A step-by-step approach to improve data quality when using commercial business lists to characterize retail food environments

Designing and Evaluating a Hierarchical Framework for Matching Food Outlets across Multi-sourced Geospatial Datasets: a Case Study of San Diego County

Research on retail food environment (RFE) relies on data availability and accuracy. However, the discrepancies in RFE datasets may lead to imprecision when measuring association with health outcomes. In this research, we present a two-tier hierarchical point of interest (POI) matching framework to compare and triangulate food outlets across multiple geospatial data sources. Two matching parameters were used including the geodesic distance between businesses and the similarity of business names according to Levenshtein distance (LD) and Double Metaphone (DM). Sensitivity analysis was conducted to determine thresholds of matching parameters. Our Tier 1 matching used more restricted parameters to generate high confidence-matched POIs, whereas in Tier 2 we opted for relaxed matching parameters and applied a weighted multi-attribute model on the previously unmatched records. Our case study in San Diego County, California used government, commercial, and crowdsourced data and returned 20.2% matched records from Tier 1 and 18.6% matched from Tier 2. Our manual validation shows a 100% matching rate for Tier 1 and up to 30.6% for Tier 2. Matched and unmatched records from Tier 1 were further analyzed for spatial patterns and categorical differences. Our hierarchical POI matching framework generated highly confident food POIs by conflating datasets and identified some food POIs that are unique to specific data sources. Triangulating RFE data can reduce uncertain and invalid POI listings when representing food environment using multiple data sources. Studies investigating associations between food environment and health outcomes may benefit from improved quality of RFE.

Concepts, Characterizations, and Cautions: A Public Health Guide and Glossary for Planning Food Environment Measurement

Background

There is no singular approach to measuring the food environment suitable for all studies. Understanding terminology, methodology, and common issues is crucial to choosing the best approach.

Objective

This review is designed to support a shared understanding so diverse multi-institutional teams engaged in food environment measurement can justify their measurement choices and have informed discussions about reasons for measurement strategies to vary across projects.

Methods

This guide defines key terms and provides annotated resources identified as a useful starting point for exploring the food environment literature. The writing team was an academic-practice collaboration, reflecting on the experience of a multi-institutional team focused on retail environments across the US relevant to cardiovascular disease.

Results

Terms and annotated resources are divided into three sections: food environment constructs, classification and measures, and errors and strategies to reduce error. Two examples of methods and challenges encountered while measuring the food environment in the context of a US health department are provided. Researchers and practice professionals are directed to the Food Environment Electronic Database Directory (https://www.foodenvironmentdirectory.com/) for comparing available data resources for food environment measurement, focused on the US; this resource incorporates updates informed by user input and literature reviews.

Discussion

Measuring the food environment is complex and risks oversimplification. This guide serves as a starting point but only partially captures some aspects of neighborhood food environment measurement.

Conclusions

No single food environment measure or data source meets all research and practice objectives. This shared starting point can facilitate theoretically grounded food environment measurement.

Impact of the built, social, and food environment on long-term weight loss within a behavioral weight loss intervention

Background

Behavioral weight loss interventions can lead to an average weight loss of 5%-10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss.

Objective

Evaluate the association between built, social, and community food environments and changes in weight, moderate-to-vigorous physical activity (MVPA), and dietary intake among adults who completed an 18-month behavioral weight loss intervention.

Methods

Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m2, 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited-service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3-day diet records) from baseline to 18 months.

Results

Grocery store density was inversely associated with change in weight (β = -0.95; p = 0.02; R 2 = 0.062) and WC (β = -1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps (β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited-service restaurant density was associated with change in % protein intake (β = 0.39; p = 0.046; R 2 = 0.051).

Conclusion

Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

The changing food environment and neighborhood prevalence of type 2 diabetes

In this ecological study, we used longitudinal data to assess if changes in neighborhood food environments were associated with type 2 diabetes mellitus (T2DM) prevalence, controlling for a host of neighborhood characteristics and spatial error correlation. We found that the population-adjusted prevalence of fast-food and pizza restaurants, grocery stores, and full-service restaurants along with changes in their numbers from 1990 to 2010 were associated with 2015 T2DM prevalence. The results suggested that neighborhoods where fast-food restaurants have increased and neighborhoods where full-service restaurants have decreased over time may be especially important targets for educational campaigns or other public health-related T2DM interventions.

Participatory mapping to address neighborhood level data deficiencies for food security assessment in Southeastern Virginia, USA

Background

Food is not equitably available. Deficiencies and generalizations limit national datasets, food security assessments, and interventions. Additional neighborhood level studies are needed to develop a scalable and transferable process to complement national and internationally comparative data sets with timely, granular, nuanced data. Participatory geographic information systems (PGIS) offer a means to address these issues by digitizing local knowledge.

Methods

The objectives of this study were two-fold: (i) identify granular locations missing from food source and risk datasets and (ii) examine the relation between the spatial, socio-economic, and agency contributors to food security. Twenty-nine subject matter experts from three cities in Southeastern Virginia with backgrounds in food distribution, nutrition management, human services, and associated research engaged in a participatory mapping process.

Results

Results show that publicly available and other national datasets are not inclusive of non-traditional food sources or updated frequently enough to reflect changes associated with closures, expansion, or new programs. Almost 6 percent of food sources were missing from publicly available and national datasets. Food pantries, community gardens and fridges, farmers markets, child and adult care programs, and meals served in community centers and homeless shelters were not well represented. Over 24 km2 of participant identified need was outside United States Department of Agriculture low income, low access areas. Economic, physical, and social barriers to food security were interconnected with transportation limitations. Recommendations address an international call from development agencies, countries, and world regions for intervention methods that include systemic and generational issues with poverty, incorporate non-traditional spaces into food distribution systems, incentivize or regulate healthy food options in stores, improve educational opportunities, increase data sharing.

Conclusions

Leveraging city and regional agency as appropriate to capitalize upon synergistic activities was seen as critical to achieve these goals, particularly for non-traditional partnership building. To address neighborhood scale food security needs in Southeastern Virginia, data collection and assessment should address both environment and utilization issues from consumer and producer perspectives including availability, proximity, accessibility, awareness, affordability, cooking capacity, and preference. The PGIS process utilized to facilitate information sharing about neighborhood level contributors to food insecurity and translate those contributors to intervention strategies through discussion with local subject matter experts and contextualization within larger scale food systems dynamics is transferable.

Active living-oriented zoning codes and cardiometabolic conditions across the lifespan

Environments that make it easier for people to incorporate physical activity into their daily life may help to reduce high rates of cardiometabolic conditions. Local zoning codes are a policy and planning tool to create more walkable and bikeable environments. This study evaluated relationships between active living-oriented zoning code environments and cardiometabolic conditions (body mass index, hyperlipidemia, hypertension). The study used county identifiers to link electronic health record and other administrative data for a sample of patients utilizing primary care services between 2012 and 2016 with county-aggregated zoning code data and built environment data. The analytic sample included 7,441,991 patients living in 292 counties in 44 states. Latent class analysis was used to summarize municipal- and unincorporated county-level data on seven zoning provisions (e.g., sidewalks, trails, street connectivity, mixed land use), resulting in classes that differed in strength of the zoning provisions. Based on the probability of class membership, counties were categorized as one of four classes. Linear and logistic regression models estimated cross-sectional associations with each cardiometabolic condition. Models were fit separately for youth (aged 5-19), adults (aged 20-59), and older adults (aged 60+). Little evidence was found that body mass index in youth, adults, or older adults or the odds of hyperlipidemia or hypertension in adults or older adults differed according to the strength of active living-oriented zoning. More research is needed to identify the health impacts of zoning codes and whether alterations to these codes would improve population health over the long term.

Nonlinear relationship between nonresidential destinations and body mass index across a wide range of development

BACKGROUND

Destination accessibility is an important measure of the built environment that is associated with active transport and body mass index (BMI). In higher density settings, an inverse association has been consistently found, but in lower density settings, findings are limited. We previously found a positive relationship between the density of nonresidential destinations (NRD) and BMI in a low-density state. We sought to test the generalizability of this unexpected finding using data from six other states that include a broader range of settlement densities.

METHODS

We obtained the address, height, and weight of 16.9 million residents with a driver's license or state identification cards, as well as the location of 3.8 million NRDs in Washington, Oregon, Texas, Illinois, Michigan, and Maine from Dun & Bradstreet. We tested the association between NRDs∙ha^-1 within 1 km of the home address, and self-reported BMI (kg∙m^-2). Visualization by locally-weighted smoothing curves (LOWESS) revealed an inverted U-shape. A multivariable piecewise regression with a random intercept for state was used to assess the relationship.

RESULTS

After accounting for age, sex, year of issue, and census tract social and economic variables, BMI correlated positively with NRDs in the low-to-mid density stratum (β = +0.005 kg∙m^-2/nonresidential building∙ha^-1; 95% CI: +0.004,+0.006) and negatively in the mid-to-high density stratum (β = -0.002; 95% CI: -0.004,-0.0003); a significant difference in slopes (P < 0.001).

CONCLUSIONS

BMI peaked in the middle density, with lower values in both the low and high-density extremes. These results suggest that the mechanisms by which NRDs are associated with obesity may differ by density level.

Business Data Categorization and Refinement for Application in Longitudinal Neighborhood Health Research: a Methodology

Retail environments, such as healthcare locations, food stores, and recreation facilities, may be relevant to many health behaviors and outcomes. However, minimal guidance on how to collect, process, aggregate, and link these data results in inconsistent or incomplete measurement that can introduce misclassification bias and limit replication of existing research. We describe the following steps to leverage business data for longitudinal neighborhood health research: re-geolocating establishment addresses, preliminary classification using standard industrial codes, systematic checks to refine classifications, incorporation and integration of complementary data sources, documentation of a flexible hierarchical classification system and variable naming conventions, and linking to neighborhoods and participant residences. We show results of this classification from a dataset of locations (over 77 million establishment locations) across the contiguous U.S. from 1990 to 2014. By incorporating complementary data sources, through manual spot checks in Google StreetView and word and name searches, we enhanced a basic classification using only standard industrial codes. Ultimately, providing these enhanced longitudinal data and supplying detailed methods for researchers to replicate our work promotes consistency, replicability, and new opportunities in neighborhood health research.

An activity space approach to understanding how food access is associated with dietary intake and BMI among urban, low-income African American women

Inconclusive evidence for how food environments affect health may result from an emphasis on residential neighborhood-based measures of exposure. We used an activity space approach to examine whether 1) measures of food access and 2) associations with diet and BMI differ between residential and activity space food environments among low-income African American women in Atlanta, Georgia (n = 199). Although residential and activity space environments differed across all dimensions of food access, being located farther away from 'unhealthy' outlets was associated with lower BMI in both environments. Future research should move beyond asking whether residential and activity space environments differ, toward examining if, how, and under what conditions these differences impact the estimation of health effects.

Validation of a province-wide commercial food store dataset in a heterogeneous predominantly rural food environment

OBJECTIVE

Commercially available business (CAB) datasets for food environments have been investigated for error in large urban contexts and some rural areas, but there is a relative dearth of literature that reports error across regions of variable rurality. The objective of the current study was to assess the validity of a CAB dataset using a government dataset at the provincial scale.

DESIGN

A ground-truthed dataset provided by the government of Newfoundland and Labrador (NL) was used to assess a popular commercial dataset. Concordance, sensitivity, positive-predictive value (PPV) and geocoding errors were calculated. Measures were stratified by store types and rurality to investigate any association between these variables and database accuracy.

SETTING

NL, Canada.

PARTICIPANTS

The current analysis used store-level (ecological) data.

RESULTS

Of 1125 stores, there were 380 stores that existed in both datasets and were considered true-positive stores. The mean positional error between a ground-truthed and test point was 17·72 km. When compared with the provincial dataset of businesses, grocery stores had the greatest agreement, sensitivity = 0·64, PPV = 0·60 and concordance = 0·45. Gas stations had the least agreement, sensitivity = 0·26, PPV = 0·32 and concordance = 0·17. Only 4 % of commercial data points in rural areas matched every criterion examined.

CONCLUSIONS

The commercial dataset exhibits a low level of agreement with the ground-truthed provincial data. Particularly retailers in rural areas or belonging to the gas station category suffered from misclassification and/or geocoding errors. Taken together, the commercial dataset is differentially representative of the ground-truthed reality based on store-type and rurality/urbanity.

GIS-Based Home Neighborhood Food Outlet Counts, Street Connectivity, and Frequency of Use of Neighborhood Restaurants and Food Stores

Researchers have linked neighborhood food availability to the overall frequency of using food outlets without noting if those outlets were within or outside of participants' neighborhoods. We aimed to examine the association of neighborhood restaurant and food store availability with frequency of use of neighborhood food outlets, and whether such an association was modified by neighborhood street connectivity using a large and diverse population-based cohort of middle-aged U.S. adults. We used self-reported frequency of use of fast food restaurants, sit-down restaurants, and grocery stores in respondents' home neighborhoods using data from the Coronary Artery Risk Development in Young Adults study Year 20 exam in 2005-2006 (n = 2860; Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA) and geographically matched GIS-measured neighborhood-level food resource, street, and U.S. Census data. We used mixed-effects logistic regression to examine the associations of the GIS-measured count of neighborhood fast food restaurants, sit-down restaurants, and grocery stores with self-reported frequency of using neighborhood restaurants and food stores and whether such associations differed by GIS-measured neighborhood street connectivity among those who perceived at least one such food outlet. In multivariate analyses, we observed a positive association between the GIS-measured count of neighborhood sit-down restaurants (OR = 1.02, 95% CI 1.00-1.04) and the self-reported frequency of using neighborhood sit-down restaurants. We observed no statistically significant association between GIS-measured count of neighborhood fast food restaurants and self-reported frequency of using neighborhood fast food restaurants, nor did we observe a statistically significant association between GIS-measured count of neighborhood grocery stores and self-reported frequency of using neighborhood grocery stores. We observed inverse associations between GIS-measured neighborhood street connectivity and the self-reported frequencies of using neighborhood fast food restaurants (OR = 0.42, 95% CI 0.26-0.68) and grocery stores (OR = - 2.26, 95% CI - 4.52 to - 0.01). Neighborhood street connectivity did not modify the association between GIS-measured neighborhood restaurant and food store count and the self-reported frequency of using neighborhood restaurants and food stores. Our findings suggest that, for those who perceived at least one sit-down restaurant in their neighborhood, individuals who have more GIS-measured sit-down restaurants in their neighborhoods reported more frequent use of sit-down restaurants than those whose neighborhoods contain fewer such restaurants. Our results also suggest that, for those who perceived at least one fast food restaurant in their neighborhood, individuals who live in neighborhoods with greater GIS-measured street connectivity reported less use of neighborhood fast food restaurants than those who live in neighborhoods with less street connectivity. The count of neighborhood sit-down restaurants and the connectivity of neighborhood street networks appear important in understanding the use of neighborhood food resources.

Associations between food environment typologies and body mass index: Evidence from Yorkshire, England

International research linking food outlets and body mass index (BMI) is largely cross-sectional, yielding inconsistent findings. However, addressing the exposure of food outlets is increasingly considered as an important adult obesity prevention strategy. Our study investigates associations between baseline food environment types and change in BMI over time. Survey data were used from the Yorkshire Health Study (n=8,864; wave one: 2010-2012, wave two: 2013-2015) for adults aged 18-86. BMI was calculated using self-reported height (cm) and weight (kg). Restaurants, cafés, fast-food, speciality, convenience and large supermarkets were identified from the Ordnance Survey Point of Interest database within 1600m radial buffer of home postcodes. K-means cluster analysis developed food environment typologies based on food outlets and population density. Large supermarkets, restaurants, cafés, fast-food, speciality and convenience food outlets all clustered together to some extent. Three neighbourhood typologies were identified. However, multilevel models revealed that relative to cluster one all were unrelated to change in BMI (cluster 2, b= -0.146 [-0.274, 0.566]; cluster 3, b= 0.065 [-0.224, 0.356]). There was also little evidence of gender-based differences in these associations when examined in a three-way interaction. Policymakers may need to begin to consider multiple types of food outlet clusters, while further research is needed to confirm how these relate to changed BMI.

Stability of activity space footprint, size, and environmental features over six months

As activity space measures are increasingly used to estimate exposure to environmental determinants of health, little is known about the stability of these measures over time. To test the stability of GPS-derived measures of activity-space footprint, size, and environmental features over time, we compared 14-day measures at baseline and six months later for 35 adults in a large city. Activity-space measures were based on convex hulls and 500 m route buffers, and included the geographic footprint (i.e. location of the activity space), size (i.e., area in square miles; (Cummins, 2007)), and environmental features including supermarket, fast-food restaurant, and parkland density. The proportion of the participants' smaller geographic footprint covered by the larger was, on average, 0.64 (SD 0.17) for the 500 m route buffer and 0.84 (SD 0.18) for the convex hull. Mean percent change in activity space size ranged from 36.3% (mean daily 500 m route buffer) to 221.3% (cumulative convex hull). Mean percent change in the density of environmental features ranged from 28.8 to 66.5%. Forty-one percent to 92.4% of the variance at one timepoint was predicted by environmental features measured within approximately six months. Activity-space size and environmental features were moderately to highly stable over six months, although there was considerable variation in stability between measures. Strategies for addressing measurement error in studies of activity space-health associations are discussed.

Would increasing access to recreational places promote healthier weights and a healthier nation?

Addressing gaps in evidence on causal associations, this study tested the hypothesis that better access to recreational places close to home helps people to maintain lower body mass index (BMI) using a retrospective longitudinal study design and up to 6 years of data for the same individuals (1,522,803 men and 183,618 women). Participants were military veterans aged 20-64 who received healthcare through the U.S. Department of Veterans Affairs in 2009-2014 and lived in a metropolitan area. Although there were cross-sectional associations, we found no longitudinal evidence that access to parks and fitness facilities was associated with BMI for either men or women in the full sample or in subgroups of residential movers and stayers. Our findings suggest that simply increasing the number of parks and fitness facilities may not be enough to achieve needed population-level reductions in weight.

Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context

In the health industry, the use of data (including Big Data) is of growing importance. The term ‘Big Data’ characterizes data by its volume, and also by its velocity, variety, and veracity. Big Data needs to have effective data governance, which includes measures to manage and control the use of data and to enhance data quality, availability, and integrity. The type and description of data quality can be expressed in terms of the dimensions of data quality. Well-known dimensions are accuracy, completeness, and consistency, amongst others. Since data quality depends on how the data is expected to be used, the most important data quality dimensions depend on the context of use and industry needs. There is a lack of current research focusing on data quality dimensions for Big Data within the health industry; this paper, therefore, investigates the most important data quality dimensions for Big Data within this context. An inner hermeneutic cycle research approach was used to review relevant literature related to data quality for big health datasets in a systematic way and to produce a list of the most important data quality dimensions. Based on a hierarchical framework for organizing data quality dimensions, the highest ranked category of dimensions was determined.

Contextual correlates of energy-dense snack food and sweetened beverage intake across the day in African American women: An application of ecological momentary assessment

This study examined relationships between contextual factors and within-person variations in snack food and sweetened beverage intake in African American women (n = 79), aged 25-65 years living in metropolitan Chicago. For seven days, participants wore a global positioning system (GPS) logger and were signaled five times per day to complete an ecological momentary assessment (EMA) survey assessing behaviors and environmental, social, and other contextual factors via smartphones. Within-person associations between snack food and beverage intake and contextual factors were analyzed using three-level logistic regressions. Participants reported consuming a snack food at 38.4% of signals and a sweetened beverage at 17.9% of signals. Fast food restaurant and convenience store density within the daily activity space was not associated with either snack food or sweetened beverage intake. However, perceptions of close proximity to fast food restaurants and convenience stores making it easier to eat/drink, while accounting for one's usual proximity, were associated with increased odds of snack intake (O.R. 2.1; 95% C.I. 1.4, 3.0) but not sweetened beverage. We also found engaging in activities such as watching television (O.R. 1.8; 95% C.I. 1.2, 2.7) and talking (O.R. 1.7; 95% C.I. 1.1, 2.6) while eating were associated with higher snack intake. These factors were not related to sweetened beverage intake. Public health interventions addressing fast food restaurant and convenience store accessibility and food offerings and marketing within these outlets may help reduce snack food intake. Additionally, to reduce concurrent activities while eating, real-time interventions using smart technology could be used to enhance attentive eating in this population.

Geographic Accessibility Of Food Outlets Not Associated With Body Mass Index Change Among Veterans, 2009-14

In recent years, various levels of government in the United States have adopted or discussed subsidies, tax breaks, zoning laws, and other public policies that promote geographic access to healthy food. However, there is little evidence from large-scale longitudinal or quasi-experimental research to suggest that the local mix of food outlets actually affects body mass index (BMI). We used a longitudinal design to examine whether the proximity of food outlets, by type, was associated with BMI changes between 2009 and 2014 among 1.7 million veterans in 382 metropolitan areas. We found no evidence that either absolute or relative geographic accessibility of supermarkets, fast-food restaurants, or mass merchandisers was associated with changes in an individual's BMI over time. While policies that alter only geographic access to food outlets may promote equitable access to healthy food and improve nutrition, our findings suggest they will do little to combat obesity in adults.

Methods for Evaluating Natural Experiments in Obesity: A Systematic Review

BACKGROUND

Given the obesity pandemic, rigorous methodological approaches, including natural experiments, are needed.

PURPOSE

To identify studies that report effects of programs, policies, or built environment changes on obesity prevention and control and to describe their methods.

DATA SOURCES

PubMed, CINAHL, PsycINFO, and EconLit (January 2000 to August 2017).

STUDY SELECTION

Natural experiments and experimental studies evaluating a program, policy, or built environment change in U.S. or non-U.S. populations by using measures of obesity or obesity-related health behaviors.

DATA EXTRACTION

2 reviewers serially extracted data on study design, population characteristics, data sources and linkages, measures, and analytic methods and independently evaluated risk of bias.

DATA SYNTHESIS

294 studies (188 U.S., 106 non-U.S.) were identified, including 156 natural experiments (53%), 118 experimental studies (40%), and 20 (7%) with unclear study design. Studies used 106 (71 U.S., 35 non-U.S.) data systems; 37% of the U.S. data systems were linked to another data source. For outcomes, 112 studies reported childhood weight and 32 adult weight; 152 had physical activity and 148 had dietary measures. For analysis, natural experiments most commonly used cross-sectional comparisons of exposed and unexposed groups (n = 55 [35%]). Most natural experiments had a high risk of bias, and 63% had weak handling of withdrawals and dropouts.

LIMITATION

Outcomes restricted to obesity measures and health behaviors; inconsistent or unclear descriptions of natural experiment designs; and imperfect methods for assessing risk of bias in natural experiments.

CONCLUSION

Many methodologically diverse natural experiments and experimental studies were identified that reported effects of U.S. and non-U.S. programs, policies, or built environment changes on obesity prevention and control. The findings reinforce the need for methodological and analytic advances that would strengthen evaluations of obesity prevention and control initiatives.

PRIMARY FUNDING SOURCE

National Institutes of Health, Office of Disease Prevention, and Agency for Healthcare Research and Quality. (PROSPERO: CRD42017055750).

Long-Term Weight Loss Effects of a Behavioral Weight Management Program: Does the Community Food Environment Matter?

This study examined whether community food environments altered the longer-term effects of a nationwide behavioral weight management program on body mass index (BMI). The sample was comprised of 98,871 male weight management program participants and 15,385 female participants, as well as 461,302 and 37,192 inverse propensity-score weighted matched male and female controls. We measured the community food environment by counting the number of supermarkets, convenience stores, and fast food restaurants within a 1-mile radius around each person's home address. We used difference-in-difference regression models with person and calendar time fixed effects to estimate MOVE! effects over time in sub-populations defined by community food environment attributes. Among men, after an initial decrease in BMI at 6 months, the effect of the program decreased over time, with BMI increasing incrementally at 12 months (0.098 kg/m², p < 0.001), 18 months (0.069 kg/m², p < 0.001), and 24 months (0.067 kg/m², p < 0.001). Among women, the initial effects of the program decreased over time as well. Women had an incremental BMI change of 0.099 kg/m² at 12 months (p < 0.05) with non-significant incremental changes at 18 months and 24 months. We found little evidence that these longer-term effects of the weight management program differed depending on the community food environment. Physiological adaptations may overwhelm environmental influences on adherence to behavioral regimens in affecting longer-term weight loss outcomes.

Neighborhood Resources to Support Healthy Diets and Physical Activity Among US Military Veterans

Introduction

Among the nearly 21 million military veterans living in the United States, 64.0% of women and 76.1% of men are overweight or obese, higher rates than in the civilian population (56.9% of women and 69.9% of men). Attributes of the residential environment are linked to obesity. The objective of this study was to characterize the residential environments of the US veteran population with respect to availability of food and recreational venues.

Methods

We used American Community Survey data to determine the concentration of veterans (the percentage of veterans among the adult population) in all continental US census tracts in 2013, and we used proprietary data to construct measures of availability of food and recreational venues per census tract. Using descriptive statistics and ordinary least-squares regression, we examined associations between the concentration of veterans per census tract and those residential environmental features.

Results

In census tracts with high concentrations of veterans, residents had, on average, 0.5 (interquartile range, 0–0.8) supermarkets within a 1-mile radius, while residents in census tracts with low concentrations of veterans had 3.2 (interquartile range, 0.6–3.7) supermarkets. Patterns were similar for grocery and convenience stores, fast food restaurants, parks, and commercial fitness facilities. In adjusted analyses controlling for census-tract–level covariates, veteran concentration remained strongly negatively associated with availability of those food and recreational venues. In nonmetropolitan tracts, adjusted associations were greatly attenuated and even positive.

Conclusion

Where veterans live is strongly associated with availability of food outlets providing healthy (and unhealthy) foods and with recreational venues, raising questions about the contributions of veterans’ residential environments to their high obesity rates. Additional research is needed to address those questions.

Assessing the validity of commercial and municipal food environment data sets in Vancouver, Canada

OBJECTIVE

The present study assessed systematic bias and the effects of data set error on the validity of food environment measures in two municipal and two commercial secondary data sets.

DESIGN

Sensitivity, positive predictive value (PPV) and concordance were calculated by comparing two municipal and two commercial secondary data sets with ground-truthed data collected within 800 m buffers surrounding twenty-six schools. Logistic regression examined associations of sensitivity and PPV with commercial density and neighbourhood socio-economic deprivation. Kendall's τ estimated correlations between density and proximity of food outlets near schools constructed with secondary data sets v. ground-truthed data.

SETTING

Vancouver, Canada.

SUBJECTS

Food retailers located within 800 m of twenty-six schools RESULTS: All data sets scored relatively poorly across validity measures, although, overall, municipal data sets had higher levels of validity than did commercial data sets. Food outlets were more likely to be missing from municipal health inspections lists and commercial data sets in neighbourhoods with higher commercial density. Still, both proximity and density measures constructed from all secondary data sets were highly correlated (Kendall's τ>0·70) with measures constructed from ground-truthed data.

CONCLUSIONS

Despite relatively low levels of validity in all secondary data sets examined, food environment measures constructed from secondary data sets remained highly correlated with ground-truthed data. Findings suggest that secondary data sets can be used to measure the food environment, although estimates should be treated with caution in areas with high commercial density.