Linking electronic health records with community-level data to understand childhood obesity risk

Does ethnic concentration buffer effects of neighborhood deprivation on early childhood growth?

BACKGROUND

Neighborhood socioeconomic marginalization and racial residential segregation are associated with differential health outcomes in adulthood and pregnancy, but the intergenerational effects of these exposures on early childhood growth are underexplored. Our objective was to investigate racial and ethnic differences in the association between neighborhood deprivation and early childhood growth trajectories, with modification by neighborhood racial concentration.

METHODS

Using longitudinal clinical data among 58,860 children receiving care in community-based clinics in the ADVANCE Clinical Data Research Network, we identified four early childhood (0-24 months) body mass index (BMI) trajectories using group-based trajectory modeling: Low, Catch-Up, Moderate, and High. In race- and ethnicity-stratified multinomial logistic regression analyses, trajectory group membership was modeled as a function of neighborhood deprivation, neighborhood racial concentration, neighborhood deprivation*racial concentration interactions, and confounders.

RESULTS

Greater neighborhood deprivation was marginally associated with greater odds of Catch-Up trajectory for most racial and ethnic groups, with a null association observed among Assimilated Hispanic children. Conversely, neighborhood deprivation was not associated with Low trajectory for non-Hispanic Black or White children; however, in Less Assimilated Hispanic children, higher neighborhood deprivation was marginally associated with higher odds of Low trajectory, most strongly in neighborhoods with higher vs. lower Hispanic concentration. Associations between neighborhood deprivation and High trajectories varied substantially by race and ethnicity, ranging from inverse among Less Assimilated Hispanic children to a positive association among non-Hispanic White children that was attenuated in neighborhoods with higher White concentration.

CONCLUSION

Greater neighborhood deprivation was generally associated with greater or similar odds of each alternative growth trajectory, most consistently for non-Hispanic White and Black children. Associations were largely similar across levels of neighborhood racial concentration. Further research is needed to understand contextual or behavioral factors that contribute to the observed racial and ethnic differences in the association between neighborhood deprivation and early childhood growth.

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions

Background

Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.

Methods

We conducted a PubMed search using "SDOH" and "EHR" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.

Discussion

Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Linking Electronic Health Records to the American Community Survey: Feasibility and Process

Objectives. To assess linkages of patient data from a health care system in the southeastern United States to microdata from the American Community Survey (ACS) with the goal of better understanding health disparities and social determinants of health in the population. Methods. Once a data use agreement was in place, a stratified random sample of approximately 200 000 was drawn of patients aged 25 to 74 years with at least 2 visits between January 1, 2016, and December 31, 2019. Information from the sampled electronic health records (EHRs) was transferred securely to the Census Bureau, put through the Census Person Identification Validation System to assign Protected Identification Keys (PIKs) as unique identifiers wherever possible. EHRs with PIKs assigned were then linked to 2001-2017 ACS records with a PIK. Results. PIKs were assigned to 94% of the sampled patients. Of patients with PIKs, 15.5% matched to persons sampled in the ACS. Conclusions. Linking data from EHRs to ACS records is feasible and, with adjustments for differential coverage, will advance understanding of social determinants and enhance the ability of integrated delivery systems to reflect and affect the health of the populations served. (Am J Public Health. 2022;112(6):923-930. https://doi.org/10.2105/AJPH.2022.306783).

Small area estimation and childhood obesity surveillance using electronic health records

There is an urgent need for childhood surveillance systems to design, implement, and evaluate interventions at the local level. We estimated obesity prevalence for individuals aged 5–17 years using a southcentral Wisconsin EHR data repository, Public Health Information Exchange (PHINEX, 2007–2012). The prevalence estimates were calculated by aggregating the estimated probability of each individual being obese, which was obtained via a generalized linear mixed model. We incorporated the random effects at the area level into our model. A weighted procedure was employed to account for missingness in EHR data. A non-parametric kernel smoothing method was used to obtain the prevalence estimates for locations with no or little data (<20 individuals) from the EHR. These estimates were compared to results from newly available obesity atlas (2015–2016) developed from various EHRs with greater statewide representation. The mean of the zip code level obesity prevalence estimates for males and females aged 5–17 years is 16.2% (SD 2.72%); 17.9% (SD 2.14%) for males and 14.4% (SD 2.00%) for females. The results were comparable to the Wisconsin Health Atlas (WHA) estimates, a much larger dataset of local community EHRs in Wisconsin. On average, prevalence estimates were 2.12% lower in this process than the WHA estimates, with lower estimation occurring more frequently for zip codes without data in PHINEX. Using this approach, we can obtain estimates for local areas that lack EHRs data. Generally, lower prevalence estimates were produced for those locations not represented in the PHINEX database when compared to WHA estimates. This underscores the need to ensure that the reference EHRs database can be made sufficiently similar to the geographic areas where synthetic estimates are being created.

Adverse social factors and all-cause mortality among male and female patients receiving care in the Veterans Health Administration

Social factors account more for health outcomes than medical care, yet health services research in this area is limited due to the lack of social factors data contained within electronic health records (EHR) systems. Few investigations have examined how cumulative burdens of co-occurring adverse social factors impact health outcomes. From 293,872 patients in one region of the Veterans Health Administration (VHA), we examined how increasing numbers of adverse social factors extracted from the EHR were associated with mortality across a one-year period for male and female patients. Adverse social factors were identified using four sources in the EHR: responses to universal VHA screens, International Classification of Disease (ICD) diagnostic codes that indicate social factors, receipt of VHA services related to social factors, and templated social work referrals. Seven types of adverse social factors were coded: violence, housing instability, employment or financial problems, legal issues, social or familial problems, lack of access to care or transportation, and nonspecific psychosocial needs. Overall, each increase in an adverse social factor was associated with 27% increased odds of mortality, after accounting for demographics, medical comorbidity, and military service-related disability. Non-specific psychosocial factors were most strongly associated with mortality, followed by social or familial problems. Although women were more likely than men to have multiple adverse social factors, social factors were not associated with mortality among women as they were among men. By incorporating social factors data, health care systems can better understand patient all-cause mortality and identify potential prevention efforts built around social determinants.

Socioeconomic gradient in the developmental health of Canadian children with disabilities at school entry: a cross-sectional study

OBJECTIVE

To examine the relationship between developmental health and neighbourhood socioeconomic status (SES) in kindergarten children with disabilities.

DESIGN

Cross-sectional study using population-level database of children's developmental health at school entry (2002-2014).

SETTING

12 of 13 Canadian provinces/territories.

MEASURES

Taxfiler and Census data between 2005 and 2006, respectively, were aggregated according to custom-created neighbourhood boundaries and used to create an index of neighbourhood-level SES. Developmental health outcomes were measured for 29 520 children with disabilities using the Early Development Instrument (EDI), a teacher-completed measure of developmental health across five domains.

ANALYSIS

Hierarchical generalised linear models were used to test the association between neighbourhood-level SES and developmental health.

RESULTS

All EDI domains were positively correlated with the neighbourhood-level SES index. The strongest association was observed for the language and cognitive development domain (β (SE): 0.29 (0.02)) and the weakest association was observed for the emotional maturity domain (β (SE): 0.12 (0.01)).

CONCLUSIONS

The magnitude of differences observed in EDI scores across neighbourhoods at the 5th and 95th percentiles are similar to the effects of more established predictors of development, such as sex. The association of SES with developmental outcomes in this population may present a potential opportunity for policy interventions to improve immediate and long-term outcomes.

Community Socioeconomic Deprivation and Nonalcoholic Fatty Liver Disease Severity

BACKGROUND AND OBJECTIVES

Nonalcoholic fatty liver disease (NAFLD) is linked to obesity. Obesity is associated with lower socioeconomic status (SES). An independent link between pediatric NAFLD and SES has not been elucidated. The objective of this study was to evaluate the distribution of socioeconomic deprivation, measured using an area-level proxy, in pediatric patients with known NAFLD and to determine whether deprivation is associated with liver disease severity.

METHODS

Retrospective study of patients <21 years with NAFLD, followed from 2009 to 2018. The patients' addresses were mapped to census tracts, which were then linked to the community deprivation index (CDI; range 0--1, higher values indicating higher deprivation, calculated from six SES-related variables available publicly in US Census databases).

RESULTS

Two cohorts were evaluated; 1 with MRI (magnetic resonance imaging) and/or MRE (magnetic resonance elastography) findings indicative of NAFLD (n = 334), and another with biopsy-confirmed NAFLD (n = 245). In the MRI and histology cohorts, the majority were boys (66%), non-Hispanic (77%-78%), severely obese (79%-80%), and publicly insured (55%-56%, respectively). The median CDI for both groups was 0.36 (range 0.15-0.85). In both cohorts, patients living above the median CDI were more likely to be younger at initial presentation, time of MRI, and time of liver biopsy. MRI-measured fat fraction and liver stiffness, as well as histologic characteristics were not different between the high- and low-deprivation groups.

CONCLUSIONS

Children with NAFLD were found across the spectrum of deprivation. Although children from more deprived neighborhoods present at a younger age, they exhibit the same degree of NAFLD severity as their peers from less deprived areas.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Objectively measured pediatric obesity prevalence using the OneFlorida Clinical Research Consortium

We characterized the prevalence of obesity among Florida children 2-19years old using electronic health records (EHRs). The obesity prevalence for 331,641 children was 16.9%. Obesity prevalence at 6-11years (19.5%) and 12-19years (18.9%) were approximately double the prevalence of obesity among children 2-5years (9.9%). The highest prevalence of severe obesity occurred in rural Florida (21.7%) and non-Hispanic children with multiple races had the highest obesity prevalence (21.1%) across all racial/ethnic groups. Our results highlight EHR as a low-cost alternative to estimate the prevalence of obesity and severe obesity in Florida children, both overall and within subpopulations.

A Solutions-Based Approach to Building Data-Sharing Partnerships

INTRODUCTION

Although researchers recognize that sharing disparate data can improve population health, barriers (technical, motivational, economic, political, legal, and ethical) limit progress. In this paper, we aim to enhance the van Panhuis et al. framework of barriers to data sharing; we present a complementary solutions-based data-sharing process in order to encourage both emerging and established researchers, whether or not in academia, to engage in data-sharing partnerships.

BRIEF DESCRIPTION OF MAJOR COMPONENTS

We enhance the van Panhuis et al. framework in three ways. First, we identify the appropriate stakeholder(s) within an organization (e.g., criminal justice agency) with whom to engage in addressing each category of barriers. Second, we provide a representative sample of specific challenges that we have faced in our data-sharing partnerships with criminal justice agencies, local clinical systems, and public health. Third, and most importantly, we suggest solutions we have found successful for each category of barriers. We grouped our solutions into five core areas that cut across the barriers as well as stakeholder groups: Preparation, Clear Communication, Funding/Support, Non-Monetary Benefits, and Regulatory Assurances.Our solutions-based process model is complementary to the enhanced framework. An important feature of the process model is the cyclical, iterative process that undergirds it. Usually, interactions with new data-sharing partner organizations begin with the leadership team and progress to both the data management and legal teams; however, the process is not always linear.

CONCLUSIONS AND NEXT STEPS

Data sharing is a powerful tool in population health research, but significant barriers hinder such partnerships. Nevertheless, by aspiring to community-based participatory research principles, including partnership engagement, development, and maintenance, we have overcome barriers identified in the van Panhuis et al. framework and have achieved success with various data-sharing partnerships.In the future, systematically studying data-sharing partnerships to clarify which elements of a solutions-based approach are essential for successful partnerships may be helpful to academic and non-academic researchers. The organizational climate is certainly a factor worth studying also because it relates both to barriers and to the potential workability of solutions.

Improving Documentation of Pediatric Height, Weight, and Body Mass Index by Primary Care Providers

The regular documentation of anthropometric data in an electronic medical record (EMR) is one tracking method used by primary care providers to follow the growth trajectory and development of children in their health care practices. EMR reminders have been proposed as a method to increase recording of pediatric height and weight by primary care providers, leading to potentially better detection and management of children classified as overweight or obese. The aim of this pre-post study was to improve a Family Health Team's physician documentation of pediatric height and weight through the implementation of an EMR reminder alert tool. The documentation rate for children 4-7 years old in the 6 months before intervention was 36% of children seen. After implementation of EMR reminder alerts, primary care physicians' documentation rate rose to 45% (9% increase; P < 0.01), but it was below the 15% target increase. Better documentation of pediatric height and weight by family physicians is needed to improve monitoring of children's growth trajectories. Additional strategies to increase documentation rates are needed.

Circannual variation in relative weight of children 5 to 16 years of age

BACKGROUND

Summer weight gain in children has been reported; however, this is usually based on two time points. Our objective was to investigate monthly variation in weight status.

METHODS

Cross-sectional, de-identified health records including height, weight and demographics, collected between 2007 and 2012 from South Central Wisconsin in 70 531 children age 5-16 years were analysed. The monthly averages in body mass index (BMI) z-score were analysed cross-sectionally followed by a paired analysis for a subset with one visit each during school and summer months.

RESULTS

BMI z-scores during the summer months (June-August) were lower than values during the school year (September-May). Of note, there was a rapid decrease in BMI z-scores from May to June, with June BMI z-score values being 0.065 units less (95% CI 0.046-0.085) than those in May, little change from June to August and a rapid increase between the August and September BMI z-scores.

CONCLUSION

The monthly pattern does not fully agree with previous two-point school-based studies. Results raise concern that the use of two time point measures of BMIs (early fall and late spring) is suboptimal for evaluation of circannual variation. We suggest that future evaluation of the effect of school-based or summer interventions utilizes additional measures in those periods so that a seasonal analysis can be performed.

Using electronic health record data for environmental and place based population health research: a systematic review

PURPOSE

We conducted a systematic review of literature published on January 2000-May 2017 that spatially linked electronic health record (EHR) data with environmental information for population health research.

METHODS

We abstracted information on the environmental and health outcome variables and the methods and data sources used.

RESULTS

The automated search yielded 669 articles; 128 articles are included in the full review. The number of articles increased by publication year; the majority (80%) were from the United States, and the mean sample size was approximately 160,000. Most articles used cross-sectional (44%) or longitudinal (40%) designs. Common outcomes were health care utilization (32%), cardiometabolic conditions/obesity (23%), and asthma/respiratory conditions (10%). Common environmental variables were sociodemographic measures (42%), proximity to medical facilities (15%), and built environment and land use (13%). The most common spatial identifiers were administrative units (59%), such as census tracts. Residential addresses were also commonly used to assign point locations, or to calculate distances or buffer areas.

CONCLUSIONS

Future research should include more detailed descriptions of methods used to geocode addresses, focus on a broader array of health outcomes, and describe linkage methods. Studies should also explore using longitudinal residential address histories to evaluate associations between time-varying environmental variables and health outcomes.

Sociodemographic associations of 4-year overweight and obese incidence among a racially diverse cohort of healthy weight 18-year-olds

BACKGROUND

Emerging adulthood is a critical time for excess weight gain. Risk can be masked if recommended overweight and obesity cut-points for Asians are not employed.

OBJECTIVES

To determine the associations among sociodemographic factors and occurrence of overweight and obesity among normal weight 18-year olds.

METHODS

Normal weight (body mass index < 25 kg m^-2 ; <23 kg m^-2 for Asians) 18-year-old (9037 boys, 13 786 girls, 36% Hispanic, 34% non-Hispanic Whites, 10% Black, 5% Asian) members of a healthcare organization in 2008 were followed through 2012 to identify incidence of overweight and obesity. Hazard ratios (HR) and 95% confidence intervals (CI) were determined controlling for sex, race/ethnicity, neighbourhood education, neighbourhood income and smoking status.

RESULTS

After 3 years of follow-up, the HR for overweight was 1.28 (95% CI: 1.12, 1.45) in the lowest quartile of neighbourhood education compared with the highest. Asians and Pacific Islanders had greater risk of overweight (HR 2.89, 95% CI: 2.55, 3.28; HR 3.13, 95% CI 2.23, 4.38) than non-Hispanic Whites. Girls and Blacks were more likely to become obese than boys and non-Hispanic Whites, as were those living in the lowest neighbourhood education quartile and lower neighbourhood income quartiles.

CONCLUSIONS

Girls, Asians, Blacks and those living in low education and income neighbourhoods during adolescence are at risk for excessive weight gain trajectories.

Electronic Health Record Data Versus the National Health and Nutrition Examination Survey (NHANES): A Comparison of Overweight and Obesity Rates

BACKGROUND

Estimating population-level obesity rates is important for informing policy and targeting treatment. The current gold standard for obesity measurement in the United States-the National Health and Nutrition Examination Survey (NHANES)-samples <0.1% of the population and does not target state-level or health system-level measurement.

OBJECTIVE

To assess the feasibility of using body mass index (BMI) data from the electronic health record (EHR) to assess rates of overweight and obesity and compare these rates to national NHANES estimates.

RESEARCH DESIGN

Using outpatient data from 42 clinics, we studied 388,762 patients in a large health system with at least 1 primary care visit in 2011-2012.

MEASURES

We compared crude and adjusted overweight and obesity rates by age category and ethnicity (white, black, Hispanic, Other) between EHR and NHANES participants. Adjusted overweight (BMI≥25) and obesity rates were calculated by a 2-step process. Step 1 accounted for missing BMI data using inverse probability weighting, whereas step 2 included a poststratification correction to adjust the EHR population to a nationally representative sample.

RESULTS

Adjusted rates of obesity (BMI≥30) for EHR patients were 37.3% [95% confidence interval (95% CI), 37.1-37.5] compared with 35.1% (95% CI, 32.3-38.1) for NHANES patients. Among the 16 different obesity class, ethnicity, and sex strata that were compared between EHR and NHANES patients, 14 (87.5%) contained similar obesity estimates (ie, overlapping 95% CIs).

CONCLUSIONS

EHRs may be an ideal tool for identifying and targeting patients with obesity for implementation of public health and/or individual level interventions.

Evaluating the Use of Electronic Health Records for Type 2 Diabetes Surveillance in 2 California Counties, 2010-2014

OBJECTIVES

Electronic health records (EHRs) and electronic laboratory records (ELRs) are increasingly seen as a rich source of data for performing public health surveillance activities and monitoring community health status. Their potential for surveillance of chronic illness, however, may be underused. Our objectives were to (1) evaluate the use of EHRs and ELRs for diabetes surveillance in 2 California counties and (2) examine disparities in diabetes prevalence by geography, income, and race/ethnicity.

METHODS

We obtained data on a clinical diagnosis of diabetes and hemoglobin A1c (HbA1c) test results for adult members of Kaiser Permanente Northern California living in Contra Costa County or Solano County at any time during 2010-2014. We evaluated the validity of using HbA1c test results to determine diabetes prevalence, using clinical diagnoses as a gold standard. We estimated disparities in diabetes prevalence by combining HbA1c test results with US Census data on income, race, and ethnicity.

RESULTS

When compared with a clinical diagnosis of diabetes, data on a patient's 5-year maximum HbA1c value ≥6.5% yielded the best combination of sensitivity (87.4%) and specificity (99.2%). The prevalence of 5-year maximum HbA1c ≥6.5% decreased with increasing median family income and increased with greater proportions of residents who were either non-Hispanic black or Hispanic.

CONCLUSIONS

Timely diabetes surveillance data from ELRs can be used to document disparities, target interventions, and evaluate changes in population health. ELR data may be easier to access than a patient's entire EHR, but outcome metric validation with diabetes diagnoses would need to be ongoing. Future research should validate ELR and EHR data across multiple providers.

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013

BACKGROUND

Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs.

METHODS

Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals).

RESULTS

3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were.

CONCLUSIONS

These findings have implications for public health practice and community planning in local health jurisdictions.

The Social Determinants of Chronic Disease

This review article addresses the concept of the social determinants of health (SDH), selected theories, and its application in studies of chronic disease. Once ignored or regarded only as distant or secondary influences on health and disease, social determinants have been increasingly acknowledged as fundamental causes of health afflictions. For the purposes of this discussion, SDH refers to SDH variables directly relevant to chronic diseases and, in some circumstances, obesity, in the research agenda of the Mid-South Transdisciplinary Collaborative Center for Health Disparities Research. The health effects of SDH are initially discussed with respect to smoking and the social gradient in mortality. Next, four leading SDH theories-life course, fundamental cause, social capital, and health lifestyle theory-are reviewed with supporting studies. The article concludes with an examination of neighborhood disadvantage, social networks, and perceived discrimination in SDH research.

Using Electronic Health Records for Population Health Research: A Review of Methods and Applications

The use and functionality of electronic health records (EHRs) have increased rapidly in the past decade. Although the primary purpose of EHRs is clinical, researchers have used them to conduct epidemiologic investigations, ranging from cross-sectional studies within a given hospital to longitudinal studies on geographically distributed patients. Herein, we describe EHRs, examine their use in population health research, and compare them with traditional epidemiologic methods. We describe diverse research applications that benefit from the large sample sizes and generalizable patient populations afforded by EHRs. These have included reevaluation of prior findings, a range of diseases and subgroups, environmental and social epidemiology, stigmatized conditions, predictive modeling, and evaluation of natural experiments. Although studies using primary data collection methods may have more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. Future EHR epidemiology with enhanced collection of social/behavior measures, linkage with vital records, and integration of emerging technologies such as personal sensing could improve clinical care and population health.