Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable

PubMed Central

2012-01-01

Background When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. Methods An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Results Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. Conclusions The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population. PMID:22716998

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

Determinants of Crime in Virginia: An Empirical Analysis

ERIC Educational Resources Information Center

Ali, Abdiweli M.; Peek, Willam

2009-01-01

This paper is an empirical analysis of the determinants of crime in Virginia. Over a dozen explanatory variables that current literature suggests as important determinants of crime are collected. The data is from 1970 to 2000. These include economic, fiscal, demographic, political, and social variables. The regression results indicate that crime…

'Food Sticking in My Throat': Videofluoroscopic Evaluation of a Common Symptom.

PubMed

Madhavan, Aarthi; Carnaby, Giselle D; Crary, Michael A

2015-06-01

Prevalence of the symptom of food 'sticking' during swallowing has been reported to range from 5 to 50%, depending on the assessment setting. However, limited objective evidence has emerged to clarify factors that contribute to this symptom. Three hundred and fifteen patient records from an outpatient dysphagia clinic were reviewed to identify patients with symptoms of 'food sticking in the throat.' Corresponding videofluoroscopic swallowing studies for patients with this complaint were reviewed for the following variables: accuracy of symptom localization, identification and characteristics (anatomic, physiologic) of an explanatory cause for the symptom, and the specific swallowed material that identified the explanatory cause. One hundred and forty one patients (45%) were identified with a complaint of food 'sticking' in their throat during swallowing. Prevalence of explanatory findings on fluoroscopy was 76% (107/141). Eighty five percent (91/107) of explanatory causes were physiologic in nature, while 15% (16/107) were anatomic. The majority of explanatory causes were identified in the esophagus (71%). Symptom localization was more accurate when the explanatory cause was anatomic versus physiologic (75 vs. 18%). A non-masticated marshmallow presented with the highest diagnostic yield in identification of explanatory causes (71%). Patients complaining of 'food sticking in the throat' are likely to present with esophageal irregularities. Thus, imaging studies of swallowing function should include the esophagus. A range of materials, including a non-masticated marshmallow, is helpful in determining the location and characteristics of swallowing deficits contributing to this symptom.

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

NASA Technical Reports Server (NTRS)

Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas

1996-01-01

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North at Fargo and Grand Forks, North Dakota, 2003-12

USGS Publications Warehouse

Galloway, Joel M.

2014-01-01

The Red River of the North (hereafter referred to as “Red River”) Basin is an important hydrologic region where water is a valuable resource for the region’s economy. Continuous water-quality monitors have been operated by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, Minnesota Pollution Control Agency, City of Fargo, City of Moorhead, City of Grand Forks, and City of East Grand Forks at the Red River at Fargo, North Dakota, from 2003 through 2012 and at Grand Forks, N.Dak., from 2007 through 2012. The purpose of the monitoring was to provide a better understanding of the water-quality dynamics of the Red River and provide a way to track changes in water quality. Regression equations were developed that can be used to estimate concentrations and loads for dissolved solids, sulfate, chloride, nitrate plus nitrite, total phosphorus, and suspended sediment using explanatory variables such as streamflow, specific conductance, and turbidity. Specific conductance was determined to be a significant explanatory variable for estimating dissolved solids concentrations at the Red River at Fargo and Grand Forks. The regression equations provided good relations between dissolved solid concentrations and specific conductance for the Red River at Fargo and at Grand Forks, with adjusted coefficients of determination of 0.99 and 0.98, respectively. Specific conductance, log-transformed streamflow, and a seasonal component were statistically significant explanatory variables for estimating sulfate in the Red River at Fargo and Grand Forks. Regression equations provided good relations between sulfate concentrations and the explanatory variables, with adjusted coefficients of determination of 0.94 and 0.89, respectively. For the Red River at Fargo and Grand Forks, specific conductance, streamflow, and a seasonal component were statistically significant explanatory variables for estimating chloride. For the Red River at Grand Forks, a time component also was a statistically significant explanatory variable for estimating chloride. The regression equations for chloride at the Red River at Fargo provided a fair relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.66 and the equation for the Red River at Grand Forks provided a relatively good relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.77. Turbidity and streamflow were statistically significant explanatory variables for estimating nitrate plus nitrite concentrations at the Red River at Fargo and turbidity was the only statistically significant explanatory variable for estimating nitrate plus nitrite concentrations at Grand Forks. The regression equation for the Red River at Fargo provided a relatively poor relation between nitrate plus nitrite concentrations, turbidity, and streamflow, with an adjusted coefficient of determination of 0.46. The regression equation for the Red River at Grand Forks provided a fair relation between nitrate plus nitrite concentrations and turbidity, with an adjusted coefficient of determination of 0.73. Some of the variability that was not explained by the equations might be attributed to different sources contributing nitrates to the stream at different times. Turbidity, streamflow, and a seasonal component were statistically significant explanatory variables for estimating total phosphorus at the Red River at Fargo and Grand Forks. The regression equation for the Red River at Fargo provided a relatively fair relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.74. The regression equation for the Red River at Grand Forks provided a good relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.87. For the Red River at Fargo, turbidity and streamflow were statistically significant explanatory variables for estimating suspended-sediment concentrations. For the Red River at Grand Forks, turbidity was the only statistically significant explanatory variable for estimating suspended-sediment concentration. The regression equation at the Red River at Fargo provided a good relation between suspended-sediment concentration, turbidity, and streamflow, with an adjusted coefficient of determination of 0.95. The regression equation for the Red River at Grand Forks provided a good relation between suspended-sediment concentration and turbidity, with an adjusted coefficient of determination of 0.96.

Empirical spatial econometric modelling of small scale neighbourhood

NASA Astrophysics Data System (ADS)

Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

Self-Consciousness and Assertiveness as Explanatory Variables of L2 Oral Ability: A Latent Variable Approach

ERIC Educational Resources Information Center

Ockey, Gary

2011-01-01

Drawing on current theories in personality, second-language (L2) oral ability, and psychometrics, this study investigates the extent to which self-consciousness and assertiveness are explanatory variables of L2 oral ability. Three hundred sixty first-year Japanese university students who were studying English as a foreign language participated in…

Random parameter models for accident prediction on two-lane undivided highways in India.

PubMed

Dinu, R R; Veeraragavan, A

2011-02-01

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation. The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models. The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations. The paper is concluded with a discussion on modeling results and the limitations of the present study. Copyright © 2010 Elsevier Ltd. All rights reserved.

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North, Fargo, North Dakota, 2003-05

USGS Publications Warehouse

Ryberg, Karen R.

2006-01-01

This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the Bureau of Reclamation, U.S. Department of the Interior, to estimate water-quality constituent concentrations in the Red River of the North at Fargo, North Dakota. Regression analysis of water-quality data collected in 2003-05 was used to estimate concentrations and loads for alkalinity, dissolved solids, sulfate, chloride, total nitrite plus nitrate, total nitrogen, total phosphorus, and suspended sediment. The explanatory variables examined for regression relation were continuously monitored physical properties of water-streamflow, specific conductance, pH, water temperature, turbidity, and dissolved oxygen. For the conditions observed in 2003-05, streamflow was a significant explanatory variable for all estimated constituents except dissolved solids. pH, water temperature, and dissolved oxygen were not statistically significant explanatory variables for any of the constituents in this study. Specific conductance was a significant explanatory variable for alkalinity, dissolved solids, sulfate, and chloride. Turbidity was a significant explanatory variable for total phosphorus and suspended sediment. For the nutrients, total nitrite plus nitrate, total nitrogen, and total phosphorus, cosine and sine functions of time also were used to explain the seasonality in constituent concentrations. The regression equations were evaluated using common measures of variability, including R2, or the proportion of variability in the estimated constituent explained by the regression equation. R2 values ranged from 0.703 for total nitrogen concentration to 0.990 for dissolved-solids concentration. The regression equations also were evaluated by calculating the median relative percentage difference (RPD) between measured constituent concentration and the constituent concentration estimated by the regression equations. Median RPDs ranged from 1.1 for dissolved solids to 35.2 for total nitrite plus nitrate. Regression equations also were used to estimate daily constituent loads. Load estimates can be used by water-quality managers for comparison of current water-quality conditions to water-quality standards expressed as total maximum daily loads (TMDLs). TMDLs are a measure of the maximum amount of chemical constituents that a water body can receive and still meet established water-quality standards. The peak loads generally occurred in June and July when streamflow also peaked.

A case study of alternative site response explanatory variables in Parkfield, California

USGS Publications Warehouse

Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Morgan, E.C.; Kaklamanos, J.

2011-01-01

The combination of densely-spaced strong-motion stations in Parkfield, California, and spectral analysis of surface waves (SASW) profiles provides an ideal dataset for assessing the accuracy of different site response explanatory variables. We judge accuracy in terms of spatial coverage and correlation with observations. The performance of the alternative models is period-dependent, but generally we observe that: (1) where a profile is available, the square-root-of-impedance method outperforms VS30 (average S-wave velocity to 30 m depth), and (2) where a profile is unavailable, the topographic-slope method outperforms surficial geology. The fundamental site frequency is a valuable site response explanatory variable, though less valuable than VS30. However, given the expense and difficulty of obtaining reliable estimates of VS30 and the relative ease with which the fundamental site frequency can be computed, the fundamental site frequency may prove to be a valuable site response explanatory variable for many applications. ?? 2011 ASCE.

Modeling Effects of Temperature, Soil, Moisture, Nutrition and Variety As Determinants of Severity of Pythium Damping-Off and Root Disease in Subterranean Clover

PubMed Central

You, Ming P.; Rensing, Kelly; Renton, Michael; Barbetti, Martin J.

2017-01-01

Subterranean clover (Trifolium subterraneum) is a critical pasture legume in Mediterranean regions of southern Australia and elsewhere, including Mediterranean-type climatic regions in Africa, Asia, Australia, Europe, North America, and South America. Pythium damping-off and root disease caused by Pythium irregulare is a significant threat to subterranean clover in Australia and a study was conducted to define how environmental factors (viz. temperature, soil type, moisture and nutrition) as well as variety, influence the extent of damping-off and root disease as well as subterranean clover productivity under challenge by this pathogen. Relationships were statistically modeled using linear and generalized linear models and boosted regression trees. Modeling found complex relationships between explanatory variables and the extent of Pythium damping-off and root rot. Linear modeling identified high-level (4 or 5-way) significant interactions for each dependent variable (dry shoot and root weight, emergence, tap and lateral root disease index). Furthermore, all explanatory variables (temperature, soil, moisture, nutrition, variety) were found significant as part of some interaction within these models. A significant five-way interaction between all explanatory variables was found for both dry shoot and root dry weights, and a four way interaction between temperature, soil, moisture, and nutrition was found for both tap and lateral root disease index. A second approach to modeling using boosted regression trees provided support for and helped clarify the complex nature of the relationships found in linear models. All explanatory variables showed at least 5% relative influence on each of the five dependent variables. All models indicated differences due to soil type, with the sand-based soil having either higher weights, greater emergence, or lower disease indices; while lowest weights and less emergence, as well as higher disease indices, were found for loam soil and low temperature. There was more severe tap and lateral root rot disease in higher moisture situations. PMID:29184544

Spatial generalised linear mixed models based on distances.

PubMed

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

The effect of topography on arctic-alpine aboveground biomass and NDVI patterns

NASA Astrophysics Data System (ADS)

Riihimäki, Henri; Heiskanen, Janne; Luoto, Miska

2017-04-01

Topography is a key factor affecting numerous environmental phenomena, including Arctic and alpine aboveground biomass (AGB) distribution. Digital Elevation Model (DEM) is a source of topographic information which can be linked to local growing conditions. Here, we investigated the effect of DEM derived variables, namely elevation, topographic position, radiation and wetness on AGB and Normalized Difference Vegetation Index (NDVI) in a Fennoscandian forest-alpine tundra ecotone. Boosted regression trees were used to derive non-parametric response curves and relative influences of the explanatory variables. Elevation and potential incoming solar radiation were the most important explanatory variables for both AGB and NDVI. In the NDVI models, the response curves were smooth compared with AGB models. This might be caused by large contribution of field and shrub layer to NDVI, especially at the treeline. Furthermore, radiation and elevation had a significant interaction, showing that the highest NDVI and biomass values are found from low-elevation, high-radiation sites, typically on the south-southwest facing valley slopes. Topographic wetness had minor influence on AGB and NDVI. Topographic position had generally weak effects on AGB and NDVI, although protected topographic position seemed to be more favorable below the treeline. The explanatory power of the topographic variables, particularly elevation and radiation demonstrates that DEM-derived land surface parameters can be used for exploring biomass distribution resulting from landform control on local growing conditions.

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms.

PubMed

Motamarri, Srinivas; Boccelli, Dominic L

2012-09-15

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data. PMID:23861737

Black-white preterm birth disparity: a marker of inequality

EPA Science Inventory

Purpose. The racial disparity in preterrn birth (PTB) is a persistent feature of perinatal epidemiology, inconsistently modeled in the literature. Rather than include race as an explanatory variable, or employ race-stratified models, we sought to directly model the PTB disparity ...

Comparison of watershed disturbance predictive models for stream benthic macroinvertebrates for three distinct ecoregions in western US

USGS Publications Warehouse

Waite, Ian R.; Brown, Larry R.; Kennen, Jonathan G.; May, Jason T.; Cuffney, Thomas F.; Orlando, James L.; Jones, Kimberly A.

2010-01-01

The successful use of macroinvertebrates as indicators of stream condition in bioassessments has led to heightened interest throughout the scientific community in the prediction of stream condition. For example, predictive models are increasingly being developed that use measures of watershed disturbance, including urban and agricultural land-use, as explanatory variables to predict various metrics of biological condition such as richness, tolerance, percent predators, index of biotic integrity, functional species traits, or even ordination axes scores. Our primary intent was to determine if effective models could be developed using watershed characteristics of disturbance to predict macroinvertebrate metrics among disparate and widely separated ecoregions. We aggregated macroinvertebrate data from universities and state and federal agencies in order to assemble stream data sets of high enough density appropriate for modeling in three distinct ecoregions in Oregon and California. Extensive review and quality assurance of macroinvertebrate sampling protocols, laboratory subsample counts and taxonomic resolution was completed to assure data comparability. We used widely available digital coverages of land-use and land-cover data summarized at the watershed and riparian scale as explanatory variables to predict macroinvertebrate metrics commonly used by state resource managers to assess stream condition. The “best” multiple linear regression models from each region required only two or three explanatory variables to model macroinvertebrate metrics and explained 41–74% of the variation. In each region the best model contained some measure of urban and/or agricultural land-use, yet often the model was improved by including a natural explanatory variable such as mean annual precipitation or mean watershed slope. Two macroinvertebrate metrics were common among all three regions, the metric that summarizes the richness of tolerant macroinvertebrates (RICHTOL) and some form of EPT (Ephemeroptera, Plecoptera, and Trichoptera) richness. Best models were developed for the same two invertebrate metrics even though the geographic regions reflect distinct differences in precipitation, geology, elevation, slope, population density, and land-use. With further development, models like these can be used to elicit better causal linkages to stream biological attributes or condition and can be used by researchers or managers to predict biological indicators of stream condition at unsampled sites.

Temporal Trends and Hydrological Controls of Fisheries Production in the Madeira River (Brazil)

NASA Astrophysics Data System (ADS)

Kaplan, D. A.; Lima, M. A.; Doria, C.

2016-12-01

Amazonian river systems are characterized by a strongly seasonal flood pulse and important hydrologic effects have been observed in the dynamics of fish stocks and fishing yields. Changes in the Amazon's freshwater ecosystems from hydropower development will have a cascade of physical, ecological, and social effects and impacts on fish and fisheries are expected to be potentially irreversible. In this work we investigate shared trends and causal factors driving fish catch in the Madeira River (a major tributary of the Amazon) before dam construction to derive relationships between catch and natural hydrologic dynamics. We applied Dynamic Factor Analysis to investigate dynamics in fish catch across ten commercially important fish species in the Madeira River using daily fish landings data including species and total weight and daily hydrological data obtained from the Brazilian Geological Service. Total annual catch averaged over the 18-yr period (1990-2007) was 849 tons yr-1. Species with the highest catch included curimatã, dourada/filhote and pacu, highlighting the importance of medium and long-distance migratory species for fisheries production. We found a four-trend dynamic factor model (DFM) to best fit the observed data, assessed using the Akaike Information Criteria. Model goodness of fit was fair (R2=0.51) but highly variable across species (0.16 ≤ R2 ≤ 0.95). Fitted trends exhibited strong and regular year-to-year variation representative of the seasonal hydrologic pulsing observed on the Madeira River. Next, we considered 11 candidate explanatory time series and found the best DFM used four explanatory variables and only one common trend. While the model fit with explanatory variables was lower (R2=0.31) it removed much reliance on unknown common trends. The most important explanatory variable in this model was maximum water level followed by days flooded, river flow of the previous year and increment. We found unique responses to hydrological variations across the ten species, suggesting that dam operating rules need to closely mimic natural hydrologic regime in order to maintain the dynamics of these ecosystems. Future multidisciplinary analyses to understand the complex social-ecological effects of dams are needed to improve management practices and support sustainable livelihoods.

Causes of Job Turnover in the Public School Superintendency: An Explanatory Analysis in the Western United States

ERIC Educational Resources Information Center

Melver, Toby A.

2011-01-01

The purpose of this mixed-methods study was to determine the factors that affect public school superintendent turnover in five western states. An explanatory theory was developed to cover all of the possible variables and show the relationship between those variables. The questions that guided this research study were: (1) What environmental…

Use of real-time monitoring to predict concentrations of select constituents in the Menomonee River drainage basin, Southeast Wisconsin, 2008-9

USGS Publications Warehouse

Baldwin, Austin K.; Graczyk, David J.; Robertson, Dale M.; Saad, David A.; Magruder, Christopher

2012-01-01

The models to estimate chloride concentrations all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity as explanatory variables. Adjusted R2 values for the chloride models ranged from 0.74 to 0.97. Models to estimate total suspended solids and total phosphorus used turbidity as the only explanatory variable. Adjusted R2 values ranged from 0.77 to 0.94 for the total suspended solids models and from 0.55 to 0.75 for the total phosphorus models. Models to estimate indicator bacteria used water temperature and turbidity as the explanatory variables, with adjusted R2 values from 0.54 to 0.69 for Escherichia coli bacteria models and from 0.54 to 0.74 for fecal coliform bacteria models. Dissolved oxygen was not used in any of the final models. These models may help managers measure the effects of land-use changes and improvement projects, establish total maximum daily loads, estimate important water-quality indicators such as bacteria concentrations, and enable informed decision making in the future.

Modification of selected South Carolina bridge-scour envelope curves

USGS Publications Warehouse

Benedict, Stephen T.; Caldwell, Andral W.

2012-01-01

Historic scour was investigated at 231 bridges in the Piedmont and Coastal Plain physiographic provinces of South Carolina by the U.S. Geological Survey in cooperation with the South Carolina Department of Transportation. These investigations led to the development of field-derived envelope curves that provided supplementary tools to assess the potential for scour at bridges in South Carolina for selected scour components that included clear-water abutment, contraction, and pier scour, and live-bed pier and contraction scour. The envelope curves consist of a single curve with one explanatory variable encompassing all of the measured field data for the respective scour components. In the current investigation, the clear-water abutment-scour and live-bed contraction-scour envelope curves were modified to include a family of curves that utilized two explanatory variables, providing a means to further refine the assessment of scour potential for those specific scour components. The modified envelope curves and guidance for their application are presented in this report.

Hypertension in the very old; prevalence, awareness, treatment and control: a cross-sectional population-based study in a Spanish municipality.

PubMed

Aguado, Alba; López, Flora; Miravet, Sonia; Oriol, Pilar; Fuentes, M Isabel; Henares, Belén; Badia, Teresa; Esteve, Lluis; Peligro, Javier

2009-05-08

Information on hypertension in the very elderly is sparse. Until recently evidence of benefits from pharmacological treatment was inconclusive. We estimated the prevalence of hypertension in subjects aged 80 or more, the proportion of awareness, treatment and control. Explanatory variables associated with good control were also studied. Cross sectional, population-based study, conducted in Martorell, an urban Spanish municipality, in 2005. By simple random sampling from the census, 323 subjects aged 80 or more were included. Patients were visited at home or in the geriatric institution and after giving informed consent, the study variables were collected. These included: supine and standing blood pressure and information about diagnosis and treatment of hypertension. The estimation and 95% confidence interval were obtained and a logistic regression model was used to study explanatory variables associated with blood pressure below 140/90 mm Hg. The prevalence of hypertension was 72.8% (95%CI: 69.5-76.6%) and 93% of the patients were aware of this condition, of whom 96.3% (95%CI: 93.65-97.9%) had been prescribed pharmacological treatment and 30.7% (95%CI: 25.8 - 36.1%) had blood pressure below 140/90 mm Hg. Some of the patients (43%) had one antihypertensive drug and 39.5% had two in combination. Explanatory variables associated with blood pressure below 140/90 mm Hg included prescription of a diuretic, OR: 0.31 (95%CI: 0.14-0.66), and history of ischemic heart disease, OR: 0.21 (95%CI: 0.1-0.47). The prevalence of hypertension in population aged 80 or more was over 70%. Most patients were aware of this condition and they had antihypertensive medication prescribed. Approximately one third of treated patients had blood pressure below 140/90 mm Hg. Patients with heart disease and with diuretics had more frequently blood pressure below this value.

Hypertension in the very old; prevalence, awareness, treatment and control: a cross-sectional population-based study in a Spanish municipality

PubMed Central

Aguado, Alba; López, Flora; Miravet, Sonia; Oriol, Pilar; Fuentes, M Isabel; Henares, Belén; Badia, Teresa; Esteve, Lluis; Peligro, Javier

2009-01-01

Background Information on hypertension in the very elderly is sparse. Until recently evidence of benefits from pharmacological treatment was inconclusive. We estimated the prevalence of hypertension in subjects aged 80 or more, the proportion of awareness, treatment and control. Explanatory variables associated with good control were also studied. Methods Cross sectional, population-based study, conducted in Martorell, an urban Spanish municipality, in 2005. By simple random sampling from the census, 323 subjects aged 80 or more were included. Patients were visited at home or in the geriatric institution and after giving informed consent, the study variables were collected. These included: supine and standing blood pressure and information about diagnosis and treatment of hypertension. The estimation and 95% confidence interval were obtained and a logistic regression model was used to study explanatory variables associated with blood pressure below 140/90 mm Hg. Results The prevalence of hypertension was 72.8% (95%CI: 69.5 – 76.6%) and 93% of the patients were aware of this condition, of whom 96.3% (95%CI: 93.65 – 97.9%) had been prescribed pharmacological treatment and 30.7% (95%CI: 25.8 – 36.1%) had blood pressure below 140/90 mm Hg. Some of the patients (43%) had one antihypertensive drug and 39.5% had two in combination. Explanatory variables associated with blood pressure below 140/90 mm Hg included prescription of a diuretic, OR: 0.31 (95%CI: 0.14 – 0.66), and history of ischemic heart disease, OR: 0.21 (95%CI: 0.1 – 0.47). Conclusion The prevalence of hypertension in population aged 80 or more was over 70%. Most patients were aware of this condition and they had antihypertensive medication prescribed. Approximately one third of treated patients had blood pressure below 140/90 mm Hg. Patients with heart disease and with diuretics had more frequently blood pressure below this value. PMID:19426484

Reporting and Methodology of Multivariable Analyses in Prognostic Observational Studies Published in 4 Anesthesiology Journals: A Methodological Descriptive Review.

PubMed

Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric

2015-10-01

Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate <36% (i.e., the 25th percentile), with some of them not identified in the STROBE checklist: blinded evaluation of the outcome (11.9%), reason for sample size (15.1%), handling of missing data (36.0%), assessment of colinearity (17.4%), assessment of interactions (13.9%), and calibration (34.9%). When reported, a few methodological shortcomings were observed, both in explanatory and predictive studies, such as an insufficient number of events of the outcome (44.6%), exclusion of cases with missing data (93.6%), or categorization of continuous variables (65.1%.). The reporting of multivariable analysis was fairly good and could be further improved by checking reporting guidelines and EQUATOR Network website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

USGS Publications Warehouse

Walter, Donald A.; Starn, J. Jeffrey

2013-01-01

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Public Views on the Gendering of Mathematics and Related Careers: International Comparisons

ERIC Educational Resources Information Center

Forgasz, Helen; Leder, Gilah; Tan, Hazel

2014-01-01

Mathematics continues to be an enabling discipline for Science, Technology, Engineering, and Mathematics (STEM)-based university studies and related careers. Explanatory models for females' underrepresentation in higher level mathematics and STEM-based courses comprise learner-related and environmental variables--including societal beliefs. Using…

Explanation and Prediction: Building a Unified Theory of Librarianship, Concept and Review.

ERIC Educational Resources Information Center

McGrath, William E.

2002-01-01

Develops a comprehensive, unified, explanatory theory of librarianship by first making an analogy to the unification of the fundamental forces of nature. Topics include dependent and independent variables; publishing; acquisitions; classification and organization of knowledge; storage, preservation, and collection management; collections; and…

Predicting daily use of urban forest recreation sites

Treesearch

John F. Dwyer

1988-01-01

A multiple linear regression model explains 90% of the variance in daily use of an urban recreation site. Explanatory variables include season, day of the week, and weather. The results offer guides for recreation site planning and management as well as suggestions for improving the model.

Predicting High Quality AFQT with Youth Attitude Tracking Study Data

DTIC Science & Technology

1991-12-01

for propensities. The history of the art of mental aptitude and psychological testing is long and convoluted. Names like Sir Francis Galton of England...Qualification Test . The explanatory variables reflect individual demographic, educational and labor market characteristics at the time of YATS interview. The...the fiftieth percentile on the Armed Forces Qualification Test . The explanatory variables reflect individual demographic, educational and labor market

Army College Fund Cost-Effectiveness Study

DTIC Science & Technology

1990-11-01

Section A.2 presents a theory of enlistment supply to provide a basis for specifying the regression model , The model Is specified in Section A.3, which...Supplementary materials are included in the final four sections. Section A.6 provides annual trends in the regression model variables. Estimates of the model ...millions, A.S. ESTIMATION OF A YOUTH EARNINGS FORECASTING MODEL Civilian pay is an important explanatory variable in the regression model . Previous

Agricultural disturbance response models for invertebrate and algal metrics from streams at two spatial scales within the U.S.

USGS Publications Warehouse

Waite, Ian R.

2014-01-01

As part of the USGS study of nutrient enrichment of streams in agricultural regions throughout the United States, about 30 sites within each of eight study areas were selected to capture a gradient of nutrient conditions. The objective was to develop watershed disturbance predictive models for macroinvertebrate and algal metrics at national and three regional landscape scales to obtain a better understanding of important explanatory variables. Explanatory variables in models were generated from landscape data, habitat, and chemistry. Instream nutrient concentration and variables assessing the amount of disturbance to the riparian zone (e.g., percent row crops or percent agriculture) were selected as most important explanatory variable in almost all boosted regression tree models regardless of landscape scale or assemblage. Frequently, TN and TP concentration and riparian agricultural land use variables showed a threshold type response at relatively low values to biotic metrics modeled. Some measure of habitat condition was also commonly selected in the final invertebrate models, though the variable(s) varied across regions. Results suggest national models tended to account for more general landscape/climate differences, while regional models incorporated both broad landscape scale and more specific local-scale variables.

Response of benthic algae to environmental gradients in an agriculturally dominated landscape

USGS Publications Warehouse

Munn, M.D.; Black, R.W.; Gruber, S.J.

2002-01-01

Benthic algal communities were assessed in an agriculturally dominated landscape in the Central Columbia Plateau, Washington, to determine which environmental variables best explained species distributions, and whether algae species optima models were useful in predicting specific water-quality parameters. Land uses in the study area included forest, range, urban, and agriculture. Most of the streams in this region can be characterized as open-channel systems influenced by intensive dryland (nonirrigated) and irrigated agriculture. Algal communities in forested streams were dominated by blue-green algae, with communities in urban and range streams dominated by diatoms. The predominance of either blue-greens or diatoms in agricultural streams varied greatly depending on the specific site. Canonical correspondence analysis (CCA) indicated a strong gradient effect of several key environmental variables on benthic algal community composition. Conductivity and % agriculture were the dominant explanatory variables when all sites (n = 24) were included in the CCA; water velocity replaced conductivity when the CCA included only agricultural and urban sites. Other significant explanatory variables included dissolved inorganic nitrogen (DIN), orthophosphate (OP), discharge, and precipitation. Regression and calibration models accurately predicted conductivity based on benthic algal communities, with OP having slightly lower predictability. The model for DIN was poor, and therefore may be less useful in this system. Thirty-four algal taxa were identified as potential indicators of conductivity and nutrient conditions, with most indicators being diatoms except for the blue-greens Anabaenasp. and Lyngbya sp.

POLO2: a user's guide to multiple Probit Or LOgit analysis

Treesearch

Robert M. Russell; N. E. Savin; Jacqueline L. Robertson

1981-01-01

This guide provides instructions for the use of POLO2, a computer program for multivariate probit or logic analysis of quantal response data. As many as 3000 test subjects may be included in a single analysis. Including the constant term, up to nine explanatory variables may be used. Examples illustrating input, output, and uses of the program's special features...

Spatial analysis of trace elements in a moss bio-monitoring data over France by accounting for source, protocol and environmental parameters.

PubMed

Lequy, Emeline; Saby, Nicolas P A; Ilyin, Ilia; Bourin, Aude; Sauvage, Stéphane; Leblond, Sébastien

2017-07-15

Air pollution in trace elements (TE) remains a concern for public health in Europe. For this reasons, networks of air pollution concentrations or exposure are deployed, including a moss bio-monitoring programme in Europe. Spatial determinants of TE concentrations in mosses remain unclear. In this study, the French dataset of TE in mosses is analyzed by spatial autoregressive model to account for spatial structure of the data and several variables proven or suspected to affect TE concentrations in mosses. Such variables include source (atmospheric deposition and soil concentrations), protocol (sampling month, collector, and moss species), and environment (forest type and canopy density, distance to the coast or the highway, and elevation). Modeled atmospheric deposition was only available for Cd and Pb and was one of the main explanatory variables of the concentrations in mosses. Predicted soil content was also an important explanatory variable except for Cr, Ni, and Zn. However, the moss species was the main factor for all the studied TE. The other environmental variables affected differently the TE. In particular, the forest type and canopy density were important in most cases. These results stress the need for further research on the effect of the moss species on the capture and retention of TE, as well as for accounting for several variables and the spatial structure of the data in statistical analyses. Copyright © 2017 Elsevier B.V. All rights reserved.

Do nurses wish to continue working for the UK National Health Service? A comparative study of three generations of nurses.

PubMed

Robson, Andrew; Robson, Fiona

2015-01-01

To identify the combination of variables that explain nurses' continuation intention in the UK National Health Service. This alternative arena has permitted the replication of a private sector Australian study. This study provides understanding about the issues that affect nurse retention in a sector where employee attrition is a key challenge, further exacerbated by an ageing workforce. A quantitative study based on a self-completion survey questionnaire completed in 2010. Nurses employed in two UK National Health Service Foundation Trusts were surveyed and assessed using seven work-related constructs and various demographics including age generation. Through correlation, multiple regression and stepwise regression analysis, the potential combined effect of various explanatory variables on continuation intention was assessed, across the entire nursing cohort and in three age-generation groups. Three variables act in combination to explain continuation intention: work-family conflict, work attachment and importance of work to the individual. This combination of significant explanatory variables was consistent across the three generations of nursing employee. Work attachment was identified as the strongest marginal predictor of continuation intention. Work orientation has a greater impact on continuation intention compared with employer-directed interventions such as leader-member exchange, teamwork and autonomy. UK nurses are homogeneous across the three age-generations regarding explanation of continuation intention, with the significant explanatory measures being recognizably narrower in their focus and more greatly concentrated on the individual. This suggests that differentiated approaches to retention should perhaps not be pursued in this sectoral context. © 2014 John Wiley & Sons Ltd.

Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school.

PubMed

Cerda, Gamal; Pérez, Carlos; Navarro, José I; Aguilar, Manuel; Casas, José A; Aragón, Estíbaliz

2015-01-01

This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students' level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students' performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.

Using Indirect Turbulence Measurements for Real-Time Parameter Estimation in Turbulent Air

NASA Technical Reports Server (NTRS)

Martos, Borja; Morelli, Eugene A.

2012-01-01

The use of indirect turbulence measurements for real-time estimation of parameters in a linear longitudinal dynamics model in atmospheric turbulence was studied. It is shown that measuring the atmospheric turbulence makes it possible to treat the turbulence as a measured explanatory variable in the parameter estimation problem. Commercial off-the-shelf sensors were researched and evaluated, then compared to air data booms. Sources of colored noise in the explanatory variables resulting from typical turbulence measurement techniques were identified and studied. A major source of colored noise in the explanatory variables was identified as frequency dependent upwash and time delay. The resulting upwash and time delay corrections were analyzed and compared to previous time shift dynamic modeling research. Simulation data as well as flight test data in atmospheric turbulence were used to verify the time delay behavior. Recommendations are given for follow on flight research and instrumentation.

Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11

USGS Publications Warehouse

Baldwin, Austin K.; Robertson, Dale M.; Saad, David A.; Magruder, Christopher

2013-01-01

In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa. The refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables. An analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs) calculated for both the original and refined models using the entire dataset showed a median improvement in RMSE of 2.1 percent, with a range of 0.0–13.9 percent. Therefore most of the original models did almost as well at estimating concentrations during the validation period (October 2009–September 2011) as the refined models, which were calibrated using those data. Application of these refined models can produce continuously estimated concentrations of chloride, total suspended solids, total phosphorus, E. coli bacteria, and fecal coliform bacteria that may assist managers in quantifying the effects of land-use changes and improvement projects, establish total maximum daily loads, and enable better informed decision making in the future.

Landscape Risk Factors for Lyme Disease in the Eastern Broadleaf Forest Province of the Hudson River Valley and the Effect of Explanatory Data Classification Resolution

EPA Science Inventory

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity)...

Belief models in first episode schizophrenia in South India.

PubMed

Saravanan, Balasubramanian; Jacob, K S; Johnson, Shanthi; Prince, Martin; Bhugra, Dinesh; David, Anthony S

2007-06-01

Existing evidence indicates that dissonance between patients' and professionals' explanatory models affects engagement of patients with psychiatric services in Western and non-Western countries. To assess qualitatively the explanatory models (EMs) of psychosis and their association with clinical variables in a representative sample of first episode patients with schizophrenia in South India. One hundred and thirty one patients with schizophrenia presenting consecutively were assessed. Measures included the patient's explanatory models, and clinician ratings of insight, symptoms of psychosis, and functioning on standard scales. The majority of patients (70%) considered spiritual and mystical factors as the cause of their predicament; 22% held multiple models of illness. Patients who held a biomedical concept of disease had significantly higher scores on the insight scale compared to those who held non-medical beliefs. Multivariate analyses identified three factors associated with holding of spiritual/mystical models (female sex, low education and visits to traditional healers); and a single factor (high level of insight) for the endorsement of biological model. Patients with schizophrenia in this region of India hold a variety of non-medical belief models, which influence patterns of health seeking. Those holding non-medical explanatory models are likey to be rated as having less insight.

Variations in non-prescription drug consumption and expenditure: Determinants and policy implications.

PubMed

Otto, Monica; Armeni, Patrizio; Jommi, Claudio

2018-01-31

This paper analyses the determinants of cross-regional variations in expenditure and consumption for non-prescription drugs using the Italian Health Care Service as a case study. This research question has never been posed in other literature contributions. Per capita income, the incidence of elderly people, the presence of distribution points alternative to community pharmacies (para-pharmacies and drug corners in supermarkets), and the disease prevalence were included as possible explanatory variables. A trade-off between consumption of non-prescription and prescription-only drugs was also investigated. Correlation was tested through linear regression models with regional fixed-effects. Demand-driven variables, including the prevalence of the target diseases and income, were found to be more influential than supply-side variables, such as the presence of alternative distribution points. Hence, the consumption of non-prescription drugs appears to respond to needs and is not induced by the supply. The expected trade-off between consumption for prescription-only and non-prescription drugs was not empirically found: increasing the use of non-prescription drugs did not automatically imply savings on prescription-only drugs covered by third payers. Despite some caveats (the short period of time covered by the longitudinal data and some missing monthly data), the regression model revealed a high explanatory power of the variability and a strong predictive ability of future values. Copyright © 2018 Elsevier B.V. All rights reserved.

Canonical Commonality Analysis.

ERIC Educational Resources Information Center

Leister, K. Dawn

Commonality analysis is a method of partitioning variance that has advantages over more traditional "OVA" methods. Commonality analysis indicates the amount of explanatory power that is "unique" to a given predictor variable and the amount of explanatory power that is "common" to or shared with at least one predictor…

The psychological factor 'self-blame' predicts overuse injury among top-level Swedish track and field athletes: a 12-month cohort study.

PubMed

Timpka, Toomas; Jacobsson, Jenny; Dahlström, Örjan; Kowalski, Jan; Bargoria, Victor; Ekberg, Joakim; Nilsson, Sverker; Renström, Per

2015-11-01

Athletes' psychological characteristics are important for understanding sports injury mechanisms. We examined the relevance of psychological factors in an integrated model of overuse injury risk in athletics/track and field. Swedish track and field athletes (n=278) entering a 12-month injury surveillance in March 2009 were also invited to complete a psychological survey. Simple Cox proportional hazards models were compiled for single explanatory variables. We also tested multiple models for 3 explanatory variable groupings: an epidemiological model without psychological variables, a psychological model excluding epidemiological variables and an integrated (combined) model. The integrated multiple model included the maladaptive coping behaviour self-blame (p=0.007; HR 1.32; 95% CI 1.08 to 1.61), and an interaction between athlete category and injury history (p<0.001). Youth female (p=0.034; HR 0.51; 95% CI 0.27 to 0.95) and youth male (p=0.047; HR 0.49; 95% CI 0.24 to 0.99) athletes with no severe injury the previous year were at half the risk of sustaining a new injury compared with the reference group. A training load index entered the epidemiological multiple model, but not the integrated model. The coping behaviour self-blame replaced training load in an integrated explanatory model of overuse injury risk in athletes. What seemed to be more strongly related to the likelihood of overuse injury was not the athletics load per se, but, rather, the load applied in situations when the athlete's body was in need of rest. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Explanatory variables for adult patients' self-reported recovery after acute lateral ankle sprain.

PubMed

van Rijn, Rogier M; Willemsen, Sten P; Verhagen, Arianne P; Koes, Bart W; Bierma-Zeinstra, Sita M A

2011-01-01

Longitudinal research on musculoskeletal disorders often makes use of a single measure of recovery, despite the large variation in reported recovery that exists. Patients with an acute ankle sprain often experience no pain or functional disability following treatment, yet report not being fully recovered, or vice versa. The purpose of this study was to find explanatory variables for reporting recovery by analyzing the extent to which different outcomes (eg, pain intensity) were associated with recovery and how baseline scores of different variables influence this association in adult patients after acute lateral ankle sprain. This was a cohort study based on data collected in a randomized controlled trial (RCT). This study was constructed within the framework of an RCT. One hundred two patients who incurred an acute ankle sprain were included. Recovery, pain intensity, giving way of the ankle, and Ankle Function Score (AFS) were assessed during the RCT at baseline and at 4 weeks, 8 weeks, 3 months, and 12 months postinjury. Mean differences were calculated between baseline and follow-up. Associations were calculated using linear mixed models, and the influence of baseline scores on these associations was determined using linear regression with interaction. Associations were found between recovery and the mean differences of pain during running on flat and rough surfaces (4 and 8 weeks, 3 months) and between recovery and the mean difference of giving way of the ankle during walking on a rough surface (8 weeks, 3 months). This study used data collected from an RCT. Therefore, the study was limited to the outcomes measured in that trial, and some explanatory factors easily could have been missed. This study is the first to identify explanatory variables for reporting recovery in adults after ankle sprain. Pain intensity and giving way of the ankle measured during high ankle load activities make it easier to measure and to generalize recovery in this population and should be the primary outcome measures of interest. This study indicates the huge need to reach consensus about primary outcome measures for research in patients sustaining ankle sprains.

Peak flow regression equations For small, ungaged streams in Maine: Comparing map-based to field-based variables

USGS Publications Warehouse

Lombard, Pamela J.; Hodgkins, Glenn A.

2015-01-01

Regression equations to estimate peak streamflows with 1- to 500-year recurrence intervals (annual exceedance probabilities from 99 to 0.2 percent, respectively) were developed for small, ungaged streams in Maine. Equations presented here are the best available equations for estimating peak flows at ungaged basins in Maine with drainage areas from 0.3 to 12 square miles (mi2). Previously developed equations continue to be the best available equations for estimating peak flows for basin areas greater than 12 mi2. New equations presented here are based on streamflow records at 40 U.S. Geological Survey streamgages with a minimum of 10 years of recorded peak flows between 1963 and 2012. Ordinary least-squares regression techniques were used to determine the best explanatory variables for the regression equations. Traditional map-based explanatory variables were compared to variables requiring field measurements. Two field-based variables—culvert rust lines and bankfull channel widths—either were not commonly found or did not explain enough of the variability in the peak flows to warrant inclusion in the equations. The best explanatory variables were drainage area and percent basin wetlands; values for these variables were determined with a geographic information system. Generalized least-squares regression was used with these two variables to determine the equation coefficients and estimates of accuracy for the final equations.

Modeling ozone bioindicator injury with microscale and landscape-scale explanatory variables: A logistic regression approach

Treesearch

John W. Coulston

2011-01-01

Tropospheric ozone occurs at phytotoxic levels in the United States (Lefohn and Pinkerton 1988). Several plant species, including commercially important timber species, are sensitive to elevated ozone levels. Exposure to elevated ozone can cause growth reduction and foliar injury and make trees more susceptible to secondary stressors such as insects and pathogens (...

Self-reported hard physical work combined with heavy smoking or overweight may result in so-called Modic changes.

PubMed

Leboeuf-Yde, Charlotte; Kjaer, Per; Bendix, Tom; Manniche, Claus

2008-01-14

Recently, the MRI finding of "Modic changes" has been identified as pathologic spinal condition that probably reflects a vertebral inflammatory process (VIP), which coincides with spinal pain in most. We hypothesized that heavy smoking in combination with macro- or repeated microtrauma could lead to VIP. The objectives were to investigate if combinations of self-reported heavy smoking, hard physical work, and overweight would be more strongly linked with VIP than with other spinal conditions, such as degenerated discs and non-specific low back pain (LBP). Secondary analysis was made of a data base pertaining to a population-based cross-sectional study. A population-generated cohort of 412 40-yr old Danes provided questionnaire information on smoking, weight, height, type of work, and LBP. MRI was used to determine the presence/absence of disc degeneration and of VIP. Associations were tested between three explanatory variables (type of work, smoking, and body mass index) and four outcome variables (LBP in the past year, more persistent LBP in the past year, disc degeneration, and VIP). Associations with these four outcome variables were studied for each single explanatory variable and for combinations of two at a time, and, finally, in a multivariable analysis including all three explanatory variables. There were no significant associations between the single explanatory variables and the two pain variables or with disc degeneration. However, VIP was found in 15% of non-smokers vs. 26% of heavy smokers. Similarly, VIP was noted in 11% of those in sedentary jobs vs. 31% of those with hard physical work. Further, the prevalence of VIP in those, who neither smoked heavily nor had a hard physical job was 13%, 25% in those who either smoked heavily or had a hard physical job, and 41% in those who both smoked heavily and worked hard. The odds ratio was 4.9 (1.6-13.0) for those who were both heavy smokers and had a hard physical job as compared to those who were classified as "neither". Similar but weaker findings were noted for the combination of overweight and hard physical work but not for the combination of smoking and overweight. Hard physical work in combination with either heavy smoking or overweight is strongly associated with VIP. If this finding can be reproduced in other studies, it may have consequences in relation to both primary and secondary prevention of LBP, because blue collar workers, who are most likely to experience the consequences of LBP, also are those who are most likely to smoke.

Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school

PubMed Central

Cerda, Gamal; Pérez, Carlos; Navarro, José I.; Aguilar, Manuel; Casas, José A.; Aragón, Estíbaliz

2015-01-01

This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students’ level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students’ performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain. PMID:26441739

A Predictive Model of Domestic Violence in Multicultural Families Focusing on Perpetrator.

PubMed

Choi, Eun Young; Hyun, Hye Jin

2016-09-01

This study was conducted to assess predictor variables of husbands in multicultural families and examine the relationship among variables after setting up a hypothetical model including influencing factors, so as to provide a framework necessary for developing nursing interventions of domestic violence. The participants were 260 husbands in multicultural families in four cities in Korea. Data were analyzed using SPSS 22.0 and AMOS 20.0. Self-control, social support, family of origin violence experience and stress on cultural adaptation directly affected to dysfunctional communication, and the explanatory power of the variables was 64.7%. Family of origin violence experience in domestic stress on cultural adaptation, and dysfunctional communication were directly related to domestic violence in multicultural families, and the explanatory power of the variables was 64.6%. We found out that all variables in the model had mediation effects to domestic violence through dysfunctional communication. In other words, self-control and social support had complete mediation effects, and family of origin violence experience in domestic violence and stress on cultural adaptation had partial mediation effects. The variables explained in this study should be considered as predictive factors of domestic violence in multicultural families, and used to provide preventive nursing intervention. Our resutls can be taken into account for developing and implementing programs on alleviating dysfunctional communication in multicultural families in Korea. Copyright © 2016. Published by Elsevier B.V.

Multiple causes of nonstationarity in the Weihe annual low-flow series

NASA Astrophysics Data System (ADS)

Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong-Yu; Li, Lingqi

2018-02-01

Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

Prediction of 222Rn in Danish dwellings using geology and house construction information from central databases.

PubMed

Andersen, Claus E; Raaschou-Nielsen, Ole; Andersen, Helle Primdal; Lind, Morten; Gravesen, Peter; Thomsen, Birthe L; Ulbak, Kaare

2007-01-01

A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%).

Development and Application of Regression Models for Estimating Nutrient Concentrations in Streams of the Conterminous United States, 1992-2001

USGS Publications Warehouse

Spahr, Norman E.; Mueller, David K.; Wolock, David M.; Hitt, Kerie J.; Gronberg, JoAnn M.

2010-01-01

Data collected for the U.S. Geological Survey National Water-Quality Assessment program from 1992-2001 were used to investigate the relations between nutrient concentrations and nutrient sources, hydrology, and basin characteristics. Regression models were developed to estimate annual flow-weighted concentrations of total nitrogen and total phosphorus using explanatory variables derived from currently available national ancillary data. Different total-nitrogen regression models were used for agricultural (25 percent or more of basin area classified as agricultural land use) and nonagricultural basins. Atmospheric, fertilizer, and manure inputs of nitrogen, percent sand in soil, subsurface drainage, overland flow, mean annual precipitation, and percent undeveloped area were significant variables in the agricultural basin total nitrogen model. Significant explanatory variables in the nonagricultural total nitrogen model were total nonpoint-source nitrogen input (sum of nitrogen from manure, fertilizer, and atmospheric deposition), population density, mean annual runoff, and percent base flow. The concentrations of nutrients derived from regression (CONDOR) models were applied to drainage basins associated with the U.S. Environmental Protection Agency (USEPA) River Reach File (RF1) to predict flow-weighted mean annual total nitrogen concentrations for the conterminous United States. The majority of stream miles in the Nation have predicted concentrations less than 5 milligrams per liter. Concentrations greater than 5 milligrams per liter were predicted for a broad area extending from Ohio to eastern Nebraska, areas spatially associated with greater application of fertilizer and manure. Probabilities that mean annual total-nitrogen concentrations exceed the USEPA regional nutrient criteria were determined by incorporating model prediction uncertainty. In all nutrient regions where criteria have been established, there is at least a 50 percent probability of exceeding the criteria in more than half of the stream miles. Dividing calibration sites into agricultural and nonagricultural groups did not improve the explanatory capability for total phosphorus models. The group of explanatory variables that yielded the lowest model error for mean annual total phosphorus concentrations includes phosphorus input from manure, population density, amounts of range land and forest land, percent sand in soil, and percent base flow. However, the large unexplained variability and associated model error precluded the use of the total phosphorus model for nationwide extrapolations.

Combined Effects of Soil Biotic and Abiotic Factors, Influenced by Sewage Sludge Incorporation, on the Incidence of Corn Stalk Rot

PubMed Central

Fortes, Nara Lúcia Perondi; Navas-Cortés, Juan A; Silva, Carlos Alberto; Bettiol, Wagner

2016-01-01

The objectives of this study were to evaluate the combined effects of soil biotic and abiotic factors on the incidence of Fusarium corn stalk rot, during four annual incorporations of two types of sewage sludge into soil in a 5-years field assay under tropical conditions and to predict the effects of these variables on the disease. For each type of sewage sludge, the following treatments were included: control with mineral fertilization recommended for corn; control without fertilization; sewage sludge based on the nitrogen concentration that provided the same amount of nitrogen as in the mineral fertilizer treatment; and sewage sludge that provided two, four and eight times the nitrogen concentration recommended for corn. Increasing dosages of both types of sewage sludge incorporated into soil resulted in increased corn stalk rot incidence, being negatively correlated with corn yield. A global analysis highlighted the effect of the year of the experiment, followed by the sewage sludge dosages. The type of sewage sludge did not affect the disease incidence. A multiple logistic model using a stepwise procedure was fitted based on the selection of a model that included the three explanatory parameters for disease incidence: electrical conductivity, magnesium and Fusarium population. In the selected model, the probability of higher disease incidence increased with an increase of these three explanatory parameters. When the explanatory parameters were compared, electrical conductivity presented a dominant effect and was the main variable to predict the probability distribution curves of Fusarium corn stalk rot, after sewage sludge application into the soil. PMID:27176597

Dengue vaccine acceptance and associated factors in Indonesia: A community-based cross-sectional survey in Aceh.

PubMed

Harapan, Harapan; Anwar, Samsul; Setiawan, Abdul Malik; Sasmono, R Tedjo

2016-07-12

The first dengue vaccine (DV) has been licensed in some countries, but an assessment of the public's acceptance of DV is widely lacking. This study aimed to explore and understand DV acceptance and its associated explanatory variables among healthy inhabitants of Aceh, Indonesia. A community-based cross-sectional survey was conducted from November 2014 to March 2015 in nine regencies of Aceh that were selected randomly. A set of validated questionnaires covering a range of explanatory variables and DV acceptance was used to conduct the interviews. A multi-step logistic regression analysis and Spearman's rank correlation were employed to assess the role of explanatory variables in DV acceptance. We included 652 community members in the final analysis and found that 77.3% of them were willing to accept the DV. Gender, monthly income, socioeconomic status (SES), attitude toward dengue fever (DF) and attitude toward vaccination practice were associated with DV acceptance in bivariate analyses (P<0.05). A correlation analysis confirmed that attitude toward vaccination practice and attitude toward DF were strongly correlated with DV acceptance, rs=0.41 and rs=0.39, respectively (P<0.001). The multivariate analysis revealed that a high monthly income, high SES, and a good attitude toward vaccination practice and toward DF were independent predictors of DV acceptance. The acceptance rate of the DV among inhabitants of Aceh, Indonesia was relatively high, and the strongest associated factors of higher support for the DV were a good attitude toward vaccination practices and a good attitude toward DF. Copyright © 2016 Elsevier Ltd. All rights reserved.

Year-class formation of upper St. Lawrence River northern pike

USGS Publications Warehouse

Smith, B.M.; Farrell, J.M.; Underwood, H.B.; Smith, S.J.

2007-01-01

Variables associated with year-class formation in upper St. Lawrence River northern pike Esox lucius were examined to explore population trends. A partial least-squares (PLS) regression model (PLS 1) was used to relate a year-class strength index (YCSI; 1974-1997) to explanatory variables associated with spawning and nursery areas (seasonal water level and temperature and their variability, number of ice days, and last day of ice presence). A second model (PLS 2) incorporated four additional ecological variables: potential predators (abundance of double-crested cormorants Phalacrocorax auritus and yellow perch Perca flavescens), female northern pike biomass (as a measure of stock-recruitment effects), and total phosphorus (productivity). Trends in adult northern pike catch revealed a decline (1981-2005), and year-class strength was positively related to catch per unit effort (CPUE; R2 = 0.58). The YCSI exceeded the 23-year mean in only 2 of the last 10 years. Cyclic patterns in the YCSI time series (along with strong year-classes every 4-6 years) were apparent, as was a dampening effect of amplitude beginning around 1990. The PLS 1 model explained over 50% of variation in both explanatory variables and the dependent variable, YCSI first-order moving-average residuals. Variables retained (N = 10; Wold's statistic ??? 0.8) included negative YCSI associations with high summer water levels, high variability in spring and fall water levels, and variability in fall water temperature. The YCSI exhibited positive associations with high spring, summer, and fall water temperature, variability in spring temperature, and high winter and spring water level. The PLS 2 model led to positive YCSI associations with phosphorus and yellow perch CPUE and a negative correlation with double-crested cormorant abundance. Environmental variables (water level and temperature) are hypothesized to regulate northern pike YCSI cycles, and dampening in YCSI magnitude may be related to a combination of factors, including wetland habitat changes, reduced nutrient loading, and increased predation by double-crested cormorants. ?? Copyright by the American Fisheries Society 2007.

Partition Coefficients of Organics between Water and Carbon Dioxide Revisited: Correlation with Solute Molecular Descriptors and Solvent Cohesive Properties.

PubMed

Roth, Michal

2016-12-06

High-pressure phase behavior of systems containing water, carbon dioxide and organics has been important in several environment- and energy-related fields including carbon capture and storage, CO 2 sequestration and CO 2 -assisted enhanced oil recovery. Here, partition coefficients (K-factors) of organic solutes between water and supercritical carbon dioxide have been correlated with extended linear solvation energy relationships (LSERs). In addition to the Abraham molecular descriptors of the solutes, the explanatory variables also include the logarithm of solute vapor pressure, the solubility parameters of carbon dioxide and water, and the internal pressure of water. This is the first attempt to include also the properties of water as explanatory variables in LSER correlations of K-factor data in CO 2 -water-organic systems. Increasing values of the solute hydrogen bond acidity, the solute hydrogen bond basicity, the solute dipolarity/polarizability, the internal pressure of water and the solubility parameter of water all tend to reduce the K-factor, that is, to favor the solute partitioning to the water-rich phase. On the contrary, increasing values of the solute characteristic volume, the solute vapor pressure and the solubility parameter of CO 2 tend to raise the K-factor, that is, to favor the solute partitioning to the CO 2 -rich phase.

Environmental, morphological, and productive characterization of Sardinian goats and use of latent explanatory factors for population analysis.

PubMed

Vacca, G M; Paschino, P; Dettori, M L; Bergamaschi, M; Cipolat-Gotet, C; Bittante, G; Pazzola, M

2016-09-01

Dairy goat farming is practiced worldwide, within a range of different farming systems. Here we investigated the effects of environmental factors and morphology on milk traits of the Sardinian goat population. Sardinian goats are currently reared in Sardinia (Italy) in a low-input context, similar to many goat farming systems, especially in developing countries. Milk and morphological traits from 1,050 Sardinian goats from 42 farms were recorded. We observed a high variability regarding morphological traits, such as coat color, ear length and direction, horn presence, and udder shape. Such variability derived partly from the unplanned repeated crossbreeding of the native Sardinian goats with exotic breeds, especially Maltese goats. The farms located in the mountains were characterized by the traditional farming system and the lowest percentage of crossbred goats. Explanatory factors analysis was used to summarize the interrelated measured milk variables. The explanatory factor related to fat, protein, and energy content of milk (the "Quality" latent variable) explained about 30% of the variance of the whole data set of measured milk traits followed by the "Hygiene" (19%), "Production" (19%), and "Acidity" (11%) factors. The "Quality" and "Hygiene" factors were not affected by any of the farm classification items, whereas "Production" and "Acidity" were affected only by altitude and size of herds, respectively, indicating the adaptation of the local goat population to different environmental conditions. The use of latent explanatory factor analysis allowed us to clearly explain the large variability of milk traits, revealing that the Sardinian goat population cannot be divided into subpopulations based on milk attitude The factors, properly integrated with genetic data, may be useful tools in future selection programs.

The cognitive and affective structure of paranoid delusions: a transdiagnostic investigation of patients with schizophrenia spectrum disorders and depression.

PubMed

Bentall, Richard P; Rowse, Georgina; Shryane, Nick; Kinderman, Peter; Howard, Robert; Blackwood, Nigel; Moore, Rosie; Corcoran, Rhiannon

2009-03-01

Paranoid delusions are a common symptom of a range of psychotic disorders. A variety of psychological mechanisms have been implicated in their cause, including a tendency to jump to conclusions, an impairment in the ability to understand the mental states of other people (theory of mind), an abnormal anticipation of threat, and an abnormal explanatory style coupled with low self-esteem. To determine the structure of the relationships among psychological mechanisms contributing to paranoia in a transdiagnostic sample. Cross-sectional design, with relationships between predictor variables and paranoia examined by structural equation models with latent variables. Publicly funded psychiatric services in London and the North West of England. One hundred seventy-three patients with schizophrenia spectrum disorders, major depression, or late-onset schizophrenia-like psychosis, subdivided according to whether they were currently experiencing paranoid delusions. Sixty-four healthy control participants matched for appropriate demographic variables were included. Assessments of theory of mind, jumping to conclusions bias, and general intellectual functioning, with measures of threat anticipation, emotion, self-esteem, and explanatory style. The best fitting (chi(2)(96) = 131.69, P = .01; comparative fit index = 0.95; Tucker-Lewis Index = 0.96; root-mean-square error of approximation = 0.04) and most parsimonious model of the data indicated that paranoid delusions are associated with a combination of pessimistic thinking style (low self-esteem, pessimistic explanatory style, and negative emotion) and impaired cognitive performance (executive functioning, tendency to jump to conclusions, and ability to reason about the mental states of others). Pessimistic thinking correlated highly with paranoia even when controlling for cognitive performance (r = 0.65, P < .001), and cognitive performance correlated with paranoia when controlling for pessimism (r = -0.34, P < .001). Both cognitive and emotion-related processes are involved in paranoid delusions. Treatment for paranoid patients should address both types of processes.

[Multilevel analysis of the technical efficiency of hospitals in the Spanish National Health System by property and type of management].

PubMed

Pérez-Romero, Carmen; Ortega-Díaz, M Isabel; Ocaña-Riola, Ricardo; Martín-Martín, José Jesús

2018-05-11

To analyze technical efficiency by type of property and management of general hospitals in the Spanish National Health System (2010-2012) and identify hospital and regional explanatory variables. 230 hospitals were analyzed combining data envelopment analysis and fixed effects multilevel linear models. Data envelopment analysis measured overall, technical and scale efficiency, and the analysis of explanatory factors was performed using multilevel models. The average rate of overall technical efficiency of hospitals without legal personality is lower than hospitals with legal personality (0.691 and 0.876 in 2012). There is a significant variability in efficiency under variable returns (TE) by direct, indirect and mixed forms of management. The 29% of the variability in TE es attributable to the Region. Legal personality increased the TE of the hospitals by 11.14 points. On the other hand, most of the forms of management (different to those of the traditional hospitals) increased TE in varying percentages. At regional level, according to the model considered, insularity and average annual income per household are explanatory variables of TE. Having legal personality favours technical efficiency. The regulatory and management framework of hospitals, more than public or private ownership, seem to explain technical efficiency. Regional characteristics explain the variability in TE. Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

ERIC Educational Resources Information Center

Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan

2017-01-01

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

Bayesian dynamical systems modelling in the social sciences.

PubMed

Ranganathan, Shyam; Spaiser, Viktoria; Mann, Richard P; Sumpter, David J T

2014-01-01

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.

Identifying Indicators of State Change and Forecasting Future Vulnerability in Alaskan Boreal Ecosystems

DTIC Science & Technology

2016-08-01

ice have catastrophic effects on facilities, infrastructure, and military testing and training. Permafrost temperature , thickness, and geographic...treeline) and fire severity (~0 to ~100% SOL consumption ), they provide an excellent suite of sites to test and quantify the effects of fire severity...stages .........................59 Table 6.1. Variables included in explanatory matrix for black spruce dominance ............68 Table 6.2. Mixed effect

Identifying Indicators of State Change and Forecasting Future Vulnerability in Alaskan Boreal Ecosystems

DTIC Science & Technology

2016-08-01

catastrophic effects on facilities, infrastructure, and military testing and training. Permafrost temperature , thickness, and geographic continuity...and fire severity (~0 to ~100% SOL consumption ), they provide an excellent suite of sites to test and quantify the effects of fire severity on plant...59 Table 6.1. Variables included in explanatory matrix for black spruce dominance ............68 Table 6.2. Mixed effect model

The use of cognitive ability measures as explanatory variables in regression analysis.

PubMed

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2012-12-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

Untangling Trends and Drivers of Changing River Discharge Along Florida's Gulf Coast

NASA Astrophysics Data System (ADS)

Glodzik, K.; Kaplan, D. A.; Klarenberg, G.

2017-12-01

Along the relatively undeveloped Big Bend coastline of Florida, discharge in many rivers and springs is decreasing. The causes are unclear, though they likely include a combination of groundwater extraction for water supply, climate variability, and altered land use. Saltwater intrusion from altered freshwater influence and sea level rise is causing transformative ecosystem impacts along this flat coastline, including coastal forest die-off and oyster reef collapse. A key uncertainty for understanding river discharge change is predicting discharge from rainfall, since Florida's karstic bedrock stores large amounts of groundwater, which has a long residence time. This study uses Dynamic Factor Analysis (DFA), a multivariate data reduction technique for time series, to find common trends in flow and reveal hydrologic variables affecting flow in eight Big Bend rivers since 1965. The DFA uses annual river flows as response time series, and climate data (annual rainfall and evapotranspiration by watershed) and climatic indices (El Niño Southern Oscillation [ENSO] Index and North Atlantic Oscillation [NAO] Index) as candidate explanatory variables. Significant explanatory variables (one evapotranspiration and three rainfall time series) explained roughly 50% of discharge variation across rivers. Significant trends (representing unexplained variation) were shared among rivers, with geographical grouping of five northern rivers and three southern rivers, along with a strong downward trend affecting six out of eight systems. ENSO and NAO had no significant impact. Advancing knowledge of these dynamics is necessary for forecasting how altered rainfall and temperatures from climate change may impact flows. Improved forecasting is especially important given Florida's reliance on groundwater extraction to support its growing population.

Longitudinal Course of Risk for Parental Post-Adoption Depression

PubMed Central

Foli, Karen J.; South, Susan C.; Lim, Eunjung; Hebdon, Megan

2016-01-01

Objective To determine whether the Postpartum Depression Predictors Inventory-Revised (PDPI-R) could be used to reveal distinct classes of adoptive parents across time. Design Longitudinal data were collected via online surveys at 4-6 weeks pre-placement, 4-6 weeks post-placement, and 5-6 months post-placement. Setting Participants were primarily clients of the largest adoption agency in the United States. Participants Participants included 127 adoptive parents (68 mothers and 59 fathers). Methods We applied a latent class growth analysis to the PDPI-R and conducted mixed effects modeling of class, time, and class×time interaction for the following categories of explanatory variables: parental expectations; interpersonal variables; psychological symptoms; and life orientation. Results Four latent trajectory classes were found. Class 1 (55% of sample) showed a stably low level of PDPI-R scores over time. Class 2 (32%) reported mean scores below the cut-off points at all three time points. Class 3 (8%) started at an intermediate level and increased after post-placement, but decreased at 5-6 months post-placement. Class 4 (5%) had high mean scores at all three time points. Significant main effects were found for almost all explanatory variables for class and for several variables for time. Significant interactions between class and time were found for expectations about the child and amount of love and ambivalence in parent's intimate relationship. Conclusion Findings may assist nurses to be alert to trajectories of risk for post-adoption depression. Additional factors, not included in the PDPI-R, to determine risk for post-adoption depression may be needed for adoptive parents. PMID:26874267

Global effects of local human population density and distance to markets on the condition of coral reef fisheries.

PubMed

Cinner, Joshua E; Graham, Nicholas A J; Huchery, Cindy; Macneil, M Aaron

2013-06-01

Coral reef fisheries support the livelihoods of millions of people but have been severely and negatively affected by anthropogenic activities. We conducted a systematic review of published data on the biomass of coral reef fishes to explore how the condition of reef fisheries is related to the density of local human populations, proximity of the reef to markets, and key environmental variables (including broad geomorphologic reef type, reef area, and net productivity). When only population density and environmental covariates were considered, high variability in fisheries conditions at low human population densities resulted in relatively weak explanatory models. The presence or absence of human settlements, habitat type, and distance to fish markets provided a much stronger explanatory model for the condition of reef fisheries. Fish biomass remained relatively low within 14 km of markets, then biomass increased exponentially as distance from reefs to markets increased. Our results suggest the need for an increased science and policy focus on markets as both a key driver of the condition of reef fisheries and a potential source of solutions. © 2012 Society for Conservation Biology.

Silent strain of caregiving: exploring the best predictors of distress in family carers of geriatric patients.

PubMed

Bień-Barkowska, Katarzyna; Doroszkiewicz, Halina; Bień, Barbara

2017-01-01

The aim of this article was to identify the best predictors of distress suffered by family carers (FCs) of geriatric patients. A cross-sectional study of 100 FC-geriatric patient dyads was conducted. The negative impact of care (NIoC) subscale of the COPE index was dichotomized to identify lower stress (score of ≤15 on the scale) and higher stress (score of ≥16 on the scale) exerted on FCs by the process of providing care. The set of explanatory variables comprised a wide range of sociodemographic and care-related attributes, including patient-related results from comprehensive geriatric assessments and disease profiles. The best combination of explanatory variables that provided the highest predictive power for distress among FCs in the multiple logistic regression (LR) model was determined according to statistical information criteria. The statistical robustness of the observed relationships and the discriminative power of the model were verified with the cross-validation method. The mean age of FCs was 57.2 (±10.6) years, whereas that of geriatric patients was 81.7 (±6.4) years. Despite the broad initial set of potential explanatory variables, only five predictors were jointly selected for the best statistical model. A higher level of distress was independently predicted by lower self-evaluation of health; worse self-appraisal of coping well as a caregiver; lower sense of general support; more hours of care per week; and the motor retardation of the cared-for person measured with the speed of the Timed Up and Go (TUG) test. Worse performance on the TUG test was only the patient-related predictor of distress among the variables examined as contributors to the higher NIoC. Enhancing the mobility of geriatric patients through suitably tailored kinesitherapeutic methods during their hospital stay may mitigate the burden endured by FCs.

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

PubMed

Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent

2014-02-01

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A tree-based statistical classification algorithm (CHAID) for identifying variables responsible for the occurrence of faecal indicator bacteria during waterworks operations

NASA Astrophysics Data System (ADS)

Bichler, Andrea; Neumaier, Arnold; Hofmann, Thilo

2014-11-01

Microbial contamination of groundwater used for drinking water can affect public health and is of major concern to local water authorities and water suppliers. Potential hazards need to be identified in order to protect raw water resources. We propose a non-parametric data mining technique for exploring the presence of total coliforms (TC) in a groundwater abstraction well and its relationship to readily available, continuous time series of hydrometric monitoring parameters (seven year records of precipitation, river water levels, and groundwater heads). The original monitoring parameters were used to create an extensive generic dataset of explanatory variables by considering different accumulation or averaging periods, as well as temporal offsets of the explanatory variables. A classification tree based on the Chi-Squared Automatic Interaction Detection (CHAID) recursive partitioning algorithm revealed statistically significant relationships between precipitation and the presence of TC in both a production well and a nearby monitoring well. Different secondary explanatory variables were identified for the two wells. Elevated water levels and short-term water table fluctuations in the nearby river were found to be associated with TC in the observation well. The presence of TC in the production well was found to relate to elevated groundwater heads and fluctuations in groundwater levels. The generic variables created proved useful for increasing significance levels. The tree-based model was used to predict the occurrence of TC on the basis of hydrometric variables.

Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS.

PubMed

Wiedermann, Wolfgang; Li, Xintong

2018-04-16

In nonexperimental data, at least three possible explanations exist for the association of two variables x and y: (1) x is the cause of y, (2) y is the cause of x, or (3) an unmeasured confounder is present. Statistical tests that identify which of the three explanatory models fits best would be a useful adjunct to the use of theory alone. The present article introduces one such statistical method, direction dependence analysis (DDA), which assesses the relative plausibility of the three explanatory models on the basis of higher-moment information about the variables (i.e., skewness and kurtosis). DDA involves the evaluation of three properties of the data: (1) the observed distributions of the variables, (2) the residual distributions of the competing models, and (3) the independence properties of the predictors and residuals of the competing models. When the observed variables are nonnormally distributed, we show that DDA components can be used to uniquely identify each explanatory model. Statistical inference methods for model selection are presented, and macros to implement DDA in SPSS are provided. An empirical example is given to illustrate the approach. Conceptual and empirical considerations are discussed for best-practice applications in psychological data, and sample size recommendations based on previous simulation studies are provided.

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

How relevant is environmental quality to per capita health expenditures? Empirical evidence from panel of developing countries.

PubMed

Yahaya, Adamu; Nor, Norashidah Mohamed; Habibullah, Muzafar Shah; Ghani, Judhiana Abd; Noor, Zaleha Mohd

2016-01-01

Developing countries have witnessed economic growth as their GDP keeps increasing steadily over the years. The growth led to higher energy consumption which eventually leads to increase in air pollutions that pose a danger to human health. People's healthcare demand, in turn, increase due to the changes in the socioeconomic life and improvement in the health technology. This study is an attempt to investigate the impact of environmental quality on per capital health expenditure in 125 developing countries within a panel cointegration framework from 1995 to 2012. We found out that a long-run relationship exists between per capita health expenditure and all explanatory variables as they were panel cointegrated. The explanatory variables were found to be statistically significant in explaining the per capita health expenditure. The result further revealed that CO2 has the highest explanatory power on the per capita health expenditure. The impact of the explanatory power of the variables is greater in the long-run compared to the short-run. Based on this result, we conclude that environmental quality is a powerful determinant of health expenditure in developing countries. Therefore, developing countries should as a matter of health care policy give provision of healthy air a priority via effective policy implementation on environmental management and control measures to lessen the pressure on health care expenditure. Moreover more environmental proxies with alternative methods should be considered in the future research.

Explanatory Power of Multi-scale Physical Descriptors in Modeling Benthic Indices Across Nested Ecoregions of the Pacific Northwest

NASA Astrophysics Data System (ADS)

Holburn, E. R.; Bledsoe, B. P.; Poff, N. L.; Cuhaciyan, C. O.

2005-05-01

Using over 300 R/EMAP sites in OR and WA, we examine the relative explanatory power of watershed, valley, and reach scale descriptors in modeling variation in benthic macroinvertebrate indices. Innovative metrics describing flow regime, geomorphic processes, and hydrologic-distance weighted watershed and valley characteristics are used in multiple regression and regression tree modeling to predict EPT richness, % EPT, EPT/C, and % Plecoptera. A nested design using seven ecoregions is employed to evaluate the influence of geographic scale and environmental heterogeneity on the explanatory power of individual and combined scales. Regression tree models are constructed to explain variability while identifying threshold responses and interactions. Cross-validated models demonstrate differences in the explanatory power associated with single-scale and multi-scale models as environmental heterogeneity is varied. Models explaining the greatest variability in biological indices result from multi-scale combinations of physical descriptors. Results also indicate that substantial variation in benthic macroinvertebrate response can be explained with process-based watershed and valley scale metrics derived exclusively from common geospatial data. This study outlines a general framework for identifying key processes driving macroinvertebrate assemblages across a range of scales and establishing the geographic extent at which various levels of physical description best explain biological variability. Such information can guide process-based stratification to avoid spurious comparison of dissimilar stream types in bioassessments and ensure that key environmental gradients are adequately represented in sampling designs.

Correlates of compliance with national comprehensive smoke-free laws.

PubMed

Peruga, Armando; Hayes, Luminita S; Aguilera, Ximena; Prasad, Vinayak; Bettcher, Douglas W

2017-12-05

To explore correlates of high compliance with smoking bans in a cross-sectional data set from the 41 countries with national comprehensive smoke-free laws in 2014 and complete data on compliance and enforcement. Outcome variable: compliance with a national comprehensive smoke-free law in each country was obtained for 2014 from the WHO global report on the global tobacco epidemic. Explanatory variables: legal enforcement requirements, penalties, infrastructure and strategy were obtained through a separate survey of governments. Also, country socioeconomic and demographic characteristics including the level of corruption control were included. an initial bivariate analysis determined the significance of each potentially relevant explanatory variable of high compliance. Differences in compliance were tested using the exact logistic regression. High compliance with the national comprehensive smoke-free law was associated with the involvement of the local jurisdictions in providing training and/or guidance for inspections (OR=10.3, 95% CI 1.7 to 117.7) and a perception of high corruption control efforts in the country (OR=7.2, 95% CI 1.1 to 85.8). The results show the importance of the depth of the enforcement infrastructure and effort represented by the degree to which the local government is involved in enforcement. They also show the significance of fighting corruption in the enforcement process, including the attempts of the tobacco industry to undermine the process, to achieve high levels of compliance with the law. The results point out to the need to invest minimal but essential enforcement resources given that national comprehensive smoke-free laws are self-enforcing in many but not all countries and sectors.

Examining school effectiveness at the fourth grade: A hierarchical analysis of the Third International Mathematics and Science Study (TIMSS)

NASA Astrophysics Data System (ADS)

Stemler, Steven Edward

This study explored school effectiveness in mathematics and science at the fourth grade using data from IEA's Third International Mathematics and Science Study (TIMSS). Fourteen of the 26 countries participating in TIMSS at the fourth grade possessed sufficient between-school variability in mathematics achievement to justify the creation of explanatory models of school effectiveness while 13 countries possessed sufficient between-school variability in science achievement. Exploratory models were developed using variables drawn from student, teacher, and school questionnaires. The variables were chosen to represent the domains of student involvement, instructional methods, classroom organization, school climate, and school structure. Six explanatory models for each subject were analyzed using two-level hierarchical linear modeling (HLM) and were compared to models using only school mean SES as an explanatory variable. The amount of variability in student achievement in mathematics attributable to differences between schools ranged from 16% in Cyprus to 56% in Latvia, while the amount of between-school variance in science achievement ranged from 12% in Korea to 59% in Latvia. In general, about one-quarter of the variability in mathematics and science achievement was found to lie between schools. The research findings revealed that after adjusting for differences in student backgrounds across schools, the most effective schools in mathematics and science had students who reported seeing a positive relationship between hard work, belief in their own abilities, and achievement. In addition, more effective schools had students who reported less frequent use of computers and calculators in the classroom. These relationships were found to be stable across explanatory models, cultural contexts, and subject areas. This study has contributed a unique element to the literature by examining school effectiveness at the fourth grade across two subject areas and across 14 different countries. The results indicate that further exploration of the relationship between school effectiveness and student locus of control warrants serious consideration. Future research on school effectiveness is recommended, perhaps using trend data and looking at different grade levels.

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

Vocational Teacher Stress and the Educational System.

ERIC Educational Resources Information Center

Adams, Elaine; Heath-Camp, Betty; Camp, William G.

1999-01-01

A multiple regression analysis of data from 235 secondary vocational teachers in Virginia found that educational system-related variables explained most teacher stress. The most important explanatory variables were task stress and role overload. (SK)

Attention Deficit Hyperactivity Disorder in Childhood: Healthcare Use in a Danish Birth Cohort during the First 12 Years of Life.

PubMed

Laugesen, Britt; Mohr-Jensen, Christina; Boldsen, Søren Kjærgaard; Jørgensen, Rikke; Sørensen, Erik Elgaard; Grønkjær, Mette; Rasmussen, Philippa; Lauritsen, Marlene Briciet

2018-06-01

To compare the mean number of medical and psychiatric hospital-based services in children with and without attention deficit hyperactivity disorder (ADHD) and to assess the effect of ADHD on hospital-based service use, including child-, parental-, and socioeconomic-related risk factors. A Danish birth cohort was followed through 12 years, and children with ADHD were identified using Danish nationwide registries. Poisson regression analyses were used to assess the association of ADHD with service use and to adjust for a comprehensive set of explanatory variables. Children diagnosed with ADHD used more medical and psychiatric hospital-based healthcare than those without ADHD. In children with ADHD, intellectual disability and parental psychiatric disorder were associated with increased medical and psychiatric service use. Low birth weight and low gestational age were associated with increased medical service use. Psychiatric comorbidity and having a divorced or single parent were associated with increased psychiatric service use. ADHD independently affected medical and psychiatric hospital-based service use even when adjusting for a comprehensive set of explanatory variables. However, the pattern of medical and psychiatric hospital-based service use is complex and cannot exclusively be explained by the child-, parental-, and socioeconomic-related variables examined in this study. Copyright © 2018 Elsevier Inc. All rights reserved.

Explanatory Models and Medication Adherence in Patients with Depression in South India

PubMed Central

Siddappa, Adarsh Lakkur; Raman, Rajesh; Hattur, Basavana Gowdappa

2017-01-01

Introduction Conceptualization of depression may have bearing on treatment seeking. It may affect adherence behaviour of the patients. Aim To find out the explanatory models and their relationship with socio-demographic variables and medication adherence in patients with depression. Materials and Methods Fifty-eight consecutive patients with depression in remission were recruited as per selection criteria. Socio-demographic details were collected. Patients were assessed using Mental Distress Explanatory Model Questionnaire (MDEMQ) and Morisky Medication Adherence Scale (MMAS). Results Significant scores were observed in all dimensions of explanatory models. In the Mann-Whitney U test the patient’s marital status (MU=113.500, p=0.05, sig≤0.05, 2-tailed), and family history of mental illness (MU=165.5, p=0.03, sig≤0.05, 2-tailed) had a statistically significant group difference in the score of MDEMQ. In linear regression analysis, four predictors (MDEMQ subscales Stress, Western physiology, Non-Western physiology and Supernatural) had significantly predicted the value of MMAS (R2=0.937, f=153.558, p<0.001). Conclusion Findings of this study suggested that patients with depression harbor multidimensional explanatory model. The levels of explanatory models are inversely associated with levels of medication adherence. PMID:28274025

How Robust Is Linear Regression with Dummy Variables?

ERIC Educational Resources Information Center

Blankmeyer, Eric

2006-01-01

Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…

The use of cognitive ability measures as explanatory variables in regression analysis

PubMed Central

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2015-01-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual’s wage, or a decision such as an individual’s education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score, constructed via standard psychometric practice from individuals’ responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a “mixed effects structural equations” (MESE) model, may be more appropriate in many circumstances. PMID:26998417

Generic Feature Selection with Short Fat Data

PubMed Central

Clarke, B.; Chu, J.-H.

2014-01-01

SUMMARY Consider a regression problem in which there are many more explanatory variables than data points, i.e., p ≫ n. Essentially, without reducing the number of variables inference is impossible. So, we group the p explanatory variables into blocks by clustering, evaluate statistics on the blocks and then regress the response on these statistics under a penalized error criterion to obtain estimates of the regression coefficients. We examine the performance of this approach for a variety of choices of n, p, classes of statistics, clustering algorithms, penalty terms, and data types. When n is not large, the discrimination over number of statistics is weak, but computations suggest regressing on approximately [n/K] statistics where K is the number of blocks formed by a clustering algorithm. Small deviations from this are observed when the blocks of variables are of very different sizes. Larger deviations are observed when the penalty term is an Lq norm with high enough q. PMID:25346546

12 CFR Appendix A to Subpart A of... - Method to Derive Pricing Multipliers and Uniform Amount

Code of Federal Regulations, 2012 CFR

2012-01-01

... explanatory variables (regressors) in the model are six financial ratios and a weighted average of the “C,” “A,” “M,” “E” and “L” component ratings. The six financial ratios included in the model are: • Tier 1... downgraded to 3 or worse within 3 to 12 months from time T. The risk measures are financial ratios as defined...

12 CFR Appendix A to Subpart A of... - Method to Derive Pricing Multipliers and Uniform Amount

Code of Federal Regulations, 2014 CFR

2014-01-01

... explanatory variables (regressors) in the model are six financial ratios and a weighted average of the “C,” “A,” “M,” “E” and “L” component ratings. The six financial ratios included in the model are: • Tier 1... downgraded to 3 or worse within 3 to 12 months from time T. The risk measures are financial ratios as defined...

12 CFR Appendix A to Subpart A of... - Method to Derive Pricing Multipliers and Uniform Amount

Code of Federal Regulations, 2013 CFR

2013-01-01

... explanatory variables (regressors) in the model are six financial ratios and a weighted average of the “C,” “A,” “M,” “E” and “L” component ratings. The six financial ratios included in the model are: • Tier 1... downgraded to 3 or worse within 3 to 12 months from time T. The risk measures are financial ratios as defined...

Psychosocial Factors Influencing Smokeless Tobacco Use by Teenage Military Dependents

DTIC Science & Technology

1994-01-01

age and grade Demographics both show strong bivariate relationships with the outcome MALE-male gender measures, we selected to use only one of these...a positive sign indicating a direct relation- no impact for either gender . Attitude and other tobacco influ- ship and a negative sign an inverse...by both genders . nlficant. However, if the interval includes one but is highly For both genders , the strongest explanatory variable for trial skewed

Exhaustive Search for Sparse Variable Selection in Linear Regression

NASA Astrophysics Data System (ADS)

Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

PubMed Central

Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel

2010-01-01

Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734

Incomes, Attitudes, and Occurrences of Invasive Species: An Application to Signal Crayfish in Sweden

NASA Astrophysics Data System (ADS)

Gren, Ing-Marie; Campos, Monica; Edsman, Lennart; Bohman, Patrik

2009-02-01

This article analyzes and carries out an econometric test of the explanatory power of economic and attitude variables for occurrences of the nonnative signal crayfish in Swedish waters. Signal crayfish are a carrier of plague which threatens the native noble crayfish with extinction. Crayfish are associated with recreational and cultural traditions in Sweden, which may run against environmental preferences for preserving native species. Econometric analysis is carried out using panel data at the municipality level with economic factors and attitudes as explanatory variables, which are derived from a simple dynamic harvesting model. A log-normal model is used for the regression analysis, and the results indicate significant impacts on occurrences of waters with signal crayfish of changes in both economic and attitude variables. Variables reflecting environmental and recreational preferences have unexpected signs, where the former variable has a positive and the latter a negative impact on occurrences of waters with signal crayfish. These effects are, however, counteracted by their respective interaction effect with income.

The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

NASA Astrophysics Data System (ADS)

Smith, Tony E.; Lee, Ka Lok

2012-01-01

There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce "spurious correlation" that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.

The effect of health and dental insurance on US children's dental care utilization for urgent and non-urgent dental problems - 2008.

PubMed

Naavaal, Shillpa; Barker, Laurie K; Griffin, Susan O

2017-12-01

We examined the association between utilization of care for a dental problem (utilization-DP) and parent-reported dental problem (DP) urgency among children with DP by type of health care insurance coverage. We used weighted 2008 National Health Interview Survey data from 2,834 children, aged 2-17 years with at least one DP within the 6 months preceding survey. Explanatory variables were selected based on Andersen's model of healthcare utilization. Need was considered urgent if DP included toothache, bleeding gums, broken or missing teeth, broken or missing filling, or decayed teeth and otherwise as non-urgent. The primary enabling variable, insurance, had four categories: none, private health no dental coverage (PHND), private health and dental (PHD), or Medicaid/State Children's Health Insurance Program (SCHIP). Predisposing variables included sociodemographic characteristics. We used bivariate and multivariate analyses to identify explanatory variables' association with utilization-DP. Using logistic regression, we obtained adjusted estimates of utilization-DP by urgency for each insurance category. In bivariate analyses, utilization-DP was associated with both insurance and urgency. In multivariate analyses, the difference in percent utilizing care for an urgent versus non-urgent DP among children covered by Medicaid/SCHIP was 32 percentage points; PHD, 25 percentage points; PHND, 12 percentage points; and no insurance, 14 percentage points. The difference in utilization by DP urgency was higher for children with Medicaid/SCHIP compared with either PHND or uninsured children. Expansion of Medicaid/SCHIP may permit children to receive care for urgent DPs who otherwise may not, due to lack of dental insurance. © 2016 American Association of Public Health Dentistry.

Health system determinants of infant, child and maternal mortality: A cross-sectional study of UN member countries

PubMed Central

2011-01-01

Objective Few studies have examined the link between health system strength and important public health outcomes across nations. We examined the association between health system indicators and mortality rates. Methods We used mixed effects linear regression models to investigate the strength of association between outcome and explanatory variables, while accounting for geographic clustering of countries. We modelled infant mortality rate (IMR), child mortality rate (CMR), and maternal mortality rate (MMR) using 13 explanatory variables as outlined by the World Health Organization. Results Significant protective health system determinants related to IMR included higher physician density (adjusted rate ratio [aRR] 0.81; 95% Confidence Interval [CI] 0.71-0.91), higher sustainable access to water and sanitation (aRR 0.85; 95% CI 0.78-0.93), and having a less corrupt government (aRR 0.57; 95% CI 0.40-0.80). Out-of-pocket expenditures on health (aRR 1.29; 95% CI 1.03-1.62) were a risk factor. The same four variables were significantly related to CMR after controlling for other variables. Protective determinants of MMR included access to water and sanitation (aRR 0.88; 95% CI 0.82-0.94), having a less corrupt government (aRR 0.49; 95%; CI 0.36-0.66), and higher total expenditures on health per capita (aRR 0.84; 95% CI 0.77-0.92). Higher fertility rates (aRR 2.85; 95% CI: 2.02-4.00) were found to be a significant risk factor for MMR. Conclusion Several key measures of a health system predict mortality in infants, children, and maternal mortality rates at the national level. Improving access to water and sanitation and reducing corruption within the health sector should become priorities. PMID:22023970

Long-term change in a behavioural trait: truncated spawning distribution and demography in Northeast Arctic cod

PubMed Central

Opdal, Anders Frugård; Jørgensen, Christian

2015-01-01

Harvesting may be a potent driver of demographic change and contemporary evolution, which both may have great impacts on animal populations. Research has focused on changes in phenotypic traits that are easily quantifiable and for which time series exist, such as size, age, sex, or gonad size, whereas potential changes in behavioural traits have been under-studied. Here, we analyse potential drivers of long-term changes in a behavioural trait for the Northeast Arctic stock of Atlantic cod Gadus morhua, namely choice of spawning location. For 104 years (1866–1969), commercial catches were recorded annually and reported by county along the Norwegian coast. During this time period, spawning ground distribution has fluctuated with a trend towards more northerly spawning. Spawning location is analysed against a suite of explanatory factors including climate, fishing pressure, density dependence, and demography. We find that demography (age or age at maturation) had the highest explanatory power for variation in spawning location, while climate had a limited effect below statistical significance. As to potential mechanisms, some effects of climate may act through demography, and explanatory variables for demography may also have absorbed direct evolutionary change in migration distance for which proxies were unavailable. Despite these caveats, we argue that fishing mortality, either through demographic or evolutionary change, has served as an effective driver for changing spawning locations in cod, and that additional explanatory factors related to climate add no significant information. PMID:25336028

Efficacy of generic allometric equations for estimating biomass: a test in Japanese natural forests.

PubMed

Ishihara, Masae I; Utsugi, Hajime; Tanouchi, Hiroyuki; Aiba, Masahiro; Kurokawa, Hiroko; Onoda, Yusuke; Nagano, Masahiro; Umehara, Toru; Ando, Makoto; Miyata, Rie; Hiura, Tsutom

2015-07-01

Accurate estimation of tree and forest biomass is key to evaluating forest ecosystem functions and the global carbon cycle. Allometric equations that estimate tree biomass from a set of predictors, such as stem diameter and tree height, are commonly used. Most allometric equations are site specific, usually developed from a small number of trees harvested in a small area, and are either species specific or ignore interspecific differences in allometry. Due to lack of site-specific allometries, local equations are often applied to sites for which they were not originally developed (foreign sites), sometimes leading to large errors in biomass estimates. In this study, we developed generic allometric equations for aboveground biomass and component (stem, branch, leaf, and root) biomass using large, compiled data sets of 1203 harvested trees belonging to 102 species (60 deciduous angiosperm, 32 evergreen angiosperm, and 10 evergreen gymnosperm species) from 70 boreal, temperate, and subtropical natural forests in Japan. The best generic equations provided better biomass estimates than did local equations that were applied to foreign sites. The best generic equations included explanatory variables that represent interspecific differences in allometry in addition to stem diameter, reducing error by 4-12% compared to the generic equations that did not include the interspecific difference. Different explanatory variables were selected for different components. For aboveground and stem biomass, the best generic equations had species-specific wood specific gravity as an explanatory variable. For branch, leaf, and root biomass, the best equations had functional types (deciduous angiosperm, evergreen angiosperm, and evergreen gymnosperm) instead of functional traits (wood specific gravity or leaf mass per area), suggesting importance of other traits in addition to these traits, such as canopy and root architecture. Inclusion of tree height in addition to stem diameter improved the performance of the generic equation only for stem biomass and had no apparent effect on aboveground, branch, leaf, and root biomass at the site level. The development of a generic allometric equation taking account of interspecific differences is an effective approach for accurately estimating aboveground and component biomass in boreal, temperate, and subtropical natural forests.

Uni- and multi-variable modelling of flood losses: experiences gained from the Secchia river inundation event.

NASA Astrophysics Data System (ADS)

Carisi, Francesca; Domeneghetti, Alessio; Kreibich, Heidi; Schröter, Kai; Castellarin, Attilio

2017-04-01

Flood risk is function of flood hazard and vulnerability, therefore its accurate assessment depends on a reliable quantification of both factors. The scientific literature proposes a number of objective and reliable methods for assessing flood hazard, yet it highlights a limited understanding of the fundamental damage processes. Loss modelling is associated with large uncertainty which is, among other factors, due to a lack of standard procedures; for instance, flood losses are often estimated based on damage models derived in completely different contexts (i.e. different countries or geographical regions) without checking its applicability, or by considering only one explanatory variable (i.e. typically water depth). We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 200 km2 in Northern Italy. In the aftermath of this event, local authorities collected flood loss data, together with additional information on affected private households and industrial activities (e.g. buildings surface and economic value, number of company's employees and others). Based on these data we implemented and compared a quadratic-regression damage function, with water depth as the only explanatory variable, and a multi-variable model that combines multiple regression trees and considers several explanatory variables (i.e. bagging decision trees). Our results show the importance of data collection revealing that (1) a simple quadratic regression damage function based on empirical data from the study area can be significantly more accurate than literature damage-models derived for a different context and (2) multi-variable modelling may outperform the uni-variable approach, yet it is more difficult to develop and apply due to a much higher demand of detailed data.

The association between depression and emotional and social loneliness in older persons and the influence of social support, cognitive functioning and personality: A cross-sectional study.

PubMed

Peerenboom, L; Collard, R M; Naarding, P; Comijs, H C

2015-08-15

We investigated the association between old age depression and emotional and social loneliness. A cross-sectional study was performed using data from the Netherlands Study of Depression in Older Persons (NESDO). A total of 341 participants diagnosed with a depressive disorder, and 125 non-depressed participants were included. Depression diagnosis was confirmed with the Composite International Diagnostic Interview. Emotional and social loneliness were assessed using the De Jong Gierveld Loneliness Scale. Socio-demographic variables, social support variables, depression characteristics (Inventory of Depressive Symptoms), cognitive functioning (Mini Mental State Examination) and personality factors (the NEO- Five Factor Inventory and the Pearlin Mastery Scale) were considered as possible explanatory factors or confounders. (Multiple) logistic regression analyses were performed. Depression was strongly associated with emotional loneliness, but not with social loneliness. A higher sense of neuroticism and lower sense of mastery were the most important explanatory factors. Also, we found several other explanatory and confounding factors in the association of depression and emotional loneliness; a lower sense of extraversion and higher severity of depression. We performed a cross-sectional observational study. Therefore we cannot add evidence in regard to causation; whether depression leads to loneliness or vice versa. Depression in older persons is strongly associated with emotional loneliness but not with social loneliness. Several personality traits and the severity of depression are important in regard to the association of depression and emotional loneliness. It is important to develop interventions in which both can be treated. Copyright © 2015 Elsevier B.V. All rights reserved.

Against Laplacian Reduction of Newtonian Mass to Spatiotemporal Quantities

NASA Astrophysics Data System (ADS)

Martens, Niels C. M.

2018-05-01

Laplace wondered about the minimal choice of initial variables and parameters corresponding to a well-posed initial value problem. Discussions of Laplace's problem in the literature have focused on choosing between spatiotemporal variables relative to absolute space (i.e. substantivalism) or merely relative to other material bodies (i.e. relationalism) and between absolute masses (i.e. absolutism) or merely mass ratios (i.e. comparativism). This paper extends these discussions of Laplace's problem, in the context of Newtonian Gravity, by asking whether mass needs to be included in the initial state at all, or whether a purely spatiotemporal initial state suffices. It is argued that mass indeed needs to be included; removing mass from the initial state drastically reduces the predictive and explanatory power of Newtonian Gravity.

Against Laplacian Reduction of Newtonian Mass to Spatiotemporal Quantities

NASA Astrophysics Data System (ADS)

Martens, Niels C. M.

2018-03-01

Laplace wondered about the minimal choice of initial variables and parameters corresponding to a well-posed initial value problem. Discussions of Laplace's problem in the literature have focused on choosing between spatiotemporal variables relative to absolute space (i.e. substantivalism) or merely relative to other material bodies (i.e. relationalism) and between absolute masses (i.e. absolutism) or merely mass ratios (i.e. comparativism). This paper extends these discussions of Laplace's problem, in the context of Newtonian Gravity, by asking whether mass needs to be included in the initial state at all, or whether a purely spatiotemporal initial state suffices. It is argued that mass indeed needs to be included; removing mass from the initial state drastically reduces the predictive and explanatory power of Newtonian Gravity.

Explaining the sense of family coherence among husbands and wives: the Israeli case.

PubMed

Kulik, Liat

2009-12-01

This study examined variables belonging to the family environment that explain the sense of family coherence among husbands (n = 133) and wives (n = 133) in Israel. Specifically, the explanatory variables tested were spousal power relations (as expressed in equality in the division of household labor and decision making), and perceived family conflict. In general, the sense of family coherence among spouses was found to be high. Perceived family conflict contributed to explaining the sense of family coherence for both husbands and wives. Equality in the division of household labor and in decision making had a greater impact on husbands than wives. Family coherence correlated negatively with age for husbands and positively with income for wives. The explanatory variables had a greater impact on the sense of family coherence among husbands than among wives.

Human influence on California fire regimes.

PubMed

Syphard, Alexandra D; Radeloff, Volker C; Keeley, Jon E; Hawbaker, Todd J; Clayton, Murray K; Stewart, Susan I; Hammer, Roger B

2007-07-01

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.

Human influence on California fire regimes

USGS Publications Warehouse

Syphard, A.D.; Radeloff, V.C.; Keeley, J.E.; Hawbaker, T.J.; Clayton, M.K.; Stewart, S.I.; Hammer, R.B.

2007-01-01

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation. ?? 2007 by the Ecological Society of America.

Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?

PubMed

Kohler, Friedbert; Renton, Roger; Dickson, Hugh G; Estell, John; Connolly, Carol E

2011-02-01

We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes.

Nitrogen deposition outweighs climatic variability in driving annual growth rate of canopy beech trees: Evidence from long-term growth reconstruction across a geographic gradient.

PubMed

Gentilesca, Tiziana; Rita, Angelo; Brunetti, Michele; Giammarchi, Francesco; Leonardi, Stefano; Magnani, Federico; van Noije, Twan; Tonon, Giustino; Borghetti, Marco

2018-07-01

In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (G V ) and height (G H ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO 2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO 2 effects on G V and G H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (N dep ) effects were repeatedly observed in G V and G H ; the positive effects of N dep on canopy height growth rates, which tended to level off at N dep values greater than approximately 1.0 g m -2  y -1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites. © 2018 John Wiley & Sons Ltd.

Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

ERIC Educational Resources Information Center

Lu, Irene R. R.; Thomas, D. Roland

2008-01-01

This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

Treatment needs and predictive capacity of explanatory variables of oral disease in young athletes with an intellectual disability in Europe and Eurasia.

PubMed

Fernandez, C; Descamps, I; Fabjanska, K; Kaschke, I; Marks, L

2016-03-01

To evaluate the oral condition and treatment needs of young athletes with intellectual disability (ID) from 53 countries of Europe and Eurasia who participated in the Special Olympics European Games held in Antwerp, October 2014. A cross- sectional study was undertaken with data collected through standardised procedures from consenting athletes under 21 years of age. Oral hygiene habits, reports of oral pain and presence of gingival signs, sealants, untreated caries and missing teeth were recorded. Data analysis was performed in SPSS to produce descriptive statistics and explanatory variables for untreated decay, and gingival signs of disease were tested with Multilevel Generalized Linear Mixed Models. Five hundred three athletes participated in this study (mean age 17 yrs). Untreated decay was recorded in 33.4% of the participants and 38.7% of them had signs of gingival disease. Absence of untreated decay was associated with lower chances of gingival signs, while absence of sealants was related with higher chances of untreated decay. There is consistent evidence of persistent need for increased promotion of oral health, as well as preventive and restorative treatment in young athletes with ID in Europe and Eurasia. Due to the limited predictive capacity of the studied variables for oral disease, further studies including other related factors are needed.

[Associations of the Employment Status during the First 2 Years Following Medical Rehabilitation and Long Term Occupational Trajectories: Implications for Outcome Measurement].

PubMed

Holstiege, J; Kaluscha, R; Jankowiak, S; Krischak, G

2017-02-01

Study Objectives: The aim was to investigate the predictive value of the employment status measured in the 6 th , 12 th , 18 th and 24 th month after medical rehabilitation for long-term employment trajectories during 4 years. Methods: A retrospective study was conducted based on a 20%-sample of all patients receiving inpatient rehabilitation funded by the German pension fund. Patients aged <62 years who were treated due to musculoskeletal, cardiovascular or psychosomatic disorders during the years 2002-2005 were included and followed for 4 consecutive years. The predictive value of the employment status in 4 predefined months after discharge (6 th , 12 th , 18 th and 24 th month), for the total number of months in employment in 4 years following rehabilitative treatment was analyzed using multiple linear regression. Per time point, separate regression analyses were conducted, including the employment status (employed vs. unemployed) at the respective point in time as explanatory variable, besides a standard set of additional prognostic variables. Results: A total of 252 591 patients were eligible for study inclusion. The level of explained variance of the regression models increased with the point in time used to measure the employment status, included as explanatory variable. Overall the R²-measure increased by 30% from the regression model that included the employment status in the 6 th month (R²=0.60) to the model that included the work status in the 24 th month (R²=0.78). Conclusion: The degree of accuracy in the prognosis of long-term employment biographies increases with the point in time used to measure employment in the first 2 years following rehabilitation. These findings should be taken into consideration for the predefinition of time points used to measure the employment status in future studies. © Georg Thieme Verlag KG Stuttgart · New York.

Attitudinal and behavioral characteristics predict high risk sexual activity in rural Tanzanian youth.

PubMed

Aichele, Stephen R; Borgerhoff Mulder, Monique; James, Susan; Grimm, Kevin

2014-01-01

The incidence of HIV infection in rural African youth remains high despite widespread knowledge of the disease within the region and increasing funds allocated to programs aimed at its prevention and treatment. This suggests that program efficacy requires a more nuanced understanding of the profiles of the most at-risk individuals. To evaluate the explanatory power of novel psychographic variables in relation to high-risk sexual behaviors, we conducted a survey to assess the effects of psychographic factors, both behavioral and attitudinal, controlling for standard predictors in 546 youth (12-26 years of age) across 8 villages in northern Tanzania. Indicators of high-risk sexual behavior included HIV testing, sexual history (i.e., virgin/non-virgin), age of first sexual activity, condom use, and number of lifetime sexual partners. Predictors in the statistical models included standard demographic variables, patterns of media consumption, HIV awareness, and six new psychographic features identified via factor analyses: personal vanity, family-building values, ambition for higher education, town recreation, perceived parental strictness, and spending preferences. In a series of hierarchical regression analyses, we find that models including psychographic factors contribute significant additional explanatory information when compared to models including only demographic and other conventional predictors. We propose that the psychographic approach used here, in so far as it identifies individual characteristics, aspirations, aspects of personal life style and spending preferences, can be used to target appropriate communities of youth within villages for leading and receiving outreach, and to build communities of like-minded youth who support new patterns of sexual behavior.

Sexual function in women in rural Tamil Nadu: disease, dysfunction, distress and norms.

PubMed

Viswanathan, Shonima; Prasad, Jasmine; Jacob, K S; Kuruvilla, Anju

2014-01-01

We examined the nature, prevalence and explanatory models of sexual concerns and dysfunction among women in rural Tamil Nadu. Married women between 18 and 65 years of age, from randomly selected villages in Kaniyambadi block, Vellore district, Tamil Nadu, were chosen by stratified sampling technique. Sexual functioning was assessed using the Female Sexual Function Index (FSFI). The modified Short Explanatory Model Interview (SEMI) was used to assess beliefs about sexual concerns and the General Health Questionnaire-12 (GHQ-12) was used to screen for common mental disorders. Sociodemographic variables and other risk factors were also assessed. Most of the women (277; 98.2%) contacted agreed to participate in the study. The prevalence of sexual dysfunction, based on the cut-off score on the FSFI, was 64.3%. However, only a minority of women considered it a problem (4.7%), expressed dissatisfaction (5.8%) or sought medical help (2.5%). The most common explanatory models offered for sexual problems included an unhappy marriage,stress and physical problems. Factors associated with lower FSFI included older age, illiteracy, as well as medical illness and sexual and marital factors such as menopause, poor quality of marital relationship, history of physical abuse and lack of privacy. The diagnosis of female sexual dysfunction needs to be nuanced and based on the broader personal and social context. Our findings argue that there is a need to use models that employ personal, local and contextual standards in assessing complex behaviours such as sexual function. Copyright 2014, NMJI.

Computation of Standard Errors

PubMed Central

Dowd, Bryan E; Greene, William H; Norton, Edward C

2014-01-01

Objectives We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages. Study Design We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects. Empirical Application Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky–Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5. Conclusions In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values. PMID:24800304

A comparison of data-driven groundwater vulnerability assessment methods

USGS Publications Warehouse

Sorichetta, Alessandro; Ballabio, Cristiano; Masetti, Marco; Robinson, Gilpin R.; Sterlacchini, Simone

2013-01-01

Increasing availability of geo-environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c-statistic. Groundwater vulnerability probability outputs, based on rank-order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).

Relations among Functional Systems in Behavior Analysis

PubMed Central

Thompson, Travis

2007-01-01

This paper proposes that an organism's integrated repertoire of operant behavior has the status of a biological system, similar to other biological systems, like the nervous, cardiovascular, or immune systems. Evidence from a number of sources indicates that the distinctions between biological and behavioral events is often misleading, engendering counterproductive explanatory controversy. A good deal of what is viewed as biological (often thought to be inaccessible or hypothetical) can become publicly measurable variables using currently available and developing technologies. Moreover, such endogenous variables can serve as establishing operations, discriminative stimuli, conjoint mediating events, and maintaining consequences within a functional analysis of behavior and need not lead to reductionistic explanation. I suggest that explanatory misunderstandings often arise from conflating different levels of analysis and that behavior analysis can extend its reach by identifying variables operating within a functional analysis that also serve functions in other biological systems. PMID:17575907

Long-term change in a behavioural trait: truncated spawning distribution and demography in Northeast Arctic cod.

PubMed

Opdal, Anders Frugård; Jørgensen, Christian

2015-04-01

Harvesting may be a potent driver of demographic change and contemporary evolution, which both may have great impacts on animal populations. Research has focused on changes in phenotypic traits that are easily quantifiable and for which time series exist, such as size, age, sex, or gonad size, whereas potential changes in behavioural traits have been under-studied. Here, we analyse potential drivers of long-term changes in a behavioural trait for the Northeast Arctic stock of Atlantic cod Gadus morhua, namely choice of spawning location. For 104 years (1866-1969), commercial catches were recorded annually and reported by county along the Norwegian coast. During this time period, spawning ground distribution has fluctuated with a trend towards more northerly spawning. Spawning location is analysed against a suite of explanatory factors including climate, fishing pressure, density dependence, and demography. We find that demography (age or age at maturation) had the highest explanatory power for variation in spawning location, while climate had a limited effect below statistical significance. As to potential mechanisms, some effects of climate may act through demography, and explanatory variables for demography may also have absorbed direct evolutionary change in migration distance for which proxies were unavailable. Despite these caveats, we argue that fishing mortality, either through demographic or evolutionary change, has served as an effective driver for changing spawning locations in cod, and that additional explanatory factors related to climate add no significant information. © 2014 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution.

PubMed

Messier, Kyle P; Jackson, Laura E; White, Jennifer L; Hilborn, Elizabeth D

2015-01-01

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution. Published by Elsevier Ltd.

Insight in psychosis: Standards, science, ethics and value judgment.

PubMed

Jacob, K S

2017-06-01

The clinical assessment of insight solely employs biomedical perspectives and criteria to the complete exclusion of context and culture and to the disregard of values and value judgments. The aim of this discussion article is to examine recent research from India on insight and explanatory models in psychosis and re-examine the framework of assessment, diagnosis and management of insight and explanatory models. Recent research from India on insight in psychosis and explanatory models is reviewed. Recent research, which has used longitudinal data and adjusted for pretreatment variables, suggests that insight and explanatory models of illness at baseline do not predict course, outcome and treatment response in schizophrenia, which seem to be dependent on the severity and quality of the psychosis. It supports the view that people with psychosis simultaneously hold multiple and contradictory explanatory models of illness, which change over time and with the trajectory of the illness. It suggests that insight, like all explanatory models, is a narrative of the person's reality and a coping strategy to handle with the varied impact of the illness. This article argues that the assessment of insight necessarily involves value entailments, commitments and consequences. It supports a need for a broad-based approach to assess awareness, attribution and action related to mental illness and to acknowledge the role of values and value judgment in the evaluation of insight in psychosis.

Poisson Regression Analysis of Illness and Injury Surveillance Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frome E.L., Watkins J.P., Ellis E.D.

2012-12-12

The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences duemore » to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra-Poisson variation. The R open source software environment for statistical computing and graphics is used for analysis. Additional details about R and the data that were used in this report are provided in an Appendix. Information on how to obtain R and utility functions that can be used to duplicate results in this report are provided.« less

Static and dynamic controls on fire activity at moderate spatial and temporal scales in the Alaskan boreal forest

USGS Publications Warehouse

Barrett, Kirsten; Loboda, Tatiana; McGuire, A. David; Genet, Hélène; Hoy, Elizabeth; Kasischke, Eric

2016-01-01

Wildfire, a dominant disturbance in boreal forests, is highly variable in occurrence and behavior at multiple spatiotemporal scales. New data sets provide more detailed spatial and temporal observations of active fires and the post-burn environment in Alaska. In this study, we employ some of these new data to analyze variations in fire activity by developing three explanatory models to examine the occurrence of (1) seasonal periods of elevated fire activity using the number of MODIS active fire detections data set (MCD14DL) within an 11-day moving window, (2) unburned patches within a burned area using the Monitoring Trends in Burn Severity fire severity product, and (3) short-to-moderate interval (<60 yr) fires using areas of burned area overlap in the Alaska Large Fire Database. Explanatory variables for these three models included dynamic variables that can change over the course of the fire season, such as weather and burn date, as well as static variables that remain constant over a fire season, such as topography, drainage, vegetation cover, and fire history. We found that seasonal periods of high fire activity are associated with both seasonal timing and aggregated weather conditions, as well as the landscape composition of areas that are burning. Important static inputs to the model of seasonal fire activity indicate that when fire weather conditions are suitable, areas that typically resist fire (e.g., deciduous stands) may become more vulnerable to burning and therefore less effective as fire breaks. The occurrence of short-to-moderate interval fires appears to be primarily driven by weather conditions, as these were the only relevant explanatory variables in the model. The unique importance of weather in explaining short-to-moderate interval fires implies that fire return intervals (FRIs) will be sensitive to projected climate changes in the region. Unburned patches occur most often in younger stands, which may be related to a greater deciduous fraction of vegetation as well as lower fuel loads compared with mature stands. The fraction of unburned patches may therefore increase in response to decreasing FRIs and increased deciduousness in the region, or these may decrease if fire weather conditions become more severe.

Effects of poor asthma control, insomnia, anxiety and depression on quality of life in young asthmatics.

PubMed

Sundbom, Fredrik; Malinovschi, Andrei; Lindberg, Eva; Alving, Kjell; Janson, Christer

2016-01-01

Asthma-related quality of life has previously been shown to be associated with asthma control. The aims of the present study were to further analyze this correlation, identify other variables with impact on asthma-related quality of life and investigate the covariance among these variables. Information was retrieved from a cohort of 369 patients, aged 12-35, with physician-diagnosed asthma requiring anti-inflammatory treatment for at least 3 months per year. Questionnaire data [including the mini-Asthma Quality of Life Questionnaire (mAQLQ), asthma control test (ACT) and Hospital Anxiety and Depression Scale (HADS)], quality of sleep, lung function data and blood samples were analyzed. Linear regression models with the mAQLQ score as the dependent scalar variable were calculated. ACT was the single variable that had the highest explanatory value for the mAQLQ score (51.5%). High explanatory power was also observed for anxiety and depression (17.0%) and insomnia (14.1%). The population was divided into groups depending on the presence of anxiety and depression, uncontrolled asthma and insomnia. The group that reported none of these conditions had the highest mean mAQLQ score (6.3 units), whereas the group reporting all of these conditions had the lowest mAQLQ score (3.8 units). The ACT score was the single most important variable in predicting asthma-related quality of life. Combining the ACT score with the data on insomnia, anxiety and depression showed considerable additive effects of the conditions. Hence, we recommend the routine use of the ACT and careful attention to symptoms of insomnia, anxiety or depression in the clinical evaluation of asthma-related quality of life.

Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA

NASA Astrophysics Data System (ADS)

Mair, Alan; El-Kadi, Aly I.

2013-10-01

Capture zone analysis combined with a subjective susceptibility index is currently used in Hawaii to assess vulnerability to contamination of drinking water sources derived from groundwater. In this study, we developed an alternative objective approach that combines well capture zones with multiple-variable logistic regression (LR) modeling and applied it to the highly-utilized Pearl Harbor and Honolulu aquifers on the island of Oahu, Hawaii. Input for the LR models utilized explanatory variables based on hydrogeology, land use, and well geometry/location. A suite of 11 target contaminants detected in the region, including elevated nitrate (> 1 mg/L), four chlorinated solvents, four agricultural fumigants, and two pesticides, was used to develop the models. We then tested the ability of the new approach to accurately separate groups of wells with low and high vulnerability, and the suitability of nitrate as an indicator of other types of contamination. Our results produced contaminant-specific LR models that accurately identified groups of wells with the lowest/highest reported detections and the lowest/highest nitrate concentrations. Current and former agricultural land uses were identified as significant explanatory variables for eight of the 11 target contaminants, while elevated nitrate was a significant variable for five contaminants. The utility of the combined approach is contingent on the availability of hydrologic and chemical monitoring data for calibrating groundwater and LR models. Application of the approach using a reference site with sufficient data could help identify key variables in areas with similar hydrogeology and land use but limited data. In addition, elevated nitrate may also be a suitable indicator of groundwater contamination in areas with limited data. The objective LR modeling approach developed in this study is flexible enough to address a wide range of contaminants and represents a suitable addition to the current subjective approach.

Prediction equations of forced oscillation technique: the insidious role of collinearity.

PubMed

Narchi, Hassib; AlBlooshi, Afaf

2018-03-27

Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.

Predictive modeling of hazardous waste landfill total above-ground biomass using passive optical and LIDAR remotely sensed data

NASA Astrophysics Data System (ADS)

Hadley, Brian Christopher

This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.

Directional Dependence in Developmental Research

ERIC Educational Resources Information Center

von Eye, Alexander; DeShon, Richard P.

2012-01-01

In this article, we discuss and propose methods that may be of use to determine direction of dependence in non-normally distributed variables. First, it is shown that standard regression analysis is unable to distinguish between explanatory and response variables. Then, skewness and kurtosis are discussed as tools to assess deviation from…

Pupil Control Ideology and the Salience of Teacher Characteristics

ERIC Educational Resources Information Center

Smyth, W. J.

1977-01-01

The explanatory power of the combined biographical variables of teacher age, experience, sex, organizational status, and academic qualifications for variances in pupil control ideology (PCI) is seriously questioned, since as little as 6 percent of PCI variance may be explained by reference to these particular variables. (Author)

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

The role of area-level deprivation and gender in participation in population-based faecal immunochemical test (FIT) colorectal cancer screening.

PubMed

Clarke, Nicholas; McNamara, Deirdre; Kearney, Patricia M; O'Morain, Colm A; Shearer, Nikki; Sharp, Linda

2016-12-01

This study aimed to investigate the effects of sex and deprivation on participation in a population-based faecal immunochemical test (FIT) colorectal cancer screening programme. The study population included 9785 individuals invited to participate in two rounds of a population-based biennial FIT-based screening programme, in a relatively deprived area of Dublin, Ireland. Explanatory variables included in the analysis were sex, deprivation category of area of residence and age (at end of screening). The primary outcome variable modelled was participation status in both rounds combined (with "participation" defined as having taken part in either or both rounds of screening). Poisson regression with a log link and robust error variance was used to estimate relative risks (RR) for participation. As a sensitivity analysis, data were stratified by screening round. In both the univariable and multivariable models deprivation was strongly associated with participation. Increasing affluence was associated with higher participation; participation was 26% higher in people resident in the most affluent compared to the most deprived areas (multivariable RR=1.26: 95% CI 1.21-1.30). Participation was significantly lower in males (multivariable RR=0.96: 95%CI 0.95-0.97) and generally increased with increasing age (trend per age group, multivariable RR=1.02: 95%CI, 1.01-1.02). No significant interactions between the explanatory variables were found. The effects of deprivation and sex were similar by screening round. Deprivation and male gender are independently associated with lower uptake of population-based FIT colorectal cancer screening, even in a relatively deprived setting. Development of evidence-based interventions to increase uptake in these disadvantaged groups is urgently required. Copyright © 2016. Published by Elsevier Inc.

Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss

PubMed Central

Bermudez, Eduardo B.; Klerman, Elizabeth B.; Czeisler, Charles A.; Cohen, Daniel A.; Wyatt, James K.; Phillips, Andrew J. K.

2016-01-01

Sleep restriction causes impaired cognitive performance that can result in adverse consequences in many occupational settings. Individuals may rely on self-perceived alertness to decide if they are able to adequately perform a task. It is therefore important to determine the relationship between an individual’s self-assessed alertness and their objective performance, and how this relationship depends on circadian phase, hours since awakening, and cumulative lost hours of sleep. Healthy young adults (aged 18–34) completed an inpatient schedule that included forced desynchrony of sleep/wake and circadian rhythms with twelve 42.85-hour “days” and either a 1:2 (n = 8) or 1:3.3 (n = 9) ratio of sleep-opportunity:enforced-wakefulness. We investigated whether subjective alertness (visual analog scale), circadian phase (melatonin), hours since awakening, and cumulative sleep loss could predict objective performance on the Psychomotor Vigilance Task (PVT), an Addition/Calculation Test (ADD) and the Digit Symbol Substitution Test (DSST). Mathematical models that allowed nonlinear interactions between explanatory variables were evaluated using the Akaike Information Criterion (AIC). Subjective alertness was the single best predictor of PVT, ADD, and DSST performance. Subjective alertness alone, however, was not an accurate predictor of PVT performance. The best AIC scores for PVT and DSST were achieved when all explanatory variables were included in the model. The best AIC score for ADD was achieved with circadian phase and subjective alertness variables. We conclude that subjective alertness alone is a weak predictor of objective vigilant or cognitive performance. Predictions can, however, be improved by knowing an individual’s circadian phase, current wake duration, and cumulative sleep loss. PMID:27019198

Detection of outliers in the response and explanatory variables of the simple circular regression model

NASA Astrophysics Data System (ADS)

Mahmood, Ehab A.; Rana, Sohel; Hussin, Abdul Ghapor; Midi, Habshah

2016-06-01

The circular regression model may contain one or more data points which appear to be peculiar or inconsistent with the main part of the model. This may be occur due to recording errors, sudden short events, sampling under abnormal conditions etc. The existence of these data points "outliers" in the data set cause lot of problems in the research results and the conclusions. Therefore, we should identify them before applying statistical analysis. In this article, we aim to propose a statistic to identify outliers in the both of the response and explanatory variables of the simple circular regression model. Our proposed statistic is robust circular distance RCDxy and it is justified by the three robust measurements such as proportion of detection outliers, masking and swamping rates.

Critical Loads of Atmospheric Nitrogen Deposition for Aquatic Ecosystems in Yosemite and Sequoia and Kings Canyon National Parks

NASA Astrophysics Data System (ADS)

Nanus, L.; Clow, D. W.; Sickman, J. O.

2016-12-01

High-elevation aquatic ecosystems in Yosemite (YOSE) and Sequoia and Kings Canyon (SEKI) National Parks are impacted by atmospheric nitrogen (N) deposition associated with local and regional air pollution. Documented effects include elevated surface water nitrate concentrations, increased algal productivity, and changes in diatom species assemblages. Annual wet inorganic N deposition maps, developed at 1-km resolution for YOSE and SEKI to quantify N deposition to sensitive high-elevation ecosystems, range from 1.0 to over 5.0 kg N ha-1 yr-1. Critical loads of N deposition for nutrient enrichment of aquatic ecosystems were quantified and mapped using a geostatistical approach, with N deposition, topography, vegetation, geology, and climate as potential explanatory variables. Multiple predictive models were created using various combinations of explanatory variables; this approach allowed us to better quantify uncertainty and more accurately identify the areas most sensitive to atmospherically deposited N. The lowest critical loads estimates and highest exceedances identified within YOSE and SEKI occurred in high-elevation basins with steep slopes, sparse vegetation, and areas of neoglacial till and talus. These results are consistent with previous analyses in the Rocky Mountains, and highlight the sensitivity of alpine ecosystems to atmospheric N deposition.

[A look at gender in research. A qualitative analysis].

PubMed

López, Mercedes Eguiluz; Lerendegui, María Luisa Samitier; Simon, Teresa Yago; Aznar, Concepción Tomas; Martin, Dolores Ariño; Briz, Teresa Oliveros; Gavin, Gema Palacio; Botaya, Rosa Magallón

2011-10-01

To find out the views of a group of national women experts on gender and health on the key elements to consider in research with a gender perspective, and what are the resistance barriers when trying to include this perspective in the research. Meeting of a group of experts. Two types of analysis, discourse analysis, analysis of group outputs were used. Zaragoza. The group consists of six experts. An expert was defined as person accredited with specific training in the subject, and/or has presented her research at seminars, workshops, conferences on gender and health in recent years, or belongs to one of the networks of research on gender and heath. Qualitative analysis. Research with a gender perspective should meet the health needs and problems of both men and women, with those issues that contribute to determining the influence of gender on people's health being of special interest. The methodology should reflect this perspective throughout the research process and the variables should have gender explanatory potential. The main resistance barriers that prevent the inclusion of this perspective were related to the scientific institution, to feminism, and to a lack of training. A project cannot be considered to have a gender perspective if it does not include the analysis of variables with a gender explanatory potential and is not designed to help reduce inequalities between men and women. Knowing the resistance barriers that hinder this approach can guide future training. Copyright © 2010 Elsevier España, S.L. All rights reserved.

Modeling and mapping abundance of American Woodcock across the Midwestern and Northeastern United States

USGS Publications Warehouse

Thogmartin, W.E.; Sauer, J.R.; Knutson, M.G.

2007-01-01

We used an over-dispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods, to model population spatial patterns of relative abundance of American woodcock (Scolopax minor) across its breeding range in the United States. We predicted North American woodcock Singing Ground Survey counts with a log-linear function of explanatory variables describing habitat, year effects, and observer effects. The model also included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land-cover composition, climate, terrain heterogeneity, and human influence. Woodcock counts were higher in landscapes with more forest, especially aspen (Populus tremuloides) and birch (Betula spp.) forest, and in locations with a high degree of interspersion among forest, shrubs, and grasslands. Woodcock counts were lower in landscapes with a high degree of human development. The most noteworthy practical application of this spatial modeling approach was the ability to map predicted relative abundance. Based on a map of predicted relative abundance derived from the posterior parameter estimates, we identified major concentrations of woodcock abundance in east-central Minnesota, USA, the intersection of Vermont, USA, New York, USA, and Ontario, Canada, the upper peninsula of Michigan, USA, and St. Lawrence County, New York. The functional relations we elucidated for the American woodcock provide a basis for the development of management programs and the model and map may serve to focus management and monitoring on areas and habitat features important to American woodcock.

Nonparametric instrumental regression with non-convex constraints

NASA Astrophysics Data System (ADS)

Grasmair, M.; Scherzer, O.; Vanhems, A.

2013-03-01

This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.

National estimates of exposure to prescription drugs with addiction potential in community-dwelling elders.

PubMed

Simoni-Wastila, Linda; Zuckerman, Ilene H; Singhal, Puneet K; Briesacher, Becky; Hsu, Van Doren

2005-03-01

The use of prescription drugs with addiction potential is an overlooked and growing problem among today's elderly. This paper provides national prevalence estimates of exposure to prescription drugs with addiction potential among community-dwelling elders and explores risk factors for such exposure. Using the Medicare Current Beneficiary Survey, a nationally-representative database of Medicare eligibles, we calculated the prevalence of abusable prescription drug use, overall, by therapeutic class, and by drug. Nearly 22% (7.22 million) of all community-dwelling Medicare elders used at least one prescription medication with addiction potential. Opioid analgesics were used most frequently (14.9%; 95% CI 14.0, 15.8%); central nervous system (CNS) depressants were used by 10.4% of the nation's elders (95% CI 9.5, 10.8%). Using logistic regression analysis, we examined the association of explanatory variables with three outcome variables: any controlled substances use, any opioid analgesic use, and any CNS depressant use. We found that females, whites, those aged 65-79, and those with non-spousal others, were significantly more likely to use one or more prescription drugs with addiction potential, controlling for health status and severity-of-illness. The significance and magnitude of several explanatory variables, including age, race, ethnicity, living arrangement, and health status, varied by therapeutic category. This paper provides an important first step in acknowledging the widespread use of abusable prescription drugs in elders, and provides a foundation for future research and practical solutions to preventing subsequent problem use of prescription drugs.

Data Mining in Institutional Economics Tasks

NASA Astrophysics Data System (ADS)

Kirilyuk, Igor; Kuznetsova, Anna; Senko, Oleg

2018-02-01

The paper discusses problems associated with the use of data mining tools to study discrepancies between countries with different types of institutional matrices by variety of potential explanatory variables: climate, economic or infrastructure indicators. An approach is presented which is based on the search of statistically valid regularities describing the dependence of the institutional type on a single variable or a pair of variables. Examples of regularities are given.

A Content Analysis of Acculturation Research in the Career Development Literature

ERIC Educational Resources Information Center

Miller, Matthew J.; Kerlow-Myers, Andrew E.

2009-01-01

The purpose of the present study was to highlight the importance of acculturation as an explanatory variable in career development and to provide an empirical review of acculturation research in the career development literature. Acculturation is a cultural variable that has been linked to a number of important career development outcomes for…

Primary School Leadership Practice: How the Subject Matters

ERIC Educational Resources Information Center

Spillane, James P.

2005-01-01

Teaching is a critical consideration in investigations of primary school leadership and not just as an outcome variable. Factoring in instruction as an explanatory variable in scholarship on school leadership involves moving away from views of teaching as a monolithic or unitary practice. When it comes to leadership in primary schools, the subject…

Clear-Water Contraction Scour at Selected Bridge Sites in the Black Prairie Belt of the Coastal Plain in Alabama, 2006

USGS Publications Warehouse

Lee, K.G.; Hedgecock, T.S.

2008-01-01

The U.S. Geological Survey, in cooperation with the Alabama Department of Transportation, made observations of clear-water contraction scour at 25 bridge sites in the Black Prairie Belt of the Coastal Plain of Alabama. These bridge sites consisted of 54 hydraulic structures, of which 37 have measurable scour holes. Observed scour depths ranged from 1.4 to 10.4 feet. Theoretical clear-water contraction-scour depths were computed for each bridge and compared with observed scour. This comparison showed that theoretical scour depths, in general, exceeded the observed scour depths by about 475 percent. Variables determined to be important in developing scour in laboratory studies along with several other hydraulic variables were investigated to understand their influence within the Alabama field data. The strongest explanatory variables for clear-water contraction scour were channel-contraction ratio and velocity index. Envelope curves were developed relating both of these explanatory variables to observed scour. These envelope curves provide useful tools for assessing reasonable ranges of scour depth in the Black Prairie Belt of Alabama.

Research of Water Level Prediction for a Continuous Flood due to Typhoons Based on a Machine Learning Method

NASA Astrophysics Data System (ADS)

Nakatsugawa, M.; Kobayashi, Y.; Okazaki, R.; Taniguchi, Y.

2017-12-01

This research aims to improve accuracy of water level prediction calculations for more effective river management. In August 2016, Hokkaido was visited by four typhoons, whose heavy rainfall caused severe flooding. In the Tokoro river basin of Eastern Hokkaido, the water level (WL) at the Kamikawazoe gauging station, which is at the lower reaches exceeded the design high-water level and the water rose to the highest level on record. To predict such flood conditions and mitigate disaster damage, it is necessary to improve the accuracy of prediction as well as to prolong the lead time (LT) required for disaster mitigation measures such as flood-fighting activities and evacuation actions by residents. There is the need to predict the river water level around the peak stage earlier and more accurately. Previous research dealing with WL prediction had proposed a method in which the WL at the lower reaches is estimated by the correlation with the WL at the upper reaches (hereinafter: "the water level correlation method"). Additionally, a runoff model-based method has been generally used in which the discharge is estimated by giving rainfall prediction data to a runoff model such as a storage function model and then the WL is estimated from that discharge by using a WL discharge rating curve (H-Q curve). In this research, an attempt was made to predict WL by applying the Random Forest (RF) method, which is a machine learning method that can estimate the contribution of explanatory variables. Furthermore, from the practical point of view, we investigated the prediction of WL based on a multiple correlation (MC) method involving factors using explanatory variables with high contribution in the RF method, and we examined the proper selection of explanatory variables and the extension of LT. The following results were found: 1) Based on the RF method tuned up by learning from previous floods, the WL for the abnormal flood case of August 2016 was properly predicted with a lead time of 6 h. 2) Based on the contribution of explanatory variables, factors were selected for the MC method. In this way, plausible prediction results were obtained.

Modeling the impact of social discrimination and financial hardship on the sexual risk of HIV among Latino and Black men who have sex with men.

PubMed

Ayala, George; Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P; Millett, Gregorio A

2012-05-01

We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants' serostatus. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM.

Determinants of urban sprawl in European cities

PubMed Central

Alvanides, Seraphim; Garrod, Guy

2015-01-01

This paper provides empirical evidence that helps to answer several key questions relating to the extent of urban sprawl in Europe. Building on the monocentric city model, this study uses existing data sources to derive a set of panel data for 282 European cities at three time points (1990, 2000 and 2006). Two indices of urban sprawl are calculated that, respectively, reflect changes in artificial area and the levels of urban fragmentation for each city. These are supplemented by a set of data on various economic and geographical variables that might explain the variation of the two indices. Using a Hausman-Taylor estimator and random regressors to control for the possible correlation between explanatory variables and unobservable city-level effects, we find that the fundamental conclusions of the standard monocentric model are valid in the European context for both indices. Although the variables generated by the monocentric model explain a large part of the variation of artificial area, their explanatory power for modelling the fragmentation index is relatively low. PMID:26321770

Determinants of urban sprawl in European cities.

PubMed

Oueslati, Walid; Alvanides, Seraphim; Garrod, Guy

2015-07-01

This paper provides empirical evidence that helps to answer several key questions relating to the extent of urban sprawl in Europe. Building on the monocentric city model, this study uses existing data sources to derive a set of panel data for 282 European cities at three time points (1990, 2000 and 2006). Two indices of urban sprawl are calculated that, respectively, reflect changes in artificial area and the levels of urban fragmentation for each city. These are supplemented by a set of data on various economic and geographical variables that might explain the variation of the two indices. Using a Hausman-Taylor estimator and random regressors to control for the possible correlation between explanatory variables and unobservable city-level effects, we find that the fundamental conclusions of the standard monocentric model are valid in the European context for both indices. Although the variables generated by the monocentric model explain a large part of the variation of artificial area, their explanatory power for modelling the fragmentation index is relatively low.

Determining Directional Dependency in Causal Associations

PubMed Central

Pornprasertmanit, Sunthud; Little, Todd D.

2014-01-01

Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D’Agostino’s K2, and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if (a) the regression error in population is normal, (b) an unobserved explanatory variable correlates with any variables equal to or less than .2, (c) a curvilinear relation between both variables is not strong (standardized regression coefficient ≤ .2), (d) there are no bivariate outliers, and (e) both variables are continuous. PMID:24683282

Identification of the need for home visiting nurse: development of a new assessment tool.

PubMed

Taguchi, Atsuko; Nagata, Satoko; Naruse, Takashi; Kuwahara, Yuki; Yamaguchi, Takuhiro; Murashima, Sachiyo

2014-01-01

To develop a Home Visiting Nursing Service Need Assessment Form (HVNS-NAF) to standardize the decision about the need for home visiting nursing service. The sample consisted of older adults who had received coordinated services by care managers. We defined the need for home visiting nursing service by elderly individuals as the decision of the need by a care manager so that the elderly can continue to live independently. Explanatory variables included demographic factors, medical procedure, severity of illness, and caregiver variables. Multiple logistic regression was carried out after univariate analyses to decide the variables to include and the weight of each variable in the HVNS-NAF. We then calculated the sensitivity and specificity of each cutoff value, and defined the score with the highest sensitivity and specificity as the cutoff value. Nineteen items were included in the final HVNS-NAF. When the cutoff value was 2 points, the sensitivity was 77.0%, specificity 68.5%, and positive predictive value 56.8%. HVNS-NAF is the first validated standard based on characteristics of elderly clients who required home visiting nursing service. Using the HVNS-NAF may result in reducing the unmet need for home visiting nursing service and preventing hospitalization.

Developing deterioration models for Wyoming bridges.

DOT National Transportation Integrated Search

2016-05-01

Deterioration models for the Wyoming Bridge Inventory were developed using both stochastic and deterministic models. : The selection of explanatory variables is investigated and a new method using LASSO regression to eliminate human bias : in explana...

Do nurse sows and foster litters have impaired animal welfare? Results from a cross-sectional study in sow herds.

PubMed

Sørensen, J T; Rousing, T; Kudahl, A B; Hansted, H J; Pedersen, L J

2016-04-01

Increasing litter size has led to introduction of so-called nurse sows in several EU countries. A nurse sow is a sow receiving piglets after having weaned her own piglets and thereby experiencing an extended lactation. In order to analyse whether nurse sows have more welfare problems than non-nurse sows a cross-sectional study was conducted in 57 sow herds in Denmark. Clinical observations were made on nurse and non-nurse sows and their litters. The clinical observations were dichotomized and the effect of being a nurse sow was analysed based on eight parameters: thin (body condition score<2.5), swollen bursae on legs, dew claw wounds, vulva lesions, poor hygiene, poor skin condition, shoulder lesions and cuts and wounds on the udder. Explanatory variables included in the eight models were: nurse sow (yes=1/no=0), age of piglets (weeks old, 1 to 7), parity (1 to 8+) and all first order interactions between these three variables. The effect of using nurse sows on piglet welfare was analysed with five models. The outcomes were: huddling, poor hygiene, lameness, snout cuts and carpal abrasions. The explanatory variables included in the five models were: nurse sow (yes=1/no=0), age of piglets (weeks old, 1 to 7), parity (1 to 8+) and all first order interactions between these three variables. Herd identity was included as a random factor in all models. The nurse sows had a significantly higher risk of swollen bursae on legs (P=0.038) and udder wounds (P=0.001). No differences in risk of being thin or having shoulder lesions were found. Foster litters had significantly higher risk of being dirty (P=0.026) and getting carpal abrasions (P=0.024) than non-foster litters. There was a tendency for higher lameness in foster litters than in non-foster litters (P=0.052). The results show that nurse sows and their piglets to some extent experience more welfare problems than non-nurse sows with piglets at a similar age.

Association of lower extremity range of motion and muscle strength with physical performance of community-dwelling older women.

PubMed

Jung, Hungu; Yamasaki, Masahiro

2016-12-08

Reduced lower extremity range of motion (ROM) and muscle strength are related to functional disability in older adults who cannot perform one or more activities of daily living (ADL) independently. The purpose of this study was to determine which factors of seven lower extremity ROMs and two muscle strengths play dominant roles in the physical performance of community-dwelling older women. Ninety-five community-dwelling older women (mean age ± SD, 70.7 ± 4.7 years; age range, 65-83 years) were enrolled in this study. Seven lower extremity ROMs (hip flexion, hip extension, knee flexion, internal and external hip rotation, ankle dorsiflexion, and ankle plantar flexion) and two muscle strengths (knee extension and flexion) were measured. Physical performance tests, including functional reach test (FRT), 5 m gait test, four square step test (FSST), timed up and go test (TUGT), and five times sit-to-stand test (FTSST) were performed. Stepwise regression models for each of the physical performance tests revealed that hip extension ROM and knee flexion strength were important explanatory variables for FRT, FSST, and FTSST. Furthermore, ankle plantar flexion ROM and knee extension strength were significant explanatory variables for the 5 m gait test and TUGT. However, ankle dorsiflexion ROM was a significant explanatory variable for FRT alone. The amount of variance on stepwise multiple regression for the five physical performance tests ranged from 25 (FSST) to 47% (TUGT). Hip extension, ankle dorsiflexion, and ankle plantar flexion ROMs, as well as knee extension and flexion strengths may play primary roles in the physical performance of community-dwelling older women. Further studies should assess whether specific intervention programs targeting older women may achieve improvements in lower extremity ROM and muscle strength, and thereby play an important role in the prevention of dependence on daily activities and loss of physical function, particularly focusing on hip extension, ankle dorsiflexion, and ankle plantar flexion ROMs as well as knee extension and flexion strength.

Adolescent oligomenorrhea in a biracial schoolgirl cohort: a simple clinical parameter predicting impaired fasting glucose plus type 2 diabetes mellitus, insulin, glucose, insulin resistance, and centripetal obesity from age 19 to 25 years.

PubMed

Morrison, John A; Glueck, Charles J; Daniels, Stephen; Wang, Ping; Stroop, Davis

2011-09-01

We hypothesized that adolescent oligomenorrhea (ages 14-19) would independently predict impaired fasting glucose (IFG; ≥110 to <126 mg/dL) plus type 2 diabetes mellitus (T2DM; ≥126 mg/dL), insulin and glucose levels, and insulin resistance (IR) in young adulthood (ages 19-25). A prospective 15-year follow-up of 370 schoolgirls starting at age 10 was performed. Age 14 waist circumference was the most important explanatory variable for IFG + T2DM during ages 19 to 24 (P = .002; odds ratio, 1.06; 95% confidence interval, 1.02-1.10), along with oligomenorrhea category from ages 14 to 19 (0, 1, 2, ≥3 reports over 6 years; P = .032; odds ratio, 1.82; 95% confidence interval, 1.05-3.14). Impaired fasting glucose + T2DM at ages 19 to 24 were more common in girls having 1 (6%), 2 (11%), and ≥3 (38%) oligomenorrhea reports from ages 14 to 19 than in girls without oligomenorrhea (3%; P = .0003). Positive explanatory variables (all Ps ≤ .05) for homeostasis model assessment of IR at ages 19 to 24 included age 14 waist (partial R(2) = 30.1%), oligomenorrhea with hyperandrogenism (polycystic ovary syndrome; partial R(2) = 4.1%), black race (3.8%), and oligomenorrhea frequency during ages 14 to 19 (0.8%); sex hormone binding globulin was a negative explanatory variable (0.7%). This is the first prospective study to report an independent association of adolescent oligomenorrhea with young adult IFG + T2DM, with insulin and glucose levels, and with IR. Age 14 waist circumference, oligomenorrhea with hyperandrogenism (polycystic ovary syndrome), black race, oligomenorrhea frequency at ages 14 to 19, and age 14 sex hormone binding globulin were independently associated with IR at ages 19 to 24, potentially facilitating primary prevention of IFG, T2DM, and hyperinsulinemia. Copyright © 2011 Elsevier Inc. All rights reserved.

Statistical summary of selected physical, chemical, and toxicity characteristics and estimates of annual constituent loads in urban stormwater, Maricopa County, Arizona

USGS Publications Warehouse

Fossum, Kenneth D.; O'Day, Christie M.; Wilson, Barbara J.; Monical, Jim E.

2001-01-01

Stormwater and streamflow in Maricopa County were monitored to (1) describe the physical, chemical, and toxicity characteristics of stormwater from areas having different land uses, (2) describe the physical, chemical, and toxicity characteristics of streamflow from areas that receive urban stormwater, and (3) estimate constituent loads in stormwater. Urban stormwater and streamflow had similar ranges in most constituent concentrations. The mean concentration of dissolved solids in urban stormwater was lower than in streamflow from the Salt River and Indian Bend Wash. Urban stormwater, however, had a greater chemical oxygen demand and higher concentrations of most nutrients. Mean seasonal loads and mean annual loads of 11 constituents and volumes of runoff were estimated for municipalities in the metropolitan Phoenix area, Arizona, by adjusting regional regression equations of loads. This adjustment procedure uses the original regional regression equation and additional explanatory variables that were not included in the original equation. The adjusted equations had standard errors that ranged from 161 to 196 percent. The large standard errors of the prediction result from the large variability of the constituent concentration data used in the regression analysis. Adjustment procedures produced unsatisfactory results for nine of the regressions?suspended solids, dissolved solids, total phosphorus, dissolved phosphorus, total recoverable cadmium, total recoverable copper, total recoverable lead, total recoverable zinc, and storm runoff. These equations had no consistent direction of bias and no other additional explanatory variables correlated with the observed loads. A stepwise-multiple regression or a three-variable regression (total storm rainfall, drainage area, and impervious area) and local data were used to develop local regression equations for these nine constituents. These equations had standard errors from 15 to 183 percent.

The association between smoking and subsequent suicide-related outcomes in the National Comorbidity Survey panel sample

PubMed Central

Kessler, Ronald C.; Borges, Guilherme; Sampson, Nancy; Miller, Matthew; Nock, Matthew K.

2009-01-01

Controversy exists about whether the repeatedly-documented associations between smoking and subsequent suicide-related outcomes (SROs; ideation, plans, gestures, and attempts) are due to unmeasured common causes or to causal effects of smoking on SROs. We address this issue by examining associations of smoking with subsequent SROs with and without controls for potential explanatory variables in the National Comorbidity Survey (NCS) panel. The latter consists of 5001 people who participated in both the 199002 NCS and the 2001–03 NCS Follow-up Survey. Explanatory variables include socio-demographics, potential common causes (parental history of mental-substance disorders; other respondent childhood adversities) and potential mediators (respondent history of DSM-III-R mental-substance disorders). Small gross (i.e., without controls) prospective associations are found between history of early-onset nicotine dependence and both subsequent suicide ideation and, among ideators, subsequent suicide plans. None of the baseline smoking measures, though, predicts subsequent suicide gestures or attempts among ideators. The smoking-ideation association largely disappear, but the association of early-onset nicotine dependence with subsequent suicide plans persists (Odds-ratio = 3.0), after adjustment for control variables. However, the latter association is as strong with remitted as active nicotine dependence, arguing against a direct causal effect of nicotine dependence on suicide plans. Decomposition of the control variable effects, furthermore, suggests that these effects are due to common causes more than to mediators. These results refine our understanding of the ways in which smoking is associated with later SROs and for the most part argue against the view that these associations are due to causal effects of smoking. PMID:18645572

Dropouts in Two-Year Colleges: Better Prediction with the Use of Moderator Subgroups.

ERIC Educational Resources Information Center

Capoor, Madan; Eagle, Norman

Failure to identify and account for the effect of moderator variables is an important reason for the low explanatory power of much educational research. Pre-existing subgroups such as sex, ethnicity, and curriculum offer an easily identifiable and theoretically meaningful source of moderator variables. Tests for intercept and slope differences in…

Management and Outcomes Following Surgery for Gastrointestinal Typhoid: An International, Prospective, Multicentre Cohort Study.

PubMed

2018-05-03

Gastrointestinal perforation is the most serious complication of typhoid fever, with a high disease burden in low-income countries. Reliable, prospective, contemporary surgical outcome data are scarce in these settings. This study aimed to investigate surgical outcomes following surgery for intestinal typhoid. Two multicentre, international prospective cohort studies of consecutive patients undergoing surgery for gastrointestinal typhoid perforation were conducted. Outcomes were measured at 30 days and included mortality, surgical site infection, organ space infection and reintervention rate. Multilevel logistic regression models were used to adjust for clinically plausible explanatory variables. Effect estimates are expressed as odds ratios (ORs) alongside their corresponding 95% confidence intervals. A total of 88 patients across the GlobalSurg 1 and GlobalSurg 2 studies were included, from 11 countries. Children comprised 38.6% (34/88) of included patients. Most patients (87/88) had intestinal perforation. The 30-day mortality rate was 9.1% (8/88), which was higher in children (14.7 vs. 5.6%). Surgical site infection was common, at 67.0% (59/88). Organ site infection was common, with 10.2% of patients affected. An ASA grade of III and above was a strong predictor of 30-day post-operative mortality, at the univariable level and following adjustment for explanatory variables (OR 15.82, 95% CI 1.53-163.57, p = 0.021). With high mortality and complication rates, outcomes from surgery for intestinal typhoid remain poor. Future studies in this area should focus on sustainable interventions which can reduce perioperative morbidity. At a policy level, improving these outcomes will require both surgical and public health system advances.

Why the Difference Between Explanation and Argument Matters to Science Education

NASA Astrophysics Data System (ADS)

Brigandt, Ingo

2016-05-01

Contributing to the recent debate on whether or not explanations ought to be differentiated from arguments, this article argues that the distinction matters to science education. I articulate the distinction in terms of explanations and arguments having to meet different standards of adequacy. Standards of explanatory adequacy are important because they correspond to what counts as a good explanation in a science classroom, whereas a focus on evidence-based argumentation can obscure such standards of what makes an explanation explanatory. I provide further reasons for the relevance of not conflating explanations with arguments (and having standards of explanatory adequacy in view). First, what guides the adoption of the particular standards of explanatory adequacy that are relevant in a scientific case is the explanatory aim pursued in this context. Apart from explanatory aims being an important aspect of the nature of science, including explanatory aims in classroom instruction also promotes students seeing explanations as more than facts, and engages them in developing explanations as responses to interesting explanatory problems. Second, it is of relevance to science curricula that science aims at intervening in natural processes, not only for technological applications, but also as part of experimental discovery. Not any argument enables intervention in nature, as successful intervention specifically presupposes causal explanations. Students can fruitfully explore in the classroom how an explanatory account suggests different options for intervention.

Repeat migration and disappointment.

PubMed

Grant, E K; Vanderkamp, J

1986-01-01

This article investigates the determinants of repeat migration among the 44 regions of Canada, using information from a large micro-database which spans the period 1968 to 1971. The explanation of repeat migration probabilities is a difficult task, and this attempt is only partly successful. May of the explanatory variables are not significant, and the overall explanatory power of the equations is not high. In the area of personal characteristics, the variables related to age, sex, and marital status are generally significant and with expected signs. The distance variable has a strongly positive effect on onward move probabilities. Variables related to prior migration experience have an important impact that differs between return and onward probabilities. In particular, the occurrence of prior moves has a striking effect on the probability of onward migration. The variable representing disappointment, or relative success of the initial move, plays a significant role in explaining repeat migration probabilities. The disappointment variable represents the ratio of actural versus expected wage income in the year after the initial move, and its effect on both repeat migration probabilities is always negative and almost always highly significant. The repeat probabilities diminish after a year's stay in the destination region, but disappointment in the most recent year still has a bearing on the delayed repeat probabilities. While the quantitative impact of the disappointment variable is not large, it is difficult to draw comparisons since similar estimates are not available elsewhere.

Self-rated health and health-strengthening factors in community-living frail older people.

PubMed

Ebrahimi, Zahra; Dahlin-Ivanoff, Synneve; Eklund, Kajsa; Jakobsson, Annika; Wilhelmson, Katarina

2015-04-01

The aim of this study was to analyse the explanatory power of variables measuring health-strengthening factors for self-rated health among community-living frail older people. Frailty is commonly constructed as a multi-dimensional geriatric syndrome ascribed to the multi-system deterioration of the reserve capacity in older age. Frailty in older people is associated with decreased physical and psychological well-being. However, knowledge about the experiences of health in frail older people is still limited. The design of the study was cross-sectional. The data were collected between October 2008 and November 2010 through face-to-face structured interviews with older people aged 65-96 years (N = 161). Binary logistic regression was used to analyse whether a set of explanatory relevant variables is associated with self-rated health. The results from the final model showed that satisfaction with one's ability to take care of oneself, having 10 or fewer symptoms and not feeling lonely had the best explanatory power for community-living frail older peoples' experiences of good health. The results indicate that a multi-disciplinary approach is desirable, where the focus should not only be on medical problems but also on providing supportive services to older people to maintain their independence and experiences of health despite frailty. © 2014 John Wiley & Sons Ltd.

The Importance of Ambient Sound Level to Characterise Anuran Habitat

PubMed Central

Goutte, Sandra; Dubois, Alain; Legendre, Frédéric

2013-01-01

Habitat characterisation is a pivotal step of any animal ecology study. The choice of variables used to describe habitats is crucial and need to be relevant to the ecology and behaviour of the species, in order to reflect biologically meaningful distribution patterns. In many species, acoustic communication is critical to individuals’ interactions, and it is expected that ambient acoustic conditions impact their local distribution. Yet, classic animal ecology rarely integrates an acoustic dimension in habitat descriptions. Here we show that ambient sound pressure level (SPL) is a strong predictor of calling site selection in acoustically active frog species. In comparison to six other habitat-related variables (i.e. air and water temperature, depth, width and slope of the stream, substrate), SPL had the most important explanatory power in microhabitat selection for the 34 sampled species. Ambient noise was particularly useful in differentiating two stream-associated guilds: torrents and calmer streams dwelling species. Guild definitions were strongly supported by SPL, whereas slope, which is commonly used in stream-associated habitat, had a weak explanatory power. Moreover, slope measures are non-standardized across studies and are difficult to assess at small scale. We argue that including an acoustic descriptor will improve habitat-species analyses for many acoustically active taxa. SPL integrates habitat topology and temporal information (such as weather and hour of the day, for example) and is a simple and precise measure. We suggest that habitat description in animal ecology should include an acoustic measure such as noise level because it may explain previously misunderstood distribution patterns. PMID:24205070

An updated geospatial liquefaction model for global application

USGS Publications Warehouse

Zhu, Jing; Baise, Laurie G.; Thompson, Eric M.

2017-01-01

We present an updated geospatial approach to estimation of earthquake-induced liquefaction from globally available geospatial proxies. Our previous iteration of the geospatial liquefaction model was based on mapped liquefaction surface effects from four earthquakes in Christchurch, New Zealand, and Kobe, Japan, paired with geospatial explanatory variables including slope-derived VS30, compound topographic index, and magnitude-adjusted peak ground acceleration from ShakeMap. The updated geospatial liquefaction model presented herein improves the performance and the generality of the model. The updates include (1) expanding the liquefaction database to 27 earthquake events across 6 countries, (2) addressing the sampling of nonliquefaction for incomplete liquefaction inventories, (3) testing interaction effects between explanatory variables, and (4) overall improving model performance. While we test 14 geospatial proxies for soil density and soil saturation, the most promising geospatial parameters are slope-derived VS30, modeled water table depth, distance to coast, distance to river, distance to closest water body, and precipitation. We found that peak ground velocity (PGV) performs better than peak ground acceleration (PGA) as the shaking intensity parameter. We present two models which offer improved performance over prior models. We evaluate model performance using the area under the curve under the Receiver Operating Characteristic (ROC) curve (AUC) and the Brier score. The best-performing model in a coastal setting uses distance to coast but is problematic for regions away from the coast. The second best model, using PGV, VS30, water table depth, distance to closest water body, and precipitation, performs better in noncoastal regions and thus is the model we recommend for global implementation.

Gender interactions and success.

PubMed

Wiggins, Carla; Peterson, Teri

2004-01-01

Does gender by itself, or does gender's interaction with career variables, better explain the difference between women and men's careers in healthcare management? US healthcare managers were surveyed regarding career and personal experiences. Gender was statistically interacted with explanatory variables. Multiple regression with backwards selection systematically removed non-significant variables. All gender interaction variables were non-significant. Much of the literature proposes that work and career factors impact working women differently than working men. We find that while gender alone is a significant predictor of income, it does not significantly interact with other career variables.

Quality of life after total vs distal gastrectomy with Roux-en-Y reconstruction: Use of the Postgastrectomy Syndrome Assessment Scale-45

PubMed Central

Takahashi, Masazumi; Terashima, Masanori; Kawahira, Hiroshi; Nagai, Eishi; Uenosono, Yoshikazu; Kinami, Shinichi; Nagata, Yasuhiro; Yoshida, Masashi; Aoyagi, Keishiro; Kodera, Yasuhiro; Nakada, Koji

2017-01-01

AIM To investigate the detrimental impact of loss of reservoir capacity by comparing total gastrectomy (TGRY) and distal gastrectomy with the same Roux-en-Y (DGRY) reconstruction. The study was conducted using an integrated questionnaire, the Postgastrectomy Syndrome Assessment Scale (PGSAS)-45, recently developed by the Japan Postgastrectomy Syndrome Working Party. METHODS The PGSAS-45 comprises 8 items from the Short Form-8, 15 from the Gastrointestinal Symptom Rating Scale, and 22 newly selected items. Uni- and multivariate analysis was performed on 868 questionnaires completed by patients who underwent either TGRY (n = 393) or DGRY (n = 475) for stage I gastric cancer (52 institutions). Multivariate analysis weighed of six explanatory variables, including the type of gastrectomy (TGRY/DGRY), interval after surgery, age, gender, surgical approach (laparoscopic/open), and whether the celiac branch of the vagus nerve was preserved/divided on the quality of life (QOL). RESULTS The patients who underwent TGRY experienced the poorer QOL compared to DGRY in the 15 of 19 main outcome measures of PGSAS-45. Moreover, multiple regression analysis indicated that the type of gastrectomy, TGRY, most strongly and broadly impaired the postoperative QOL among six explanatory variables. CONCLUSION The results of the present study suggested that TGRY had a certain detrimental impact on the postoperative QOL, and the loss of reservoir capacity could be a major cause. PMID:28373774

Risky online behaviors among adolescents: Longitudinal relations among problematic Internet use, cyberbullying perpetration, and meeting strangers online.

PubMed

Gámez-Guadix, Manuel; Borrajo, Erika; Almendros, Carmen

2016-03-01

Background and aims This study aims to analyze the cross-sectional and longitudinal relationship between three major risky online behaviors during adolescence: problematic Internet use, cyberbullying perpetration, and meeting strangers online. An additional objective was to study the role of impulsivity-irresponsibility as a possible explanatory variable of the relationships between these risky online behaviors. Methods The study sample was 888 adolescents that completed self-report measures at time 1 and time 2 with an interval of 6 months. Results The findings showed a significant cross-sectional relationship between the risky online behaviors analyzed. At the longitudinal level, problematic Internet use at time 1 predicted an increase in the perpetration of cyberbullying and meeting strangers online at time 2. Furthermore, meeting strangers online increased the likelihood of cyberbullying perpetration at time 2. Finally, when impulsivity-irresponsibility was included in the model as an explanatory variable, the relationships previously found remained significant. Discussion These results extend traditional problem behavior theory during adolescence, also supporting a relationship between different risky behaviors in cyberspace. In addition, findings highlighted the role of problematic Internet use, which increased the chances of developing cyberbullying perpetration and meeting strangers online over time. However, the results suggest a limited role of impulsivity-irresponsibility as an explicative mechanism. Conclusions The findings suggest that various online risk activities ought to be addressed together when planning assessment, prevention and intervention efforts.

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

USGS Publications Warehouse

Cross, Paul C.; Klaver, Robert W.; Brennan, Angela; Creel, Scott; Beckmann, Jon P.; Higgs, Megan D.; Scurlock, Brandon M.

2013-01-01

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (2) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km2) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

The cumulative effects of life event, personal and social resources on subjective well-being of elderly widowers.

PubMed

Balaswamy, S; Richardson, V E

2001-01-01

A multidimensional Life Stress Model was used to test the independent contributions of background characteristics, personal resources, life event, and environmental influences on 200 widowers' levels of well-being, measured by the Affect Balance Scale. Stepwise regression analyses revealed that environmental resources were unrelated to negative affect which is influenced more by the life event and personal resource variables. The environmental resource variables, particularly interactions with friends and neighbors, mostly influenced positive affect. The explanatory model for well-being included multiple variables and explained 33 percent of the variance. Although background characteristics had the greatest impact, absence of hospitalization, higher mastery, higher self-esteem, contacts with friends, and interaction with neighbors enhanced well-being. The results support previous speculations on the importance of positive exchanges for positive affect. African-American widowers showed higher levels of well-being than Caucasian widowers did. The results advance knowledge about differences among elderly men.

Developing Passenger Demand Models for International Aviation from/to Egypt: A Case Study of Cairo Airport and Egyptair

NASA Technical Reports Server (NTRS)

Abbas, Khaled A.; Fattah, Nabil Abdel; Reda, Hala R.

2003-01-01

This research is concerned with developing passenger demand models for international aviation from/to Egypt. In this context, aviation sector in Egypt is represented by the biggest and main airport namely Cairo airport as well as by the main Egyptian international air carrier namely Egyptair. The developed models utilize two variables to represent aviation demand, namely total number of international flights originating from and attracted to Cairo airport as well as total number of passengers using Egyptair international flights originating from and attracted to Cairo airport. Such demand variables were related, using different functional forms, to several explanatory variables including population, GDP and number of foreign tourists. Finally, two models were selected based on their logical acceptability, best fit and statistical significance. To demonstrate usefulness of developed models, these were used to forecast future demand patterns.

Classification and regression trees

Treesearch

G. G. Moisen

2008-01-01

Frequently, ecologists are interested in exploring ecological relationships, describing patterns and processes, or making spatial or temporal predictions. These purposes often can be addressed by modeling the relationship between some outcome or response and a set of features or explanatory variables.

Access to health programs at the workplace and the reduction of work presenteeism: a population-based cross-sectional study.

PubMed

Bustillos, Arnaldo Sanchez; Trigoso, Oswaldo Ortiz

2013-11-01

To examine access to health programs at workplace as a determinant of presenteeism among adults. Data source was a subsample of the 2009-2010 Canadian Community Health Survey. The outcome was self-reported reduced activities at work (presenteeism). The explanatory variable was self-reported access to a health program at workplace. Logistic regression was used to measure the association between outcome and explanatory variables adjusting for potential confounders. Adjusting for sex, age, education, income, work stress, and chronic conditions, presenteeism was not associated with having access to a health program at workplace (adjusted odds ratio, 1.23; 95% confidence interval, 0.91 to 1.65). The odds of presenteeism were higher in workers who reported high work stress and those with chronic medical conditions. This study found that access to health programs at workplace is not significantly associated with a decline in presenteeism.

Remotely sensed vegetation moisture as explanatory variable of Lyme borreliosis incidence

NASA Astrophysics Data System (ADS)

Barrios, J. M.; Verstraeten, W. W.; Maes, P.; Clement, J.; Aerts, J. M.; Farifteh, J.; Lagrou, K.; Van Ranst, M.; Coppin, P.

2012-08-01

The strong correlation between environmental conditions and abundance and spatial spread of the tick Ixodes ricinus is widely documented. I. ricinus is in Europe the main vector of the bacterium Borrelia burgdorferi, the pathogen causing Lyme borreliosis (LB). Humidity in vegetated systems is a major factor in tick ecology and its effects might translate into disease incidence in humans. Time series of two remotely sensed indices with sensitivity to vegetation greenness and moisture were tested as explanatory variables of LB incidence. Wavelet-based multiresolution analysis allowed the examination of these signals at different temporal scales in study sites in Belgium, where increases in LB incidence were reported in recent years. The analysis showed the potential of the tested indices for disease monitoring, the usefulness of analyzing the signal in different time frames and the importance of local characteristics of the study area for the selection of the vegetation index.

Factors influencing storm-generated suspended-sediment concentrations and loads in four basins of contrasting land use, humid-tropical Puerto Rico

Treesearch

A. C. Gellis; NO-VALUE

2013-01-01

The significant characteristics controlling the variability in storm-generated suspended-sediment loads and concentrations were analyzed for four basins of differing land use (forest, pasture, cropland, and urbanizing) in humid-tropical Puerto Rico. Statistical analysis involved stepwise regression on factor scores. The explanatory variables were attributes of flow,...

Forest structure estimation and pattern exploration from discrete return lidar in subalpine forests of the Central Rockies

Treesearch

K. R. Sherrill; M. A. Lefsky; J. B. Bradford; M. G. Ryan

2008-01-01

This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory...

Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies

Treesearch

K.R. Sherrill; M.A. Lefsky; J.B. Bradford; M.G. Ryan

2008-01-01

This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory...

The Impact of Household Heads' Education Levels on the Poverty Risk: The Evidence from Turkey

ERIC Educational Resources Information Center

Bilenkisi, Fikret; Gungor, Mahmut Sami; Tapsin, Gulcin

2015-01-01

This study aims to analyze the relationship between the education levels of household heads and the poverty risk of households in Turkey. The logistic regression models have been estimated with the poverty risk of a household as a dependent variable and a set of educational levels as explanatory variables for all households. There are subgroups of…

Using a Market Ratio Factor in Faculty Salary Equity Studies. Professional File Number 103, Spring 2007

ERIC Educational Resources Information Center

Luna, Andrew L.

2007-01-01

This study used two multiple regression analyses to develop an explanatory model to determine which model might best explain faculty salaries. The central purpose of the study was to determine if using a single market ratio variable was a stronger predictor for faculty salaries than the use of dummy variables representing various disciplines.…

Modeling the Impact of Social Discrimination and Financial Hardship on the Sexual Risk of HIV Among Latino and Black Men Who Have Sex With Men

PubMed Central

Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P.; Millett, Gregorio A.

2012-01-01

Objectives. We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. Methods. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Results. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants’ serostatus. Conclusions. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM. PMID:22401516

Explanatory models and distress in primary caregivers of patients with acute psychotic presentations: A study from South India.

PubMed

Joy, Deepa S; Manoranjitham, S D; Samuel, P; Jacob, K S

2017-11-01

Emotional distress among caregivers of people with mental illness is common, changes overtime and requires appropriate coping strategies to prevent long-term disability. Explanatory models, which underpin understanding of disease and illness, are crucial to coping. To study the association of explanatory models and distress among caregivers of people with acute psychotic illness. A total of 60 consecutive patients and their primary caregivers who presented to the Department of Psychiatry, Christian Medical College, Vellore, were recruited for the study. Positive and Negative Syndrome Scale (PANSS), Short Explanatory Model Interview (SEMI) and the General Health Questionnaire-12 (GHQ-12) were used to assess severity of psychosis, explanatory models of illness and emotional distress. Standard bivariate and multivariable statistics were employed. Majority of the caregivers simultaneously held multiple models of illness, which included medical and non-medical perspectives. The GHQ-12 score were significantly lower in people who held multiple explanatory models of illness when compared to the caregivers who believed single explanations. Explanatory models affect coping in caregivers of patients with acute psychotic presentations. There is a need to have a broad-based approach to recovery and care.

Insight, psychopathology, explanatory models and outcome of schizophrenia in India: a prospective 5-year cohort study.

PubMed

Johnson, Shanthi; Sathyaseelan, Manoranjitham; Charles, Helen; Jeyaseelan, Visalakshi; Jacob, Kuruthukulangara Sebastian

2012-09-27

The sole focus of models of insight on bio-medical perspectives to the complete exclusion of local, non-medical and cultural constructs mandates review. This study attempted to investigate the impact of insight, psychopathology, explanatory models of illness on outcome of first episode schizophrenia. Patients diagnosed to have DSM IV schizophrenia (n = 131) were assessed prospectively for insight, psychopathology, explanatory models of illness at baseline, 6, 12 and 60 months using standard instruments. Multiple linear and logistic regression and generalized estimating equations (GEE) were employed to assess predictors of outcome. We could follow up 95 (72.5%) patients. Sixty-five of these patients (68.4%) achieved remission. There was a negative relationship between psychosis rating and insight scores. Urban residence, fluctuating course of the initial illness, and improvement in global functioning at 6 months and lower psychosis rating at 12 months were significantly related to remission at 5 years. Insight scores, number of non-medical explanatory models and individual explanatory models held during the later course of the illness were significantly associated with outcome. Analysis of longitudinal data using GEE showed that women, rural residence, insight scores and number of non-medical explanatory models of illness held were significantly associated with BPRS scores during the study period. Insight, the disease model and the number of non-medical model positively correlated with improvement in psychosis arguing for a complex interaction between the culture, context and illness variables. These finding argue that insight and explanatory models are secondary to psychopathology, course and outcome of the illness. The awareness of mental illness is a narrative act in which people make personal sense of the many challenges they face. The course and outcome of the illness, cultural context, acceptable cultural explanations and the prevalent social stigma interact to produce a complex and multifaceted understanding of the issues. This complexity calls for a nuanced framing of insight.

Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA.

PubMed

Mair, Alan; El-Kadi, Aly I

2013-10-01

Capture zone analysis combined with a subjective susceptibility index is currently used in Hawaii to assess vulnerability to contamination of drinking water sources derived from groundwater. In this study, we developed an alternative objective approach that combines well capture zones with multiple-variable logistic regression (LR) modeling and applied it to the highly-utilized Pearl Harbor and Honolulu aquifers on the island of Oahu, Hawaii. Input for the LR models utilized explanatory variables based on hydrogeology, land use, and well geometry/location. A suite of 11 target contaminants detected in the region, including elevated nitrate (>1 mg/L), four chlorinated solvents, four agricultural fumigants, and two pesticides, was used to develop the models. We then tested the ability of the new approach to accurately separate groups of wells with low and high vulnerability, and the suitability of nitrate as an indicator of other types of contamination. Our results produced contaminant-specific LR models that accurately identified groups of wells with the lowest/highest reported detections and the lowest/highest nitrate concentrations. Current and former agricultural land uses were identified as significant explanatory variables for eight of the 11 target contaminants, while elevated nitrate was a significant variable for five contaminants. The utility of the combined approach is contingent on the availability of hydrologic and chemical monitoring data for calibrating groundwater and LR models. Application of the approach using a reference site with sufficient data could help identify key variables in areas with similar hydrogeology and land use but limited data. In addition, elevated nitrate may also be a suitable indicator of groundwater contamination in areas with limited data. The objective LR modeling approach developed in this study is flexible enough to address a wide range of contaminants and represents a suitable addition to the current subjective approach. © 2013 Elsevier B.V. All rights reserved.

Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds.

PubMed

Schefers, J M; Weigel, K A; Rawson, C L; Zwald, N R; Cook, N B

2010-04-01

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies.

PubMed

Miñano Pérez, Pablo; Castejón Costa, Juan-Luis; Gilar Corbí, Raquel

2012-03-01

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement.

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

PubMed Central

Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo

2007-01-01

Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584

The Health Belief Model as an Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation

PubMed Central

Jones, Christina L.; Jensen, Jakob D.; Scherr, Courtney L.; Brown, Natasha R.; Christy, Katheryn; Weaver, Jeremy

2015-01-01

The Health Belief Model (HBM) posits that messages will achieve optimal behavior change if they successfully target perceived barriers, benefits, self-efficacy, and threat. While the model seems to be an ideal explanatory framework for communication research, theoretical limitations have limited its use in the field. Notably, variable ordering is currently undefined in the HBM. Thus, it is unclear whether constructs mediate relationships comparably (parallel mediation), in sequence (serial mediation), or in tandem with a moderator (moderated mediation). To investigate variable ordering, adults (N = 1,377) completed a survey in the aftermath of an 8-month flu vaccine campaign grounded in the HBM. Exposure to the campaign was positively related to vaccination behavior. Statistical evaluation supported a model where the indirect effect of exposure on behavior through perceived barriers and threat was moderated by self-efficacy (moderated mediation). Perceived barriers and benefits also formed a serial mediation chain. The results indicate that variable ordering in the Health Belief Model may be complex, may help to explain conflicting results of the past, and may be a good focus for future research. PMID:25010519

Ordinal probability effect measures for group comparisons in multinomial cumulative link models.

PubMed

Agresti, Alan; Kateri, Maria

2017-03-01

We consider simple ordinal model-based probability effect measures for comparing distributions of two groups, adjusted for explanatory variables. An "ordinal superiority" measure summarizes the probability that an observation from one distribution falls above an independent observation from the other distribution, adjusted for explanatory variables in a model. The measure applies directly to normal linear models and to a normal latent variable model for ordinal response variables. It equals Φ(β/2) for the corresponding ordinal model that applies a probit link function to cumulative multinomial probabilities, for standard normal cdf Φ and effect β that is the coefficient of the group indicator variable. For the more general latent variable model for ordinal responses that corresponds to a linear model with other possible error distributions and corresponding link functions for cumulative multinomial probabilities, the ordinal superiority measure equals exp(β)/[1+exp(β)] with the log-log link and equals approximately exp(β/2)/[1+exp(β/2)] with the logit link, where β is the group effect. Another ordinal superiority measure generalizes the difference of proportions from binary to ordinal responses. We also present related measures directly for ordinal models for the observed response that need not assume corresponding latent response models. We present confidence intervals for the measures and illustrate with an example. © 2016, The International Biometric Society.

Whole Trait Theory

PubMed Central

Fleeson, William; Jayawickreme, Eranda

2014-01-01

Personality researchers should modify models of traits to include mechanisms of differential reaction to situations. Whole Trait Theory does so via five main points. First, the descriptive side of traits should be conceptualized as density distributions of states. Second, it is important to provide an explanatory account of the Big 5 traits. Third, adding an explanatory account to the Big 5 creates two parts to traits, an explanatory part and a descriptive part, and these two parts should be recognized as separate entities that are joined into whole traits. Fourth, Whole Trait Theory proposes that the explanatory side of traits consists of social-cognitive mechanisms. Fifth, social-cognitive mechanisms that produce Big-5 states should be identified. PMID:26097268

[Treatment of functional somatic syndrome with abdominal pain].

PubMed

Abe, Tetsuya; Kanbara, Kenji; Mizuno, Yasuyuki; Fukunaga, Mikihiko

2009-09-01

Functional somatic syndrome (FSS) with abdominal pain include functional gastrointestinal disorder, chronic pancreatitis, chronic pelvic pain syndrome, which generally contain autonomic dysfunction. Regarding the treatment of FSS, it is important to know about FSS for a therapist at first. Secondly, the therapist should find out physical dysfunction of patients positively, and confirm objectively the hypotheses about both peripheral and central pathophysiological mechanisms as much as possible. Heart rate variability is an easy method, and useful to assess autonomic function. After grasping the patient's explanatory model about the illness, the therapist showes the most acceptable treatment for the patient at last.

[Access to care and prevention for people with disabilities in France: Analysis based on data from the 2008 French health and disabilities households surveys (Handicap-Santé-Ménages)].

PubMed

Pichetti, S; Penneau, A; Lengagne, P; Sermet, C

2016-04-01

Using data from the 2008 French health and disabilities households surveys, this study examines the use of three types of routine medical care (dental, ophthalmological and gynecological care) and four preventive services (cervical cancer screening, breast cancer screening, colon cancer screening and vaccination against hepatitis B) both for people with disabilities and for those without. Two definitions of disability were retained: (1) functional limitations (motor, cognitive, visual or hearing limitations) and (2) administrative recognition of disability. For each type of care, binary logistic regression was used to test whether access to care is influenced by any of the disability indicators as well as by other explanatory variables. Two set of explanatory variables were included successively: (1) sociodemographic variables such as age, gender as well as a proxy variable representing medical needs and (2) socioeconomic variables such as level of education, household income per consumption unit, supplementary health insurance coverage, co-payment exemption and geographic variables. Persons reporting functional limitations are less likely to access to all types of care, in a proportion that varies between 5 to 27 points, compared to persons without functional limitations, except for eye care for which no gap is observed. The same results are obtained for persons reporting an administrative recognition of disability, and more precisely for those who benefit from the Disability allowance for adults (Allocation adulte handicapé [AAH]). After adding the social variables to the model, problems of access to health care decrease significantly, showing that disabled persons' social situation tends to reduce their access to care. This study reveals, for a broad range of care, a negative differential access to care for persons reporting functional limitations compared to those without limitations which is confirmed when identifying disability through administrative recognition. Furthermore, it also discusses factors explaining these differentials. It highlights the role of the social situation of disabled people as an additional barrier to already limited access to healthcare. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Monitoring and modeling to predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania

USGS Publications Warehouse

Zimmerman, Tammy M.

2006-01-01

The Lake Erie shoreline in Pennsylvania spans nearly 40 miles and is a valuable recreational resource for Erie County. Nearly 7 miles of the Lake Erie shoreline lies within Presque Isle State Park in Erie, Pa. Concentrations of Escherichia coli (E. coli) bacteria at permitted Presque Isle beaches occasionally exceed the single-sample bathing-water standard, resulting in unsafe swimming conditions and closure of the beaches. E. coli concentrations and other water-quality and environmental data collected at Presque Isle Beach 2 during the 2004 and 2005 recreational seasons were used to develop models using tobit regression analyses to predict E. coli concentrations. All variables statistically related to E. coli concentrations were included in the initial regression analyses, and after several iterations, only those explanatory variables that made the models significantly better at predicting E. coli concentrations were included in the final models. Regression models were developed using data from 2004, 2005, and the combined 2-year dataset. Variables in the 2004 model and the combined 2004-2005 model were log10 turbidity, rain weight, wave height (calculated), and wind direction. Variables in the 2005 model were log10 turbidity and wind direction. Explanatory variables not included in the final models were water temperature, streamflow, wind speed, and current speed; model results indicated these variables did not meet significance criteria at the 95-percent confidence level (probabilities were greater than 0.05). The predicted E. coli concentrations produced by the models were used to develop probabilities that concentrations would exceed the single-sample bathing-water standard for E. coli of 235 colonies per 100 milliliters. Analysis of the exceedence probabilities helped determine a threshold probability for each model, chosen such that the correct number of exceedences and nonexceedences was maximized and the number of false positives and false negatives was minimized. Future samples with computed exceedence probabilities higher than the selected threshold probability, as determined by the model, will likely exceed the E. coli standard and a beach advisory or closing may need to be issued; computed exceedence probabilities lower than the threshold probability will likely indicate the standard will not be exceeded. Additional data collected each year can be used to test and possibly improve the model. This study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to issue beach advisories or closings.

Temporal self-regulation theory: a neurobiologically informed model for physical activity behavior

PubMed Central

Hall, Peter A.; Fong, Geoffrey T.

2015-01-01

Dominant explanatory models for physical activity behavior are limited by the exclusion of several important components, including temporal dynamics, ecological forces, and neurobiological factors. The latter may be a critical omission, given the relevance of several aspects of cognitive function for the self-regulatory processes that are likely required for consistent implementation of physical activity behavior in everyday life. This narrative review introduces temporal self-regulation theory (TST; Hall and Fong, 2007, 2013) as a new explanatory model for physical activity behavior. Important features of the model include consideration of the default status of the physical activity behavior, as well as the disproportionate influence of temporally proximal behavioral contingencies. Most importantly, the TST model proposes positive feedback loops linking executive function (EF) and the performance of physical activity behavior. Specifically, those with relatively stronger executive control (and optimized brain structures supporting it, such as the dorsolateral prefrontal cortex (PFC)) are able to implement physical activity with more consistency than others, which in turn serves to strengthen the executive control network itself. The TST model has the potential to explain everyday variants of incidental physical activity, sport-related excellence via capacity for deliberate practice, and variability in the propensity to schedule and implement exercise routines. PMID:25859196

A Test of the Big Fish in a Little Pond Hypothesis: An Investigation into the Feelings of Seven-Year-Old Pupils in School.

ERIC Educational Resources Information Center

Tymms, Peter

2001-01-01

The feelings (self-concepts and attitudes) of 21,000 British 7-year-olds toward math, reading, and school were investigated using multivariate multilevel models. The most important explanatory variables were the teacher and pupils' academic level. Other variables (age, sex, and first language) were weakly connected to attitude measures. (Contains…

The Predictive Influence of Family and Community Demographic Variables on Grade 7 Student Achievement in Language Arts and Mathematics

ERIC Educational Resources Information Center

Wolfe, Adam

2016-01-01

This correlational, explanatory, longitudinal study sought to determine the combination of community and family-level demographic variables found in the 2010 U.S. Census data that most accurately predicted a New Jersey school district's percentage of students scoring proficient or above on the 2010, 2011, and 2012 NJ ASK 7 in Language Arts and…

Gender quotas for women in national politics: A comparative analysis across development thresholds.

PubMed

Rosen, Jennifer

2017-08-01

Women's share of global lower or single house parliamentary seats has increased by over 70% over the course of the 21st century. Yet these increases have not been uniform across countries. Rather countries with low levels of socioeconomic development have outpaced developed democracies in terms of the gains made in the formal political representation of women. One reasonable explanation for this trend is the adoption in many poorer countries of national gender quota legislation, that is, affirmative action laws intended to compensate for sex discrimination in the electoral process. Yet, cross-national analyses examining quotas as an explanatory factor typically use a simple binary (yes/no) variable that either conflates the diverse intra-quota variations into a single variable or includes only one part of the many quota variations. By contrast, using an originally compiled dataset that includes 167 countries from 1992 to 2012, this paper employs measures of gender quota legislation that capture the complexity and considerable diversity of existing quota legislation. These measures allow us to identify the specific factors that have helped so many less developed countries rise to the top of international rankings in recent years. The findings indicate that the effect of each type of gender quota, as well as other explanatory variables, do not operate in the same way across all countries. Specifically, voluntary political party quotas are substantially more effective in developed countries, while reserved seat quotas are only significant in least developed countries. Electoral candidate quotas, on the other hand, can be significant across all countries, however only have a positive effect when they are accompanied by placement mandates that ensure women are placed in winnable positions, sanctions for non-compliance that are significant enough to force adherence, and a minimum mandated threshold of at least 30%. Copyright © 2017 Elsevier Inc. All rights reserved.

Identification of the need for home visiting nurse: development of a new assessment tool

PubMed Central

Taguchi, Atsuko; Nagata, Satoko; Naruse, Takashi; Kuwahara, Yuki; Yamaguchi, Takuhiro; Murashima, Sachiyo

2014-01-01

Objective To develop a Home Visiting Nursing Service Need Assessment Form (HVNS-NAF) to standardize the decision about the need for home visiting nursing service. Methods The sample consisted of older adults who had received coordinated services by care managers. We defined the need for home visiting nursing service by elderly individuals as the decision of the need by a care manager so that the elderly can continue to live independently. Explanatory variables included demographic factors, medical procedure, severity of illness, and caregiver variables. Multiple logistic regression was carried out after univariate analyses to decide the variables to include and the weight of each variable in the HVNS-NAF. We then calculated the sensitivity and specificity of each cutoff value, and defined the score with the highest sensitivity and specificity as the cutoff value. Results Nineteen items were included in the final HVNS-NAF. When the cutoff value was 2 points, the sensitivity was 77.0%, specificity 68.5%, and positive predictive value 56.8%. Conclusions HVNS-NAF is the first validated standard based on characteristics of elderly clients who required home visiting nursing service. Using the HVNS-NAF may result in reducing the unmet need for home visiting nursing service and preventing hospitalization. PMID:24665229

Racialized customer service in restaurants: a quantitative assessment of the statistical discrimination explanatory framework.

PubMed

Brewster, Zachary W

2012-01-01

Despite popular claims that racism and discrimination are no longer salient issues in contemporary society, racial minorities continue to experience disparate treatment in everyday public interactions. The context of full-service restaurants is one such public setting wherein racial minority patrons, African Americans in particular, encounter racial prejudices and discriminate treatment. To further understand the causes of such discriminate treatment within the restaurant context, this article analyzes primary survey data derived from a community sample of servers (N = 200) to assess the explanatory power of one posited explanation—statistical discrimination. Taken as a whole, findings suggest that while a statistical discrimination framework toward understanding variability in servers’ discriminatory behaviors should not be disregarded, the framework’s explanatory utility is limited. Servers’ inferences about the potential profitability of waiting on customers across racial groups explain little of the overall variation in subjects’ self-reported discriminatory behaviors, thus suggesting that other factors not explored in this research are clearly operating and should be the focus of future inquires.

A Longitudinal Analysis of the Effects of Socioeconomic Factors, Foreign Media, and Attitude toward Appearance on General and Central Adiposity in Chinese Adolescents.

PubMed

Felicitas, Jamie Q; Tanenbaum, Hilary C; Li, Yawen; Chou, Chih-Ping; Palmer, Paula H; Spruijt-Metz, Donna; Reynolds, Kim D; Johnson, C Anderson; Xie, Bin

This paper explores the longitudinal effects of socioeconomic factors (i.e., parent education and family income level), foreign media, and attitude toward appearance on general and central adiposity among Chinese adolescents. A longitudinal analysis was performed using data from the China Seven Cities Study, a health promotion and smoking prevention study conducted in seven cities across Mainland China between 2002 and 2005. Participants included 5,020 middle and high school students and their parents. Explanatory variables included foreign media exposure, attitude toward appearance, parent education, and family income. Three-level, random-effect models were used to predict general adiposity (i.e., body mass index) and central adiposity (i.e., waist circumference). The Generalized Estimating Equation approach was utilized to determine the effect of explanatory variables on overweight status. Among girls, foreign media exposure was significantly negatively associated with general adiposity over time (β=-0.06, p=0.01 for middle school girls; β=-0.06, p=0.03 for high school girls). Attitude toward appearance was associated with lesser odds of being overweight, particularly among high school girls (OR=0.86, p<0.01). Among boys, parental education was significantly positively associated with general adiposity (β=0.62, p<0.01 for middle school boys; β=0.37, p=0.02 for high school boys) and associated with greater odds of being overweight (OR=1.55, p<0.01 for middle school boys; OR=1.26, p=0.04 for high school boys). Across all gender and grade levels, family income was significantly negatively associated with central adiposity over time. Interventions addressing Chinese adolescent overweight/obesity should consider these factors as potential focus areas.

Beyond imperviousness: A statistical approach to identifying functional differences between development morphologies on variable source area-type response in urbanized watersheds

NASA Astrophysics Data System (ADS)

Lim, T. C.

2016-12-01

Empirical evidence has shown linkages between urbanization, hydrological regime change, and degradation of water quality and aquatic habitat. Percent imperviousness, has long been suggested as the dominant source of these negative changes. However, recent research identifying alternative pathways of runoff production at the watershed scale have called into question percent impervious surface area's primacy in urban runoff production compared to other aspects of urbanization including change in vegetative cover, imported water and water leakages, and the presence of drainage infrastructure. In this research I show how a robust statistical methodology can detect evidence of variable source area (VSA)-type hydrologic response associated with incremental hydraulic connectivity in watersheds. I then use logistic regression to explore how evidence of VSA-type response relates to the physical and meterological characteristics of the watershed. I find that impervious surface area is highly correlated with development, but does not add significant explanatory power beyond percent developed in predicting VSA-type response. Other aspects of development morphology, including percent developed open space and type of drainage infrastructure also do not add to the explanatory power of undeveloped land in predicting VSA-type response. Within only developed areas, the effect of developed open space was found to be more similar to that of total impervious area than to undeveloped land. These findings were consistent when tested across a national cross-section of urbanized watersheds, a higher resolution dataset of Baltimore Metropolitan Area watersheds, and a subsample of watersheds confirmed not to be served by combined sewer systems. These findings suggest that land development policies that focus on lot coverage should be revisited, and more focus should be placed on preserving native vegetation and soil conditions alongside development.

Climate Change Impacts on Migration in the Vulnerable Countries

NASA Astrophysics Data System (ADS)

An, Nazan; Incealtin, Gamze; Kurnaz, M. Levent; Şengün Ucal, Meltem

2014-05-01

This work focuses on the economic, demographic and environmental drivers of migration related with the sustainable development in underdeveloped and developed countries, which are the most vulnerable to the climate change impacts through the Climate-Development Modeling including climate modeling and panel logit data analysis. We have studied some countries namely Bangladesh, Netherlands, Morocco, Malaysia, Ethiopia and Bolivia. We have analyzed these countries according to their economic, demographic and environmental indicators related with the determinants of migration, and we tried to indicate that their conditions differ according to all these factors concerning with the climate change impacts. This modeling covers some explanatory variables, which have the relationship with the migration, including GDP per capita, population, temperature and precipitation, which indicate the seasonal differences according to the years, the occurrence of natural hazards over the years, coastal location of countries, permanent cropland areas and fish capture which represents the amount of capturing over the years. We analyzed that whether there is a relationship between the migration and these explanatory variables. In order to achieve sustainable development by preventing or decreasing environmental migration due to climate change impacts or related other factors, these countries need to maintain economic, social, political, demographic, and in particular environmental performance. There are some significant risks stemming from climate change, which is not under control. When the economic and environmental conditions are considered, we have to regard climate change to be the more destructive force for those who are less defensible against all of these risks and impacts of uncontrolled climate change. This work was supported by the BU Research Fund under the project number 6990. One of the authors (MLK) was partially supported by Mercator-IPC Fellowship Program.

Ethnic differences in parents' coresidence with adult children in peninsular Malaysia.

PubMed

Chan, A; Davanzo, J

1996-03-01

This study examines how benefits, costs, opportunities, and preferences affect ethnic differences in parent-child coresidence in Malaysia. The conceptual model is described in greater detail in a companion paper. Data were obtained from the senior sample of the Second Malaysian Family Life Survey of 1988-89. The nationally representative sample includes 1229 persons aged over 50 years living in private households. Retirement age in Malaysia is 45 years for women and 55 years for men. Ethnicity includes Malay, Chinese, and Indians. Adult children are aged 20 years and older. The analysis pertains to 802 married and 427 unmarried seniors. Chinese tended to live in the most expensive areas and urban areas. Malays tended to live in the least expensive areas and rural areas. Health perception ranged from good to fair to poor. About 20% of married seniors had wives aged under 50 years. Income refers to average monthly unearned income, excluding transfers from other households or public sources. The relative roles of ethnic differences in each explanatory variable are estimated. Findings indicate that the higher incidence of remarriage and lower housing costs for married Malays explain their lower coresidence rates. The poorer health of Indians and better health of Malays also explain coresidence differences for the married. The higher incidence of daughter-only families among Malays explains coresidence differences. The explanatory variables of remarriage, housing costs, health, and daughter-only families explain little for the unmarried. Among the unmarried and the married, older age was associated with greater coresidence for the Chinese only. Chinese and Malay coresidence declined with increased educational levels. Coresidence rates were lower for Malays and higher for Indians.

Transportation economics and energy

NASA Astrophysics Data System (ADS)

Soltani Sobh, Ali

The overall objective of this research is to study the impacts of technology improvement including fuel efficiency increment, extending the use of natural gas vehicle and electric vehicles on key parameters of transportation. In the first chapter, a simple economic analysis is used in order to demonstrate the adoption rate of natural gas vehicles as an alternative fuel vehicle. The effect of different factors on adoption rate of commuters is calculated in sensitivity analysis. In second chapter the VMT is modeled and forecasted under influence of CNG vehicles in different scenarios. The VMT modeling is based on the time series data for Washington State. In order to investigate the effect of population growth on VMT, the per capita model is also developed. In third chapter the effect of fuel efficiency improvement on fuel tax revenue and greenhouse emission is examined. The model is developed based on time series data of Washington State. The rebound effect resulted from fuel efficiency improvement is estimated and is considered in fuel consumption forecasting. The reduction in fuel tax revenue and greenhouse gas (GHG) emissions as two outcomes of lower fuel consumption are computed. In addition, the proper fuel tax rate to restitute the revenue is suggested. In the fourth chapter effective factors on electric vehicles (EV) adoption is discussed. The constructed model is aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different states over time. Various factors are incorporated in the utility function as explanatory variables in order to quantify their effect on EV adoption choices. The explanatory variables include income, VMT, electricity price, gasoline price, urban area and number of EV stations.

Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia.

PubMed

Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe; Tarantola, Arnaud; Cappelle, Julien

2017-01-01

Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable-the time elapsed since the first flooding of the year-was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10-1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25-3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia.

Scale effects on spatially varying relationships between urban landscape patterns and water quality.

PubMed

Sun, Yanwei; Guo, Qinghai; Liu, Jian; Wang, Run

2014-08-01

Scientific interpretation of the relationships between urban landscape patterns and water quality is important for sustainable urban planning and watershed environmental protection. This study applied the ordinary least squares regression model and the geographically weighted regression model to examine the spatially varying relationships between 12 explanatory variables (including three topographical factors, four land use parameters, and five landscape metrics) and 15 water quality indicators in watersheds of Yundang Lake, Maluan Bay, and Xinglin Bay with varying levels of urbanization in Xiamen City, China. A local and global investigation was carried out at the watershed-level, with 50 and 200 m riparian buffer scales. This study found that topographical features and landscape metrics are the dominant factors of water quality, while land uses are too weak to be considered as a strong influential factor on water quality. Such statistical results may be related with the characteristics of land use compositions in our study area. Water quality variations in the 50 m buffer were dominated by topographical variables. The impact of landscape metrics on water quality gradually strengthen with expanding buffer zones. The strongest relationships are obtained in entire watersheds, rather than in 50 and 200 m buffer zones. Spatially varying relationships and effective buffer zones were verified in this study. Spatially varying relationships between explanatory variables and water quality parameters are more diversified and complex in less urbanized areas than in highly urbanized areas. This study hypothesizes that all these varying relationships may be attributed to the heterogeneity of landscape patterns in different urban regions. Adjustment of landscape patterns in an entire watershed should be the key measure to successfully improving urban lake water quality.

Methods for estimating the magnitude and frequency of peak discharges of rural, unregulated streams in Virginia

USGS Publications Warehouse

Bisese, James A.

1995-01-01

Methods are presented for estimating the peak discharges of rural, unregulated streams in Virginia. A Pearson Type III distribution is fitted to the logarithms of the unregulated annual peak-discharge records from 363 stream-gaging stations in Virginia to estimate the peak discharge at these stations for recurrence intervals of 2 to 500 years. Peak-discharge characteristics for 284 unregulated stations are divided into eight regions based on physiographic province, and regressed on basin characteristics, including drainage area, main channel length, main channel slope, mean basin elevation, percentage of forest cover, mean annual precipitation, and maximum rainfall intensity. Regression equations for each region are computed by use of the generalized least-squares method, which accounts for spatial and temporal correlation between nearby gaging stations. This regression technique weights the significance of each station to the regional equation based on the length of records collected at each cation, the correlation between annual peak discharges among the stations, and the standard deviation of the annual peak discharge for each station.Drainage area proved to be the only significant explanatory variable in four regions, while other regions have as many as three significant variables. Standard errors of the regression equations range from 30 to 80 percent. Alternate equations using drainage area only are provided for the five regions with more than one significant explanatory variable.Methods and sample computations are provided to estimate peak discharges at gaged and engaged sites in Virginia for recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, and to adjust the regression estimates for sites on gaged streams where nearby gaging-station records are available.

Rationale for hedging initiatives: Empirical evidence from the energy industry

NASA Astrophysics Data System (ADS)

Dhanarajata, Srirajata

Theory offers different rationales for hedging including (i) financial distress and bankruptcy cost, (ii) capacity to capture attractive investment opportunities, (iii) information asymmetry, (iv) economy of scale, (v) substitution for hedging, (vi) managerial risk aversion, and (vii) convexity of tax schedule. The purpose of this dissertation is to empirically test the explanatory power of the first five theoretical rationales on hedging done by oil and gas exploration and production (E&P) companies. The level of hedging is measured by the percentage of production effectively hedged, calculated based on the concept of delta and delta-gamma hedging. I employ Tobit regression, principal components, and panel data analysis on dependent and raw independent variables. Tobit regression is applied due to the fact that the dependent variable used in the analysis is non-negative. Principal component analysis helps to reduce the dimension of explanatory variables while panel data analysis combines/pools the data that is a combination of time-series and cross-sectional. Based on the empirical results, leverage level is consistently found to be a significant factor on hedging activities, either due to an attempt to avoid financial distress by the firm, or an attempt to control agency cost by debtholders, or both. The effect of capital expenditures and discretionary cash flows are both indeterminable due possibly to a potential mismatch in timing of realized cash flow items and hedging decision. Firm size is found to be positively related to hedging supporting economy of scale hypothesis, which is introduced in past literature, as well as the argument that large firm usually are more sophisticated and should be more willing and more comfortable to use hedge instruments than smaller firms.

Exploring socioeconomic disparities in self-reported oral health among adolescents in california.

PubMed

Telford, Claire; Coulter, Ian; Murray, Liam

2011-01-01

Socioeconomic factors are associated with disparities in oral health among adolescents; however, the underlying reasons are not clear. The authors conducted a study to determine if known indicators of oral health can explain such disparities. The authors examined data from a 2007 California Health Interview Survey of adolescents. The outcome of interest was self-reported condition of the teeth; covariates were socioeconomic status (SES) (that is, family poverty level and parental education) and a range of other variables representing health-influencing behaviors, dental care and other social factors. The authors conducted analyses by using logistic regression to explain disparities in self-reported condition of the teeth associated with SES. The authors found that socioeconomic disparities decreased substantially after they added all potential explanatory variables to the model, leaving poverty level as the only variable associated with differences in the self-reported condition of the teeth. Adolescents living below the federal poverty guidelines were more likely to report that the condition of their teeth was fair or poor than were adolescents who were least poor (odds ratio = 1.58; 95 percent confidence interval, 1.04-2.41). In multivariate analyses, further oral health disparities existed in relation to behaviors that influence health, social environment and dental care. The results of this study showed that a number of factors decreased, but did not eliminate, the observed relationship between SES and oral health in Californian adolescents. Most of these explanatory factors are modifiable, indicating that socioeconomic differences associated with oral health among adolescents may be amenable to change. Practice Implications. By promoting a healthy lifestyle (including healthy diet, exercise and regular dental attendance) and conveying to patients in languages other than English how to maintain oral health, dentists may be able to ameliorate the effects of socioeconomic disparities in oral health.

ASSESSING ACCURACY OF NET CHANGE DERIVED FROM LAND COVER MAPS

EPA Science Inventory

Net change derived from land-cover maps provides important descriptive information for environmental monitoring and is often used as an input or explanatory variable in environmental models. The sampling design and analysis for assessing net change accuracy differ from traditio...

Multilevel Modeling with Correlated Effects

ERIC Educational Resources Information Center

Kim, Jee-Seon; Frees, Edward W.

2007-01-01

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized…

Bias, Confounding, and Interaction: Lions and Tigers, and Bears, Oh My!

PubMed

Vetter, Thomas R; Mascha, Edward J

2017-09-01

Epidemiologists seek to make a valid inference about the causal effect between an exposure and a disease in a specific population, using representative sample data from a specific population. Clinical researchers likewise seek to make a valid inference about the association between an intervention and outcome(s) in a specific population, based upon their randomly collected, representative sample data. Both do so by using the available data about the sample variable to make a valid estimate about its corresponding or underlying, but unknown population parameter. Random error in an experiment can be due to the natural, periodic fluctuation or variation in the accuracy or precision of virtually any data sampling technique or health measurement tool or scale. In a clinical research study, random error can be due to not only innate human variability but also purely chance. Systematic error in an experiment arises from an innate flaw in the data sampling technique or measurement instrument. In the clinical research setting, systematic error is more commonly referred to as systematic bias. The most commonly encountered types of bias in anesthesia, perioperative, critical care, and pain medicine research include recall bias, observational bias (Hawthorne effect), attrition bias, misclassification or informational bias, and selection bias. A confounding variable is a factor associated with both the exposure of interest and the outcome of interest. A confounding variable (confounding factor or confounder) is a variable that correlates (positively or negatively) with both the exposure and outcome. Confounding is typically not an issue in a randomized trial because the randomized groups are sufficiently balanced on all potential confounding variables, both observed and nonobserved. However, confounding can be a major problem with any observational (nonrandomized) study. Ignoring confounding in an observational study will often result in a "distorted" or incorrect estimate of the association or treatment effect. Interaction among variables, also known as effect modification, exists when the effect of 1 explanatory variable on the outcome depends on the particular level or value of another explanatory variable. Bias and confounding are common potential explanations for statistically significant associations between exposure and outcome when the true relationship is noncausal. Understanding interactions is vital to proper interpretation of treatment effects. These complex concepts should be consistently and appropriately considered whenever one is not only designing but also analyzing and interpreting data from a randomized trial or observational study.

Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes

DOE PAGES

Zscheischler, Jakob; Fatichi, Simone; Wolf, Sebastian; ...

2016-08-08

Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their explanatory power is limited and uncertainties remain as to their relative contributions. Recent results show that the annual count of hours where evapotranspiration (ET) is larger than its 95th percentile is strongly correlated with the annual variability of ET and gross primary production (GPP) in an ecosystem model. This suggests that the occurrence ofmore » favorable conditions has a strong influence on the annual carbon budget. Here we analyzed data from eight forest sites of the AmeriFlux network with at least 7 years of continuous measurements. We show that for ET and the carbon fluxes GPP, ecosystem respiration (RE), and net ecosystem production, counting the “most active hours/days” (i.e., hours/days when the flux exceeds a high percentile) correlates well with the respective annual sums, with correlation coefficients generally larger than 0.8. Phenological transitions have much weaker explanatory power. By exploiting the relationship between most active hours and interannual variability, we classify hours as most active or less active and largely explain interannual variability in ecosystem fluxes, particularly for GPP and RE. Our results suggest that a better understanding and modeling of the occurrence of large values in high-frequency ecosystem fluxes will result in a better understanding of interannual variability of these fluxes.« less

A hierarchical spatial model of avian abundance with application to Cerulean Warblers

USGS Publications Warehouse

Thogmartin, Wayne E.; Sauer, John R.; Knutson, Melinda G.

2004-01-01

Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response is counts, such as animal counts, activity (e.g.,nest) counts, or species richness. The most noteworthy practical application of this spatial modeling approach is the ability to map relative species abundance. The functional relationships that we elucidated for the Cerulean Warbler provide a basis for the development of management programs and may serve to focus management and monitoring on areas and habitat variables important to Cerulean Warblers.

Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

EIA Publications

2010-01-01

The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

Improved algorithms for estimating Total Alkalinity in Northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Devkota, M.; Dash, P.

2017-12-01

Ocean Acidification (OA) is one of the serious challenges that have significant impacts on ocean. About 25% of anthropologically generated CO2 is absorbed by the oceans which decreases average ocean pH. This change has critical impacts on marine species, ocean ecology, and associated economics. 35 years of observation concluded that the rate of alteration in OA parameters varies geographically with higher variations in the northern Gulf of Mexico (N-GoM). Several studies have suggested that the Mississippi River affects the carbon dynamics of the N-GoM coastal ecosystem significantly. Total Alkalinity (TA) algorithms developed for major ocean basins produce inaccurate estimations in this region. Hence, a local algorithm to estimate TA is the need for this region, which would incorporate the local effects of oceanographic processes and complex spatial influences. In situ data collected in N-GoM region during the GOMECC-I and II cruises, and GISR Cruises (G-1, 3, 5) from 2007 to 2013 were assimilated and used to calculate the efficiency of the existing TA algorithm that uses Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) as explanatory variables. To improve this algorithm, firstly, statistical analyses were performed to improve the coefficients and the functional form of this algorithm. Then, chlorophyll a (Chl-a) was included as an additional explanatory variable in the multiple linear regression approach in addition to SST and SSS. Based on the average concentration of Chl-a for last 15 years, the N-GoM was divided into two regions, and two separate algorithms were developed for each region. Finally, to address spatial non-stationarity, a Geographically Weighted Regression (GWR) algorithm was developed. The existing TA algorithm resulted considerable algorithm bias with a larger bias in the coastal waters. Chl-a as an additional explanatory variable reduced the bias in the residuals and improved the algorithm efficiency. Chl-a worked as a proxy for addressing the organic pump's pronounced effects in the coastal waters. The GWR algorithm provided a raster surface of the coefficients with even more reliable algorithms to estimate TA with least error. The GWR algorithm addressed the spatial non-stationarity of OA in N-GoM, which apparently was not addressed in the previously developed algorithms.

The Association between Regional Environmental Factors and Road Trauma Rates: A Geospatial Analysis of 10 Years of Road Traffic Crashes in British Columbia, Canada

PubMed Central

Brubacher, Jeffrey R.; Chan, Herbert; Erdelyi, Shannon; Schuurman, Nadine; Amram, Ofer

2016-01-01

Background British Columbia, Canada is a geographically large jurisdiction with varied environmental and socio-cultural contexts. This cross-sectional study examined variation in motor vehicle crash rates across 100 police patrols to investigate the association of crashes with key explanatory factors. Methods Eleven crash outcomes (total crashes, injury crashes, fatal crashes, speed related fatal crashes, total fatalities, single-vehicle night-time crashes, rear-end collisions, and collisions involving heavy vehicles, pedestrians, cyclists, or motorcyclists) were identified from police collision reports and insurance claims and mapped to police patrols. Six potential explanatory factors (intensity of traffic law enforcement, speed limits, climate, remoteness, socio-economic factors, and alcohol consumption) were also mapped to police patrols. We then studied the association between crashes and explanatory factors using negative binomial models with crash count per patrol as the response variable and explanatory factors as covariates. Results Between 2003 and 2012 there were 1,434,239 insurance claim collisions, 386,326 police reported crashes, and 3,404 fatal crashes. Across police patrols, there was marked variation in per capita crash rate and in potential explanatory factors. Several factors were associated with crash rates. Percent roads with speed limits ≤ 60 km/hr was positively associated with total crashes, injury crashes, rear end collisions, and collisions involving pedestrians, cyclists, and heavy vehicles; and negatively associated with single vehicle night-time crashes, fatal crashes, fatal speeding crashes, and total fatalities. Higher winter temperature was associated with lower rates of overall collisions, single vehicle night-time collisions, collisions involving heavy vehicles, and total fatalities. Lower socio-economic status was associated with higher rates of injury collisions, pedestrian collisions, fatal speeding collisions, and fatal collisions. Regions with dedicated traffic officers had fewer fatal crashes and fewer fatal speed related crashes but more rear end crashes and more crashes involving cyclists or pedestrians. The number of traffic citations per 1000 drivers was positively associated with total crashes, fatal crashes, total fatalities, fatal speeding crashes, injury crashes, single vehicle night-time crashes, and heavy vehicle crashes. Possible explanations for these associations are discussed. Conclusions There is wide variation in per capita rates of motor vehicle crashes across BC police patrols. Some variation is explained by factors such as climate, road type, remoteness, socioeconomic variables, and enforcement intensity. The ability of explanatory factors to predict crash rates would be improved if considered with local traffic volume by all travel modes. PMID:27099930

Impact of Québec's healthcare reforms on the organization of primary healthcare (PHC): a 2003-2010 follow-up.

PubMed

Pineault, Raynald; Borgès Da Silva, Roxane; Prud'homme, Alexandre; Fournier, Michel; Couture, Audrey; Provost, Sylvie; Levesque, Jean-Frédéric

2014-05-21

Healthcare reforms initiated in the early 2000s in Québec involved the implementation of new modes of primary healthcare (PHC) delivery and the creation of Health and Social Services Centers (HSSCs) to support it. The objective of this article is to assess and explain the degree of PHC organizational change achieved following these reforms. We conducted two surveys of PHC organizations, in 2005 and 2010, in two regions of the province of Québec, Canada. From the responses to these surveys, we derived a measure of organizational change based on an index of conformity to an ideal type (ICIT). One set of explanatory variables was contextual, related to coercive, normative and mimetic influences; the other consisted of organizational variables that measured receptivity towards new PHC models. Multilevel analyses were performed to examine the relationships between ICIT change in the post-reform period and the explanatory variables. Positive results were attained, as expressed by increase in the ICIT score in the post-reform period, mainly due to implementation of new types of PHC organizations (Family Medicine Groups and Network Clinics). Organizational receptivity was the main explanatory variable mediating the effect of coercive and mimetic influences. Normative influence was not a significant factor in explaining changes. Changes were modest at the system level but important with regard to new forms of PHC organizations. The top-down decreed reform was a determining factor in initiating change whereas local coercive and normative influences did not play a major role. The exemplar role played by certain PHC organizations through mimetic influence was more important. Receptivity of individual organizations was both a necessary condition and a mediating factor in influencing change. This supports the view that a combination of top-down and bottom-up strategy is best suited for achieving substantial changes in PHC local organization.

Impact of Québec’s healthcare reforms on the organization of primary healthcare (PHC): a 2003-2010 follow-up

PubMed Central

2014-01-01

Background Healthcare reforms initiated in the early 2000s in Québec involved the implementation of new modes of primary healthcare (PHC) delivery and the creation of Health and Social Services Centers (HSSCs) to support it. The objective of this article is to assess and explain the degree of PHC organizational change achieved following these reforms. Methods We conducted two surveys of PHC organizations, in 2005 and 2010, in two regions of the province of Québec, Canada. From the responses to these surveys, we derived a measure of organizational change based on an index of conformity to an ideal type (ICIT). One set of explanatory variables was contextual, related to coercive, normative and mimetic influences; the other consisted of organizational variables that measured receptivity towards new PHC models. Multilevel analyses were performed to examine the relationships between ICIT change in the post-reform period and the explanatory variables. Results Positive results were attained, as expressed by increase in the ICIT score in the post-reform period, mainly due to implementation of new types of PHC organizations (Family Medicine Groups and Network Clinics). Organizational receptivity was the main explanatory variable mediating the effect of coercive and mimetic influences. Normative influence was not a significant factor in explaining changes. Conclusion Changes were modest at the system level but important with regard to new forms of PHC organizations. The top-down decreed reform was a determining factor in initiating change whereas local coercive and normative influences did not play a major role. The exemplar role played by certain PHC organizations through mimetic influence was more important. Receptivity of individual organizations was both a necessary condition and a mediating factor in influencing change. This supports the view that a combination of top-down and bottom-up strategy is best suited for achieving substantial changes in PHC local organization. PMID:24886490

The connection between nursing diagnosis and the use of healthcare resources.

PubMed

Company-Sancho, María Consuelo; Estupiñán-Ramírez, Marcos; Sánchez-Janáriz, Hilda; Tristancho-Ajamil, Rita

The health service invests up to 75% of its resources on chronic care where the focus should be on caring rather than curing. Nursing staff focuses their work on such care. Care requires being redorded in health histories through the standardized languages. These records enable useful analyses to organisational and healthcare decision-making. Our proposal is to know the association of between nursing diagnosis and a higher total expenditure on health. An observational cross-sectional analytical study was performed based on data from electronic health records in Primary Care (Drago-AP), hospital discharges (CMBD-AH) and prescriptions (REC-SCS) of patients over 50 from 2012-2013 in the Canary Islands. A descriptive, bivariate and multivariate analysis was undertaken to create a predictive model on the use of resources. Sociodemographic (age, sex, type of health-care affiliation, type of prescription charge) and nursing diagnosis (ND) recorded in late 2012. Dependent variables: Resources consumed in 2013. 582,171 patients met the criteria for inclusion. 53.0% of them were women with an average age of 64.3 years (SD 10.8years). 53.2% were pensioners. 49% of the included population had an ND, with an average of 2.1ND per patient. The average costs per patient were 1824.62€ (with a median of 827.5€) 25 and 27 percentiles of 264.1€ and 1824.7€, respectively. The bivariate analysis showed a significant correlation between these expenses and all the demographic variables; the expenses increased when a nursing diagnosis has been made (Spearman's rank=0.37: the more diagnoses, the more expenses). In the multivariate analysis, a first linear regression with the sociodemographic variables as independent variables explains 13.7% of the variability of the logarithm of the full costs (R 2 =0.137). If we add to this model the presence of nursing diagnoses, the explanatory capacity reaches 19.77% (R 2 =0.1977). Compared with a model that only consists of sociodemographic variables, nursing diagnoses can enhance the explanatory capacity of the use of healthcare resources. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

[Satisfaction with primary care nursing: use of measurement tools and explanatory factors].

PubMed

Martín-Fernández, J; Ariza-Cardiel, G; Rodríguez-Martínez, G; Gayo-Milla, M; Martínez-Gil, M; Alzola-Martín, C; Fernández-San Martín, M I

2015-01-01

This study aims to assess the psychometric properties of two measurement tools for patient satisfaction with nursing care in Primary Care, the satisfaction level, and the personal and consultation characteristics associated with its variability. Subjects randomly selected in 23 Health Care centres in the Community of Madrid were included. Satisfaction was measured by means of the AMABLE and Baker questionnaires, in which the psychometric properties were evaluated. Sociodemographic characteristics of the consultations, variables related to health status, and other related to the consultation process were collected. An explanatory model using Generalized Estimating Equations was constructed. The 662 subjects expressed a mean satisfaction of 4.95/5 (SD .25) with AMABLE, and 4.83/5 (SD .42) with the Baker questionnaire. AMABLE had a single dimension (Cronbach's alpha .85), and Baker three: professional care (mean 4.76, SD .48 Cronbach's alpha .74), depth of relationship (mean 3.76, SD 1.18, Cronbach's alpha .73), and perceived time (mean 4.42, SD .86, Cronbach's alpha .47). Ageing, a better perception of health status, and appointments arranged by nurses were associated with higher expressed satisfaction. Home care, hospital admissions, delayed consultation, extended family, or high family income were associated with lower satisfaction. Satisfaction with nurse consultations in Primary Care was very high, and varied depending on personal characteristics and on the type of consultation. The assessed tools allowed this outcome to be measured properly. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.

Risky online behaviors among adolescents: Longitudinal relations among problematic Internet use, cyberbullying perpetration, and meeting strangers online

PubMed Central

Gámez-Guadix, Manuel; Borrajo, Erika; Almendros, Carmen

2016-01-01

Background and aims This study aims to analyze the cross-sectional and longitudinal relationship between three major risky online behaviors during adolescence: problematic Internet use, cyberbullying perpetration, and meeting strangers online. An additional objective was to study the role of impulsivity–irresponsibility as a possible explanatory variable of the relationships between these risky online behaviors. Methods The study sample was 888 adolescents that completed self-report measures at time 1 and time 2 with an interval of 6 months. Results The findings showed a significant cross-sectional relationship between the risky online behaviors analyzed. At the longitudinal level, problematic Internet use at time 1 predicted an increase in the perpetration of cyberbullying and meeting strangers online at time 2. Furthermore, meeting strangers online increased the likelihood of cyberbullying perpetration at time 2. Finally, when impulsivity–irresponsibility was included in the model as an explanatory variable, the relationships previously found remained significant. Discussion These results extend traditional problem behavior theory during adolescence, also supporting a relationship between different risky behaviors in cyberspace. In addition, findings highlighted the role of problematic Internet use, which increased the chances of developing cyberbullying perpetration and meeting strangers online over time. However, the results suggest a limited role of impulsivity–irresponsibility as an explicative mechanism. Conclusions The findings suggest that various online risk activities ought to be addressed together when planning assessment, prevention and intervention efforts. PMID:28092196

Learning Molecular Behaviour May Improve Student Explanatory Models of the Greenhouse Effect

ERIC Educational Resources Information Center

Harris, Sara E.; Gold, Anne U.

2018-01-01

We assessed undergraduates' representations of the greenhouse effect, based on student-generated concept sketches, before and after a 30-min constructivist lesson. Principal component analysis of features in student sketches revealed seven distinct and coherent explanatory models including a new "Molecular Details" model. After the…

[Analysis of the technical efficiency of hospitals in the Spanish National Health Service].

PubMed

Pérez-Romero, Carmen; Ortega-Díaz, M Isabel; Ocaña-Riola, Ricardo; Martín-Martín, José Jesús

To analyse the technical efficiency and productivity of general hospitals in the Spanish National Health Service (NHS) (2010-2012) and identify explanatory hospital and regional variables. 230 NHS hospitals were analysed by data envelopment analysis for overall, technical and scale efficiency, and Malmquist index. The robustness of the analysis is contrasted with alternative input-output models. A fixed effects multilevel cross-sectional linear model was used to analyse the explanatory efficiency variables. The average rate of overall technical efficiency (OTE) was 0.736 in 2012; there was considerable variability by region. Malmquist index (2010-2012) is 1.013. A 23% variability in OTE is attributable to the region in question. Statistically significant exogenous variables (residents per 100 physicians, aging index, average annual income per household, essential public service expenditure and public health expenditure per capita) explain 42% of the OTE variability between hospitals and 64% between regions. The number of residents showed a statistically significant relationship. As regards regions, there is a statistically significant direct linear association between OTE and annual income per capita and essential public service expenditure, and an indirect association with the aging index and annual public health expenditure per capita. The significant room for improvement in the efficiency of hospitals is conditioned by region-specific characteristics, specifically aging, wealth and the public expenditure policies of each one. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Analysis of the Explanatory Variables of the Differences in Perceptions of Cyberbullying: A Role-Based-Model Approach.

PubMed

Fernández-Antelo, Inmaculada; Cuadrado-Gordillo, Isabel

2018-04-01

The controversies that exist regarding the delimitation of the cyberbullying construct demonstrate the need for further research focused on determining the criteria that shape the structure of the perceptions that adolescents have of this phenomenon and on seeking explanations of this behavior. The objectives of this study were to (a) construct possible explanatory models of the perception of cyberbullying from identifying and relating the criteria that form this construct and (b) analyze the influence of previous cyber victimization and cyber aggression experiences in the construction of explanatory models of the perception of cyberbullying. The sample consisted of 2,148 adolescents (49.1% girls; SD = 0.5) aged from 12 to 16 years ( M = 13.9 years; SD = 1.2). The results have shown that previous cyber victimization and cyber aggression experiences lead to major differences in the explanatory models to interpret cyber-abusive behavior as cyberbullying episodes, or as social relationship mechanisms, or as a revenge reaction. We note that the aggressors' explanatory model is based primarily on a strong reciprocal relationship between the imbalance of power and intentionality, that it functions as a link promoting indirect causal relationships of the anonymity and repetition factors with the cyberbullying construct. The victims' perceptual structure is based on three criteria-imbalance of power, intentionality, and publicity-where the key factor in this structure is the intention to harm. These results allow to design more effective measures of prevention and intervention closely tailored to addressing directly the factors that are considered to be predictors of risk.

Time-Frequency Analysis of Beach Bacteria Variations and its Implication for Recreational Water Quality Modeling

EPA Science Inventory

This paper explores the potential of time-frequency wavelet analysis in resolving beach bacteria concentration and possible explanatory variables across multiple time scales with temporal information still preserved. The wavelet scalograms of E. coli concentrations and the explan...

The Determinants of College Student Retention

ERIC Educational Resources Information Center

Guerrero, Adam A.

2010-01-01

This study attempts to add to the college student dropout literature by examining persistence decisions at private, non-selective university using previously unstudied explanatory variables and advanced econometric methods. Three main contributions are provided. First, proprietary data obtained from a type of university that is underrepresented in…

Optical Properties of Three Beach Waters: Implications for Predictive Modeling of Enterococci

EPA Science Inventory

Sunlight plays an important role in the inactivation of fecal indicator bacteria in recreational waters. Solar radiation can explain temporal trends in bacterial counts and is commonly used as an explanatory variable in predictive models. Broadband surface radiation provides a ba...

Predictive Modeling of a Fecal Indicator at a Subtropical Marine Beach

EPA Science Inventory

The Virtual Beach Model Builder (VBMB) is a software tool that can be used to develop predictive models at beaches based on microbial data and observations (explanatory variables) that describe hydrometeorological and biogeochemical conditions. During the summer of 2008, a study...

Computer-mediated communication and interpersonal attraction: an experimental test of two explanatory hypotheses.

PubMed

Antheunis, Marjolijn L; Valkenburg, Patti M; Peter, Jochen

2007-12-01

The aims of this study were (a) to investigate the influence of computer-mediated communication (CMC) on interpersonal attraction and (b) to examine two underlying processes in the CMC-interpersonal attraction relationship. We identified two variables that may mediate the influence of CMC on interpersonal attraction: self-disclosure and direct questioning. Focusing on these potential mediating variables, we tested two explanatory hypotheses: the CMC-induced direct questioning hypothesis and the CMC-induced self-disclosure hypothesis. Eighty-one cross-sex dyads were randomly assigned to one of three experimental conditions: text-only CMC, visual CMC, and face-to-face communication. We did not find a direct effect of CMC on interpersonal attraction. However, we did find two positive indirect effects of text-only CMC on interpersonal attraction: text-only CMC stimulated both self-disclosure and direct questioning, both of which in turn enhanced interpersonal attraction. Results are discussed in light of uncertainty reduction theory and CMC theories.

Determination of Optimal Subsidy for Materials Saving Investment through Recycle/Recovery at Industrial Level

NASA Astrophysics Data System (ADS)

Batzias, Dimitris F.

2009-08-01

This work deals with a methodological framework under the form of a simple/short algorithmic procedure (including 11 activity steps and 3 decision nodes) designed/developed for the determination of optimal subsidy for materials saving investment through recycle/recovery (RR) at industrial level. Two case examples are presented, covering both aspects, without and with recycling. The expected Relative Cost Decrease (RCD) because of recycling, which forms a critical index for decision making on subsidizing, is estimated. The developed procedure can be extended outside the industrial unit to include collection/transportation/processing of recyclable wasted products. Since, in such a case, transportation cost and processing cost are conflict depended variables (when the quantity collected/processed Q is the independent/explanatory variable), the determination of Qopt is examined under energy crises conditions, when corresponding subsidies might be granted to re-set the original equilibrium and avoid putting the recycling enterprise in jeopardize due to dangerous lowering of the first break-even point.

Editorial: Let's talk about sex - the gender binary revisited.

PubMed

Oldehinkel, Albertine J

2017-08-01

Sex refers to biological differences and gender to socioculturally delineated masculine and feminine roles. Sex or gender are included as a covariate or effect modifier in the majority of child psychology and psychiatry studies, and differences found between boys and girls have inspired many researchers to postulate underlying mechanisms. Empirical tests of whether including these proposed explanatory variables actually reduces the variance explained by gender are lagging behind somewhat. That is a pity, because a lot can be gained from a greater focus on the active agents of specific gender differences. As opposed to biological sex as such, some of the processes explaining why a specific outcome shows gender differences may be changeable and so possible prevention targets. Moreover, while the sex binary may be reasonable adequate as a classification variable, the gender binary is far from perfect. Gender is a multidimensional, partly context-dependent factor, and the dichotomy generally used in research does not do justice to the diversity existing within boys and girls. © 2017 Association for Child and Adolescent Mental Health.

Do gender gaps in education and health affect economic growth? A cross-country study from 1975 to 2010.

PubMed

Mandal, Bidisha; Batina, Raymond G; Chen, Wen

2018-05-01

We use system-generalized method-of-moments to estimate the effect of gender-specific human capital on economic growth in a cross-country panel of 127 countries between 1975 and 2010. There are several benefits of using this methodology. First, a dynamic lagged dependent econometric model is suitable to address persistence in per capita output. Second, the generalized method-of-moments estimator uses dynamic properties of the data to generate appropriate instrumental variables to address joint endogeneity of the explanatory variables. Third, we allow the measurement error to include unobserved country-specific effect and random noise. We include two gender-disaggregated measures of human capital-education and health. We find that gender gap in health plays a critical role in explaining economic growth in developing countries. Our results provide aggregate evidence that returns to investments in health systematically differ across gender and between low-income and high-income countries. Copyright © 2018 John Wiley & Sons, Ltd.

A study on reproductive performance and related factors of zebu cows in pastoral herds in a semi-arid area of Tanzania.

PubMed

Kanuya, N L; Matiko, M K; Kessy, B M; Mgongo, F O; Ropstad, E; Reksen, O

2006-06-01

A prospective longitudinal study was carried out from September 2001 to June 2004 in three adjacent villages in a semi-arid area of Tanzania. The objectives of this study were to measure the intervals between calving and either resumption of cyclical activity or confirmation of pregnancy, to estimate calving intervals, and to investigate the effect of factors assumed to be related to postpartum reproductive performance. A total of 275 lactation periods from 177 Tanzanian Shorthorn Zebu cows managed in a traditional pastoral system in 46 households were initially included. Animals were initially screened for brucelosis and thereafter examined by palpation per rectum at 2-week intervals. Body condition score (scale 1 to 5) was assessed and girth measurement (cm) taken. Occurrence of other reproductive events such as calving, abortion, death of calf, culling and reason for culling were recorded. In a subset of 98 lactation periods from 91 cows milk samples for progesterone (P4) determination were collected twice per week from day 7 after calving to the time of confirmed pregnancy or until milk production ceased before pregnancy. The data were analysed both univariately and in multivariable Cox proportional hazard (frailty) models. The mean (+/-S.E.M.) calving interval was 500+/-13.6 days. Positive reactors in the brucellosis test were 15.6% of the tested animals. Milk P4 analysis showed the rate of abortion/late embryo loss to be 14.3%. Calf mortality rates varied between 14.6 and 17.4%. A positive relationship was found between the outcome variables likelihood of cyclical activity and likelihood of pregnancy in the Cox model, and the explanatory variables: parity and body condition score (BCS) at calving. A negative relationship was found between the outcome variables, and the explanatory variables: maximum BCS loss and calf survival/mortality. Calving in the rainy season was associated with an increased likelihood of pregnancy.

Public funding of pharmaceuticals in The Netherlands: investigating the effect of evidence, process and context on CVZ decision-making.

PubMed

Cerri, Karin H; Knapp, Martin; Fernandez, Jose-Luis

2014-09-01

The College Voor Zorgverzekeringen (CVZ) provides guidance to the Dutch healthcare system on funding and use of new pharmaceutical technologies. This study examined the impact of evidence, process and context factors on CVZ decisions in 2004-2009. A data set of CVZ decisions pertaining to pharmaceutical technologies was created, including 29 variables extracted from published information. A three-category outcome variable was used, defined as the decision to 'recommend', 'restrict' or 'not recommend' a technology. Technologies included in list 1A/1B or on the expensive drug list were considered recommended; those included in list 2 or for which patient co-payment is required were considered restricted; technologies not included on any reimbursement list were classified as 'not recommended'. Using multinomial logistic regression, the relative contribution of explanatory variables on CVZ decisions was assessed. In all, 244 technology appraisals (256 technologies) were analysed, with 51%, of technologies recommended, 33% restricted and 16% not recommended by CVZ for funding. The multinomial model showed significant associations (p ≤ 0.10) between CVZ outcome and several variables, including: (1) use of an active comparator and demonstration of statistical superiority of the primary endpoint in clinical trials, (2) pharmaceutical budget impact associated with introduction of the technology, (3) therapeutic indication and (4) prevalence of the target population. Results confirm the value of a comprehensive and multivariate approach to understanding CVZ decision-making.

Understanding logistic regression analysis.

PubMed

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

A geospatial model of ambient sound pressure levels in the contiguous United States.

PubMed

Mennitt, Daniel; Sherrill, Kirk; Fristrup, Kurt

2014-05-01

This paper presents a model that predicts measured sound pressure levels using geospatial features such as topography, climate, hydrology, and anthropogenic activity. The model utilizes random forest, a tree-based machine learning algorithm, which does not incorporate a priori knowledge of source characteristics or propagation mechanics. The response data encompasses 270 000 h of acoustical measurements from 190 sites located in National Parks across the contiguous United States. The explanatory variables were derived from national geospatial data layers and cross validation procedures were used to evaluate model performance and identify variables with predictive power. Using the model, the effects of individual explanatory variables on sound pressure level were isolated and quantified to reveal systematic trends across environmental gradients. Model performance varies by the acoustical metric of interest; the seasonal L50 can be predicted with a median absolute deviation of approximately 3 dB. The primary application for this model is to generalize point measurements to maps expressing spatial variation in ambient sound levels. An example of this mapping capability is presented for Zion National Park and Cedar Breaks National Monument in southwestern Utah.

Modification of the Integrated Sasang Constitutional Diagnostic Model

PubMed Central

Nam, Jiho

2017-01-01

In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to address quantification problems in the existing Sasang constitutional diagnostic method. However, certain issues have been raised regarding a revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problems of the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absolute shrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly, proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the test set for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables of individual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the male SY type (36.4% → 62.0%) and the female SE type (43.7% → 64.5%), which were areas of limitation of the previous integrated diagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasang constitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine. PMID:29317897

The influences of canopy species and topographic variables on understory species diversity and composition in coniferous forests.

PubMed

Huo, Hong; Feng, Qi; Su, Yong-hong

2014-01-01

Understanding the factors that influence the distribution of understory vegetation is important for biological conservation and forest management. We compared understory species composition by multi-response permutation procedure and indicator species analysis between plots dominated by Qinghai spruce (Picea crassifolia Kom.) and Qilian juniper (Sabina przewalskii Kom.) in coniferous forests of the Qilian Mountains, northwestern China. Understory species composition differed markedly between the forest types. Many heliophilous species were significantly associated with juniper forest, while only one species was indicative of spruce forest. Using constrained ordination and the variation partitioning model, we quantitatively assessed the relative effects of two sets of explanatory variables on understory species composition. The results showed that topographic variables had higher explanatory power than did site conditions for understory plant distributions. However, a large amount of the variation in understory species composition remained unexplained. Forward selection revealed that understory species distributions were primarily affected by elevation and aspect. Juniper forest had higher species richness and α-diversity and lower β-diversity in the herb layer of the understory plant community than spruce forest, suggesting that the former may be more important in maintaining understory biodiversity and community stability in alpine coniferous forest ecosystems.

The NASA-Sponsored Study of Cataract in Astronauts (NASCA). Relationship of Exposure to Radiation in Space and the Risk of Cataract Incidence and Progression. Report 1: Recruitment and Methodology

NASA Technical Reports Server (NTRS)

Chylack, Leo T.; Peterson, Leif E.; Feiveson, Alan H.; Wear, Mary; Manuel, F. Keith

2007-01-01

The NASA Study of Cataract in Astronauts (NASCA) is a five-year, multi-centered, investigation of lens opacification in populations of U.S. astronauts, military pilots, and ground-based (nonaviator) comparison participants. For astronauts, the explanatory variable of most interest is radiation exposure during space flight, however to properly evaluate its effect, the secondary effects of age, nutrition, general health, solar ocular exposure, and other confounding variables encountered in non-space flight must also be considered. NASCA contains an initial baseline, cross-sectional objective assessment of the severity of cortical (C), nuclear (N), and posterior subcapsular (PSC) lens opacification, and annual follow-on assessments of severity and progression of these opacities in the population of astronauts and in participants sampled from populations of military pilots and ground-based exposure controls. From these data, NASCA will estimate the degree to which space radiation affects lens opacification for astronauts and how the overall risks of each cataract type for astronauts compared with those of the other exposure control groups after adjusting for differences in age and other explanatory variables.

Conducting systematic reviews of association (etiology): The Joanna Briggs Institute's approach.

PubMed

Moola, Sandeep; Munn, Zachary; Sears, Kim; Sfetcu, Raluca; Currie, Marian; Lisy, Karolina; Tufanaru, Catalin; Qureshi, Rubab; Mattis, Patrick; Mu, Peifan

2015-09-01

The systematic review of evidence is the research method which underpins the traditional approach to evidence-based healthcare. There is currently no uniform methodology for conducting a systematic review of association (etiology). This study outlines and describes the Joanna Briggs Institute's approach and guidance for synthesizing evidence related to association with a predominant focus on etiology and contributes to the emerging field of systematic review methodologies. It should be noted that questions of association typically address etiological or prognostic issues.The systematic review of studies to answer questions of etiology follows the same basic principles of systematic review of other types of data. An a priori protocol must inform the conduct of the systematic review, comprehensive searching must be performed and critical appraisal of retrieved studies must be carried out.The overarching objective of systematic reviews of etiology is to identify and synthesize the best available evidence on the factors of interest that are associated with a particular disease or outcome. The traditional PICO (population, interventions, comparators and outcomes) format for systematic reviews of effects does not align with questions relating to etiology. A systematic review of etiology should include the following aspects: population, exposure of interest (independent variable) and outcome (dependent variable).Studies of etiology are predominantly explanatory or predictive. The objective of reviews of explanatory or predictive studies is to contribute to, and improve our understanding of, the relationship of health-related events or outcomes by examining the association between variables. When interpreting possible associations between variables based on observational study data, caution must be exercised due to the likely presence of confounding variables or moderators that may impact on the results.As with all systematic reviews, there are various approaches to present the results, including a narrative, graphical or tabular summary, or meta-analysis. When meta-analysis is not possible, a set of alternative methods for synthesizing research is available. On the basis of the research question and objectives, narrative, tabular and/or visual approaches can be used for data synthesis. There are some special considerations when conducting meta-analysis for questions related to risk and correlation. These include, but are not limited to, causal inference.Systematic review and meta-analysis of studies related to etiology is an emerging methodology in the field of evidence synthesis. These reviews can provide useful information for healthcare professionals and policymakers on the burden of disease. The standardized Joanna Briggs Institute approach offers a rigorous and transparent method to conduct reviews of etiology.

An outline of graphical Markov models in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1999-12-01

In the usual multiple regression model there is one response variable and one block of several explanatory variables. In contrast, in reality there may be a block of several possibly interacting response variables one would like to explain. In addition, the explanatory variables may split into a sequence of several blocks, each block containing several interacting variables. The variables in the second block are explained by those in the first block; the variables in the third block by those in the first and the second block etc. During recent years methods have been developed allowing analysis of problems where the data set has the above complex structure. The models involved are called graphical models or graphical Markov models. The main result of an analysis is a picture, a conditional independence graph with precise statistical meaning, consisting of circles representing variables and lines or arrows representing significant conditional associations. The absence of a line between two circles signifies that the corresponding two variables are independent conditional on the presence of other variables in the model. An example from epidemiology is presented in order to demonstrate application and use of the models. The data set in the example has a complex structure consisting of successive blocks: the variable in the first block is year of investigation; the variables in the second block are age and gender; the variables in the third block are indices of calculus, gingivitis and mutans streptococci and the final response variables in the fourth block are different indices of caries. Since the statistical methods may not be easily accessible to dentists, this article presents them in an introductory form. Graphical models may be of great value to dentists in allowing analysis and visualisation of complex structured multivariate data sets consisting of a sequence of blocks of interacting variables and, in particular, several possibly interacting responses in the final block.

The interaction rainfall vs. weight as determinant of total mercury concentration in fish from a tropical estuary.

PubMed

Barletta, M; Lucena, L R R; Costa, M F; Barbosa-Cintra, S C T; Cysneiros, F J A

2012-08-01

Mercury loads in tropical estuaries are largely controlled by the rainfall regime that may cause biodilution due to increased amounts of organic matter (both live and non-living) in the system. Top predators, as Trichiurus lepturus, reflect the changing mercury bioavailability situations in their muscle tissues. In this work two variables [fish weight (g) and monthly total rainfall (mm)] are presented as being important predictors of total mercury concentration (T-Hg) in fish muscle. These important explanatory variables were identified by a Weibull Regression model, which best fit the dataset. A predictive model using readily available variables as rainfall is important, and can be applied for human and ecological health assessments and decisions. The main contribution will be to further protect vulnerable groups as pregnant women and children. Nature conservation directives could also improve by considering monitoring sample designs that include this hypothesis, helping to establish complete and detailed mercury contamination scenarios. Copyright © 2012 Elsevier Ltd. All rights reserved.

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

NASA Astrophysics Data System (ADS)

Gundogdu, Ismail Bulent

2017-01-01

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

Forecasting the stochastic demand for inpatient care: the case of the Greek national health system.

PubMed

Boutsioli, Zoe

2010-08-01

The aim of this study is to estimate the unexpected demand of Greek public hospitals. A multivariate model with four explanatory variables is used. These are as follows: the weekend effect, the duty effect, the summer holiday and the official holiday. The method of the ordinary least squares is used to estimate the impact of these variables on the daily hospital emergency admissions series. The forecasted residuals of hospital regressions for each year give the estimated stochastic demand. Daily emergency admissions decline during weekends, summer months and official holidays, and increase on duty hospital days. Stochastic hospital demand varies both among hospitals and over the five-year time period under investigation. Variations among hospitals are larger than time variations. Hospital managers and health policy-makers can be availed by forecasting the future flows of emergent patients. The benefit can be both at managerial and economical level. More advanced models including additional daily variables such as the weather forecasts could provide more accurate estimations.

The relationship between employer health insurance characteristics and the provision of employee assistance programs.

PubMed

Zarkin, G A; Garfinkel, S A

1994-01-01

Workplace drug and alcohol abuse imposes substantial costs on employers. In response, employers have implemented a variety of programs to decrease substance abuse in the workplace, including drug testing, health and wellness programs, and employee assistance programs (EAPs). This paper focuses on the relationship between enterprises' organizational and health insurance characteristics and the firms' decisions to provide EAPs. Using data from the 1989 Survey of Health Insurance Plans (SHIP), sponsored by the Health Care Financing Administration (HCFA), we estimated the prevalence of EAPs by selected organizational and health insurance characteristics for those firms that offer health insurance to their workers. In addition, we estimated logistic models of the enterprises' decisions to provide EAPs as functions of the extent of state substance abuse and mental health insurance mandates, state-level demographic variables, and organizational and health insurance characteristics. Our results suggest that state mandates and demographic variables, as well as organizational and health insurance characteristics, are important explanatory variables of enterprises' decisions to provide EAPs.

Schoolyard Science. Grades 2-4.

ERIC Educational Resources Information Center

Perdue, Peggy K.

This book includes 25 science activities in the fields of environmental science, soil science, life science, and physical science. The activities are designed to be used in outdoor settings. Each activity is composed of two parts--an explanatory section for the teacher and a student lab sheet. The teacher explanatory section begins with a brief…

An Economic Model of U.S. Airline Operating Expenses

NASA Technical Reports Server (NTRS)

Harris, Franklin D.

2005-01-01

This report presents a new economic model of operating expenses for 67 airlines. The model is based on data that the airlines reported to the United States Department of Transportation in 1999. The model incorporates expense-estimating equations that capture direct and indirect expenses of both passenger and cargo airlines. The variables and business factors included in the equations are detailed enough to calculate expenses at the flight equipment reporting level. Total operating expenses for a given airline are then obtained by summation over all aircraft operated by the airline. The model's accuracy is demonstrated by correlation with the DOT Form 41 data from which it was derived. Passenger airlines are more accurately modeled than cargo airlines. An appendix presents a concise summary of the expense estimating equations with explanatory notes. The equations include many operational and aircraft variables, which accommodate any changes that airline and aircraft manufacturers might make to lower expenses in the future. In 1999, total operating expenses of the 67 airlines included in this study amounted to slightly over $100.5 billion. The economic model reported herein estimates $109.3 billion.

Adolescent Self-Reported and Peer-Reported Self-Esteem.

ERIC Educational Resources Information Center

O'Donnell, William James

1979-01-01

The study is an examination of the relationship between adolescents' self-reported and peer-reported self-esteem and how this relationship is affected by sex, race, and age variables. Significant sex and race variations interacted with age. Explanatory hypotheses for these findings are given. (Author/KC)

Student Effort, Consistency, and Online Performance

ERIC Educational Resources Information Center

Patron, Hilde; Lopez, Salvador

2011-01-01

This paper examines how student effort, consistency, motivation, and marginal learning, influence student grades in an online course. We use data from eleven Microeconomics courses taught online for a total of 212 students. Our findings show that consistency, or less time variation, is a statistically significant explanatory variable, whereas…

Drought tolerance in cacao is mediated by root phenotypic plasticity

USDA-ARS?s Scientific Manuscript database

This study aimed to evaluate phenotypic relationships and their direct and indirect effects through path analysis, and evaluate the use of the phenotypic plasticity index as criteria for the estimation of the basic and explanatory variables used to analysis several cacao progenies subjected to soil ...

Exploring the Wisdom Structure: Validation of the Spanish New Short Three-Dimensional Wisdom Scale (3D-WS) and Its Explanatory Power on Psychological Health-Related Variables.

PubMed

García-Campayo, Javier; Del Hoyo, Yolanda L; Barceló-Soler, Alberto; Navarro-Gil, Mayte; Borao, Luis; Giarin, Veronica; Tovar-Garcia, R Raziel; Montero-Marin, Jesus

2018-01-01

Introduction: Personal wisdom has demonstrated important implications for the health of individuals. The aim of the present study was to validate a Spanish version of the Three-Dimensional Wisdom Scale (3D-WS), exploring the structure of a possible general factor, and assessing its explanatory power on psychological health-related variables. Methods: A cross-sectional study design was used, with a total sample of 624 Spanish participants recruited on the Internet and randomly split into two halves. The following instruments were applied: 3D-WS, Purpose in Life (PIL), Multidimensional State Boredom Scale (MSBS), Positive and Negative Affect Scale (PANAS), and Difficulties in Emotion Regulation Scale (DERS). Factorial structures were analyzed through exploratory and confirmatory factor analysis (EFA and CFA), and the general factor was characterized by using bifactor models. The explanatory power of the 3D-WS was established by multiple regression. Results: The original long and short versions of the 3D-WS were not replicated in the first subsample using EFA, and there was a high rate of cross-loadings. Thus, a new short 3D-WS was proposed by ordering the original items according to factorial weights. This three-correlated-factor (reflective, cognitive, and affective) proposal was tested by means of CFA in the second subsample, with adequate psychometrics and invariance, and a good fit (χ 2 /df = 1.98; CFI = 0.946; RMSEA = 0.056; 90% CI = 0.040-0.072). A bifactor structure, in which the reflective trait of wisdom was integrated into a general factor (G-Reflective) improved the model fit (χ 2 /df = 1.85; CFI = 0.959; RMSEA = 0.052; 90% CI = 0.035-0.070). The explained common variance of G-Reflective was 0.53; therefore, the new short 3D-WS should not be considered essentially unidimensional. The new short 3D-WS showed positive relationships with the PIL and PANAS-positive, and negative associations with the MSBS, PANAS-negative and DERS, contributing to explain all the referred variables. These results were consistent across subsamples. Conclusion: The new short 3D-WS appears to be a reliable instrument for measuring wisdom in the Spanish general population. The reflective facet might influence the cognitive and affective wisdom components through the G-Reflective general factor. There seems to be a high explanatory power of the 3D-WS on psychological health-related variables. This study will facilitate the development of future research and psychological knowledge regarding wisdom.

Use of regression‐based models to map sensitivity of aquatic resources to atmospheric deposition in Yosemite National Park, USA

USGS Publications Warehouse

Clow, David W.; Nanus, Leora; Huggett, Brian

2010-01-01

An abundance of exposed bedrock, sparse soil and vegetation, and fast hydrologic flushing rates make aquatic ecosystems in Yosemite National Park susceptible to nutrient enrichment and episodic acidification due to atmospheric deposition of nitrogen (N) and sulfur (S). In this study, multiple linear regression (MLR) models were created to estimate fall‐season nitrate and acid neutralizing capacity (ANC) in surface water in Yosemite wilderness. Input data included estimated winter N deposition, fall‐season surface‐water chemistry measurements at 52 sites, and basin characteristics derived from geographic information system layers of topography, geology, and vegetation. The MLR models accounted for 84% and 70% of the variance in surface‐water nitrate and ANC, respectively. Explanatory variables (and the sign of their coefficients) for nitrate included elevation (positive) and the abundance of neoglacial and talus deposits (positive), unvegetated terrain (positive), alluvium (negative), and riparian (negative) areas in the basins. Explanatory variables for ANC included basin area (positive) and the abundance of metamorphic rocks (positive), unvegetated terrain (negative), water (negative), and winter N deposition (negative) in the basins. The MLR equations were applied to 1407 stream reaches delineated in the National Hydrography Data Set for Yosemite, and maps of predicted surface‐water nitrate and ANC concentrations were created. Predicted surface‐water nitrate concentrations were highest in small, high‐elevation cirques, and concentrations declined downstream. Predicted ANC concentrations showed the opposite pattern, except in high‐elevation areas underlain by metamorphic rocks along the Sierran Crest, which had relatively high predicted ANC (>200 μeq L−1). Maps were created to show where basin characteristics predispose aquatic resources to nutrient enrichment and acidification effects from N and S deposition. The maps can be used to help guide development of water‐quality programs designed to monitor and protect natural resources in national parks.

Understanding burnout according to individual differences: ongoing explanatory power evaluation of two models for measuring burnout types

PubMed Central

2012-01-01

Background The classic determination of burnout is by means of the dimensions exhaustion, cynicism and inefficacy. A new definition of the syndrome is based on clinical subtypes, consisting of “frenetic” (involved, ambitious, overloaded), “underchallenged” (indifferent, bored, with lack of personal development) and “worn-out” (neglectful, unacknowledged, with little control). The dimensions of overload, lack of development and neglect form a shortened version of this perspective. The aims of this study were to estimate and to compare the explanatory power of both typological models, short and long, with the standard measurement. Methods This was a cross-sectional survey with a randomly sample of university employees (n=409). Multivariate linear regression models were constructed between the “Maslach Burnout Inventory General Survey” (MBI-GS) dimensions, as dependent variables, and the “Burnout Clinical Subtype Questionnaire” (BCSQ-36 and BCSQ-12) dimensions, as independent variables. Results The BCSQ-36 subscales together explained 53% of ‘exhaustion’ (p<0.001), 59% of ‘cynicism’ (p<0.001) and 37% of ‘efficacy’ (p<0.001), while BCSQ-12 subscales explained 44% of ‘exhaustion’ (p<0.001), 44% of ‘cynicism’ (p<0.001), and 30% of ‘efficacy’ (p<0.001). The difference in the explanatory power of both models was significant for ‘exhaustion’ (p<0.001), and for ‘cynicism’ (p<0.001) and ‘efficacy (p<0.001). Conclusions Both BCSQ-36 and BCSQ-12 demonstrate great explanatory power over the standard MBI-GS, while offering a useful characterization of the syndrome for the evaluation and design of interventions tailored to the characteristics of each individual. The BCSQ-36 may be very useful in mental health services, given that it provides a good deal of information, while the BCSQ-12 could be used as a screening measure in primary care consultations owing to its simplicity and functional nature. PMID:23110723

Biodiversity response to natural gradients of multiple stressors on continental margins

PubMed Central

Sperling, Erik A.; Frieder, Christina A.; Levin, Lisa A.

2016-01-01

Sharp increases in atmospheric CO2 are resulting in ocean warming, acidification and deoxygenation that threaten marine organisms on continental margins and their ecological functions and resulting ecosystem services. The relative influence of these stressors on biodiversity remains unclear, as well as the threshold levels for change and when secondary stressors become important. One strategy to interpret adaptation potential and predict future faunal change is to examine ecological shifts along natural gradients in the modern ocean. Here, we assess the explanatory power of temperature, oxygen and the carbonate system for macrofaunal diversity and evenness along continental upwelling margins using variance partitioning techniques. Oxygen levels have the strongest explanatory capacity for variation in species diversity. Sharp drops in diversity are seen as O2 levels decline through the 0.5–0.15 ml l−1 (approx. 22–6 µM; approx. 21–5 matm) range, and as temperature increases through the 7–10°C range. pCO2 is the best explanatory variable in the Arabian Sea, but explains little of the variance in diversity in the eastern Pacific Ocean. By contrast, very little variation in evenness is explained by these three global change variables. The identification of sharp thresholds in ecological response are used here to predict areas of the seafloor where diversity is most at risk to future marine global change, noting that the existence of clear regional differences cautions against applying global thresholds. PMID:27122565

Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response.

PubMed

Binder, Harald; Sauerbrei, Willi; Royston, Patrick

2013-06-15

In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R(2)  = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd.

Using a Six Sigma Fishbone Analysis Approach To Evaluate the Effect of Extreme Weather Events on Salmonella Positives in Young Chicken Slaughter Establishments.

PubMed

Linville, John W; Schumann, Douglas; Aston, Christopher; Defibaugh-Chavez, Stephanie; Seebohm, Scott; Touhey, Lucy

2016-12-01

A six sigma fishbone analysis approach was used to develop a machine learning model in SAS, Version 9.4, by using stepwise linear regression. The model evaluated the effect of a wide variety of variables, including slaughter establishment operational measures, normal (30-year average) weather, and extreme weather events on the rate of Salmonella -positive carcasses in young chicken slaughter establishments. Food Safety and Inspection Service (FSIS) verification carcass sampling data, as well as corresponding data from the National Oceanographic and Atmospheric Administration and the Federal Emergency Management Agency, from September 2011 through April 2015, were included in the model. The results of the modeling show that in addition to basic establishment operations, normal weather patterns, differences from normal and disaster events, including time lag weather and disaster variables, played a role in explaining the Salmonella percent positive that varied by slaughter volume quartile. Findings show that weather and disaster events should be considered as explanatory variables when assessing pathogen-related prevalence analysis or research and slaughter operational controls. The apparent significance of time lag weather variables suggested that at least some of the impact on Salmonella rates occurred after the weather events, which may offer opportunities for FSIS or the poultry industry to implement interventions to mitigate those effects.

Simulation of changes in heavy metal contamination in farmland soils of a typical manufacturing center through logistic-based cellular automata modeling.

PubMed

Qiu, Menglong; Wang, Qi; Li, Fangbai; Chen, Junjian; Yang, Guoyi; Liu, Liming

2016-01-01

A customized logistic-based cellular automata (CA) model was developed to simulate changes in heavy metal contamination (HMC) in farmland soils of Dongguan, a manufacturing center in Southern China, and to discover the relationship between HMC and related explanatory variables (continuous and categorical). The model was calibrated through the simulation and validation of HMC in 2012. Thereafter, the model was implemented for the scenario simulation of development alternatives for HMC in 2022. The HMC in 2002 and 2012 was determined through soil tests and cokriging. Continuous variables were divided into two groups by odds ratios. Positive variables (odds ratios >1) included the Nemerow synthetic pollution index in 2002, linear drainage density, distance from the city center, distance from the railway, slope, and secondary industrial output per unit of land. Negative variables (odds ratios <1) included elevation, distance from the road, distance from the key polluting enterprises, distance from the town center, soil pH, and distance from bodies of water. Categorical variables, including soil type, parent material type, organic content grade, and land use type, also significantly influenced HMC according to Wald statistics. The relative operating characteristic and kappa coefficients were 0.91 and 0.64, respectively, which proved the validity and accuracy of the model. The scenario simulation shows that the government should not only implement stricter environmental regulation but also strengthen the remediation of the current polluted area to effectively mitigate HMC.

Statistical Modelling of Temperature and Moisture Uptake of Biochars Exposed to Selected Relative Humidity of Air.

PubMed

Bastistella, Luciane; Rousset, Patrick; Aviz, Antonio; Caldeira-Pires, Armando; Humbert, Gilles; Nogueira, Manoel

2018-02-09

New experimental techniques, as well as modern variants on known methods, have recently been employed to investigate the fundamental reactions underlying the oxidation of biochar. The purpose of this paper was to experimentally and statistically study how the relative humidity of air, mass, and particle size of four biochars influenced the adsorption of water and the increase in temperature. A random factorial design was employed using the intuitive statistical software Xlstat. A simple linear regression model and an analysis of variance with a pairwise comparison were performed. The experimental study was carried out on the wood of Quercus pubescens , Cyclobalanopsis glauca , Trigonostemon huangmosun , and Bambusa vulgaris , and involved five relative humidity conditions (22, 43, 75, 84, and 90%), two mass samples (0.1 and 1 g), and two particle sizes (powder and piece). Two response variables including water adsorption and temperature increase were analyzed and discussed. The temperature did not increase linearly with the adsorption of water. Temperature was modeled by nine explanatory variables, while water adsorption was modeled by eight. Five variables, including factors and their interactions, were found to be common to the two models. Sample mass and relative humidity influenced the two qualitative variables, while particle size and biochar type only influenced the temperature.

N-of-1 study of weight loss maintenance assessing predictors of physical activity, adherence to weight loss plan and weight change.

PubMed

Kwasnicka, Dominika; Dombrowski, Stephan U; White, Martin; Sniehotta, Falko F

2017-06-01

Behaviour change interventions are effective in supporting individuals to achieve clinically significant weight loss, but weight loss maintenance (WLM) is less often attained. This study examined predictive variables associated with WLM. N-of-1 study with daily ecological momentary assessment combined with objective measurement of weight and physical activity, collected with wireless devices (Fitbit™) for six months. Eight previously obese adults who had lost over 5% of their body weight in the past year took part. Data were analysed using time series methods. Predictor variables were based on five theoretical themes: maintenance motives, self-regulation, personal resources, habits, and environmental influences. Dependent variables were: objectively estimated step count and weight, and self-reported WLM plan adherence. For all participants, daily fluctuations in self-reported adherence to their WLM plan were significantly associated with most of the explanatory variables, including maintenance motivation and satisfaction with outcomes, self-regulation, habit, and stable environment. Personal resources were not a consistent predictor of plan adherence. This is the first study to assess theoretical predictions of WLM within individuals. WLM is a dynamic process including the interplay of motivation, self-regulation, habit, resources, and perceptions of environmental context. Individuals maintaining their weight have unique psychological profiles which could be accounted for in interventions.

Explanatory Supplement to the AllWISE Data Release Products

NASA Astrophysics Data System (ADS)

Cutri, R. M.; Wright, E. L.; Conrow, T.; Fowler, J. W.; Eisenhardt, P. R. M.; Grillmair, C.; Kirkpatrick, J. D.; Masci, F.; McCallon, H. L.; Wheelock, S. L.; Fajardo-Acosta, S.; Yan, L.; Benford, D.; Harbut, M.; Jarrett, T.; Lake, S.; Leisawitz, D.; Ressler, M. E.; Stanford, S. A.; Tsai, C. W.; Liu, F.; Helou, G.; Mainzer, A.; Gettings, D.; Gonzalez, A.; Hoffman, D.; Marsh, K. A.; Padgett, D.; Skrutskie, M. F.; Beck, R. P.; Papin, M.; Wittman, M.

2013-11-01

The AllWISE program builds upon the successful Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010) mission by combining data from all WISE and NEOWISE (Mainzer et al. 2011) survey phases to form the most comprehensive view of the mid-infrared sky currently available. By combining the data from two complete sky coverage epochs in an advanced data processing system, AllWISE has generated new products that have enhanced photometric sensitivity and accuracy, and improved astrometric precision compared with the earlier WISE All-Sky Data Release. Exploiting the 6 month baseline between the WISE sky coverage epochs enables AllWISE to measure source motions for the first time, and to compute improved flux variability statistics. AllWISE data release products include: a Source Catalog that contains 4-band fluxes, positions, apparent motion measurements, and flux variability statistics for over 747 million objects detected at SNR>5 in the combined exposures; a Multiepoch Photometry Database containing over 42 billion time-tagged, single-exposure fluxes for each object detected on the combined exposures; and an Image Atlas of 18,240 4-band calibrated FITS images, depth-of-coverage and noise maps that cover the sky produced by coadding nearly 7.9 million single-exposure images from the cryogenic and post-cryogenic survey phases. The Explanatory Supplement to the AllWISE Data Release Products is a general guide for users of the AllWISE data. The Supplement contains detailed descriptions of the format and characteristics of the AllWISE data products, as well as a summary of cautionary notes that describe known limitations. The Supplement is an on-line document that is updated frequently to provide the most current information for users of the AllWISE data products. The Explanatory Supplement is maintained at: http://wise2.ipac.caltech.edu/docs/release/allwise/expsup/index.html AllWISE makes use of data from WISE, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, and NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the National Aeronautics and Space Administration.

An Empirical Examination of the Anomie Theory of Drug Use.

ERIC Educational Resources Information Center

Dull, R. Thomas

1983-01-01

Investigated the relationship between anomie theory, as measured by Srole's Anomie Scale, and self-admitted drug use in an adult population (N=1,449). Bivariate cross-comparison correlations indicated anomie was significantly correlated with several drug variables, but these associations were extremely weak and of little explanatory value.…

Improving Audience Learning from Television News through Between-Channel Redundancy.

ERIC Educational Resources Information Center

Reese, Stephen D.

A study tested the effects of between-channel redundancy on television news learning. Redundancy, defined as shared information, was proposed as an explanatory variable that considers the relationship between information in three channels: the audio, the nonverbal pictorial, and visual-verbal print channel. It was hypothesized that pictures would…

Eating Disorders: Explanatory Variables in Caucasian and Hispanic College Women

ERIC Educational Resources Information Center

Aviña, Vanessa; Day, Susan X.

2016-01-01

The authors explored Hispanic and Caucasian college women's (N = 264) behavioral and attitudinal symptoms of eating disorders after controlling for body mass index and internalization of the thinness ideal, as well as the roles of ethnicity and ethnic identity in symptomatology. Correlational analysis, multivariate analysis of variance, and…

Understanding the Nature and Determinants of Critical Thinking among Senior Business Undergraduate Students

ERIC Educational Resources Information Center

Brown, F. William; Bielinska-Kwapisz, Agnieszka

2015-01-01

The authors examine the dimensions and determinants of critical thinking skills, as measured by the California Critical Thinking Skills Test, among graduating senior students enrolled in an Association to Advance Collegiate Schools of Business-accredited undergraduate business program. Utilizing explanatory variables, a methodology for predicting…

Social Inequality and Labor Force Participation.

ERIC Educational Resources Information Center

King, Jonathan

The labor force participation rates of whites, blacks, and Spanish-Americans, grouped by sex, are explained in a linear regression model fitted with 1970 U. S. Census data on Standard Metropolitan Statistical Area (SMSA). The explanatory variables are: average age, average years of education, vocational training rate, disabled rate, unemployment…

Cognitive Style Predictors of Affect Change in Older Adults

ERIC Educational Resources Information Center

Isaacowitz, Derek M.; Seligman, Martin E. P.

2002-01-01

Cognitive styles are the lenses through which individuals habitually process information from their environment. In this study, we evaluated whether different cognitive style individual difference variables, such as explanatory style and dispositional optimism, could predict changes in affective state over time in community-dwelling older adults.…

A Theory of School Achievement: A Quantum View

ERIC Educational Resources Information Center

Phelps, James L.

2012-01-01

In most school achievement research, the relationships between achievement and explanatory variables follow the Newton and Einstein concept/principle and the viewpoint of the macro-observer: Deterministic measures based on the mean value of a sufficiently large number of schools. What if the relationships between achievement and explanatory…

Marital Conflict and Children's Emotional Security in the Context of Parental Depression

ERIC Educational Resources Information Center

Kouros, Chrystyna D.; Merrilees, Christine E.; Cummings, E. Mark

2008-01-01

Evidence has emerged for emotional security as an explanatory variable linking marital conflict to children's adjustment. Further evidence suggests parental psychopathology is a key factor in child development. To advance understanding of the pathways by which these family risk factors impact children's development, the mediational role of…

A Note on the Heterogeneous Choice Model

ERIC Educational Resources Information Center

Rohwer, Goetz

2015-01-01

The heterogeneous choice model (HCM) has been proposed as an extension of the standard logit and probit models, which allows taking into account different error variances of explanatory variables. In this note, I show that in an important special case, this model is just another way to specify an interaction effect.

Lexicography and Mathematics Learning: A Case Study of "Variable."

ERIC Educational Resources Information Center

Frawley, William

1992-01-01

Lexicography is shown to offer some useful new tools to researchers in mathematics education. The paper examines the relationship between the sublanguage of mathematics and the acquisition of mathematical knowledge, and also the use of definitions in research and curriculum design. An Explanatory Combinatorial Dictionary is advocated for improving…

Toxic Familial Effects of Parental Hostility.

ERIC Educational Resources Information Center

Buri, John R.; And Others

In research efforts to account for the variance in parent-child interactions, two variables have been cited repeatedly for their explanatory cogency--nurturance and authority. This study was conducted to examine the relation of parents' Hostility (Ho) scores from the Minnesota Multiphasic Personality Inventory-based Cook and Medley Hostility Scale…

Performance Measurement: Does Education Impact Productivity?

ERIC Educational Resources Information Center

Larbi-Apau, Josephine A.; Sarpong, Daniel Bruce

2010-01-01

This study investigated the impact of managers' educational levels on productivity in the commercial poultry industry in Ghana. The level of education of 33 production managers of the poultry farms were factored into a Cobb-Douglas production function with other explanatory variables. The computed percentage change in productivity due to higher…

Defining conservation priorities using fragmentation forecasts

Treesearch

David Wear; John Pye; Kurt H. Riitters

2004-01-01

Methods are developed for forecasting the effects of population and economic growth on the distribution of interior forest habitat. An application to the southeastern United States shows that models provide significant explanatory power with regard to the observed distribution of interior forest. Estimates for economic and biophysical variables are significant and...

Dynamical Systems Approaches to Emotional Development

ERIC Educational Resources Information Center

Camras, Linda A.; Witherington, David C.

2005-01-01

Within the last 20 years, transitions in the conceptualization of emotion and its development have given rise to calls for an explanatory framework that captures emotional development in all its organizational complexity and variability. Recent attempts have been made to couch emotional development in terms of a dynamical systems approach through…

Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm

NASA Astrophysics Data System (ADS)

Creaco, E.; Berardi, L.; Sun, Siao; Giustolisi, O.; Savic, D.

2016-04-01

The growing availability of field data, from information and communication technologies (ICTs) in "smart" urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multiobjective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure.

Science Is an Action Word! Grades 1-3.

ERIC Educational Resources Information Center

Perdue, Peggy K.

This book includes 20 science activities in the fields of scientific method, earth science, life science, and physical science. Each activity is composed of two parts--an explanatory section for the teacher and a student lab sheet. The explanatory section begins with a brief introduction designed to give an overview of the activity's main concept.…

Moderating effects of social engagement on driving cessation in older women.

PubMed

Pachana, Nancy A; Leung, Janni K; Gardiner, Paul A; McLaughlin, Deirdre

2016-08-01

Driving cessation in later life is associated with depression. This study examines if social support can buffer the negative effects of driving cessation on older women's mental health. Participants were drawn from the 1921-1926 cohort of the Australian Longitudinal Study on Women's Health (ALSWH) and included 4,075 older women (aged 76-87 years) who drove at baseline, following them for three years to assess driving cessation. The outcome variable was mental health, measured by the mental health index (MHI) of the SF-36. The explanatory variables were social support factors, including social interaction, whether the women were living alone or with others, and engagement in social activities. Control variables included age, country of birth, area of residence, ability to manage on income, marital status, and general health. Main effect results showed that poor mental health was predicted by driving cessation, low levels of social interaction, and non-engagement in social activities. There was a significant interaction effect of driving status by social activities engagement on mental health. Women who remained active in their engagement of social activities were able to maintain a good level of mental health despite driving cessation. Engagement and participation in social activities can help older women who stopped driving maintain a good level of mental health.

Seasonality of tuberculosis in Israel, 2001-2011.

PubMed

Margalit, I; Block, C; Mor, Z

2016-12-01

Several studies have suggested that the incidence of tuberculosis (TB) varies with the seasons. To determine the seasonality of TB in Israel and to explore possible associations with climatic variables. Laboratory-confirmed TB cases reported between 2001 and 2011 in individuals resident in Israel for at least 1 year before diagnosis were included in the study. Climatic variables included average temperature and average ultraviolet radiation. The mean serum 25-hydroxyvitamin D level of the population was also recorded. Of all 2653 TB cases, incidence peaked during spring (n = 712) and reached its nadir during the fall (n = 577), with a case proportion amplitude (CPA) of 5.1% (P = 0.036). Individuals born in the Horn of Africa exhibited a CPA of 9.5% (P = 0.077). Mean population 25-hydroxyvitamin D level was significantly correlated with the seasonal pattern of the disease. Southern Israel had the highest global radiation and, counter-instinctively, the highest TB incidence. TB exhibited a seasonal tendency in Israel, with the spring peak/fall nadir pattern found elsewhere. Vitamin D is suspected to be an explanatory variable for this seasonal phenomenon. The finding that the highest incidence is in the area receiving the highest global radiation suggests population-related vulnerability to vitamin D deficiency.

Interpretation of tropospheric ozone variability in data with different vertical and temporal resolution

NASA Astrophysics Data System (ADS)

Petropavlovskikh, I. V.; Disterhoft, P.; Johnson, B. J.; Rieder, H. E.; Manney, G. L.; Daffer, W.

2012-12-01

This work attributes tropospheric ozone variability derived from the ground-based Dobson and Brewer Umkehr measurements and from ozone sonde data to local sources and transport. It assesses capability and limitations in both types of measurements that are often used to analyze long- and short-term variability in tropospheric ozone time series. We will address the natural and instrument-related contribution to the variability found in both Umkehr and sonde data. Validation of Umkehr methods is often done by intercomparisons against independent ozone measuring techniques such as ozone sounding. We will use ozone-sounding in its original and AK-smoothed vertical profiles for assessment of ozone inter-annual variability over Boulder, CO. We will discuss possible reasons for differences between different ozone measuring techniques and its effects on the derived ozone trends. Next to standard evaluation techniques we utilize a STL-decomposition method to address temporal variability and trends in the Boulder Umkehr data. Further, we apply a statistical modeling approach to the ozone data set to attribute ozone variability to individual driving forces associated with natural and anthropogenic causes. To this aim we follow earlier work applying a backward selection method (i.e., a stepwise elimination procedure out of a set of total 44 explanatory variables) to determine those explanatory variables which contribute most significantly to the observed variability. We will present also some results associated with completeness (sampling rate) of the existing data sets. We will also use MERRA (Modern-Era Retrospective analysis for Research and Applications) re-analysis results selected for Boulder location as a transfer function in understanding of the effects that the temporal sampling and vertical resolution bring into trend and ozone variability analysis. Analyzing intra-annual variability in ozone measurements over Boulder, CO, in relation to the upper tropospheric subtropical and polar jets, we will address the stratospheric and tropospheric intrusions in the middle latitude troposphere ozone field.

An Explanatory Model of Self-Service on the Internet

NASA Astrophysics Data System (ADS)

Oliver, Dave; Livermore, Celia Romm; Farag, Neveen Awad

This chapter describes research that identifies and classifies the dimensions of self-service activity enabled through the Internet. Self-service is effected by organizations providing ways and means whereby customers perform tasks related to the procurement of goods and services. We describe how an instrument used to measure Internet-based self-service was developed, validated and applied. The results from applying the instrument to a large number of Web sites, covering a range of industries, countries and cultures, are analyzed and discussed. The study presents a model in which type of industry, level of technological development, income and cultural factors are proposed as explanatory variables for Web-based self-service. We conclude with an assessment of this program of research’s achievements so far.

Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model

NASA Astrophysics Data System (ADS)

Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho

2016-06-01

Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

Intensity of interprofessional collaboration among intensive care nurses at a tertiary hospital.

PubMed

Serrano-Gemes, G; Rich-Ruiz, M

To measure the intensity of interprofessional collaboration (IPC) in nurses of an intensive care unit (ICU) at a tertiary hospital, to check differences between the dimensions of the Intensity of Interprofessional Collaboration Questionnaire, and to identify the influence of personal variables. A cross-sectional descriptive study was conducted with 63 intensive care nurses selected by simple random sampling. Explanatory variables: age, sex, years of experience in nursing, years of experience in critical care, workday type and work shift type; variable of outcome: IPC. The IPC was measured by: Intensity of Interprofessional Collaboration Questionnaire. Descriptive and bivariate statistical analysis (IPC and its dimensions with explanatory variables). 73.8% were women, with a mean age of 46.54 (±6.076) years. The average years experience in nursing and critical care was 23.03 (±6.24) and 14.25 (±8.532), respectively. 77% had a full time and 95.1% had a rotating shift. 62.3% obtained average IPC values. Statistically significant differences were found (P<.05) between IPC (overall score) and overall assessment with years of experience in critical care. This study shows average levels of IPC; the nurses with less experience in critical care obtained higher IPC and overall assessment scores. Copyright © 2016 Sociedad Española de Enfermería Intensiva y Unidades Coronarias (SEEIUC). Publicado por Elsevier España, S.L.U. All rights reserved.

Correction of the significance level when attempting multiple transformations of an explanatory variable in generalized linear models

PubMed Central

2013-01-01

Background In statistical modeling, finding the most favorable coding for an exploratory quantitative variable involves many tests. This process involves multiple testing problems and requires the correction of the significance level. Methods For each coding, a test on the nullity of the coefficient associated with the new coded variable is computed. The selected coding corresponds to that associated with the largest statistical test (or equivalently the smallest pvalue). In the context of the Generalized Linear Model, Liquet and Commenges (Stat Probability Lett,71:33–38,2005) proposed an asymptotic correction of the significance level. This procedure, based on the score test, has been developed for dichotomous and Box-Cox transformations. In this paper, we suggest the use of resampling methods to estimate the significance level for categorical transformations with more than two levels and, by definition those that involve more than one parameter in the model. The categorical transformation is a more flexible way to explore the unknown shape of the effect between an explanatory and a dependent variable. Results The simulations we ran in this study showed good performances of the proposed methods. These methods were illustrated using the data from a study of the relationship between cholesterol and dementia. Conclusion The algorithms were implemented using R, and the associated CPMCGLM R package is available on the CRAN. PMID:23758852

Bayesian spatio-temporal modeling of particulate matter concentrations in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Manga, Edna; Awang, Norhashidah

2016-06-01

This article presents an application of a Bayesian spatio-temporal Gaussian process (GP) model on particulate matter concentrations from Peninsular Malaysia. We analyze daily PM10 concentration levels from 35 monitoring sites in June and July 2011. The spatiotemporal model set in a Bayesian hierarchical framework allows for inclusion of informative covariates, meteorological variables and spatiotemporal interactions. Posterior density estimates of the model parameters are obtained by Markov chain Monte Carlo methods. Preliminary data analysis indicate information on PM10 levels at sites classified as industrial locations could explain part of the space time variations. We include the site-type indicator in our modeling efforts. Results of the parameter estimates for the fitted GP model show significant spatio-temporal structure and positive effect of the location-type explanatory variable. We also compute some validation criteria for the out of sample sites that show the adequacy of the model for predicting PM10 at unmonitored sites.

MMI: Multimodel inference or models with management implications?

USGS Publications Warehouse

Fieberg, J.; Johnson, Douglas H.

2015-01-01

We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density

NASA Astrophysics Data System (ADS)

Seino, Junji; Kageyama, Ryo; Fujinami, Mikito; Ikabata, Yasuhiro; Nakai, Hiromi

2018-06-01

A semi-local kinetic energy density functional (KEDF) was constructed based on machine learning (ML). The present scheme adopts electron densities and their gradients up to third-order as the explanatory variables for ML and the Kohn-Sham (KS) kinetic energy density as the response variable in atoms and molecules. Numerical assessments of the present scheme were performed in atomic and molecular systems, including first- and second-period elements. The results of 37 conventional KEDFs with explicit formulae were also compared with those of the ML KEDF with an implicit formula. The inclusion of the higher order gradients reduces the deviation of the total kinetic energies from the KS calculations in a stepwise manner. Furthermore, our scheme with the third-order gradient resulted in the closest kinetic energies to the KS calculations out of the presented functionals.

Investigation of the marked and long-standing spatial inhomogeneity of the Hungarian suicide rate: a spatial regression approach.

PubMed

Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan

2014-02-01

In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.

Adherence to physical activity in an unsupervised setting: Explanatory variables for high attrition rates among fitness center members.

PubMed

Sperandei, Sandro; Vieira, Marcelo C; Reis, Arianne C

2016-11-01

To evaluate the attrition rate of members of a fitness center in the city of Rio de Janeiro and the potential explanatory variables for the phenomenon. An exploratory, observational study using a retrospective longitudinal frame. The records of 5240 individuals, members of the fitness center between January-2005 and June-2014, were monitored for 12 months or until cancellation of membership, whichever occurred first. A Cox proportional hazard regression model was adjusted to identify variables associated to higher risk of 'abandonment' of activities. This study was approved by Southern Cross University's Human Research Ethics Committee (approval number: ECN-15-176). The general survival curve shows that 63% of new members will abandon activities before the third month, and less than 4% will remain for more than 12 months of continuous activity. The regression model showed that age, previous level of physical activity, initial body mass index and motivations related to weight loss, hypertrophy, health, and aesthetics are related to risk of abandonment. Combined, those variables represent an important difference in the probability to abandon the gym between individuals with the best and worse combination of variables. Even individuals presenting the best combination of variables still present a high risk of abandonment before completion of 12 months of fitness center membership. Findings can assist in the identification of high risk individuals and therefore help in the development of strategies to prevent abandonment of physical activity practice. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

The gender earnings gap among pharmacists.

PubMed

Carvajal, Manuel J; Armayor, Graciela M; Deziel, Lisa

2012-01-01

A gender earnings gap exists across professions. Compared with men, women earn consistently lower income levels. The determinants of wages and salaries should be explored to assess whether a gender earnings gap exists in the pharmacy profession. The objectives of this study were to (1) compare the responses of male and female pharmacists' earnings with human-capital stock, workers' preferences, and opinion variables and (2) assess whether the earnings determination models for male and female pharmacists yielded similar results in estimating the wage-and-salary gap through earnings projections, the influence of each explanatory variable, and gender differences in statistical significance. Data were collected through the use of a 37-question survey mailed to registered pharmacists in South Florida, United States. Earnings functions were formulated and tested separately for male and female pharmacists using unlogged and semilog equation forms. Number of hours worked, human-capital stock, job preferences, and opinion variables were hypothesized to explain wage-and-salary differentials. The empirical evidence led to 3 major conclusions: (1) men's and women's earnings sometimes were influenced by different stimuli, and when they responded to the same variables, the effect often was different; (2) although the influence of some explanatory variables on earnings differed in the unlogged and semilog equations, the earnings projections derived from both equation forms for male and female pharmacists were remarkably similar and yielded nearly identical male-female earnings ratios; and (3) controlling for number of hours worked, human-capital stock, job preferences, and opinion variables reduced the initial unadjusted male-female earnings ratios only slightly, which pointed toward the presence of gender bias. After controlling for human-capital stock, job-related characteristics, and opinion variables, male pharmacists continued to earn higher income levels than female pharmacists. Copyright © 2012 Elsevier Inc. All rights reserved.

Model for the separate collection of packaging waste in Portuguese low-performing recycling regions.

PubMed

Oliveira, V; Sousa, V; Vaz, J M; Dias-Ferreira, C

2018-06-15

Separate collection of packaging waste (glass; plastic/metals; paper/cardboard), is currently a widespread practice throughout Europe. It enables the recovery of good quality recyclable materials. However, separate collection performance are quite heterogeneous, with some countries reaching higher levels than others. In the present work, separate collection of packaging waste has been evaluated in a low-performance recycling region in Portugal in order to investigate which factors are most affecting the performance in bring-bank collection system. The variability of separate collection yields (kg per inhabitant per year) among 42 municipalities was scrutinized for the year 2015 against possible explanatory factors. A total of 14 possible explanatory factors were analysed, falling into two groups: socio-economic/demographic and waste collection service related. Regression models were built in an attempt to evaluate the individual effect of each factor on separate collection yields and predict changes on the collection yields by acting on those factors. The best model obtained is capable to explain 73% of the variation found in the separate collection yields. The model includes the following statistically significant indicators affecting the success of separate collection yields: i) inhabitants per bring-bank; ii) relative accessibility to bring-banks; iii) degree of urbanization; iv) number of school years attended; and v) area. The model presented in this work was developed specifically for the bring-bank system, has an explanatory power and quantifies the impact of each factor on separate collection yields. It can therefore be used as a support tool by local and regional waste management authorities in the definition of future strategies to increase collection of recyclables of good quality and to achieve national and regional targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Watershed Influences on Residence Time and Oxygen Reduction Rates in an Agricultural Landscape

NASA Astrophysics Data System (ADS)

Shope, C. L.; Tesoriero, A. J.

2015-12-01

Agricultural use of synthetic fertilizers and animal manure has led to increased crop production, but also elevated nitrogen concentrations in groundwater, resulting in impaired water quality. Groundwater oxygen concentrations are a key indicator of potential biogeochemical processes, which control water/aquifer interactions and contaminant transport. The U.S. Geological Survey's National Water-Quality Assessment Program has a long-history of studying nutrient transport and processing across the United States and the Glacial Aquifer system in particular. A series of groundwater well networks in Eastern Wisconsin is being used to evaluate the distribution of redox reaction rates over a range of scales with a focus on dissolved O2 reduction rates. An analysis of these multi-scale networks elucidates the influence of explanatory variables (i.e.: soil type, land use classification) on reduction rates and redox reactions throughout the Fox-Wolf-Peshtigo watersheds. Multiple tracers including dissolved gasses, tritium, helium, chlorofluorocarbons, sulfur hexafluoride, and carbon-14 were used to estimate groundwater ages (0.8 to 61.2 yr) at over 300 locations. Our results indicate O2 reduction rates along a flowpath study area (1.2 km2) of 0.15 mg O2 L-1 yr-1 (0.12 to 0.18 mg O2 L-1 yr-1) up to 0.41 mg O2 L-1 yr-1 (0.23 to 0.89 mg O2 L-1 yr-1) for a larger scale land use study area (3,300 km2). Preliminary explanatory variables that can be used to describe the variability in reduction rates include soil type (hydrologic group, bulk density) and chemical concentrations (nitrite plus nitrate, silica). The median residence time expected to reach suboxic conditions (≤ 0.4 mg O2 L-1) for the flowpath and the land use study areas was 66 and 25 yr, respectively. These results can be used to elucidate and differentiate the impact of residence time on groundwater quality vulnerability and sustainability in agricultural regions without complex flow models.

The sequential pathway between trauma-related symptom severity and cognitive-based smoking processes through perceived stress and negative affect reduction expectancies among trauma exposed smokers.

PubMed

Garey, Lorra; Cheema, Mina K; Otal, Tanveer K; Schmidt, Norman B; Neighbors, Clayton; Zvolensky, Michael J

2016-10-01

Smoking rates are markedly higher among trauma-exposed individuals relative to non-trauma-exposed individuals. Extant work suggests that both perceived stress and negative affect reduction smoking expectancies are independent mechanisms that link trauma-related symptoms and smoking. Yet, no work has examined perceived stress and negative affect reduction smoking expectancies as potential explanatory variables for the relation between trauma-related symptom severity and smoking in a sequential pathway model. Methods The present study utilized a sample of treatment-seeking, trauma-exposed smokers (n = 363; 49.0% female) to examine perceived stress and negative affect reduction expectancies for smoking as potential sequential explanatory variables linking trauma-related symptom severity and nicotine dependence, perceived barriers to smoking cessation, and severity of withdrawal-related problems and symptoms during past quit attempts. As hypothesized, perceived stress and negative affect reduction expectancies had a significant sequential indirect effect on trauma-related symptom severity and criterion variables. Findings further elucidate the complex pathways through which trauma-related symptoms contribute to smoking behavior and cognitions, and highlight the importance of addressing perceived stress and negative affect reduction expectancies in smoking cessation programs among trauma-exposed individuals. (Am J Addict 2016;25:565-572). © 2016 American Academy of Addiction Psychiatry.

[Validity of adjusted morbidity groups with respect to clinical risk groups in the field of primary care].

PubMed

Monterde, David; Vela, Emili; Clèries, Montse; García Eroles, Luis; Pérez Sust, Pol

2018-02-09

To compare the performance in terms of goodness of fit and explanatory power of 2morbidity groupers in primary care (PC): adjusted morbidity groups (AMG) and clinical risk groups (CRG). Cross-sectional study. PC in the Catalan Institute for the Health (CIH), Catalonia, Spain. Population allocated in primary care centers of the CIH for the year 2014. Three indicators of interest are analyzed such as urgent hospitalization, number of visits and spending in pharmacy. A stratified analysis by centers is applied adjusting generalized lineal models from the variables age, sex and morbidity grouping to explain each one of the 3variables of interest. The statistical measures to analyze the performance of the different models applied are the Akaike index, the Bayes index and the pseudo-variability explained by deviance change. The results show that in the area of the primary care the explanatory power of the AMGs is higher to that offered by the CRGs, especially for the case of the visits and the pharmacy. The performance of GMAs in the area of the CIH PC is higher than that shown by the CRGs. Copyright © 2018 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.

Biodiversity of shallow subtidal, under-rock invertebrates in Europe's first marine reserve: Effects of physical factors and scientific sampling

NASA Astrophysics Data System (ADS)

Trowbridge, Cynthia D.; Kachmarik, Katy; Plowman, Caitlin Q.; Little, Colin; Stirling, Penny; McAllen, Rob

2017-03-01

At Lough Hyne Marine Reserve in SW Ireland, shallow subtidal, under-rock biodiversity was investigated to assess (i) any deleterious effects of scientific sampling and (ii) quantitative baseline community patterns. Comparisons were made between 10 sites with annual rock-turning disturbance and 10 with multi-decadal (historical) disturbance. At each site, shallow subtidal rocks (N = 1289 total) were lifted, organisms recorded, and rocks replaced in their original position. Biodiversity indices were calculated to evaluate how diversity varied with location within the lough, frequency of sampling disturbance, degree of hypoxia/anoxia, dissolved oxygen (DO) concentration, and number of rocks turned. The richness of solitary invertebrates surveyed in situ averaged 21 taxa per site with significantly more in the South Basin (near the lough's connection to the ocean) than in the North Basin. The Shannon-Wiener Index did not differ significantly with variables investigated. However, evenness was higher at annually disturbed sites than at historical ones where anemones with algal symbionts often dominated. Several sites were hypoxic to anoxic under the shallow subtidal rocks. Cup corals were most abundant in the South Basin; DO was a crucial explanatory variable of these sensitive species. Solitary ascidians were most abundant at South-Basin annual sites with DO levels being a highly significant explanatory variable.

Axial cervical vertebrae-based multivariate regression model for the estimation of skeletal-maturation status.

PubMed

Yang, Y-M; Lee, J; Kim, Y-I; Cho, B-H; Park, S-B

2014-08-01

This study aimed to determine the viability of using axial cervical vertebrae (ACV) as biological indicators of skeletal maturation and to build models that estimate ossification level with improved explanatory power over models based only on chronological age. The study population comprised 74 female and 47 male patients with available hand-wrist radiographs and cone-beam computed tomography images. Generalized Procrustes analysis was used to analyze the shape, size, and form of the ACV regions of interest. The variabilities of these factors were analyzed by principal component analysis. Skeletal maturation was then estimated using a multiple regression model. Separate models were developed for male and female participants. For the female estimation model, the adjusted R(2) explained 84.8% of the variability of the Sempé maturation level (SML), representing a 7.9% increase in SML explanatory power over that using chronological age alone (76.9%). For the male estimation model, the adjusted R(2) was over 90%, representing a 1.7% increase relative to the reference model. The simplest possible ACV morphometric information provided a statistically significant explanation of the portion of skeletal-maturation variability not dependent on chronological age. These results verify that ACV is a strong biological indicator of ossification status. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The N-shaped environmental Kuznets curve: an empirical evaluation using a panel quantile regression approach.

PubMed

Allard, Alexandra; Takman, Johanna; Uddin, Gazi Salah; Ahmed, Ali

2018-02-01

We evaluate the N-shaped environmental Kuznets curve (EKC) using panel quantile regression analysis. We investigate the relationship between CO 2 emissions and GDP per capita for 74 countries over the period of 1994-2012. We include additional explanatory variables, such as renewable energy consumption, technological development, trade, and institutional quality. We find evidence for the N-shaped EKC in all income groups, except for the upper-middle-income countries. Heterogeneous characteristics are, however, observed over the N-shaped EKC. Finally, we find a negative relationship between renewable energy consumption and CO 2 emissions, which highlights the importance of promoting greener energy in order to combat global warming.

Blue and Fin Whale Habitat Modeling from Long-Term Year-Round Passive Acoustic Data from the Southern California Bight

DTIC Science & Technology

2015-09-30

SST), sea surface height anomaly (SSH), chlorophyll a concentration (Chla), and primary productivity (PP). These data are available on similar...between the high and low area, and in areas with low abundance, chlorophyll a concentration was also a significant explanatory variable. For fin

Correlating Parental Involvement and Mathematics Achievement of African American Eighth-Grade Students

ERIC Educational Resources Information Center

Oyerinde, Bolanle Adenike

2014-01-01

Low involvement of African American parents in middle school education is a concern in a school district in the southeastern United States. The purpose of this quantitative study was to investigate the relationships between the explanatory variables of parental involvement, socioeconomic status, and level of education, and the achievement of…

Language Attitudes in Catalan Multilingual Classrooms: Educational Implications

ERIC Educational Resources Information Center

Madariaga, José-María; Huguet, Ángel; Janés, Judit

2016-01-01

Catalonia is the Autonomous Community of Spain with the highest proportion of immigrant students. This study analyses the language attitudes of Catalan, as well as the possible explanatory variables for such attitudes, for a large sample with a high proportion of immigrant students and a great linguistic diversity. A questionnaire was given to…

A Structural Equation Model Explaining 8th Grade Students' Mathematics Achievements

ERIC Educational Resources Information Center

Yurt, Eyüp; Sünbül, Ali Murat

2014-01-01

The purpose of this study is to investigate, via a model, the explanatory and predictive relationships among the following variables: Mathematical Problem Solving and Reasoning Skills, Sources of Mathematics Self-Efficacy, Spatial Ability, and Mathematics Achievements of Secondary School 8th Grade Students. The sample group of the study, itself…

Factors Affecting Smoking Tendency and Smoking Intensity

ERIC Educational Resources Information Center

David, Nissim Ben; Zion, Uri Ben

2009-01-01

Purpose: The purpose of this paper is to measure the relative effect of relevant explanatory variable on smoking tendency and smoking intensity. Design/methodology/approach: Using survey data collected by the Israeli Bureau of Statistics in 2003-2004, a probit procedure is estimated for analyzing factors that affect the probability of being a…

Making the Case for Space: The Effect of Learning Spaces on Teaching and Learning

ERIC Educational Resources Information Center

Byers, Terry; Imms, Wesley; Hartnell-Young, Elizabeth

2014-01-01

An explanatory, mixed method study examined the impact of learning spaces on teachers' pedagogy, student engagement and student learning outcomes in a technology-rich school setting. Its quasi-experimental design allowed examination of differences in these variables between two settings--'traditional' classrooms, and 'new generation learning…

The Impact of Education on Income Distribution.

ERIC Educational Resources Information Center

Tinbergen, Jan

The author's previously developed theory on income distribution, in which two of the explanatory variables are the average level and the distribution of education, is refined and tested on data selected and processed by the author and data from three studies by Americans. The material consists of data on subdivisions of three countries, the United…

The Economics of Persistence: Graduation Rates of Athletes as Labor Market Choice.

ERIC Educational Resources Information Center

DeBrock, Lawrence; And Others

1996-01-01

Analysis of data from NCAA Division I schools for male football and male and female basketball players shows that traditional labor market opportunities unrelated to sports are significant explanatory variables for athletes' academic persistence. Professional sports opportunities also have a significant impact on the graduation rate of athletes.…

Does Price Matter? Overseas Students in UK Higher Education

ERIC Educational Resources Information Center

Soo, Kwok Tong; Elliott, Caroline

2010-01-01

This paper explores the determinants of the choice of UK universities by overseas undergraduate applicants. We use data on overseas applicants in Business Studies and Engineering from 2002 to 2007, to 97 UK universities. Estimating using a Hausman-Taylor model to control for the possible correlation between our explanatory variables and…

Economic factors influencing land use changes in the South-Central United States

Treesearch

Ralph J. Alig; Fred C. White; Brian C. Murray

1988-01-01

Econometric models of land use change were estimated for two physiographic regions in the South-Central United States. Results are consistent-with the economic hierarchy of land use, with population and personal income being significant explanatory variables. Findings regarding the importance of relative agricultural and forestry market-based incomes in influencing...

Blame Attribution as a Moderator of Perceptions of Sexual Orientation-Based Hate Crimes

ERIC Educational Resources Information Center

Cramer, Robert J.; Chandler, Joseph F.; Wakeman, Emily E.

2010-01-01

Blame attribution is a valuable mechanism explaining decision making. However, present literature mainly employs blame attribution as a dependent variable. The shortcoming of this fact is that blame attribution offers a potentially valuable explanatory mechanism for decision making. The authors designed two studies to investigate blame attribution…

Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…

Function and Functional Explanation in Social Capital Theory: A Philosophical Appraisal

ERIC Educational Resources Information Center

Vorhaus, John

2014-01-01

Social capital is frequently offered up as a variable to explain such educational outcomes as academic attainment, drop-out rates and cognitive development. Yet, despite its popularity amongst social scientists, social capital theory remains the object of some scepticism, particularly in respect of its explanatory ambitions. I provide an account…

Relationship between Self-Control and Facebook Use: Case of CEIT Students

ERIC Educational Resources Information Center

Firat, Mehmet

2017-01-01

This is an explanatory mixed-method study that analyzes the relationship between the variables of students' self-control and Facebook usage. TIME's online Facebook calculator and the Brief Self-Control Scale are used for data collection. The research participants are 60 students in a department of computer education and instructional technology…

Personality Type and Student Performance in Upper-Level Economics Courses: The Importance of Race and Gender.

ERIC Educational Resources Information Center

Borg, Mary O.; Stranahan, Harriet A.

2002-01-01

Demonstrates that personality type is an important explanatory variable in student performance in upper level economics courses. Finds that certain personality types, combined with race and gender effects, produce students who outperform other students. Introverts and those with the Keirsey-Bates temperament combination of sensing/judging…

Assault Injury Rates, Social Capital, and Fear of Neighborhood Crime

ERIC Educational Resources Information Center

Kruger, Daniel J.; Hutchison, Peter; Monroe, Matthew G.; Reischl, Thomas; Morrel-Samuels, Susan

2007-01-01

This study develops an explanatory framework for fear of neighborhood crime based on respondents' social context and local rates of assault injuries. Rates of assault injuries within zip codes are based on hospital discharge records. We find that only four variables have a significant unique contribution to fear of crime: respondent's sex,…

Organizing for Instruction in Education Systems and School Organizations: "How" the Subject Matters

ERIC Educational Resources Information Center

Spillane, James P.; Hopkins, Megan

2013-01-01

Teaching, the core technology of schooling, is an essential consideration in investigations of education systems and school organizations. Taking teaching seriously as an explanatory variable in research on education systems and organizations necessitates moving beyond treating it as a unitary practice, so as to take account of the school subjects…

Physiologic Factors Influencing the Arterial-To-End-Tidal CO2 Difference and the Alveolar Dead Space Fraction in Spontaneously Breathing Anesthetised Horses.

PubMed

Mosing, Martina; Böhm, Stephan H; Rasis, Anthea; Hoosgood, Giselle; Auer, Ulrike; Tusman, Gerardo; Bettschart-Wolfensberger, Regula; Schramel, Johannes P

2018-01-01

The arterial to end-tidal CO 2 difference (P (a-ET) CO 2 ) and alveolar dead space fraction (VDalv frac  = P (a-ET) CO 2 /PaCO 2 ), are used to estimate Enghoff's "pulmonary dead space" (V/Q Eng ), a factor which is also influenced by venous admixture and other pulmonary perfusion abnormalities and thus is not just a measure of dead space as the name suggests. The aim of this experimental study was to evaluate which factors influence these CO 2 indices in anesthetized spontaneously breathing horses. Six healthy adult horses were anesthetized in dorsal recumbency breathing spontaneously for 3 h. Data to calculate the CO 2 indices (response variables) and dead space variables were measured every 30 min. Bohr's physiological and alveolar dead space variables, cardiac output (CO), mean pulmonary pressure (MPP), venous admixture [Formula: see text], airway dead space, tidal volume, oxygen consumption, and slope III of the volumetric capnogram were evaluated (explanatory variables). Univariate Pearson correlation was first explored for both CO 2 indices before V/Q Eng and the explanatory variables with rho were reported. Multiple linear regression analysis was performed on P (a-ET) CO 2 and VDalv frac assessing which explanatory variables best explained the variance in each response. The simplest, best-fit model was selected based on the maximum adjusted R 2 and smallest Mallow's p (C p ). The R 2 of the selected model, representing how much of the variance in the response could be explained by the selected variables, was reported. The highest correlation was found with the alveolar part of V/Q Eng to alveolar tidal volume ratio for both, P (a-ET) CO 2 ( r  = 0.899) and VDalv frac ( r  = 0.938). Venous admixture and CO best explained P (a-ET) CO 2 ( R 2  = 0.752; C p  = 4.372) and VDalv frac ( R 2  = 0.711; C p  = 9.915). Adding MPP (P (a-ET) CO 2 ) and airway dead space (VDalv frac ) to the models improved them only marginally. No "real" dead space variables from Bohr's equation contributed to the explanation of the variance of the two CO 2 indices. P (a-ET) CO 2 and VDalv frac were closely associated with the alveolar part of V/Q Eng and as such, were also influenced by variables representing a dysfunctional pulmonary perfusion. Neither P (a-ET) CO 2 nor VDalv frac should be considered pulmonary dead space, but used as global indices of V/Q mismatching under the described conditions.

The utility of Cox's Interaction Model to direct the assessment and prevention of adolescent aggression.

PubMed

DiNapoli, Pamela Pershing

2003-02-01

The current study of violence prevention is hampered by a lack of consensus on the definition of violence. There is, however, some agreement about the behavioral cues that may predict violent behavior such as aggression. Although it has been shown that individual-level variables (e.g., race, gender, and ethnicity) are correlated with aggressive behavior, it is clear that they alone are not explanatory of aggressive behavior. This article demonstrates how the Interaction Model of Client Health Behavior is an important health behavior framework for the assessment of aggression in adolescents, offering insight into the contextual nature of adolescent aggression. Victimization and witnessing of violence, frequently identified to be precursors of adolescent aggression in current epidemiologic studies, are examined within this framework. On the basis of the interactional nature of the phenomenon, necessary components for successful prevention programs are suggested. Finally, future research implications calling for a well-designed study that integrates individual and contextual variables with the use of this theory-driven explanatory framework are proposed. Copyright 2003, Elsevier Science (USA). All rights reserved.

Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario.

PubMed

Lee, Chris; Li, Xuancheng

2014-10-01

This study analyzes driver's injury severity in single- and two-vehicle crashes and compares the effects of explanatory variables among various types of crashes. The study identified factors affecting injury severity and their effects on severity levels using 5-year crash records for provincial highways in Ontario, Canada. Considering heteroscedasticity in the effects of explanatory variables on injury severity, the heteroscedastic ordered logit (HOL) models were developed for single- and two-vehicle crashes separately. The results of the models show that there exists heteroscedasticity for young drivers (≤30), safety equipment and ejection in the single-vehicle crash model, and female drivers, safety equipment and head-on collision in the two-vehicle crash models. The results also show that young car drivers have opposite effects between single-car and car-car crashes, and sideswipe crashes have opposite effects between car-car and truck-truck crashes. The study demonstrates that separate HOL models for single-vehicle and different types of two-vehicle crashes can identify differential effects of factors on driver's injury severity. Copyright © 2014 Elsevier Ltd. All rights reserved.

John Snow, William Farr and the 1849 outbreak of cholera that affected London: a reworking of the data highlights the importance of the water supply.

PubMed

Bingham, P; Verlander, N Q; Cheal, M J

2004-09-01

This paper examines why Snow's contention that cholera was principally spread by water was not accepted in the 1850s by the medical elite. The consequence of rejection was that hundreds in the UK continued to die. Logistic regression was used to re-analyse data, first published in 1852 by William Farr, consisting of the 1849 mortality rate from cholera and eight potential explanatory variables for the 38 registration districts of London. Logistic regression does not support Farr's original conclusion that a district's elevation above high water was the most important explanatory variable. Elevation above high water, water supply and poor rate each have an independent significant effect on district cholera mortality rate, but in terms of size of effect, it can be argued that water supply most strongly 'invited' further consideration. The science of epidemiology, that Farr helped to found, has continued to advance. Had logistic regression been available to Farr, its application to his 1852 data set would have changed his conclusion.

Coping and back problems: analysis of multiple data sources on an entire cross-sectional cohort of Swedish military recruits.

PubMed

Leboeuf-Yde, Charlotte; Larsen, Kristian; Ahlstrand, Ingvar; Volinn, Ernest

2006-05-03

As the literature now stands, a bewildering number and variety of biological, psychological and social factors are, apparently, implicated in back problems. However, if and how these have a direct influence on back problems is not clear. Obesity, for example, has in many studies been shown to be associated with back problems but there is no evidence for a causal link. This could be explained by a dearth of suitably designed studies but also because obesity may be but a proxy for some other, truly explanatory variable. Coping has been linked with, particularly, persistent back problems as well as with health in general. The question is, whether coping could be the explanatory link between, for example, these two variables. A cross-sectional study was undertaken using data from the Swedish Army, consisting of the entire cohort of males (N = 48,502) summoned in 1998 to serve in the military. The purpose of the study was to investigate the relation between five independent variables and two dependent variables ("outcome variables"). The independent variables were two anthropomorphic variables (height and body mass index), two psychological variables (intellectual capacity and coping in relation to stress), and one social variable (type of education). The two outcome variables were back problems and ill health. In particular, we wanted to determine whether controlling for coping would affect the associations between the other four independent variables and the two outcome variables. Data for the analysis come from a battery of standardized examinations, including medical examinations, a test of intellectual capacity, and a test of coping in relation to stress. Each of these examinations was conducted independently of the others. Unadjusted and adjusted odds ratios were calculated for the outcome variables of back problems and ill health. The associations between height, body mass index, intellectual capacity, type of education and the two outcome variables (back problems and ill health) were weak to moderate. Additionally, there were strong associations between coping and the two outcome variables and when controlling for coping the previously noted associations diminished or disappeared, whereas none of the other variables had a large effect on the association between coping and the two outcome variables. Coping emerged as strongly associated with both back problem and ill health and coping had a leveling effect on the associations between the other independent variables and the two outcome variables. This study is noteworthy particularly because the association with coping is so robust. It is a retrospective, cross-sectional study, however, and, as such it raises questions of causality; which - if any - came first, inability to cope or back pain? The results of this study call attention to the need for a prospective study, in which coping is clearly defined. Such a study has been undertaken and will be presented separately. Index terms: back pain, coping, education, height, BMI, intellectual capacity, bio-psycho-social model, epidemiology, cohort, cross-sectional study.

Can we explain increases in young people’s psychological distress over time?

PubMed Central

Sweeting, Helen; West, Patrick; Young, Robert; Der, Geoff

2010-01-01

This paper aims to explain previously described increases in self-reported psychological distress between 1987 and 2006 among samples identical in respect of age (15 years), school year and geographical location (West of Scotland). Such increases might be explained by changes in exposure (changes in levels of risk or protective factors) and/or by changes in vulnerability (changes in the relationship between risk/protective factors and psychological distress). Key areas of social change over this time period allow identification of potential explanatory factors, categorised as economic, family, educational, values and lifestyle and represented by variables common to each study. Psychological distress was measured via the 12-item General Health Questionnaire, Likert scored. Analyses were conducted on those with complete data on all variables (N = 3276 of 3929), and separately for males and females. Between 1987 and 2006, levels of almost every potential explanatory factor changed in line with general societal trends. Associations between explanatory factors and GHQ tended to be stronger among females, and at the later date. The strongest associations were with worries, arguments with parents, and, at the later date, school disengagement. The factors which best accounted for the increase in mean GHQ between 1987 and 2006 were arguments with parents, school disengagement, worry about school and, for females, worry about family relationships, reflecting both increasing exposure and vulnerability to these risk factors. A number of limitations to our analysis can be identified. However, our results reinforce the conclusions of others in highlighting the role of family and educational factors as plausible explanations for increases in young people’s psychological distress. PMID:20870334

Sensitivity of Alpine and Subalpine Lakes to Atmospheric Deposition in Grand Teton National Park and Yellowstone National Park, Wyoming

NASA Astrophysics Data System (ADS)

Nanus, L.; Campbell, D. H.; Williams, M. W.

2004-12-01

Acidification of high-elevation lakes in the Western United States is of concern because of the storage and release of pollutants in snowmelt runoff combined with steep topography, granitic bedrock, and limited soils and biota. Land use managers have limited resources for sampling and thus need direction on how best to design monitoring programs. We evaluated the sensitivity of 400 lakes in Grand Teton (GRTE) and Yellowstone (YELL) National Parks to acidification from atmospheric deposition of nitrogen and sulfur based on statistical relations between acid-neutralizing capacity (ANC) concentrations and basin characteristics to aid in the design of a long-term monitoring plan for Outstanding Natural Resource Waters. ANC concentrations that were measured at 52 lakes in GRTE and 23 lakes in YELL during synoptic surveys were used to calibrate the statistical models. Basin-characteristic information was derived from Geographic Information System data sets. The explanatory variables that were considered included bedrock type, basin slope, basin aspect, basin elevation, lake area, basin area, inorganic nitrogen (N) deposition, sulfate deposition, hydrogen ion deposition, basin precipitation, soil type, and vegetation type. A logistic regression model was developed and applied to lake basins greater than 1 hectare (ha) in GRTE (n=106) and YELL (n=294). For GRTE, 36 percent of lakes had a greater than 60-percent probability of having ANC concentrations less than 100 microequivalents per liter, and 14 percent of lakes had a greater than 80-percent probability of having ANC concentrations less than 100 microequivalents per liter. The elevation of the lake outlet and the area of the basin with northeast aspects were determined to be statistically significant and were used as the explanatory variables in the multivariate logistic regression model. For YELL, results indicated that 13 percent of lakes had a greater than 60-percent probability of having ANC concentrations less than 100 microequivalents per liter, and 9 percent of lakes had a greater than 80-percent probability of having ANC concentrations less than 100 microequivalents per liter. Only the elevation of the lake outlet was determined to be statistically significant and was used as the explanatory variable in the multivariate logistic regression model. The lakes that exceeded 80-percent probability of having an ANC concentration less than 100 microequivalents per liter, and therefore had the greatest sensitivity to acidification from atmospheric deposition, are located at elevations greater than 2,810 meters (m) in GRTE, and greater than 2,655 m in YELL.

The variability puzzle in human memory.

PubMed

Kahana, Michael J; Aggarwal, Eash V; Phan, Tung D

2018-04-26

Memory performance exhibits a high level of variability from moment to moment. Much of this variability may reflect inadequately controlled experimental variables, such as word memorability, past practice and subject fatigue. Alternatively, stochastic variability in performance may largely reflect the efficiency of endogenous neural processes that govern memory function. To help adjudicate between these competing views, the authors conducted a multisession study in which subjects completed 552 trials of a delayed free-recall task. Applying a statistical model to predict variability in each subject's recall performance uncovered modest effects of word memorability, proactive interference, and other variables. In contrast to the limited explanatory power of these experimental variables, performance on the prior list strongly predicted current list recall. These findings suggest that endogenous factors underlying successful encoding and retrieval drive variability in performance. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Regression model development and computational procedures to support estimation of real-time concentrations and loads of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-9

USGS Publications Warehouse

Lee, Michael T.; Asquith, William H.; Oden, Timothy D.

2012-01-01

In December 2005, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (Escherichia coli and total coliform), atrazine, and suspended sediment at two USGS streamflow-gaging stations that represent watersheds contributing to Lake Houston (08068500 Spring Creek near Spring, Tex., and 08070200 East Fork San Jacinto River near New Caney, Tex.). Data from the discrete water-quality samples collected during 2005–9, in conjunction with continuously monitored real-time data that included streamflow and other physical water-quality properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), were used to develop regression models for the estimation of concentrations of water-quality constituents of substantial source watersheds to Lake Houston. The potential explanatory variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, and time (to account for seasonal variations inherent in some water-quality data). The response variables (the selected constituents) at each site were nitrite plus nitrate nitrogen, total phosphorus, total organic carbon, E. coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities to serve as potential surrogate variables to estimate concentrations of the selected constituents through statistical regression. Statistical regression also facilitates accompanying estimates of uncertainty in the form of prediction intervals. Each regression model potentially can be used to estimate concentrations of a given constituent in real time. Among other regression diagnostics, the diagnostics used as indicators of general model reliability and reported herein include the adjusted R-squared, the residual standard error, residual plots, and p-values. Adjusted R-squared values for the Spring Creek models ranged from .582–.922 (dimensionless). The residual standard errors ranged from .073–.447 (base-10 logarithm). Adjusted R-squared values for the East Fork San Jacinto River models ranged from .253–.853 (dimensionless). The residual standard errors ranged from .076–.388 (base-10 logarithm). In conjunction with estimated concentrations, constituent loads can be estimated by multiplying the estimated concentration by the corresponding streamflow and by applying the appropriate conversion factor. The regression models presented in this report are site specific, that is, they are specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the general methods that were developed and documented could be applied to most perennial streams for the purpose of estimating real-time water quality data.

unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, Ian J.; Chandler, Richard B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.

Culture, cultural factors and psychiatric diagnosis: review and projections.

PubMed

Alarcón, Renato D

2009-10-01

This paper aims to provide conceptual justifications for the inclusion of culture and cultural factors in psychiatric diagnosis, and logistic suggestions as to the content and use of this approach. A discussion of the scope and limitations of current diagnostic practice, criticisms from different quarters, and the role and relevance of culture in the diagnostic encounter, precede the examination of advantages and disadvantages of the approach. The cultural content of psychiatric diagnosis should include the main, well-recognized cultural variables, adequate family data, explanatory models, and strengths and weaknesses of every individual patient. The practical aspects include the acceptance of "cultural discordances" as a component of an updated definition of mental disorder, and the use of a refurbished cultural formulation. Clinical "telescoping" strategies to obtain relevant cultural data during the diagnostic interview, and areas of future research (including field trials on the cultural formulation and on "culture bound syndromes"), are outlined.

Clinical correlates of hypoglycaemia over 4 years in people with type 2 diabetes starting insulin: An analysis from the CREDIT study

PubMed Central

Calvi‐Gries, Francoise; Blonde, Lawrence; Pilorget, Valerie; Berlingieri, Joseph; Freemantle, Nick

2018-01-01

Aim To identify factors associated with documented symptomatic and severe hypoglycaemia over 4 years in people with type 2 diabetes starting insulin therapy. Materials and methods CREDIT, a prospective international observational study, collected data over 4 years on people starting any insulin in 314 centres; 2729 and 2271 people had hypoglycaemia data during the last 6 months of years 1 and 4, respectively. Multivariable logistic regression was used to select the characteristics associated with documented symptomatic hypoglycaemia, and the model was tested against severe hypoglycaemia. Results The proportions of participants reporting ≥1 non‐severe event were 18.5% and 16.6% in years 1 and 4; the corresponding proportions of those achieving a glycated haemoglobin (HbA1c) concentration <7.0% (<53 mmol/mol) were 24.6% and 18.3%, and 16.5% and 16.2% of those who did not. For severe hypoglycaemia, the proportions were 3.0% and 4.6% of people reaching target vs 1.5% and 1.1% of those not reaching target. Multivariable analysis showed that, for documented symptomatic hypoglycaemia at both years 1 and 4, baseline lower body mass index and more physical activity were predictors, and lower HbA1c was an explanatory variable in the respective year. Models for documented symptomatic hypoglycaemia predicted severe hypoglycaemia. Insulin regimen was a univariate explanatory variable, and was not retained in the multivariable analysis. Conclusions Hypoglycaemia occurred at significant rates, but was stable over 4 years despite increased insulin doses. The association with insulin regimen and with oral agent use declined over that time. Associated predictors and explanatory variables for documented symptomatic hypoglycaemia conformed to clinical impressions and could be extended to severe hypoglycaemia. Better achieved HbA1c was associated with a higher risk of hypoglycaemia. PMID:29205734

Clinical correlates of hypoglycaemia over 4 years in people with type 2 diabetes starting insulin: An analysis from the CREDIT study.

PubMed

Home, Philip; Calvi-Gries, Francoise; Blonde, Lawrence; Pilorget, Valerie; Berlingieri, Joseph; Freemantle, Nick

2018-04-01

To identify factors associated with documented symptomatic and severe hypoglycaemia over 4 years in people with type 2 diabetes starting insulin therapy. CREDIT, a prospective international observational study, collected data over 4 years on people starting any insulin in 314 centres; 2729 and 2271 people had hypoglycaemia data during the last 6 months of years 1 and 4, respectively. Multivariable logistic regression was used to select the characteristics associated with documented symptomatic hypoglycaemia, and the model was tested against severe hypoglycaemia. The proportions of participants reporting ≥1 non-severe event were 18.5% and 16.6% in years 1 and 4; the corresponding proportions of those achieving a glycated haemoglobin (HbA1c) concentration <7.0% (<53 mmol/mol) were 24.6% and 18.3%, and 16.5% and 16.2% of those who did not. For severe hypoglycaemia, the proportions were 3.0% and 4.6% of people reaching target vs 1.5% and 1.1% of those not reaching target. Multivariable analysis showed that, for documented symptomatic hypoglycaemia at both years 1 and 4, baseline lower body mass index and more physical activity were predictors, and lower HbA1c was an explanatory variable in the respective year. Models for documented symptomatic hypoglycaemia predicted severe hypoglycaemia. Insulin regimen was a univariate explanatory variable, and was not retained in the multivariable analysis. Hypoglycaemia occurred at significant rates, but was stable over 4 years despite increased insulin doses. The association with insulin regimen and with oral agent use declined over that time. Associated predictors and explanatory variables for documented symptomatic hypoglycaemia conformed to clinical impressions and could be extended to severe hypoglycaemia. Better achieved HbA1c was associated with a higher risk of hypoglycaemia. © 2017 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

Analysis of the individual factors affecting mobile phone use while driving in France: socio-demographic characteristics, car and phone use in professional and private contexts.

PubMed

Brusque, Corinne; Alauzet, Aline

2008-01-01

In France, as in many other countries, phoning while driving is legally restricted because of its negative impact on driving performance which increases accident risk. Nevertheless, it is still a frequently observed practice and one which has not been analyzed in detail. This study attempts to identify the profiles of those who use mobile phones while at the wheel and determine the forms taken by this use. A representative sample of 1973 French people was interviewed by phone on their driving practices and mobile phone use in everyday life and their mobile phone use while driving. Logistics regressions have been conducted to highlight the explanatory factors of phoning while driving. Strong differences between males and females have been shown. For the male population, age is the main explanatory factor of phoning while driving, followed by phone use for work-related reasons and extensive mobile phone use in everyday life. For females, high mileage and intensive use of mobile phone are the only two explanatory factors. We defined the intensive phone use at the wheel group as drivers who receive or send at least five or more calls per day while driving. There is no socio-demographic variable related to this practice. Car and phone uses in everyday life are the only explanatory factors for this intensive mobile use of the phone at the wheel.

A digital spatial predictive model of land-use change using economic and environmental inputs and a statistical tree classification approach: Thailand, 1970s--1990s

NASA Astrophysics Data System (ADS)

Felkner, John Sames

The scale and extent of global land use change is massive, and has potentially powerful effects on the global climate and global atmospheric composition (Turner & Meyer, 1994). Because of this tremendous change and impact, there is an urgent need for quantitative, empirical models of land use change, especially predictive models with an ability to capture the trajectories of change (Agarwal, Green, Grove, Evans, & Schweik, 2000; Lambin et al., 1999). For this research, a spatial statistical predictive model of land use change was created and run in two provinces of Thailand. The model utilized an extensive spatial database, and used a classification tree approach for explanatory model creation and future land use (Breiman, Friedman, Olshen, & Stone, 1984). Eight input variables were used, and the trees were run on a dependent variable of land use change measured from 1979 to 1989 using classified satellite imagery. The derived tree models were used to create probability of change surfaces, and these were then used to create predicted land cover maps for 1999. These predicted 1999 maps were compared with actual 1999 landcover derived from 1999 Landsat 7 imagery. The primary research hypothesis was that an explanatory model using both economic and environmental input variables would better predict future land use change than would either a model using only economic variables or a model using only environmental. Thus, the eight input variables included four economic and four environmental variables. The results indicated a very slight superiority of the full models to predict future agricultural change and future deforestation, but a slight superiority of the economic models to predict future built change. However, the margins of superiority were too small to be statistically significant. The resulting tree structures were used, however, to derive a series of principles or "rules" governing land use change in both provinces. The model was able to predict future land use, given a series of assumptions, with 90 percent overall accuracies. The model can be used in other developing or developed country locations for future land use prediction, determination of future threatened areas, or to derive "rules" or principles driving land use change.

Statistical evaluation of the influence of soil properties on recoveries and matrix effects during the analysis of pharmaceutical compounds and steroids by quick, easy, cheap, effective, rugged and safe extraction followed by liquid chromatography-tandem mass spectrometry.

PubMed

Salvia, Marie-Virginie; Cren-Olivé, Cécile; Vulliet, Emmanuelle

2013-11-08

Numerous chemical products are dispersed in our environment. Many of them are recognized as harmful to humans and the ecosystem. Among these harmful substances are antibiotics and steroid hormones. Currently, very few data are available on the presence and fate of these substances in the environment, in particular for solid matrices, mainly due to a lack of analytical methodologies. Indeed, soil is a very complex matrix, and the nature and composition of the soil has a significant impact on the extraction efficiency and the sensitivity of the method. For this reason a statistical approach was performed to study the influence of soil parameters (clay, silt, sand and organic carbon percentages and cation exchange capacity (CEC)) on recoveries and matrix effects of various pharmaceuticals and steroids. Thus, an analysis of covariance (ANCOVA) was performed when several substances were analyzed simultaneously, whereas a Pearson correlation was used to study the compounds individually. To the best of our knowledge, this study is the first time such an experiment was performed. The results showed that clay and organic carbon percentages as well as the CEC have an impact on the recoveries of most of the target substances, the variables being anti-correlated. This result suggests that the compounds are trapped in soils with high levels of clay and organic carbon and a high CEC. For the matrix effects, it was shown that the organic carbon content has a significant effect on steroid hormones and penicillin G matrix effects (positive correlation). Finally, interaction effects (first order) were evaluated. This latter point corresponds to the crossed effects that occur between explanatory variables (soil parameters). Indeed, the value taken by an explanatory variable can have an influence on the effect that another explanatory variable has on a dependent variable. For instance, it was shown that some parameters (silt, sand) have an impact on the effect that clay content has on recoveries. Besides, CEC and silt affect the influence that organic carbon percentage has on matrix effect. This original approach provides a better understanding of the complex interactions that occur in soil and could be useful to understand and predict the performance of an analytical method. Copyright © 2013 Elsevier B.V. All rights reserved.

Individual factors and perceived community characteristics in relation to mental health and mental well-being.

PubMed

McAneney, Helen; Tully, Mark A; Hunter, Ruth F; Kouvonen, Anne; Veal, Philip; Stevenson, Michael; Kee, Frank

2015-12-12

It has been argued that though correlated with mental health, mental well-being is a distinct entity. Despite the wealth of literature on mental health, less is known about mental well-being. Mental health is something experienced by individuals, whereas mental well-being can be assessed at the population level. Accordingly it is important to differentiate the individual and population level factors (environmental and social) that could be associated with mental health and well-being, and as people living in deprived areas have a higher prevalence of poor mental health, these relationships should be compared across different levels of neighbourhood deprivation. A cross-sectional representative random sample of 1,209 adults from 62 Super Output Areas (SOAs) in Belfast, Northern Ireland (Feb 2010 - Jan 2011) were recruited in the PARC Study. Interview-administered questionnaires recorded data on socio-demographic characteristics, health-related behaviours, individual social capital, self-rated health, mental health (SF-8) and mental well-being (WEMWBS). Multi-variable linear regression analyses, with inclusion of clustering by SOAs, were used to explore the associations between individual and perceived community characteristics and mental health and mental well-being, and to investigate how these associations differed by the level of neighbourhood deprivation. Thirty-eight and 30 % of variability in the measures of mental well-being and mental health, respectively, could be explained by individual factors and the perceived community characteristics. In the total sample and stratified by neighbourhood deprivation, age, marital status and self-rated health were associated with both mental health and well-being, with the 'social connections' and local area satisfaction elements of social capital also emerging as explanatory variables. An increase of +1 in EQ-5D-3 L was associated with +1SD of the population mean in both mental health and well-being. Similarly, a change from 'very dissatisfied' to 'very satisfied' for local area satisfaction would result in +8.75 for mental well-being, but only in the more affluent of areas. Self-rated health was associated with both mental health and mental well-being. Of the individual social capital explanatory variables, 'social connections' was more important for mental well-being. Although similarities in the explanatory variables of mental health and mental well-being exist, socio-ecological interventions designed to improve them may not have equivalent impacts in rich and poor neighbourhoods.

Explanatory Style in Patients with Rheumatoid Arthritis: An Unrecognized Predictor of Mortality

PubMed Central

Crowson, Aaron D.; Colligan, Robert C.; Matteson, Eric L.; Davis, John M.; Crowson, Cynthia S.

2016-01-01

Objective To determine whether pessimistic explanatory style altered the risk for and mortality of rheumatoid arthritis (RA) patients. Methods The study included subjects from a population-based cohort with incident RA and non-RA comparison cohort who completed the Minnesota Multiphasic Personality Inventory (MMPI). Results Among 148 RA and 135 non-RA subjects, pessimism was associated with development of rheumatoid factor positive (RF+) RA. Pessimism was associated with an increased risk of mortality (hazard ratio [HR]:2.88 with similar magnitude to RF+ (HR:2.28). Conclusion Pessimistic explanatory style was associated with an increased risk of developing RA and increased mortality rate in patients with RA. PMID:28148754

Modeling the association between HR variability and illness in elite swimmers

PubMed Central

Hellard, Philippe; Guimaraes, Fanny; Avalos, Marta; Houel, Nicolas; Hausswirth, Christophe; Toussaint, Jean François

2011-01-01

Purpose To determine whether heart rate variability, an indirect measure of autonomic control, is associated with upper respiratory tract and pulmonary infections, muscular affections and all-type pathologies in elite swimmers. Methods Seven elite international and 11 national swimmers were followed weekly for two years. The indexes of cardiac autonomic regulation in supine and orthostatic position were assessed as explanatory variables by time-domain (SD1, SD2) and spectral analyses (high frequency- HF; 0.15 Hz-0.40Hz, low frequency-LF; 0.04-0.15 Hz and HF/LF ratio) of heart rate variability. Logistic mixed models described the relationship between the explanatory variables and the risk of upper respiratory tract and pulmonary infections, muscular affections and all-type pathologies. Results The risk of all-type pathologies was higher for national swimmers and in winter (p<0.01). An increase in the parasympathetic indexes (HF, SD1) in supine position assessed one week earlier was linked to a higher risk of upper respiratory tract and pulmonary infections (p<0.05), and to a higher risk of muscular affections (increase in HF, p<0.05). Multivariate analyses showed: (1) a higher all-type pathologies risk in winter, and for an increase in the total power of heart rate variability associated with a decline SD1 in supine position; (2) a higher all-type pathologies risk in winter associated with a decline in HF assessed one week earlier in orthostatic position; and (3) a higher risk of muscular affections in winter associated with a decrease SD1 and an increase LF in orthostatic position. Conclusion Swimmers’ health maintenance requires particular attention when autonomic balance shows a sudden increase in parasympathetic indices in supine position assessed one week earlier evolving toward sympathetic predominance in supine and orthostatic positions. PMID:21085039

Complex, Dynamic Combination of Physical, Chemical and Nutritional Variables Controls Spatio-Temporal Variation of Sandy Beach Community Structure

PubMed Central

Ortega Cisneros, Kelly; Smit, Albertus J.; Laudien, Jürgen; Schoeman, David S.

2011-01-01

Sandy beach ecological theory states that physical features of the beach control macrobenthic community structure on all but the most dissipative beaches. However, few studies have simultaneously evaluated the relative importance of physical, chemical and biological factors as potential explanatory variables for meso-scale spatio-temporal patterns of intertidal community structure in these systems. Here, we investigate macroinfaunal community structure of a micro-tidal sandy beach that is located on an oligotrophic subtropical coast and is influenced by seasonal estuarine input. We repeatedly sampled biological and environmental variables at a series of beach transects arranged at increasing distances from the estuary mouth. Sampling took place over a period of five months, corresponding with the transition between the dry and wet season. This allowed assessment of biological-physical relationships across chemical and nutritional gradients associated with a range of estuarine inputs. Physical, chemical, and biological response variables, as well as measures of community structure, showed significant spatio-temporal patterns. In general, bivariate relationships between biological and environmental variables were rare and weak. However, multivariate correlation approaches identified a variety of environmental variables (i.e., sampling session, the C∶N ratio of particulate organic matter, dissolved inorganic nutrient concentrations, various size fractions of photopigment concentrations, salinity and, to a lesser extent, beach width and sediment kurtosis) that either alone or combined provided significant explanatory power for spatio-temporal patterns of macroinfaunal community structure. Overall, these results showed that the macrobenthic community on Mtunzini Beach was not structured primarily by physical factors, but instead by a complex and dynamic blend of nutritional, chemical and physical drivers. This emphasises the need to recognise ocean-exposed sandy beaches as functional ecosystems in their own right. PMID:21858213

Complex, dynamic combination of physical, chemical and nutritional variables controls spatio-temporal variation of sandy beach community structure.

PubMed

Ortega Cisneros, Kelly; Smit, Albertus J; Laudien, Jürgen; Schoeman, David S

2011-01-01

Sandy beach ecological theory states that physical features of the beach control macrobenthic community structure on all but the most dissipative beaches. However, few studies have simultaneously evaluated the relative importance of physical, chemical and biological factors as potential explanatory variables for meso-scale spatio-temporal patterns of intertidal community structure in these systems. Here, we investigate macroinfaunal community structure of a micro-tidal sandy beach that is located on an oligotrophic subtropical coast and is influenced by seasonal estuarine input. We repeatedly sampled biological and environmental variables at a series of beach transects arranged at increasing distances from the estuary mouth. Sampling took place over a period of five months, corresponding with the transition between the dry and wet season. This allowed assessment of biological-physical relationships across chemical and nutritional gradients associated with a range of estuarine inputs. Physical, chemical, and biological response variables, as well as measures of community structure, showed significant spatio-temporal patterns. In general, bivariate relationships between biological and environmental variables were rare and weak. However, multivariate correlation approaches identified a variety of environmental variables (i.e., sampling session, the C∶N ratio of particulate organic matter, dissolved inorganic nutrient concentrations, various size fractions of photopigment concentrations, salinity and, to a lesser extent, beach width and sediment kurtosis) that either alone or combined provided significant explanatory power for spatio-temporal patterns of macroinfaunal community structure. Overall, these results showed that the macrobenthic community on Mtunzini Beach was not structured primarily by physical factors, but instead by a complex and dynamic blend of nutritional, chemical and physical drivers. This emphasises the need to recognise ocean-exposed sandy beaches as functional ecosystems in their own right.

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

Lutzomyia longipalpis Presence and Abundance Distribution at Different Micro-spatial Scales in an Urban Scenario

PubMed Central

Santini, María Soledad; Utgés, María Eugenia; Berrozpe, Pablo; Manteca Acosta, Mariana; Casas, Natalia; Heuer, Paola; Salomón, O. Daniel

2015-01-01

The principal objective of this study was to assess a modeling approach to Lu. longipalpis distribution in an urban scenario, discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector. For this objective, we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously, so we constructed a set of 13 models to account for micro-habitats, macro-habitats and mixed-habitats. We captured a total of 853 sandflies, of which 98.35% were Lu. longipalpis. We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled. Positive rK39 dogs represented 16.75% of the total, of which 47% were asymptomatic. Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables, all positive, for the presence of sandflies in the city. All variables in the abundance model ended to be explanatory, trees around the trap, distance to the stream and its quadratic, being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream. The spatial distribution of dogs infected with L. infantum showed a heterogeneous pattern throughout the city; however, we could not confirm an association of the distribution with the variables assessed. In relation to Lu. longipalpis distribution, the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables. Based on the variables associated with Lu. longipalpis, the model will be validated in other cities and environmental surveillance, and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village (mesoscale) strategies. PMID:26274318

Lutzomyia longipalpis Presence and Abundance Distribution at Different Micro-spatial Scales in an Urban Scenario.

PubMed

Santini, María Soledad; Utgés, María Eugenia; Berrozpe, Pablo; Manteca Acosta, Mariana; Casas, Natalia; Heuer, Paola; Salomón, O Daniel

2015-01-01

The principal objective of this study was to assess a modeling approach to Lu. longipalpis distribution in an urban scenario, discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector. For this objective, we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously, so we constructed a set of 13 models to account for micro-habitats, macro-habitats and mixed-habitats. We captured a total of 853 sandflies, of which 98.35% were Lu. longipalpis. We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled. Positive rK39 dogs represented 16.75% of the total, of which 47% were asymptomatic. Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables, all positive, for the presence of sandflies in the city. All variables in the abundance model ended to be explanatory, trees around the trap, distance to the stream and its quadratic, being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream. The spatial distribution of dogs infected with L. infantum showed a heterogeneous pattern throughout the city; however, we could not confirm an association of the distribution with the variables assessed. In relation to Lu. longipalpis distribution, the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables. Based on the variables associated with Lu. longipalpis, the model will be validated in other cities and environmental surveillance, and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village (mesoscale) strategies.

Smooth conditional distribution function and quantiles under random censorship.

PubMed

Leconte, Eve; Poiraud-Casanova, Sandrine; Thomas-Agnan, Christine

2002-09-01

We consider a nonparametric random design regression model in which the response variable is possibly right censored. The aim of this paper is to estimate the conditional distribution function and the conditional alpha-quantile of the response variable. We restrict attention to the case where the response variable as well as the explanatory variable are unidimensional and continuous. We propose and discuss two classes of estimators which are smooth with respect to the response variable as well as to the covariate. Some simulations demonstrate that the new methods have better mean square error performances than the generalized Kaplan-Meier estimator introduced by Beran (1981) and considered in the literature by Dabrowska (1989, 1992) and Gonzalez-Manteiga and Cadarso-Suarez (1994).

Racism, Gun Ownership and Gun Control: Biased Attitudes in US Whites May Influence Policy Decisions

PubMed Central

O’Brien, Kerry; Forrest, Walter; Lynott, Dermot; Daly, Michael

2013-01-01

Objective Racism is related to policies preferences and behaviors that adversely affect blacks and appear related to a fear of blacks (e.g., increased policing, death penalty). This study examined whether racism is also related to gun ownership and opposition to gun controls in US whites. Method The most recent data from the American National Election Study, a large representative US sample, was used to test relationships between racism, gun ownership, and opposition to gun control in US whites. Explanatory variables known to be related to gun ownership and gun control opposition (i.e., age, gender, education, income, conservatism, anti-government sentiment, southern vs. other states, political identification) were entered in logistic regression models, along with measures of racism, and the stereotype of blacks as violent. Outcome variables included; having a gun in the home, opposition to bans on handguns in the home, support for permits to carry concealed handguns. Results After accounting for all explanatory variables, logistic regressions found that for each 1 point increase in symbolic racism there was a 50% increase in the odds of having a gun at home. After also accounting for having a gun in the home, there was still a 28% increase in support for permits to carry concealed handguns, for each one point increase in symbolic racism. The relationship between symbolic racism and opposition to banning handguns in the home (OR1.27 CI 1.03,1.58) was reduced to non-significant after accounting for having a gun in the home (OR1.17 CI.94,1.46), which likely represents self-interest in retaining property (guns). Conclusions Symbolic racism was related to having a gun in the home and opposition to gun control policies in US whites. The findings help explain US whites’ paradoxical attitudes towards gun ownership and gun control. Such attitudes may adversely influence US gun control policy debates and decisions. PMID:24204867

Racism, gun ownership and gun control: biased attitudes in US whites may influence policy decisions.

PubMed

O'Brien, Kerry; Forrest, Walter; Lynott, Dermot; Daly, Michael

2013-01-01

Racism is related to policies preferences and behaviors that adversely affect blacks and appear related to a fear of blacks (e.g., increased policing, death penalty). This study examined whether racism is also related to gun ownership and opposition to gun controls in US whites. The most recent data from the American National Election Study, a large representative US sample, was used to test relationships between racism, gun ownership, and opposition to gun control in US whites. Explanatory variables known to be related to gun ownership and gun control opposition (i.e., age, gender, education, income, conservatism, anti-government sentiment, southern vs. other states, political identification) were entered in logistic regression models, along with measures of racism, and the stereotype of blacks as violent. Outcome variables included; having a gun in the home, opposition to bans on handguns in the home, support for permits to carry concealed handguns. After accounting for all explanatory variables, logistic regressions found that for each 1 point increase in symbolic racism there was a 50% increase in the odds of having a gun at home. After also accounting for having a gun in the home, there was still a 28% increase in support for permits to carry concealed handguns, for each one point increase in symbolic racism. The relationship between symbolic racism and opposition to banning handguns in the home (OR1.27 CI 1.03,1.58) was reduced to non-significant after accounting for having a gun in the home (OR1.17 CI.94,1.46), which likely represents self-interest in retaining property (guns). Symbolic racism was related to having a gun in the home and opposition to gun control policies in US whites. The findings help explain US whites' paradoxical attitudes towards gun ownership and gun control. Such attitudes may adversely influence US gun control policy debates and decisions.

Key factors affecting urban runoff pollution under cold climatic conditions

NASA Astrophysics Data System (ADS)

Valtanen, Marjo; Sillanpää, Nora; Setälä, Heikki

2015-10-01

Urban runoff contains various pollutants and has the potential of deteriorating the quality of aquatic ecosystems. In this study our objective is to shed light on the factors that control the runoff water quality in urbanized catchments. The effects of runoff event characteristics, land use type and catchment imperviousness on event mass loads (EML) and event mean concentrations (EMC) were studied during warm and cold periods in three study catchments (6.1, 6.5 and 12.6 ha in size) in the city of Lahti, Finland. Runoff and rainfall were measured continuously for two years at each catchment. Runoff samples were taken for total nutrients (tot-P and tot-N), total suspended solids (TSS), heavy metals (Zn, Cr, Al, Co, Ni, Cu, Pb, Mn) and total organic carbon (TOC). Stepwise multiple linear regression analysis (SMLR) was used to identify general relationships between the following variables: event water quality, runoff event characteristics and catchment characteristics. In general, the studied variables explained 50-90% of the EMLs but only 30-60% of the EMCs, with runoff duration having an important role in most of the SMLR models. Mean runoff intensity or peak flow was also often included in the runoff quality models. Yet, the importance (being the first, second or third best) and role (negative or positive impact) of the explanatory variables varied between the cold and warm period. Land use type often explained cold period concentrations, but imperviousness alone explained EMCs weakly. As for EMLs, the influence of imperviousness and/or land use was season and pollutant dependent. The study suggests that pollutant loads can be - throughout the year - adequately predicted by runoff characteristics given that seasonal differences are taken into account. Although pollutant concentrations were sensitive to variation in seasonal and catchment conditions as well, the accurate estimation of EMCs would require a more complete set of explanatory factors than used in this study.

Developmental delays and dental caries in low-income preschoolers in the USA: a pilot cross-sectional study and preliminary explanatory model

PubMed Central

2013-01-01

Background Anecdotal evidence suggests that low-income preschoolers with developmental delays are at increased risk for dental caries and poor oral health, but there are no published studies based on empirical data. The purpose of this pilot study was two-fold: to examine the relationship between developmental delays and dental caries in low-income preschoolers and to present a preliminary explanatory model on the determinants of caries for enrollees in Head Start, a U.S. school readiness program for low-income preschool-aged children. Methods Data were collected on preschoolers ages 3–5 years at two Head Start centers in Washington, USA (N = 115). The predictor variable was developmental delay status (no/yes). The outcome variable was the prevalence of decayed, missing, and filled surfaces (dmfs) on primary teeth. We used multiple variable Poisson regression models to test the hypothesis that within a population of low-income preschoolers, those with developmental delays would have increased dmfs prevalence than those without developmental delays. Results Seventeen percent of preschoolers had a developmental delay and 51.3% of preschoolers had ≥1 dmfs. Preschoolers with developmental delays had a dmfs prevalence ratio that was 1.26 times as high as preschoolers without developmental delays (95% CI: 1.01, 1.58; P < .04). Other factors associated with increased dmfs prevalence ratios included: not having a dental home (P = .01); low caregiver education (P < .001); and living in a non-fluoridated community (P < .001). Conclusions Our pilot data suggest that developmental delays among low-income preschoolers are associated with increased primary tooth dmfs. Additional research is needed to further examine this relationship. Future interventions and policies should focus on caries prevention strategies within settings like Head Start classrooms that serve low-income preschool-aged children with additional targeted home- and community-based interventions for those with developmental delays. PMID:24119240

The Social Explanatory Styles Questionnaire: Assessing Moderators of Basic Social-Cognitive Phenomena Including Spontaneous Trait Inference, the Fundamental Attribution Error, and Moral Blame

PubMed Central

Gill, Michael J.; Andreychik, Michael R.

2014-01-01

Why is he poor? Why is she failing academically? Why is he so generous? Why is she so conscientious? Answers to such everyday questions—social explanations—have powerful effects on relationships at the interpersonal and societal levels. How do people select an explanation in particular cases? We suggest that, often, explanations are selected based on the individual's pre-existing general theories of social causality. More specifically, we suggest that over time individuals develop general beliefs regarding the causes of social events. We refer to these beliefs as social explanatory styles. Our goal in the present article is to offer and validate a measure of individual differences in social explanatory styles. Accordingly, we offer the Social Explanatory Styles Questionnaire (SESQ), which measures three independent dimensions of social explanatory style: Dispositionism, historicism, and controllability. Studies 1–3 examine basic psychometric properties of the SESQ and provide positive evidence regarding internal consistency, factor structure, and both convergent and divergent validity. Studies 4–6 examine predictive validity for each subscale: Does each explanatory dimension moderate an important phenomenon of social cognition? Results suggest that they do. In Study 4, we show that SESQ dispositionism moderates the tendency to make spontaneous trait inferences. In Study 5, we show that SESQ historicism moderates the tendency to commit the Fundamental Attribution Error. Finally, in Study 6 we show that SESQ controllability predicts polarization of moral blame judgments: Heightened blaming toward controllable stigmas (assimilation), and attenuated blaming toward uncontrollable stigmas (contrast). Decades of research suggest that explanatory style regarding the self is a powerful predictor of self-functioning. We think it is likely that social explanatory styles—perhaps comprising interactive combinations of the basic dimensions tapped by the SESQ—will be similarly potent predictors of social functioning. We hope the SESQ will be a useful tool for exploring that possibility. PMID:25007152

Impact of housing improvement and the socio-physical environment on the mental health of children's carers: a cohort study in Australian Aboriginal communities.

PubMed

Bailie, Ross S; Stevens, Matthew; McDonald, Elizabeth L

2014-05-19

The mental health of carers is an important proximate factor in the causal web linking housing conditions to child health, as well as being important in its own right. Improved understanding of the nature of the relationships between housing conditions, carer mental health and child health outcomes is therefore important for informing the development of housing programs. This paper examines the relationship between the mental health of the carers of young children, housing conditions, and other key factors in the socio-physical environment. This analysis is part of a broader prospective cohort study of children living in Aboriginal communities in the Northern Territory (NT) of Australia at the time of major new community housing programs. Carer's mental health was assessed using two validated scales: the Affect Balance scale and the Brief Screen for Depression. The quality of housing infrastructure was assessed through detailed surveys. Secondary explanatory variables included a range of socio-environmental factors, including validated measures of stressful life events. Hierarchical regression modelling was used to assess associations between outcome and explanatory variables at baseline, and associations between change in housing conditions and change in outcomes between baseline and follow-up. There was no clear or consistent evidence of a causal relationship between the functional state of household infrastructure and the mental health of carers of young children. The strongest and most consistent associations with carer mental health were the measures of negative life events, with a dose-response relationship, and adjusted odds ratio of over 6 for carers in the highest stress exposure category at baseline, and consistent associations in the follow up analysis. The findings highlight the need for housing programs to be supported by social, behavioral and community-wide environmental programs if potential health gains are to be more fully realized, and for rigorous evaluation of such programs for the purpose of informing future housing initiatives.

Multiscale Framework for Assessing Critical Loads of Atmospheric Nitrogen Deposition for Aquatic Ecosystems in Wilderness Areas of the Western United States

NASA Astrophysics Data System (ADS)

Nanus, Leora; Clow, David; Saros, Jasmine; McMurray, Jill; Blett, Tamara; Sickman, James

2017-04-01

High-elevation aquatic ecosystems in Wilderness areas of the western United States are impacted by current and historic atmospheric nitrogen (N) deposition associated with local and regional air pollution. Documented effects include elevated surface water nitrate concentrations, increased algal productivity, and changes in diatom species assemblages. A predictive framework was developed for sensitive high-elevation basins across the western United States at multiple spatial scales including the Rocky Mountain Region (Rockies), the Greater Yellowstone Area (GYA), and Yosemite (YOSE) and Sequoia & Kings Canyon (SEKI) National Parks. Spatial trends in critical loads of N deposition for nutrient enrichment of aquatic ecosystems were quantified and mapped using a geostatistical approach, with modeled N deposition, topography, vegetation, geology, and climate as potential explanatory variables. Multiple predictive models were created using various combinations of explanatory variables; this approach allowed for better quantification of uncertainty and identification of areas most sensitive to high atmospheric N deposition (> 3 kg N ha-1 yr-1). For multiple spatial scales, the lowest critical loads estimates (<1.5 + 1 kg N ha-1 yr-1) occurred in high-elevation basins with steep slopes, sparse vegetation, and exposed bedrock and talus. Based on a nitrate threshold of 1 μmol L-1, estimated critical load exceedances (>1.5 + 1 kg N ha-1 yr-1) correspond with areas of high N deposition and vary spatially ranging from less than 20% to over 40% of the study area for the Rockies, GYA, YOSE, and SEKI. These predictive models and maps identify sensitive aquatic ecosystems that may be impacted by excess atmospheric N deposition and can be used to help protect against future anthropogenic disturbance. The approach presented here may be transferable to other remote and protected high-elevation ecosystems at multiple spatial scales that are sensitive to adverse effects of pollutant loading in the US and around the world.

Factors affecting hatch success of hawksbill sea turtles on Long Island, Antigua, West Indies.

PubMed

Ditmer, Mark Allan; Stapleton, Seth Patrick

2012-01-01

Current understanding of the factors influencing hawksbill sea turtle (Eretmochelys imbricata) hatch success is disparate and based on relatively short-term studies or limited sample sizes. Because global populations of hawksbills are heavily depleted, evaluating the parameters that impact hatch success is important to their conservation and recovery. Here, we use data collected by the Jumby Bay Hawksbill Project (JBHP) to investigate hatch success. The JBHP implements saturation tagging protocols to study a hawksbill rookery in Antigua, West Indies. Habitat data, which reflect the varied nesting beaches, are collected at egg deposition, and nest contents are exhumed and categorized post-emergence. We analyzed hatch success using mixed-model analyses with explanatory and predictive datasets. We incorporated a random effect for turtle identity and evaluated environmental, temporal and individual-based reproductive variables. Hatch success averaged 78.6% (SD: 21.2%) during the study period. Highly supported models included multiple covariates, including distance to vegetation, deposition date, individual intra-seasonal nest number, clutch size, organic content, and sand grain size. Nests located in open sand were predicted to produce 10.4 more viable hatchlings per clutch than nests located >1.5 m into vegetation. For an individual first nesting in early July, the fourth nest of the season yielded 13.2 more viable hatchlings than the initial clutch. Generalized beach section and inter-annual variation were also supported in our explanatory dataset, suggesting that gaps remain in our understanding of hatch success. Our findings illustrate that evaluating hatch success is a complex process, involving multiple environmental and individual variables. Although distance to vegetation and hatch success were inversely related, vegetation is an important component of hawksbill nesting habitat, and a more complete assessment of the impacts of specific vegetation types on hatch success and hatchling sex ratios is needed. Future research should explore the roles of sand structure, nest moisture, and local weather conditions.

Factors Affecting Hatch Success of Hawksbill Sea Turtles on Long Island, Antigua, West Indies

PubMed Central

Ditmer, Mark Allan; Stapleton, Seth Patrick

2012-01-01

Current understanding of the factors influencing hawksbill sea turtle (Eretmochelys imbricata) hatch success is disparate and based on relatively short-term studies or limited sample sizes. Because global populations of hawksbills are heavily depleted, evaluating the parameters that impact hatch success is important to their conservation and recovery. Here, we use data collected by the Jumby Bay Hawksbill Project (JBHP) to investigate hatch success. The JBHP implements saturation tagging protocols to study a hawksbill rookery in Antigua, West Indies. Habitat data, which reflect the varied nesting beaches, are collected at egg deposition, and nest contents are exhumed and categorized post-emergence. We analyzed hatch success using mixed-model analyses with explanatory and predictive datasets. We incorporated a random effect for turtle identity and evaluated environmental, temporal and individual-based reproductive variables. Hatch success averaged 78.6% (SD: 21.2%) during the study period. Highly supported models included multiple covariates, including distance to vegetation, deposition date, individual intra-seasonal nest number, clutch size, organic content, and sand grain size. Nests located in open sand were predicted to produce 10.4 more viable hatchlings per clutch than nests located >1.5 m into vegetation. For an individual first nesting in early July, the fourth nest of the season yielded 13.2 more viable hatchlings than the initial clutch. Generalized beach section and inter-annual variation were also supported in our explanatory dataset, suggesting that gaps remain in our understanding of hatch success. Our findings illustrate that evaluating hatch success is a complex process, involving multiple environmental and individual variables. Although distance to vegetation and hatch success were inversely related, vegetation is an important component of hawksbill nesting habitat, and a more complete assessment of the impacts of specific vegetation types on hatch success and hatchling sex ratios is needed. Future research should explore the roles of sand structure, nest moisture, and local weather conditions. PMID:22802928

A longitudinal analysis of the effects of socioeconomic factors, foreign media, and attitude toward appearance on general and central adiposity in Chinese adolescents

PubMed Central

Felicitas, Jamie Q.; Tanenbaum, Hilary C.; Li, Yawen; Chou, Chih-Ping; Palmer, Paula H.; Spruijt-Metz, Donna; Reynolds, Kim D.; Johnson, C. Anderson; Xie, Bin

2015-01-01

This paper explores the longitudinal effects of socioeconomic factors (i.e., parent education and family income level), foreign media, and attitude toward appearance on general and central adiposity among Chinese adolescents. A longitudinal analysis was performed using data from the China Seven Cities Study, a health promotion and smoking prevention study conducted in seven cities across Mainland China between 2002 and 2005. Participants included 5,020 middle and high school students and their parents. Explanatory variables included foreign media exposure, attitude toward appearance, parent education, and family income. Three-level, random-effect models were used to predict general adiposity (i.e., body mass index) and central adiposity (i.e., waist circumference). The Generalized Estimating Equation approach was utilized to determine the effect of explanatory variables on overweight status. Among girls, foreign media exposure was significantly negatively associated with general adiposity over time (β = − 0.06, p = 0.01 for middle school girls; β = − 0.06, p = 0.03 for high school girls). Attitude toward appearance was associated with lesser odds of being overweight, particularly among high school girls (OR = 0.86, p < 0.01). Among boys, parental education was significantly positively associated with general adiposity (β = 0.62, p < 0.01 for middle school boys; β = 0.37, p = 0.02 for high school boys) and associated with greater odds of being overweight (OR = 1.55, p < 0.01 for middle school boys; OR = 1.26, p = 0.04 for high school boys). Across all gender and grade levels, family income was significantly negatively associated with central adiposity over time. Interventions addressing Chinese adolescent overweight/obesity should consider these factors as potential focus areas. PMID:26279973

Comparison of stream invertebrate response models for bioassessment metric

USGS Publications Warehouse

Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2012-01-01

We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.

The relative influence of nutrients and habitat on stream metabolism in agricultural streams

USGS Publications Warehouse

Frankforter, J.D.; Weyers, H.S.; Bales, J.D.; Moran, P.W.; Calhoun, D.L.

2010-01-01

Stream metabolism was measured in 33 streams across a gradient of nutrient concentrations in four agricultural areas of the USA to determine the relative influence of nutrient concentrations and habitat on primary production (GPP) and respiration (CR-24). In conjunction with the stream metabolism estimates, water quality and algal biomass samples were collected, as was an assessment of habitat in the sampling reach. When data for all study areas were combined, there were no statistically significant relations between gross primary production or community respiration and any of the independent variables. However, significant regression models were developed for three study areas for GPP (r 2 = 0.79-0.91) and CR-24 (r 2 = 0.76-0.77). Various forms of nutrients (total phosphorus and area-weighted total nitrogen loading) were significant for predicting GPP in two study areas, with habitat variables important in seven significant models. Important physical variables included light availability, precipitation, basin area, and in-stream habitat cover. Both benthic and seston chlorophyll were not found to be important explanatory variables in any of the models; however, benthic ash-free dry weight was important in two models for GPP. ?? 2009 The Author(s).

Importance of scale, land cover, and weather on the abundance of bird species in a managed forest

USGS Publications Warehouse

Grinde, Alexis R.; Hiemi, Gerald J.; Sturtevant, Brian R.; Panci, Hannah; Thogmartin, Wayne E.; Wolter, Peter

2017-01-01

Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared < 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage.

A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level.

PubMed

Gasqui, Patrick; Trommenschlager, Jean-Marie

2017-08-21

Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood's model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., "problem finding"), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.

Spatial patterns of development drive water use

USGS Publications Warehouse

Sanchez, G.M.; Smith, J.W.; Terando, Adam J.; Sun, G.; Meentemeyer, R.K.

2018-01-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non‐spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio‐economic and environmental variables and two water use variables: a) domestic water use, and b) total development‐related water use (a combination of public supply, domestic self‐supply and industrial self‐supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio‐economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water‐efficient land use planning.

Spatial Patterns of Development Drive Water Use

NASA Astrophysics Data System (ADS)

Sanchez, G. M.; Smith, J. W.; Terando, A.; Sun, G.; Meentemeyer, R. K.

2018-03-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non-spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio-economic and environmental variables and two water use variables: a) domestic water use, and b) total development-related water use (a combination of public supply, domestic self-supply and industrial self-supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio-economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water-efficient land use planning.

Identifying the Driving Factors of Water Quality in a Sub-Watershed of the Republican River Basin, Kansas USA.

PubMed

Burke, Morgen W V; Shahabi, Mojtaba; Xu, Yeqian; Zheng, Haochi; Zhang, Xiaodong; VanLooy, Jeffrey

2018-05-22

Studies have shown that the agricultural expansion and land use changes in the Midwest of the U.S. are major drivers for increased nonpoint source pollution throughout the regional river systems. In this study, we empirically examined the relationship of planted area and production of three dominant crops with nitrate flux in the Republican River, Kansas, a sub-watershed of Mississippi River Basin. Our results show that land use in the region could not explain the observed changes in nitrate flux in the river. Instead, after including explanatory variables such as precipitation, growing degree days, and well water irrigation in the regression model we found that irrigation and spring precipitation could explain >85% of the variability in nitrate flux from 2000 to 2014. This suggests that changes in crop acreage and production alone cannot explain variability in nitrate flux. Future agricultural policy for the region should focus on controlling both the timing and amount of fertilizer applied to the field to reduce the potential leaching of excess fertilizer through spring time runoff and/or over-irrigation into nearby river systems.

Cost efficiency of university hospitals in the Nordic countries: a cross-country analysis.

PubMed

Medin, Emma; Anthun, Kjartan S; Häkkinen, Unto; Kittelsen, Sverre A C; Linna, Miika; Magnussen, Jon; Olsen, Kim; Rehnberg, Clas

2011-12-01

This paper estimates cost efficiency scores using the bootstrap bias-corrected procedure, including variables for teaching and research, for the performance of university hospitals in the Nordic countries. Previous research has shown that hospital provision of research and education interferes with patient care routines and inflates the costs of health care services, turning university hospitals into outliers in comparative productivity and efficiency analyses. The organisation of patient care, medical education and clinical research as well as available data at the university hospital level are highly similar in the Nordic countries, creating a data set of comparable decision-making units suitable for a cross-country cost efficiency analysis. The results demonstrate significant differences in university hospital cost efficiency when variables for teaching and research are entered into the analysis, both between and within the Nordic countries. The results of a second-stage analysis show that the most important explanatory variables are geographical location of the hospital and the share of discharges with a high case weight. However, a substantial amount of the variation in cost efficiency at the university hospital level remains unexplained.

Myxomatosis in wild rabbit: design of control programs in Mediterranean ecosystems.

PubMed

García-Bocanegra, Ignacio; Astorga, Rafael Jesús; Napp, Sebastián; Casal, Jordi; Huerta, Belén; Borge, Carmen; Arenas, Antonio

2010-01-01

A cross-sectional study was carried out in natural wild rabbit (Oryctolagus cuniculus) populations from southern Spain to identify risk factors associated to myxoma virus infection. Blood samples from 619 wild rabbits were collected, and questionnaires which included variables related to host, disease, game management and environment were completed. A logistic regression analysis was conducted to investigate the associations between myxomatosis seropositivity (dependent variable) across 7 hunting estates and an extensive set of explanatory variables obtained from the questionnaires. The prevalence of antibodies against myxomatosis virus was 56.4% (95% CI: 52.5-60.3) and ranged between 21.4% (95% CI: 9.0-33.8) and 70.2% (95% CI: 58.3-82.1) among the different sampling areas. The logistic regression analysis showed that autumn (OR 9.0), high abundance of mosquitoes (OR 8.2), reproductive activity (OR 4.1), warren's insecticide treatment (OR 3.7), rabbit haemorrhagic disease (RHD) seropositivity (OR 2.6), high hunting pressure (OR 6.3) and sheep presence (OR 6.4) were associated with seropositivity to myxomatosis. Based on the results, diverse management measures for myxomatosis control are proposed.

Assessing groundwater vulnerability to agrichemical contamination in the Midwest US

USGS Publications Warehouse

Burkart, M.R.; Kolpin, D.W.; James, D.E.

1999-01-01

Agrichemicals (herbicides and nitrate) are significant sources of diffuse pollution to groundwater. Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.

Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers

USGS Publications Warehouse

Runkel, Robert L.; Crawford, Charles G.; Cohn, Timothy A.

2004-01-01

LOAD ESTimator (LOADEST) is a FORTRAN program for estimating constituent loads in streams and rivers. Given a time series of streamflow, additional data variables, and constituent concentration, LOADEST assists the user in developing a regression model for the estimation of constituent load (calibration). Explanatory variables within the regression model include various functions of streamflow, decimal time, and additional user-specified data variables. The formulated regression model then is used to estimate loads over a user-specified time interval (estimation). Mean load estimates, standard errors, and 95 percent confidence intervals are developed on a monthly and(or) seasonal basis. The calibration and estimation procedures within LOADEST are based on three statistical estimation methods. The first two methods, Adjusted Maximum Likelihood Estimation (AMLE) and Maximum Likelihood Estimation (MLE), are appropriate when the calibration model errors (residuals) are normally distributed. Of the two, AMLE is the method of choice when the calibration data set (time series of streamflow, additional data variables, and concentration) contains censored data. The third method, Least Absolute Deviation (LAD), is an alternative to maximum likelihood estimation when the residuals are not normally distributed. LOADEST output includes diagnostic tests and warnings to assist the user in determining the appropriate estimation method and in interpreting the estimated loads. This report describes the development and application of LOADEST. Sections of the report describe estimation theory, input/output specifications, sample applications, and installation instructions.

Missed Nursing Care and Unit-Level Nurse Workload in the Acute and Post-Acute Settings.

PubMed

Orique, Sabrina B; Patty, Christopher M; Woods, Ellen

2016-01-01

This study replicates previous research on the nature and causes of missed nursing care and adds an explanatory variable: unit-level nurse workload (patient turnover percentage). The study was conducted in California, which legally mandates nurse staffing ratios. Findings demonstrated no significant relationship between patient turnover and missed nursing care.

Mercury concentrations in lentic fish populations related to ecosystem and watershed characteristics

Treesearch

Andrew L. Rypel

2010-01-01

Predicting mercury (Hg) concentrations of fishes at large spatial scales is a fundamental environmental challenge with the potential to improve human health. In this study, mercury concentrations were examined for five species across 161 lakes and ecosystem, and watershed parameters were investigated as explanatory variables in statistical models. For all species, Hg...

A Strategy for Assessing the Impact of Time-Varying Family Risk Factors on High School Dropout

ERIC Educational Resources Information Center

Randolph, Karen A.; Fraser, Mark W.; Orthner, Dennis K.

2006-01-01

Human behavior is dynamic, influenced by changing situations over time. Yet the impact of the dynamic nature of important explanatory variables on outcomes has only recently begun to be estimated in developmental models. Using a risk factor perspective, this article demonstrates the potential benefits of regressing time-varying outcome measures on…

Nature and Nurture by Definition Means Both: A Response to Males

ERIC Educational Resources Information Center

DeLisi, Matt; Wright, John Paul; Vaughn, Michael G.; Beaver, Kevin M.

2010-01-01

Recognition of the interplay between nature and nurture is decades old in fields such as psychiatry, but other fields in the social sciences continue to be hampered by the idea that social and biological variables compete for explanatory relevance. In a recent study of the adolescent brain and risk taking, Males critiqued biologically oriented…

Broken Homes: Stable Risk, Changing Reasons, Changing Forms.

ERIC Educational Resources Information Center

Sweetser, Dorrian Apple

1985-01-01

Cohort membership and two measures of social disadvantage were used as explanatory variables in analysis of the risk of growing up in a broken home and of the living arrangements of children with broken homes. The risk of a broken home by age 16 proved to be stable across cohorts and greater for those from disadvantaged homes. (Author/BL)

Calibration between Undergraduate Students' Prediction of and Actual Performance: The Role of Gender and Performance Attributions

ERIC Educational Resources Information Center

Gutierrez, Antonio P.; Price, Addison F.

2017-01-01

This study investigated changes in male and female students' prediction and postdiction calibration accuracy and bias scores, and the predictive effects of explanatory styles on these variables beyond gender. Seventy undergraduate students rated their confidence in performance before and after a 40-item exam. There was an improvement in students'…

12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications

Code of Federal Regulations, 2013 CFR

2013-01-01

....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...

12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications

Code of Federal Regulations, 2011 CFR

2011-01-01

....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...

Comparing the Utility of the 2000 and 2005 Carnegie Classification Systems in Research on Students' College Experiences and Outcomes

ERIC Educational Resources Information Center

McCormick, Alexander C.; Pike, Gary R.; Kuh, George D.; Chen, Pu-Shih Daniel

2009-01-01

This study compares the explanatory power of the 2000 edition of Carnegie Classification, the 2005 revision of the classification, and selected variables underlying Carnegie's expanded 2005 classification system using data from the National Survey of Student Engagement's spring 2004 administration. Results indicate that the 2000 and 2005…

Location and Lifestyle: The Comparative Explanatory Ability of Urbanism and Rurality

ERIC Educational Resources Information Center

Lowe, George D.; Peek, Charles W.

1974-01-01

The article focuses on 2 questions pivotal to the issue of rural-urban differences: 1) "Do attitudinal differences remain among the rural and urban residents independent of differences generated by other potent variables?"; and 2) "Will any increase in the predictive utility of rurality be generated by use of a composite definition (residence plus…

Five Decades of Educational Assortative Mating in 10 East Asian Societies

ERIC Educational Resources Information Center

Smits, Jeroen; Park, Hyunjoon

2009-01-01

We study trends in educational homogamy at six boundaries in the educational structure of 10 East-Asian societies and explain its variation using explanatory variables at the country, cohort and boundary level. Educational homogamy was higher at the higher boundaries in the educational structure. Since the 1950s it decreased at all but the lowest…

Different Pathways to Juvenile Delinquency: Characteristics of Early and Late Starters in a Sample of Previously Incarcerated Youth

ERIC Educational Resources Information Center

Alltucker, Kevin W.; Bullis, Michael; Close, Daniel; Yovanoff, Paul

2006-01-01

We examined the differences between early and late start juvenile delinquents in a sample of 531 previously incarcerated youth in Oregon's juvenile justice system. Data were analyzed with logistic regression to predict early start delinquency based on four explanatory variables: foster care experience, family criminality, special education…

Dealing with Item Nonresponse in Large-Scale Cognitive Assessments: The Impact of Missing Data Methods on Estimated Explanatory Relationships

ERIC Educational Resources Information Center

Köhler, Carmen; Pohl, Steffi; Carstensen, Claus H.

2017-01-01

Competence data from low-stakes educational large-scale assessment studies allow for evaluating relationships between competencies and other variables. The impact of item-level nonresponse has not been investigated with regard to statistics that determine the size of these relationships (e.g., correlations, regression coefficients). Classical…

Psychosocial Development in a Danish Population of Children with Cochlear Implants and Deaf and Hard-of-Hearing Children

ERIC Educational Resources Information Center

Dammeyer, Jesper

2010-01-01

Research has shown a prevalence of psychosocial difficulties ranging from about 20% to 50% among children with hearing loss. This study evaluates the prevalence of psychosocial difficulties in a Danish population in relation to different explanatory variables. Five scales and questionnaires measuring sign language, spoken language, hearing…

Predicting Item Difficulty of Science National Curriculum Tests: The Case of Key Stage 2 Assessments

ERIC Educational Resources Information Center

El Masri, Yasmine H.; Ferrara, Steve; Foltz, Peter W.; Baird, Jo-Anne

2017-01-01

Predicting item difficulty is highly important in education for both teachers and item writers. Despite identifying a large number of explanatory variables, predicting item difficulty remains a challenge in educational assessment with empirical attempts rarely exceeding 25% of variance explained. This paper analyses 216 science items of key stage…

Basic Math Skills and Performance in an Introductory Economics Class

ERIC Educational Resources Information Center

Ballard, Charles L.; Johnson, Marianne F.

2004-01-01

The authors measure math skills with a broader set of explanatory variables than have been used in previous studies. To identify what math skills are important for student success in introductory microeconomics, they examine (1) the student's score on the mathematics portion of the ACT Assessment Test, (2) whether the student has taken calculus,…

A Question of Effectiveness: Recruitment of Special Educators within High School Peer Support Groups

ERIC Educational Resources Information Center

Zascavage, Victoria; Winterman, Kathy; Armstrong, Philip; Schroeder-Steward, Jennifer

2008-01-01

The present study combines information about support groups for students with disabilities from 187 East Texas high schools with explanatory variables taken from data of the Texas Education Agency Academic Excellence Indicator System. This study is a tangential section of a larger study on the influence of peer support groups in East Texas…

12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications

Code of Federal Regulations, 2012 CFR

2012-01-01

....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...

12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications

Code of Federal Regulations, 2014 CFR

2014-01-01

....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...

Use of Continuous Monitors and Autosamplers to Predict Unmeasured Water-Quality Constituents in Tributaries of the Tualatin River, Oregon

USGS Publications Warehouse

Anderson, Chauncey W.; Rounds, Stewart A.

2010-01-01

Management of water quality in streams of the United States is becoming increasingly complex as regulators seek to control aquatic pollution and ecological problems through Total Maximum Daily Load programs that target reductions in the concentrations of certain constituents. Sediment, nutrients, and bacteria, for example, are constituents that regulators target for reduction nationally and in the Tualatin River basin, Oregon. These constituents require laboratory analysis of discrete samples for definitive determinations of concentrations in streams. Recent technological advances in the nearly continuous, in situ monitoring of related water-quality parameters has fostered the use of these parameters as surrogates for the labor intensive, laboratory-analyzed constituents. Although these correlative techniques have been successful in large rivers, it was unclear whether they could be applied successfully in tributaries of the Tualatin River, primarily because these streams tend to be small, have rapid hydrologic response to rainfall and high streamflow variability, and may contain unique sources of sediment, nutrients, and bacteria. This report evaluates the feasibility of developing correlative regression models for predicting dependent variables (concentrations of total suspended solids, total phosphorus, and Escherichia coli bacteria) in two Tualatin River basin streams: one draining highly urbanized land (Fanno Creek near Durham, Oregon) and one draining rural agricultural land (Dairy Creek at Highway 8 near Hillsboro, Oregon), during 2002-04. An important difference between these two streams is their response to storm runoff; Fanno Creek has a relatively rapid response due to extensive upstream impervious areas and Dairy Creek has a relatively slow response because of the large amount of undeveloped upstream land. Four other stream sites also were evaluated, but in less detail. Potential explanatory variables included continuously monitored streamflow (discharge), stream stage, specific conductance, turbidity, and time (to account for seasonal processes). Preliminary multiple-regression models were identified using stepwise regression and Mallow's Cp, which maximizes regression correlation coefficients and accounts for the loss of additional degrees of freedom when extra explanatory variables are used. Several data scenarios were created and evaluated for each site to assess the representativeness of existing monitoring data and autosampler-derived data, and to assess the utility of the available data to develop robust predictive models. The goodness-of-fit of candidate predictive models was assessed with diagnostic statistics from validation exercises that compared predictions against a subset of the available data. The regression modeling met with mixed success. Functional model forms that have a high likelihood of success were identified for most (but not all) dependent variables at each site, but there were limitations in the available datasets, notably the lack of samples from high-flows. These limitations increase the uncertainty in the predictions of the models and suggest that the models are not yet ready for use in assessing these streams, particularly under high-flow conditions, without additional data collection and recalibration of model coefficients. Nonetheless, the results reveal opportunities to use existing resources more efficiently. Baseline conditions are well represented in the available data, and, for the most part, the models reproduced these conditions well. Future sampling might therefore focus on high flow conditions, without much loss of ability to characterize the baseline. Seasonal cycles, as represented by trigonometric functions of time, were not significant in the evaluated models, perhaps because the baseline conditions are well characterized in the datasets or because the other explanatory variables indirectly incorporate seasonal aspects. Multicollinearity among independent variabl

Socioeconomic Inequalities in Mortality and Repeated Measurement of Explanatory Risk Factors in a 25 Years Follow-Up

PubMed Central

Skalická, Věra; Ringdal, Kristen; Witvliet, Margot I.

2015-01-01

Background Socioeconomic inequalities in mortality can be explained by different groups of risk factors. However, little is known whether repeated measurement of risk factors can provide better explanation of socioeconomic inequalities in health. Our study examines the extent to which relative educational and income inequalities in mortality might be explained by explanatory risk factors (behavioral, psychosocial, biomedical risk factors and employment) measured at two points in time, as compared to one measurement at baseline. Methods and Findings From the Norwegian total county population-based HUNT Study (years 1984–86 and 1995–1997, respectively) 61 513 men and women aged 25–80 (82.5% of all enrolled) were followed-up for mortality in 25 years until 2009, employing a discrete time survival analysis. Socioeconomic inequalities in mortality were observed. As compared to their highest socioeconomic counterparts, the lowest educated men had an OR (odds ratio) of 1.41 (95% CI 1.29–1.55) and for the lowest income quartile OR = 1.59 (1.48–1.571), for women OR = 1.35 (1.17–1.55), and OR = 1.40 (1.28–1.52), respectively. Baseline explanatory variables attenuated the association between education and income with mortality by 54% and 54% in men, respectively, and by 69% and 18% in women. After entering time-varying variables, this attainment increased to 63% and 59% in men, respectively, and to 25% (income) in women, with no improvement in regard to education in women. Change in biomedical factors and employment did not amend the explanation. Conclusions Addition of a second measurement for risk factors provided only a modest improvement in explaining educational and income inequalities in mortality in Norwegian men and women. Accounting for change in behavior provided the largest improvement in explained inequalities in mortality for both men and women, as compared to measurement at baseline. Psychosocial factors explained the largest share of income inequalities in mortality for men, but repeated measurement of these factors contributed only to modest improvement in explanation. Further comparative research on the relative importance of explanatory pathways assessed over time is needed. PMID:25853571

Socioeconomic inequalities in mortality and repeated measurement of explanatory risk factors in a 25 years follow-up.

PubMed

Skalická, Věra; Ringdal, Kristen; Witvliet, Margot I

2015-01-01

Socioeconomic inequalities in mortality can be explained by different groups of risk factors. However, little is known whether repeated measurement of risk factors can provide better explanation of socioeconomic inequalities in health. Our study examines the extent to which relative educational and income inequalities in mortality might be explained by explanatory risk factors (behavioral, psychosocial, biomedical risk factors and employment) measured at two points in time, as compared to one measurement at baseline. From the Norwegian total county population-based HUNT Study (years 1984-86 and 1995-1997, respectively) 61 513 men and women aged 25-80 (82.5% of all enrolled) were followed-up for mortality in 25 years until 2009, employing a discrete time survival analysis. Socioeconomic inequalities in mortality were observed. As compared to their highest socioeconomic counterparts, the lowest educated men had an OR (odds ratio) of 1.41 (95% CI 1.29-1.55) and for the lowest income quartile OR = 1.59 (1.48-1.571), for women OR = 1.35 (1.17-1.55), and OR = 1.40 (1.28-1.52), respectively. Baseline explanatory variables attenuated the association between education and income with mortality by 54% and 54% in men, respectively, and by 69% and 18% in women. After entering time-varying variables, this attainment increased to 63% and 59% in men, respectively, and to 25% (income) in women, with no improvement in regard to education in women. Change in biomedical factors and employment did not amend the explanation. Addition of a second measurement for risk factors provided only a modest improvement in explaining educational and income inequalities in mortality in Norwegian men and women. Accounting for change in behavior provided the largest improvement in explained inequalities in mortality for both men and women, as compared to measurement at baseline. Psychosocial factors explained the largest share of income inequalities in mortality for men, but repeated measurement of these factors contributed only to modest improvement in explanation. Further comparative research on the relative importance of explanatory pathways assessed over time is needed.

Episodic acidification of small streams in the northeastern united states: Fish mortality in field bioassays

USGS Publications Warehouse

Van Sickle, J.; Baker, J.P.; Simonin, H.A.; Baldigo, Barry P.; Kretser, W.A.; Sharpe, W.E.

1996-01-01

In situ bioassays were performed as part of the Episodic Response Project, to evaluate the effects of episodic stream acidification on mortality of brook trout (Salvelinus fontinalis) and forage fish species. We report the results of 122 bioassays in 13 streams of the three study regions: the Adirondack mountains of New York, the Catskill mountains of New York, and the Northern Appalachian Plateau of Pennsylvania. Bioassays during acidic episodes had significantly higher mortality than did bioassays conducted under nonacidic conditions, but there was little difference in mortality rates in bioassays experiencing acidic episodes and those experiencing acidic conditions throughout the test period. Multiple logistic regression models were used to relate bioassay mortality rates to summary statistics of time-varying stream chemistry (inorganic monomeric aluminum, calcium, pH, and dissolved organic carbon) estimated for the 20-d bioassay periods. The large suite of candidate regressors also included biological, regional, and seasonal factors, as well as several statistics summarizing various features of aluminum exposure duration and magnitude. Regressor variable selection and model assessment were complicated by multicol-linearity and overdispersion. For the target fish species, brook trout, bioassay mortality was most closely related to time-weighted median inorganic aluminum. Median Ca and minimum pH offered additional explanatory power, as did stream-specific aluminum responses. Due to high multicollinearity, the relative importance of different aluminum exposure duration and magnitude variables was difficult to assess, but these variables taken together added no significant explanatory power to models already containing median aluminum. Between 59 and 79% of the variation in brook trout mortality was explained by models employing between one and five regressors. Simpler models were developed for smaller sets of bioassays that tested slimy and mottled sculpin (Cottus cognatus and C. bairdi) as well as blacknose dace (Rhinichthys atratulus). For these forage species a single inorganic aluminum exposure variable successfully accounted for 86-98% of the observed mortality. Even though field bioassays showed evidence of multiple toxicity factors, model results suggest that adequate mortality predictions can be obtained from a single index of inorganic Al concentrations during exposure periods.

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

10 CFR Appendix I to Part 1050 - DOE Form 3735.2-Foreign Gifts Statement

Code of Federal Regulations, 2010 CFR

2010-01-01

... should always be indicated in item 1; if the employee is the recipient of the gift then items 5 and 6... information should be included in items 5 and 6. Item 2.Self explanatory. Items 3 and 4.The Office or Division... employee or a spouse or dependent. Items 5 and 6.See above, Item 1. Item 7.Self explanatory. Item 8.Self...

Perceived Risks and Normative Beliefs as Explanatory Models for College Student Alcohol Involvement: An Assessment of a Campus with Conventional Alcohol Control Policies and Enforcement Practices

ERIC Educational Resources Information Center

Lewis, Todd F.; Thombs, Dennis L.

2005-01-01

The aim of this study was to conduct a multivariate assessment of college student drinking motivations at a campus with conventional alcohol control policies and enforcement practices, including the establishment and dissemination of alcohol policies and the use of warnings to arouse fear of sanctions. Two explanatory models were compared:…

Applying the Expectancy-Value Model to understand health values.

PubMed

Zhang, Xu-Hao; Xie, Feng; Wee, Hwee-Lin; Thumboo, Julian; Li, Shu-Chuen

2008-03-01

Expectancy-Value Model (EVM) is the most structured model in psychology to predict attitudes by measuring attitudinal attributes (AAs) and relevant external variables. Because health value could be categorized as attitude, we aimed to apply EVM to explore its usefulness in explaining variances in health values and investigate underlying factors. Focus group discussion was carried out to identify the most common and significant AAs toward 5 different health states (coded as 11111, 11121, 21221, 32323, and 33333 in EuroQol Five-Dimension (EQ-5D) descriptive system). AAs were measured in a sum of multiplications of subjective probability (expectancy) and perceived value of attributes with 7-point Likert scales. Health values were measured using visual analog scales (VAS, range 0-1). External variables (age, sex, ethnicity, education, housing, marital status, and concurrent chronic diseases) were also incorporated into survey questionnaire distributed by convenience sampling among eligible respondents. Univariate analyses were used to identify external variables causing significant differences in VAS. Multiple linear regression model (MLR) and hierarchical regression model were used to investigate the explanatory power of AAs and possible significant external variable(s) separately or in combination, for each individual health state and a mixed scenario of five states, respectively. Four AAs were identified, namely, "worsening your quality of life in terms of health" (WQoL), "adding a burden to your family" (BTF), "making you less independent" (MLI) and "unable to work or study" (UWS). Data were analyzed based on 232 respondents (mean [SD] age: 27.7 [15.07] years, 49.1% female). Health values varied significantly across 5 health states, ranging from 0.12 (33333) to 0.97 (11111). With no significant external variables identified, EVM explained up to 62% of the variances in health values across 5 health states. The explanatory power of 4 AAs were found to be between 13% and 28% in separate MLR models (P < 0.05). When data were analyzed for each health state, variances in health values became small and explanatory power of EVM was reduced to a range between 8% and 23%. EVM was useful in explaining variances of health values and predicting important factors. Its power to explain small variances might be restricted due to limitations of 7-point Likert scale to measure AAs accurately. With further improvement and validation of a compatible continuous scale for more accurate measurement, EVM is expected to explain health values to a larger extent.

Phytoplankton dynamics of a subtropical reservoir controlled by the complex interplay among hydrological, abiotic, and biotic variables.

PubMed

Kuo, Yi-Ming; Wu, Jiunn-Tzong

2016-12-01

This study was conducted to identify the key factors related to the spatiotemporal variations in phytoplankton abundance in a subtropical reservoir from 2006 to 2010 and to assist in developing strategies for water quality management. Dynamic factor analysis (DFA), a dimension-reduction technique, was used to identify interactions between explanatory variables (i.e., environmental variables) and abundance (biovolume) of predominant phytoplankton classes. The optimal DFA model significantly described the dynamic changes in abundances of predominant phytoplankton groups (including dinoflagellates, diatoms, and green algae) at five monitoring sites. Water temperature, electrical conductivity, water level, nutrients (total phosphorus, NO 3 -N, and NH 3 -N), macro-zooplankton, and zooplankton were the key factors affecting the dynamics of aforementioned phytoplankton. Therefore, transformations of nutrients and reactions between water quality variables and aforementioned processes altered by hydrological conditions may also control the abundance dynamics of phytoplankton, which may represent common trends in the DFA model. The meandering shape of Shihmen Reservoir and its surrounding rivers caused a complex interplay between hydrological conditions and abiotic and biotic variables, resulting in phytoplankton abundance that could not be estimated using certain variables. Additional water quality and hydrological variables at surrounding rivers and monitoring plans should be executed a few days before and after reservoir operations and heavy storm, which would assist in developing site-specific preventive strategies to control phytoplankton abundance.

Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia

PubMed Central

Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe

2017-01-01

Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable—the time elapsed since the first flooding of the year—was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10–1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25–3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia. PMID:28704461

Height loss in elderly women is preceded by osteoporosis and is associated with digestive problems and urinary incontinence.

PubMed

Berecki-Gisolf, J; Spallek, M; Hockey, R; Dobson, A

2010-03-01

This study explores risk factors for height loss and consequences in terms of health and wellbeing, in older women. Osteoporosis, low body-mass index, being born in Europe and using medications for both sleep and anxiety were risk factors for height loss. Height loss was associated with digestive problems; excessive height loss was also associated with urinary stress-incontinence and a decline in self-rated health. Height loss is associated with osteoporosis, but little is known about its consequences. We aimed to examine the risk factors for height loss and the symptoms associated with height loss. Elderly participants of the Australian Longitudinal Study on Women's Health (aged 70-75 in 1996) who provided data on height at any two consecutive surveys (held in 1996, 1999, 2002, and 2005) were included (N = 9,852). A regression model was fitted with height loss as the outcome and sociodemographics, osteoporosis, and other risk factors as explanatory variables. Symptoms related to postural changes or raised intra-abdominal pressure were analyzed using height loss as an explanatory variable. Over 9 years, average height loss per year was -0.12% (95% confidence intervals [95% CI] = -0.13 to -0.12) of height at baseline. Height loss was greater among those with osteoporosis and low body mass index and those taking medications for sleep and anxiety. After adjusting for confounders, symptoms associated with height loss of > or =2% were heartburn/indigestion (odds ratio [OR] = 1.19, 95% CI = 1.01 to 1.40), constipation (OR = 1.18, 95% CI = 1.01 to 1.37), and urinary stress incontinence (OR = 1.20, 95% CI = 1.02 to 1.41). These findings highlight the importance of monitoring height among the elderly in general practice and targeting associated symptoms.

Freshwater Vulnerability to Nitrate Contamination as an Indicator of Sustainability and Resilience within the Water-Energy-Food Nexus of the California Coastal Basins

NASA Astrophysics Data System (ADS)

Nanus, L.; Geyer, G.; Gurdak, J. J.; Orencio, P. M.; Endo, A.; Taniguchi, M.

2014-12-01

The California Coastal Basin (CCB) aquifers are representative of many coastal aquifers that are vulnerable to nonpoint-source (NPS) contamination from intense agriculture and increased urbanization combined with historical groundwater use and overdraft conditions. Overdraft has led to seawater intrusion along parts of the central California coast, which negatively affects food production because of high salinity concentrations in groundwater used for irrigation. Recent drought conditions in California have led to an increased need to further understand freshwater sustainability and resilience within the water-energy-food (WEF) nexus. Assessing the vulnerability of NPS contamination in groundwater provides valuable information for optimal resource management and policy. Vulnerability models of nitrate contamination in the CCB were developed as one of many indicators to evaluate risk in terms of susceptibility of the physical environment at local and regional scales. Multivariate logistic regression models were developed to predict the probability of NPS nitrate contamination in recently recharged groundwater and to identify significant explanatory variables as controlling factors in the CCB. Different factors were found to be significant in the sub-regions of the CCB and issues of scale are important. For example, land use is scale dependent because of the difference in land management practices between the CCB sub-regions. However, dissolved oxygen concentrations in groundwater, farm fertilizer, and soil thickness are scale invariant because they are significant both regionally and sub-regionally. Thus, the vulnerability models for the CCB show that different explanatory variables are scale invariant. This finding has important implications for accurately quantifying linkages between vulnerability and consequences within the WEF nexus, including inherent tradeoffs in water and food production in California and associated impacts on the local and regional economy, governance, environment, and society at multiple scales.

Pre-post evaluation of effects of a titanium dioxide coating on environmental contamination of an intensive care unit: the TITANIC study.

PubMed

de Jong, B; Meeder, A M; Koekkoek, K W A C; Schouten, M A; Westers, P; van Zanten, A R H

2018-07-01

Among patients admitted to European hospitals or intensive care units (ICUs), 5.7% and 19.5% will encounter healthcare-associated infections (HAIs), respectively, and antimicrobial resistance is emerging. As hospital surfaces are contaminated with potentially pathogenic bacteria, environmental cleanliness is an essential aspect to reduce HAIs. To address the efficacy of a titanium dioxide coating in reducing the microbial colonization of environmental surfaces in an ICU. A prospective, controlled, single-centre pilot study was conducted to examine the effect of a titanium dioxide coating on the microbial colonization of surfaces in an ICU. During the pre- and post-intervention periods, surfaces were cultured with agar contact plates (BBL RODAC plates). Factors that were potentially influencing the bacterial colonization of surfaces were recorded. A repeated measurements analysis within a hierarchic multi-level framework was used to analyse the effect of the intervention, controlling for the explanatory variables. The mean ratio for the total number of colony-forming units (cfus) in a room between the pre- and post-intervention periods was 0.86 (standard deviation 0.57). The optimal model included the following explanatory variables: intervention (P=0.065), week (P=0.002), culture surfaces (P<0.001), ICU room (P=0.039), and interaction between intervention and week (P=0.002) and between week and culture surfaces (P=0.031). The effect of the intervention on the number of cfus from all culture plates in Week 4 between the pre- and post-intervention periods was -0.47 (95% confidence interval -0.24 to - 0.70). This study found that a titanium dioxide coating had no effect on the microbial colonization of surfaces in an ICU. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Willingness to pay for a dengue vaccine and its associated determinants in Indonesia: A community-based, cross-sectional survey in Aceh.

PubMed

Harapan, Harapan; Anwar, Samsul; Bustamam, Aslam; Radiansyah, Arsil; Angraini, Pradiba; Fasli, Riny; Salwiyadi, Salwiyadi; Bastian, Reza Akbar; Oktiviyari, Ade; Akmal, Imaduddin; Iqbalamin, Muhammad; Adil, Jamalul; Henrizal, Fenni; Darmayanti, Darmayanti; Mahmuda, Mahmuda; Mudatsir, Mudatsir; Imrie, Allison; Sasmono, R Tedjo; Kuch, Ulrich; Shkedy, Ziv; Pramana, Setia

2017-02-01

Vaccination strategies are being considered as a part of dengue prevention programs in endemic countries. To accelerate the introduction of dengue vaccine into the public sector program and private markets, understanding the private economic benefits of a dengue vaccine is therefore essential. The aim of this study was to assess the willingness to pay (WTP) for a dengue vaccine among community members in Indonesia and its associated explanatory variables. A community-based, cross-sectional survey was conducted in nine regencies of Aceh province, Indonesia, from November 2014 to March 2015. A pre-tested validated questionnaire was used to facilitate the interviews. To assess the explanatory variables influencing participants' WTP for a dengue vaccine, a linear regression analysis was employed. We interviewed 677 healthy community members; 476 participants (87.5% of the total) were included in the final analysis. An average individual was willing to pay around US-$ 4 (mean: US-$ 4.04; median: US-$ 3.97) for a dengue vaccine. Our final multivariate model revealed that working as a civil servant, living in the city, and having good knowledge on dengue viruses, a good attitude towards dengue, and good preventive practice against dengue virus infection were associated with a higher WTP (P<0.05). Our model suggests that marketing efforts should be directed to community members who are working in the suburbs especially as farmers. In addition, the results of our study underscore the need for low-cost quality vaccines, public sector subsidies for vaccinations, and intensifying efforts to further educate and encourage households regarding other dengue preventive measures, using trusted individuals as facilitators. Copyright © 2016 Elsevier B.V. All rights reserved.

The role of ability, motivation, and opportunity to work in the transition from work to early retirement--testing and optimizing the Early Retirement Model.

PubMed

de Wind, Astrid; Geuskens, Goedele A; Ybema, Jan Fekke; Bongers, Paulien M; van der Beek, Allard J

2015-01-01

Determinants in the domains health, job characteristics, skills, and social and financial factors may influence early retirement through three central explanatory variables, namely, the ability, motivation, and opportunity to work. Based on the literature, we created the Early Retirement Model. This study aims to investigate whether data support the model and how it could be improved. Employees aged 58-62 years (N=1862), who participated in the first three waves of the Dutch Study on Transitions in Employment, Ability and Motivation (STREAM) were included. Determinants were assessed at baseline, central explanatory variables after one year, and early retirement after two years. Structural equation modeling was applied. Testing the Early Retirement Model resulted in a model with good fit. Health, job characteristics, skills, and social and financial factors were related to the ability, motivation and/or opportunity to work (significant β range: 0.05-0.31). Lower work ability (β=-0.13) and less opportunity to work (attitude colleagues and supervisor about working until age 65: β=-0.24) predicted early retirement, whereas the motivation to work (work engagement) did not. The model could be improved by adding direct effects of three determinants on early retirement, ie, support of colleagues and supervisor (β=0.14), positive attitude of the partner with respect to early retirement (β=0.15), and not having a partner (β=-0.13). The Early Retirement Model was largely supported by the data but could be improved. The prolongation of working life might be promoted by work-related interventions focusing on health, work ability, the social work climate, social norms on prolonged careers, and the learning environment.

Unmodeled observation error induces bias when inferring patterns and dynamics of species occurrence via aural detections

USGS Publications Warehouse

McClintock, Brett T.; Bailey, Larissa L.; Pollock, Kenneth H.; Simons, Theodore R.

2010-01-01

The recent surge in the development and application of species occurrence models has been associated with an acknowledgment among ecologists that species are detected imperfectly due to observation error. Standard models now allow unbiased estimation of occupancy probability when false negative detections occur, but this is conditional on no false positive detections and sufficient incorporation of explanatory variables for the false negative detection process. These assumptions are likely reasonable in many circumstances, but there is mounting evidence that false positive errors and detection probability heterogeneity may be much more prevalent in studies relying on auditory cues for species detection (e.g., songbird or calling amphibian surveys). We used field survey data from a simulated calling anuran system of known occupancy state to investigate the biases induced by these errors in dynamic models of species occurrence. Despite the participation of expert observers in simplified field conditions, both false positive errors and site detection probability heterogeneity were extensive for most species in the survey. We found that even low levels of false positive errors, constituting as little as 1% of all detections, can cause severe overestimation of site occupancy, colonization, and local extinction probabilities. Further, unmodeled detection probability heterogeneity induced substantial underestimation of occupancy and overestimation of colonization and local extinction probabilities. Completely spurious relationships between species occurrence and explanatory variables were also found. Such misleading inferences would likely have deleterious implications for conservation and management programs. We contend that all forms of observation error, including false positive errors and heterogeneous detection probabilities, must be incorporated into the estimation framework to facilitate reliable inferences about occupancy and its associated vital rate parameters.

Spatial modelling of landscape aesthetic potential in urban-rural fringes.

PubMed

Sahraoui, Yohan; Clauzel, Céline; Foltête, Jean-Christophe

2016-10-01

The aesthetic potential of landscape has to be modelled to provide tools for land-use planning. This involves identifying landscape attributes and revealing individuals' landscape preferences. Landscape aesthetic judgments of individuals (n = 1420) were studied by means of a photo-based survey. A set of landscape visibility metrics was created to measure landscape composition and configuration in each photograph using spatial data. These metrics were used as explanatory variables in multiple linear regressions to explain aesthetic judgments. We demonstrate that landscape aesthetic judgments may be synthesized in three consensus groups. The statistical results obtained show that landscape visibility metrics have good explanatory power. Ultimately, we propose a spatial modelling of landscape aesthetic potential based on these results combined with systematic computation of visibility metrics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Techniques used to identify tornado producing thunderstorms using geosynchronous satellite data

NASA Technical Reports Server (NTRS)

Schrab, Kevin J.; Anderson, Charles E.; Monahan, John F.

1992-01-01

Satellite imagery in the outbreak region in the time prior to and during tornado occurrence was examined in detail to obtain descriptive characteristics of the anvil plume. These characteristics include outflow strength (UMAX), departure of anvil centerline from the storm relative ambient wind (MDA), storm relative ambient wind (SRAW), and maximum surface vorticity (SFCVOR). It is shown that by using satellite derived parameters which characterize the flow field in the anvil region, the occurrence and intensity of tornadoes, which the parent thunderstorm produces, can be identified. Analysis of the censored regression models revealed that the five explanatory variables (UMAX, MDA, SRAW, UMAX-2, and SFCVOR) were all significant predictors in the identification of tornadic intensity of a particular thunderstorm.

Predictors of elderly mortality: health status, socioeconomic characteristics and social determinants of health.

PubMed

Mete, Cem

2005-02-01

This paper uses longitudinal survey data from Taiwan to investigate the predictors of elderly mortality. The empirical analysis confirms a relationship between socioeconomic characteristics and mortality, but this relationship weakens considerably when estimates are conditional on the health status at the time of the first wave survey. In terms of predictive power, the models with an activities of daily living index fare better (as opposed to models with self-evaluated health or self-reported illnesses). Having said that there is a payoff to the consideration of self-evaluated health jointly with other 'objective' health indicators. Other findings include a strong association between life satisfaction and survival, which prevails even after controlling for other explanatory variables. Copyright (c) 2004 John Wiley & Sons, Ltd.

Modeling sheep pox disease from the 1994-1998 epidemic in Evros Prefecture, Greece.

PubMed

Malesios, C; Demiris, N; Abas, Z; Dadousis, K; Koutroumanidis, T

2014-10-01

Sheep pox is a highly transmissible disease which can cause serious loss of livestock and can therefore have major economic impact. We present data from sheep pox epidemics which occurred between 1994 and 1998. The data include weekly records of infected farms as well as a number of covariates. We implement Bayesian stochastic regression models which, in addition to various explanatory variables like seasonal and environmental/meteorological factors, also contain serial correlation structure based on variants of the Ornstein-Uhlenbeck process. We take a predictive view in model selection by utilizing deviance-based measures. The results indicate that seasonality and the number of infected farms are important predictors for sheep pox incidence. Copyright © 2014 Elsevier Ltd. All rights reserved.

Coping with Stress and Types of Burnout: Explanatory Power of Different Coping Strategies

PubMed Central

Montero-Marin, Jesus; Prado-Abril, Javier; Piva Demarzo, Marcelo Marcos; Gascon, Santiago; García-Campayo, Javier

2014-01-01

Background Burnout occurs when professionals use ineffective coping strategies to try to protect themselves from work-related stress. The dimensions of ‘overload’, ‘lack of development’ and ‘neglect’, belonging to the ‘frenetic’, ‘under-challenged’ and ‘worn-out’ subtypes, respectively, comprise a brief typological definition of burnout. The aim of the present study was to estimate the explanatory power of the different coping strategies on the development of burnout subtypes. Methods This was a cross-sectional survey with a random sample of university employees, stratified by occupation (n = 429). Multivariate linear regression models were constructed between the ‘Burnout Clinical Subtypes Questionnaire’, with its three dimensions –overload, lack of development and neglect– as dependent variables, and the ‘Coping Orientation for Problem Experiences’, with its fifteen dimensions, as independent variables. Adjusted multiple determination coefficients and beta coefficients were calculated to evaluate and compare the explanatory capacity of the different coping strategies. Results The ‘Coping Orientation for Problem Experiences’ subscales together explained 15% of the ‘overload’ (p<0.001), 9% of the ‘lack of development’ (p<0.001), and 21% of the ‘neglect’ (p<0.001). ‘Overload’ was mainly explained by ‘venting of emotions’ (Beta = 0.34; p<0.001); ‘lack of development’ by ‘cognitive avoidance’ (Beta = 0.21; p<0.001); and ‘neglect’ by ‘behavioural disengagement’ (Beta = 0.40; p<0.001). Other interesting associations were observed. Conclusions These findings further our understanding of the way in which the effectiveness of interventions for burnout may be improved, by influencing new treatments and preventive programmes using features of the strategies for handling stress in the workplace. PMID:24551223

Comparability of nurse staffing measures in examining the relationship between RN staffing and unit-acquired pressure ulcers: a unit-level descriptive, correlational study.

PubMed

Choi, JiSun; Staggs, Vincent S

2014-10-01

Various staffing measures have been used in examining the relationship between nurse staffing and patient outcomes. Little research has been conducted to compare these measures based on their explanatory power as predictors of nursing-sensitive outcomes. In this study, both administrative and nurse-reported measures were examined. Administrative measures included registered nurse (RN) skill mix and three versions of nursing hours per patient day (HPPD); nurse-reported measures included RN-reported number of assigned patients and RN-perceived staffing adequacy. To examine correlations among six nurse staffing measures and to compare their explanatory power in relation to unit-acquired pressure ulcers (UAPUs). Descriptive, correlational study. 2397 nursing units in 409 U.S. acute care hospitals. Random-intercept logistic regression analyses were performed using 2011 data from a national database. Relationships between nurse staffing measures and UAPU occurrences were examined in eight models, each with one or more staffing measures as predictors. Characteristics of nursing units (RN workgroup education level and RN workgroup unit tenure) and hospitals (size, teaching status, and Magnet status) were included as control variables. Two versions of HPPD (total nursing HPPD and RN HPPD) and RN skill mix were significantly correlated with RN-reported number of assigned patients (r range=-0.87 to -0.75). These staffing measures had weaker correlations with RN-perceived staffing adequacy (r range=0.16 to 0.23). Of the six staffing variables, only RN-perceived staffing adequacy and RN skill mix were significantly associated with UAPU odds, the former being the better predictor. Although RN-perceived staffing adequacy was not highly correlated with administrative measures of HPPD and RN skill mix, it was the strongest predictor of UAPU occurrences. RN-perceived staffing adequacy can serve as a more appropriate measure of staffing for nursing-sensitive outcomes research than administrative measures, as it reflects relevant aspects of staffing and involves an implicit adjustment for patient acuity. Copyright © 2014 Elsevier Ltd. All rights reserved.

[Evaluation of continuous education: from the satisfaction to the impact. With regard to a formative programme in minor surgery in a health area].

PubMed

Martín Fernández, J; Martínez Marcos, M; Ferrándiz Santos, J

2001-04-30

To compare the evaluation of reaction of an activity of continuous education (CE) in minor surgery (MS), with the impact in the realization of MS in a health area. Observational cross-sectional study. Setting. 27 centers in a health area that offer MS between their services. The valuation was studied in a scale from 1 to 10, of 9 theoretical-practical activities of CE in MS and the consideration of its utility. The number of activities of MS (NMS) carried out in all the units, was picked up during one year, and a model of lineal regression was built. The independent variable was the NMS, and the explanatory ones the assistance pressure (AP), the postgraduate formation (PF), the staff of the unit, the equipment (E), and the carried out CE. The valuation of the CE had a median of 8 (with interquartile range 1), 85.1% of the people who realized CE in MS said that this would be of utility. However in the explanatory regression model the PF was the only significant variable (beta = 6.7; 95% CI, 0.12-12.22). Neither the CE, nor the AP, nor the E, explained the variability among the NMS. The CE in MS with conventional methodology has a very positive reaction evaluation, but its impact in the later realization of MS is not significant.

Empirical analysis of farmers' drought risk perception: objective factors, personal circumstances, and social influence.

PubMed

Duinen, Rianne van; Filatova, Tatiana; Geurts, Peter; Veen, Anne van der

2015-04-01

Drought-induced water shortage and salinization are a global threat to agricultural production. With climate change, drought risk is expected to increase as drought events are assumed to occur more frequently and to become more severe. The agricultural sector's adaptive capacity largely depends on farmers' drought risk perceptions. Understanding the formation of farmers' drought risk perceptions is a prerequisite to designing effective and efficient public drought risk management strategies. Various strands of literature point at different factors shaping individual risk perceptions. Economic theory points at objective risk variables, whereas psychology and sociology identify subjective risk variables. This study investigates and compares the contribution of objective and subjective factors in explaining farmers' drought risk perception by means of survey data analysis. Data on risk perceptions, farm characteristics, and various other personality traits were collected from farmers located in the southwest Netherlands. From comparing the explanatory power of objective and subjective risk factors in separate models and a full model of risk perception, it can be concluded that farmers' risk perceptions are shaped by both rational and emotional factors. In a full risk perception model, being located in an area with external water supply, owning fields with salinization issues, cultivating drought-/salt-sensitive crops, farm revenue, drought risk experience, and perceived control are significant explanatory variables of farmers' drought risk perceptions. © 2014 Society for Risk Analysis.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Factors affecting unmet need for family planning in southern nations, nationalities and peoples region, ethiopia.

PubMed

Hailemariam, Assefa; Haddis, Fikrewold

2011-07-01

High fertility and low contraceptive prevalence characterize Southern Nations, Nationalities and Peoples Region. In such populations, unmet needs for contraception have a tendency to be high, mainly due to the effect of socio-economic and demographic variables. However, there has not been any study examining the relationship between these variables and unmet need in the region. This study, therefore, identifies the key socio- demographic determinants of unmet need for family planning in the region. The study used data from the 2000 and 2005 Ethiopian Demographic and Health Surveys. A total of 2,133 currently married women age 15-49 from the 2000 survey and 1,988 from the 2005 survey were included in the study. Unmet need for spacing, unmet need for limiting and total unmet need were used as dependent variables. Socio- demographic variables (respondent's age, age at marriage, number of living children, sex composition of living children, child mortality experience, place of residence, respondent's and partner's education, religion and work status) were treated as explanatory variables and their relative importance was examined on each of the dependent variables using multinomial and binary logistic regression models. Unmet need for contraception increased from 35.1% in 2000 to 37.4% in 2005. Unmet need for spacing remained constant at about 25%, while unmet need for limiting increased by 20% between 2000 and 2005. Age, age at marriage, number of living children, place of residence, respondent's education, knowledge of family planning, respondent's work status, being visited by a family planning worker and survey year emerged as significant factors affecting unmet need. On the other hand, number of living children, education, age and age at marriage were the only explanatory variables affecting unmet need for limiting. Number of living children, place of residence, age and age at marriage were also identified as factors affecting total unmet need for contraception. unmet need for spacing is more prevalent than unmet need for limiting. Women with unmet need for both spacing and limiting are more likely to be living in rural areas, have lower level of education, lower level of knowledge about family planning methods, have no work other than household chores, and have never been visited by a family planning worker. In order to address unmet need for family planning in the region, policy should set mechanisms to enforce the law on minimum age for marriage, improve child survival and increase educational access to females. In addition, the policy should promote awareness creation about family planning in rural areas.

The Mole as an Explanatory Device: How Do You Know a Mole if You See One? A Manual for Chemistry Students. Sample Teaching Materials: The Explanatory Modes Project.

ERIC Educational Resources Information Center

Roberts, Douglas A.

This booklet is designed to supplement the study of introductory chemistry. It deals particularly with the mole concept but also includes ideas for analyzing the kinds of statements that appear in all science textbooks and scientific writing. The material in the booklet should be studied after the completion of an introductory textbook study of…

The impact of medication regimen factors on adherence to chronic treatment: a review of literature

PubMed Central

Cohen, Jessye

2010-01-01

This article reviews recent literature in chronic illness or long-term health management including asthma, contraception, diabetes, HIV disease, and hypertension/cardiovascular disease, mental disorders, pain, and other diseases to determine the relationship between regimen factors and adherence to medications. The authors conducted an electronic literature search to detect articles published between 1998 and 2007. Articles were included if they pertained to a chronic illness or to contraception, included a clear definition of how adherence was measured, and included regimen factors as primary or secondary explanatory variables. Methodology of the studies varied greatly, as did methods of measuring adherence and regimen factors. Surprisingly few of these articles concerned (1) chronic treatment, (2) regimen factors such as dosing, pill burden, and regimen complexity, and (3) adherence measured in a clear manner. Most studies failed to use state-of-the-art methods of measuring adherence. Despite these flaws, a suggestive pattern of the importance of regimen factors, specifically dose frequency and regimen complexity, emerged from this review. PMID:18202907

Buying a Better Air Force

DTIC Science & Technology

2006-03-01

identify if an explanatory variable may have been omitted due to model misspecification ( Ramsey , 1979). The RESET test resulted in failure to...Prob > F 0.0094 This model was also regressed using Huber-White estimators. Again, the Ramsey RESET test was done to ensure relevant...Aircraft. Annapolis, MD: Naval Institute Press, 2004. Ramsey , J. B. “ Tests for Specification Errors in Classical Least-Squares Regression Analysis

Differential Use of Learning Strategies in First-Year Higher Education: The Impact of Personality, Academic Motivation, and Teaching Strategies

ERIC Educational Resources Information Center

Donche, Vincent; De Maeyer, Sven; Coertjens, Liesje; Van Daal, Tine; Van Petegem, Peter

2013-01-01

Background. Although the evidence in support of the variability of students' learning strategies has expanded in recent years, less is known about the explanatory base of these individual differences in terms of the joint Influences of personal and contextual characteristics. Aims. Previous studies have often investigated how student learning is…

College Student Alcohol Use and Abuse: Social Norms, Health Beliefs, and Selected Socio-Demographic Variables as Explanatory Factors

ERIC Educational Resources Information Center

Champion, Denisha A.; Lewis, Todd F.; Myers, Jane E.

2015-01-01

The U.S. Surgeon General described college alcohol abuse as the most significant public health concern on university campuses (DHHS, 2007). Social norms have been identified as a strong predictor of college drinking and yet programs based on norms have had limited effectiveness in changing drinking behavior. Other theoretical explanations, such as…

Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

ERIC Educational Resources Information Center

Calvert, Carol Elaine

2014-01-01

This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

Can Animation Be Used to Improve Comprehension of Instructional Text?

ERIC Educational Resources Information Center

Moremoholo, T. P.

2008-01-01

The aim of the study was to determine whether the animation of a linear process, requiring explanatory text, can assist students to form a better understanding of the text. Tertiary students (N = 61) participated in a pre-test, post-test experimental study during which they were exposed to 4 treatment variables: text (T), video and text (VT),…

Do Pre-Service Teachers Feel Ready to Teach in Inclusive Classrooms? A Four Country Study of Teaching Self-Efficacy

ERIC Educational Resources Information Center

Loreman, Tim; Sharma, Umesh; Forlin, Chris

2013-01-01

This paper reports the results of an international study examining pre-service teacher reports of teaching self-efficacy for inclusive education; principally focusing on the explanatory relationship between a scale designed to measure teaching self-efficacy in this area and key demographic variables within Canada, Australia, Hong Kong, and…

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

Fuel load modeling from mensuration attributes in temperate forests in northern Mexico

Treesearch

Maricela Morales-Soto; Marín Pompa-Garcia

2013-01-01

The study of fuels is an important factor in defining the vulnerability of ecosystems to forest fires. The aim of this study was to model a dead fuel load based on forest mensuration attributes from forest management inventories. A scatter plot analysis was performed and, from explanatory trends between the variables considered, correlation analysis was carried out...

Does One Size Fit All? University Differences in the Influence of Wages, Financial Aid, and Integration on Student Retention

ERIC Educational Resources Information Center

Kerkvliet, J.; Nowell, C.

2005-01-01

We develop and empirically implement a model of university student retention using opportunity cost, financial aid, academic and social integration, and students' background explanatory variables. For one year, we tracked students from Weber State University (WSU) and Oregon State University (OSU) to learn whether they remained enrolled for 0, 1,…

The Impact of Normative Environments on Learner Motivation and L2 Reading Ability Growth

ERIC Educational Resources Information Center

Sasaki, Miyuki; Kozaki, Yoko; Ross, Steven J.

2017-01-01

This study explores the effects of various motivational variables operating within 44 English classes on 1-year-long gains in the English reading proficiency of 1,149 Japanese university students. The study adds new knowledge to the recent outcomes of second language (L2) motivational studies in 3 major ways. First, the explanatory variables…

Admissions and Plebe Year Data as Indicators of Academic Success in Engineering Majors at the United States Naval Academy

DTIC Science & Technology

2002-06-01

3 = Divorced ) Number of Dependents Self-Explanatory Continuous Variable Related Job Experience Was job experience related to college program...Crawford Naval Postgraduate School Monterrey , CA 7. Professor Roger Little U. S. Naval Academy Annapolis, MD 8. LT Nicholas A. Kristof Chester, MD 9. Mr. and Mrs. Zoltan J. Kristof Pittsburgh, PA

Techniques of contributing-area delineation for analysis of nonpoint-source contamination of Long Island, New York

USGS Publications Warehouse

Misut, P.

1995-01-01

Ninety shallow monitoring wells on Long Island, N.Y., were used to test the hypothesis that the correlation between the detection of volatile organic compounds (VOC's) at a well and explanatory variables representing land use, population density, and hydrogeologic conditions around the well is affected by the size and shape of the area defined as the contributing area. Explanatory variables are quantified through overlay of various specified contributing areas on 1:24 000-scale landuse and population-density geographic information system (GIS) coverages. Four methods of contributing-area delineation were used: (a) centering a circle of selected radius on the well site, (b) orienting a triangular area along the direction of horizontal ground-water flow to the well, (c) generating a shaped based on direction and magnitude of horizontal flow to the well, and (d) generating a shape based on three-dimensional particle pathlines backtracked from the well screen to the water table. The strongest correlations with VOC detections were obtained from circles of 400- to 1 000-meter radius. Improvement in correlation through delineations based on ground-water flow would require geographic overlay on more highly detailed GIS coverages than those used in the study.

Estimation of Polychlorinated Biphenyl Sources in Industrial Port Sediments Using a Bayesian Semifactor Model Considering Unidentified Sources.

PubMed

Anezaki, Katsunori; Nakano, Takeshi; Kashiwagi, Nobuhisa

2016-01-19

Using the chemical balance method, and considering the presence of unidentified sources, we estimated the origins of PCB contamination in surface sediments of Muroran Port, Japan. It was assumed that these PCBs originated from four types of Kanechlor products (KC300, KC400, KC500, and KC600), combustion and two kinds of pigments (azo and phthalocyanine). The characteristics of these congener patterns were summarized on the basis of principal component analysis and explanatory variables determined. A Bayesian semifactor model (CMBK2) was applied to the explanatory variables to analyze the sources of PCBs in the sediments. The resulting estimates of the contribution ratio of each kind of sediment indicate that the existence of unidentified sources can be ignored and that the assumed seven sources are adequate to account for the contamination. Within the port, the contribution ratio of KC500 and KC600 (used as paints for ship hulls) was extremely high, but outside the port, the influence of azo pigments was observable to a limited degree. This indicates that environmental PCBs not derived from technical PCBs are present at levels that cannot be ignored.

Variable selection with stepwise and best subset approaches

PubMed Central

2016-01-01

While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values “forward”, “backward” and “both”. The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion. PMID:27162786

Correlates of blood pressure in young insulin-dependent diabetics and their families.

PubMed

Tarn, A C; Thomas, J M; Drury, P L

1990-09-01

We compared the correlates of blood pressure in 163 young patients with insulin-dependent diabetes and in 232 of their non-diabetic siblings. A single observer recorded blood pressure in all subjects, plus all their available parents, using a standardized technique. Other variables recorded included age, weight, height, presence of diabetes and urinary albumin. The major factors accounting for over 50% of the variance of systolic blood pressure (SBP) in both groups were age, weight, paternal SBP and sex. In addition, in the diabetic group the logarithm of the random urinary albumin concentration was a significant explanatory variable. For diastolic blood pressure (DBP) approximately 16% of the variance was explained by age, weight and maternal DBP. Parental blood pressure was an important determinant of blood pressure in both the diabetic and non-diabetic sibling groups. The similarity of the correlates of blood pressure in the two groups suggests that the determinants of blood pressure in young insulin-dependent diabetic patients and in the general population are similar.

[Sedentary leisure time and food consumption among Brazilian adolescents: the Brazilian National School-Based Adolescent Health Survey (PeNSE), 2009].

PubMed

Camelo, Lidyane do Valle; Rodrigues, Jôsi Fernandes de Castro; Giatti, Luana; Barreto, Sandhi Maria

2012-11-01

The objective of this paper was to investigate whether sedentary leisure time was associated with increased regular consumption of unhealthy foods, independently of socio-demographic indicators and family context. The analysis included 59,809 students from the Brazilian National School-Based Adolescent Health Survey (PeNSE) in 2009. The response variable was sedentary leisure time, defined as watching more than two hours of TV daily. The target explanatory variables were regular consumption of soft drinks, sweets, cookies, and processed meat. Odds ratios (OR) and 95% confidence limits (95%CI) were obtained by multiple logistic regression. Prevalence of sedentary leisure time was 65%. Regular consumption of unhealthy foods was statistically higher among students reporting sedentary leisure time, before and after adjusting for sex, age, skin color, school administration (public versus private), household assets index, and household composition. The results indicate the need for integrated interventions to promote healthy leisure-time activities and healthy eating habits among young people.

Determinants of choice of market-oriented indigenous Horo cattle production in Dano district of western Showa, Ethiopia.

PubMed

Alemayehu, Befikadu; Bogale, Ayalneh; Wollny, Clemens; Tesfahun, Girma

2010-12-01

Based on a survey data collected from 150 farming households in Dano district of western Showa of Ethiopia, this paper analyzes determinants of smallholders' choice for market oriented indigenous Horo cattle production and tries to suggest policy alternatives for sustainable use of animal genetic resource in the study area. Descriptive statistics and binary logistic model were employed to analyze the data. Eight explanatory variables including age of the household head, size of the grazing land, total size of cultivated land, farmer's experience in indigenous cattle production, farmer's attitude towards productivity of local breed, off-farm income, fattening practice, and availability of information and training of the head of the household regarding conservation, management and sustainable use indigenous cattle were found to be statistically significant variables to explain farmers' choice for market oriented indigenous cattle production activities. Besides, possible policy implications were made in order to improve conservation, management and sustainable use of market oriented indigenous cattle genetic resources.

Sweetened Soft Drinks Consumption Is Associated with Metabolic Syndrome: Cross-sectional Analysis from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

PubMed

Velasquez-Melendez, Gustavo; Molina, Maria Del Carmen B; Benseñor, Isabela M; Cardoso, Leticia O; Fonseca, Maria de Jesus M; Moreira, Alexandra D; Pereira, Taísa Sabrina S; Barreto, Sandhi M

2017-02-01

To estimate the association between regular consumption of sweetened soft drinks, natural fruit juice, and coconut water with metabolic syndrome (MetS). This was a cross-sectional study including men and women aged 35-74 years from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Study, excluding patients with type 2 diabetes. The main explanatory variables were beverage consumption and the outcome variable was metabolic syndrome (Adult Treatment Panel III). After adjustments, a daily intake of 250 ml of soft drink increased the chance of metabolic syndrome (odds ratio [OR] = 1.95; 95% confidence interval [CI], 1.60-2.38). There was no association between coconut water and MetS. Moderate consumption of fruit juices has low odds of MetS compared to no consumption. Our results add evidence to potential negative effects of sweetened soft drinks on cluster metabolic abnormalities in middle-income countries.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand's southern beech treelines.

PubMed

Case, Bradley S; Buckley, Hannah L

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand’s southern beech treelines

PubMed Central

Buckley, Hannah L.

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments. PMID:26528407

Separating the effects of environment and space on tree species distribution: from population to community.

PubMed

Lin, Guojun; Stralberg, Diana; Gong, Guiquan; Huang, Zhongliang; Ye, Wanhui; Wu, Linfang

2013-01-01

Quantifying the relative contributions of environmental conditions and spatial factors to species distribution can help improve our understanding of the processes that drive diversity patterns. In this study, based on tree inventory, topography and soil data from a 20-ha stem-mapped permanent forest plot in Guangdong Province, China, we evaluated the influence of different ecological processes at different spatial scales using canonical redundancy analysis (RDA) at the community level and multiple linear regression at the species level. At the community level, the proportion of explained variation in species distribution increased with grid-cell sizes, primarily due to a monotonic increase in the explanatory power of environmental variables. At the species level, neither environmental nor spatial factors were important determinants of overstory species' distributions at small cell sizes. However, purely spatial variables explained most of the variation in the distributions of understory species at fine and intermediate cell sizes. Midstory species showed patterns that were intermediate between those of overstory and understory species. At the 20-m cell size, the influence of spatial factors was stronger for more dispersal-limited species, suggesting that much of the spatial structuring in this community can be explained by dispersal limitation. Comparing environmental factors, soil variables had higher explanatory power than did topography for species distribution. However, both topographic and edaphic variables were highly spatial structured. Our results suggested that dispersal limitation has an important influence on fine-intermediate scale (from several to tens of meters) species distribution, while environmental variability facilitates species distribution at intermediate (from ten to tens of meters) and broad (from tens to hundreds of meters) scales.

[Factors conditioning primary care services utilization. Empirical evidence and methodological inconsistencies].

PubMed

Sáez, M

2003-01-01

In Spain, the degree and characteristics of primary care services utilization have been the subject of analysis since at least the 1980s. One of the main reasons for this interest is to assess the extent to which utilization matches primary care needs. In fact, the provision of an adequate health service for those who most need it is a generally accepted priority. The evidence shows that individual characteristics, mainly health status, are the factors most closely related to primary care utilization. Other personal characteristics, such as gender and age, could act as modulators of health care need. Some family and/or cultural variables, as well as factors related to the health care professional and institutions, could explain some of the observed variability in primary care services utilization. Socioeconomic variables, such as income, reveal a paradox. From an aggregate perspective, income is the main determinant of utilization as well as of health care expenditure. When data are analyzed for individuals, however, income is not related to primary health utilization. The situation is controversial, with methodological implications and, above all, consequences for the assessment of the efficiency in primary care utilization. Review of the literature reveals certain methodological inconsistencies that could at least partly explain the disparity of the empirical results. Among others, the following flaws can be highlighted: design problems, measurement errors, misspecification, and misleading statistical methods.Some solutions, among others, are quasi-experiments, the use of large administrative databases and of primary data sources (design problems); differentiation between types of utilization and between units of analysis other than consultations, and correction of measurement errors in the explanatory variables (measurement errors); consideration of relevant explanatory variables (misspecification); and the use of multilevel models (statistical methods).

A successful backward step correlates with hip flexion moment of supporting limb in elderly people.

PubMed

Takeuchi, Yahiko

2018-01-01

The objective of this study was to determine the positional relationship between the center of mass (COM) and the center of pressure (COP) at the time of step landing, and to examine their relationship with the joint moments exerted by the supporting limb, with regard to factors of the successful backward step response. The study population comprised 8 community-dwelling elderly people that were observed to take successive multi steps after the landing of a backward stepping. Using a motion capture system and force plate, we measured the COM, COP and COM-COP deviation distance on landing during backward stepping. In addition, we measured the moment of the supporting limb joint during backward stepping. The multi-step data were compared with data from instances when only one step was taken (single-step). Variables that differed significantly between the single- and multi-step data were used as objective variables and the joint moments of the supporting limb were used as explanatory variables in single regression analyses. The COM-COP deviation in the anteroposterior was significantly larger in the single-step. A regression analysis with COM-COP deviation as the objective variable obtained a significant regression equation in the hip flexion moment (R2 = 0.74). The hip flexion moment of supporting limb was shown to be a significant explanatory variable in both the PS and SS phases for the relationship with COM-COP distance. This study found that to create an appropriate backward step response after an external disturbance (i.e. the ability to stop after 1 step), posterior braking of the COM by a hip flexion moment are important during the single-limbed standing phase.

[Factors affecting infant mortality (author's transl)].

PubMed

Chackiel, J

1982-04-01

The purpose of this paper is to analyze the differentials and detect factors affecting infant mortality on the basis of data obtained from the fertility surveys from those countries participating in the World Fertility Survey. In particular, this includes the surveys carried out in Colombia, Peru, Costa Rica, Panama, and the Dominican Republic. 3 types of explanatory variables may be considered from the information available: 1) context variables related to the mother's environment; 2) socioeconomic variables based on the educational and economic characteristics of the mother and her last husband; and 3) biological factors (from each woman's pregnancy history) such as mother's age at birth of the child, order of birth, interbirth interval, etc. The countries, whether high or low mortality, present great differences in child mortality in most of the variables considered. In Panama and Costa Rica there are population sectors with infant mortality rates of around 100/1000 live births, whereas in Peru these are over 150/1000 (children from mothers without education, low agricultural strata, etc.). Besides presenting the differentials, a methodological test is made through the application to Costa Rica and Peru of the Proportional Hazards Model which permits analysis of the effects of variables when acting simultaneously upon mortality in early childhood. The variables which show the highest disparity in mortality level are: natural region among the context variables, education of mother among the socioeconomic variables, and interbirth interval and maternal age at birth of their children among the biological ones.

Spatial analysis of agri-environmental policy uptake and expenditure in Scotland.

PubMed

Yang, Anastasia L; Rounsevell, Mark D A; Wilson, Ronald M; Haggett, Claire

2014-01-15

Agri-environment is one of the most widely supported rural development policy measures in Scotland in terms of number of participants and expenditure. It comprises 69 management options and sub-options that are delivered primarily through the competitive 'Rural Priorities scheme'. Understanding the spatial determinants of uptake and expenditure would assist policy-makers in guiding future policy targeting efforts for the rural environment. This study is unique in examining the spatial dependency and determinants of Scotland's agri-environmental measures and categorised options uptake and payments at the parish level. Spatial econometrics is applied to test the influence of 40 explanatory variables on farming characteristics, land capability, designated sites, accessibility and population. Results identified spatial dependency for each of the dependent variables, which supported the use of spatially-explicit models. The goodness of fit of the spatial models was better than for the aspatial regression models. There was also notable improvement in the models for participation compared with the models for expenditure. Furthermore a range of expected explanatory variables were found to be significant and varied according to the dependent variable used. The majority of models for both payment and uptake showed a significant positive relationship with SSSI (Sites of Special Scientific Interest), which are designated sites prioritised in Scottish policy. These results indicate that environmental targeting efforts by the government for AEP uptake in designated sites can be effective. However habitats outside of SSSI, termed here the 'wider countryside' may not be sufficiently competitive to receive funding in the current policy system. Copyright © 2013 Elsevier Ltd. All rights reserved.

Groundwater salinity in a floodplain forest impacted by saltwater intrusion

NASA Astrophysics Data System (ADS)

Kaplan, David A.; Muñoz-Carpena, Rafael

2014-11-01

Coastal wetlands occupy a delicate position at the intersection of fresh and saline waters. Changing climate and watershed hydrology can lead to saltwater intrusion into historically freshwater systems, causing plant mortality and loss of freshwater habitat. Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals, however finding direct relationships between hydrological inputs and floodplain hydrology is complicated by interactions between surface water, groundwater, and atmospheric fluxes in variably saturated soils with heterogeneous vegetation and topography. Thus, an alternative method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unexplained common variability, and explanatory variables. DFA was applied to model shallow groundwater salinity in the forested floodplain wetlands of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes. Long-term, high-resolution groundwater salinity datasets revealed dynamics over seasonal and yearly time periods as well as over tidal cycles and storm events. DFA identified shared trends among salinity time series and a full dynamic factor model simulated observed series well (overall coefficient of efficiency, Ceff = 0.85; 0.52 ≤ Ceff ≤ 0.99). A reduced multilinear model based solely on explanatory variables identified in the DFA had fair to good results (Ceff = 0.58; 0.38 ≤ Ceff ≤ 0.75) and may be used to assess the effects of restoration and management scenarios on shallow groundwater salinity in the Loxahatchee River floodplain.

The linkage between geopotential height and monthly precipitation in Iran

NASA Astrophysics Data System (ADS)

Shirvani, Amin; Fadaei, Amir Sabetan; Landman, Willem A.

2018-04-01

This paper investigates the linkage between large-scale atmospheric circulation and monthly precipitation during November to April over Iran. Canonical correlation analysis (CCA) is used to set up the statistical linkage between the 850 hPa geopotential height large-scale circulation and monthly precipitation over Iran for the period 1968-2010. The monthly precipitation dataset for 50 synoptic stations distributed in different climate regions of Iran is considered as the response variable in the CCA. The monthly geopotential height reanalysis dataset over an area between 10° N and 60° N and from 20° E to 80° E is utilized as the explanatory variable in the CCA. Principal component analysis (PCA) as a pre-filter is used for data reduction for both explanatory and response variables before applying CCA. The optimal number of principal components and canonical variables to be retained in the CCA equations is determined using the highest average cross-validated Kendall's tau value. The 850 hPa geopotential height pattern over the Red Sea, Saudi Arabia, and Persian Gulf is found to be the major pattern related to Iranian monthly precipitation. The Pearson correlation between the area averaged of the observed and predicted precipitation over the study area for Jan, Feb, March, April, November, and December months are statistically significant at the 5% significance level and are 0.78, 0.80, 0.82, 0.74, 0.79, and 0.61, respectively. The relative operating characteristic (ROC) indicates that the highest scores for the above- and below-normal precipitation categories are, respectively, for February and April and the lowest scores found for December.

On the effect of networks of cycle-tracks on the risk of cycling. The case of Seville.

PubMed

Marqués, R; Hernández-Herrador, V

2017-05-01

We analyze the evolution of the risk of cycling in Seville before and after the implementation of a network of segregated cycle tracks in the city. Specifically, we study the evolution of the risk for cyclists of being involved in a collision with a motor vehicle, using data reported by the traffic police along the period 2000-2013, i.e. seven years before and after the network was built. A sudden drop of such risk was observed after the implementation of the network of bikeways. We study, through a multilinear regression analysis, the evolution of the risk by means of explanatory variables representing changes in the built environment, specifically the length of the bikeways and a stepwise jump variable taking the values 0/1 before/after the network was implemented. We found that this last variable has a high value as explanatory variable, even higher than the length of the network, thus suggesting that networking the bikeways has a substantial effect on cycling safety by itself and beyond the mere increase in the length of the bikeways. We also analyze safety in numbers through a non-linear regression analysis. Our results fully agree qualitatively and quantitatively with the results previously reported by Jacobsen (2003), thus providing an independent confirmation of Jacobsen's results. Finally, the mutual causal relationships between the increase in safety, the increase in the number of cyclists and the presence of the network of bikeways are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Health status identification and their relationship with socieconomics and geographic characteristics: a population survey analysis in México].

PubMed

Vargas-Palacios, Armando; Gutiérrez, Juan Pablo; Carreón-Rodríguez, Víctor

2006-01-01

To estimate the effectiveness of using standardized health vignettes to adjust self-reported health taking into account household and community variables to correct for systematic bias. The national health survey "Evaluación del Desempeño 2002" (Mexican component of the World Health Survey) was used. This survey analyzed subject's health perception based on their responses to hypothetical questions referring to third parties in the vignettes within eight domains. Variations in responses were attributed to socio-demographic, socioeconomic, community, differences of the subjects. To assess those variations, an index for each domain was constructed and used as a variable in a series of linear regression models to estimate the relation between health perceptions, self-reported health, socioeconomic and socio-demographic characteristics. The health perception index derived from the vignettes showed a positive, logarithmic correlation with household expenditure for each health domain, after controlling for socio-demographic, health and community characteristics. No relationship was found between the health status described in the vignettes and self-reported health status. In no case was the explanatory power above 10%. The low explanatory power of the models, and the lack of correlation between self reported health status and the health perception index, suggest that the variability in the vignettes responses cannot be explained by differences in self-reported health status or socioeconomic and socio-demographic characteristics. These results from Mexico suggest that vignette-based methods to correct for systematic variability in perception of own health status are of limited efficacy and reinforce the importance of collecting objective measures of health status in health surveys.

Explanatory factors and predictors of fatigue in persons with rheumatoid arthritis: A longitudinal study.

PubMed

Feldthusen, Caroline; Grimby-Ekman, Anna; Forsblad-d'Elia, Helena; Jacobsson, Lennart; Mannerkorpi, Kaisa

2016-04-28

To investigate the impact of disease-related aspects on long-term variations in fatigue in persons with rheumatoid arthritis. Observational longitudinal study. Sixty-five persons with rheumatoid arthritis, age range 20-65 years, were invited to a clinical examination at 4 time-points during the 4 seasons. Outcome measures were: general fatigue rated on visual analogue scale (0-100) and aspects of fatigue assessed by the Bristol Rheumatoid Arthritis Fatigue Multidimensional Questionnaire. Disease-related variables were: disease activity (erythrocyte sedimentation rate), pain threshold (pressure algometer), physical capacity (six-minute walk test), pain (visual analogue scale (0-100)), depressive mood (Hospital Anxiety and Depression scale, depression subscale), personal factors (age, sex, body mass index) and season. Multivariable regression analysis, linear mixed effects models were applied. The strongest explanatory factors for all fatigue outcomes, when recorded at the same time-point as fatigue, were pain threshold and depressive mood. Self-reported pain was an explanatory factor for physical aspects of fatigue and body mass index contributed to explaining the consequences of fatigue on everyday living. For predicting later fatigue pain threshold and depressive mood were the strongest predictors. Pain threshold and depressive mood were the most important factors for fatigue in persons with rheumatoid arthritis.

Social support and clinical and functional outcome in people with schizophrenia.

PubMed

Vázquez Morejón, Antonio J; León Rubio, Jose Mª; Vázquez-Morejón, Raquel

2018-05-01

The impact of Social Support (SS) on the clinical and functional evolution of patients diagnosed with schizophrenia was studied from a multidimensional concept of SS in the framework of the vulnerability-stress model. In total, 152 patients diagnosed with schizophrenia according to the International Classification of Diseases, Tenth Edition (ICD-10) treated in a Community Mental Health Unit were assessed using the Mannheim Interview on Social Support (MISS) and the Brief Psychiatric Rating Scale (BPRS). Then they were followed up for 3 years with a final assessment for the period using the Social Functioning Scale. The impact of SS was explored in clinical and functional measurements with a multiple regression analysis in a 3-year longitudinal prospective design. The quality of Global Social Support (GSS) and satisfaction with GSS appeared to be protective factors from frequency and duration of hospital admissions, with explanatory intensity varying from 9% in survival time to relapse to 13% in number of relapses. Concerning functional measurements, GSS quantity, quality and satisfaction showed an explanatory power for several different dimensions of social functioning, varying from 12% in isolation to 20% in communication. The results confirm SS as a protective factor in the evolution of schizophrenia patients and enable the SS variables with the most explanatory power in their clinical and functional evolution to be identified.

Improving Space Project Cost Estimating with Engineering Management Variables

NASA Technical Reports Server (NTRS)

Hamaker, Joseph W.; Roth, Axel (Technical Monitor)

2001-01-01

Current space project cost models attempt to predict space flight project cost via regression equations, which relate the cost of projects to technical performance metrics (e.g. weight, thrust, power, pointing accuracy, etc.). This paper examines the introduction of engineering management parameters to the set of explanatory variables. A number of specific engineering management variables are considered and exploratory regression analysis is performed to determine if there is statistical evidence for cost effects apart from technical aspects of the projects. It is concluded that there are other non-technical effects at work and that further research is warranted to determine if it can be shown that these cost effects are definitely related to engineering management.

An Update on Statistical Boosting in Biomedicine.

PubMed

Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf

2017-01-01

Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.

Some methodological issues in the longitudinal analysis of demographic data.

PubMed

Krishinan, P

1982-12-01

Most demographic data are macro (or aggregate) in nature. Some relevant methodological issues are presented here in a time series study using aggregate data. The micro-macro distinction is relative. Time enters into the micro and macro variables in different ways. A simple micro model of rural-urban migration is given. Method 1 is to assume homogeneity in behavior. Method 2 is a Bayesian estimation. A discusssion of the results follows. Time series models of aggregate data are given. The nature of the model--predictive or explanatory--must be decided on. Explanatory models in longitudinal studies have been developed. Ways to go to the micro level from the macro are discussed. The aggregation-disaggregation problem in demography is not similar to that in econometrics. To understand small populations, separate micro level data have to be collected and analyzed and appropriate models developed. Both types of models have their uses.

Creating a non-linear total sediment load formula using polynomial best subset regression model

NASA Astrophysics Data System (ADS)

Okcu, Davut; Pektas, Ali Osman; Uyumaz, Ali

2016-08-01

The aim of this study is to derive a new total sediment load formula which is more accurate and which has less application constraints than the well-known formulae of the literature. 5 most known stream power concept sediment formulae which are approved by ASCE are used for benchmarking on a wide range of datasets that includes both field and flume (lab) observations. The dimensionless parameters of these widely used formulae are used as inputs in a new regression approach. The new approach is called Polynomial Best subset regression (PBSR) analysis. The aim of the PBRS analysis is fitting and testing all possible combinations of the input variables and selecting the best subset. Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable. While selecting the best subset a multistep approach is used that depends on significance values and also the multicollinearity degrees of inputs. The new formula is compared to others in a holdout dataset and detailed performance investigations are conducted for field and lab datasets within this holdout data. Different goodness of fit statistics are used as they represent different perspectives of the model accuracy. After the detailed comparisons are carried out we figured out the most accurate equation that is also applicable on both flume and river data. Especially, on field dataset the prediction performance of the proposed formula outperformed the benchmark formulations.

An investigation on fatality of drivers in vehicle-fixed object accidents on expressways in China: Using multinomial logistic regression model.

PubMed

Peng, Yong; Peng, Shuangling; Wang, Xinghua; Tan, Shiyang

2018-06-01

This study aims to identify the effects of characteristics of vehicle, roadway, driver, and environment on fatality of drivers in vehicle-fixed object accidents on expressways in Changsha-Zhuzhou-Xiangtan district of Hunan province in China by developing multinomial logistic regression models. For this purpose, 121 vehicle-fixed object accidents from 2011-2017 are included in the modeling process. First, descriptive statistical analysis is made to understand the main characteristics of the vehicle-fixed object crashes. Then, 19 explanatory variables are selected, and correlation analysis of each two variables is conducted to choose the variables to be concluded. Finally, five multinomial logistic regression models including different independent variables are compared, and the model with best fitting and prediction capability is chosen as the final model. The results showed that the turning direction in avoiding fixed objects raised the possibility that drivers would die. About 64% of drivers died in the accident were found being ejected out of the car, of which 50% did not use a seatbelt before the fatal accidents. Drivers are likely to die when they encounter bad weather on the expressway. Drivers with less than 10 years of driving experience are more likely to die in these accidents. Fatigue or distracted driving is also a significant factor in fatality of drivers. Findings from this research provide an insight into reducing fatality of drivers in vehicle-fixed object accidents.

[Adult mortality differentials in Argentina].

PubMed

Rofman, R

1994-06-01

Adult mortality differentials in Argentina are estimated and analyzed using data from the National Social Security Administration. The study of adult mortality has attracted little attention in developing countries because of the scarcity of reliable statistics and the greater importance assigned to demographic phenomena traditionally associated with development, such as infant mortality and fertility. A sample of 39,421 records of retired persons surviving as of June 30, 1988, was analyzed by age, sex, region of residence, relative amount of pension, and social security fund of membership prior to the consolidation of the system in 1967. The thirteen former funds were grouped into the five categories of government, commerce, industry, self-employed, and other, which were assumed to be proxies for the activity sector in which the individual spent his active life. The sample is not representative of the Argentine population, since it excludes the lowest and highest socioeconomic strata and overrepresents men and urban residents. It is, however, believed to be adequate for explaining mortality differentials for most of the population covered by the social security system. The study methodology was based on the technique of logistic analysis and on the use of regional model life tables developed by Coale and others. To evaluate the effect of the study variables on the probability of dying, a regression model of maximal verisimilitude was estimated. The model relates the logit of the probability of death between ages 65 and 95 to the available explanatory variables, including their possible interactions. Life tables were constructed by sex, region of residence, previous pension fund, and income. As a test of external consistency, a model including only age and sex as explanatory variables was constructed using the methodology. The results confirmed consistency between the estimated values and other published estimates. A significant conclusion of the study was that social security data are a satisfactory source for study of adult mortality, a finding of importance in cases where vital statistics systems are deficient. Mortality differentials by income level and activity sector were significant, representing up to 11.5 years in life expectancy at age 20 and 4.4 years at age 65. Mortality differentials by region were minor, probably due to the nature of the sample. The lowest observed mortality levels were in own-account workers, independent professionals, and small businessmen.

Impact of Ego-resilience and Family Function on Quality of Life in Childhood Leukemia Survivors

PubMed Central

CHO, Ok-Hee; YOO, Yang-Sook; HWANG, Kyung-Hye

2016-01-01

Background: This study aimed to examine the impact of ego-resilience and family function on quality of life in childhood leukemia survivors. Methods: This study targeted 100 pediatric leukemia survivors, who visited the Pediatric Hemato-Oncology Center in South Korea from Aug to Dec 2011. A structured questionnaire of ego-resilience, family function and quality of life used to collect data through direct interview with the pediatric patients and their parents. The correlation between the study variables analyzed using the Pearson’s correlation coefficient, and the impact on quality of life analyzed using a stepwise multiple regression. Results: Ego-resilience (r = 0.69, P<0.001) and family function (r =0.46, P< 0.001) had a positive correlation with quality of life and all the sub-categories of quality of life. Ego-resilience was a major factor affecting quality of life in childhood leukemia survivors, with an explanatory power of 48%. The explanatory power for quality of life increased to 53% when age and family function were included. Conclusion: Ego-resilience, age, and family function affect quality of life in childhood leukemia survivors. Hence, strategies are required to construct age-matched programs to improve quality of life, in order to help restore the necessary ego-resilience and to strengthen family function in childhood leukemia survivors. PMID:28032062

What Is the Role of Education in the Recruitment Process? Employers' Practices and Experiences of Graduates from Tertiary Educational Institutions in Estonia

ERIC Educational Resources Information Center

Saar, Ellu; Unt, Marge; Helemäe, Jelena; Oras, Kaja; Täht, Kadri

2014-01-01

Since the 1980s, growing globalisation and economic restructuring coupled with expansion of tertiary education contributed to tremendous change in the labour market entry process in Europe. Most previous studies have been quantitative, concentrated on the supply aspect and analysed the role of education as the explanatory variable of youth labour…

Predictive equations for dimensions and leaf area of coastal Southern California street trees

Treesearch

P.J. Peper; E.G. McPherson; S.M. Mori

2001-01-01

Tree height, crown height, crown width, diameter at breast height (dbh), and leaf area were measured for 16 species of commonly planted street trees in the coastal southern California city of Santa Monica, USA. The randomly sampled trees were planted from 1 to 44 years ago. Using number of years after planting or dbh as explanatory variables, mean values of dbh, tree...

Modeling Alcohol Use Intensity among Students at a Historically Black University: The Role of Social Norms, Perceptions for Risk, and Selected Demographic Variables

ERIC Educational Resources Information Center

Lewis, Todd F.; Likis-Werle, Elizabeth; Fulton, Cheryl L.

2012-01-01

Drinking patterns and rates at historically Black colleges and universities (HBCU) are not well understood. Social norms and perceptions of risk are two explanatory mechanisms that have accounted for a significant amount of variance in college student drinking at predominantly White campuses. However, these models have not been examined among…

Efficacy and Academic Emphasis: A Leadership Factor in Elementary School Principals, and Its Relationship to Hope, Resilience, Optimism, and View of Intelligence

ERIC Educational Resources Information Center

Riegel, Lisa A.

2012-01-01

The goal of this research was to explore the construct of academic optimism at the principal level and examine possible explanatory variables for the factors that emerged from the principal academic optimism scale. Academic optimism contains efficacy, trust and academic emphasis (Hoy, Tarter & Woolfolk Hoy, 2006). It has been studied at the…

The independent relationship between triglycerides and coronary heart disease.

PubMed

Morrison, Alan; Hokanson, John E

2009-01-01

The aim was to review epidemiologic studies to reassess whether serum levels of triglycerides should be considered independently of high-density lipoprotein-cholesterol (HDL-C) as a predictor of coronary heart disease (CHD). We systematically reviewed population-based cohort studies in which baseline serum levels of triglycerides and HDL-C were included as explanatory variables in multivariate analyses with the development of CHD (coronary events or coronary death) as dependent variable. A total of 32 unique reports describing 38 cohorts were included. The independent association between elevated triglycerides and risk of CHD was statistically significant in 16 of 30 populations without pre-existing CHD. Among populations with diabetes mellitus or pre-existing CHD, or the elderly, triglycerides were not significantly independently associated with CHD in any of 8 cohorts. Triglycerides and HDL-C were mutually exclusive predictors of coronary events in 12 of 20 analyses of patients without pre-existing CHD. Epidemiologic studies provide evidence of an association between triglycerides and the development of primary CHD independently of HDL-C. Evidence of an inverse relationship between triglycerides and HDL-C suggests that both should be considered in CHD risk estimation and as targets for intervention.

The independent relationship between triglycerides and coronary heart disease

PubMed Central

Morrison, Alan; Hokanson, John E

2009-01-01

Aims: The aim was to review epidemiologic studies to reassess whether serum levels of triglycerides should be considered independently of high-density lipoprotein-cholesterol (HDL-C) as a predictor of coronary heart disease (CHD). Methods and results: We systematically reviewed population-based cohort studies in which baseline serum levels of triglycerides and HDL-C were included as explanatory variables in multivariate analyses with the development of CHD (coronary events or coronary death) as dependent variable. A total of 32 unique reports describing 38 cohorts were included. The independent association between elevated triglycerides and risk of CHD was statistically significant in 16 of 30 populations without pre-existing CHD. Among populations with diabetes mellitus or pre-existing CHD, or the elderly, triglycerides were not significantly independently associated with CHD in any of 8 cohorts. Triglycerides and HDL-C were mutually exclusive predictors of coronary events in 12 of 20 analyses of patients without pre-existing CHD. Conclusions: Epidemiologic studies provide evidence of an association between triglycerides and the development of primary CHD independently of HDL-C. Evidence of an inverse relationship between triglycerides and HDL-C suggests that both should be considered in CHD risk estimation and as targets for intervention. PMID:19436658

Does health status influence acceptance of illness in patients with chronic respiratory diseases?

PubMed

Kurpas, D; Mroczek, B; Brodowski, J; Urban, M; Nitsch-Osuch, A

2015-01-01

The level of illness acceptance correlates positively with compliance to the doctor's recommendations, and negatively with the frequency and intensity of complications of chronic diseases. The purpose of this study was to determine the influence of the clinical condition on the level of illness acceptance, and to find variables which would have the most profound effect on the level of illness acceptance in patients with chronic respiratory diseases. The study group consisted of 594 adult patients (mean age: 60 ± 15 years) with mixed chronic respiratory diseases, recruited from patients of 136 general practitioners. The average score in the Acceptance of Illness Scale was 26.2 ± 7.6. The low level of illness acceptance was noted in 174 (62.6 %) and high in 46 (16.6 %) patients. Analysis of multiple regressions was used to examine the influence of explanatory variables on the level of illness acceptance. The variables which shaped the level of illness acceptance in our patients included: improvement of health, intensity of symptoms, age, marital status, education level, place of residence, BMI, and the number of chronic diseases. All above mentioned variables should be considered during a design of prevention programs for patients with mixed chronic respiratory diseases.

Risk-adjusted antibiotic consumption in 34 public acute hospitals in Ireland, 2006 to 2014

PubMed Central

Oza, Ajay; Donohue, Fionnuala; Johnson, Howard; Cunney, Robert

2016-01-01

As antibiotic consumption rates between hospitals can vary depending on the characteristics of the patients treated, risk-adjustment that compensates for the patient-based variation is required to assess the impact of any stewardship measures. The aim of this study was to investigate the usefulness of patient-based administrative data variables for adjusting aggregate hospital antibiotic consumption rates. Data on total inpatient antibiotics and six broad subclasses were sourced from 34 acute hospitals from 2006 to 2014. Aggregate annual patient administration data were divided into explanatory variables, including major diagnostic categories, for each hospital. Multivariable regression models were used to identify factors affecting antibiotic consumption. Coefficient of variation of the root mean squared errors (CV-RMSE) for the total antibiotic usage model was very good (11%), however, the value for two of the models was poor (> 30%). The overall inpatient antibiotic consumption increased from 82.5 defined daily doses (DDD)/100 bed-days used in 2006 to 89.2 DDD/100 bed-days used in 2014; the increase was not significant after risk-adjustment. During the same period, consumption of carbapenems increased significantly, while usage of fluoroquinolones decreased. In conclusion, patient-based administrative data variables are useful for adjusting hospital antibiotic consumption rates, although additional variables should also be employed. PMID:27541730

Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, I.J.; Chandler, R.B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientic questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mecha- nisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unied modeling in- terface. The R package unmarked provides such a unied modeling framework, including tools for data exploration, model tting, model criticism, post-hoc analysis, and model comparison.

Multivariate dynamic Tobit models with lagged observed dependent variables: An effectiveness analysis of highway safety laws.

PubMed

Dong, Chunjiao; Xie, Kun; Zeng, Jin; Li, Xia

2018-04-01

Highway safety laws aim to influence driver behaviors so as to reduce the frequency and severity of crashes, and their outcomes. For one specific highway safety law, it would have different effects on the crashes across severities. Understanding such effects can help policy makers upgrade current laws and hence improve traffic safety. To investigate the effects of highway safety laws on crashes across severities, multivariate models are needed to account for the interdependency issues in crash counts across severities. Based on the characteristics of the dependent variables, multivariate dynamic Tobit (MVDT) models are proposed to analyze crash counts that are aggregated at the state level. Lagged observed dependent variables are incorporated into the MVDT models to account for potential temporal correlation issues in crash data. The state highway safety law related factors are used as the explanatory variables and socio-demographic and traffic factors are used as the control variables. Three models, a MVDT model with lagged observed dependent variables, a MVDT model with unobserved random variables, and a multivariate static Tobit (MVST) model are developed and compared. The results show that among the investigated models, the MVDT models with lagged observed dependent variables have the best goodness-of-fit. The findings indicate that, compared to the MVST, the MVDT models have better explanatory power and prediction accuracy. The MVDT model with lagged observed variables can better handle the stochasticity and dependency in the temporal evolution of the crash counts and the estimated values from the model are closer to the observed values. The results show that more lives could be saved if law enforcement agencies can make a sustained effort to educate the public about the importance of motorcyclists wearing helmets. Motor vehicle crash-related deaths, injuries, and property damages could be reduced if states enact laws for stricter text messaging rules, higher speeding fines, older licensing age, and stronger graduated licensing provisions. Injury and PDO crashes would be significantly reduced with stricter laws prohibiting the use of hand-held communication devices and higher fines for drunk driving. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exploring biological, chemical and geomorphological patterns in fluvial ecosystems with Structural Equation Modelling

NASA Astrophysics Data System (ADS)

Bizzi, S.; Surridge, B.; Lerner, D. N.:

2009-04-01

River ecosystems represent complex networks of interacting biological, chemical and geomorphological processes. These processes generate spatial and temporal patterns in biological, chemical and geomorphological variables, and a growing number of these variables are now being used to characterise the status of rivers. However, integrated analyses of these biological-chemical-geomorphological networks have rarely been undertaken, and as a result our knowledge of the underlying processes and how they generate the resulting patterns remains weak. The apparent complexity of the networks involved, and the lack of coherent datasets, represent two key challenges to such analyses. In this paper we describe the application of a novel technique, Structural Equation Modelling (SEM), to the investigation of biological, chemical and geomorphological data collected from rivers across England and Wales. The SEM approach is a multivariate statistical technique enabling simultaneous examination of direct and indirect relationships across a network of variables. Further, SEM allows a-priori conceptual or theoretical models to be tested against available data. This is a significant departure from the solely exploratory analyses which characterise other multivariate techniques. We took biological, chemical and river habitat survey data collected by the Environment Agency for 400 sites in rivers spread across England and Wales, and created a single, coherent dataset suitable for SEM analyses. Biological data cover benthic macroinvertebrates, chemical data relate to a range of standard parameters (e.g. BOD, dissolved oxygen and phosphate concentration), and geomorphological data cover factors such as river typology, substrate material and degree of physical modification. We developed a number of a-priori conceptual models, reflecting current research questions or existing knowledge, and tested the ability of these conceptual models to explain the variance and covariance within the dataset. The conceptual models we developed were able to explain correctly the variance and covariance shown by the datasets, proving to be a relevant representation of the processes involved. The models explained 65% of the variance in indices describing benthic macroinvertebrate communities. Dissolved oxygen was of primary importance, but geomorphological factors, including river habitat type and degree of habitat degradation, also had significant explanatory power. The addition of spatial variables, such as latitude or longitude, did not provide additional explanatory power. This suggests that the variables already included in the models effectively represented the eco-regions across which our data were distributed. The models produced new insights into the relative importance of chemical and geomorphological factors for river macroinvertebrate communities. The SEM technique proved a powerful tool for exploring complex biological-chemical-geomorphological networks, for example able to deal with the co-correlations that are common in rivers due to multiple feedback mechanisms.

Demand impact and policy implications from taxing nitrogen fertilizer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foltz, J.C.

1992-12-01

Recent concern has focused on nitrogen fertilizer as a potential contaminant of groundwater. A demand function for fertilizer was developed using the quantity of fertilizer purchased, corn yield, real price of nitrogen fertilizer, lagged fertilizer purchases, a land value variable and the real price of corn as explanatory variables. Short and long-run price elasticities of demand were estimated to be inelastic. Support was found for the hypothesis that demand for nitrogen fertilizer has become more price inelastic over time. From a policy standpoint, a tax on nitrogen fertilizer may not be the most effective method to reduce consumption.

Neglected children, shame-proneness, and depressive symptoms.

PubMed

Bennett, David S; Sullivan, Margaret Wolan; Lewis, Michael

2010-11-01

Neglected children may be at increased risk for depressive symptoms. This study examines shame-proneness as an outcome of child neglect and as a potential explanatory variable in the relation between neglect and depressive symptoms. Participants were 111 children (52 with a Child Protective Services [CPS] allegation of neglect) seen at age 7. Neglected children reported more shame-proneness and more depressive symptoms than comparison children. Guilt-proneness, in contrast, was unrelated to neglect and depressive symptoms, indicating specificity for shame-proneness. The potential role of shame as a process variable that can help explain how some neglected children exhibit depressive symptoms is discussed.

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

PubMed Central

Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson

2015-01-01

Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (<100 ms) spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365

Factors influencing riverine fish assemblages in Massachusetts

USGS Publications Warehouse

Armstrong, David S.; Richards, Todd A.; Levin, Sara B.

2011-01-01

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Massachusetts Department of Environmental Protection, and the Massachusetts Department of Fish and Game, conducted an investigation of fish assemblages in small- to medium-sized Massachusetts streams. The objective of this study was to determine relations between fish-assemblage characteristics and anthropogenic factors, including impervious cover and estimated flow alteration, relative to the effects of environmental factors, including physical-basin characteristics and land use. The results of this investigation supersede those of a preliminary analysis published in 2010. Fish data were obtained for 669 fish-sampling sites from the Massachusetts Division of Fisheries and Wildlife fish-community database. A review of the literature was used to select fish metrics - species richness, abundance of individual species, and abundances of species grouped on life history traits - responsive to flow alteration. The contributing areas to the fish-sampling sites were delineated and used with a geographic information system to determine a set of environmental and anthropogenic factors that were tested for use as explanatory variables in regression models. Reported and estimated withdrawals and return flows were used together with simulated unaltered streamflows to estimate altered streamflows and indicators of flow alteration for each fish-sampling site. Altered streamflows and indicators of flow alteration were calculated on the basis of methods developed in a previous U.S. Geological Survey study in which unaltered daily streamflows were simulated for a 44-year period (water years 1961-2004), and streamflow alterations were estimated by use of water-withdrawal and wastewater-return data previously reported to the State for the 2000-04 period and estimated domestic-well withdrawals and septic-system discharges. A variable selection process, conducted using principal components analysis and Spearman rank correlation, was used to select a set of 15 non-redundant environmental and anthropogenic factors to test for use as explanatory variables in the regression analyses. Twenty-one fish species were used in a multivariate analysis of fish-assemblage patterns. Results of nonmetric multidimensional scaling and hierarchical cluster analysis were used to group fish species into fluvial and macrohabitat generalist habitat-use classes. Two analytical techniques, quantile regression and generalized linear modeling, were applied to characterize the association between fish-response variables and environmental and anthropogenic explanatory variables. Quantile regression demonstrated that as percent impervious cover and an indicator of percent alteration of August median flow from groundwater withdrawals increase, the relative abundance and species richness of fluvial fish decrease. The quantile regression plots indicate that (1) as many as seven fluvial fish species are expected in streams with little flow alteration or impervious cover, (2) no more than four fluvial fish species are expected in streams where flow alterations from groundwater withdrawals exceed 50 percent of the August median flow or the percent area of impervious cover exceeds 15 percent, and (3) few fluvial fish remain at high rates of withdrawal (approaching 100 percent) or high rates of impervious cover (between 25 and 30 percent). Three generalized linear models (GLMs) were developed to quantify the response of fluvial fish to multiple environmental and anthropogenic variables. All variables in the GLM equations were demonstrated to be significant (p less than 0.05, with most less than 0.01). Variables in the fluvial-fish relative-abundance model were channel slope, estimated percent alteration of August median flow from groundwater withdrawals, percent wetland in a 240-meter buffer strip, and percent impervious cover. Variables in the fluvial-fish species-richness model were drainage area, channel slope, total undammed reach length, percent wetland in a 240-meter buffer strip, and percent impervious cover. Variables in the brook trout relativeabundance model were drainage area, percent open water, and percent impervious cover. The variability explained by the GLM models, as measured by the pseudo R2, ranged from 18.2 to 34.6, and correlations between observed and predicted values ranged from 0.50 to 0.60. Results of GLM models indicated that, keeping all other variables the same, a one-unit (1 percent) increase in the percent depletion of August median flow would result in a 0.9-percent decrease in the relative abundance (in counts per hour) of fluvial fish. The results of GLM models also indicated that a unit increase in impervious cover (1 percent) resulted in a 3.7-percent decrease in the relative abundance of fluvial fish, a 5.4-percent decrease in fluvial-fish species richness, and an 8.7-percent decrease in brook trout relative abundance.

Identifying the Driving Factors of Water Quality in a Sub-Watershed of the Republican River Basin, Kansas USA

PubMed Central

Burke, Morgen W. V.; Xu, Yeqian; Zheng, Haochi; VanLooy, Jeffrey

2018-01-01

Studies have shown that the agricultural expansion and land use changes in the Midwest of the U.S. are major drivers for increased nonpoint source pollution throughout the regional river systems. In this study, we empirically examined the relationship of planted area and production of three dominant crops with nitrate flux in the Republican River, Kansas, a sub-watershed of Mississippi River Basin. Our results show that land use in the region could not explain the observed changes in nitrate flux in the river. Instead, after including explanatory variables such as precipitation, growing degree days, and well water irrigation in the regression model we found that irrigation and spring precipitation could explain >85% of the variability in nitrate flux from 2000 to 2014. This suggests that changes in crop acreage and production alone cannot explain variability in nitrate flux. Future agricultural policy for the region should focus on controlling both the timing and amount of fertilizer applied to the field to reduce the potential leaching of excess fertilizer through spring time runoff and/or over-irrigation into nearby river systems. PMID:29789462

Watershed regressions for pesticides (warp) models for predicting atrazine concentrations in Corn Belt streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.

2012-01-01

Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.

Female homicide in Rio Grande do Sul, Brazil.

PubMed

Leites, Gabriela Tomedi; Meneghel, Stela Nazareth; Hirakata, Vania Noemi

2014-01-01

This study aimed to assess the female homicide rate due to aggression in Rio Grande do Sul, Brazil, using this as a "proxy" of femicide. This was an ecological study which correlated the female homicide rate due to aggression in Rio Grande do Sul, according to the 35 microregions defined by the Brazilian Institute of Geography and Statistics (IBGE), with socioeconomic and demographic variables access and health indicators. Pearson's correlation test was performed with the selected variables. After this, multiple linear regressions were performed with variables with p < 0.20. The standardized average of female homicide rate due to aggression in the period from 2003 to 2007 was 3.1 obits per 100 thousand. After multiple regression analysis, the final model included male mortality due to aggression (p = 0.016), the percentage of hospital admissions for alcohol (p = 0.005) and the proportion of ill-defined deaths (p = 0.015). The model have an explanatory power of 39% (adjusted r2 = 0.391). The results are consistent with other studies and indicate a strong relationship between structural violence in society and violence against women, in addition to a higher incidence of female deaths in places with high alcohol hospitalization.

Measurement error in epidemiologic studies of air pollution based on land-use regression models.

PubMed

Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

2013-10-15

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

The role of family-of-origin violence in men's marital violence perpetration.

PubMed

Delsol, Catherine; Margolin, Gayla

2004-03-01

This paper presents overall transmission rates between family-of-origin violence and marital violence, as well as theoretical and empirical work on possible mechanisms of transmission. In identified samples, approximately 60% of the maritally violent men report family-of-origin violence, whereas slightly over 20% of the comparison group of maritally nonviolent men report family-of-origin violence. Modest associations between experiencing violence in the family of origin and marital violence are found in community samples and in studies with prospective and longitudinal designs. Variables that intervene in the association between family-of-origin violence and marital violence are reviewed, with a focus on personal characteristics such as antisocial personality, psychological distress, and attitudes condoning violence, as well as on contextual factors, such as marital problems and conflict resolution style. Variables associated with nonviolence in men who grew up in violent families also are identified, including strong interpersonal connections and the ability to create psychological distance from the family-of-origin violence. Continued empirical investigation of variables that potentiate or mitigate the association between family-of-origin violence and marital violence at different developmental stages is needed to identify explanatory mechanisms and, ultimately, to interrupt the intergenerational transmission of marital violence.

Estimation of Nitrogen Vertical Distribution by Bi-Directional Canopy Reflectance in Winter Wheat

PubMed Central

Huang, Wenjiang; Yang, Qinying; Pu, Ruiliang; Yang, Shaoyuan

2014-01-01

Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively. PMID:25353983

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Mediation and moderation of treatment effects in randomised controlled trials of complex interventions.

PubMed

Emsley, Richard; Dunn, Graham; White, Ian R

2010-06-01

Complex intervention trials should be able to answer both pragmatic and explanatory questions in order to test the theories motivating the intervention and help understand the underlying nature of the clinical problem being tested. Key to this is the estimation of direct effects of treatment and indirect effects acting through intermediate variables which are measured post-randomisation. Using psychological treatment trials as an example of complex interventions, we review statistical methods which crucially evaluate both direct and indirect effects in the presence of hidden confounding between mediator and outcome. We review the historical literature on mediation and moderation of treatment effects. We introduce two methods from within the existing causal inference literature, principal stratification and structural mean models, and demonstrate how these can be applied in a mediation context before discussing approaches and assumptions necessary for attaining identifiability of key parameters of the basic causal model. Assuming that there is modification by baseline covariates of the effect of treatment (i.e. randomisation) on the mediator (i.e. covariate by treatment interactions), but no direct effect on the outcome of these treatment by covariate interactions leads to the use of instrumental variable methods. We describe how moderation can occur through post-randomisation variables, and extend the principal stratification approach to multiple group methods with explanatory models nested within the principal strata. We illustrate the new methodology with motivating examples of randomised trials from the mental health literature.

Longitudinal Analysis of Outpatient Physician Visits in the Oldest Old: Results of the AgeQualiDe Prospective Cohort Study.

PubMed

Hajek, A; Brettschneider, C; van den Bussche, H; Kaduszkiewicz, H; Oey, A; Wiese, B; Weyerer, S; Werle, J; Fuchs, A; Pentzek, M; Stein, J; Luck, T; Bickel, H; Mösch, E; Heser, K; Bleckwenn, M; Scherer, M; Riedel-Heller, S G; Maier, W; König, H-H

2018-01-01

The aim of this study was to identify determinants of outpatient health care utilization among the oldest old in Germany longitudinally. Multicenter prospective cohort "Study on Needs, health service use, costs and health-related quality of life in a large sample of oldest-old primary care patients (85+)" (AgeQualiDe). Individuals in very old age were recruited via GP offices at six study centers in Germany. The course of outpatient health care was observed over 10 months (two waves). Primary care patients aged 85 years and over (at baseline: n=861, with mean age of 89.0 years±2.9 years; 85-100 years). Self-reported numbers of outpatient visits to general practitioners (GP) and specialists in the past three months were used as dependent variables. Widely used scales were used to quantify explanatory variables (e.g., Geriatric Depression Scale, Instrumental Activities of Daily Living Scale, or Global Deterioration Scale). Fixed effects regressions showed that increases in GP visits were associated with increases in cognitive impairment, whereas they were not associated with changes in marital status, functional decline, increasing number of chronic conditions, increasing age, and changes in social network. Increases in specialist visits were not associated with changes in the explanatory variables. Our findings underline the importance of cognitive impairment for GP visits. Creating strategies to postpone cognitive decline might be beneficial for the health care system.

Factors related to dietary habits and body mass index among Turkish school children: a Cox's interaction model-based study.

PubMed

Haney, Meryem Ozturk; Erdogan, Semra

2013-06-01

To report a study conducted to describe the determinants of Turkish school-aged children's dietary habits and body mass index. Over the past two decades, children's unhealthy dietary habits and obesity have increased rapidly. Nurses have an essential role in minimizing health-risk behaviours and promoting healthy lifestyles. Using the Interaction Model of Client Health Behavior to measure children's dietary habits and body mass index values helps to prepare health-promotion interventions. A descriptive, correlational study. The study was conducted, based on a sample of 420 fifth-grade students and their parents in one city in Turkey. The data were collected during 2007 using a questionnaire designed to assess the dietary habits and anthropometric indices. Data were analysed using quantitative analysis to identify key variables. The girls scored healthier on dietary habits than did the boys. Although dietary self-efficacy was statistically significant as an explanatory variable of dietary habits for both genders, the dietary attitude was the only explanatory variable of dietary habits for the girls. No difference was detected in the prevalence of overweight between boys and girls. Nurses are well-situated to give children dietary self-efficacy improvement, dietary attitude enhancement, and family-centred and school-based intervention programmes to reduce their unhealthy dietary habits. The model guides researchers to identify the background characteristics of children that result in the body mass index. © 2012 Blackwell Publishing Ltd.

Agreement between self-reported sleep patterns and actigraphy in fibromyalgia and healthy women.

PubMed

Segura-Jiménez, Víctor; Camiletti-Moirón, Daniel; Munguía-Izquierdo, Diego; Álvarez-Gallardo, Inmaculada C; Ruiz, Jonatan R; Ortega, Francisco B; Delgado-Fernández, Manuel

2015-01-01

To examine the agreement between objective (accelerometer) and subjective measures of sleep in fibromyalgia women (FW) and healthy women (HW). To identify explanatory variables of the discrepancies between the objective and subjective measures in FW and in HW. 127 diagnosed FW and 53 HW filled the Fibromyalgia Impact Questionnaire (FIQ) and wore the SenseWear Pro Armband (SWA) for 7 days in order to assess sleep over the last week. Participants completed the Pittsburgh Sleep Quality Index (PSQI) when the SWA was returned. The SWA showed greater total duration (74 vs. 88 min/day) and average duration (7 vs. 9 min) of wake after sleep onset in FW compared with HW. The PSQI showed poorer sleep quality in all the variables studied in FW than in HW (all, p<0.001), except time in bed. There was a lack of inter-method agreement for total sleep time, sleep time without naps and sleep latency in FW. Age and educational status explained the inter-method mean difference in sleep time in FW. High discrepancy in sleep time between the SWA and the PSQI was related to higher FIQ scores (p<0.05). The objective measure only showed higher frequency and average duration of wake after sleep onset in FW compared with HW. The agreement between the SWA and the PSQI measures of sleep were poor in the FW group. Age, educational level and the impact of fibromyalgia might be explanatory variables of the inter-method discrepancies in FW.

Estimation of nitrogen vertical distribution by bi-directional canopy reflectance in winter wheat.

PubMed

Huang, Wenjiang; Yang, Qinying; Pu, Ruiliang; Yang, Shaoyuan

2014-10-28

Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively.

Induction-related cost of patients with acute myeloid leukaemia in France.

PubMed

Nerich, Virginie; Lioure, Bruno; Rave, Maryline; Recher, Christian; Pigneux, Arnaud; Witz, Brigitte; Escoffre-Barbe, Martine; Moles, Marie-Pierre; Jourdan, Eric; Cahn, Jean Yves; Woronoff-Lemsi, Marie-Christine

2011-04-01

The economic profile of acute myeloid leukaemia (AML) is badly known. The few studies published on this disease are now relatively old and include small numbers of patients. The purpose of this retrospective study was to evaluate the induction-related cost of 500 patients included in the AML 2001 trial, and to determine the explanatory factors of cost. "Induction" patient's hospital stay from admission for "induction" to discharge after induction. The study was performed from the French Public Health insurance perspective, restrictive to hospital institution costs. The average management of a hospital stay for "induction" was evaluated according to the analytical accounting of Besançon University Teaching Hospital and the French public Diagnosis-Related Group database. Multiple linear regression was used to search for explanatory factors. Only direct medical costs were included: treatment and hospitalisation. Mean induction-related direct medical cost was estimated at €41,852 ± 6,037, with a mean length of hospital stay estimated at 36.2 ± 10.7 days. After adjustment for age, sex and performance status, only two explanatory factors were found: an additional induction course and salvage course increased induction-related cost by 38% (± 4) and 15% (± 1) respectively, in comparison to one induction. These explanatory factors were associated with a significant increase in the mean length of hospital stay: 45.8 ± 11.6 days for 2 inductions and 38.5 ± 15.5 if the patient had a salvage course, in comparison to 32.9 ± 7.7 for one induction (P < 10⁻⁴). This result is robust and was confirmed by sensitivity analysis. Consideration of economic constraints in health care is now a reality. Only the control of length of hospital stay may lead to a decrease in induction-related cost for patients with AML.

The Mid-Career Malaise of Teachers: An Examination of Job Attitudes and the Factors Influencing Job Satisfaction in the Middle Years. Final Report.

ERIC Educational Resources Information Center

Lowther, Malcolm A.; And Others

This study examined the quality of teachers' work lives, teachers' job satisfaction, and the relationship between teachers' work experiences and their wider network of life experiences. Age was used as a key explanatory variable in each phase of this analysis. Data analyzed were from three sets of national surveys: (1) the 1969 Survey of Working…

Using a Market Ratio Factor in Faculty Salary Equity Studies. AIR Professional File. Number 103, Spring 2007

ERIC Educational Resources Information Center

Luna, Andrew L.

2007-01-01

The purpose of this study was to determine if a market ratio factor was a better predictor of faculty salaries than the use of k-1 dummy variables representing the various disciplines. This study used two multiple regression analyses to develop an explanatory model to determine which model might best explain faculty salaries. A total of 20 out of…

Equal Pay for Equal Work in Academic Obstetrics and Gynecology.

PubMed

Eichelberger, Kacey Y

2018-02-01

The most compelling data suggest women in academic obstetrics and gynecology earn approximately $36,000 less than male colleagues per year in regression models correcting for commonly cited explanatory variables. Although residual confounding may exist, academic departments in the United States should consider rigorous examination of their own internal metrics around salary to ensure gender-neutral compensation, commonly referred to as equal pay for equal work.

A data-centric approach to understanding the pricing of financial options

NASA Astrophysics Data System (ADS)

Healy, J.; Dixon, M.; Read, B.; Cai, F. F.

2002-05-01

We investigate what can be learned from a purely phenomenological study of options prices without modelling assumptions. We fitted neural net (NN) models to LIFFE ``ESX'' European style FTSE 100 index options using daily data from 1992 to 1997. These non-parametric models reproduce the Black-Scholes (BS) analytic model in terms of fit and performance measures using just the usual five inputs (S, X, t, r, IV). We found that adding transaction costs (bid-ask spread) to these standard five parameters gives a comparable fit and performance. Tests show that the bid-ask spread can be a statistically significant explanatory variable for option prices. The difference in option prices between the models with transaction costs and those without ranges from about -3.0 to +1.5 index points, varying with maturity date. However, the difference depends on the moneyness (S/X), being greatest in-the-money. This suggests that use of a five-factor model can result in a pricing difference of up to #10 to #30 per call option contract compared with modelling under transaction costs. We found that the influence of transaction costs varied between different yearly subsets of the data. Open interest is also a significant explanatory variable, but volume is not.

Individual Factors Affecting Self-esteem, and Relationships Among Self-esteem, Body Mass Index, and Body Image in Patients With Schizophrenia.

PubMed

Oh, EunJung; Song, EunJu; Shin, JungEun

2017-12-01

The purposes of this study were to identify correlations between body mass index, body image, and self-esteem in patients with schizophrenia and to analyse the specific factors affecting self-esteem. This study had a descriptive design, utilising a cross-sectional survey. Participants were patients with schizophrenia who were admitted to a mental health facility in South Korea. A total of 180 questionnaires were distributed, and an appropriate total sample size of 167 valid questionnaires was analysed. Self-esteem was significantly correlated with body image, the subscale of appearance orientation, and body areas satisfaction. However, body mass index exhibited no significant correlation with any variable. The variables found to have a significant explanatory power of 21.4% were appearance orientation and body areas satisfaction. The explanatory power of all factors was 33.6%. The self-esteem of patients with schizophrenia was influenced by body mass index and body image. The positive symptoms of schizophrenia can be controlled by medication, whereas negative symptoms can be improved through education and nursing care with medication. Thus, psychiatric nurses should develop education and care programs that contribute to the positive body image and self-esteem of patients with schizophrenia. Copyright © 2017 Elsevier Inc. All rights reserved.

Information Sources as Explanatory Variables for the Belgian Health-Related Risk Perception of the Fukushima Nuclear Accident.

PubMed

Vyncke, Bart; Perko, Tanja; Van Gorp, Baldwin

2017-03-01

The media play an important role in risk communication, providing information about accidents, both nearby and far away. Each media source has its own presentation style, which could influence how the audience perceives the presented risk. This study investigates the explanatory power of 12 information sources (traditional media, new media, social media, and interpersonal communication) for the perceived risk posed by radiation released from the damaged Fukushima nuclear power plant on respondents' own health and that of the population in general. The analysis controlled for attitude toward nuclear energy, gender, education, satisfaction with the media coverage, and duration of attention paid to the coverage. The study uses a large empirical data set from a public opinion survey, which is representative for the Belgian population with respect to six sociodemographic variables. Results show that three information sources are significant regressors of perceived health-related risk of the nuclear accident: television, interpersonal communication, and the category of miscellaneous online sources. More favorable attitudes toward nuclear power, longer attention to the coverage, and higher satisfaction with the provided information lead to lower risk perception. Taken together, the results suggest that the media can indeed have a modest influence on how the audience perceives a risk. © 2016 Society for Risk Analysis.

Groundwater salinity in a floodplain forest impacted by saltwater intrusion.

PubMed

Kaplan, David A; Muñoz-Carpena, Rafael

2014-11-15

Coastal wetlands occupy a delicate position at the intersection of fresh and saline waters. Changing climate and watershed hydrology can lead to saltwater intrusion into historically freshwater systems, causing plant mortality and loss of freshwater habitat. Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals, however finding direct relationships between hydrological inputs and floodplain hydrology is complicated by interactions between surface water, groundwater, and atmospheric fluxes in variably saturated soils with heterogeneous vegetation and topography. Thus, an alternative method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unexplained common variability, and explanatory variables. DFA was applied to model shallow groundwater salinity in the forested floodplain wetlands of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes. Long-term, high-resolution groundwater salinity datasets revealed dynamics over seasonal and yearly time periods as well as over tidal cycles and storm events. DFA identified shared trends among salinity time series and a full dynamic factor model simulated observed series well (overall coefficient of efficiency, Ceff=0.85; 0.52≤Ceff≤0.99). A reduced multilinear model based solely on explanatory variables identified in the DFA had fair to good results (Ceff=0.58; 0.38≤Ceff≤0.75) and may be used to assess the effects of restoration and management scenarios on shallow groundwater salinity in the Loxahatchee River floodplain. Copyright © 2014 Elsevier B.V. All rights reserved.

Spatial Modeling of Flood Duration in Amazonian Floodplains Through Radar Remote Sensing and Generalized Linear Models

NASA Astrophysics Data System (ADS)

Ferreira-Ferreira, J.; Francisco, M. S.; Silva, T. S. F.

2017-12-01

Amazon floodplains play an important role in biodiversity maintenance and provide important ecosystem services. Flood duration is the prime factor modulating biogeochemical cycling in Amazonian floodplain systems, as well as influencing ecosystem structure and function. However, due to the absence of accurate terrain information, fine-scale hydrological modeling is still not possible for most of the Amazon floodplains, and little is known regarding the spatio-temporal behavior of flooding in these environments. Our study presents an new approach for spatial modeling of flood duration, using Synthetic Aperture Radar (SAR) and Generalized Linear Modeling. Our focal study site was Mamirauá Sustainable Development Reserve, in the Central Amazon. We acquired a series of L-band ALOS-1/PALSAR Fine-Beam mosaics, chosen to capture the widest possible range of river stage heights at regular intervals. We then mapped flooded area on each image, and used the resulting binary maps as the response variable (flooded/non-flooded) for multiple logistic regression. Explanatory variables were accumulated precipitation 15 days prior and the water stage height recorded in the Mamirauá lake gauging station observed for each image acquisition date, Euclidean distance from the nearest drainage, and slope, terrain curvature, profile curvature, planform curvature and Height Above the Nearest Drainage (HAND) derived from the 30-m SRTM DEM. Model results were validated with water levels recorded by ten pressure transducers installed within the floodplains, from 2014 to 2016. The most accurate model included water stage height and HAND as explanatory variables, yielding a RMSE of ±38.73 days of flooding per year when compared to the ground validation sites. The largest disagreements were 57 days and 83 days for two validation sites, while remaining locations achieved absolute errors lower than 38 days. In five out of nine validation sites, the model predicted flood durations with disagreements lower than 20 days. The method extends our current capability to answer relevant scientific questions regarding floodplain ecological structure and functioning, and allows forecasting of ecological and biogeochemical alterations under climate change scenarios, using readily available datasets.

Dynamic factor analysis of groundwater quality trends in an agricultural area adjacent to Everglades National Park.

PubMed

Muñoz-Carpena, R; Ritter, A; Li, Y C

2005-11-01

The extensive eastern boundary of Everglades National Park (ENP) in south Florida (USA) is subject to one of the most expensive and ambitious environmental restoration projects in history. Understanding and predicting the water quality interactions between the shallow aquifer and surface water is a key component in meeting current environmental regulations and fine-tuning ENP wetland restoration while still maintaining flood protection for the adjacent developed areas. Dynamic factor analysis (DFA), a recent technique for the study of multivariate non-stationary time-series, was applied to study fluctuations in groundwater quality in the area. More than two years of hydrological and water quality time series (rainfall; water table depth; and soil, ground and surface water concentrations of N-NO3-, N-NH4+, P-PO4(3-), Total P, F-and Cl-) from a small agricultural watershed adjacent to the ENP were selected for the study. The unexplained variability required for determining the concentration of each chemical in the 16 wells was greatly reduced by including in the analysis some of the observed time series as explanatory variables (rainfall, water table depth, and soil and canal water chemical concentration). DFA results showed that groundwater concentration of three of the agrochemical species studied (N-NO3-, P-PO4(3-)and Total P) were affected by the same explanatory variables (water table depth, enriched topsoil, and occurrence of a leaching rainfall event, in order of decreasing relative importance). This indicates that leaching by rainfall is the main mechanism explaining concentration peaks in groundwater. In the case of N-NH4+, in addition to leaching, groundwater concentration is governed by lateral exchange with canals. F-and Cl- are mainly affected by periods of dilution by rainfall recharge, and by exchange with the canals. The unstructured nature of the common trends found suggests that these are related to the complex spatially and temporally varying land use patterns in the watershed. The results indicate that peak concentrations of agrochemicals in groundwater could be reduced by improving fertilization practices (by splitting and modifying timing of applications) and by operating the regional canal system to maintain the water table low, especially during the rainy periods.

Dynamic factor analysis of groundwater quality trends in an agricultural area adjacent to Everglades National Park

NASA Astrophysics Data System (ADS)

Muñoz-Carpena, R.; Ritter, A.; Li, Y. C.

2005-11-01

The extensive eastern boundary of Everglades National Park (ENP) in south Florida (USA) is subject to one of the most expensive and ambitious environmental restoration projects in history. Understanding and predicting the water quality interactions between the shallow aquifer and surface water is a key component in meeting current environmental regulations and fine-tuning ENP wetland restoration while still maintaining flood protection for the adjacent developed areas. Dynamic factor analysis (DFA), a recent technique for the study of multivariate non-stationary time-series, was applied to study fluctuations in groundwater quality in the area. More than two years of hydrological and water quality time series (rainfall; water table depth; and soil, ground and surface water concentrations of N-NO 3-, N-NH 4+, P-PO 43-, Total P, F -and Cl -) from a small agricultural watershed adjacent to the ENP were selected for the study. The unexplained variability required for determining the concentration of each chemical in the 16 wells was greatly reduced by including in the analysis some of the observed time series as explanatory variables (rainfall, water table depth, and soil and canal water chemical concentration). DFA results showed that groundwater concentration of three of the agrochemical species studied (N-NO 3-, P-PO 43-and Total P) were affected by the same explanatory variables (water table depth, enriched topsoil, and occurrence of a leaching rainfall event, in order of decreasing relative importance). This indicates that leaching by rainfall is the main mechanism explaining concentration peaks in groundwater. In the case of N-NH 4+, in addition to leaching, groundwater concentration is governed by lateral exchange with canals. F -and Cl - are mainly affected by periods of dilution by rainfall recharge, and by exchange with the canals. The unstructured nature of the common trends found suggests that these are related to the complex spatially and temporally varying land use patterns in the watershed. The results indicate that peak concentrations of agrochemicals in groundwater could be reduced by improving fertilization practices (by splitting and modifying timing of applications) and by operating the regional canal system to maintain the water table low, especially during the rainy periods.

Predicting factors for malaria re-introduction: an applied model in an elimination setting to prevent malaria outbreaks.

PubMed

Ranjbar, Mansour; Shoghli, Alireza; Kolifarhood, Goodarz; Tabatabaei, Seyed Mehdi; Amlashi, Morteza; Mohammadi, Mahdi

2016-03-02

Malaria re-introduction is a challenge in elimination settings. To prevent re-introduction, receptivity, vulnerability, and health system capacity of foci should be monitored using appropriate tools. This study aimed to design an applicable model to monitor predicting factors of re-introduction of malaria in highly prone areas. This exploratory, descriptive study was conducted in a pre-elimination setting with a high-risk of malaria transmission re-introduction. By using nominal group technique and literature review, a list of predicting indicators for malaria re-introduction and outbreak was defined. Accordingly, a checklist was developed and completed in the field for foci affected by re-introduction and for cleared-up foci as a control group, for a period of 12 weeks before re-introduction and for the same period in the previous year. Using field data and analytic hierarchical process (AHP), each variable and its sub-categories were weighted, and by calculating geometric means for each sub-category, score of corresponding cells of interaction matrices, lower and upper threshold of different risks strata, including low and mild risk of re-introduction and moderate and high risk of malaria outbreaks, were determined. The developed predictive model was calibrated through resampling with different sets of explanatory variables using R software. Sensitivity and specificity of the model were calculated based on new samples. Twenty explanatory predictive variables of malaria re-introduction were identified and a predictive model was developed. Unpermitted immigrants from endemic neighbouring countries were determined as a pivotal factor (AHP score: 0.181). Moreover, quality of population movement (0.114), following malaria transmission season (0.088), average daily minimum temperature in the previous 8 weeks (0.062), an outdoor resting shelter for vectors (0.045), and rainfall (0.042) were determined. Positive and negative predictive values of the model were 81.8 and 100 %, respectively. This study introduced a new, simple, yet reliable model to forecast malaria re-introduction and outbreaks eight weeks in advance in pre-elimination and elimination settings. The model incorporates comprehensive deterministic factors that can easily be measured in the field, thereby facilitating preventive measures.

A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts.

PubMed

Brunwasser, Steven M; Gebretsadik, Tebeb; Gold, Diane R; Turi, Kedir N; Stone, Cosby A; Datta, Soma; Gern, James E; Hartert, Tina V

2018-01-01

The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0-2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders. We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with asthma morbidity, as demonstrated in two ECHO birth cohort studies, and provides a more statistically powerful method of testing etiologic hypotheses of childhood wheezing illness and asthma.

Scrub typhus islands in the Taiwan area and the association between scrub typhus disease and forest land use and farmer population density: geographically weighted regression.

PubMed

Tsai, Pui-Jen; Yeh, Hsi-Chyi

2013-04-29

The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined. We applied Pearson's product moment correlation to calculate the correlation between the incidence of scrub typhus and meteorological factors among 10 local climate regions. We used the geographically weighted regression (GWR) method, a type of spatial regression that generates parameters disaggregated by the spatial units of analysis, to detail and map each regression point for the response variables of the standardized incidence ratio (SIR)-district scrub typhus. We also applied the GWR to examine the explanatory variables of types of forest-land use and farm worker density in Taiwan in 2005. In the Taiwan Area, scrub typhus endemic areas are located in the southeastern regions and mountainous townships of Taiwan, as well as the Pescadore, Kinmen, and Matou Islands. Among these islands and low-incidence areas in the central western and southwestern regions of Taiwan, we observed a significant correlation between scrub typhus incidence and surface temperature. No similar significant correlation was found in the endemic areas (e.g., the southeastern region and the mountainous area of Taiwan). Precipitation correlates positively with scrub typhus incidence in 3 local climate regions (i.e., Taiwan's central western and southwestern regions, and the Kinmen Islands). Relative humidity correlates positively with incidence in Southwestern Taiwan and the Kinmen Islands. The number of wet days correlates positively with incidence in Southwestern Taiwan. The duration of sunshine correlates positively with incidence in Central Western Taiwan, as well as the Kinmen and Matou Islands. In addition, the 10 local climatic regions can be classified into the following 3 groups, based on the warm-cold seasonal fluctuations in scrub typhus incidence: (a) Type 1, evident in 5 local climate regions (Taiwan's northern, northwestern, northeastern, and southeastern regions, as well as the mountainous area); (b) Type 2 (Taiwan's central western and southwestern regions, and the Pescadore Islands); and (c) Type 3 (the Kinmen and Matou Islands). In the GWR models, the response variable of the SIR-district scrub typhus has a statistically significantly positive association with 2 explanatory variables (farm worker population density and timber management). In addition, other explanatory variables (recreational forests, natural reserves, and "other purpose" areas) show positive or negative signs for parameter estimates in various locations in Taiwan. Negative signs of parameter estimates occurred only for the explanatory variables of national protectorates, plantations, and clear-cut areas. The results of this study show that scrub typhus in Taiwan can be classified into 3 types. Type 1 exhibits no climatic effect, whereas the incidence of Type 2 correlates positively with higher temperatures during the warm season, and the incidence of Type 3 correlates positively with higher surface temperatures and longer hours of sunshine. The results also show that in the mountainous township areas of Taiwan's central and southern regions, as well as in Southeastern Taiwan, higher SIR values for scrub typhus are associated with the following variables: farm worker population density, timber management, and area type (i.e., recreational forest, natural reserve, or other purpose).

Scrub typhus islands in the Taiwan area and the association between scrub typhus disease and forest land use and farmer population density: geographically weighted regression

PubMed Central

2013-01-01

Background The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined. Methods We applied Pearson’s product moment correlation to calculate the correlation between the incidence of scrub typhus and meteorological factors among 10 local climate regions. We used the geographically weighted regression (GWR) method, a type of spatial regression that generates parameters disaggregated by the spatial units of analysis, to detail and map each regression point for the response variables of the standardized incidence ratio (SIR)-district scrub typhus. We also applied the GWR to examine the explanatory variables of types of forest-land use and farm worker density in Taiwan in 2005. Results In the Taiwan Area, scrub typhus endemic areas are located in the southeastern regions and mountainous townships of Taiwan, as well as the Pescadore, Kinmen, and Matou Islands. Among these islands and low-incidence areas in the central western and southwestern regions of Taiwan, we observed a significant correlation between scrub typhus incidence and surface temperature. No similar significant correlation was found in the endemic areas (e.g., the southeastern region and the mountainous area of Taiwan). Precipitation correlates positively with scrub typhus incidence in 3 local climate regions (i.e., Taiwan’s central western and southwestern regions, and the Kinmen Islands). Relative humidity correlates positively with incidence in Southwestern Taiwan and the Kinmen Islands. The number of wet days correlates positively with incidence in Southwestern Taiwan. The duration of sunshine correlates positively with incidence in Central Western Taiwan, as well as the Kinmen and Matou Islands. In addition, the 10 local climatic regions can be classified into the following 3 groups, based on the warm-cold seasonal fluctuations in scrub typhus incidence: (a) Type 1, evident in 5 local climate regions (Taiwan’s northern, northwestern, northeastern, and southeastern regions, as well as the mountainous area); (b) Type 2 (Taiwan’s central western and southwestern regions, and the Pescadore Islands); and (c) Type 3 (the Kinmen and Matou Islands). In the GWR models, the response variable of the SIR-district scrub typhus has a statistically significantly positive association with 2 explanatory variables (farm worker population density and timber management). In addition, other explanatory variables (recreational forests, natural reserves, and “other purpose” areas) show positive or negative signs for parameter estimates in various locations in Taiwan. Negative signs of parameter estimates occurred only for the explanatory variables of national protectorates, plantations, and clear-cut areas. Conclusion The results of this study show that scrub typhus in Taiwan can be classified into 3 types. Type 1 exhibits no climatic effect, whereas the incidence of Type 2 correlates positively with higher temperatures during the warm season, and the incidence of Type 3 correlates positively with higher surface temperatures and longer hours of sunshine. The results also show that in the mountainous township areas of Taiwan’s central and southern regions, as well as in Southeastern Taiwan, higher SIR values for scrub typhus are associated with the following variables: farm worker population density, timber management, and area type (i.e., recreational forest, natural reserve, or other purpose). PMID:23627966

Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany

NASA Astrophysics Data System (ADS)

Lüttger, Andrea B.; Feike, Til

2018-04-01

Climate change constitutes a major challenge for high productivity in wheat, the most widely grown crop in Germany. Extreme weather events including dry spells and heat waves, which negatively affect wheat yields, are expected to aggravate in the future. It is crucial to improve the understanding of the spatiotemporal development of such extreme weather events and the respective crop-climate relationships in Germany. Thus, the present study is a first attempt to evaluate the historic development of relevant drought and heat-related extreme weather events from 1901 to 2010 on county level (NUTS-3) in Germany. Three simple drought indices and two simple heat stress indices were used in the analysis. A continuous increase in dry spells over time was observed over the investigated periods from 1901-1930, 1931-1960, 1961-1990 to 2001-2010. Short and medium dry spells, i.e., precipitation-free periods longer than 5 and 8 days, respectively, increased more strongly compared to longer dry spells (longer than 11 days). The heat-related stress indices with maximum temperatures above 25 and 28 °C during critical wheat growth phases showed no significant increase over the first three periods but an especially sharp increase in the final 1991-2010 period with the increases being particularly pronounced in parts of Southwestern Germany. Trend analysis over the entire 110-year period using Mann-Kendall test revealed a significant positive trend for all investigated indices except for heat stress above 25 °C during flowering period. The analysis of county-level yield data from 1981 to 2010 revealed declining spatial yield variability and rather constant temporal yield variability over the three investigated (1981-1990, 1991-2000, and 2001-2010) decades. A clear spatial gradient manifested over time with variability in the West being much smaller than in the east of Germany. Correlating yield variability with the previously analyzed extreme weather indices revealed strong spatiotemporal fluctuations in explanatory power of the different indices over all German counties and the three time periods. Over the 30 years, yield deviations were increasingly well correlated with heat and drought-related indices, with the number of days with maximum temperature above 25 °C during anthesis showing a sharp increase in explanatory power over entire Germany in the final 2001-2010 period.

Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07

USGS Publications Warehouse

Oden, Timothy D.; Asquith, William H.; Milburn, Matthew S.

2009-01-01

In December 2005, the U.S. Geological Survey in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (total coliform and Escherichia coli), atrazine, and suspended sediment at two U.S. Geological Survey streamflow-gaging stations upstream from Lake Houston near Houston (08068500 Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Texas). The data from the discrete water-quality samples collected during 2005-07, in conjunction with monitored real-time data already being collected - physical properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), streamflow, and rainfall - were used to develop regression models for predicting water-quality constituent concentrations for inflows to Lake Houston. Rainfall data were obtained from a rain gage monitored by Harris County Homeland Security and Emergency Management and colocated with the Spring Creek station. The leaps and bounds algorithm was used to find the best subsets of possible regression models (minimum residual sum of squares for a given number of variables). The potential explanatory or predictive variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, rainfall, and time (to account for seasonal variations inherent in some water-quality data). The response variables at each site were nitrite plus nitrate nitrogen, total phosphorus, organic carbon, Escherichia coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities as a means to estimate concentrations of the various constituents under investigation, with accompanying estimates of measurement uncertainty. Each regression equation can be used to estimate concentrations of a given constituent in real time. In conjunction with estimated concentrations, constituent loads were estimated by multiplying the estimated concentration by the corresponding streamflow and applying the appropriate conversion factor. By computing loads from estimated constituent concentrations, a continuous record of estimated loads can be available for comparison to total maximum daily loads. The regression equations presented in this report are site specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the methods that were developed and documented could be applied to other tributaries to Lake Houston for estimating real-time water-quality data for streams entering Lake Houston.