Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events.

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries' ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

Mapping Juvenile Justice: Identifying Existing Structural Barriers to Accessing Probation Services.

The majority of justice-involved youth are placed on probation; however, many of those same youth struggle to comply with probation requirements and are subsequently confined. In Baltimore, 20% of newly committed youth were detained for violations of probation. While there are various reasons youth fail to comply with probation requirements, there have been recent calls to consider the impact of structural and spatial barriers to accessing probation programs and services. Centering the goals of community psychology, we aim to identify how existing structural barriers in Baltimore City may be contributing to social injustice through inequitable access to probation services for youth and their families. In this study, we take a novel, interdisciplinary approach to identify structural or spatial barriers facing justice-involved youth in Baltimore, MD. Specifically, we explore transportation barriers (i.e., vehicle access) and spatial disparities between youth residences and probation office locations. Our findings suggest that there are several barriers facing Baltimore's justice-involved youth that may impact access to and engagement with juvenile probation. Specifically, we found that 1 in 3 youths reside in areas with extremely low levels of vehicle access and where the median household income is 25% below the city median. We also find that the majority of youth live beyond walking distances; many would require lengthy transit commutes. These findings highlight the structural and spatial barriers facing justice-involved youth that may impact access to and engagement with probation services.