Table 4.
Built environment characteristics and their relationship with change in weight (in kilograms) at 1, 3, and 5 years from baseline (mean difference) comparing the third and first tertile of residential unit density and any vs. no fast food and supermarkets at a 1600 m buffer, after mutually adjusting for baseline BE characteristics and demographics, weight, and property values
| Built environment characteristic | 1 year | 3 year | 5 year | |||
|---|---|---|---|---|---|---|
|
| ||||||
| Wt. Change (95% CI) | P-value | Wt. Change (95% CI) | P-value | Wt. Change (95% CI) | P-value | |
| Residential unit density tertiles (1600 m) | ||||||
| Tertile 1 (0.0 to <5.9) | 0.18 (0.08, 0.28) | 0.84 (0.72, 0.96) | 1.24 (1.11, 1.38) | |||
| Tertile 2 (5.9 to <10.5) | 0.12 (0.03, 0.20) | 0.72 (0.62, 0.82) | 1.00 (0.89, 1.11) | |||
| Tertile 3 (10.5 to 87.4) | −0.06 (−0.14, 0.03) | <0.001 | 0.50 (0.40, 0.60) | <0.001 | 0.78 (0.67, 0.89) | <0.001 |
| Fast food count (1,600 m) | ||||||
| None | 0.10 (0.00, 0.19) | 0.62 (0.51, 0.73) | 0.87 (0.75, 1.00) | |||
| Any | 0.09 (0.01, 0.18) | 0.890 | 0.69 (0.59, 0.79) | 0.170 | 0.97 (0.86, 1.08) | 0.110 |
| Supermarket count (1,600 m) | ||||||
| None | 0.11 (0.02, 0.19) | 0.65 (0.54, 0.76) | 0.90 (0.78, 1.02) | |||
| Any | 0.09 (0.00, 0.17) | 0.710 | 0.68 (0.58, 0.78) | 0.560 | 0.96 (0.85, 1.07) | 0.280 |
Wt = weight, CI = confidence interval
Note: Residential density based on Euclidean distance and calculated as units per hectare. Fast food and supermarket counts based on network-based buffer. Models adjust for sex (male and female), baseline age (nonlinearly via spline terms with 10 DF), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian, Hawai’ian / Pacific Islander, Native American / Alaskan Native, and Other), Medicaid (yes/no), and baseline weight (nonlinearly via spline terms with 5 DF, allowing association to differ by gender), and patient residential property values. Separate model fit for each combination of residential unit density and fast food count and residential unit density and supermarket count. Models for fast food and supermarket counts at 1600 m are binary comparisons of any vs. none, not tertiles. P-values compare 1, 3, and 5-year weight change between the third and first tertile (or any versus none for binary variables).