Exploring racial and ethnic diversity trajectories and diabetes prevalence in the United States.

This study investigates whether long-term changes in neighborhood racial and ethnic diversity are associated with adult diabetes prevalence across the contiguous United States. We calculated diversity indexes for US census tracts (n = 72,033) using decennial census data from 1990 to 2020. Five diversity trajectory clusters were identified using K-means clustering. Diabetes prevalence in 2019 was obtained from the CDC's PLACES dataset. Linear mixed models (LMMs) assessed global associations between diversity trajectories and diabetes prevalence, adjusting for age, sex, poverty, marital status, and public insurance. Geographically weighted regression (GWR) examined local variations in these associations. Compared to the reference group (low and stable diversity), tracts with increasing or high diversity had significantly lower diabetes prevalence. In fully adjusted LMMs, diversity trajectory clusters characterized by large increases or high diversity by 2020 showed the largest negative associations with diabetes prevalence (e.g., cluster 4: 0.71, 95% CI: 0.76 to -0.65). GWR revealed spatial heterogeneity in these relationships: tracts with significant negative associations were most concentrated in the South, but also appeared in urban areas of the Midwest and Northeast. Neighborhoods with increasing racial and ethnic diversity over time were associated with lower diabetes prevalence, independent of key socioeconomic factors. These findings suggest that historical trajectories of integration may shape present-day diabetes prevalence at the neighborhood level and highlight the value of incorporating demographic change into spatially aware public health strategies.