IMPORTANCE Clinical prediction models estimated with health records data may perpetuate inequities.OBJECTIVE To evaluate racial/ethnic differences in the performance of statistical models that predict suicide.

Current health policy calls for greater use of evidence based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a pre-specified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel 'Robust-ITS' model that overcomes these omissions and limitations. The Robust-ITS model formally performs inference on: (a) identifying the change point; (b) differences in pre-and post-intervention correlation; (c) differences in the outcome variance pre-and post-intervention; and (d) differences in the mean pre-and post-intervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The Robust-ITS model is implemented in a R Shiny toolbox which is freely available to the community.

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-meter buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1,600, and 5,000-meter 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 kilograms at 1 year (95% CI: 0.03, 0.10); 0.64 kilograms at 3 years (95% CI: 0.59, 0.68), and 0.95 kilograms 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 kilograms. Conclusion: 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.

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 5,000-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 3 rd versus 1 st 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.