Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study

Residential Density Is Associated With BMI Trajectories in Children and Adolescents: Findings From the Moving to Health Study

Introduction

This study investigates the associations between built environment features and 3-year BMI trajectories in children and adolescents.

Methods

This retrospective cohort study utilized electronic health records of individuals aged 5-18 years living in King County, Washington, from 2005 to 2017. Built environment features such as residential density; counts of supermarkets, fast-food restaurants, and parks; and park area were measured using SmartMaps at 1,600-meter buffers. Linear mixed-effects models performed in 2022 tested whether built environment variables at baseline were associated with BMI change within age cohorts (5, 9, and 13 years), adjusting for sex, age, race/ethnicity, Medicaid, BMI, and residential property values (SES measure).

Results

At 3-year follow-up, higher residential density was associated with lower BMI increase for girls across all age cohorts and for boys in age cohorts of 5 and 13 years but not for the age cohort of 9 years. Presence of fast food was associated with higher BMI increase for boys in the age cohort of 5 years and for girls in the age cohort of 9 years. There were no significant associations between BMI change and counts of parks, and park area was only significantly associated with BMI change among boys in the age cohort of 5 years.

Conclusions

Higher residential density was associated with lower BMI increase in children and adolescents. The effect was small but may accumulate over the life course. Built environment factors have limited independent impact on 3-year BMI trajectories in children and adolescents.

Neighborhood built and food environment in relation to glycemic control in people with type 2 diabetes in the moving to health study

OBJECTIVE

To examine whether built environment and food metrics are associated with glycemic control in people with type 2 diabetes.

RESEARCH DESIGN AND METHODS

We included 14,985 patients with type 2 diabetes using electronic health records from Kaiser Permanente Washington. Patient addresses were geocoded with ArcGIS using King County and Esri reference data. Built environment exposures estimated from geocoded locations included residential unit density, transit threshold residential unit density, park access, and having supermarkets and fast food restaurants within 1600-m Euclidean buffers. Linear mixed effects models compared mean changes of HbA1c from baseline at 1, 3 (primary) and 5 years by each built environment variable.

RESULTS

Patients (mean age = 59.4 SD = 13.2, 49.5% female, 16.6% Asian, 9.8% Black, 5.5% Latino/Hispanic, 57.1% White, 20% insulin dependent, mean BMI = 32.7±7.7) had an average of 6 HbA1c measures available. Participants in the 1st tertile of residential density (lowest) had a greater decline in HbA1c (-0.42, -0.43, and -0.44 in years 1, 3, and 5 respectively) than those in the 3rd tertile (HbA1c = -0.37 at 1- and 3-years and -0.36 at 5-years; all p-values <0.05). Having any supermarkets within 1600 m of home was associated with a greater decrease in HbA1c at 1-year and 3-years compared to having none (all p-values <0.05).

CONCLUSIONS

Lower residential density and better proximity to supermarkets may benefit HbA1c control in people with people with type 2 diabetes. However, effects were small and indicate limited clinical significance.

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

BACKGROUND

Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight.

METHODS

Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight.

RESULTS

In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]).

CONCLUSIONS

Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III

Objective

To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1- and 2-year changes in body mass index (BMI).

Methods

The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1–3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education.

Results

Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity.

Conclusion

Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1- and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

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Strong, inverse cross-sectional relationships between the built environment, residential property values (a proxy for individual socioeconomic status), and measured BMI were observed.

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Measures of the built environment and residential property values showed modest and inconsistent associations with 1- and 2-year BMI change.

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There was suggestive evidence that age may moderate the association between urban density and 1- and 2-year BMI change while education may moderate the association between residential property values and 2-year BMI change.

Differential associations of the built environment on weight gain by sex and race/ethnicity but not age

OBJECTIVE

To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults.

METHODS

Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values.

RESULTS

Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures.

CONCLUSION

The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

From the clinic to the community: Can health system data accurately estimate population obesity prevalence?

OBJECTIVE

Health system data were assessed for how well they can estimate obesity prevalence in census tracts.

METHODS

Clinical visit data were available from two large health systems (Kaiser Permanente Washington and University of Washington Medicine) in King County, Washington, as were census tract-level obesity prevalence estimates from the Behavioral Risk Factor Surveillance System (BRFSS). The health system data were geocoded to identify each patient's tract of residence, and the cross-sectional concordance between census tract-level obesity prevalence estimates computed from the two health systems in 2005 to 2006 and the concordance between University of Washington Medicine and BRFSS from 2012 to 2016 were assessed.

RESULTS

The spatial distribution of obesity was similar between the health systems (Spearman r = 0.63). The University of Washington Medicine estimates of rank order correlated well with BRFSS estimates (Spearman r = 0.85), though prevalence estimates from BRFSS were lower (mean obesity prevalence = 26% for University of Washington Medicine versus 20% for BRFSS, Wilcoxon rank sum test p < 0.001). Across all data sources, obesity was more prevalent in tracts with less educational attainment.

CONCLUSIONS

Health system clinical weight data can reliably replicate census tract-level spatial patterns in the ranking of obesity prevalence. Health system data may be an efficient resource for geographic obesity surveillance.

Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain

OBJECTIVE

To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults.

METHODS

Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values.

RESULTS

Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg.

CONCLUSIONS

Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.