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.

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.

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…

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.

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.

Spatial regression analysis on 32 years of total column ozone data

NASA Astrophysics Data System (ADS)

Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.

2014-08-01

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.

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.

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.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

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 public health model of Medicaid emergency room use.

PubMed

de Alteriis, M; Fanning, T

1991-01-01

This study builds a public health model of Medicaid emergency room use for 57 upstate counties in New York from 1985 to 1987. The principle explanatory variables are primary care use (based in physicians' offices, freestanding clinics, and hospital outpatient departments), the concentration of poverty, and geographic and hospital availability. These factors influence the emergency room use of all Medicaid aid categories apart from the Supplemental Security Income recipients. Inherent in these findings are a number of policy implications that are explored in this article.

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.

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

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

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Sheyenne River, North Dakota, 1980-2006

USGS Publications Warehouse

Ryberg, Karen R.

2007-01-01

This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the North Dakota State Water Commission, to estimate water-quality constituent concentrations at seven sites on the Sheyenne River, N. Dak. Regression analysis of water-quality data collected in 1980-2006 was used to estimate concentrations for hardness, dissolved solids, calcium, magnesium, sodium, and sulfate. The explanatory variables examined for the regression relations were continuously monitored streamflow, specific conductance, and water temperature. For the conditions observed in 1980-2006, streamflow was a significant explanatory variable for some constituents. Specific conductance was a significant explanatory variable for all of the constituents, and water temperature was not a statistically significant explanatory variable for any of the constituents in this study. The regression relations were evaluated using common measures of variability, including R2, the proportion of variability in the estimated constituent concentration explained by the explanatory variables and regression equation. R2 values ranged from 0.784 for calcium to 0.997 for dissolved solids. The regression relations 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 1.7 for dissolved solids to 11.5 for sulfate. The regression relations also may be used to estimate daily constituent loads. The relations should be monitored for change over time, especially at sites 2 and 3 which have a short period of record. In addition, caution should be used when the Sheyenne River is affected by ice or when upstream sites are affected by isolated storm runoff. Almost all of the outliers and highly influential samples removed from the analysis were made during periods when the Sheyenne River might be affected by ice.

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.

PubMed

Bertl, Johanna; Guo, Qianyun; Juul, Malene; Besenbacher, Søren; Nielsen, Morten Muhlig; Hornshøj, Henrik; Pedersen, Jakob Skou; Hobolth, Asger

2018-04-19

Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

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.

Spatial analysis of participation in the Waterloo Residential Energy Efficiency Project

NASA Astrophysics Data System (ADS)

Song, Ge Bella

Researchers are in broad agreement that energy-conserving actions produce economic as well as energy savings. Household energy rating systems (HERS) have been established in many countries to inform households of their house's current energy performance and to help reduce their energy consumption and greenhouse gas emissions. In Canada, the national EnerGuide for Houses (EGH) program is delivered by many local delivery agents, including non-profit green community organizations. Waterloo Region Green Solutions is the local non-profit that offers the EGH residential energy evaluation service to local households. The purpose of this thesis is to explore the determinants of household's participation in the residential energy efficiency program (REEP) in Waterloo Region, to explain the relationship between the explanatory variables and REEP participation, and to propose ways to improve this kind of program. A spatial (trend) analysis was conducted within a geographic information system (GIS) to determine the spatial patterns of the REEP participation in Waterloo Region from 1999 to 2006. The impact of sources of information on participation and relationships between participation rates and explanatory variables were identified. GIS proved successful in presenting a visual interpretation of spatial patterns of the REEP participation. In general, the participating households tend to be clustered in urban areas and scattered in rural areas. Different sources of information played significant roles in reaching participants in different years. Moreover, there was a relationship between each explanatory variable and the REEP participation rates. Statistical analysis was applied to obtain a quantitative assessment of relationships between hypothesized explanatory variables and participation in the REEP. The Poisson regression model was used to determine the relationship between hypothesized explanatory variables and REEP participation at the CDA level. The results show that all of the independent variables have a statistically significant positive relationship with REEP participation. These variables include level of education, average household income, employment rate, home ownership, population aged 65 and over, age of home, and number of eligible dwellings. The logistic regression model was used to assess the ability of the hypothesized explanatory variables to predict whether or not households would participate in a second follow-up evaluation after completing upgrades to their home. The results show all the explanatory variables have significant relationships with the dependent variable. The increased rating score, average household income, aged population, and age of home are positively related to the dependent variable. While the dwelling size and education has negative relationships with the dependent variable. In general, the contribution of this work provides a practical understanding of how the energy efficiency program operates, and insight into the type of variables that may be successful in bringing about changes in performance in the energy efficiency project in Waterloo Region. Secondly, with the completion of this research, future residential energy efficiency programs can use the information from this research and emulate or expand upon the efforts and lessons learned from the Residential Energy Efficiency Project in Waterloo Region case study. Thirdly, this research also contributes to practical experience on how to integrate different datasets using GIS.

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.

Adaptive capacity of the Adjusted Clinical Groups Case-Mix System to the cost of primary healthcare in Catalonia (Spain): a observational study.

PubMed

Sicras-Mainar, Antoni; Velasco-Velasco, Soledad; Navarro-Artieda, Ruth; Prados-Torres, Alexandra; Bolibar-Ribas, Buenaventura; Violan-Fors, Concepción

2012-01-01

To describe the adaptive capacity of the Adjusted Clinical Groups (ACG) system to the cost of care in primary healthcare centres in Catalonia (Spain). Retrospective study (multicentres) conducted using computerised medical records. 13 primary care teams in 2008 were included. All patients registered in the study centres who required care between 1 January and 31 December 2008 were finally studied. Patients not registered in the study centres during the study period were excluded. Demographic (age and sex), dependent (cost of care) and case-mix variables were studied. The cost model for each patient was established by differentiating the fixed and variable costs. To evaluate the adaptive capacity of the ACG system, Pearson's coefficient of variation and the percentage of outliers were calculated. To evaluate the explanatory power of the ACG system, the authors used the coefficient of determination (R(2)). The number of patients studied was 227 235 (frequency: 5.9 visits per person per year), with a mean of 4.5 (3.2) episodes and 8.1 (8.2) visits per patient per year. The mean total cost was €654.2. The explanatory power of the ACG system was 36.9% for costs (56.5% without outliers). 10 ACG categories accounted for 60.1% of all cases and 19 for 80.9%. 5 categories represented 71% of poor performance (N=78 887, 34.7%), particularly category 0300-Acute Minor, Age 6+ (N=26 909, 11.8%), which had a coefficient of variation =139% and 6.6% of outliers. The ACG system is an appropriate manner of classifying patients in routine clinical practice in primary healthcare centres in Catalonia, although improvements to the adaptive capacity through disaggregation of some categories according to age groups and, especially, the number of acute episodes in paediatric patients would be necessary to reduce intra-group variation.

Explanatory Variables Associated with Campylobacter and Escherichia coli Concentrations on Broiler Chicken Carcasses during Processing in Two Slaughterhouses.

PubMed

Pacholewicz, Ewa; Swart, Arno; Wagenaar, Jaap A; Lipman, Len J A; Havelaar, Arie H

2016-12-01

This study aimed at identifying explanatory variables that were associated with Campylobacter and Escherichia coli concentrations throughout processing in two commercial broiler slaughterhouses. Quantative data on Campylobacter and E. coli along the processing line were collected. Moreover, information on batch characteristics, slaughterhouse practices, process performance, and environmental variables was collected through questionnaires, observations, and measurements, resulting in data on 19 potential explanatory variables. Analysis was conducted separately in each slaughterhouse to identify which variables were related to changes in concentrations of Campylobacter and E. coli during the processing steps: scalding, defeathering, evisceration, and chilling. Associations with explanatory variables were different in the slaughterhouses studied. In the first slaughterhouse, there was only one significant association: poorer uniformity of the weight of carcasses within a batch with less decrease in E. coli concentrations after defeathering. In the second slaughterhouse, significant statistical associations were found with variables, including age, uniformity, average weight of carcasses, Campylobacter concentrations in excreta and ceca, and E. coli concentrations in excreta. Bacterial concentrations in excreta and ceca were found to be the most prominent variables, because they were associated with concentration on carcasses at various processing points. Although the slaughterhouses produced specific products and had different batch characteristics and processing parameters, the effect of the significant variables was not always the same for each slaughterhouse. Therefore, each slaughterhouse needs to determine its particular relevant measures for hygiene control and process management. This identification could be supported by monitoring changes in bacterial concentrations during processing in individual slaughterhouses. In addition, the possibility that management and food handling practices in slaughterhouses contribute to the differences in bacterial contamination between slaughterhouses needs further investigation.

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.

Variables affecting learning in a simulation experience: a mixed methods study.

PubMed

Beischel, Kelly P

2013-02-01

The primary purpose of this study was to test a hypothesized model describing the direct effects of learning variables on anxiety and cognitive learning outcomes in a high-fidelity simulation (HFS) experience. The secondary purpose was to explain and explore student perceptions concerning the qualities and context of HFS affecting anxiety and learning. This study used a mixed methods quantitative-dominant explanatory design with concurrent qualitative data collection to examine variables affecting learning in undergraduate, beginning nursing students (N = 124). Being ready to learn, having a strong auditory-verbal learning style, and being prepared for simulation directly affected anxiety, whereas learning outcomes were directly affected by having strong auditory-verbal and hands-on learning styles. Anxiety did not quantitatively mediate cognitive learning outcomes as theorized, although students qualitatively reported debilitating levels of anxiety. This study advances nursing education science by providing evidence concerning variables affecting learning outcomes in HFS.

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.

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).

Dose-Response Calculator for ArcGIS

USGS Publications Warehouse

Hanser, Steven E.; Aldridge, Cameron L.; Leu, Matthias; Nielsen, Scott E.

2011-01-01

The Dose-Response Calculator for ArcGIS is a tool that extends the Environmental Systems Research Institute (ESRI) ArcGIS 10 Desktop application to aid with the visualization of relationships between two raster GIS datasets. A dose-response curve is a line graph commonly used in medical research to examine the effects of different dosage rates of a drug or chemical (for example, carcinogen) on an outcome of interest (for example, cell mutations) (Russell and others, 1982). Dose-response curves have recently been used in ecological studies to examine the influence of an explanatory dose variable (for example, percentage of habitat cover, distance to disturbance) on a predicted response (for example, survival, probability of occurrence, abundance) (Aldridge and others, 2008). These dose curves have been created by calculating the predicted response value from a statistical model at different levels of the explanatory dose variable while holding values of other explanatory variables constant. Curves (plots) developed using the Dose-Response Calculator overcome the need to hold variables constant by using values extracted from the predicted response surface of a spatially explicit statistical model fit in a GIS, which include the variation of all explanatory variables, to visualize the univariate response to the dose variable. Application of the Dose-Response Calculator can be extended beyond the assessment of statistical model predictions and may be used to visualize the relationship between any two raster GIS datasets (see example in tool instructions). This tool generates tabular data for use in further exploration of dose-response relationships and a graph of the dose-response curve.

Population characteristics, mechanisms of primary care and premature mortality in England: a cross-sectional study.

PubMed

Baker, Richard; Honeyford, Kate; Levene, Louis S; Mainous, Arch G; Jones, David R; Bankart, M John; Stokes, Tim

2016-02-11

Health systems with strong primary care tend to have better population outcomes, but in many countries demand for care is growing. We sought to identify mechanisms of primary care that influence premature mortality. We developed a conceptual model of the mechanisms by which primary care influences premature mortality, and undertook a cross-sectional study in which population and primary care variables reflecting the model were used to explain variations in mortality of those aged under 75 years. The premature standardised mortality ratios (SMRs) for each practice, available from the Department of Health, had been calculated from numbers of deaths in the 5 years from 2006 to 2010. A regression model was undertaken with explanatory variables for the year 2009/2010, and repeated to check stability using data for 2008/2009 and 2010/2011. All general practices in England were eligible for inclusion and, of the total of 8290, complete data were available for 7858. Population variables, particularly deprivation, were the most powerful predictors of premature mortality, but the mechanisms of primary care depicted in our model also affected mortality. The number of GPs/1000 population and detection of hypertension were negatively associated with mortality. In less deprived practices, continuity of care was also negatively associated with mortality. Greater supply of primary care is associated with lower premature mortality even in a health system that has strong primary care (England). Health systems need to sustain the capacity of primary care to deliver effective care, and should assist primary care providers in identifying and meeting the needs of socioeconomically deprived groups. 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/

Validation of ACG Case-mix for equitable resource allocation in Swedish primary health care.

PubMed

Zielinski, Andrzej; Kronogård, Maria; Lenhoff, Håkan; Halling, Anders

2009-09-18

Adequate resource allocation is an important factor to ensure equity in health care. Previous reimbursement models have been based on age, gender and socioeconomic factors. An explanatory model based on individual need of primary health care (PHC) has not yet been used in Sweden to allocate resources. The aim of this study was to examine to what extent the ACG case-mix system could explain concurrent costs in Swedish PHC. Diagnoses were obtained from electronic PHC records of inhabitants in Blekinge County (approx. 150,000) listed with public PHC (approx. 120,000) for three consecutive years, 2004-2006. The inhabitants were then classified into six different resource utilization bands (RUB) using the ACG case-mix system. The mean costs for primary health care were calculated for each RUB and year. Using linear regression models and log-cost as dependent variable the adjusted R2 was calculated in the unadjusted model (gender) and in consecutive models where age, listing with specific PHC and RUB were added. In an additional model the ACG groups were added. Gender, age and listing with specific PHC explained 14.48-14.88% of the variance in individual costs for PHC. By also adding information on level of co-morbidity, as measured by the ACG case-mix system, to specific PHC the adjusted R2 increased to 60.89-63.41%. The ACG case-mix system explains patient costs in primary care to a high degree. Age and gender are important explanatory factors, but most of the variance in concurrent patient costs was explained by the ACG case-mix system.

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…

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.

Some Behaviorial Science Measurement Concerns and Proposals.

PubMed

Nesselroade, John R; Molenaar, Peter C M

2016-01-01

Primarily from a measurement standpoint, we question some basic beliefs and procedures characterizing the scientific study of human behavior. The relations between observed and unobserved variables are key to an empirical approach to building explanatory theories and we are especially concerned about how the former are used as proxies for the latter. We believe that behavioral science can profitably reconsider the prevailing version of this arrangement because of its vulnerability to limiting idiosyncratic aspects of observed/unobserved variable relations. We describe a general measurement approach that takes into account idiosyncrasies that should be irrelevant to the measurement process but can intrude and may invalidate it in ways that distort and weaken relations among theoretically important variables. To clarify further our major concerns, we briefly describe one version of the measurement approach that fundamentally supports the individual as the primary unit of analysis orientation that we believe should be preeminent in the scientific study of human behavior.

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

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.

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.

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.

Factors affecting plant species composition of hedgerows: relative importance and hierarchy

NASA Astrophysics Data System (ADS)

Deckers, Bart; Hermy, Martin; Muys, Bart

2004-07-01

Although there has been a clear quantitative and qualitative decline in traditional hedgerow network landscapes during last century, hedgerows are crucial for the conservation of rural biodiversity, functioning as an important habitat, refuge and corridor for numerous species. To safeguard this conservation function, insight in the basic organizing principles of hedgerow plant communities is needed. The vegetation composition of 511 individual hedgerows situated within an ancient hedgerow network landscape in Flanders, Belgium was recorded, in combination with a wide range of explanatory variables, including a selection of spatial variables. Non-parametric statistics in combination with multivariate data analysis techniques were used to study the effect of individual explanatory variables. Next, variables were grouped in five distinct subsets and the relative importance of these variable groups was assessed by two related variation partitioning techniques, partial regression and partial canonical correspondence analysis, taking into account explicitly the existence of intercorrelations between variables of different factor groups. Most explanatory variables affected significantly hedgerow species richness and composition. Multivariate analysis showed that, besides adjacent land use, hedgerow management, soil conditions, hedgerow type and origin, the role of other factors such as hedge dimensions, intactness, etc., could certainly not be neglected. Furthermore, both methods revealed the same overall ranking of the five distinct factor groups. Besides a predominant impact of abiotic environmental conditions, it was found that management variables and structural aspects have a relatively larger influence on the distribution of plant species in hedgerows than their historical background or spatial configuration.

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.

Seasonally adjusted birth frequencies follow the Poisson distribution.

PubMed

Barra, Mathias; Lindstrøm, Jonas C; Adams, Samantha S; Augestad, Liv A

2015-12-15

Variations in birth frequencies have an impact on activity planning in maternity wards. Previous studies of this phenomenon have commonly included elective births. A Danish study of spontaneous births found that birth frequencies were well modelled by a Poisson process. Somewhat unexpectedly, there were also weekly variations in the frequency of spontaneous births. Another study claimed that birth frequencies follow the Benford distribution. Our objective was to test these results. We analysed 50,017 spontaneous births at Akershus University Hospital in the period 1999-2014. To investigate the Poisson distribution of these births, we plotted their variance over a sliding average. We specified various Poisson regression models, with the number of births on a given day as the outcome variable. The explanatory variables included various combinations of years, months, days of the week and the digit sum of the date. The relationship between the variance and the average fits well with an underlying Poisson process. A Benford distribution was disproved by a goodness-of-fit test (p < 0.01). The fundamental model with year and month as explanatory variables is significantly improved (p < 0.001) by adding day of the week as an explanatory variable. Altogether 7.5% more children are born on Tuesdays than on Sundays. The digit sum of the date is non-significant as an explanatory variable (p = 0.23), nor does it increase the explained variance. INERPRETATION: Spontaneous births are well modelled by a time-dependent Poisson process when monthly and day-of-the-week variation is included. The frequency is highest in summer towards June and July, Friday and Tuesday stand out as particularly busy days, and the activity level is at its lowest during weekends.

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

PubMed Central

Shao, Yuehjen E.

2014-01-01

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. PMID:24723804

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; (Peter) He, Qinghua; Lillard, James W.

2015-01-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

[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.

What variables should be considered in allocating Primary health care Pharmaceutical budgets to districts in Uganda?

PubMed

Mujasi, Paschal N; Puig-Junoy, Jaume

2015-01-01

A key policy question for the government of Uganda is how to equitably allocate primary health care pharmaceutical budgets to districts. This paper seeks to identify variables influencing current primary health care pharmaceutical expenditure and their usefulness in allocating prospective pharmaceutical budgets to districts. This was a cross sectional, retrospective observational study using secondary administrative data. We collected data on the value of pharmaceuticals procured by primary health care facilities in each district from National Medical Stores for the financial year 2011/2012. The dependent variable was expressed as per capita district pharmaceutical expenditure. By reviewing literature we identified 26 potential explanatory variables. They include supply, need and demand, and health system organization variables that may influence the demand and supply of health services and the corresponding pharmaceutical expenditure. We collected secondary data for these variables for all the districts in Uganda (n = 112). We performed econometric analysis to estimate parameters of various regression models. There is a significant correlation between per capita district pharmaceutical expenditure and total district population, rural poverty, access to drinking water and outpatient department (OPD) per capita utilisation.(P < 0.01). The percentage of health centre IIIs (HC III) among each district's health facilities is significantly correlated with per capita pharmaceutical expenditure (P < 0.05). OPD per capita utilisation has a relatively strong correlation with per capita pharmaceutical expenditure (r = 0.498); all the other significant factors are weakly correlated with per capita pharmaceutical expenditure (r < 0.5). From several iterations of an initially developed model, the proposed final model for explaining per capita pharmaceutical expenditure explains about 53% of the variation in pharmaceutical expenditure among districts in Uganda (Adjusted R(2) = 0.528). All variables in the model are significant (p < 0.01). From evaluation of the various models, proposed variables to consider in allocating prospective primary health care pharmaceutical budgets to districts in Uganda are: district outpatient department attendance per capita, total district population, total number of government health facilities in the district and the district human poverty index.

Analysis of nitrate in near-surface aquifers in the midcontinental United States: An application of the inverse hyperbolic sine Tobit model

USGS Publications Warehouse

Yen, Steven T.; Liu, Shiping; Kolpin, Dana W.

1996-01-01

A nonnormal and heteroscedastic Tobit model is used to determine the primary factors that affect nitrate concentrations in near-surface aquifers, using data from the U.S. Geological Survey collected in 1991. Both normality and homoscedasticity of errors are rejected, justifying the use of a nonnormal and heteroscedastic model. The following factors are found to have significant impacts on nitrate concentrations in groundwater: well screen interval, depth to top of aquifers, percentages of urban residential, forest land, and pasture within 3.2 km, dissolved oxygen concentration level, and presence of a chemical facility and feedlot. The effects of explanatory variables on nitrate concentration are explored further by calculating elasticities. Dissolved oxygen concentration level has more notable effects on nitrate concentrations in groundwater than other variables.

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…

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.

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

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.

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.

Modeling the Commuting Travel Activities within Historic Districts in Chinese Cities

PubMed Central

Yin, Fengjun; Hu, Qizhou

2014-01-01

The primary objective of this study is to analyze the characteristics of commuting activities within the historical districts in cities of China. The impacts of various explanatory variables on commuters' travels are evaluated using the structural equation modeling (SEM) approach. The household survey was conducted in the historical districts in Yangzhou, China. Based on the data, various individual and household attributes were considered exogenous variables, while the subsistence activity characteristics, travel times, numbers of three typical home-based trip chains, trip chains, and travel mode were considered as the endogenous variables. Commuters in our study were classified into two main groups according to their working location, which were the commuters in the historic district and those out of the district. The modeling results show that several individual and household attributes of commuters in historic district have significant impacts on the characteristics of travel activities. Additionally, the characteristics of travel activities within the two groups are quite different, and the contributing factors related to commuting travels are different as well. PMID:25435864

Modeling the commuting travel activities within historic districts in Chinese cities.

PubMed

Ye, Mao; Yu, Miao; Li, Zhibin; Yin, Fengjun; Hu, Qizhou

2014-01-01

The primary objective of this study is to analyze the characteristics of commuting activities within the historical districts in cities of China. The impacts of various explanatory variables on commuters' travels are evaluated using the structural equation modeling (SEM) approach. The household survey was conducted in the historical districts in Yangzhou, China. Based on the data, various individual and household attributes were considered exogenous variables, while the subsistence activity characteristics, travel times, numbers of three typical home-based trip chains, trip chains, and travel mode were considered as the endogenous variables. Commuters in our study were classified into two main groups according to their working location, which were the commuters in the historic district and those out of the district. The modeling results show that several individual and household attributes of commuters in historic district have significant impacts on the characteristics of travel activities. Additionally, the characteristics of travel activities within the two groups are quite different, and the contributing factors related to commuting travels are different as well.

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.

Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

The Development of Valid Subtypes for Depression in Primary Care Settings

PubMed Central

Karasz, Alison

2009-01-01

A persistent theme in the debate on the classification of depressive disorders is the distinction between biological and environmental depressions. Despite decades of research, there remains little consensus on how to distinguish between depressive subtypes. This preliminary study describes a method that could be useful, if implemented on a larger scale, in the development of valid subtypes of depression in primary care settings, using explanatory models of depressive illness. Seventeen depressed Hispanic patients at an inner city general practice participated in explanatory model interviews. Participants generated illness narratives, which included details about symptoms, cause, course, impact, health seeking, and anticipated outcome. Two distinct subtypes emerged from the analysis. The internal model subtype was characterized by internal attributions, specifically the notion of an “injured self.” The external model subtype conceptualized depression as a reaction to life situations. Each subtype was associated with a distinct constellation of clinical features and health seeking experiences. Future directions for research using explanatory models to establish depressive subtypes are explored. PMID:18414123

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.

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.

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)

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

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.

Use of social adaptability index to explain self-care and diabetes outcomes.

PubMed

Campbell, Jennifer A; Walker, Rebekah J; Smalls, Brittany L; Egede, Leonard E

2017-06-20

To examine whether the social adaptability index (SAI) alone or components of the index provide a better explanatory model for self-care and diabetes outcomes. Six hundred fifteen patients were recruited from two primary care settings. A series of multiple linear regression models were run to assess (1) associations between the SAI and diabetes self-care/outcomes, and (2) associations between individual SAI indicator variables and diabetes self-care/outcomes. Separate models were run for each self-care behavior and outcome. Two models were run for each dependent variable to compare associations with the SAI and components of the index. The SAI has a significant association with the mental component of quality of life (0.23, p < 0.01). In adjusted analyses, the SAI score did not have a significant association with any of the self-care behaviors. Individual components from the index had significant associations between self-care and multiple SAI indicator variables. Significant associations also exist between outcomes and the individual SAI indicators for education and employment. In this population, the SAI has low explanatory power and few significant associations with diabetes self-care/outcomes. While the use of a composite index to predict outcomes within a diabetes population would have high utility, particularly for clinical settings, this SAI lacks statistical and clinical significance in a representative diabetes population. Based on these results, the index does not provide a good model fit and masks the relationship of individual components to diabetes self-care and outcomes. These findings suggest that five items alone are not adequate to explain or predict outcomes for patients with type 2 diabetes.

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.

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

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

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.

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 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.

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 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.

Factors associated with patient satisfaction with primary care in Europe: results from the EUprimecare project.

PubMed

Sánchez-Piedra, Carlos Alberto; Prado-Galbarro, Francisco Javier; García-Pérez, Sonia; Santamera, Antonio Sarría

2014-01-01

EUprimecare is a study funded by the Seventh Framework Programme of the European Union, aimed at analysing the quality of the different models of primary care in Europe. The objective of this study was to describe and analyse the determinants associated with patient satisfaction in primary care in Europe. We conducted telephone population surveys among primary care users in each EUprimecare consortium country (Germany, Spain, Estonia, Finland, Hungary, Italy and Lithuania). The survey was conducted with 3020 patients and the questionnaire included sociodemographic variables, health status, and use and satisfaction with primary care services. We undertook descriptive analyses, bivariate correlations and an ordinal regression model to study the direct relationship between levels of satisfaction and the explanatory variables for demographics, health status and health services for patients. We present the regression coefficients (β) with 95% confidence intervals and associated tests of statistical significance. The mean age of the respondents was 51 years (SD 14.1). We found significant associations between the level of satisfaction and age (β = 0.008), specialist visits (β = -0.030), not having a general practitioner (β = 0.70), not measuring weight, cholesterol and blood pressure (β = 0.52), country (β1 Germany = -1.08 and β2 Lithuania = -0.60; β3 Hungary = 0.50 and β4 Italy = 0.53) and a better perception of health status (β = 0.33). Specialist visits had a negative association with satisfaction. Overall, the results indicate factors that may be related to greater satisfaction with primary care services: age, visits to a specialist, having a doctor assigned to primary care and measurement of control parameters are associated with a better perception of the care received.

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.

Primary production in a tropical large lake: the role of phytoplankton composition.

PubMed

Darchambeau, F; Sarmento, H; Descy, J-P

2014-03-01

Phytoplankton biomass and primary production in tropical large lakes vary at different time scales, from seasons to centuries. We provide a dataset made of 7 consecutive years of phytoplankton biomass and production in Lake Kivu (Eastern Africa). From 2002 to 2008, bi-weekly samplings were performed in a pelagic site in order to quantify phytoplankton composition and biomass, using marker pigments determined by HPLC. Primary production rates were estimated by 96 in situ (14)C incubations. A principal component analysis showed that the main environmental gradient was linked to a seasonal variation of the phytoplankton assemblage, with a clear separation between diatoms during the dry season and cyanobacteria during the rainy season. A rather wide range of the maximum specific photosynthetic rate (PBm) was found, ranging between 1.15 and 7.21 g carbong(-1)chlorophyll ah(-1), and was best predicted by a regression model using phytoplankton composition as an explanatory variable. The irradiance at the onset of light saturation (Ik) ranged between 91 and 752 μE m(-2)s(-1) and was linearly correlated with the mean irradiance in the mixed layer. The inter-annual variability of phytoplankton biomass and production was high, ranging from 53 to 100 mg chlorophyll am(-2) (annual mean) and from 143 to 278 g carbon m(-2)y(-1), respectively. The degree of seasonal mixing determined annual production, demonstrating the sensitivity of tropical lakes to climate variability. A review of primary production of other African great lakes allows situating Lake Kivu productivity in the same range as that of lakes Tanganyika and Malawi, even if mean phytoplankton biomass was higher in Lake Kivu. Copyright © 2013 Elsevier B.V. All rights reserved.

Analysis of low flows and selected methods for estimating low-flow characteristics at partial-record and ungaged stream sites in western Washington

USGS Publications Warehouse

Curran, Christopher A.; Eng, Ken; Konrad, Christopher P.

2012-01-01

Regional low-flow regression models for estimating Q7,10 at ungaged stream sites are developed from the records of daily discharge at 65 continuous gaging stations (including 22 discontinued gaging stations) for the purpose of evaluating explanatory variables. By incorporating the base-flow recession time constant τ as an explanatory variable in the regression model, the root-mean square error for estimating Q7,10 at ungaged sites can be lowered to 72 percent (for known values of τ), which is 42 percent less than if only basin area and mean annual precipitation are used as explanatory variables. If partial-record sites are included in the regression data set, τ must be estimated from pairs of discharge measurements made during continuous periods of declining low flows. Eight measurement pairs are optimal for estimating τ at partial-record sites, and result in a lowering of the root-mean square error by 25 percent. A low-flow survey strategy that includes paired measurements at partial-record sites requires additional effort and planning beyond a standard strategy, but could be used to enhance regional estimates of τ and potentially reduce the error of regional regression models for estimating low-flow characteristics at ungaged sites.

Emotion Understanding, Social Competence and School Achievement in Children from Primary School in Portugal

PubMed Central

Franco, Maria da Glória; Beja, Maria J.; Candeias, Adelinda; Santos, Natalie

2017-01-01

This study analyzes the relationship between emotion understanding and school achievement in children of primary school, considering age, gender, fluid intelligence, mother’s educational level and social competence. In this study participated 406 children of primary school. The instruments used were the Test of Emotion Comprehension, Colored Progressive Matrices of Raven, Socially Action and Interpersonal Problem Solving Scale. The structural equation model showed the relationship between the emotion understanding and school performance depends on a mediator variable that in the context of the study was designated social competence. Age appear as an explanatory factor of the differences found, the mother’s educational level only predicts significantly social emotional competence, fluid intelligence is a predictor of emotion understanding, school achievement and social emotional competence. Regarding the influence of sex, emotional understanding does not emerge as a significant predictor of social emotional competence in girls or boys. Multiple relationships between the various factors associated with school achievement and social emotional competence are discussed as well as their implications in promoting child development and school success. PMID:28861014

Improving Global Gross Primary Productivity Estimates by Computing Optimum Light Use Efficiencies Using Flux Tower Data

NASA Astrophysics Data System (ADS)

Madani, Nima; Kimball, John S.; Running, Steven W.

2017-11-01

In the light use efficiency (LUE) approach of estimating the gross primary productivity (GPP), plant productivity is linearly related to absorbed photosynthetically active radiation assuming that plants absorb and convert solar energy into biomass within a maximum LUE (LUEmax) rate, which is assumed to vary conservatively within a given biome type. However, it has been shown that photosynthetic efficiency can vary within biomes. In this study, we used 149 global CO2 flux towers to derive the optimum LUE (LUEopt) under prevailing climate conditions for each tower location, stratified according to model training and test sites. Unlike LUEmax, LUEopt varies according to heterogeneous landscape characteristics and species traits. The LUEopt data showed large spatial variability within and between biome types, so that a simple biome classification explained only 29% of LUEopt variability over 95 global tower training sites. The use of explanatory variables in a mixed effect regression model explained 62.2% of the spatial variability in tower LUEopt data. The resulting regression model was used for global extrapolation of the LUEopt data and GPP estimation. The GPP estimated using the new LUEopt map showed significant improvement relative to global tower data, including a 15% R2 increase and 34% root-mean-square error reduction relative to baseline GPP calculations derived from biome-specific LUEmax constants. The new global LUEopt map is expected to improve the performance of LUE-based GPP algorithms for better assessment and monitoring of global terrestrial productivity and carbon dynamics.

'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.

Meteorological influences on the interannual variability of meningitis incidence in northwest Nigeria.

NASA Astrophysics Data System (ADS)

Abdussalam, Auwal; Monaghan, Andrew; Dukic, Vanja; Hayden, Mary; Hopson, Thomas; Leckebusch, Gregor

2013-04-01

Northwest Nigeria is a region with high risk of bacterial meningitis. Since the first documented epidemic of meningitis in Nigeria in 1905, the disease has been endemic in the northern part of the country, with epidemics occurring regularly. In this study we examine the influence of climate on the interannual variability of meningitis incidence and epidemics. Monthly aggregate counts of clinically confirmed hospital-reported cases of meningitis were collected in northwest Nigeria for the 22-year period spanning 1990-2011. Several generalized linear statistical models were fit to the monthly meningitis counts, including generalized additive models. Explanatory variables included monthly records of temperatures, humidity, rainfall, wind speed, sunshine and dustiness from weather stations nearest to the hospitals, and a time series of polysaccharide vaccination efficacy. The effects of other confounding factors -- i.e., mainly non-climatic factors for which records were not available -- were estimated as a smooth, monthly-varying function of time in the generalized additive models. Results reveal that the most important explanatory climatic variables are mean maximum monthly temperature, relative humidity and dustiness. Accounting for confounding factors (e.g., social processes) in the generalized additive models explains more of the year-to-year variation of meningococcal disease compared to those generalized linear models that do not account for such factors. Promising results from several models that included only explanatory variables that preceded the meningitis case data by 1-month suggest there may be potential for prediction of meningitis in northwest Nigeria to aid decision makers on this time scale.

Predictors of primary health care pharmaceutical expenditure by districts in Uganda and implications for budget setting and allocation.

PubMed

Mujasi, Paschal N; Puig-Junoy, Jaume

2015-08-20

There is need for the Uganda Ministry of Health to understand predictors of primary health care pharmaceutical expenditure among districts in order to guide budget setting and to improve efficiency in allocation of the set budget among districts. Cross sectional, retrospective observational study using secondary data. The value of pharmaceuticals procured by primary health care facilities in 87 randomly selected districts for the Financial Year 2011/2012 was collected. Various specifications of the dependent variable (pharmaceutical expenditure) were used: total pharmaceutical expenditure, Per capita district pharmaceutical expenditure, pharmaceutical expenditure per district health facility and pharmaceutical expenditure per outpatient department visit. Andersen's behaviour model of health services utilisation was used as conceptual framework to identify independent variables likely to influence health care utilisation and hence pharmaceutical expenditure. Econometric analysis was conducted to estimate parameters of various regression models. All models were significant overall (P < 0.01), with explanatory power ranging from 51 to 82%. The log linear model for total pharmaceutical expenditure explained about 80% of the observed variation in total pharmaceutical expenditure (Adjusted R(2) = 0.797) and contained the following variables: Immunisation coverage, Total outpatient department attendance, Urbanisation, Total number of government health facilities and total number of Health Centre IIs. The model based on Per capita Pharmaceutical expenditure explained about 50% of the observed variation in per capita pharmaceutical expenditure (Adjusted R(2) = 0.513) and was more balanced with the following variables: Outpatient per capita attendance, percentage of rural population below poverty line 2005, Male Literacy rate, Whether a district is characterised by MOH as difficult to reach or not and the Human poverty index. The log-linear model based on total pharmaceutical expenditure works acceptably well and can be considered useful for predicting future total pharmaceutical expenditure following observed trends. It can be used as a simple tool for rough estimation of the potential overall national primary health pharmaceutical expenditure to guide budget setting. The model based on pharmaceutical expenditure per capita is a more balanced model containing both need and enabling factor variables. These variables would be useful in allocating any set budget to districts.

Longitudinal associations between children’s dental caries and risk factors

PubMed Central

Chankanka, Oitip; Cavanaugh, Joseph E.; Levy, Steven M.; Marshall, Teresa A.; Warren, John J; Broffitt, Barbara; Kolker, Justine L.

2015-01-01

Dental caries is a common disease in children of all ages. It is desirable to know whether children with primary, mixed and permanent dentitions share risk factors for cavitated and non-cavitated caries. Objective To assess the longitudinal associations between caries outcomes and modifiable risk factors. Methods One hundred and fifty-six children in the Iowa Fluoride Study met inclusion criteria of three dental examinations and caries-related risk factor assessments preceding each examination. Surface-specific counts of new non-cavitated caries and cavitated caries at the primary (Exam 1: age 5), mixed (Exam 2: age 9) and permanent (Exam 3: age 13) dentition examinations were outcome variables. Explanatory variables were caries-related factors, including averaged beverage exposure frequencies, toothbrushing frequencies, and composite water fluoride levels collected from 3–5, 6–8, and 11–13 years, dentition category, socioeconomic status and gender. Generalized linear mixed models (GLMMs) were used to explore the relationships between new non-cavitated or cavitated caries and caries-related variables. Results Greater frequency of 100% juice exposure was significantly associated with fewer non-cavitated and cavitated caries surfaces. Greater toothbrushing frequency and high SES were significantly associated with fewer new non-cavitated caries. Children had significantly more new cavitated caries surfaces at the mixed dentition examination than at the primary and permanent dentition examinations. Conclusions There were common caries-related factors for more new non-cavitated caries across the three exams, including less frequent 100% juice exposure, lower toothbrushing frequency and lower socioeconomic status. Less frequent 100% juice exposures might be associated with higher exposures to several other cariogenic beverages. PMID:22320287

A multimodal dataset for various forms of distracted driving

PubMed Central

Taamneh, Salah; Tsiamyrtzis, Panagiotis; Dcosta, Malcolm; Buddharaju, Pradeep; Khatri, Ashik; Manser, Michael; Ferris, Thomas; Wunderlich, Robert; Pavlidis, Ioannis

2017-01-01

We describe a multimodal dataset acquired in a controlled experiment on a driving simulator. The set includes data for n=68 volunteers that drove the same highway under four different conditions: No distraction, cognitive distraction, emotional distraction, and sensorimotor distraction. The experiment closed with a special driving session, where all subjects experienced a startle stimulus in the form of unintended acceleration—half of them under a mixed distraction, and the other half in the absence of a distraction. During the experimental drives key response variables and several explanatory variables were continuously recorded. The response variables included speed, acceleration, brake force, steering, and lane position signals, while the explanatory variables included perinasal electrodermal activity (EDA), palm EDA, heart rate, breathing rate, and facial expression signals; biographical and psychometric covariates as well as eye tracking data were also obtained. This dataset enables research into driving behaviors under neatly abstracted distracting stressors, which account for many car crashes. The set can also be used in physiological channel benchmarking and multispectral face recognition. PMID:28809848

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).

Regression Analysis of Stage Variability for West-Central Florida Lakes

USGS Publications Warehouse

Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy

2008-01-01

The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground-water exchange in controlling the stage of karst lakes in Florida. Regression equations were used to predict lake-stage variability for the recent period for 12 additional lakes, and the median difference between predicted and observed values ranged from 11 to 23 percent. Coefficients of determination for the historical period were considerably lower (maximum R2 of 0.28) than for the recent period. Reasons for these low R2 values are probably related to the small number of lakes (20) with stage data for an equivalent time period that were unaffected by ground-water pumping, the similarity of many of the lake types (large surface-water drainage lakes), and the greater uncertainty in defining historical basin characteristics. The lack of lake-stage data unaffected by ground-water pumping and the poor regression results obtained for that group of lakes limit the ability to predict natural lake-stage variability using this method in west-central Florida.

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

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.

Individual and contextual factors associated to the self-perception of oral health in Brazilian adults.

PubMed

Silva, Janmille Valdivino da; Oliveira, Angelo Giuseppe Roncalli da Costa

2018-04-09

To analyze how individual characteristics and the social context, together, are associated with self-perception of the oral health. A multilevel cross-sectional study with data from the Brazilian National Health Survey 2013, the United Nations Development Program, and the National Registry of Health Establishments. The explanatory variables for the "oral health perception" outcome were grouped, according to the study framework, into biological characteristics (sex, color, age), proximal social determinants (literacy, household crowding, and socioeconomic stratification), and distal (years of schooling expectancy at age 18, GINI, Human Development Index, and per capita income). The described analysis was performed, along with bivariate Poisson analysis and multilevel Poisson analysis for the construction of the explanatory model of oral health perception. All analyzes considered the sample weights. Both the biological characteristics and the proximal and distal social determinants were associated with the perception of oral health in the bivariate analysis. A higher prevalence of bad oral health was associated to lower years of schooling expectancy (PR = 1.31), lower per capita income (PR = 1.45), higher income concentration (PR = 1.41), and worse human development (PR = 1.45). Inversely, oral health services in both primary and secondary care were negatively associated with oral health perception. All the biological and individual social characteristics, except reading and writing, made up the final explanatory model along with the distal social determinants of the Human Development Index and coverage of basic care in the multilevel analysis. Biological factors, individual and contextual social determinants were associate synergistically with the population's perception of oral health. It is necessary to improve individual living conditions and the implementation of public social policies to improve the oral health of the population.

Perceived Problem-Solving Deficits and Suicidal Ideation: Evidence for the Explanatory Roles of Thwarted Belongingness and Perceived Burdensomeness in Five Samples.

PubMed

Chu, Carol; Walker, Kristin L; Stanley, Ian H; Hirsch, Jameson K; Greenberg, Jeffrey H; Rudd, M David; Joiner, Thomas E

2017-06-26

Perceived social problem-solving deficits are associated with suicide risk; however, little research has examined the mechanisms underlying this relationship. The interpersonal theory of suicide proposes 2 mechanisms in the pathogenesis of suicidal desire: intractable feelings of thwarted belongingness (TB) and perceived burdensomeness (PB). This study tested whether TB and PB serve as explanatory links in the relationship between perceived social problem-solving (SPS) deficits and suicidal thoughts and behaviors cross-sectionally and longitudinally. The specificity of TB and PB was evaluated by testing depression as a rival mediator. Self-report measures of perceived SPS deficits, TB, PB, suicidal ideation, and depression were administered in 5 adult samples: 336 and 105 undergraduates from 2 universities, 53 homeless individuals, 222 primary care patients, and 329 military members. Bias-corrected bootstrap mediation and meta-analyses were conducted to examine the magnitude of the direct and indirect effects, and the proposed mediation paths were tested using zero-inflated negative binomial regressions. Cross-sectionally, TB and PB were significant parallel mediators of the relationship between perceived SPS deficits and ideation, beyond depression. Longitudinally and beyond depression, in 1 study, both TB and PB emerged as significant explanatory factors, and in the other, only PB was a significant mediator. Findings supported the specificity of TB and PB: Depression and SPS deficits were not significant mediators. The relationship between perceived SPS deficits and ideation was explained by interpersonal theory variables, particularly PB. Findings support a novel application of the interpersonal theory, and bolster a growing compendium of literature implicating perceived SPS deficits in suicide risk. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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.

Do different styles of antismoking ads influence the types of smokers who call quitlines?

PubMed

Nonnemaker, James M; Farrelly, Matthew C; Kamyab, Kian; MacMonegle, Anna J

2013-02-01

This study examined the relationship between television antismoking advertisements and the proportion of smokers who call a smokers' quitline who are ready to quit or have high confidence in quitting. The primary data of interest came from completed intake interviews of smokers. Using a generalized linear model, we modeled the proportion of Quitline callers who are ready to quit and/or have high confidence in quitting. The primary explanatory variable was monthly target audience rating points (TARPs) for antismoking advertisements, a measure of broadcast media exposure, obtained from the state's media buyer. The proportions of callers ready to quit and with high confidence in quitting were negatively associated with total TARPs. This result, over all ad types, was driven by why to quit-graphic ads. These results suggest that why to quit-graphic ads influence smokers who are less ready to quit or have lower confidence they can quit, likely new quitters, to call the Quitline.

Stratification and mobility in contemporary Egypt.

PubMed

Nagi, Saad Z; Nagi, Omar

2011-01-01

The objectives in this statement are to characterize and explain the patterns of change in stratification and mobility in Egypt, over the last half century, by placing them within conceptual, explanatory, and historical contexts. First, literature relevant to the primary concepts of "class" and "status", is reviewed. Second, four institutions whose influence is fundamental in shaping these patterns are identified to form an explanatory context: family, polity, economy, and education. And third, an historical account is presented to demonstrate the interplay of these institutions and their consequences for stratification and mobility. For this, four periods are identified that are marked by change in the dominance of institutions and their corresponding influence on stratification and mobility. In addition to data available in relevant literature, this analysis utilizes primary data generated through a national probability household survey.

Evaluation of stream flow effects on smolt survival in the Yakima River basin, Washington

USGS Publications Warehouse

Courter,; Garrison,; Kock, Tobias J.; Perry, Russell W.

2012-01-01

Yearling Chinook smolt survival and travel time estimates from 2012 suggest that migration rates and survival rates in the Roza Reach may be associated with stream flow, water temperature, release timing (i.e. migratory disposition), and fish size, but the extent to which each variable influenced survival is yet to be determined. The lowest survival rate (61%) and longest travel time (median 2.26 days) was observed in Release Group 1, which had the smallest size distribution and experienced the lowest flows, lowest temperatures, and earliest release date among the three groups. Release Groups 2 and 3 survived at 96% and 98% respectively and traveled through the Roza Reach in less than eight hours. The primary focus of years two and three of this study will be to collect data that minimizes the effect of confounding explanatory variables, so that flow effects on emigration survival can be quantified independent of these other influential 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)

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…

Introduction to Multilevel Item Response Theory Analysis: Descriptive and Explanatory Models

ERIC Educational Resources Information Center

Sulis, Isabella; Toland, Michael D.

2017-01-01

Item response theory (IRT) models are the main psychometric approach for the development, evaluation, and refinement of multi-item instruments and scaling of latent traits, whereas multilevel models are the primary statistical method when considering the dependence between person responses when primary units (e.g., students) are nested within…

[Impact of cardiovascular risk factors on the consumption of resources in Primary Care according to Clinical Risk Groups].

PubMed

Millá Perseguer, Magdalena; Guadalajara Olmeda, Natividad; Vivas Consuelo, David

2018-06-13

To analyze the prevalence of Cardiovascular Risk Factors (CVRF) in the context of a Basic Health Area and the impact they generate on morbidity and consumption of healthcare resources in the stratified population according to the Clinical System Risk Groups (CRG) in Primary Care, with the purpose of identifying the population with multimorbidity to apply preventive measures, as well as the one that generates the highest care burden and social needs. Observational, cross-sectional and population-based study for a basic health area during 2013. Department of Health 2 (Castellón), Comunidad Valenciana (CV). Includes outpatient care in Primary Care and specialized. All citizens registered in the Population Information System, N=32,667. From the computerized system Abucasis we obtained the demographic, clinical and consumption variables of health resources. We consider the prevalence of CVRF based on the presence or absence of the ICD.9.MC diagnostic codes. The relationship of the CVRF with the 9 CRG health states was analyzed and a predictive analysis was performed with the logistic regression model to evaluate the explanatory capacity of each variable. In addition, an explanatory model of ambulatory pharmaceutical expenditure was obtained through multivariate regression. The population of health status CRG4 and above had multimorbidity. The CRG7 and 6 health states have a higher prevalence of CVRF; it was predictive that the higher the morbidity, the greater the consumption of resources through OR above the mean, p<0.05 and the 95% confidence intervals. It was observed that 59.8% of ambulatory pharmaceutical expenditure was explained by the CRG system and all the CVRF (p<0.05 and R 2 corrected=0.598). Regarding the effect of the CVRF on the CRG health states, there was a significant association (p<0.05) for the alteration of blood glucose, dyslipidemia and HBP in all the CRG states. The study of CVRF in a stratified population using the CRG system identifies and predicts where the greatest impact on morbidity and consumption of healthcare resources is generated. It allows us to know the groups of patients where to develop prevention and chronicity strategies. At the level of clinical practice, a new concept of multimorbidity is provided, defined from the state of health CRG 4 and above. Publicado por Elsevier España, S.L.U.

The effect of implementation strength of basic emergency obstetric and newborn care (BEmONC) on facility deliveries and the met need for BEmONC at the primary health care level in Ethiopia.

PubMed

Tiruneh, Gizachew Tadele; Karim, Ali Mehryar; Avan, Bilal Iqbal; Zemichael, Nebreed Fesseha; Wereta, Tewabech Gebrekiristos; Wickremasinghe, Deepthi; Keweti, Zinar Nebi; Kebede, Zewditu; Betemariam, Wuleta Aklilu

2018-05-02

Basic emergency obstetric and newborn care (BEmONC) is a primary health care level initiative promoted in low- and middle-income countries to reduce maternal and newborn mortality. Tailored support, including BEmONC training to providers, mentoring and monitoring through supportive supervision, provision of equipment and supplies, strengthening referral linkages, and improving infection-prevention practice, was provided in a package of interventions to 134 health centers, covering 91 rural districts of Ethiopia to ensure timely BEmONC care. In recent years, there has been a growing interest in measuring program implementation strength to evaluate public health gains. To assess the effectiveness of the BEmONC initiative, this study measures its implementation strength and examines the effect of its variability across intervention health centers on the rate of facility deliveries and the met need for BEmONC. Before and after data from 134 intervention health centers were collected in April 2013 and July 2015. A BEmONC implementation strength index was constructed from seven input and five process indicators measured through observation, record review, and provider interview; while facility delivery rate and the met need for expected obstetric complications were measured from service statistics and patient records. We estimated the dose-response relationships between outcome and explanatory variables of interest using regression methods. The BEmONC implementation strength index score, which ranged between zero and 10, increased statistically significantly from 4.3 at baseline to 6.7 at follow-up (p < .05). Correspondingly, the health center delivery rate significantly increased from 24% to 56% (p < .05). There was a dose-response relationship between the explanatory and outcome variables. For every unit increase in BEmONC implementation strength score there was a corresponding average of 4.5 percentage points (95% confidence interval: 2.1-6.9) increase in facility-based deliveries; while a higher score for BEmONC implementation strength of a health facility at follow-up was associated with a higher met need. The BEmONC initiative was effective in improving institutional deliveries and may have also improved the met need for BEmONC services. The BEmONC implementation strength index can be potentially used to monitor the implementation of BEmONC interventions.

Do doctors' attachment styles and emotional intelligence influence patients' emotional expressions in primary care consultations? An exploratory study using multilevel analysis.

PubMed

Cherry, M Gemma; Fletcher, Ian; Berridge, Damon; O'Sullivan, Helen

2018-04-01

To investigate whether and how doctors' attachment styles and emotional intelligence (EI) might influence patients' emotional expressions in general practice consultations. Video recordings of 26 junior doctors consulting with 173 patients were coded using the Verona Coding Definition of Emotional Sequences (VR-CoDES). Doctors' attachment style was scored across two dimensions, avoidance and anxiety, using the Experiences in Close Relationships: Short Form questionnaire. EI was assessed with the Mayer-Salovey-Caruso Emotional Intelligence Test. Multilevel Poisson regressions modelled the probability of patients' expressing emotional distress, considering doctors' attachment styles and EI and demographic and contextual factors. Both attachment styles and EI were significantly associated with frequency of patients' cues, with patient- and doctor-level explanatory variables accounting for 42% of the variance in patients' cues. The relative contribution of attachment styles and EI varied depending on whether patients' presenting complaints were physical or psychosocial in nature. Doctors' attachment styles and levels of EI are associated with patients' emotional expressions in primary care consultations. Further research is needed to investigate how these two variables interact and influence provider responses and patient outcomes. Understanding how doctors' psychological characteristics influence PPC may help to optimise undergraduate and postgraduate medical education. Copyright © 2017 Elsevier B.V. All rights reserved.

Risk factors in the development of behaviour difficulties among students with special educational needs and disabilities: A multilevel analysis.

PubMed

Oldfield, Jeremy; Humphrey, Neil; Hebron, Judith

2017-06-01

Students with special educational needs and disabilities (SEND) are more likely to exhibit behaviour difficulties than their typically developing peers. The aim of this study was to identify specific risk factors that influence variability in behaviour difficulties among individuals with SEND. The study sample comprised 4,228 students with SEND, aged 5-15, drawn from 305 primary and secondary schools across England. Explanatory variables were measured at the individual and school levels at baseline, along with a teacher-reported measure of behaviour difficulties (assessed at baseline and at 18-month follow-up). Hierarchical linear modelling of data revealed that differences between schools accounted for between 13% (secondary) and 15.4% (primary) of the total variance in the development of students' behaviour difficulties, with the remainder attributable to individual differences. Statistically significant risk markers for these problems across both phases of education were being male, eligibility for free school meals, being identified as a bully, and lower academic achievement. Additional risk markers specific to each phase of education at the individual and school levels are also acknowledged. Behaviour difficulties are affected by risks across multiple ecological levels. Addressing any one of these potential influences is therefore likely to contribute to the reduction in the problems displayed. © 2017 The British Psychological Society.

Intrinsic and extrinsic motivations in primary care: an explanatory study among French general practitioners.

PubMed

Sicsic, Jonathan; Le Vaillant, Marc; Franc, Carine

2012-12-01

Like many other OECD nations, France has implemented a pay-for-performance (P4P) model in primary care. However, the benefits have been debated, particularly regarding the possibly undesirable effects of extrinsic motivation (EM) on intrinsic motivation (IM). To examine the relationship between French GPs' IM and EM based on an intrinsic motivation composite score (IMCS) developed for this purpose. If a negative relationship is found, P4P schemes could have side effects on GPs' IM that is a key determinant of quality of care. From data on 423 GPs practicing in a region of France, IM indicators are selected using a multiple correspondence analysis and aggregated from a multilevel model. Several doctors' characteristics have significant impacts on IMCS variability, especially group practice and salaried practice. Qualitative EM variables are negatively correlated with the IMCS: GPs who report not being satisfied with their income or feeling "often" constrained by patients' requests in terms of consultations length and office appointments obtain a lower mean IMCS than other GPs. Our results provide a cautionary message to regulators who should take into account the potential side effects of increasing EM through policies such as P4P. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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.

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.

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…

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.

[Evolution of burnout and associated factors in primary care physicians].

PubMed

Matía Cubillo, Angel Carlos; Cordero Guevara, José; Mediavilla Bravo, José Javier; Pereda Riguera, Maria José; González Castro, Maria Luisa; González Sanz, Ana

2012-09-01

To analyse the course of burnout and develop an explanatory model. Prospective cohort dynamics. SITE: All primary health care centres in Burgos. All physicians except medical emergencies, paediatrics and residents. Anonymous self-report questionnaire: Maslach Burnout Inventory (MBI) and related variables. An analysis was performed using the Student-t, X(2) test and logistic regression. The response rate was 47.76% in 2007, which was lower than that of 2005. There were significant differences between 2005 and 2007, for increases in the percentage of physicians who smoked, postgraduate training, residency, and those who believe that coordination with nursing and specialist care and institutional communication is appropriate. There was an increase in the prevalence of burnout by almost one point compared with 2005, a decrease in maximum burnout and emotional exhaustion (EC), and an increase in depersonalisation (DP) and personal accomplishment (RP). The incidence density of burnout was 1/113. 5 primary care physicians per year. The existence of burnout is associated with the use of chronic medication and inadequate coordination between nursing and EC, and also with the high workload. The increase in the prevalence found is consistent with the idea of burnout as a dynamic development and the theoretical model described. Stable and quality employment is one way to indirectly mitigate (by encouraging internal communication) professional burnout. In the multivariate analysis, the most critical variable in the onset of burnout is the inadequate coordination with nursing. Copyright © 2008 Elsevier España, S.L. All rights reserved.

Polypharmacy: a challenge for the primary health care of the Brazilian Unified Health System

PubMed Central

do Nascimento, Renata Cristina Rezende Macedo; Álvares, Juliana; Guerra, Augusto Afonso; Gomes, Isabel Cristina; Silveira, Micheline Rosa; Costa, Ediná Alves; Leite, Silvana Nair; Costa, Karen Sarmento; Soeiro, Orlando Mario; Guibu, Ione Aquemi; Karnikowski, Margô Gomes de Oliveira; Acurcio, Francisco de Assis

2017-01-01

ABSTRACT OBJECTIVE To characterize the polypharmacy in primary health care patients and to identify its associated factors. METHODS This is a cross-sectional, exploratory, and evaluative study, part of the Pesquisa Nacional sobre Acesso, Utilização e Promoção do Uso Racional de Medicamentos – Serviços, 2015 (PNAUM – National Survey on Access, Use and Promotion of Rational Use of Medicines – Services, 2015). The variable of interest was polypharmacy, defined as the use of five or more medicines. We sought to identify the association of sociodemographic variables and indicators of health conditions to polypharmacy. For group comparison, the Pearson’s Chi-square test was used. The association between polypharmacy and explanatory variables was evaluated by logistic regression model (p < 0.05). The quality of the adjustment was verified by Hosmer-Lemeshow test. RESULTS The prevalence of polypharmacy among medicine users was 9.4% (95%CI 7.8–12.0) in the general population and 18.1% (95%CI 13.6–22.8) in older adults above 65 years old. We found statistically significant association between polypharmacy and age above 45 years, lower self-perception of health, presence of chronic diseases, having health insurance, care in emergency services, and region of the Country. South users presented the highest chances to polypharmacy. The most used medicines were those of the cardiovascular system, being compatible with the national epidemiological profile. CONCLUSIONS Polypharmacy is a reality in the population met within the primary care of Brazilian Unified Health System and may be related to excessive or inappropriate use of medicines. The main challenge to qualify health care is to ensure that prescription of multiple medicines be appropriate and safe. PMID:29160460

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.

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.

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.

[Comparison of three methods for measuring multiple morbidity according to the use of health resources in primary healthcare].

PubMed

Sicras-Mainar, Antoni; Velasco-Velasco, Soledad; Navarro-Artieda, Ruth; Blanca Tamayo, Milagrosa; Aguado Jodar, Alba; Ruíz Torrejón, Amador; Prados-Torres, Alexandra; Violan-Fors, Concepción

2012-06-01

To compare three methods of measuring multiple morbidity according to the use of health resources (cost of care) in primary healthcare (PHC). Retrospective study using computerized medical records. Thirteen PHC teams in Catalonia (Spain). Assigned patients requiring care in 2008. The socio-demographic variables were co-morbidity and costs. Methods of comparison were: a) Combined Comorbidity Index (CCI): an index itself was developed from the scores of acute and chronic episodes, b) Charlson Index (ChI), and c) Adjusted Clinical Groups case-mix: resource use bands (RUB). The cost model was constructed by differentiating between fixed (operational) and variable costs. 3 multiple lineal regression models were developed to assess the explanatory power of each measurement of co-morbidity which were compared from the determination coefficient (R(2)), p< .05. The study included 227,235 patients. The mean unit of cost was €654.2. The CCI explained an R(2)=50.4%, the ChI an R(2)=29.2% and BUR an R(2)=39.7% of the variability of the cost. The behaviour of the ICC is acceptable, albeit with low scores (1 to 3 points), showing inconclusive results. The CCI may be a simple method of predicting PHC costs in routine clinical practice. If confirmed, these results will allow improvements in the comparison of the case-mix. Copyright © 2011 Elsevier España, S.L. All rights reserved.

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.

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.

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.

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.

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...

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.

Homework and Primary-School Students' Academic Achievement in Latin America

ERIC Educational Resources Information Center

Murillo, F. Javier; Martinez-Garrido, Cynthia

2014-01-01

This paper explores teachers' habits (1) in terms of setting homework for their students and (2) in terms of building on homework in the classroom. Based on data collected in UNESCO's Second Regional Comparative and Explanatory Study (SERCE), the sample size of this analysis is about 200,000 Primary Grade 3 and 6 students in 16 Latin American…

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…

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/

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.

[Quality perceived by users of minor surgery according to care level and the professionals that carried it out].

PubMed

Oltra Rodríguez, Enrique; Fernández García, Benjamín; Cabiedes Miragaya, Laura; Riestra Rodríguez, Rosario; González Aller, Cristina; Osorio Álvarez, Sofía

2018-04-26

Nowadays minor surgery is performed by di- fferent professionals at primary as well as specialized care. Being a healthcare technology, minor surgery must be assessed in order to achieve an organizational efficiency. User's satisfaction must be one of the quality criteria. That is why an analysis of the quality perceived by users according to where minor surgery takes place and who carries it out is made. This study explores, conducting telephone surveys, the satisfaction of a sample of 275 minor surgery patients of two hospitals and three primary healthcare areas of Asturias. The survey is based on the SERVQUAL model adapting the one used by the Spanish Ministry of Health in 1977. A behavior pattern of satisfaction was established in terms of the variables that increase or diminish it. In every item, satisfaction was perceived as good or very good at least in 84% of the survey users and in the majority was over 95%. There was a significant difference in favour of primary care with respect to waiting time (p less than 0,001), explanations received (p=0,002) and security perceived (p=0,015). The more explanatory variables of excellent satisfaction were the sense of security and the staff attention. The kind of professional did not represent a conditioning factor and the level of healthcare only appeared to be so among those who did not feel safe showing to be less satisfied those treated in primary care. Good quality perceived by users does not seem to be penalized by the fact that minor surgery can be carried out at different healthcare levels or which specialist performs it.

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.

An Analytical Model / Emotional Intelligence Quotient and QOL in Mothers with Infants in Japan.

PubMed

Ohashi, Junko; Katsura, Toshiki; Hoshino, Akiko; Usui, Kanae

2013-01-01

The purpose of this study was to examine the relationship between the emotional intelligence quotient and health-related quality of life using structural equation modeling. A self-administered questionnaire survey was conducted among 1,911 mothers who visited the Health Center for an infant medical examination. A hypothetical model was constructed using variables of the emotional intelligence quotient, social support, coping, parenting stress, and perceived health competence. There were a total of 1,104 valid responses (57.8%). Significant standardized estimates were obtained, confirming the goodness of fit issues with the model. The emotional intelligence quotient had a strong impact on physical and psychological quality of life, and showed the greatest association with coping. This study differed from previous studies in that, due to the inclusion of social support and explanatory variables in coping, an increase in coping strategies was more highly associated with emotional intelligence quotient levels than with social support. An enhanced emotional intelligence quotient should be considered a primary objective to promote the health of mothers with infant children.

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.

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.

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.…

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

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

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.

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.

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.

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

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.

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.

Effects of mining-associated lead and zinc soil contamination on native floristic quality.

PubMed

Struckhoff, Matthew A; Stroh, Esther D; Grabner, Keith W

2013-04-15

We assessed the quality of plant communities across a range of lead (Pb) and zinc (Zn) soil concentrations at a variety of sites associated with Pb mining in southeast Missouri, USA. In a novel application, two standard floristic quality measures, Mean Coefficient of Conservatism (Mean C) and Floristic Quality Index (FQI), were examined in relation to concentrations of Pb and Zn, soil nutrients, and other soil characteristics. Nonmetric Multidimensional Scaling and Regression Tree Analyses identified soil Pb and Zn concentrations as primary explanatory variables for plant community composition and indicated negative relationships between soil metals concentrations and both Mean C and FQI. Univariate regression also demonstrated significant negative relationships between metals concentrations and floristic quality. The negative effects of metals in native soils with otherwise relatively undisturbed conditions indicate that elevated soil metals concentrations adversely affect native floristic quality where no other human disturbance is evident. Published by Elsevier Ltd.

Population-based study of food insecurity and HIV transmission risk behaviors and symptoms of sexually transmitted infections among linked couples in Nepal

PubMed Central

Tsai, Alexander C.; Weiser, Sheri D.

2014-01-01

Food insecurity has recently emerged as an important risk factor for HIV acquisition among women worldwide. No previous studies have used linked data that would permit investigation of the extent to which food insecurity may have differential associations with HIV transmission risk behaviors or symptoms of sexually transmitted infections (STIs) among men and women in the same households. We used nationally representative data on linked couples from the Nepal 2011 Demographic and Health Survey. The primary explanatory variable of interest was food insecurity, measured with the Household Food Insecurity Access Scale. In multivariable logistic regression models, women in food insecure householdswere less likely to report recent condom use and more likely to report symptoms consistent with STIs. These patterns were absent among men. Interventions targeting food insecurity may have beneficial implications for both HIV prevention and gender equity in Nepal. PMID:24833522

Effects of mining-associated lead and zinc soil contamination on native floristic quality

USGS Publications Warehouse

Struckhoff, Matthew A.; Stroh, Esther D.; Grabner, Keith W.

2013-01-01

We assessed the quality of plant communities across a range of lead (Pb) and zinc (Zn) soil concentrations at a variety of sites associated with Pb mining in southeast Missouri, USA. In a novel application, two standard floristic quality measures, Mean Coefficient of Conservatism (Mean C) and Floristic Quality Index (FQI), were examined in relation to concentrations of Pb and Zn, soil nutrients, and other soil characteristics. Nonmetric Multidimensional Scaling and Regression Tree Analyses identified soil Pb and Zn concentrations as primary explanatory variables for plant community composition and indicated negative relationships between soil metals concentrations and both Mean C and FQI. Univariate regression also demonstrated significant negative relationships between metals concentrations and floristic quality. The negative effects of metals in native soils with otherwise relatively undisturbed conditions indicate that elevated soil metals concentrations adversely affect native floristic quality where no other human disturbance is evident.

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…

Jaundice in primary care: a cohort study of adults aged >45 years using electronic medical records.

PubMed

Taylor, Anna; Stapley, Sally; Hamilton, William

2012-08-01

Jaundice is a rare but important symptom of malignant and benign conditions. When patients present in primary care, understanding the relative likelihood of different disease processes can help GPs to investigate and refer patients appropriately. To identify and quantify the various causes of jaundice in adults presenting in primary care. Historical cohort study using electronic primary care records. UK General Practice Research Database. Participants (186 814 men and women) aged >45 years with clinical events recorded in primary care records between 1 January 2005 and 31 December 2007. Data were searched for episodes of jaundice and explanatory diagnoses identified within the subsequent 12 months. If no diagnosis was found, the patient's preceding medical record was searched for relevant chronic diseases. From the full cohort, 277 patients had at least one record of jaundice between 1 January 2005 and 31 December 2006. Ninety-two (33%) were found to have bile duct stones; 74 (27%) had an explanatory cancer [pancreatic cancer 34 (12%), cholangiocarcinoma 13 (5%) and other diagnosed primary malignancy 27 (10%)]. Liver disease attributed to excess alcohol explained 26 (9%) and other diagnoses were identified in 24 (9%). Sixty-one (22%) had no diagnosis related to jaundice recorded. Although the most common cause of jaundice is bile duct stones, cancers are present in over a quarter of patients with jaundice in this study, demonstrating the importance of urgent investigation into the underlying cause.

Relationship between organisational commitment and burnout syndrome: a canonical correlation approach.

PubMed

Enginyurt, Ozgur; Cankaya, Soner; Aksay, Kadir; Tunc, Taner; Koc, Bozkurt; Bas, Orhan; Ozer, Erdal

2016-04-01

Objective Burnout syndrome can significantly reduce the performance of health workers. Although many factors have been identified as antecedents of burnout, few studies have investigated the role of organisational commitment in its development. The purpose of the present study was to examine the relationships between subdimensions of burnout syndrome (emotional exhaustion, depersonalisation and personal accomplishment) and subdimensions of organisational commitment (affective commitment, continuance commitment and normative commitment). Methods The present study was a cross-sectional survey of physicians and other healthcare employees working in the Ministry of Health Ordu University Education and Research Hospital. The sample consisted of 486 healthcare workers. Data were collected using the Maslach Burnout Inventory and the Organisation Commitment Scale, and were analysed using the canonical correlation approach. Results The first of three canonical correlation coefficients between pairs of canonical variables (Ui , burnout syndrome and Vi, organisational commitment) was found to be statistically significant. Emotional exhaustion was found to contribute most towards the explanatory capacity of canonical variables estimated from the subdimensions of burnout syndrome, whereas affective commitment provided the largest contribution towards the explanatory capacity of canonical variables estimated from the subdimensions of organisational commitment. Conclusions The results of the present study indicate that affective commitment is the primary determinant of burnout syndrome in healthcare professionals. What is known about the topic? Organisational commitment and burnout syndrome are the most important criteria in predicting health workforce performance. An increasing number of studies in recent years have clearly indicated the field's continued relevance and importance. Conversely, canonical correlation analysis (CCA) is a technique for describing the relationship between two variable sets simultaneously to produce both structural and spatial meaning. What does this paper add? To our knowledge, CCA has not been used to determine the relationships between burnout and organisational commitment of physicians and other healthcare staff. Accordingly, the present study adds information regarding the relationship between burnout and organisational commitment variables determined using CCA. This analysis is used to describe the relationship between two variable sets simultaneously and allows for an easy method of interpretation. What are the implications for practitioners? Burnout syndrome is a major threat to both the health workforce and its organisations. In addition, it affects the quality and effectiveness of health care. Thus, the findings of the present study offer a solid foundation from which actions to decrease burnout levels in healthcare professionals can be implemented by successfully increasing levels of organisational commitment.

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.

Distribution and relative abundance of humpback whales in relation to environmental variables in coastal British Columbia and adjacent waters

NASA Astrophysics Data System (ADS)

Dalla Rosa, Luciano; Ford, John K. B.; Trites, Andrew W.

2012-03-01

Humpback whales are common in feeding areas off British Columbia (BC) from spring to fall, and are widely distributed along the coast. Climate change and the increase in population size of North Pacific humpback whales may lead to increased anthropogenic impact and require a better understanding of species-habitat relationships. We investigated the distribution and relative abundance of humpback whales in relation to environmental variables and processes in BC waters using GIS and generalized additive models (GAMs). Six non-systematic cetacean surveys were conducted between 2004 and 2006. Whale encounter rates and environmental variables (oceanographic and remote sensing data) were recorded along transects divided into 4 km segments. A combined 3-year model and individual year models (two surveys each) were fitted with the mgcv R package. Model selection was based primarily on GCV scores. The explained deviance of our models ranged from 39% for the 3-year model to 76% for the 2004 model. Humpback whales were strongly associated with latitude and bathymetric features, including depth, slope and distance to the 100-m isobath. Distance to sea-surface-temperature fronts and salinity (climatology) were also constantly selected by the models. The shapes of smooth functions estimated for variables based on chlorophyll concentration or net primary productivity with different temporal resolutions and time lags were not consistent, even though higher numbers of whales seemed to be associated with higher primary productivity for some models. These and other selected explanatory variables may reflect areas of higher biological productivity that favor top predators. Our study confirms the presence of at least three important regions for humpback whales along the BC coast: south Dixon Entrance, middle and southwestern Hecate Strait and the area between La Perouse Bank and the southern edge of Juan de Fuca Canyon.

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 ...

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…

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.

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

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

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.

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

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.

Effects of Dementia-Care Mapping on Residents and Staff of Care Homes: A Pragmatic Cluster-Randomised Controlled Trial

PubMed Central

van de Ven, Geertje; Draskovic, Irena; Adang, Eddy M. M.; Donders, Rogier; Zuidema, Sytse U.; Koopmans, Raymond T. C. M.; Vernooij-Dassen, Myrra J. F. J.

2013-01-01

Background The effectiveness of dementia-care mapping (DCM) for institutionalised people with dementia has been demonstrated in an explanatory cluster-randomised controlled trial (cRCT) with two DCM researchers carrying out the DCM intervention. In order to be able to inform daily practice, we studied DCM effectiveness in a pragmatic cRCT involving a wide range of care homes with trained nursing staff carrying out the intervention. Methods Dementia special care units were randomly assigned to DCM or usual care. Nurses from the intervention care homes received DCM training and conducted the 4-months DCM-intervention twice during the study. The primary outcome was agitation, measured with the Cohen-Mansfield agitation inventory (CMAI). The secondary outcomes included residents’ neuropsychiatric symptoms (NPSs) and quality of life, and staff stress and job satisfaction. The nursing staff made all measurements at baseline and two follow-ups at 4-month intervals. We used linear mixed-effect models to test treatment and time effects. Results 34 units from 11 care homes, including 434 residents and 382 nursing staff members, were randomly assigned. Ten nurses from the intervention units completed the basic and advanced DCM training. Intention-to-treat analysis showed no statistically significant effect on the CMAI (mean difference between groups 2·4, 95% CI −2·7 to 7·6; p = 0·34). More NPSs were reported in the intervention group than in usual care (p = 0·02). Intervention staff reported fewer negative and more positive emotional reactions during work (p = 0·02). There were no other significant effects. Conclusions Our pragmatic findings did not confirm the effect on the primary outcome of agitation in the explanatory study. Perhaps the variability of the extent of implementation of DCM may explain the lack of effect. Trial Registration Dutch Trials Registry NTR2314. PMID:23844003

Effects of dementia-care mapping on residents and staff of care homes: a pragmatic cluster-randomised controlled trial.

PubMed

van de Ven, Geertje; Draskovic, Irena; Adang, Eddy M M; Donders, Rogier; Zuidema, Sytse U; Koopmans, Raymond T C M; Vernooij-Dassen, Myrra J F J

2013-01-01

The effectiveness of dementia-care mapping (DCM) for institutionalised people with dementia has been demonstrated in an explanatory cluster-randomised controlled trial (cRCT) with two DCM researchers carrying out the DCM intervention. In order to be able to inform daily practice, we studied DCM effectiveness in a pragmatic cRCT involving a wide range of care homes with trained nursing staff carrying out the intervention. Dementia special care units were randomly assigned to DCM or usual care. Nurses from the intervention care homes received DCM training and conducted the 4-months DCM-intervention twice during the study. The primary outcome was agitation, measured with the Cohen-Mansfield agitation inventory (CMAI). The secondary outcomes included residents' neuropsychiatric symptoms (NPSs) and quality of life, and staff stress and job satisfaction. The nursing staff made all measurements at baseline and two follow-ups at 4-month intervals. We used linear mixed-effect models to test treatment and time effects. 34 units from 11 care homes, including 434 residents and 382 nursing staff members, were randomly assigned. Ten nurses from the intervention units completed the basic and advanced DCM training. Intention-to-treat analysis showed no statistically significant effect on the CMAI (mean difference between groups 2·4, 95% CI -2·7 to 7·6; p = 0·34). More NPSs were reported in the intervention group than in usual care (p = 0·02). Intervention staff reported fewer negative and more positive emotional reactions during work (p = 0·02). There were no other significant effects. Our pragmatic findings did not confirm the effect on the primary outcome of agitation in the explanatory study. Perhaps the variability of the extent of implementation of DCM may explain the lack of effect. Dutch Trials Registry NTR2314.

Preparedness of lower-level health facilities and the associated factors for the outpatient primary care of hypertension: Evidence from Tanzanian national survey.

PubMed

Bintabara, Deogratius; Mpondo, Bonaventura C T

2018-01-01

Sub-Saharan Africa is experiencing a rapid rise in the burden of non-communicable diseases in both urban and rural areas. Data on health system preparedness to manage hypertension and other non-communicable diseases remains scarce. This study aimed to assess the preparedness of lower-level health facilities for outpatient primary care of hypertension in Tanzania. This study used data from the 2014-2015 Tanzania Service Provision Assessment survey. The facility was considered as prepared for the outpatient primary care of hypertension if reported at least half (≥50%) of the items listed from each of the three domains (staff training and guideline, basic diagnostic equipment, and basic medicines) as identified by World Health Organization-Service Availability and Readiness Assessment manual. Data were analyzed using Stata 14. An unadjusted logistic regression model was used to assess the association between outcome and explanatory variables. All variables with a P value < 0.2 were fitted into the multiple logistic regression models using a 5% significance level. Out of 725 health facilities involved in the current study, about 68% were public facilities and 73% located in rural settings. Only 28% of the assessed facilities were considered prepared for the outpatient primary care of hypertension. About 9% and 42% of the assessed facilities reported to have at least one trained staff and guidelines for hypertension respectively. In multivariate analysis, private facilities [AOR = 2.7, 95% CI; 1.2-6.1], urban location [AOR = 2.2, 95% CI; 1.2-4.2], health centers [AOR = 5.2, 95% CI; 3.1-8.7] and the performance of routine management meetings [AOR = 2.6, 95% CI; 1.1-5.9] were significantly associated with preparedness for the outpatient primary care of hypertension. The primary healthcare system in Tanzania is not adequately equipped to cope with the increasing burden of hypertension and other non-communicable diseases. Rural location, public ownership, and absence of routine management meetings were associated with being not prepared. There is a need to strengthen the primary healthcare system in Tanzania for better management of chronic diseases and curb their rising impact on health outcomes.

[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.

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.

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...

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%).

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.

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.

The upper bound of Pier Scour defined by selected laboratory and field data

USGS Publications Warehouse

Benedict, Stephen; Caldwell, Andral W.

2015-01-01

The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, conducted several field investigations of pier scour in South Carolina (Benedict and Caldwell, 2006; Benedict and Caldwell, 2009) and used that data to develop envelope curves defining the upper bound of pier scour. To expand upon this previous work, an additional cooperative investigation was initiated to combine the South Carolina data with pier-scour data from other sources and evaluate the upper bound of pier scour with this larger data set. To facilitate this analysis, a literature review was made to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet consisting of approximately 570 laboratory and 1,880 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 24 states within the United States and six other countries. This extensive database was used to define the upper bound of pier-scour depth with respect to pier width encompassing the laboratory and field data. Pier width is a primary variable that influences pier-scour depth (Laursen and Toch, 1956; Melville and Coleman, 2000; Mueller and Wagner, 2005, Ettema et al. 2011, Arneson et al. 2012) and therefore, was used as the primary explanatory variable in developing the upper-bound envelope curve. The envelope curve provides a simple but useful tool for assessing the potential maximum pier-scour depth for pier widths of about 30 feet or less.

Systematic age-related differences in chronic disease management in a population-based cohort study: a new paradigm of primary care is required.

PubMed

Buja, Alessandra; Damiani, Gianfranco; Gini, Rosa; Visca, Modesta; Federico, Bruno; Donato, Daniele; Francesconi, Paolo; Marini, Alessandro; Donatini, Andrea; Brugaletta, Salvatore; Baldo, Vincenzo; Donata Bellentani, Maria

2014-01-01

Our interest in chronic conditions is due to the fact that, worldwide, chronic diseases have overtaken infectious diseases as the leading cause of death and disability, so their management represents an important challenge for health systems. The aim of this study was to compare the performance of primary health care services in managing diabetes, congestive heart failure (CHF) and coronary heart disease (CHD), by age group. This population-based retrospective cohort study was conducted in Italy, enrolling 1,948,622 residents ≥ 16 years old. A multilevel regression model was applied to analyze compliance to care processes with explanatory variables at both patient and district level, using age group as an independent variable, and adjusting for sex, citizenship, disease duration, and Charlson index on the first level, and for District Health Unit on the second level. The quality of chronic disease management showed an inverted U-shaped relationship with age. In particular, our findings indicate lower levels for young adults (16-44 year-olds), adults (45-64), and oldest old (+85) than for patients aged 65-74 in almost all quality indicators of CHD, CHF and diabetes management. Young adults (16-44 y), adults (45-64 y), the very old (75-84 y) and the oldest old (+85 y) patients with CHD, CHF and diabetes are less likely than 65-74 year-old patients to be monitored and treated using evidence-based therapies, with the exceptions of echocardiographic monitoring for CHF in young adult patients, and renal monitoring for CHF and diabetes in the very old. Our study shows that more effort is needed to ensure that primary health care systems are sensitive to chronic conditions in the young and in the very elderly.

The UK-PBC risk scores: Derivation and validation of a scoring system for long-term prediction of end-stage liver disease in primary biliary cholangitis.

PubMed

Carbone, Marco; Sharp, Stephen J; Flack, Steve; Paximadas, Dimitrios; Spiess, Kelly; Adgey, Carolyn; Griffiths, Laura; Lim, Reyna; Trembling, Paul; Williamson, Kate; Wareham, Nick J; Aldersley, Mark; Bathgate, Andrew; Burroughs, Andrew K; Heneghan, Michael A; Neuberger, James M; Thorburn, Douglas; Hirschfield, Gideon M; Cordell, Heather J; Alexander, Graeme J; Jones, David E J; Sandford, Richard N; Mells, George F

2016-03-01

The biochemical response to ursodeoxycholic acid (UDCA)--so-called "treatment response"--strongly predicts long-term outcome in primary biliary cholangitis (PBC). Several long-term prognostic models based solely on the treatment response have been developed that are widely used to risk stratify PBC patients and guide their management. However, they do not take other prognostic variables into account, such as the stage of the liver disease. We sought to improve existing long-term prognostic models of PBC using data from the UK-PBC Research Cohort. We performed Cox's proportional hazards regression analysis of diverse explanatory variables in a derivation cohort of 1,916 UDCA-treated participants. We used nonautomatic backward selection to derive the best-fitting Cox model, from which we derived a multivariable fractional polynomial model. We combined linear predictors and baseline survivor functions in equations to score the risk of a liver transplant or liver-related death occurring within 5, 10, or 15 years. We validated these risk scores in an independent cohort of 1,249 UDCA-treated participants. The best-fitting model consisted of the baseline albumin and platelet count, as well as the bilirubin, transaminases, and alkaline phosphatase, after 12 months of UDCA. In the validation cohort, the 5-, 10-, and 15-year risk scores were highly accurate (areas under the curve: >0.90). The prognosis of PBC patients can be accurately evaluated using the UK-PBC risk scores. They may be used to identify high-risk patients for closer monitoring and second-line therapies, as well as low-risk patients who could potentially be followed up in primary care. © 2015 by the American Association for the Study of Liver Diseases.

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.

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.

Differentiated Success: Combining Theories to Explain Learning

ERIC Educational Resources Information Center

Jorgensen, Robyn; Larkin, Kevin

2015-01-01

This paper explores the value of different paradigms to explain dispositions towards mathematics among primary school students from different social backgrounds. As part of a larger project designed to elicit students' thinking and attitudes towards mathematics, we seek to develop an explanatory model for the socially-differentiated outcomes in…

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.

Obtaining the mean relative weights of the cost of care in Catalonia (Spain): retrospective application of the adjusted clinical groups case-mix system in primary health care.

PubMed

Sicras-Mainar, Antoni; Velasco-Velasco, Soledad; Navarro-Artieda, Ruth; Aguado Jodar, Alba; Plana-Ripoll, Oleguer; Hermosilla-Pérez, Eduardo; Bolibar-Ribas, Bonaventura; Prados-Torres, Alejandra; Violan-Fors, Concepción

2013-04-01

The study aims to obtain the mean relative weights (MRWs) of the cost of care through the retrospective application of the adjusted clinical groups (ACGs) in several primary health care (PHC) centres in Catalonia (Spain) in routine clinical practice. This is a retrospective study based on computerized medical records. All patients attended by 13 PHC teams in 2008 were included. The principle measurements were: demographic variables (age and sex), dependent variables (number of diagnoses and total costs), and case-mix or co-morbidity variables (International Classification of Primary Care). The costs model for each patient was established by differentiating the fix costs from the variable costs. In the bivariate analysis, the Student's t, analysis of variance, chi-squared, Pearson's linear correlation and Mann-Whitney-Wilcoxon tests were used. In order to compare the MRW of the present study with those of the United States (US), the concordance [intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC)] and the correlation (coefficient of determination: R²) were measured. The total number of patients studied was 227,235, and the frequentation was 5.9 visits/habitant/year) and with a mean diagnoses number of 4.5 (3.2). The distribution of costs was €148.7 million, of which 29.1% were fixed costs. The mean total cost per patient/year was €654.2 (851.7), which was considered to be the reference MRW. Relationship between study-MRW and US-MRW: ICC was 0.40 [confidential interval (CI) 95%: 0.21-0.60] and the CCC was 0.42 (CI 95%: 0.35-0.49). The correlation between the US MRW and the MRW of the present study can be seen; the adjusted R² value is 0.691. The explanatory power of the ACG classification was 36.9% for the total costs. The R² of the total cost without considering outliers was 56.9%. The methodology has been shown appropriate for promoting the calculation of the MRW for each category of the classification. The results provide a possible practical application in PHC clinical management. © 2012 Blackwell Publishing Ltd.

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

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…

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…

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.

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.

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.

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…

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.

Predictors of Health Service Utilization Among Older Men in Jamaica.

PubMed

Willie-Tyndale, Douladel; McKoy Davis, Julian; Holder-Nevins, Desmalee; Mitchell-Fearon, Kathryn; James, Kenneth; Waldron, Norman K; Eldemire-Shearer, Denise

2018-01-03

To determine the relative influence of sociodemographic, socioeconomic, psychosocial, and health variables on health service utilization in the last 12 months. Data were analyzed for 1,412 men ≥60 years old from a 2012 nationally representative community-based survey in Jamaica. Associations between six health service utilization variables and several explanatory variables were explored. Logistic regression models were used to identify independent predictors of each utilization measure and determine the strengths of associations. More than 75% reported having health visits and blood pressure checks. Blood sugar (69.6%) and cholesterol (63.1%) checks were less common, and having a prostate check (35.1%) was the least utilized service. Adjusted models confirmed that the presence of chronic diseases and health insurance most strongly predicted utilization. A daughter or son as the main source of financial support (vs self) doubled or tripled, respectively, the odds of routine doctors' visits. Compared with primary or lower education, tertiary education doubled [2.37 (1.12, 4.95)] the odds of a blood pressure check. Regular attendance at club/society/religious organizations' meetings increased the odds of having a prostate check by 45%. Although need and financial resources most strongly influenced health service utilization, psychosocial variables may be particularly influential for underutilized services. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

HIV Education and Welfare Services in Primary Care: An Empirical Model of Integration in Brazil’s Unified Health System

PubMed Central

Rahman, Rahbel; Pinto, Rogério M.; Wall, Melanie M.

2017-01-01

Integration of health education and welfare services in primary care systems is a key strategy to solve the multiple determinants of chronic diseases, such as Human Immunodeficiency Virus Infection and Acquired Immune Deficiency Syndrome (HIV/AIDS). However, there is a scarcity of conceptual models from which to build integration strategies. We provide a model based on cross-sectional data from 168 Community Health Agents, 62 nurses, and 32 physicians in two municipalities in Brazil’s Unified Health System (UHS). The outcome, service integration, comprised HIV education, community activities (e.g., health walks and workshops), and documentation services (e.g., obtainment of working papers and birth certificates). Predictors included individual factors (provider confidence, knowledge/skills, perseverance, efficacy); job characteristics (interprofessional collaboration, work-autonomy, decision-making autonomy, skill variety); and organizational factors (work conditions and work resources). Structural equation modeling was used to identify factors associated with service integration. Knowledge and skills, skill variety, confidence, and perseverance predicted greater integration of HIV education alongside community activities and documentation services. Job characteristics and organizational factors did not predict integration. Our study offers an explanatory model that can be adapted to examine other variables that may influence integration of different services in global primary healthcare systems. Findings suggest that practitioner trainings to improve integration should focus on cognitive constructs—confidence, perseverance, knowledge, and skills. PMID:28335444

Prevalence of chronic diseases by immigrant status and disparities in chronic disease management in immigrants: a population-based cohort study, Valore Project.

PubMed

Buja, Alessandra; Gini, Rosa; Visca, Modesta; Damiani, Gianfranco; Federico, Bruno; Francesconi, Paolo; Donato, Daniele; Marini, Alessandro; Donatini, Andrea; Brugaletta, Salvatore; Baldo, Vincenzo; Bellentani, Mariadonata

2013-05-24

For chronic conditions, disparities can take effect cumulatively at various times as the disease progresses, even when care is provided. The aim of this study was to quantify the prevalence of diabetes, congestive heart failure (CHF) and coronary heart disease (CHD) in adults by citizenship, and to compare the performance of primary care services in managing these chronic conditions, again by citizenship. This is a population-based retrospective cohort study on 1,948,622 people aged 16 years or more residing in Italy. A multilevel regression model was applied to analyze adherence to care processes using explanatory variables at both patient and district level. The age-adjusted prevalence of diabetes was found higher among immigrants from high migratory pressure countries (HMPC) than among Italians, while the age-adjusted prevalence of CHD and CHF was higher for Italians than for HMPC immigrants or those from highly-developed countries (HDC). Our results indicate lower levels in all quality management indicators for citizens from HMPC than for Italians, for all the chronic conditions considered. Patients from HDC did not differ from Italian in their adherence to disease management schemes. This study revealed a different prevalence of chronic diseases by citizenship, implying a different burden of primary care by citizenship. Our findings show that more effort is needed to guarantee migrant-sensitive primary health care.

Factors associated to clinical learning in nursing students in primary health care: an analytical cross-sectional study

PubMed Central

Serrano-Gallardo, Pilar; Martínez-Marcos, Mercedes; Espejo-Matorrales, Flora; Arakawa, Tiemi; Magnabosco, Gabriela Tavares; Pinto, Ione Carvalho

2016-01-01

ABSTRACT Objective: to identify the students' perception about the quality of clinical placements and asses the influence of the different tutoring processes in clinical learning. Methods: analytical cross-sectional study on second and third year nursing students (n=122) about clinical learning in primary health care. The Clinical Placement Evaluation Tool and a synthetic index of attitudes and skills were computed to give scores to the clinical learning (scale 0-10). Univariate, bivariate and multivariate (multiple linear regression) analyses were performed. Results: the response rate was 91.8%. The most commonly identified tutoring process was "preceptor-professor" (45.2%). The clinical placement was assessed as "optimal" by 55.1%, relationship with team-preceptor was considered good by 80.4% of the cases and the average grade for clinical learning was 7.89. The multiple linear regression model with more explanatory capacity included the variables "Academic year" (beta coefficient = 1.042 for third-year students), "Primary Health Care Area (PHC)" (beta coefficient = 0.308 for Area B) and "Clinical placement perception" (beta coefficient = - 0.204 for a suboptimal perception). Conclusions: timeframe within the academic program, location and clinical placement perception were associated with students' clinical learning. Students' perceptions of setting quality were positive and a good team-preceptor relationship is a matter of relevance. PMID:27627124

HIV Education and Welfare Services in Primary Care: An Empirical Model of Integration in Brazil's Unified Health System.

PubMed

Rahman, Rahbel; Pinto, Rogério M; Wall, Melanie M

2017-03-14

Integration of health education and welfare services in primary care systems is a key strategy to solve the multiple determinants of chronic diseases, such as Human Immunodeficiency Virus Infection and Acquired Immune Deficiency Syndrome (HIV/AIDS). However, there is a scarcity of conceptual models from which to build integration strategies. We provide a model based on cross-sectional data from 168 Community Health Agents, 62 nurses, and 32 physicians in two municipalities in Brazil's Unified Health System (UHS). The outcome, service integration, comprised HIV education, community activities (e.g., health walks and workshops), and documentation services (e.g., obtainment of working papers and birth certificates). Predictors included individual factors (provider confidence, knowledge/skills, perseverance, efficacy); job characteristics (interprofessional collaboration, work-autonomy, decision-making autonomy, skill variety); and organizational factors (work conditions and work resources). Structural equation modeling was used to identify factors associated with service integration. Knowledge and skills, skill variety, confidence, and perseverance predicted greater integration of HIV education alongside community activities and documentation services. Job characteristics and organizational factors did not predict integration. Our study offers an explanatory model that can be adapted to examine other variables that may influence integration of different services in global primary healthcare systems. Findings suggest that practitioner trainings to improve integration should focus on cognitive constructs-confidence, perseverance, knowledge, and skills.

[Determinants of the use of different healthcare levels in the General System of Social Security in Health in Colombia and the Unified Health System in Brazil].

PubMed

Garcia-Subirats, Irene; Vargas Lorenzo, Ingrid; Mogollón-Pérez, Amparo Susana; De Paepe, Pierre; da Silva, Maria Rejane Ferreira; Unger, Jean Pierre; Vázquez Navarrete, María Luisa

2014-01-01

To compare the use of different healthcare levels, and its determinants, in two different health systems, the General System of Social Security in Health (GSSSH) and the Unified Health System (UHS) in municipalities in Colombia and Brazil. A cross-sectional study was carried out, based on a population survey in two municipalities in Colombia (n=2163) and two in Brazil (n=2155). Outcome variables consisted of the use of primary care services, outpatient secondary care services, and emergency care in the previous 3 months. Explanatory variables were need and predisposing and enabling factors. Bivariate and multivariate logistic regression analyses were performed by healthcare level and country. The determinants of use differed by healthcare level and country: having a chronic disease was associated with a greater use of primary and outpatient secondary care in Colombia, and was also associated with the use of emergency care in Brazil. In Colombia, persons enrolled in the contributory scheme more frequently used the services of the GSSSH than persons enrolled with subsidized contributions in primary and outpatient secondary care and more than persons without insurance in any healthcare level. In Brazil, the low-income population and those without private insurance more frequently used the UHS at any level. In both countries, the use of primary care was increased when persons knew the healthcare center to which they were assigned and if they had a regular source of care. Knowledge of the referral hospital increased the use of outpatient secondary care and emergency care. In both countries, the influence of the determinants of use differed according to the level of care used, emphasizing the need to analyze healthcare use by disaggregating it by level of care. Copyright © 2014. Published by Elsevier Espana.

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.

[Relationship between personality organization and the prevalence of symptoms of depression, anxiety and stress among university students in health careers in the Region of Coquimbo, Chile].

PubMed

López, René Barraza; Navarro, Nadia Muñoz; Astorga, Ana Contreras

The literature reports a set of variables associated with depression, anxiety and stress in health career students. The only one of these that could have a constant input is the structure of personality organisation. The present study aims to determine the relationship between the dimensions of personality organization and depression, anxiety, and stress symptoms reported by first-year university health career students. Under a non-experimental ex-post-facto design, the personality organisation was evaluated in 235 1st year university, medical, nursing, and kinesiology from three universities of La Serena and Coquimbo (Chile). Inventory of personality organization and scale of depression, anxiety and stress to sift participants was used. The relationship of personality with depressive, anxiety and stress symptoms was determined by multiple regression analysis. It was found that the primary and overall personality dimensions explained 28% of the variance of depression (P<.01), 20% of anxiety, and stress 22%, with the use of primitive defenses and identity diffusion dimensions that largely contribute to the explanatory model. The dimensions of personality organization could have a significant relationship with the emergence of depression, anxiety and stress, as the explanatory burden dimension provides the primitive defenses and identity diffusion. These results may be useful for early recognition of aspects of personality of applicants, and to perform actions that strengthen them in order to improve efficiency. Copyright © 2016 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

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.

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

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.

Examination of the Relationships between Fifth Graders' Self-Regulated Learning Strategies, Motivational Beliefs, Attitudes, and Achievement

ERIC Educational Resources Information Center

Ocak, Gurbuz; Yamac, Ahmet

2013-01-01

The aim of current study was to examine predictor and explanatory relationships between fifth graders' self-regulated learning strategies, motivational beliefs, attitudes towards mathematics, and academic achievement. The study was conducted on a sample of 204 students studying in the primary schools of Afyonkarahisar province. Motivated…

Is the Perception of Music Related to Musical Motivation in School?

ERIC Educational Resources Information Center

Arriaga Sanz, Cristina; Madariaga Orbea, José-María

2014-01-01

This paper contains research on the relationship between factors that explain the pupils' perception of music and musical education and their motivation rates to study the subject of music based on motivational explanatory models of cognitive orientation. One hundred and sixteen, sixth-year primary education students belonging to four schools in…

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.

Factors attributable for the prevalence of dental caries in Queensland children.

PubMed

Do, Loc Giang; Ha, Diep Hong; Spencer, A John

2015-10-01

Dental caries is a multifactorial condition, prevention of which requires comprehensive understanding of both contextual and compositional determinants and their population impact. To investigate contextual and compositional factors associated with the prevalence of dental caries in children and to estimate the population impact of those factors. Children in one Australian state were selected through stratified random sampling selection in 2010-2011. Oral epidemiological examinations provided individual-level outcomes: prevalence of dental caries in the primary (among 5- to 8-year-olds) and permanent dentitions (9- to 14-year-olds). Socioeconomic status, oral health behaviours and practices and dietary patterns were explanatory factors at the individual-level, school-level and area-level fluoridation status. Three-level multilevel multivariable models were sequentially specified for the prevalence of dental caries to estimate prevalence ratios (PR) associated with explanatory factors, adjusting for covariates and between- and within-group variances. Population attributable fraction (PAF) was estimated as the population impact of the statistically significant explanatory factors. Data from 2214 5- to 8-year-olds and 3186 9- to 14-year-olds from 207 schools in 16 areas were analysed. The prevalence of dental caries in the primary and the permanent dentitions was 47.1% (43.9-50.4) and 38.8% (36.1-41.6), respectively. The highest prevalence of dental caries was observed in the nonfluoridated areas. In bivariate associations, factors at three levels were associated with prevalence of dental caries. In the full models, children in the nonfluoridated areas had significantly higher prevalence of dental caries [PR for the primary: 1.29 (1.11-1.50); PR for the permanent 1.49 (1.01-2.21)] compared with children in fluoridated areas, controlling for other factors. PAF estimates indicated that lack of water fluoridation attributed to 21% and 31% of primary and permanent dental caries, respectively in this child population. A multitude of factors had significant population impact on the prevalence of dental caries in children. Water fluoridation has a significant population impact on dental caries experience in this child population. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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.

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.

An explanatory model of maths achievement:Perceived parental involvement and academic motivation.

PubMed

Rodríguez, Susana; Piñeiro, Isabel; Gómez-Taibo, Mª L; Regueiro, Bibiana; Estévez, Iris; Valle, Antonio

2017-05-01

Although numerous studies have tried to explain performance in maths very few have deeply explored the relationship between different variables and how they jointly explain mathematical performance. With a sample of 897 students in 5th and 6th grade in Primary Education and using structural equation modeling (SEM), this study analyzes how the perception of parents’ beliefs is related to children´s beliefs, their involvement in mathematical tasks and their performance. Perceived parental involvement contributes to the motivation of their children in mathematics. Direct supervision of students’ academic work by parents may increase students’ concerns about the image and rating of their children, but not their academic performance. In fact, maths achievement depends directly and positively on the parents’ expectations and children’s maths self-efficacy and negatively on the parents’ help in tasks and performance goal orientation. Perceived parental involvement contributes to children’s motivation in maths essentially conveying confidence in their abilities and showing interest in their progress and schoolwork.

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

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

Stress-related sickness absence and return to labour market in Sweden.

PubMed

Engström, Lars-Gunnar; Janson, Staffan

2007-03-15

To analyse factors influencing chances of returning to work after long-term sickness absence with a stress-related psychiatric diagnosis. Primary focus is on employer- and occupational categories as explanatory variables. Data was collected from the regional social insurance office in the county of Värmland for 911 individuals, all with stress-related sickness absences during November in the year 2000. Logistic regressions were carried out on outcome states from long-term sickness absence on two follow-up occasions after two and three years. The results indicate that the employer- and occupational categories only had a minor effect on return to work after the long-term sickness absence. Age and health-related factors together with time factors seem to be more relevant in explaining return to work. The findings suggest that individual labour market position, as occupation, employer, branch etc, seems to be less important than expected in explaining return to work from sickness absence due to stress-related psychiatric disorders.

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

The role of patients' explanatory models and daily-lived experience in hypertension self-management.

PubMed

Bokhour, Barbara G; Cohn, Ellen S; Cortés, Dharma E; Solomon, Jeffrey L; Fix, Gemmae M; Elwy, A Rani; Mueller, Nora; Katz, Lois A; Haidet, Paul; Green, Alexander R; Borzecki, Ann M; Kressin, Nancy R

2012-12-01

Uncontrolled hypertension remains a significant problem for many patients. Few interventions to improve patients' hypertension self-management have had lasting effects. Previous work has focused largely on patients' beliefs as predictors of behavior, but little is understood about beliefs as they are embedded in patients' social contexts. This study aims to explore how patients' "explanatory models" of hypertension (understandings of the causes, mechanisms or pathophysiology, course of illness, symptoms and effects of treatment) and social context relate to their reported daily hypertension self-management behaviors. Semi-structured qualitative interviews with a diverse group of patients at two large urban Veterans Administration Medical centers. PARTICIPANTS (OR PATIENTS OR SUBJECTS): African-American, white and Latino Veterans Affairs (VA) primary care patients with uncontrolled blood pressure. We conducted thematic analysis using tools of grounded theory to identify key themes surrounding patients' explanatory models, social context and hypertension management behaviors. Patients' perceptions of the cause and course of hypertension, experiences of hypertension symptoms, and beliefs about the effectiveness of treatment were related to different hypertension self-management behaviors. Moreover, patients' daily-lived experiences, such as an isolated lifestyle, serious competing health problems, a lack of habits and routines, barriers to exercise and prioritizing lifestyle choices, also interfered with optimal hypertension self-management. Designing interventions to improve patients' hypertension self-management requires consideration of patients' explanatory models and their daily-lived experience. We propose a new conceptual model - the dynamic model of hypertension self-management behavior - which incorporates these key elements of patients' experiences.

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.

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.

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.

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.

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…

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)...

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.

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.

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.

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.

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

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

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.

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).

Who Governs? Academic Decision-Making in US Four-Year Colleges and Universities, 2000-2012

ERIC Educational Resources Information Center

Apkarian, Jacob; Mulligan, Kerry; Rotondi, Matthew B.; Brint, Steven

2014-01-01

This study compares the explanatory power of two models of academic governance: dual and managerial control. The research is based on characterizations by chief academic officers of the primary decision-makers involved in 13 types of recurrent academic decisions. We examine change between responses to surveys fielded to US four-year colleges and…

Teachers' Adoptation Level of Student Centered Education Approach

ERIC Educational Resources Information Center

Arseven, Zeynep; Sahin, Seyma; Kiliç, Abdurrahman

2016-01-01

The aim of this study is to identify how far the student centered education approach is applied in the primary, middle and high schools in Düzce. Explanatory design which is one type of mixed research methods and "sequential mixed methods sampling" were used in the study. 685 teachers constitute the research sample of the quantitative…

Provider continuity in family medicine: does it make a difference for total health care costs?

PubMed

De Maeseneer, Jan M; De Prins, Lutgarde; Gosset, Christiane; Heyerick, Jozef

2003-01-01

International comparisons of health care systems have shown a relationship at the macro level between a well-structured primary health care plan and lower total health care costs. The objective of this study was to assess whether provider continuity with a family physician is related to lower health care costs using the individual patient as the unit of analysis. We undertook a study of a stratified sample of patients (age, sex, region, insurance company) for which 2 cohorts were constructed based on the patients' utilization pattern of family medicine (provider continuity or not). Patient utilization patterns were observed for 2 years. The setting was the Belgian health care system. The participants were 4,134 members of the 2 largest health insurance companies in 2 regions (Aalst and Liège). The main outcome measures were the total health care costs of patients with and without provider continuity with a family physician, controlling for variables known to influence health care utilization (need factors, predisposing factors, enabling factors). Bivariate analyses showed that patients who were visiting the same family physician had a lower total cost for medical care. A multivariate linear regression showed that provider continuity with a family physician was one of the most important explanatory variables related to the total health care cost. Provider continuity with a family physician is related to lower total health care costs. This finding brings evidence to the debate on the importance of structured primary health care (with high continuity for family practice) for a cost-effective health policy.

Avoidable hospitalizations in Switzerland: a small area analysis on regional variation, density of physicians, hospital supply and rurality.

PubMed

Berlin, Claudia; Busato, André; Rosemann, Thomas; Djalali, Sima; Maessen, Maud

2014-07-03

Avoidable hospitalizations (AH) are hospital admissions for diseases and conditions that could have been prevented by appropriate ambulatory care. We examine regional variation of AH in Switzerland and the factors that determine AH. We used hospital service areas, and data from 2008-2010 hospital discharges in Switzerland to examine regional variation in AH. Age and sex standardized AH were the outcome variable, and year of admission, primary care physician density, medical specialist density, rurality, hospital bed density and type of hospital reimbursement system were explanatory variables in our multilevel poisson regression. Regional differences in AH were as high as 12-fold. Poisson regression showed significant increase of all AH over time. There was a significantly lower rate of all AH in areas with more primary care physicians. Rates increased in areas with more specialists. Rates of all AH also increased where the proportion of residences in rural communities increased. Regional hospital capacity and type of hospital reimbursement did not have significant associations. Inconsistent patterns of significant determinants were found for disease specific analyses. The identification of regions with high and low AH rates is a starting point for future studies on unwarranted medical procedures, and may help to reduce their incidence. AH have complex multifactorial origins and this study demonstrates that rurality and physician density are relevant determinants. The results are helpful to improve the performance of the outpatient sector with emphasis on local context. Rural and urban differences in health care delivery remain a cause of concern in Switzerland.

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.

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.

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

Burnout Subtypes and Absence of Self-Compassion in Primary Healthcare Professionals: A Cross-Sectional Study

PubMed Central

Montero-Marin, Jesus; Zubiaga, Fernando; Cereceda, Maria; Piva Demarzo, Marcelo Marcos; Trenc, Patricia; Garcia-Campayo, Javier

2016-01-01

Background Primary healthcare professionals report high levels of distress and burnout. A new model of burnout has been developed to differentiate three clinical subtypes: ‘frenetic’, ‘underchallenged’ and ‘worn-out’. The aim of this study was to confirm the validity and reliability of the burnout subtype model in Spanish primary healthcare professionals, and to assess the explanatory power of the self-compassion construct as a possible protective factor. Method The study employed a cross-sectional design. A sample of n = 440 Spanish primary healthcare professionals (214 general practitioners, 184 nurses, 42 medical residents) completed the Burnout Clinical Subtype Questionnaire (BCSQ-36), the Maslach Burnout Inventory General Survey (MBI-GS), the Self-Compassion Scale (SCS), the Utrecht Work Engagement Scale (UWES) and the Positive and Negative Affect Schedule (PANAS). The factor structure of the BCSQ-36 was estimated using confirmatory factor analysis (CFA) by the unweighted least squares method from polychoric correlations. Internal consistency (R) was assessed by squaring the correlation between the latent true variable and the observed variables. The relationships between the BCSQ-36 and the other constructs were analysed using Spearman’s r and multiple linear regression models. Results The structure of the BCSQ-36 fit the data well, with adequate CFA indices for all the burnout subtypes. Reliability was adequate for all the scales and sub-scales (R≥0.75). Self-judgement was the self-compassion factor that explained the frenetic subtype (Beta = 0.36; p<0.001); isolation explained the underchallenged (Beta = 0.16; p = 0.010); and over-identification the worn-out (Beta = 0.25; p = 0.001). Other significant associations were observed between the different burnout subtypes and the dimensions of the MBI-GS, UWES and PANAS. Conclusions The typological definition of burnout through the BCSQ-36 showed good structure and appropriate internal consistence in Spanish primary healthcare professionals. The negative self-compassion dimensions seem to play a relevant role in explaining the burnout profiles in this population, and they should be considered when designing specific treatments and interventions tailored to the specific vulnerability of each subtype. PMID:27310426

Tentative explanatory variable of lung dust concentration in gold miners exposed to crystalline silica.

PubMed

Dufresne, A; Loosereewanich, P; Bégin, R; Dion, C; Ecobichon, D; Muir, D C; Ritchie, A C; Perrault, G

1998-01-01

The first objective of the study was to investigate the relationships between quantitative lung mineral dust burdens, dust exposure history, and pathological fibrosis grading in silicotic workers. The second objective was to evaluate the association between particle size parameters, concentration of retained silica particles and the severity of the silicosis. Sixty-seven paraffin-embedded lung tissue samples of silicotic patients were analyzed. The cases of silicosis included 39 non-lung cancer patients and 28 patients with lung cancer. All of the cases were gold miners in the Province of Ontario, Canada. Particles, both angular and fibrous, were extracted from lung parenchyma by a bleach digestion method, mounted on copper microscopic grids by a carbon replica technique, and analyzed by transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS). Quartz concentration was also determined by X-ray diffraction (XRD) on a silver membrane filter after the extraction from the lung parenchyma. Total particles, silica, clay, and quartz also increase in concentration with increased age at death, although the trends are not statistically significant. Quartz concentration has a statistically significant correlation with the silicosis severity score (r = +0.45, p < 0.001), with the geometric mean concentration increasing from 2.24 micrograms/mg in the group having silicosis severity score less than 1 to 4.80 micrograms/mg in group with highest score. Quartz concentration is the only significant explanatory variable of the silicosis severity with a regression coefficient of +0.41 (p < 0.001). Among several dust exposure variables extracted from the work history of the miners, the calendar year of first exposure was the primary significant determinant of lung retained total particles, silica, and clay minerals, except for quartz. A statistically significant linear relationship between lung quartz concentration and silicosis severity in the gold miners was observed (p < 0.001). Among the several types of lung particles detected, quartz was the only significant determinant of the silicosis severity in the gold miners in this study and vice versa, although it explained only 20% of the variation in the severity. This study suggested no significant linear relationship between the duration of dust exposure and the lung burden of any particle types in the gold miners.

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

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.

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.

Preparedness of lower-level health facilities and the associated factors for the outpatient primary care of hypertension: Evidence from Tanzanian national survey

PubMed Central

Mpondo, Bonaventura C. T.

2018-01-01

Introduction Sub-Saharan Africa is experiencing a rapid rise in the burden of non-communicable diseases in both urban and rural areas. Data on health system preparedness to manage hypertension and other non-communicable diseases remains scarce. This study aimed to assess the preparedness of lower-level health facilities for outpatient primary care of hypertension in Tanzania. Methods This study used data from the 2014–2015 Tanzania Service Provision Assessment survey. The facility was considered as prepared for the outpatient primary care of hypertension if reported at least half (≥50%) of the items listed from each of the three domains (staff training and guideline, basic diagnostic equipment, and basic medicines) as identified by World Health Organization-Service Availability and Readiness Assessment manual. Data were analyzed using Stata 14. An unadjusted logistic regression model was used to assess the association between outcome and explanatory variables. All variables with a P value < 0.2 were fitted into the multiple logistic regression models using a 5% significance level. Results Out of 725 health facilities involved in the current study, about 68% were public facilities and 73% located in rural settings. Only 28% of the assessed facilities were considered prepared for the outpatient primary care of hypertension. About 9% and 42% of the assessed facilities reported to have at least one trained staff and guidelines for hypertension respectively. In multivariate analysis, private facilities [AOR = 2.7, 95% CI; 1.2–6.1], urban location [AOR = 2.2, 95% CI; 1.2–4.2], health centers [AOR = 5.2, 95% CI; 3.1–8.7] and the performance of routine management meetings [AOR = 2.6, 95% CI; 1.1–5.9] were significantly associated with preparedness for the outpatient primary care of hypertension. Conclusion The primary healthcare system in Tanzania is not adequately equipped to cope with the increasing burden of hypertension and other non-communicable diseases. Rural location, public ownership, and absence of routine management meetings were associated with being not prepared. There is a need to strengthen the primary healthcare system in Tanzania for better management of chronic diseases and curb their rising impact on health outcomes. PMID:29447231

[Prevalence and factors associated with metabolic syndrome in users of primary healthcare units in São Paulo--SP, Brazil].

PubMed

Leitão, Maria Paula Carvalho; Martins, Ignez Salas

2012-01-01

To determine the relationship between metabolic syndrome (MS) and socioeconomic level, life style, health status, family history of morbidity, and residence areas. This is a cross-sectional cohort study. The random sample consisted of users of two primary health care units (Unidades Básicas de Saúde--UBSs) in the city of São Paulo--Jardim Comercial (UBS1), and Jardim Germânia (UBS2), a total of 452 subjects. The NCEP ATP IIIcriterion was used to diagnose MS. Weight, height, abdominal and hip circumferences were measured for the anthropometric evaluation. A general questionnaire was used to obtain sociodemographic and socioeconomic data; family history; medical history; behavioral habits such as smoking, drinking, and physical activity. Multivariate logistic regression was used to establish the association between explanatory variables of interest and MS. At UBS1, MS percentage was 56.1%; at UBS2, 34.0%. There was a direct and significant association between MS and age, female gender, race, smoking, drinking, physical activity level, stress, and family history of heart disease and diabetes mellitus. Education level showed an inverse association. Subjects living in a lower socioeconomic level neighborhood had a higher MS risk. The results suggest that the morbidities that compose MS are a serious publichealth problem in that population.

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.

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.

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).

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.

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.

Regional price differences and food consumption frequency among elementary school children.

PubMed

Sturm, R; Datar, A

2011-03-01

Food prices may affect diet and weight gain among youth and lead to geographic disparities in obesity. This paper examines the association between regional prices and consumption frequency of fruit/vegetables and snack items among elementary school children in the USA. Observational study using individual-level survey data of fifth-grade children (average age 11 years) and regional food prices based on store visits in 2004. Dependent variables are self-reported consumption frequency in fifth grade; primary explanatory variables are metropolitan area food prices relative to cost of living. Multivariate regression analysis. Price variation across metropolitan areas exists, and lower real prices for vegetables and fruits predict significantly higher intake frequency. Higher dairy prices predict lower frequency of milk consumption, while higher meat prices predict increased milk consumption. Similar price effects were not found for fast food or soft drink consumption. The geographic variation in food prices across the USA is sufficiently large to affect dietary patterns among youth for fruit, vegetables and milk. The price variation is either too small to affect children's consumption frequency of fast food or soft drinks, or the consumption of these foods is less price sensitive. Copyright © 2010 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Cognitive components of a mathematical processing network in 9-year-old children.

PubMed

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-07-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.

Cognitive components of a mathematical processing network in 9-year-old children

PubMed Central

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-01-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular ‘number sense’. We suggest an ‘executive memory function centric’ model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors. PMID:25089322

Regional price differences and food consumption frequency among elementary school children

PubMed Central

Sturm, R.; Datar, A.

2010-01-01

SUMMARY Objective Food prices may affect diet and weight gain among youth and lead to geographic disparities in obesity. This paper examines the association between regional prices and consumption frequency of fruit/vegetables and snack items among elementary school children in the USA. Study design Observational study using individual-level survey data of fifth-grade children (average age 11 years) and regional food prices based on store visits in 2004. Methods Dependent variables are self-reported consumption frequency in fifth grade; primary explanatory variables are metropolitan area food prices relative to cost of living. Multivariate regression analysis. Results Price variation across metropolitan areas exists, and lower real prices for vegetables and fruits predict significantly higher intake frequency. Higher dairy prices predict lower frequency of milk consumption, while higher meat prices predict increased milk consumption. Similar price effects were not found for fast food or soft drink consumption. Discussion The geographic variation in food prices across the USA is sufficiently large to affect dietary patterns among youth for fruit, vegetables and milk. This suggests that either the price variation is too small to affect children’s consumption frequency of fast food or soft drinks, or that the consumption of these foods is less price sensitive. PMID:21315395

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…

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 (...

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...

Insight in Psychosis: An Indicator of Severity of Psychosis, an Explanatory Model of Illness, and a Coping Strategy

PubMed Central

Jacob, K. S.

2016-01-01

Recent studies related to insight, explanatory models (EMs) of illness and their relationship to outcome of psychosis are reviewed. The traditional argument that insight predicts outcome in psychosis is not supported by recent longitudinal data, which has been analyzed using multivariable statistics that adjust for severity and quality of illness. While all cognition will have a neurobiological representation, if “insight” is related to the primary psychotic process, then insight cannot be seen as an independent predictor of outcome but a part of the progression of illness. The evidence suggests insight, like all EMs, is belief which interacts with the trajectory of the person's illness and the local culture to produce a unique understanding of the illness for the particular individual and his/her family. PMID:27335513

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.

Spatiotemporal Anopheles Population Dynamics, Response to Climatic Conditions: The Case of Chabahar, South Baluchistan, Iran.

PubMed

Farajzadeh, Manuchehr; Halimi, Mansour; Ghavidel, Yousef; Delavari, Mahdi

2015-01-01

An understanding of the factors that affect the abundance of Anopheline species provides an opportunity to better understand the dynamics of malaria transmission in different regions. Chabahar, located south east of Iran, is the most malarious region in the country. The main aim of this study was to quantify the spatiotemporal Anopheles population dynamics, response to climatic conditions in Chabahar. Satellite-based and land-based climatic data were used as explanatory variables. Monthly caught mosquitoes in 6 village sites of Chabahar were used as dependent variable. The spatiotemporal associations were first investigated by inspection of scatter plots and single variable regression analysis. A multivariate linear regression model was developed to reveal the association between environmental variables and the monthly mosquito abundance at a 95% confidence level (P ≤ 0.5). Results indicated that Anopheles mosquitoes can be found all year in Chabahar with 2 significant seasonal peaks from March to June (primary peak) and September to November (secondary peak). Results of the present study showed that 0.77 of yearly mosquito abundance emerges in the thermal range of 24°C to 30°C and the humidity range of 0.70 to 0.80 in Chabahar. According to the developed multivariate linear model, 0.88 of temporal variance of mosquito abundance, nighttime land surface temperature, and relative humidity of 15 Universal Time Coordinated (18.30 Iran) are the main drivers of mosquito population dynamics in Chabahar. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Large interannual variability in net ecosystem carbon dioxide exchange of a disturbed temperate peatland.

PubMed

Aslan-Sungur, Guler; Lee, Xuhui; Evrendilek, Fatih; Karakaya, Nusret

2016-06-01

Peatland ecosystems play an important role in the global carbon (C) cycle as significant C sinks. However, human-induced disturbances can turn these sinks into sources of atmospheric CO2. Long-term measurements are needed to understand seasonal and interannual variability of net ecosystem CO2 exchange (NEE) and effects of hydrological conditions and their disturbances on C fluxes. Continuous eddy-covariance measurements of NEE were conducted between August 2010 and April 2014 at Yenicaga temperate peatland (Turkey), which was drained for agricultural usage and for peat mining until 2009. Annual NEE during the three full years of measurement indicated that the peatland acted as a CO2 source with large interannual variability, at rates of 246, 244 and 663 g Cm(-2)yr(-1) for 2011, 2012, and 2013 respectively, except for June 2011, and May to July 2012. The emission strengths were comparable to those found for severely disturbed tropical peatlands. The peak CO2 emissions occurred in the dry summer of 2013 when water table level (WTL) was below a threshold value of -60 cm and soil water content (SCW) below a threshold value of 70% by volume. Water availability index was found to have a stronger explanatory power for variations in monthly ecosystem respiration (ER) than the traditional water status indicators (SCW and WTL). Air temperature, evapotranspiration and vapor pressure deficient were the most significant variables strongly correlated with NEE and its component fluxes of gross primary production and ER. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.

The role of pragmatism in explaining heterogeneity in meta-analyses of randomised trials: a protocol for a cross-sectional methodological review.

PubMed

Aves, Theresa; Allan, Katherine S; Lawson, Daeria; Nieuwlaat, Robby; Beyene, Joseph; Mbuagbaw, Lawrence

2017-09-03

There has been increasing interest in pragmatic trials methodology. As a result, tools such as the Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) are being used prospectively to help researchers design randomised controlled trials (RCTs) within the pragmatic-explanatory continuum. There may be value in applying the PRECIS-2 tool retrospectively in a systematic review setting as it could provide important information about how to pool data based on the degree of pragmatism. To investigate the role of pragmatism as a source of heterogeneity in systematic reviews by (1) identifying systematic reviews with meta-analyses of RCTs that have moderate to high heterogeneity, (2) applying PRECIS-2 to RCTs of systematic reviews, (3) evaluating the inter-rater reliability of PRECIS-2, (4) determining how much of this heterogeneity may be explained by pragmatism. A cross-sectional methodological review will be conducted on systematic reviews of RCTs published in the Cochrane Library from 1 January 2014 to 1 January 2017. Included systematic reviews will have a minimum of 10 RCTs in the meta-analysis of the primary outcome and moderate to substantial heterogeneity (I 2 ≥50%). Of the eligible systematic reviews, a random selection of 10 will be included for quantitative evaluation. In each systematic review, RCTs will be scored using the PRECIS-2 tool, in duplicate. Agreement between raters will be measured using the intraclass correlation coefficient. Subgroup analyses and meta-regression will be used to evaluate how much variability in the primary outcome may be due to pragmatism. This review will be among the first to evaluate the PRECIS-2 tool in a systematic review setting. Results from this research will provide inter-rater reliability information about PRECIS-2 and may be used to provide methodological guidance when dealing with pragmatism in systematic reviews and subgroup considerations. On completion, this review will be submitted to a peer-reviewed journal for publication. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Individual Differences in Learning from Verbal and Figural Materials. Technical Report No. 12: Aptitude Research Project.

ERIC Educational Resources Information Center

Yalow, Elanna

Of primary interest in this study was the effect of general ability on learning. It was hypothesized that students higher in general ability would obtain higher posttest scores on the average than lower ability students, and that verbal and figural explanatory supplements to minimal instructional materials would reduce the regression of general…

Making Sense of Iconic Symbols: A Study of Preschool Children Conducting a Refuse-Sorting Task

ERIC Educational Resources Information Center

Ljung-Djärf, Agneta; Åberg-Bengtsson, Lisbeth; Ottosson, Torgny; Beach, Dennis

2015-01-01

This article is part of a larger project focusing upon explanatory illustrations that children encounter in pre- and primary school education. The research questions concerned (a) how preschool children make sense of iconic symbols when placing items of refuse on illustrations of refuse bins in a sorting task and (b) what stumbling blocks they…

Why Do Older Men Report Low Stress Ratings? Findings from the Veterans Affairs Normative Aging Study

ERIC Educational Resources Information Center

Boeninger, Daria K.; Shiraishi, Ray W.; Aldwin, Carolyn M.; Spiro, Avron, III

2009-01-01

We examined the interplay between three explanatory hypotheses for why older adults appear to rate their problems as less stressful than do younger adults: age-related differences in personality, in types of problems, and in the appraisal process--specifically, the number of primary stress appraisals. A sample of 1,054 men from the Normative Aging…

Keep it simple? Predicting primary health care costs with clinical morbidity measures

PubMed Central

Brilleman, Samuel L.; Gravelle, Hugh; Hollinghurst, Sandra; Purdy, Sarah; Salisbury, Chris; Windmeijer, Frank

2014-01-01

Models of the determinants of individuals’ primary care costs can be used to set capitation payments to providers and to test for horizontal equity. We compare the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs and examine capitation payments based on them. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in the Quality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 Expanded Diagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient records of 86,100 individuals in 174 English practices. For a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power. The EDC measures performed best followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Comparisons of predictive power for different morbidity measures were similar for linear and exponential models, but the relative predictive power of the models varied with the morbidity measure. Capitation payments for an individual patient vary considerably with the different morbidity measures included in the cost model. Even for the best fitting model large differences between expected cost and capitation for some types of patient suggest incentives for patient selection. Models with any of the morbidity measures show higher cost for more deprived patients but the positive effect of deprivation on cost was smaller in better fitting models. PMID:24657375

Determinants of Objectively Measured Physical Functional Performance in Early to Mid-stage Parkinson Disease

PubMed Central

Kluger, Benzi M.; Brown, R. Preston; Aerts, Shanae; Schenkman, Margaret

2014-01-01

Background Parkinson disease (PD) may lead to functional limitations through both motor and non-motor symptoms. While patients with advanced disease have well-documented and profound functional limitations, less is known about the determinants of function in early to mid-stage disease where interventions may be more likely to benefit and preserve function. Objective The objective of the current study was to identify motor, cognitive and gait determinants of physical functional performance in patients with early to mid-stage PD. Design Secondary analysis of cross-sectional baseline data from a randomized clinical trial of exercise. Setting Tertiary academic medical center. Participants 121 patients with early to mid-stage PD. Methods Our functional performance outcomes included: 1) the Continuous Scale Functional Performance Test (CS-PFP; primary outcome); 2) the timed up and go (TUG) tests; and Section 2 (Activities of Daily Living) of the Unified Parkinson's Disease Rating Scale (UPDRS). Explanatory variables included measures of disease severity, motor function, cognitive function, balance and gait. Step-wise linear regression models were used to determine correlations between explanatory variables and outcome measures. Results In our regression models the CS-PFP significantly correlated with walking endurance (six minute walk; r2 = 0.12, p < .0001), turning ability (360 degree turn; r2 = .03, p = .002), attention (brief test of attention; r2 = .01, p = .03), overall cognitive status (Mini-mental State Examination; r2 = .01, p = .04) and bradykinesia (timed tapping; r2 = .02, p = .02). The TUG significantly correlated with walking speed (5 meter walk; r2 = 0.33, p <.0001), stride length (r2 = 0.25, p <.0001), turning ability (360 turn r2 = .05, p = .0003) and attention (r2 = .016, p = .03). Section 2 of the UPDRS was significantly correlated with endurance (r2 = .09, p < .0001), turning ability (r2 = .03, p = .001) and attention (r2 = .01, p = .03). Conclusions Gait, motor and cognitive function all contribute to objectively measured global functional ability in mild to moderate PD. Subjectively measured functional activity outcomes may underestimate the impact of both motor and non-motor symptoms. PMID:24880056

Ten problems and solutions when predicting individual outcome from lesion site after stroke.

PubMed

Price, Cathy J; Hope, Thomas M; Seghier, Mohamed L

2017-01-15

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Ten problems and solutions when predicting individual outcome from lesion site after stroke

PubMed Central

Price, Cathy J.; Hope, Thomas M.; Seghier, Mohamed L.

2016-01-01

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual’s functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients. PMID:27502048

Religion and survival in a secular region. A twenty year follow-up of 734 Danish adults born in 1914.

PubMed

la Cour, Peter; Avlund, Kirsten; Schultz-Larsen, Kirsten

2006-01-01

The aim of the study was to analyse associations of religiosity and mortality in a secular region. The sample consisted of 734 Danish, community dwelling elderly persons, living in a secular culture, and all aged 70 when primary data were collected. Secondary data consisted of a 20 year follow-up on vital status or exact age of death. The study was designed to be highly comparable to studies conducted in more religious environments in order to compare results. Three variables of religion were investigated in relation to survival: importance of affiliation, church attendance and listening to religious media. Relative hazards (RH) of dying were controlled in models including gender, education, medical and mental health, social relations, help given and received, and health behaviour. The results showed significant and positive associations between claiming religious affiliation important and survival (relative hazard of dying=RH .70; 95% CI .58-.85) and church attendance and survival (RH .73; 95% CI .64-.87). Results decreased and only stayed significant regarding church attendance when controlled for covariates. Nearly all significant effects were seen in women, but not in men. The effect size of the full sample is less than in more religious environments in United States samples. Although the positive overall RHs are comparable to those of other studies, the mediating variables and pathways of effects seem dissimilar in this sample from a secular environment. Receiving and especially giving help to others are suggested as variables of explanatory value.

[Cardiovascular risk factors in the population at risk of poverty and social exclusión].

PubMed

Álvarez-Fernández, Carlos; Vaquero-Abellán, Manuel; Ruíz-Gandara, África; Romero-Saldaña, Manuel; Álvarez-López, Carlos

2017-03-01

Detect if there are differences in prevalence, distribution of cardiovascular risk factors and risk according to REGICOR and SCORE's function; between people belonging to different occupational classes and population at risk of social exclusion. Cross-sectional. SITE: Occupational health unit of the City Hall of Córdoba. Sample availability of 628 people, excluding 59 by age or incomplete data. The group of municipal workers was obtained randomly while all contracted exclusion risk was taken. No preventive, diagnostic or therapeutic actions that modify the course of the previous situation of workers were applied. Smoke, glucose, lipids, blood pressure and BMI as main variables. T-student were used for comparison of means and percentages for Chi 2 . Statistical significance attached to an alpha error <5% and confidence interval with a 95% security. Receiver operator curves (ROC) were employed to find out what explanatory variables predict group membership of workers at risk of exclusion. Smoking (95% CI: -.224;-.443), hypercholesterolemia (95% CI: -.127;-.320), obesity (95% CI: -.005;-0.214), diabetes (95% CI: -.060;-.211) and cardiovascular risk were higher in men at risk of exclusion. In women there were differences in the same variables except smoking (P=.053). The existence of inequalities in prevalence of cardiovascular risk factors is checked. In a context of social crisis, health promotion and primary prevention programs directing to the most vulnerable, they are needed to mit. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

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.

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

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.

[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.

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.

Systematic Age-Related Differences in Chronic Disease Management in a Population-Based Cohort Study: A New Paradigm of Primary Care Is Required

PubMed Central

Buja, Alessandra; Damiani, Gianfranco; Gini, Rosa; Visca, Modesta; Federico, Bruno; Donato, Daniele; Francesconi, Paolo; Marini, Alessandro; Donatini, Andrea; Brugaletta, Salvatore; Baldo, Vincenzo; Donata Bellentani, Maria

2014-01-01

Background Our interest in chronic conditions is due to the fact that, worldwide, chronic diseases have overtaken infectious diseases as the leading cause of death and disability, so their management represents an important challenge for health systems. The aim of this study was to compare the performance of primary health care services in managing diabetes, congestive heart failure (CHF) and coronary heart disease (CHD), by age group. Methods This population-based retrospective cohort study was conducted in Italy, enrolling 1,948,622 residents ≥16 years old. A multilevel regression model was applied to analyze compliance to care processes with explanatory variables at both patient and district level, using age group as an independent variable, and adjusting for sex, citizenship, disease duration, and Charlson index on the first level, and for District Health Unit on the second level. Results The quality of chronic disease management showed an inverted U-shaped relationship with age. In particular, our findings indicate lower levels for young adults (16–44 year-olds), adults (45–64), and oldest old (+85) than for patients aged 65–74 in almost all quality indicators of CHD, CHF and diabetes management. Young adults (16–44 y), adults (45–64 y), the very old (75–84 y) and the oldest old (+85 y) patients with CHD, CHF and diabetes are less likely than 65–74 year-old patients to be monitored and treated using evidence-based therapies, with the exceptions of echocardiographic monitoring for CHF in young adult patients, and renal monitoring for CHF and diabetes in the very old. Conclusion Our study shows that more effort is needed to ensure that primary health care systems are sensitive to chronic conditions in the young and in the very elderly. PMID:24632818

“Gaining or losing”: The importance of the perspective in primary care health services valuation

PubMed Central

Ariza-Cardiel, Gloria; Peña-Longobardo, Luz Mª; Polentinos-Castro, Elena; Oliva-Moreno, Juan; Gil-Lacruz, Ana Isabel; Medina-Palomino, Héctor; del Cura-González, Isabel

2017-01-01

Rationale and objectives Economic theory classifies an intervention as socially beneficial if the total Willingness to Pay (WTP) of those who gain exceeds the total Willingness to accept (WTA) of those who are harmed. This paper examines the differences in health system users’ valuation of a health care service in primary care setting based on the WTP and WTA perspectives, discussing the impact of personal and service variables, including risk attitudes, on these disparities. Method Six hundred and sixty two subjects who asked for care in health centres in the Region of Madrid (Spain) were interviewed, using the contingent valuation method to estimate WTP and WTA. Patient sociodemographic characteristics, health needs, satisfaction with the service and risk attitude and behaviour under risk (measured by self-reported scales and lottery games respectively) were collected. Generalised Linear Models were used to estimate the association between the explanatory variables and the WTA/WTP ratio. Results We obtained the WTA/WTP ratio for 570 subjects (mean 1.66 CI 95%: 1.53–1.79; median 1, interquartile range 1–2). People with higher education or in high social groups expressed WTA values closest to WTP. The opposite occurred in patients with the greatest health needs or who were born abroad. Self-reported expression of risk aversion appeared also related to increases in the WTA/WTP ratio. Satisfaction with the service evaluated was the most influential factor in the WTA/WTP ratio. Conclusion Health need, difficulty in obtaining substitutes and satisfaction with the service could serve for profiling people averse to loss for health care services in primary care setting. Self-reported expression of risk aversion could also be related to increases in the WTA/WTP ratio. This would mean that these characteristics should be taken into account both in the design and implementation of new healthcare interventions, as in the making decision for disinvestment. PMID:29206847

"Gaining or losing": The importance of the perspective in primary care health services valuation.

PubMed

Martín-Fernández, Jesús; Ariza-Cardiel, Gloria; Peña-Longobardo, Luz Mª; Polentinos-Castro, Elena; Oliva-Moreno, Juan; Gil-Lacruz, Ana Isabel; Medina-Palomino, Héctor; Del Cura-González, Isabel

2017-01-01

Economic theory classifies an intervention as socially beneficial if the total Willingness to Pay (WTP) of those who gain exceeds the total Willingness to accept (WTA) of those who are harmed. This paper examines the differences in health system users' valuation of a health care service in primary care setting based on the WTP and WTA perspectives, discussing the impact of personal and service variables, including risk attitudes, on these disparities. Six hundred and sixty two subjects who asked for care in health centres in the Region of Madrid (Spain) were interviewed, using the contingent valuation method to estimate WTP and WTA. Patient sociodemographic characteristics, health needs, satisfaction with the service and risk attitude and behaviour under risk (measured by self-reported scales and lottery games respectively) were collected. Generalised Linear Models were used to estimate the association between the explanatory variables and the WTA/WTP ratio. We obtained the WTA/WTP ratio for 570 subjects (mean 1.66 CI 95%: 1.53-1.79; median 1, interquartile range 1-2). People with higher education or in high social groups expressed WTA values closest to WTP. The opposite occurred in patients with the greatest health needs or who were born abroad. Self-reported expression of risk aversion appeared also related to increases in the WTA/WTP ratio. Satisfaction with the service evaluated was the most influential factor in the WTA/WTP ratio. Health need, difficulty in obtaining substitutes and satisfaction with the service could serve for profiling people averse to loss for health care services in primary care setting. Self-reported expression of risk aversion could also be related to increases in the WTA/WTP ratio. This would mean that these characteristics should be taken into account both in the design and implementation of new healthcare interventions, as in the making decision for disinvestment.

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,…

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...

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.

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.

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

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.

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.

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.

Cross-sectional associations between high-deprivation home and neighbourhood environments, and health-related variables among Liverpool children

PubMed Central

Noonan, Robert J; Boddy, Lynne M; Knowles, Zoe R; Fairclough, Stuart J

2016-01-01

Objectives (1) To investigate differences in health-related, home and neighbourhood environmental variables between Liverpool children living in areas of high deprivation (HD) and medium-to-high deprivation (MD) and (2) to assess associations between these perceived home and neighbourhood environments and health-related variables stratified by deprivation group. Design Cross-sectional study. Setting 10 Liverpool primary schools in 2014. Participants 194 children aged 9–10 years. Main outcome measures Health-related variables (self-reported physical activity (PA) (Physical Activity Questionnaire for Older Children, PAQ-C), cardiorespiratory fitness, body mass index (BMI) z-scores, waist circumference), home environment variables: (garden/backyard access, independent mobility, screen-based media restrictions, bedroom media) and neighbourhood walkability (Neighbourhood Environment Walkability Scale for Youth, NEWS-Y). Explanatory measures Area deprivation. Results There were significant differences between HD and MD children's BMI z-scores (p<0.01), waist circumference (p<0.001) and cardiorespiratory fitness (p<0.01). HD children had significantly higher bedroom media availability (p<0.05) and independent mobility scores than MD children (p<0.05). MD children had significantly higher residential density and neighbourhood aesthetics scores, and lower crime safety, pedestrian and road traffic safety scores than HD children, all of which indicated higher walkability (p<0.01). HD children's BMI z-scores (β=−0.29, p<0.01) and waist circumferences (β=−0.27, p<0.01) were inversely associated with neighbourhood aesthetics. HD children's PA was negatively associated with bedroom media (β=−0.24, p<0.01), and MD children's PA was positively associated with independent mobility (β=0.25, p<0.01). MD children's independent mobility was inversely associated with crime safety (β=−0.28, p<0.01) and neighbourhood aesthetics (β=−0.24, p<0.05). Conclusions Children living in HD areas had the least favourable health-related variables and were exposed to home and neighbourhood environments that are unconducive to health-promoting behaviours. Less access to bedroom media equipment and greater independent mobility were strongly associated with higher PA in HD and MD children, respectively. Facilitating independent mobility and encouraging outdoor play may act as effective strategies to enhance PA levels and reduce sedentary time in primary school-aged children. PMID:26769779

Primary History: Using the Evidence of the Historic Environment. A Teacher's Guide.

ERIC Educational Resources Information Center

Corbishley, Mike; Fordham, Jennie; Gay, Susan; Hollinshead, Liz; Spicer, Suzanne; Walmsley, David

This guide aims to help busy elementary teachers make the best use of a whole range of evidence for teaching history in schools today, covering areas of study at both Key Stages 1 and 2 (in the British school system). The guide includes: clear, richly illustrated explanatory text for the non-expert, with practical advice and project ideas; how to…

Conceptions of Pupils of the Primary on the Topic of an Electric Circuit in Three Countries (Canada, France and Morocco)

ERIC Educational Resources Information Center

Métioui, Abdeljalil; MacWillie, Mireille Baulu; Trudel, Louis

2016-01-01

Qualitative research conducted with 237 pupils from Canada, France, and Morocco, between 10 and 12 years of age, on the setting and functioning of simple electric circuits, demonstrates that similar explanatory systems of the students. For this, we had given them a paper and pencil questionnaire of a sixty minutes duration. The first question was…

School Infrastructure and Resources Do Matter: Analysis of the Incidence of School Resources on the Performance of Latin American Students

ERIC Educational Resources Information Center

Murillo, F. Javier; Roman, Marcela

2011-01-01

The purpose of this investigation is to determine the incidence of school infrastructure and resources and its impact on the academic performance of primary education students in Latin America. A 4-level multilevel model was applied to the data of the Second Regional Comparative and Explanatory Study (SERCE) conducted by UNESCO, which researched…

Young Primary Students Making Sense of Text and Illustrations about How Refuse Can Become Soil

ERIC Educational Resources Information Center

Åberg-Bengtsson, Lisbeth; Beach, Dennis; Ljung-Djärf, Agneta

2017-01-01

Explanatory pictures and models are frequently used in teaching and learning situations. However, it seems to be simply assumed that they are always beneficial. In this article results from an investigation with 16 Swedish pupils aged 7-9 year are presented based on an analysis that has examined how well this assumption holds up. Concepts from…

An explanatory model for state Medicaid per capita prescription drug expenditures.

PubMed

Roy, Sanjoy; Madhavan, S Suresh

2012-01-01

Rising prescription drug expenditure is a growing concern for publicly funded drug benefit programs like Medicaid. To be able to contain drug expenditures in Medicaid, it is important that cause(s) for such increases are identified. This study attempts to establish an explanatory model for Medicaid prescription drugs expenditure based on the impacts of key influencers/predictors identified using a comprehensive framework of drug utilization. A modified Andersen's behavior model of health services utilization is employed to identify potential determinants of pharmaceutical expenditures in state Medicaid programs. Level of federal matching funds, access to primary care, severity of diseases, unemployment, and education levels were found to be key influencers of Medicaid prescription drug expenditure. Increases in all, except education levels, were found to result in increases in drug expenditures. Findings from this study could better inform intervention policies and cost-containment strategies for state Medicaid drug benefit programs.

The nature of primary consciousness. A new synthesis.

PubMed

Feinberg, Todd E; Mallatt, Jon

2016-07-01

While the philosophical puzzles about "life" that once confounded biology have all been solved by science, much of the "mystery of consciousness" remains unsolved due to multiple "explanatory gaps" between the brain and conscious experience. One reason for this impasse is that diverse brain architectures both within and across species can create consciousness, thus making any single neurobiological feature insufficient to explain it. We propose instead that an array of general biological features that are found in all living things, combined with a suite of special neurobiological features unique to animals with consciousness, evolved to create subjective experience. Combining philosophical, neurobiological and evolutionary approaches to consciousness, we review our theory of neurobiological naturalism that we argue closes the "explanatory gaps" between the brain and subjective experience and naturalizes the "experiential gaps" between subjectivity and third-person observation of the brain. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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.

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.

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.

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.

[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.

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.

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

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.

Health-Related Quality of Life in Primary Care: Which Aspects Matter in Multimorbid Patients with Type 2 Diabetes Mellitus in a Community Setting?

PubMed

Kamradt, Martina; Krisam, Johannes; Kiel, Marion; Qreini, Markus; Besier, Werner; Szecsenyi, Joachim; Ose, Dominik

2017-01-01

Knowledge about predictors of health-related quality of life for multimorbid patients with type 2 diabetes mellitus in primary care could help to improve quality and patient-centeredness of care in this specific group of patients. Thus, the aim of this study was to investigate the impact of several patient characteristics on health-related quality of life of multimorbid patients with type 2 diabetes mellitus in a community setting. A cross-sectional study with 32 primary care practice teams in Mannheim, Germany, and randomly selected multimorbid patients with type 2 diabetes mellitus (N = 495) was conducted. In order to analyze associations of various patient characteristics with health-related quality of life (EQ-5D index) a multilevel analysis was applied. After excluding patients with missing data, the cohort consisted of 404 eligible patients. The final multilevel model highlighted six out of 14 explanatory patient variables which were significantly associated with health-related quality of life: female gender (r = -0.0494; p = .0261), school education of nine years or less (r = -0.0609; p = .0006), (physical) mobility restrictions (r = -0.1074; p = .0003), presence of chronic pain (r = -0.0916; p = .0004), diabetes-related distress (r = -0.0133; p < .0001), and BMI (r = -0.0047; p = .0045). The findings of this study suggest that increased diabetes-related distress, chronic pain, restrictions in (physical) mobility, female gender, as well as lower education and, increased BMI have a noteworthy impact on health-related quality of life in multimorbid patients with type 2 diabetes mellitus seen in primary care practices in a community setting. The highlighted aspects should gain much more attention when treating multimorbid patients with type 2 diabetes mellitus.

The relationship between species richness and aboveground biomass in a primary Pinus kesiya forest of Yunnan, southwestern China.

PubMed

Li, Shuaifeng; Lang, Xuedong; Liu, Wande; Ou, Guanglong; Xu, Hui; Su, Jianrong

2018-01-01

The relationship between biodiversity and biomass is an essential element of the natural ecosystem functioning. Our research aims at assessing the effects of species richness on the aboveground biomass and the ecological driver of this relationship in a primary Pinus kesiya forest. We sampled 112 plots of the primary P. kesiya forests in Yunnan Province. The general linear model and the structural equation model were used to estimate relative effects of multivariate factors among aboveground biomass, species richness and the other explanatory variables, including climate moisture index, soil nutrient regime and stand age. We found a positive linear regression relationship between the species richness and aboveground biomass using ordinary least squares regressions. The species richness and soil nutrient regime had no direct significant effect on aboveground biomass. However, the climate moisture index and stand age had direct effects on aboveground biomass. The climate moisture index could be a better link to mediate the relationship between species richness and aboveground biomass. The species richness affected aboveground biomass which was mediated by the climate moisture index. Stand age had direct and indirect effects on aboveground biomass through the climate moisture index. Our results revealed that climate moisture index had a positive feedback in the relationship between species richness and aboveground biomass, which played an important role in a link between biodiversity maintenance and ecosystem functioning. Meanwhile, climate moisture index not only affected positively on aboveground biomass, but also indirectly through species richness. The information would be helpful in understanding the biodiversity-aboveground biomass relationship of a primary P. kesiya forest and for forest management.

The relationship between species richness and aboveground biomass in a primary Pinus kesiya forest of Yunnan, southwestern China

PubMed Central

Li, Shuaifeng; Lang, Xuedong; Liu, Wande; Ou, Guanglong; Xu, Hui

2018-01-01

The relationship between biodiversity and biomass is an essential element of the natural ecosystem functioning. Our research aims at assessing the effects of species richness on the aboveground biomass and the ecological driver of this relationship in a primary Pinus kesiya forest. We sampled 112 plots of the primary P. kesiya forests in Yunnan Province. The general linear model and the structural equation model were used to estimate relative effects of multivariate factors among aboveground biomass, species richness and the other explanatory variables, including climate moisture index, soil nutrient regime and stand age. We found a positive linear regression relationship between the species richness and aboveground biomass using ordinary least squares regressions. The species richness and soil nutrient regime had no direct significant effect on aboveground biomass. However, the climate moisture index and stand age had direct effects on aboveground biomass. The climate moisture index could be a better link to mediate the relationship between species richness and aboveground biomass. The species richness affected aboveground biomass which was mediated by the climate moisture index. Stand age had direct and indirect effects on aboveground biomass through the climate moisture index. Our results revealed that climate moisture index had a positive feedback in the relationship between species richness and aboveground biomass, which played an important role in a link between biodiversity maintenance and ecosystem functioning. Meanwhile, climate moisture index not only affected positively on aboveground biomass, but also indirectly through species richness. The information would be helpful in understanding the biodiversity-aboveground biomass relationship of a primary P. kesiya forest and for forest management. PMID:29324901

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.

Building a Computer Program to Support Children, Parents, and Distraction during Healthcare Procedures

PubMed Central

McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W. Nick; Zimmerman, M. Bridget; Ersig, Anne L.

2012-01-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children’s responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, the Children, Parents and Distraction (CPaD), is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure. PMID:22805121

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.

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...

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

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…

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.

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.

End-Point Variability Is Not Noise in Saccade Adaptation

PubMed Central

Herman, James P.; Cloud, C. Phillip; Wallman, Josh

2013-01-01

When each of many saccades is made to overshoot its target, amplitude gradually decreases in a form of motor learning called saccade adaptation. Overshoot is induced experimentally by a secondary, backwards intrasaccadic target step (ISS) triggered by the primary saccade. Surprisingly, however, no study has compared the effectiveness of different sizes of ISS in driving adaptation by systematically varying ISS amplitude across different sessions. Additionally, very few studies have examined the feasibility of adaptation with relatively small ISSs. In order to best understand saccade adaptation at a fundamental level, we addressed these two points in an experiment using a range of small, fixed ISS values (from 0° to 1° after a 10° primary target step). We found that significant adaptation occurred across subjects with an ISS as small as 0.25°. Interestingly, though only adaptation in response to 0.25° ISSs appeared to be complete (the magnitude of change in saccade amplitude was comparable to size of the ISS), further analysis revealed that a comparable proportion of the ISS was compensated for across conditions. Finally, we found that ISS size alone was sufficient to explain the magnitude of adaptation we observed; additional factors did not significantly improve explanatory power. Overall, our findings suggest that current assumptions regarding the computation of saccadic error may need to be revisited. PMID:23555763

DETERMINANTS OF THE INTENTION TO USE TELEMEDICINE: EVIDENCE FROM PRIMARY CARE PHYSICIANS.

PubMed

Saigi-Rubió, Francesc; Jiménez-Zarco, Ana; Torrent-Sellens, Joan

2016-01-01

While most studies have focused on analyzing the results of telemedicine use, it is crucial to consider the determinants of its use to fully understand the issue. This article aims to provide evidence on the determinants of telemedicine use in clinical practice. The survey targeted a total population of 398 medical professionals from a healthcare institution in Spain. The study sample was formed by the ninety-three primary care physicians who responded. Using an extended Technology Acceptance Model and microdata for the ninety-six physicians, binary logistic regression analysis was carried out. The analysis performed confirmed the model's goodness-of-fit, which allowed 48.1 percent of the dependent variable's variance to be explained. The outcomes revealed that the physicians at the healthcare institution placed greater importance on telemedicine's potential to reduce costs, and on its usefulness to the medical profession. The perception of medical information security and confidentiality and the patients' predisposition toward telemedicine were the second explanatory factors in order of importance. A third set of moderating effects would appear to corroborate the importance of the physicians' own opinions. These results have revealed the need for a dynamic approach to the design of telemedicine use, especially when it targets a variety of end-users. Hence, the importance of conducting studies before using telemedicine, and attempting to identify which of the above-mentioned predictors exert an influence and how.

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.

Associations between alcohol outlets and emergency department injury presentations: effects of distance from the central business district.

PubMed

Hobday, Michelle; Meuleners, Lynn; Liang, Wenbin; Gilmore, William; Chikritzhs, Tanya

2016-02-01

To examine the effects of licensed outlets and sales on levels of alcohol-related injuries presenting to emergency departments (EDs) in the Inner, Middle and Outer postcode zones of Perth, Australia. Using panel data (2002-2010), a surrogate measure (based on day of week and time of day of presentation) was used to identify alcohol-related injuries presenting at EDs. Postcodes were grouped according to their distance from the central business district (CBD). Numbers of alcohol outlets and their sales were the primary explanatory variables. Data were analysed using negative binomial regression with random effects. In the Inner and Outer postcode zones, counts of on-site outlets were positively associated with alcohol-related injury (IRR: 1.008; 95%CI 1.003-1.013 and IRR: 1.021; 95%CI 1.013-1.030 respectively). An additional off-site outlet was associated with 6.8% fewer alcohol-related injuries (95%CI 0.887-0.980). In the Middle postcode zone, mean off-site sales were positively associated with injury (IRR: 1.024; 95%CI 1.003-1.044). Associations between alcohol availability variables and injury differed by outlet type and distance from the CBD. These findings provide further evidence to support stronger controls on liquor licensing, and indicate the need for different controls according to the location and type of licence. © 2015 Public Health Association of Australia.

Increasing educational attainment and mortality reduction: a systematic review and taxonomy.

PubMed

Byhoff, Elena; Hamati, Mary C; Power, Robyn; Burgard, Sarah A; Chopra, Vineet

2017-09-18

Understanding the relationship between increasing educational attainment and mortality reduction has important policy and public health implications. This systematic review of the literature establishes a taxonomy to facilitate evaluation of the association between educational attainment and early mortality. Following PRISMA guidelines, we searched Ovid Medline, Embase, PubMed and hand searches of references for English-language primary data analyses using education as an independent variable and mortality as a dependent variable. Initial searches were undertaken in February 2015 and updated in April 2016. One thousand, seven hundred and eleven unique articles were identified, 418 manuscripts were screened and 262 eligible studies were included in the review. After an iterative review process, the literature was divided into four study domains: (1) all-cause mortality (n = 68, 26.0%), (2) outcome-specific mortality (n = 89, 34.0%), (3) explanatory pathways (n = 51, 19.5%), and (4) trends over time (n = 54, 20.6%). These four domains comprise a novel taxonomy that can be implemented to better quantify the relationship between education and mortality. We propose an organizational taxonomy for the education-mortality literature based upon study characteristics that will allow for a more in-depth understanding of this association. Our review suggests that studies that include mediators or subgroups can explain part, but not all, of the relationship between education and early mortality. PROSPERO registration # CRD42015017182 .

Spatial Analysis of Severe Fever with Thrombocytopenia Syndrome Virus in China Using a Geographically Weighted Logistic Regression Model

PubMed Central

Wu, Liang; Deng, Fei; Xie, Zhong; Hu, Sheng; Shen, Shu; Shi, Junming; Liu, Dan

2016-01-01

Severe fever with thrombocytopenia syndrome (SFTS) is caused by severe fever with thrombocytopenia syndrome virus (SFTSV), which has had a serious impact on public health in parts of Asia. There is no specific antiviral drug or vaccine for SFTSV and, therefore, it is important to determine the factors that influence the occurrence of SFTSV infections. This study aimed to explore the spatial associations between SFTSV infections and several potential determinants, and to predict the high-risk areas in mainland China. The analysis was carried out at the level of provinces in mainland China. The potential explanatory variables that were investigated consisted of meteorological factors (average temperature, average monthly precipitation and average relative humidity), the average proportion of rural population and the average proportion of primary industries over three years (2010–2012). We constructed a geographically weighted logistic regression (GWLR) model in order to explore the associations between the selected variables and confirmed cases of SFTSV. The study showed that: (1) meteorological factors have a strong influence on the SFTSV cover; (2) a GWLR model is suitable for exploring SFTSV cover in mainland China; (3) our findings can be used for predicting high-risk areas and highlighting when meteorological factors pose a risk in order to aid in the implementation of public health strategies. PMID:27845737

A method to identify the areas at risk for the introduction of avian influenza virus into poultry flocks through direct contact with wild ducks.

PubMed

Galletti, G; Santi, A; Guberti, V; Paternoster, G; Licata, E; Loli Piccolomini, L; Procopio, A; Tamba, M

2018-02-22

Wild dabbling ducks are the main reservoir for avian influenza (AI) viruses and pose an ongoing threat to commercial poultry flocks. Combining the (i) size of that population, (ii) their flight distances and (iii) their AI prevalence, the density of AI-infected dabbling ducks (DID) was calculated as a risk factor for the introduction of AI viruses into poultry holdings of Emilia-Romagna region, Northern Italy. Data on 747 poultry holdings and on 39 AI primary outbreaks notified in Emilia-Romagna between 2000 and 2017 were used to validate that risk factor. A multivariable Bayesian logistic regression was performed to assess whether DID could be associated with the occurrence of AI primary outbreaks. DID value, being an outdoor flock, hobby poultry trading, species reared, length of cycle and flock size were used as explanatory variables. Being an outdoor poultry flock was significantly associated with a higher risk of AI outbreak occurrence. The probability of DID to be a risk factor for AI virus introduction was estimated to be 90%. A DID cut-off of 0.23 was identified to define high-risk areas for AI virus introduction. Using this value, the high-risk area covers 43% of the region. Seventy-four per cent of the primary AI outbreaks have occurred in that area, containing 39% of the regional poultry holdings. Poultry holdings located in areas with a high DID value should be included in a risk-based surveillance programme aimed at AI early detection. © 2018 Blackwell Verlag GmbH.

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.

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.

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.

Levels and trends of childhood undernutrition by wealth and education according to a Composite Index of Anthropometric Failure: evidence from 146 Demographic and Health Surveys from 39 countries.

PubMed

Vollmer, Sebastian; Harttgen, Kenneth; Kupka, Roland; Subramanian, S V

2017-01-01

Governments have endorsed global targets to reduce childhood undernutrition as part of the Sustainable Development Goals. Understanding the socioeconomic differences in childhood undernutrition has the potential to be helpful for targeting policy to reach these goals. We specify a logistic regression model with the Composite Index of Anthropometric Failure (CIAF) as the outcome and indicator variables for wealth quartiles, maternal education categories and a set of covariates as explanatory variables. Wealth and education variables are interacted with a period indicator for 1990-2000 compared with 2001-2014 to observe differences over time. Based on these regressions we calculate predicted CIAF prevalence by wealth and education categories and over time. The sample included 146 surveys from 39 low-income and lower-middle-income countries with an overall sample size of 533 217 children. CIAF prevalence was 47.5% in 1990-2000, and it declined to 42.6% in 2001-2014. In 1990-2000 the CIAF prevalence of children with mothers with less than primary education was 31 percentage points higher than for mothers with secondary or higher education. This difference slightly decreased to 27 percentage points in 2001-2014. The difference in predicted CIAF prevalence of children from the highest and lowest wealth quartiles was 21 percentage points and did not change over time. We find evidence for persistent and even increasing socioeconomic inequalities in childhood undernutrition, which underlines the importance of previous calls for equity-driven approaches targeting the most vulnerable to reduce childhood malnutrition.

Health related quality of life among elderly living in region of high vulnerability for health in Belo Horizonte, Minas Gerais, Brazil.

PubMed

Camelo, Lidyane do Valle; Giatti, Luana; Barreto, Sandhi Maria

2016-01-01

To investigate whether social relations, sociodemographic characteristics, lifestyle, and health conditions are associated with health-related quality of life (HRQOL) among elderly persons living in regions classified as high vulnerable in terms of health. A cross-sectional study conducted with a population-based random sample of 366 elderly (≥ 60 years of age) persons registered at a primary health-care unit in Belo Horizonte, Minas Gerais, Brazil. HRQOL was measured using the Medical Outcomes Study 12-Item Short-Form Health Survey (SF-12) and the scores obtained in the physical component score (PCS) and mental component score (MCS) were our response variables. Social relations, sociodemographic characteristics, lifestyle, and health conditions were considered our groups of explanatory variables. Multiple linear regression models were used for the analysis. In the final multivariate models, we found that elevated number of diagnosis of chronic diseases, and being bedridden for the last 15 days were variables associated with worse PCS and MCS. However, lack of education, dissatisfaction with personal relationships, lack of support and help when bedridden or to go to the doctor, and to prepare meals were associated with worse HRQOL only in MCS. Participants who reported black race/color, absence of work activity, lack of physical activity, no alcohol consumption, and hospitalization in the last 12 months had worse HRQOL only in PCS. In addition to the aspects related to social adversity, lifestyle, and health conditions, some functional aspects of social relations were important for understanding the HRQOL in elderly persons living in social vulnerability.

Explaining direct care resource use of nursing home residents: findings from time studies in four states.

PubMed

Arling, Greg; Kane, Robert L; Mueller, Christine; Lewis, Teresa

2007-04-01

To explain variation in direct care resource use (RU) of nursing home residents based on the Resource Utilization Groups III (RUG-III) classification system and other resident- and unit-level explanatory variables. Primary data were collected on 5,314 nursing home residents in 156 nursing units in 105 facilities from four states (CO, IN, MN, MS) from 1998 to 2004. Study Design. Nurses and other direct care staff recorded resident-specific and other time caring for all residents on sampled nursing units. Care time was linked to resident data from the Minimum Data Set assessment instrument. Major variables were: RUG-III group (34-group), other health and functional conditions, licensed and other professional minutes per day, unlicensed minutes per day, and direct care RU (wage-weighted minutes). Resident- and unit-level relationships were examined through hierarchical linear modeling. Time study data were recorded with hand-held computers, verified for accuracy by project staff at the data collection sites and then merged into resident and unit-level data sets. Resident care time and RU varied between and within nursing units. RUG-III group was related to RU; variables such as length of stay and unit percentage of high acuity residents also were significantly related. Case-mix indices (CMIs) constructed from study data displayed much less variation across RUG-III groups than CMIs from earlier time studies. Results from earlier time studies may not be representative of care patterns of Medicaid and private pay residents. New RUG-III CMIs should be developed to better reflect the relative costs of caring for these residents.

Experimental Concepts for Testing Seismic Hazard Models

NASA Astrophysics Data System (ADS)

Marzocchi, W.; Jordan, T. H.

2015-12-01

Seismic hazard analysis is the primary interface through which useful information about earthquake rupture and wave propagation is delivered to society. To account for the randomness (aleatory variability) and limited knowledge (epistemic uncertainty) of these natural processes, seismologists must formulate and test hazard models using the concepts of probability. In this presentation, we will address the scientific objections that have been raised over the years against probabilistic seismic hazard analysis (PSHA). Owing to the paucity of observations, we must rely on expert opinion to quantify the epistemic uncertainties of PSHA models (e.g., in the weighting of individual models from logic-tree ensembles of plausible models). The main theoretical issue is a frequentist critique: subjectivity is immeasurable; ergo, PSHA models cannot be objectively tested against data; ergo, they are fundamentally unscientific. We have argued (PNAS, 111, 11973-11978) that the Bayesian subjectivity required for casting epistemic uncertainties can be bridged with the frequentist objectivity needed for pure significance testing through "experimental concepts." An experimental concept specifies collections of data, observed and not yet observed, that are judged to be exchangeable (i.e., with a joint distribution independent of the data ordering) when conditioned on a set of explanatory variables. We illustrate, through concrete examples, experimental concepts useful in the testing of PSHA models for ontological errors in the presence of aleatory variability and epistemic uncertainty. In particular, we describe experimental concepts that lead to exchangeable binary sequences that are statistically independent but not identically distributed, showing how the Bayesian concept of exchangeability generalizes the frequentist concept of experimental repeatability. We also address the issue of testing PSHA models using spatially correlated data.

Detecting Subtle Shifts in Ecosystem Functioning in a Dynamic Estuarine Environment

PubMed Central

Pratt, Daniel R.; Lohrer, Andrew M.; Thrush, Simon F.; Hewitt, Judi E.; Townsend, Michael; Cartner, Katie; Pilditch, Conrad A.; Harris, Rachel J.; van Colen, Carl; Rodil, Iván F.

2015-01-01

Identifying the effects of stressors before they impact ecosystem functioning can be challenging in dynamic, heterogeneous ‘real-world’ ecosystems. In aquatic systems, for example, reductions in water clarity can limit the light available for photosynthesis, with knock-on consequences for secondary consumers, though in naturally turbid wave-swept estuaries, detecting the effects of elevated turbidity can be difficult. The objective of this study was to investigate the effects of shading on ecosystem functions mediated by sandflat primary producers (microphytobenthos) and deep-dwelling surface-feeding macrofauna (Macomona liliana; Bivalvia, Veneroida, Tellinidae). Shade cloths (which reduced incident light intensity by ~80%) were deployed on an exposed, intertidal sandflat to experimentally stress the microphytobenthic community associated with the sediment surface. After 13 weeks, sediment properties, macrofauna and fluxes of oxygen and inorganic nutrients across the sediment-water interface were measured. A multivariate metric of ecosystem function (MF) was generated by combining flux-based response variables, and distance-based linear models were used to determine shifts in the drivers of ecosystem function between non-shaded and shaded plots. No significant differences in MF or in the constituent ecosystem function variables were detected between the shaded and non-shaded plots. However, shading reduced the total explained variation in MF (from 64% in non-shaded plots to 15% in shaded plots) and affected the relative influence of M. liliana and other explanatory variables on MF. This suggests that although shade stress may shift the drivers of ecosystem functioning (consistent with earlier investigations of shading effects on sandflat interaction networks), ecosystem functions appear to have a degree of resilience to those changes. PMID:26214854

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.

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.

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 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.

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

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.

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

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

Serial killers with military experience: applying learning theory to serial murder.

PubMed

Castle, Tammy; Hensley, Christopher

2002-08-01

Scholars have endeavored to study the motivation and causality behind serial murder by researching biological, psychological, and sociological variables. Some of these studies have provided support for the relationship between these variables and serial murder. However, the study of serial murder continues to be an exploratory rather than explanatory research topic. This article examines the possible link between serial killers and military service. Citing previous research using social learning theory for the study of murder, this article explores how potential serial killers learn to reinforce violence, aggression, and murder in military boot camps. As with other variables considered in serial killer research, military experience alone cannot account for all cases of serial murder. Future research should continue to examine this possible link.

Sentiment analysis in twitter data using data analytic techniques for predictive modelling

NASA Astrophysics Data System (ADS)

Razia Sulthana, A.; Jaithunbi, A. K.; Sai Ramesh, L.

2018-04-01

Sentiment analysis refers to the task of natural language processing to determine whether a piece of text contains subjective information and the kind of subjective information it expresses. The subjective information represents the attitude behind the text: positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great concern. We have made data analysis with huge amount of tweets taken as big data and thereby classifying the polarity of words, sentences or entire documents. We use linear regression for modelling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. We conduct a series of experiments to test the performance of the system.

Birth Order and health: major issues.

PubMed

Elliott, B A

1992-08-01

Birth Order has been described as a variable with a complex relationship to child and adult outcomes. A review of the medical literature over the past 5 years identified 20 studies that investigated the relationship between Birth Order and a health outcome. Only one of the studies established a relationship between Birth Order and a health outcome: third and fourth-born children have a higher incidence of accidents that result in hospitalization. The other demonstrated relationships are each explained by intervening variables or methodological limitations. Although Birth Order is not a strongly independent explanatory factor in understanding health outcomes, it is an important marker variable. Statistically significant relationships between Birth Order and health outcomes yield insights into the ways a family influences an individual's health.

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

An instrumental variable random-coefficients model for binary outcomes

PubMed Central

Chesher, Andrew; Rosen, Adam M

2014-01-01

In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration. PMID:25798048

Multicollinearity may lead to artificial interaction: an example from a cross sectional study of biomarkers.

PubMed

Sithisarankul, P; Weaver, V M; Diener-West, M; Strickland, P T

1997-06-01

Collinearity is the situation which arises in multiple regression when some or all of the explanatory variables are so highly correlated with one another that it becomes very difficult, if not impossible, to disentangle their influences and obtain a reasonably precise estimate of their effects. Suppressor variable is one of the extreme situations of collinearity that one variable can substantially increase the multiple correlation when combined with a variable that is only modestly correlated with the response variable. In this study, we describe the process by which we disentangled and discovered multicollinearity and its consequences, namely artificial interaction, using the data from cross-sectional quantification of several biomarkers. We showed how the collinearity between one biomarker (blood lead level) and another (urinary trans, trans-muconic acid) and their interaction (blood lead level* urinary trans, trans-muconic acid) can lead to the observed artificial interaction on the third biomarker (urinary 5-aminolevulinic acid).

Parent Predictors of Adolescents' Explanatory Style

ERIC Educational Resources Information Center

Vélez, Clorinda E.; Krause, Elizabeth D.; Brunwasser, Steven M.; Freres, Derek R.; Abenavoli, Rachel M.; Gillham, Jane E.

2015-01-01

The current study tested the prospective relations (6-month lag) between three aspects of the parent-child relationship at Time 1 (T1) and adolescents' explanatory styles at Time 2 (T2): caregiving behaviors, parents' explanatory style for their own negative events, and parents' explanatory style for their children's negative events. The sample…

Explanatory style across the life span: evidence for stability over 52 years.

PubMed

Burns, M O; Seligman, M E

1989-03-01

Analyzed explanatory style across the life span. 30 Ss whose average age was 72 responded to questions about their current life and provided diaries or letters written in their youth, an average of 52 years earlier. A blind content analysis of explanatory style derived from these 2 sources revealed that explanatory style for negative events was stable throughout adult life (r = .54, p less than .002). In contrast, there appeared to be no stability of explanatory style for positive events between the same 2 time periods. These results suggest that explanatory style for negative events may persist across the life span and may constitute an enduring risk factor for depression, low achievement, and physical illness.

FAST INdiCATE Trial protocol. Clinical efficacy of functional strength training for upper limb motor recovery early after stroke: neural correlates and prognostic indicators.

PubMed

Pomeroy, Valerie M; Ward, Nick S; Johansen-Berg, Heidi; van Vliet, Paulette; Burridge, Jane; Hunter, Susan M; Lemon, Roger N; Rothwell, John; Weir, Christopher J; Wing, Alan; Walker, Andrew A; Kennedy, Niamh; Barton, Garry; Greenwood, Richard J; McConnachie, Alex

2014-02-01

Functional strength training in addition to conventional physical therapy could enhance upper limb recovery early after stroke more than movement performance therapy plus conventional physical therapy. To determine (a) the relative clinical efficacy of conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy for upper limb recovery; (b) the neural correlates of response to conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy; (c) whether any one or combination of baseline measures predict motor improvement in response to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Randomized, controlled, observer-blind trial. The sample will consist of 288 participants with upper limb paresis resulting from a stroke that occurred within the previous 60 days. All will be allocated to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Functional strength training and movement performance therapy will be undertaken for up to 1·5 h/day, five-days/week for six-weeks. Measurements will be undertaken before randomization, six-weeks thereafter, and six-months after stroke. Primary efficacy outcome will be the Action Research Arm Test. Explanatory measurements will include voxel-wise estimates of brain activity during hand movement, brain white matter integrity (fractional anisotropy), and brain-muscle connectivity (e.g. latency of motor evoked potentials). The primary clinical efficacy analysis will compare treatment groups using a multilevel normal linear model adjusting for stratification variables and for which therapist administered the treatment. Effect of conventional physical therapy combined with functional strength training versus conventional physical therapy combined with movement performance therapy will be summarized using the adjusted mean difference and 95% confidence interval. To identify the neural correlates of improvement in both groups, we will investigate associations between change from baseline in clinical outcomes and each explanatory measure. To identify baseline measurements that independently predict motor improvement, we will develop a multiple regression model. © 2013 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization.

Estimating integrated variance in the presence of microstructure noise using linear regression

NASA Astrophysics Data System (ADS)

Holý, Vladimír

2017-07-01

Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

Discrete factor approximations in simultaneous equation models: estimating the impact of a dummy endogenous variable on a continuous outcome.

PubMed

Mroz, T A

1999-10-01

This paper contains a Monte Carlo evaluation of estimators used to control for endogeneity of dummy explanatory variables in continuous outcome regression models. When the true model has bivariate normal disturbances, estimators using discrete factor approximations compare favorably to efficient estimators in terms of precision and bias; these approximation estimators dominate all the other estimators examined when the disturbances are non-normal. The experiments also indicate that one should liberally add points of support to the discrete factor distribution. The paper concludes with an application of the discrete factor approximation to the estimation of the impact of marriage on wages.

A big data approach to macrofaunal baseline assessment, monitoring and sustainable exploitation of the seabed.

PubMed

Cooper, K M; Barry, J

2017-09-29

In this study we produce a standardised dataset for benthic macrofauna and sediments through integration of data (33,198 samples) from 777 grab surveys. The resulting dataset is used to identify spatial and temporal patterns in faunal distribution around the UK, and the role of sediment composition and other explanatory variables in determining such patterns. We show how insight into natural variability afforded by the dataset can be used to improve the sustainability of activities which affect sediment composition, by identifying conditions which should remain favourable for faunal recolonisation. Other big data applications and uses of the dataset are discussed.

Rates and predictors of depression in adoptive mothers: moving toward theory.

PubMed

Foli, Karen J; South, Susan C; Lim, Eunjung

2012-01-01

There are approximately 1.8 million adopted children living in the United States. Adoptive parents may experience depressive symptoms and put their children at risk for negative outcomes. The results of this study describe the rates of depression in 300 adoptive mothers and associations with hypothesized explanatory variables, which predict approximately half of the variance in maternal depressive symptoms: expectations of themselves as mothers, the child, and family and friends; feeling of rest; past and present psychiatric difficulties (self-esteem, history of depression); and interpersonal variables (bonding, marital satisfaction, perceived support). These findings are useful in planning effective interventions to mitigate depressive symptoms.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

[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.

Legitimate Techniques for Improving the R-Square and Related Statistics of a Multiple Regression Model

DTIC Science & Technology

1981-01-01

explanatory variable has been ommitted. Ramsey (1974) has developed a rather interesting test for detecting specification errors using estimates of the...Peter. (1979) A Guide to Econometrics , Cambridge, MA: The MIT Press. Ramsey , J.B. (1974), "Classical Model Selection Through Specification Error... Tests ," in P. Zarembka, Ed. Frontiers in Econometrics , New York: Academia Press. Theil, Henri. (1971), Principles of Econometrics , New York: John Wiley

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...

The Impact of Pay on Navy Physician Retention in a Health Care Reform Environment.

DTIC Science & Technology

1998-03-01

Analyses 33 6. Generalist Physician Supply Under Alternative Specialty Mix 33 7. Specialist Physician Supply Under Alternative Specialty Mix 34 8...Fiscal Years 1992 to 1997 40 5. Comparison of proposed alternative pay plans 54 6. Evaluation of expected outcomes of alternative pay plans 55 7...Explanatory variables with the expected sign of regression coefficient 76 8. Comparison of Navy physicians’ pay to alternative civilian pay for selected

Theoretical Models for Aircraft Availability: Classical Approach to Identification of Trends, Seasonality, and System Constraints in the Development of Realized Models

DTIC Science & Technology

2004-03-01

predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted

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

How well can body size represent effects of the environment on demographic rates? Disentangling correlated explanatory variables.

PubMed

Brooks, Mollie E; Mugabo, Marianne; Rodgers, Gwendolen M; Benton, Timothy G; Ozgul, Arpat

2016-03-01

Demographic rates are shaped by the interaction of past and current environments that individuals in a population experience. Past environments shape individual states via selection and plasticity, and fitness-related traits (e.g. individual size) are commonly used in demographic analyses to represent the effect of past environments on demographic rates. We quantified how well the size of individuals captures the effects of a population's past and current environments on demographic rates in a well-studied experimental system of soil mites. We decomposed these interrelated sources of variation with a novel method of multiple regression that is useful for understanding nonlinear relationships between responses and multicollinear explanatory variables. We graphically present the results using area-proportional Venn diagrams. Our novel method was developed by combining existing methods and expanding upon them. We showed that the strength of size as a proxy for the past environment varied widely among vital rates. For instance, in this organism with an income breeding life history, the environment had more effect on reproduction than individual size, but with substantial overlap indicating that size encompassed some of the effects of the past environment on fecundity. This demonstrates that the strength of size as a proxy for the past environment can vary widely among life-history processes within a species, and this variation should be taken into consideration in trait-based demographic or individual-based approaches that focus on phenotypic traits as state variables. Furthermore, the strength of a proxy will depend on what state variable(s) and what demographic rate is being examined; that is, different measures of body size (e.g. length, volume, mass, fat stores) will be better or worse proxies for various life-history processes. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

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.

Causal explanations of distress and general practitioners' assessments of common mental disorder among punjabi and English attendees.

PubMed

Bhui, Kamaldeep; Bhugra, Dinesh; Goldberg, David

2002-01-01

The literature on the primary care assessment of mental distress among Indian subcontinent origin patients suggests frequent presentations to general practitioner, but rarely for recognisable psychiatric disorders. This study investigates whether cultural variations in patients' causal explanatory models account for cultural variations in the assessment of non-psychotic mental disorders in primary care. In a two-phase survey, 272 Punjabi and 269 English subjects were screened. The second phase was completed by 209 and 180 subjects, respectively. Causal explanatory models were elicited as explanations of two vignette scenarios. One of these emphasised a somatic presentation and the other anxiety symptoms. Psychiatric disorder was assessed by GPs on a Likert scale and by a psychiatrist on the Clinical Interview Schedule. Punjabis more commonly expressed medical/somatic and religious beliefs. General practitioners were more likely to assess any subject giving psychological explanations to vignette A and English subjects giving religious explanations to vignette B as having a significant psychiatric disorder. Where medical/somatic explanations of distress were most prevalent in response to the somatic vignette, psychological, religious and work explanations were less prevalent among Punjabis but not among English subjects. Causal explanations did not fully explain cultural differences in assessments. General practitioners' assessments and causal explanations are related and influenced by culture, but causal explanations do not fully explain cultural differences in assessments.

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.

Illness perceptions and explanatory models of viral hepatitis B & C among immigrants and refugees: a narrative systematic review.

PubMed

Owiti, John A; Greenhalgh, Trisha; Sweeney, Lorna; Foster, Graham R; Bhui, Kamaldeep S

2015-02-15

Hepatitis B and C (HBV, HCV) infections are associated with high morbidity and mortality. Many countries with traditionally low prevalence (such as UK) are now planning interventions (screening, vaccination, and treatment) of high-risk immigrants from countries with high prevalence. This review aimed to synthesise the evidence on immigrants' knowledge of HBV and HCV that might influence the uptake of clinical interventions. The review was also used to inform the design and successful delivery of a randomised controlled trial of targeted screening and treatment. Five databases (PubMed, CINHAL, SOCIOFILE, PsycINFO & Web of Science) were systematically searched, supplemented by reference tracking, searches of selected journals, and of relevant websites. We aimed to identify qualitative and quantitative studies that investigated knowledge of HBV and HCV among immigrants from high endemic areas to low endemic areas. Evidence, extracted according to a conceptual framework of Kleinman's explanatory model, was subjected to narrative synthesis. We adapted the PEN-3 model to categorise and analyse themes, and recommend strategies for interventions to influence help-seeking behaviour. We identified 51 publications including quantitative (n = 39), qualitative (n = 11), and mixed methods (n = 1) designs. Most of the quantitative studies included small samples and had heterogeneous methods and outcomes. The studies mainly concentrated on hepatitis B and ethnic groups of South East Asian immigrants residing in USA, Canada, and Australia. Many immigrants lacked adequate knowledge of aetiology, symptoms, transmission risk factors, prevention strategies, and treatment, of hepatitis HBV and HCV. Ethnicity, gender, better education, higher income, and English proficiency influenced variations in levels and forms of knowledge. Immigrants are vulnerable to HBV and HCV, and risk life-threatening complications from these infections because of poor knowledge and help-seeking behaviour. Primary studies in this area are extremely diverse and of variable quality precluding meta-analysis. Further research is needed outside North America and Australia.

A Mixed-Method Analysis of Reports on 100 Cases of Improper Prescribing of Controlled Substances

PubMed Central

DuBois, James M.; Chibnall, John T.; Anderson, Emily E.; Eggers, Michelle; Baldwin, Kari; Vasher, Meghan

2017-01-01

Improper prescribing of controlled substances contributes to opioid addictions and deaths by overdose. Studies conducted to-date have largely lacked a theoretical framework and ignored the interaction of individual with environmental factors. We conducted a mixed-method analysis of published reports on 100 cases that occurred in the United States. An average of 17 reports (e.g., from medical boards) per case were coded for 38 dichotomous variables describing the physician, setting, patients, and investigation. A theory on how the case occurred was developed for each case. Explanatory typologies were developed and then validated through hierarchical cluster analysis. Most cases involved physicians who were male (88%), >40 years old (90%), non-board certified (63%), and in small private practices (97%); 54% of cases reported facts about the physician indicative of self-centered personality traits. Three explanatory typologies were validated. Increasing oversight provided by peers and trainees may help prevent improper prescribing of controlled substances. PMID:28663601

The Importance of Field in Understanding Persistence among Science and Engineering Majors

NASA Astrophysics Data System (ADS)

Wyer, Mary

Many contemporary studies of persistence in science and engineering majors find that students' gender is an important explanatory variable in understanding why students leave and stay in their majors. This study revisits an earlier report by this author to explore the relative impact of gender on persistence when field of major is held constant, using the same data as the earlier work, drawing on survey responses from 285 students in required biology and engineering courses. The author argues two points: that effects of field on students' persistence are often misinterpreted as gender effects and that gender is too often understood in the narrow sense of students' sex. The author finds that when field is taken into account, students' sex has little explanatory power for understanding persistence rates. In contrast, students' positive experiences with and positive attitudes about gender equity significantly enhance commitments to science and engineering majors, advanced degrees, and careers. The results suggest the need for field-specific interventions to promote the advancement of undergraduate women in science and engineering.

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.

An Instrumental Variable Probit (IVP) analysis on depressed mood in Korea: the impact of gender differences and other socio-economic factors.

PubMed

Gitto, Lara; Noh, Yong-Hwan; Andrés, Antonio Rodríguez

2015-04-16

Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people's quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression), might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES) were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females), aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP) regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status) and socio-economic factors (such as education, residence in metropolitan areas, and so on). As the results of the Wald test carried out after the estimations did not allow to reject the null hypothesis of endogeneity, a probit model was run too. Overall, women tend to develop depression more frequently than men. There is an inverse effect of education on depressed mood (probability of -24.6% to report a depressed mood due to high school education, as it emerges from the probit model marginal effects), while marital status and the number of family members may act as protective factors (probability to report a depressed mood of -1.0% for each family member). Depression is significantly associated with socio-economic conditions, such as work and income. Living in metropolitan areas is inversely correlated with depression (probability of -4.1% to report a depressed mood estimated through the probit model): this could be explained considering that, in rural areas, people rarely have immediate access to high-quality health services. This study outlines the factors that are more likely to impact on depression, and applies an IVP model to take into account the potential endogeneity of some of the predictors of depressive mood, such as female participation to workforce and health status. A probit model has been estimated too. Depression is associated with a wide range of socio-economic factors, although the strength and direction of the association can differ by gender. Prevention approaches to contrast depressive symptoms might take into consideration the evidence offered by the present study. © 2015 by Kerman University of Medical Sciences.

An Instrumental Variable Probit (IVP) analysis on depressed mood in Korea: the impact of gender differences and other socio-economic factors

PubMed Central

Gitto, Lara; Noh, Yong-Hwan; Andrés, Antonio Rodríguez

2015-01-01

Background: Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people’s quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression), might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Methods: Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES) were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females), aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP) regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status) and socio-economic factors (such as education, residence in metropolitan areas, and so on). As the results of the Wald test carried out after the estimations did not allow to reject the null hypothesis of endogeneity, a probit model was run too. Results: Overall, women tend to develop depression more frequently than men. There is an inverse effect of education on depressed mood (probability of -24.6% to report a depressed mood due to high school education, as it emerges from the probit model marginal effects), while marital status and the number of family members may act as protective factors (probability to report a depressed mood of -1.0% for each family member). Depression is significantly associated with socio-economic conditions, such as work and income. Living in metropolitan areas is inversely correlated with depression (probability of -4.1% to report a depressed mood estimated through the probit model): this could be explained considering that, in rural areas, people rarely have immediate access to high-quality health services. Conclusion: This study outlines the factors that are more likely to impact on depression, and applies an IVP model to take into account the potential endogeneity of some of the predictors of depressive mood, such as female participation to workforce and health status. A probit model has been estimated too. Depression is associated with a wide range of socio-economic factors, although the strength and direction of the association can differ by gender. Prevention approaches to contrast depressive symptoms might take into consideration the evidence offered by the present study. PMID:26340392

Benthic algae of benchmark streams in agricultural areas of eastern Wisconsin

USGS Publications Warehouse

Scudder, Barbara C.; Stewart, Jana S.

2001-01-01

Multivariate analyses indicated multiple scales of environmental factors affect algae. Although two-way indicator species analysis (TWINSPAN), detrended correspondence analysis (DCA), and canonical correspondence analysis (CCA) generally separated sites according to RHU, only DCA ordination indicated a separation of sites according to ecoregion. Environmental variables con-elated with DCA axes 1 and 2 and therefore indicated as important explanatory factors for algal distribution and abundance were factors related to stream size, basin land use/cover, geomorphology, hydrogeology, and riparian disturbance. CCA analyses with a more limited set of environmental variables indicated that pH, average width of natural riparian vegetation (segment scale), basin land use/cover and Q/Q2 were the most important variables affecting the distribution and relative abundance of benthic algae at the 20 benchmark streams,

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.