Client Racial Composition in First-Episode Psychosis Programs Compared With Compositions in Program Service Areas.

OBJECTIVE: The authors examined the extent to which clients served by first-episode psychosis programs reflected the racial composition of the surrounding service area and, to the extent that they did not, explored possible explanatory factors. METHODS: As part of a national study of coordinated specialty care (CSC) sites in the United States, 35 programs documented race for 772 clients. Programs identified a geographic service area for their clients. Using Census data, the authors identified the proportion of clients in this service area who were Black and then examined the extent of disproportionality, calculated as a risk ratio and as a relative difference in racial composition between CSC programs and their service areas. RESULTS: Overall, 71% of CSC programs had a disproportionately greater proportion of Black clients than Black residents within the service area. This disproportionality was still evident after conducting sensitivity analyses that included adjusting for sampling error in the service area population estimates; however, smaller study sites displayed greater fluctuations in disproportionality in the sensitivity analyses. CONCLUSIONS: Using data from diverse CSC programs, the authors illustrate that the odds of Blacks receiving services through a CSC program are much higher than would be expected on the basis of the population living in the area being served by the program. Multiple reasons may explain this finding, but in the absence of clear explanatory factors, this result may be ripe for discussion and further investigation.

Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study.

Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012-2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using "gold standard" rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: -2 to 168) and 9 m (IQR: -80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: -1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.