Exploring non-linear relationships between neighbourhood walkability and health: a cross-sectional study among US primary care patients with chronic conditions.

BACKGROUND: A recent study of licensed drivers found a non-linear relationship between density of non-residential destinations (NRDs), a proxy for walkability and body mass index (BMI) across a wide range of development patterns. It is unclear if this relationship can be replicated in a population with multiple chronic conditions or translated to health outcomes other than BMI. METHODS: We obtained health data and home addresses for 2405 adults with multiple chronic conditions from 44 primary care clinics across 13 states using the Integrating Behavioral health and Primary Care Trial. In this cross-sectional study, the relationships between density of NRDs (from a commercial database) within 1 km of the home address and self-reported BMI, and mental and physical health indices were assessed using several non-linear methods, including restricted cubic splines, LOWESS smoothing curves, non-parametric regression with a spline basis and piecewise linear regression. RESULTS: All methods demonstrated similar non-linear relationships. Piecewise linear regression was selected for ease of interpretation. BMI had a positive marginal rate of change below the NRD density inflection point of 15 establishments/hectare (ß=+0.09 kg/m2/non-residential buildings ha-1; 95% CI +0.01 to +0.14), and a negative marginal rate of change above the inflection point (ß=-0.02; 95% CI -0.06 to 0.02). Mental health decreased with NRD density below the inflection point (ß=-0.24; 95% CI -0.31 to -0.17) and increased above it (ß=+0.03; 95% CI -0.00 to +0.07). Results were similar for physical health (ß= -0.28; 95% CI -0.35 to -0.20) and (ß=+0.06; 95% CI 0.01 to +0.10). CONCLUSION: Health indicators were the lowest in middle density (typically suburban) areas and got progressively better moving in either direction from the peak. NRDs may affect health differently depending on home-address NRD density. TRIAL REGISTRATION NUMBER: NCT02868983.

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 (ß = +0.005 kg∙m-2/nonresidential building∙ha-1; 95% CI: +0.004,+0.006) and negatively in the mid-to-high density stratum (ß = -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.

Relationship Between the Built Environment and Body Mass Index in a Rural Context: A Cross-Sectional Study from Vermont.

Objective To evaluate the association between a marker of urban development (commercial building density) and body mass index (BMI) in a predominantly rural context. Methods A cross-sectional analysis of two geocoded datasets from Vermont. The first includes subjects from the Vermont Diabetes Information System (VDIS), an extensively attributed dataset of adult diabetics (n = 610); the second was the complete driver's license records for Vermont (n = 401,367). The dependent variable was BMI, measured objectively for the VDIS data and self-reported for the driver's license data. The explanatory variable was commercial buildings per hectare within 250 m of the home address used as a proxy for walkability. We regressed BMI against density in both datasets, controlling for age and gender; a separate regression was run for the VDIS data, controlling for a number of additional confounders related to health, activity, diet, and income. Results All models demonstrated a significant positive relationship between BMI and commercial building density. For the three VDIS data models, coefficients of density were +0.75, +0.79, and +0.90, all of which indicate an approximate ¾ kg/m2 increase in BMI for each additional commercial facility per hectare (p < 0.01). For the driver's license data, the coefficient was +0.16, which also indicates an increase in BMI with increasing density (p < 0.01). Discussion We found that BMI displays a positive association with commercial building density in Vermont, which is inconsistent with previous findings. The difference may be due to the unique rural focus of this study. Other characteristics of rural life may be associated with lower incidence of obesity and should be studied further.

The Relationship Between Access to Natural Environmental Amenities and Obesity.

Background Various aspects of the environment are correlated with obesity. Most of the previous work in this area centers on the built environment. We sought to better understand the association of the natural environment with obesity. Methods We used the Natural Amenities Scale to characterize the attractiveness of 2,545 US counties based on access to open water, varied topography, and mild climate. We obtained the height, weight, age, sex, and address of adults from three different sources. The Departments of Motor Vehicles from seven US states provided over 38 million records. A web survey contributed 3,012 from 48 states and the District of Columbia. A clinical study of adults with diabetes from four states provided 974 more for a total of 38,159,046 analyzable records. We used logistic regression to model the association of obesity with natural amenities while controlling for age, sex, year of data collection, and various socioeconomic characteristics of the county. Results Natural amenities were inversely associated with obesity in all three populations. Over 20% of residents of low amenity areas were obese, but less than 10% of those living with the best natural amenities were obese. Conclusions The natural environment may affect health. Residing in areas with access to open water and a variety of topographic features as well as cool, dry summers and warm, sunny winters is associated with lower rates of obesity.

Predicting opportunities for greening and patterns of vegetation on private urban lands.

This paper examines predictors of vegetative cover on private lands in Baltimore, Maryland. Using high-resolution spatial data, we generated two measures: "possible stewardship," which is the proportion of private land that does not have built structures on it and hence has the possibility of supporting vegetation, and "realized stewardship," which is the proportion of possible stewardship land upon which vegetation is growing. These measures were calculated at the parcel level and averaged by US Census block group. Realized stewardship was further defined by proportion of tree canopy and grass. Expenditures on yard supplies and services, available by block group, were used to help understand where vegetation condition appears to be the result of current activity, past legacies, or abandonment. PRIZM market segmentation data were tested as categorical predictors of possible and realized stewardship and yard expenditures. PRIZM segmentations are hierarchically clustered into 5, 15, and 62 categories, which correspond to population density, social stratification (income and education), and lifestyle clusters, respectively. We found that PRIZM 15 best predicted variation in possible stewardship and PRIZM 62 best predicted variation in realized stewardship. These results were further analyzed by regressing each dependent variable against a set of continuous variables reflective of each of the three PRIZM groupings. Housing age, vacancy, and population density were found to be critical determinants of both stewardship metrics. A number of lifestyle factors, such as average family size, marriage rates, and percentage of single-family detached homes, were strongly related to realized stewardship. The percentage of African Americans by block group was positively related to realized stewardship but negatively related to yard expenditures.

The use of insulin declines as patients live farther from their source of care: results of a survey of adults with type 2 diabetes.

BACKGROUND: Although most diabetic patients do not achieve good physiologic control, patients who live closer to their source of primary care tend to have better glycemic control than those who live farther away. We sought to assess the role of travel burden as a barrier to the use of insulin in adults with diabetes. METHODS: 781 adults receiving primary care for type 2 diabetes were recruited from the Vermont Diabetes Information System. They completed postal surveys and were interviewed at home. Travel burden was estimated as the shortest possible driving distance from the patient's home to the site of primary care. Medication use, age, sex, race, marital status, education, health insurance, duration of diabetes, and frequency of care were self-reported. Body mass index was measured by a trained field interviewer. Glycemic control was measured by the glycosolated hemoglobin A1C assay. RESULTS: Driving distance was significantly associated with insulin use, controlling for the covariates and potential confounders. The odds ratio for using insulin associated with each kilometer of driving distance was 0.97 (95% confidence interval 0.95, 0.99; P = 0.01). The odds ratio for using insulin for those living within 10 km (compared to those with greater driving distances) was 2.29 (1.35, 3.88; P = 0.02). DISCUSSION: Adults with type 2 diabetes who live farther from their source of primary care are significantly less likely to use insulin. This association is not due to confounding by age, sex, race, education, income, health insurance, body mass index, duration of diabetes, use of oral agents, glycemic control, or frequency of care, and may be responsible for the poorer physiologic control noted among patients with greater travel burdens.