Small-area estimation and prioritizing communities for obesity control in Massachusetts

The relationship of within-individual and between-individual variation in mental health with bodyweight: An exploratory longitudinal study

BACKGROUND

Poor mental health is associated with obesity, but existing studies are either cross-sectional or have long time periods between measurements of mental health and weight. It is, therefore, unclear how small fluctuations in mental wellbeing within individuals predict bodyweight over short time periods, e.g. within the next month. Studying this could identify modifiable determinants of weight changes and highlight opportunities for early intervention.

METHODS

2,133 UK adults from a population-based cohort completed monthly mental health and weight measurements using a mobile app over a period of 6-9 months. We used random intercept regression models to examine longitudinal associations of depressive symptoms, anxiety symptoms and stress with subsequent weight. In sub-group analyses, we included interaction terms of mental health variables with baseline characteristics. Mental health variables were split into "between-individual" measurements (= the participant's median score across all timepoints) and "within-individual" measurements (at each timepoint, the difference between the participant's current score and their median).

RESULTS

Within-individual variation in depressive symptoms predicted subsequent weight (0.045kg per unit of depressive symptom severity, 95% CI 0.021-0.069). We found evidence of a moderation effect of baseline BMI on the association between within-individual fluctuation in depressive symptoms and subsequent weight: The association was only apparent in those with overweight/obesity, and it was stronger in those with obesity than those with overweight (BMI<25kg/m2: 0.011kg per unit of depressive symptom severity [95% CI -0.017 to 0.039]; BMI 25-29.9kg/m2: 0.052kg per unit of depressive symptom severity [95%CI 0.010-0.094kg]; BMI≥30kg/m2: 0.071kg per unit of depressive symptom severity [95%CI 0.013-0.129kg]). We found no evidence for other interactions, associations of stress and anxiety with weight, or for a reverse direction of association.

CONCLUSION

In this exploratory study, individuals with overweight or obesity were more vulnerable to weight gain following higher-than-usual (for that individual) depressive symptoms than individuals with a BMI<25kg/m2.

Development and Validation of a Claims-Based Model to Predict Categories of Obesity

We developed and validated a claims-based algorithm that classifies patients into obesity categories. Using Medicare (2007-2017) and Medicaid (2000-2014) claims data linked to 2 electronic health record (EHR) systems in Boston, Massachusetts, we identified a cohort of patients with an EHR-based body mass index (BMI) measurement (calculated as weight (kg)/height (m)2). We used regularized regression to select from 137 variables and built generalized linear models to classify patients with BMIs of ≥25, ≥30, and ≥40. We developed the prediction model using EHR system 1 (training set) and validated it in EHR system 2 (validation set). The cohort contained 123,432 patients in the Medicare population and 40,736 patients in the Medicaid population. The model comprised 97 variables in the Medicare set and 95 in the Medicaid set, including BMI-related diagnosis codes, cardiovascular and antidiabetic drugs, and obesity-related comorbidities. The areas under the receiver-operating-characteristic curve in the validation set were 0.72, 0.75, and 0.83 (Medicare) and 0.66, 0.66, and 0.70 (Medicaid) for BMIs of ≥25, ≥30, and ≥40, respectively. The positive predictive values were 81.5%, 80.6%, and 64.7% (Medicare) and 81.6%, 77.5%, and 62.5% (Medicaid), for BMIs of ≥25, ≥30, and ≥40, respectively. The proposed model can identify obesity categories in claims databases when BMI measurements are missing and can be used for confounding adjustment, defining subgroups, or probabilistic bias analysis.

Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation

Background

Electronic Health Record (EHR) data are increasingly being used to monitor population health on account of their timeliness, granularity, and large sample sizes. While EHR data are often sufficient to estimate disease prevalence and trends for large geographic areas, the same accuracy and precision may not carry over for smaller areas that are sparsely represented by non-random samples.

Methods

We developed small-area estimation models using a combination of EHR data drawn from MDPHnet, an EHR-based public health surveillance network in Massachusetts, the American Community Survey, and state hospitalization data. We estimated municipality-specific prevalence rates of asthma, diabetes, hypertension, obesity, and smoking in each of the 351 municipalities in Massachusetts in 2016. Models were compared against Behavioral Risk Factor Surveillance System (BRFSS) state and small area estimates for 2016.

Results

Integrating progressively more variables into prediction models generally reduced mean absolute error (MAE) relative to municipality-level BRFSS small area estimates: asthma (2.24% MAE crude, 1.02% MAE modeled), diabetes (3.13% MAE crude, 3.48% MAE modeled), hypertension (2.60% MAE crude, 1.48% MAE modeled), obesity (4.92% MAE crude, 4.07% MAE modeled), and smoking (5.33% MAE crude, 2.99% MAE modeled). Correlation between modeled estimates and BRFSS estimates for the 13 municipalities in Massachusetts covered by BRFSS’s 500 Cities ranged from 81.9% (obesity) to 96.7% (diabetes).

Conclusions

Small-area estimation using EHR data is feasible and generates estimates comparable to BRFSS state and small-area estimates. Integrating EHR data with survey data can provide timely and accurate disease monitoring tools for areas with sparse data coverage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13809-2.

Assessing comparative asset-based measures of material wealth as predictors of physical growth and mortality

Social scientists and policymakers have increasingly relied on asset-based indices of household wealth to assess social disparities and to identify economically vulnerable populations in low- and middle-income countries. In the last decade, researchers have proposed a number of asset-based measures that permit global comparisons of household wealth across populations in different countries and over time. Each of these measures relies on different assumptions and indicators, and little is known about the relative performance of these measures in assessing disparities. In this study, we assess four comparative, asset-based measures of wealth-the Absolute Wealth Estimate (AWE), the International Wealth Index (IWI), the Comparative Wealth Index (CWI), and the "Standard of Living" portion of the Multi-Dimensional Poverty Index (MPI), along with a variable measuring television ownership-and compare how well each predicts health related variables such as women's BMI, children's height-for-age Z scores, and infant mortality at the household and survey level. Analyzing data from over 300 Demographic and Health surveys in 84 countries (n = 2,304,928 households), we found that AWE, IWI, CWI, MPI are all highly correlated (r = 0.7 to 0.9). However, IWI which is based on a common set of universally weighted indicators, typically best accounts for variation in all three health measures. We discuss the implications of these findings for choosing and interpreting these measures of wealth for different purposes.

Classes of lifetime adversities among emerging adult women by race/ethnicity and their associations with weight status in the United States

This cross-sectional study examines the association of childhood and adolescent/adult adversities with obesity across four racial/ethnic groups among emerging adult women aged 18 to 25 (n = 9310). Latent class analysis was used to identify racial/ethnicity-specific classes arising from childhood and adolescent/adult adversity indicators in the 2015 and 2018 College Student Health Surveys (sampled from Minnesota, U.S.) Distal outcome procedure and Bolck-Croon-Hagenaars methods were used to assess each class's association with body mass index (BMI) and obesity probability. Models were adjusted for post-secondary school type and parental education. We identified 7 classes for White women, 4 classes for Asian and Latina women, and 5 classes for Black women. Weight distributions of Black and Latina women leaned towards "overweight", whereas White and Asian women's BMI leaned towards "normal weight." Latina and Black women had a wider BMI range (~5 kg/m²) between classes with the highest versus lowest BMI than White and Asian women (~3 kg/m²), suggesting a stronger association between adversities and BMI. For Asian, Black, and White women, the "Low Adversities" class had the lowest obesity prevalence, while the "High Lifetime Adversities" class had the highest prevalence. In contrast, Latina women had the lowest obesity prevalence in the "High Adolescent/Adult Adversities & Low Childhood Adversities" class, and highest prevalence in the "Household Mental Illness" class. Results indicate that racial/ethnic disparities in obesity-related measures are reduced when racial/ethnic groups experience low adversity. Future research should explore tailored adversity interventions that consider adversity exposure differences across race/ethnicity as a strategy for reducing obesity risk.

Estimates of Childhood Overweight and Obesity at the Region, State, and County Levels: A Multilevel Small-Area Estimation Approach

Local-level childhood overweight and obesity data are often used to implement and evaluate community programs, as well as allocate resources to combat overweight and obesity. The most current substate estimates of US childhood obesity use data collected in 2007. Using a spatial multilevel model and the 2016 National Survey of Children's Health, we estimated childhood overweight and obesity prevalence rates at the Census regional division, state, and county levels using small-area estimation with poststratification. A sample of 24,162 children aged 10-17 years was used to estimate a national overweight and obesity rate of 30.7% (95% confidence interval: 27.0%, 34.9%). There was substantial county-to-county variability (range, 7.0% to 80.9%), with 31 out of 3,143 counties having an overweight and obesity rate significantly different from the national rate. Estimates from counties located in the Pacific region had higher uncertainty than other regions, driven by a higher proportion of underrepresented sociodemographic groups. Child-level overweight and obesity was related to race/ethnicity, sex, parental highest education (P < 0.01 for all), county-level walkability (P = 0.03), and urban/rural designation (P = 0.02). Overweight and obesity remains a vital issue for US youth, with substantial area-level variability. The additional uncertainty for underrepresented groups shows surveys need to better target diverse samples.

Modeling the associations between internal body orientation, body appreciation, and intuitive eating among early-adult and middle-adult men and women: A multigroup structural invariance analysis

This cross-sectional study investigated the associations among internal body orientation, body appreciation, intuitive eating, age, and BMI in men and women between 30 and 70 years old as delineated in the acceptance model of intuitive eating. Self-report measures were administered to a final sample of 522 individuals consisting of early-adult men (ages 30-44; n = 153), middle-adult men (ages 45-70; n = 108), early-adult women (ages 30-44; n = 135), and middle-adult women (ages 45-70; n = 126). Overall mean age was 45.03 (SD = 10.95). Structural equation modeling evidenced that, for both age groups of men and women, internal body orientation was positively associated with body appreciation and body appreciation was positively associated with intuitive eating. Internal body orientation was positively associated with intuitive eating in each group, except early-adult women. The associations among age and BMI with the aforementioned variables were inconsistent. Although evidencing measurement non-invariance among a number of parameters, multigroup structural invariance analyses showed that the associations among internal body orientation, body appreciation, intuitive eating, age, and BMI were invariant across each gender and age. These results further confirm components of the acceptance model of intuitive eating among men and women in early-adulthood and middle-adulthood.

A randomized study of effects of obesity framing on weight stigma

OBJECTIVE

Growing evidence suggests highly processed foods may trigger an addictive-like process, which is associated with obesity. Other research suggests an addictive-like process occurs in response to eating itself, rather than specific foods. Addiction-based obesity explanations raise concerns about double stigmatization of people with obesity and addiction. This study compared effects of obesity framings on external and internalized weight stigma.

METHODS

The study was preregistered via Open Science Framework. Four hundred and forty-seven adults read an informational passage that described food addiction, eating addiction, or calorie balance explanations for obesity or a control passage about memory. Participants then completed external and internalized weight stigma measures.

RESULTS

Participants in the food addiction condition reported higher internalized weight stigma compared with those in the control condition. Obesity framing did not significantly affect external weight stigma compared with the control.

CONCLUSIONS

These findings suggest that food addiction explanations for obesity may elicit greater internalized weight stigma than non-obesity-related messages. Addiction-based and traditional obesity explanations do not appear to influence external weight stigma. Illuminating the effects of obesity framing on stigma will help researchers communicate discoveries in ways that mitigate stigma.

Relationships between patterns of technology-based weight-related self-monitoring and eating disorder behaviors among first year university students

OBJECTIVE

To identify patterns of technology-based weight-related self-monitoring (WRSM) and assess associations between identified patterns and eating disorder behaviors among first year university students.

METHODS

First year university students (n = 647) completed a web-based survey to assess their use of technology-based WRSM and eating disorder behaviors. The cross-sectional data were analyzed using gender-stratified latent class analysis to identify patterns of WRSM, followed by logistic regression to calculate the predicted probability of eating disorder behaviors for each pattern of WRSM.

RESULTS

Technology-based WRSM is common among first year university students, with patterns of WRSM differing by student gender. Further, unique patterns of WRSM were associated with differing probability of engaging in eating disorder behaviors. For example, compared to the 67.0% of females who did not use technology-based WRSM, females engaging in high amounts of technology-based WRSM (33.0%) were more likely to report fasting, skipping meals, excessively exercising, and using supplements. Among males, those who reported all forms of WRSM (9.5%) were more likely to report fasting, skipping meals, purging, and using supplements but those who only used exercise self-monitoring (11.9%) did not have increased likelihood of eating disorder behaviors.

CONCLUSIONS

Using multiple forms of technology-based WRSM is associated with increased likelihood of engaging in eating disorder behaviors among both female and male, first year university students. Assessing technology-based WRSM may be a simple method to screen for elevated eating disorder risk among first year students.

Life-course trajectories of body mass index from adolescence to old age: Racial and educational disparities

No research exists on how body mass index (BMI) changes with age over the full life span and social disparities therein. This study aims to fill the gap using an innovative life-course research design and analytic methods to model BMI trajectories from early adolescence to old age across 20th-century birth cohorts and test sociodemographic variation in such trajectories. We conducted the pooled integrative data analysis (IDA) to combine data from four national population-based NIH longitudinal cohort studies that collectively cover multiple stages of the life course (Add Health, MIDUS, ACL, and HRS) and estimate mixed-effects models of age trajectories of BMI for men and women. We examined associations of BMI trajectories with birth cohort, race/ethnicity, parental education, and adult educational attainment. We found higher mean levels of and larger increases in BMI with age across more recent birth cohorts as compared with earlier-born cohorts. Black and Hispanic excesses in BMI compared with Whites were present early in life and persisted at all ages, and, in the case of Black-White disparities, were of larger magnitude for more recent cohorts. Higher parental and adulthood educational attainment were associated with lower levels of BMI at all ages. Women with college-educated parents also experienced less cohort increase in mean BMI. Both race and education disparities in BMI trajectories were larger for women compared with men.

Relationships between patterns of weight-related self-monitoring and eating disorder symptomology among undergraduate and graduate students

OBJECTIVE

To characterize patterns of weight-related self-monitoring (WRSM) among US undergraduate and graduate students and examine associations between identified patterns of WRSM and eating disorder symptomology.

METHOD

Undergraduate and graduate students from 12 US colleges and universities (N = 10,010) reported the frequency with which they use WRSM, including self-weighing and dietary self-monitoring. Eating disorder symptomology was assessed using the Eating Disorder Examination Questionnaire. Gender-specific patterns of WRSM were identified using latent class analysis, and logistic regressions were used to identify differences in the odds of eating disorder symptomology across patterns of WRSM.

RESULTS

Among this sample, 32.7% weighed themselves regularly; 44.1% reported knowing the nutrition facts of the foods they ate; 33.6% reported knowing the caloric content of the foods they ate; and 12.8% counted the calories they ate. Among women, four patterns of WRSM were identified: "no WRSM," "all forms of WRSM," "knowing nutrition/calorie facts," and "self-weigh only." Compared with the "no WRSM" pattern, women in all other patterns experienced increased eating disorder symptomology. Among men, three patterns were identified: "no WRSM," "all forms of WRSM," and "knowing nutrition/calorie facts." Only men in the "all forms WRSM" pattern had increased eating disorder symptomatology compared with those in the "no WRSM" pattern.

DISCUSSION

In a large sample of undergraduate and graduate students, engaging in any WRSM was associated with increased eating disorder symptomology among women, particularly for those who engaged in all forms. Among men, engaging in all forms of WRSM was the only pattern associated with higher eating disorder symptomology.

Trends in Tract-Level Prevalence of Obesity in Philadelphia by Race-Ethnicity, Space, and Time

The growing recognition of often substantial neighborhood variation in health within cities has motivated greater demand for reliable data on small-scale variations in health outcomes. The goal of this article is to explore temporal changes in geographic disparities in obesity prevalence in the City of Philadelphia by race and sex over the period 2000-2015. Our data consist of self-reported survey responses of non-Hispanic whites, non-Hispanic blacks, and Hispanics from the Southeastern Pennsylvania Household Health Survey. To analyze these data-and to obtain more reliable estimates of the prevalence of obesity-we apply a Bayesian model that simultaneously accounts for spatial-, temporal-, and between-race/ethnicity dependence structures. This approach yields estimates of the obesity prevalence by age, race/ethnicity, sex, and poverty status for each census tract at all time-points in our study period. While the data suggest that the prevalence of obesity has increased at the city-level for men and women of all three race/ethnicities, the magnitude and geographic distribution of these increases differ substantially by race/ethnicity and sex. The method can be flexibly used to describe and visualize spatial heterogeneities in levels, trends, and in disparities. This is useful for targeting, surveillance, and etiologic research.

How youth cognitive and sociodemographic factors relate to the development of overweight and obesity in the UK and the USA: a prospective cross-cohort study of the National Child Development Study and National Longitudinal Study of Youth 1979

OBJECTIVES

We investigated how youth cognitive and sociodemographic factors are associated with the aetiology of overweight and obesity. We examined both onset (who is at early risk for overweight and obesity) and development (who gains weight and when).

DESIGN

Prospective cohort study.

SETTING

We used data from the US National Longitudinal Study of Youth 1979 (NLSY) and the UK National Child Development Study (NCDS); most of both studies completed a cognitive function test in youth.

PARTICIPANTS

12 686 and 18 558 members of the NLSY and NCDS, respectively, with data on validated measures of youth cognitive function, youth socioeconomic disadvantage (eg, parental occupational class and time spent in school) and educational attainment. Height, weight and income data were available from across adulthood, from individuals' 20s into their 50s.

PRIMARY AND SECONDARY OUTCOME MEASURES

Body mass index (BMI) for four time points in adulthood. We modelled gain in BMI using latent growth curve models to capture linear and quadratic components of change in BMI over time.

RESULTS

Across cohorts, higher cognitive function was associated with lower overall BMI. In the UK, 1 SD higher score in cognitive function was associated with lower BMI (β=-0.20, 95% CI -0.33 to -0.06 kg/m²). In America, this was true only for women (β=-0.53, 95% CI -0.90 to -0.15 kg/m²), for whom higher cognitive function was associated with lower BMI. In British participants only, we found limited evidence for negative and positive associations, respectively, between education (β=-0.15, 95% CI -0.26 to -0.04 kg/m²) and socioeconomic disadvantage (β=0.33, 95% CI 0.23 to 0.43 kg/m²) and higher BMI. Overall, no cognitive or socioeconomic factors in youth were associated with longitudinal changes in BMI.

CONCLUSIONS

While sociodemographic and particularly cognitive factors can explain some patterns in individuals' overall weight levels, differences in who gains weight in adulthood could not be explained by any of these factors.

Measuring Subcounty Differences in Population Health Using Hospital and Census-Derived Data Sets: The Missouri ZIP Health Rankings Project

CONTEXT

Measures of population health at the subcounty level are needed to identify areas for focused interventions and to support local health improvement activities.

OBJECTIVE

To extend the County Health Rankings population health measurement model to the ZIP code level using widely available hospital and census-derived data sources.

DESIGN

Retrospective administrative data study.

SETTING

Missouri.

POPULATION

Missouri FY 2012-2014 hospital inpatient, outpatient, and emergency department discharge encounters (N = 36 176 377) and 2015 Nielsen data.

MAIN OUTCOME MEASURES

ZIP code-level health factors and health outcomes indices.

RESULTS

Statistically significant measures of association were observed between the ZIP code-level population health indices and published County Health Rankings indices. Variation within counties was observed in both urban and rural areas. Substantial variation of the derived measures was observed at the ZIP code level with 20 (17.4%) Missouri counties having ZIP codes in both the top and bottom quintiles of health factors and health outcomes. Thirty of the 46 (65.2%) counties in the top 2 county quintiles had ZIP codes in the bottom 2 quintiles.

CONCLUSIONS

This proof-of-concept analysis suggests that readily available hospital and census-derived data can be used to create measures of population health at the subcounty level. These widely available data sources could be used to identify areas of potential need within counties, engage community stakeholders, and target interventions.

Geographic variation in cardiometabolic risk distribution: A cross-sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia

INTRODUCTION

Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches.

METHOD

A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into 'higher risk' and 'lower risk' values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran's I test and Local Indicators of Spatial Autocorrelation (LISA).

RESULTS

A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of 'higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran's I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area.

CONCLUSION

The study describes a range of CMRFs with different distributions in the study region. The results allow generation of hypotheses to test in future research concerning location specific population health approaches.

Gender effects of agricultural cropping work and nutrition status in Tanzania

Although agriculture is an important source of food and income for food expenditures, women's involvement in the agricultural cropping production process could increase their work load and reduce their BMI. Using three waves of the Tanzania National Panel Survey, we investigate the extent to which time spent in agricultural crop production affects women and men's nutritional status among non-overweight individuals (age 20-65). We also test whether the impact of agricultural cropping work on nutritional status is modified by access to agricultural equipment, and whether gender differences exist. The study finds that time spent in agricultural cropping work is negatively associated with BMI for non-overweight individuals, albeit of small magnitude, and this finding is consistent across different crop production processes. This suggests that agricultural interventions should not ignore the implications of increasing work intensities on nutrition. While increased agricultural production could improve nutritional status by increasing agricultural income and food, the gains in nutritional status could be offset by an increase in work effort of doing agricultural work. Our results suggest that it is possible that access to equipment reduced effort for one production activity, but increased work for other activities in the production process, such as in harvesting. Furthermore, we find that the BMI of women in households with a hand powered sprayer is positively related to time spent in weeding, fertilizing, and non-harvest activities, while it is negatively correlated for men. It is possible that access to a hand powered sprayer may have helped reduce women's work, for example, in weeding, while this was not the case for men's work such as in ridging and fertilizing. Further disaggregation of agricultural activities in the dataset would have been helpful to provide more insights on the gender roles.

Subnational estimation of modern contraceptive prevalence in five sub-Saharan African countries: a Bayesian hierarchical approach

BACKGROUND

Global monitoring efforts have relied on national estimates of modern contraceptive prevalence rate (mCPR) for many low-income countries. However, most contraceptive delivery programs are implemented by health departments at lower administrative levels, reflecting a persisting gap between the availability of and need for subnational mCPR estimates.

METHODS

Using woman-level data from multiple semi-annual national survey rounds conducted between 2013 and 2016 in five sub-Saharan African countries (Burkina Faso, Ethiopia, Ghana, Kenya, and Uganda) by the Performance, Monitoring and Accountability 2020 project, we propose a Bayesian Hierarchical Model with a standard set of covariates and temporally correlated random effects to estimate the level and trend of mCPR for first level administrative divisions in each country.

RESULTS

There is considerable narrowing of the uncertainty interval (UI) around the model-based estimates, compared to the estimates directly based on the survey data. We find substantial variations in the estimated subnational mCPRs. Uganda, for example, shows a gain in mCPR of 6.4% (95% UI: 4.5-8.3) based on model estimates of 20.9% (19.6-22.2) in mid-2014 and 27.3% (26.0-28.8) in mid-2016, with change across 10 regions ranging from - 0.6 points in Karamoja to 9.4 points in Central 2 region. The lower bound of the UIs of the change over four rounds was above 0 in 6 regions. Similar upward trends are observed for most regions in the other four countries, and there is noticeable within-country geographic variation.

CONCLUSIONS

Reliable subnational estimates of mCPR empower health departments in evidence-based policy making. Despite nationally increasing mCPRs, regional disparities exist within countries suggesting uneven contraceptive access. Raising investments in disadvantaged areas may be warranted to increase equity in access to modern contraceptive methods.

Prevalence of Chronic Disease and Their Risk Factors Among Iranian, Ukrainian, Vietnamese Refugees in California, 2002-2011

Little is known about how the health status of incoming refugees to the United States compares to that of the general population. We used logistic regression to assess whether country of origin is associated with prevalence of hypertension, obesity, type-II diabetes, and tobacco-use among Iranian, Ukrainian and Vietnamese refugees arriving in California from 2002 to 2011 (N = 21,968). We then compared the prevalence among refugees to that of the Californian general population (CGP). Ukrainian origin was positively associated with obesity and negatively with smoking, while the opposite was true for Vietnamese (p < 0.001). Iranian origin was positively associated with type-II diabetes and smoking (p < 0.001). After accounting for age and gender differences, refugees had lower prevalence of obesity and higher prevalence of smoking than CGP. Individually, all refugee groups had lower type-II diabetes prevalence than CGP. Grouping all refugees together can hide distinct health needs associated with country of origin.

Comparison of Methods for Estimating Prevalence of Chronic Diseases and Health Behaviors for Small Geographic Areas: Boston Validation Study, 2013

Introduction

Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract–level prevalence estimates of 27 measures for the 500 largest US cities.

Methods

To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code–level estimates for the city of Boston, Massachusetts.

Results

By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators.

Conclusion

Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.

Metabolic Health Status and the Obesity Paradox in Older Adults

The explanation for reduced mortality among older persons with overweight or class I obesity compared to those of desirable weight remains unclear. Our objective was to investigate the joint effects of body mass index (BMI) and metabolic health status on all-cause mortality in a cohort of advanced age. Adults aged 74 ± 4.7 (mean ± SD) years at baseline (n = 4551) were categorized according to BMI (18.5-24.9, 25.0-29.9, 30.0-34.9, and ≥35.0 kg/m(2)) and the presence or absence of a metabolically healthy phenotype (i.e., 0 or 1 risk factors based on a modified Adult Treatment Panel III). Metabolically unhealthy was ≥2 risk factors. There were 2294 deaths over a mean 10.9 years of follow up. Relative to metabolically healthy desirable weight, metabolically healthy overweight or class I obesity was not associated with a greater mortality risk (HR 0.90; 95 CI% 0.73-1.13 and HR 0.58; 95 CI% 0.42-0.80, respectively) (P-interaction <0.001). Results remained consistent in rigorous sensitivity analyses. The "obesity paradox" may be partially explained by the inclusion of metabolically healthy overweight and obese older persons, who do not have elevated mortality risk, in population studies of BMI and mortality.

Using Monte Carlo/Gaussian Based Small Area Estimates to Predict Where Medicaid Patients Reside

Electronic Health Records (EHR) are rapidly becoming accepted as tools for planning and population health^1,2. With the national dialogue around Medicaid expansion¹², the role of EHR data has become even more important. For their potential to be fully realized and contribute to these discussions, techniques for creating accurate small area estimates is vital. As such, we examined the efficacy of developing small area estimates for Medicaid patients in two locations, Albuquerque and Chicago, by using a Monte Carlo/Gaussian technique that has worked in accurately locating registered voters in North Carolina¹¹. The Albuquerque data, which includes patient address, will first be used to assess the accuracy of the methodology. Subsequently, it will be combined with the EHR data from Chicago to develop a regression that predicts Medicaid patients by US Block Group. We seek to create a tool that is effective in translating EHR data's potential for population health studies.

Body mass index and all-cause mortality among older adults

OBJECTIVE

To examine the association between baseline body mass index (BMI, kg/m(2) ) and all-cause mortality in a well-characterized cohort of older persons.

METHODS

The association between BMI (both as a categorical and continuous variable) and all-cause mortality was investigated using 4,565 Geisinger Rural Aging Study participants with baseline age 74.0 ± 4.7 years (mean ± SD) and BMI 29.5 ± 5.3 kg/m(2) over a mean of 10.9 ± 3.8 years of follow-up.

RESULTS

The relationship between BMI (as a continuous variable) and all-cause mortality was found to be U-shaped (P nonlinearity <0.001). Controlling for age, sex, smoking, alcohol, laboratory values, medications, and comorbidity status, underweight (BMI <18.5 kg/m(2) ) individuals had significantly greater adjusted risk of all-cause mortality than persons of BMI 18.5 to 24.9 kg/m(2) (reference range). Participants with overweight (BMI 25.0-29.9 kg/m(2) ) and class I obesity (BMI 30.0-34.9 kg/m(2) ) had significantly lower adjusted-risk of all-cause mortality. Those with classes II/III obesity (BMI ≥ 35.0 kg/m(2) ) did not have significantly greater adjusted-risk of all-cause mortality. Findings were consistent using propensity score weights and among never-smokers with 2- and 5-year lag analysis and among those with no identified chronic disease.

CONCLUSIONS

A U-shaped association was observed between BMI and all-cause mortality with lower risk among older persons with overweight and class I obesity in comparison with those with BMI 18.5 to 24.9 kg/m(2) .

Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach

Background

An important public health goal is to decrease the prevalence of key behavioural risk factors, such as tobacco use and obesity. Survey information is often available at the regional level, but heterogeneity within large geographic regions cannot be assessed. Advanced spatial analysis techniques are demonstrated to produce sensible micro area estimates of behavioural risk factors that enable identification of areas with high prevalence.

Methods

A spatial Bayesian hierarchical model was used to estimate the micro area prevalence of current smoking and excess bodyweight for the Erie-St. Clair region in southwestern Ontario. Estimates were mapped for male and female respondents of five cycles of the Canadian Community Health Survey (CCHS). The micro areas were 2006 Census Dissemination Areas, with an average population of 400–700 people. Two individual-level models were specified: one controlled for survey cycle and age group (model 1), and one controlled for survey cycle, age group and micro area median household income (model 2). Post-stratification was used to derive micro area behavioural risk factor estimates weighted to the population structure. SaTScan analyses were conducted on the granular, postal-code level CCHS data to corroborate findings of elevated prevalence.

Results

Current smoking was elevated in two urban areas for both sexes (Sarnia and Windsor), and an additional small community (Chatham) for males only. Areas of excess bodyweight were prevalent in an urban core (Windsor) among males, but not females. Precision of the posterior post-stratified current smoking estimates was improved in model 2, as indicated by narrower credible intervals and a lower coefficient of variation. For excess bodyweight, both models had similar precision. Aggregation of the micro area estimates to CCHS design-based estimates validated the findings.

Conclusions

This is among the first studies to apply a full Bayesian model to complex sample survey data to identify micro areas with variation in risk factor prevalence, accounting for spatial correlation and other covariates. Application of micro area analysis techniques helps define areas for public health planning, and may be informative to surveillance and research modeling of relevant chronic disease outcomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3144-4) contains supplementary material, which is available to authorized users.

Using Small-Area Estimation to Calculate the Prevalence of Smoking by Subcounty Geographic Areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013

Introduction

King County, Washington, fares well overall in many health indicators. However, county-level data mask disparities among subcounty areas. For disparity-focused assessment, a demand exists for examining health data at subcounty levels such as census tracts and King County health reporting areas (HRAs).

Methods

We added a “nearest intersection” question to the Behavioral Risk Factor Surveillance System (BRFSS) and geocoded the data for subcounty geographic areas, including census tracts. To overcome small sample size at the census tract level, we used hierarchical Bayesian models to obtain smoothed estimates in cigarette smoking rates at the census tract and HRA levels. We also used multiple imputation to adjust for missing values in census tracts.

Results

Direct estimation of adult smoking rates at the census tract level ranged from 0% to 56% with a median of 10%. The 90% confidence interval (CI) half-width for census tract with nonzero rates ranged from 1 percentage point to 37 percentage points with a median of 13 percentage points. The smoothed-multiple–imputation rates ranged from 5% to 28% with a median of 12%. The 90% CI half-width ranged from 4 percentage points to 13 percentage points with a median of 8 percentage points.

Conclusion

The nearest intersection question in the BRFSS provided geocoded data at subcounty levels. The Bayesian model provided estimation with improved precision at the census tract and HRA levels. Multiple imputation can be used to account for missing geographic data. Small-area estimation, which has been used for King County public health programs, has increasingly become a useful tool to meet the demand of presenting data at more granular levels.

Generating Health Estimates by Zip Code: A Semiparametric Small Area Estimation Approach Using the California Health Interview Survey

OBJECTIVES

We propose a method to meet challenges in generating health estimates for granular geographic areas in which the survey sample size is extremely small.

METHODS

Our generalized linear mixed model predicts health outcomes using both individual-level and neighborhood-level predictors. The model's feature of nonparametric smoothing function on neighborhood-level variables better captures the association between neighborhood environment and the outcome. Using 2011 to 2012 data from the California Health Interview Survey, we demonstrate an empirical application of this method to estimate the fraction of residents without health insurance for Zip Code Tabulation Areas (ZCTAs).

RESULTS

Our method generated stable estimates of uninsurance for 1519 of 1765 ZCTAs (86%) in California. For some areas with great socioeconomic diversity across adjacent neighborhoods, such as Los Angeles County, the modeled uninsured estimates revealed much heterogeneity among geographically adjacent ZCTAs.

CONCLUSIONS

The proposed method can increase the value of health surveys by providing modeled estimates for health data at a granular geographic level. It can account for variations in health outcomes at the neighborhood level as a result of both socioeconomic characteristics and geographic locations.

Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system

Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.

Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data

Introduction

Interventions for pediatric obesity can be geographically targeted if high-risk populations can be identified. We developed an approach to estimate the percentage of overweight or obese children aged 2 to 17 years in small geographic areas using publicly available data. We piloted our approach for Georgia.

Methods

We created a logistic regression model to estimate the individual probability of high body mass index (BMI), given data on the characteristics of the survey participants. We combined the regression model with a simulation to sample subpopulations and obtain prevalence estimates. The models used information from the 2001–2010 National Health and Nutrition Examination Survey, the 2010 Census, and the 2010 American Community Survey. We validated our results by comparing 1) estimates for adults in Georgia produced by using our approach with estimates from the Centers for Disease Control and Prevention (CDC) and 2) estimates for children in Arkansas produced by using our approach with school examination data. We generated prevalence estimates for census tracts in Georgia and prioritized areas for interventions.

Results

In DeKalb County, the mean prevalence among census tracts varied from 27% to 40%. For adults, the median difference between our estimates and CDC estimates was 1.3 percentage points; for Arkansas children, the median difference between our estimates and examination-based estimates data was 1.7 percentage points.

Conclusion

Prevalence estimates for census tracts can be different from estimates for the county, so small-area estimates are crucial for designing effective interventions. Our approach validates well against external data, and it can be a relevant aid for planning local interventions for children.

'In general, how do you feel today?'--self-rated health in the context of aging in India

This thesis is centered on self-rated health (SRH) as an outcome measure, as a predictor, and as a marker. The thesis uses primary data from the WHO Study on global AGEing and adult health (SAGE) implemented in India in 2007. The structural equation modeling approach is employed to understand the pathways through which the social environment, disability, disease, and sociodemographic characteristics influence SRH among older adults aged 50 years and above. Cox proportional hazard model is used to explore the role of SRH as a predictor for mortality and the role of disability in modifying this effect. The hierarchical ordered probit modeling approach, which combines information from anchoring vignettes with SRH, was used to address the long overlooked methodological concern of interpersonal incomparability. Finally, multilevel model-based small area estimation techniques were used to demonstrate the use of large national surveys and census information to derive precise SRH prevalence estimates at the district and sub-district level. The thesis advocates the use of such a simple measure to identify vulnerable communities for targeted health interventions, to plan and prioritize resource allocation, and to evaluate health interventions in resource-scarce settings. The thesis provides the basis and impetus to generate and integrate similar and harmonized adult health and aging data platforms within demographic surveillance systems in different regions of India and elsewhere.

Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the behavioral risk factor surveillance system

A variety of small-area statistical models have been developed for health surveys, but none are sufficiently flexible to generate small-area estimates (SAEs) to meet data needs at different geographic levels. We developed a multilevel logistic model with both state- and nested county-level random effects for chronic obstructive pulmonary disease (COPD) using 2011 data from the Behavioral Risk Factor Surveillance System. We applied poststratification with the (decennial) US Census 2010 counts of census-block population to generate census-block-level SAEs of COPD prevalence which could be conveniently aggregated to all other census geographic units, such as census tracts, counties, and congressional districts. The model-based SAEs and direct survey estimates of COPD prevalence were quite consistent at both the county and state levels. The Pearson correlation coefficient was 0.99 at the state level and ranged from 0.88 to 0.95 at the county level. Our extended multilevel regression modeling and poststratification approach could be adapted for other geocoded national health surveys to generate reliable SAEs for population health outcomes at all administrative and legislative geographic levels of interest in a scalable framework.

A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran's I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran's I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran's I=0.452, P=0.001), and in the OLS regression residuals (Global Moran's I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=-0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=-0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.

Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India

We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH.

Tailoring community-based wellness initiatives with latent class analysis--Massachusetts Community Transformation Grant projects

INTRODUCTION

Community-based approaches to preventing chronic diseases are attractive because of their broad reach and low costs, and as such, are integral components of health care reform efforts. Implementing community-based initiatives across Massachusetts' municipalities presents both programmatic and evaluation challenges. For effective delivery and evaluation of the interventions, establishing a community typology that groups similar municipalities provides a balanced and cost-effective approach.

METHODS

Through a series of key informant interviews and exploratory data analysis, we identified 55 municipal-level indicators of 6 domains for the typology analysis. The domains were health behaviors and health outcomes, housing and land use, transportation, retail environment, socioeconomics, and demographic composition. A latent class analysis was used to identify 10 groups of municipalities based on similar patterns of municipal-level indicators across the domains.

RESULTS

Our model with 10 latent classes yielded excellent classification certainty (relative entropy = .995, minimum class probability for any class = .871), and differentiated distinct groups of municipalities based on health-relevant needs and resources. The classes differentiated healthy and racially and ethnically diverse urban areas from cities with similar population densities and diversity but worse health outcomes, affluent communities from lower-income rural communities, and mature suburban areas from rapidly suburbanizing communities with different healthy-living challenges.

CONCLUSION

Latent class analysis is a tool that may aid in the planning, communication, and evaluation of community-based wellness initiatives such as Community Transformation Grants projects administrated by the Centers for Disease Control and Prevention.

Neighborhood commuting environment and obesity in the United States: an urban-rural stratified multilevel analysis

OBJECTIVE

Automobile dependency and longer commuting are associated with current obesity epidemic. We aimed to examine the urban-rural differential effects of neighborhood commuting environment on obesity in the US METHODS: The 1997-2005 National Health Interview Survey (NHIS) were linked to 2000 US Census data to assess the effects of neighborhood commuting environment: census tract-level automobile dependency and commuting time, on individual obesity status.

RESULTS

Higher neighborhood automobile dependency was associated with increased obesity risk in urbanized areas (large central metro (OR 1.11[1.09, 1.12]), large fringe metro (OR 1.17[1.13, 1.22]), medium metro (OR 1.22[1.16, 1.29]), small metro (OR 1.11[1.04, 1.19]), and micropolitan (OR 1.09[1.00, 1.19])), but not in non-core rural areas (OR 1.00[0.92, 1.08]). Longer neighborhood commuting time was associated with increased obesity risk in large central metro (OR 1.09[1.04, 1.13]), and less urbanized areas (small metro (OR 1.08[1.01, 1.16]), micropolitan (OR 1.06[1.01, 1.12]), and non-core rural areas (OR 1.08[1.01, 1.17])), but not in (large fringe metro (OR 1.05[1.00, 1.11]), and medium metro (OR 1.04[0.98, 1.10])).

CONCLUSION

The link between commuting environment and obesity differed across the regional urbanization levels. Urban and regional planning policies may improve current commuting environment and better support healthy behaviors and healthy community development.

Examination of how neighborhood definition influences measurements of youths' access to tobacco retailers: a methodological note on spatial misclassification

Measurements of neighborhood exposures likely vary depending on the definition of "neighborhood" selected. This study examined the extent to which neighborhood definition influences findings regarding spatial accessibility to tobacco retailers among youth. We defined spatial accessibility to tobacco retailers (i.e., tobacco retail density, closest tobacco retailer, and average distance to the closest 5 tobacco retailers) on the basis of circular and network buffers of 400 m and 800 m, census block groups, and census tracts by using residential addresses from the 2008 Boston Youth Survey Geospatial Dataset (n = 1,292). Friedman tests (to compare overall differences in neighborhood definitions) were applied. There were differences in measurements of youths' access to tobacco retailers according to the selected neighborhood definitions, and these were marked for the 2 spatial proximity measures (both P < 0.01 for all differences). For example, the median average distance to the closest 5 tobacco retailers was 381.50 m when using specific home addresses, 414.00 m when using census block groups, and 482.50 m when using census tracts, illustrating how neighborhood definition influences the measurement of spatial accessibility to tobacco retailers. These analyses suggest that, whenever possible, egocentric neighborhood definitions should be used. The use of larger administrative neighborhood definitions can bias exposure estimates for proximity measures.

The geographic distribution of obesity by census tract among 59 767 insured adults in King County, WA

OBJECTIVE

To evaluate the geographic concentration of adult obesity prevalence by census tract (CT) in King County, WA, in relation to social and economic factors.

METHODS AND DESIGN

Measured heights and weights from 59 767 adult men and women enrolled in the Group Health (GH) healthcare system were used to estimate obesity prevalence at the CT level. CT-level measures of socioeconomic status (SES) were median home values of owner-occupied housing units, percent of residents with a college degree and median household incomes, all drawn from the 2000 Census. Spatial regression models were used to assess the relation between CT-level obesity prevalence and socioeconomic variables.

RESULTS

Smoothed CT obesity prevalence, obtained using an Empirical Bayes tool, ranged from 16.2-43.7% (a 2.7-fold difference). The spatial pattern of obesity was non-random, showing a concentration in south and southeast King County. In spatial regression models, CT-level home values and college education were more strongly associated with obesity than household incomes. For each additional $100 000 in median home values, CT obesity prevalence was 2.3% lower. The three SES factors together explained 70% of the variance in CT obesity prevalence after accounting for population density, race/ethnicity, age and spatial dependence.

CONCLUSIONS

To our knowledge, this is the first report to show major social disparities in adult obesity prevalence at the CT scale that is based, moreover, on measured heights and weights. Analyses of data at sufficiently fine geographic scale are needed to guide targeted local interventions to stem the obesity epidemic.

Factors affecting healthy eating and physical activity behaviors among multiethnic blue- and white-collar workers: a case study of one healthcare institution

Worksite health promotion programs can reduce prevalence of chronic disease among employees, but little research has been done to discern whether they meet the needs and incorporate the preferences of workers of different occupational types. The objective of this study is to examine differences in influences to healthy eating and physical activity and preferences for programs among multiethnic blue- and white-collar workers in Hawai'i. A total of 57 employees from a major health care corporation in Hawai'i participated. A mixed-methods approach was employed, in which findings from focus groups with white-collar workers (WCW) (n=18) were used to inform development of a questionnaire with closed and open-ended items for use with blue-collar workers (BCW) (n=39), whose jobs did not provide adequate time to participate in focus groups. Focus groups with WCW revealed that onsite availability of healthy food and fitness opportunities provided the most support for healthy eating and physical activity at work; work demands, easy access to unhealthy foods, and lack of onsite fitness opportunities were barriers; and lifestyle management was a topic of substantial interest. BCW cited the ability to bring home lunch and their (physically active) jobs as being supportive of healthy behaviors; not having enough time to eat and personal illness/injury were barriers; and chronic disease topics were of greatest interest. Knowing differences in influences to healthy eating and physical activity, as well as preferences for worksite wellness programming, among BCW and WCW, is important when planning and implementing worksite health promotion programs.

The geography of recreational open space: influence of neighborhood racial composition and neighborhood poverty

The geography of recreational open space might be inequitable in terms of minority neighborhood racial/ethnic composition and neighborhood poverty, perhaps due in part to residential segregation. This study evaluated the association between minority neighborhood racial/ethnic composition, neighborhood poverty, and recreational open space in Boston, Massachusetts (US). Across Boston census tracts, we computed percent non-Hispanic Black, percent Hispanic, and percent families in poverty as well as recreational open space density. We evaluated spatial autocorrelation in study variables and in the ordinary least squares (OLS) regression residuals via the Global Moran's I. We then computed Spearman correlations between the census tract socio-demographic characteristics and recreational open space density, including correlations adjusted for spatial autocorrelation. After this, we computed OLS regressions or spatial regressions as appropriate. Significant positive spatial autocorrelation was found for neighborhood socio-demographic characteristics (all p value = 0.001). We found marginally significant positive spatial autocorrelation in recreational open space (Global Moran's I = 0.082; p value = 0.053). However, we found no spatial autocorrelation in the OLS regression residuals, which indicated that spatial models were not appropriate. There was a negative correlation between census tract percent non-Hispanic Black and recreational open space density (r S = -0.22; conventional p value = 0.005; spatially adjusted p value = 0.019) as well as a negative correlation between predominantly non-Hispanic Black census tracts (>60 % non-Hispanic Black in a census tract) and recreational open space density (r S = -0.23; conventional p value = 0.003; spatially adjusted p value = 0.007). In bivariate and multivariate OLS models, percent non-Hispanic Black in a census tract and predominantly Black census tracts were associated with decreased density of recreational open space (p value < 0.001). Consistent with several previous studies in other geographic locales, we found that Black neighborhoods in Boston were less likely to have recreational open spaces, indicating the need for policy interventions promoting equitable access. Such interventions may contribute to reductions and disparities in obesity.

Use of practice-based research network data to measure neighborhood smoking prevalence

Introduction

Practice-Based Research Networks (PBRNs) and health systems may provide timely, reliable data to guide the development and distribution of public health resources to promote healthy behaviors, such as quitting smoking. The objective of this study was to determine if PBRN data could be used to make neighborhood-level estimates of smoking prevalence.

Methods

We estimated the smoking prevalence in 32 greater Boston neighborhoods (population = 877,943 adults) by using the electronic health record data of adults who in 2009 visited one of 26 Partners Primary Care PBRN practices (n = 77,529). We compared PBRN-derived estimates to population-based estimates derived from 1999–2009 Behavioral Risk Factor Surveillance System (BRFSS) data (n = 20,475).

Results

The PBRN estimates of neighborhood smoking status ranged from 5% to 22% and averaged 11%. The 2009 neighborhood-level smoking prevalence estimates derived from the BRFSS ranged from 5% to 26% and averaged 13%. The difference in smoking prevalence between the PBRN and the BRFSS averaged −2 percentage points (standard deviation, 3 percentage points).

Conclusion

Health behavior data collected during routine clinical care by PBRNs and health systems could supplement or be an alternative to using traditional sources of public health data.

A multilevel approach to estimating small area childhood obesity prevalence at the census block-group level

Introduction

Traditional survey methods for obtaining nationwide small-area estimates (SAEs) of childhood obesity are costly. This study applied a geocoded national health survey in a multilevel modeling framework to estimate prevalence of childhood obesity at the census block-group level.

Methods

We constructed a multilevel logistic regression model to evaluate the influence of individual demographic characteristics, zip code, county, and state on the childhood obesity measures from the 2007 National Survey of Children’s Health. The obesity risk for a child in each census block group was then estimated on the basis of this multilevel model. We compared direct survey and model-based SAEs to evaluate the model specification.

Results

Multilevel models in this study explained about 60% of state-level variances associated with childhood obesity, 82.8% to 86.5% of county-level, and 93.1% of zip code-level. The 95% confidence intervals of block- group level SAEs have a wide range (0.795-20.0), a low median of 2.02, and a mean of 2.12. The model-based SAEs of childhood obesity prevalence ranged from 2.3% to 54.7% with a median of 16.0% at the block-group level.

Conclusion

The geographic variances among census block groups, counties, and states demonstrate that locale may be as significant as individual characteristics such as race/ethnicity in the development of the childhood obesity epidemic. Our estimates provide data to identify priority areas for local health programs and to establish feasible local intervention goals. Model-based SAEs of population health outcomes could be a tool of public health assessment and surveillance.

Where is obesity prevention on the map?: distribution and predictors of local health department prevention activities in relation to county-level obesity prevalence in the United States

CONTEXT

The system of local health departments (LHDs) in the United States has the potential to advance a locally oriented public health response in obesity control and reduce geographic disparities. However, the extent to which obesity prevention programs correspond to local obesity levels is unknown.

OBJECTIVE

This study examines the extent to which LHDs across the United States have responded to local levels of obesity by examining the association between jurisdiction-level obesity prevalence and the existence of obesity prevention programs.

DESIGN

Data on LHD organizational characteristics from the Profile Study of Local Health Departments and county-level estimates of obesity from the Behavioral Risk Factor Surveillance System were analyzed (n = 2300). Since local public health systems are nested within state infrastructure, multilevel models were used to examine the relationship between county-level obesity prevalence and LHD obesity prevention programming and to assess the impact of state-level clustering.

SETTING

Two thousand three hundred local health department jurisdictions defined with respect to county boundaries.

PARTICIPANTS

Practitioners in local health departments who responded to the 2005 Profile Study of Local Health Departments.

MAIN OUTCOME MEASURES

Likelihood of having obesity prevention activities and association with area-level obesity prevalence.

RESULTS

The existence of obesity prevention activities was not associated with the prevalence of obesity in the jurisdiction. A substantial portion of the variance in LHD activities was explained by state-level clustering.

CONCLUSIONS

This article identified a gap in the local public health response to the obesity epidemic and underscores the importance of multilevel modeling in examining predictors of LHD performance.

Predictors of obesity in Michigan Operating Engineers

Blue collar workers are at risk for obesity. Little is known about obesity in Operating Engineers, a group of blue collar workers, who operate heavy earth-moving equipment in road building and construction. Therefore, 498 Operating Engineers in Michigan were recruited to participate in a cross-sectional survey to determine variables related to obesity in this group. Bivariate and multivariate analyses were conducted to determine personal, psychological, and behavioral factors predicting obesity. Approximately 45% of the Operating Engineers screened positive for obesity, and another 40% were overweight. Multivariate analysis revealed that younger age, male sex, higher numbers of self-reported co-morbidities, not smoking, and low physical activity levels were significantly associated with obesity among Operating Engineers. Operating Engineers are significantly at risk for obesity, and workplace interventions are needed to address this problem.

Small area estimates reveal high cigarette smoking prevalence in low-income cities of Los Angeles county

Los Angeles County has among the lowest smoking rates of large urban counties in the USA. Nevertheless, concerning disparities persist as high smoking prevalence is found among certain subgroups. We calculated adult smoking prevalence in the incorporated cities of Los Angeles County in order to identify cities with high smoking prevalence. The prevalence was estimated by a model-based small area estimation method with utilization of three data sources, including the 2007 Los Angeles County Health Survey, the 2000 Census, and the 2007 Los Angeles County Population Estimates and Projection System. Smoking prevalence varied considerably across cities, with a more than fourfold difference between the lowest (5.3%) and the highest prevalence (21.7%). Higher smoking prevalence was generally found in socioeconomically disadvantaged cities. The disparities identified here add another layer of data to our knowledge of the health inequities experienced by low-income urban communities and provide much sought data for local tobacco control. Our study also demonstrates the feasibility of providing credible local estimates of smoking prevalence using the model-based small area estimation method.

[Regionalization of health indicators. Results from the GEDA-Study 2009]

The representative health surveys conducted by the Department of Epidemiology and Health Reporting weren't used before to provide estimates for the spatial distribution of health outcomes. We are discussing the possibilities of providing these outcomes using methods for 'Small-Area-Estimation'. In the study we are using data of the "German Health Update 2009" (GEDA) to analyze regional inequalities for self-assessed health status, smoking and obesity on the district level in Germany. The small area estimates are provided by multilevel logistic regression models using additional regional statistical data from the official INKAR 2009 database of regional indicators for Germany. We are mapping the results of our analysis for the district level (NUTS-3) using simple thematic maps. Afterwards we compared the results of our small area models with conventional estimates that were based on the official German small scale census. The results showed that our estimates are in line with the prevalences of the census. Overall the results suggest that Small-Area-Estimation methods have a big potential to provide regionalized health indicators for the health reporting in Germany.

Space, race, and poverty: Spatial inequalities in walkable neighborhood amenities?

BACKGROUND

Multiple and varied benefits have been suggested for increased neighborhood walkability. However, spatial inequalities in neighborhood walkability likely exist and may be attributable, in part, to residential segregation.

OBJECTIVE

Utilizing a spatial demographic perspective, we evaluated potential spatial inequalities in walkable neighborhood amenities across census tracts in Boston, MA (US).

METHODS

The independent variables included minority racial/ethnic population percentages and percent of families in poverty. Walkable neighborhood amenities were assessed with a composite measure. Spatial autocorrelation in key study variables were first calculated with the Global Moran’s I statistic. Then, Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were calculated as well as Spearman correlations accounting for spatial autocorrelation. We fit ordinary least squares (OLS) regression and spatial autoregressive models, when appropriate, as a final step.

RESULTS

Significant positive spatial autocorrelation was found in neighborhood socio-demographic characteristics (e.g. census tract percent Black), but not walkable neighborhood amenities or in the OLS regression residuals. Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were not statistically significant, nor were neighborhood socio-demographic characteristics significantly associated with walkable neighborhood amenities in OLS regression models.

CONCLUSIONS

Our results suggest that there is residential segregation in Boston and that spatial inequalities do not necessarily show up using a composite measure.

COMMENTS

Future research in other geographic areas (including international contexts) and using different definitions of neighborhoods (including small-area definitions) should evaluate if spatial inequalities are found using composite measures but also should use measures of specific neighborhood amenities.

Using small-area estimation method to calculate county-level prevalence of obesity in Mississippi, 2007-2009

INTRODUCTION

Obesity is one of Mississippi's pressing public health problems. Since 2005, the state has ranked first in the nation in adult obesity prevalence. For authorities to take targeted action against the obesity epidemic, counties, regions, and subpopulations that are most affected by obesity need to be identified. The objective of this study was to assess the scope, socioeconomic and geographic characteristics, and temporal trends of the obesity epidemic in Mississippi.

METHODS

Using 2007-2009 Mississippi Behavioral Risk Factor Surveillance System data and auxiliary data, we applied a small-area estimation method to estimate county-level obesity prevalence in 2007 through 2009, to assess the association between obesity and socioeconomic factors and to evaluate temporal trends. We determined geographic patterns by mapping obesity prevalence. We appraised the precision of estimates by the width of 95% confidence intervals, and we validated our small-area estimates by comparing them with direct estimates.

RESULTS

In 2009, the county prevalence of obesity ranged from 30.5% to 44.2%. Counties with the highest prevalence of obesity were in the Delta region and along the Mississippi River. The obesity prevalence increased from 2007 through 2009. Age, sex, race, education, and employment status were associated with obesity.

CONCLUSION

The 2009 obesity prevalence in all Mississippi counties was substantially higher than the national average and differed by geography and race. Although urgent intervention measures are needed in the entire state, policies and programs giving higher priority to higher-risk areas and subpopulations identified by this study may be better strategies.

Overweight and obesity in long-term breast cancer survivors: how does sexual orientation impact BMI?

In noncancer populations lesbians have greater odds of obesity compared with heterosexual women, suggesting a similar pattern among cancer survivors. Weight of cancer survivors is an important area of study because obesity is associated with an increased risk of recurrence and shorter survival. Sixty-nine lesbian and bisexual and 257 heterosexual survivors of breast cancer were recruited to participate in a one-time telephone survey. Multinomial logit models do not support disparities in obesity due to sexual orientation. Our findings in breast cancer survivors suggest that lesbians are more likely to improve their weight-related behaviors after cancer than heterosexual women.

Data and measurement issues in the analysis of health disparities

OBJECTIVE

To describe measurement challenges and strategies in identifying and analyzing health disparities and inequities.

METHODS

We discuss the limitations of existing data sources for measuring health disparities and inequities, describe current strategies to address those limitations, and explore the potential of emerging strategies.

PRINCIPAL FINDINGS

Larger national sample sizes are necessary to identify disparities for major population subgroups. Collecting self-reported race and granular ethnicity data may reduce some measurement errors, but it raises other methodological questions. The assessment of health inequities presents particular challenges, requiring analysis of the interactive effects of multiple determinants of health. Indirect estimation and modeling methods are likely to be important tools for estimating health disparities and inequities for the foreseeable future.

CONCLUSIONS

Interdisciplinary training and collaborative research models will be essential for future disparities research. Evaluation of evolving methodologies for assessing health disparities should be a priority for health services researchers in the next decade.