A Geospatial Analysis of Social and Structural Determinants of Health and High HIV Prevalence in Alabama, USA

OBJECTIVE

This study utilizes geospatial analytic techniques to examine HIV hotspots in Alabama leveraging Medicaid utilization data.

METHODS

This cross-sectional study leveraged Medicaid utilization data from Alabama's 67 counties, averaging 9,861 Medicaid recipients aged > 18 years old per county. We used Alabama Medicaid administrative claims data from January 1, 2016, to December 31, 2020, to identify individuals with HIV. Using Microsoft SQL Server, we obtained the average annual count of HIV Medicaid claims in each of the 67 Alabama counties (numerator) and the number of adult Medicaid recipients in each county (denominator), and standardized with a multiplier of 100,000. We also examined several other area-level summary variables (e.g., non-high school completion, income greater than four times the federal poverty level, social associations, urbanicity/rurality) as social and structural determinants of health. County-boundary choropleth maps were created representing the geographic distribution of HIV rates per 100,000 adult Medicaid recipients in Alabama. Leveraging ESRI ArcGIS and local indicators of spatial association (LISA), results were examined using local Moran's I to identify geographic hotspots.

RESULTS

Eleven counties had HIV rates higher than 100 per 100,000. Three were hotspots. Being an HIV hotspot was significantly associated with relatively low educational attainment and less severe poverty than other areas in the state.

CONCLUSIONS

Findings suggesting that the HIV clusters in Alabama were categorized by significantly less severe poverty and lower educational attainment can aid ongoing efforts to strategically target resources and end the HIV epidemic in U.S.' Deep South.

Exploring the Relationship Between Community Program Location and Community Needs

INTRODUCTION

Health behaviors, mental health, and social needs impact health, but addressing these needs is difficult. Clinicians can partner with community programs to provide patients support. The relationship between program location and community need is uncertain.

METHODS

We identified and geolocated community programs in Richmond, Virginia, that aid with 9 domains of needs (mental health, smoking, unhealthy alcohol use, nutrition, physical activity, transportation, financial, housing, food insecurity). For each census tract, we identified needs from public data sources. We used 2 methods to compare program location and need: (1) hotspot analysis and (2) a negative binomial regression model.

RESULTS

We identified 280 community programs that provide aid for the 9 domains. Programs most often provided financial assistance (n = 121) and housing support (n = 73). The regression analysis showed no relationship between the number of community programs and the level of need in census tracts, with 2 exceptions. There was a positive association between financial programs and financial need and a negative association between housing programs and housing need.

CONCLUSIONS

Community programs are generally not colocated with need. This poses a barrier for people who need help addressing these domains.

Identifying Priority Areas for Increasing the Supply of Medication-Assisted Treatments for Opioid Use Disorder: A Geospatial Approach

BACKGROUND

The opioid epidemic has disproportionately affected several areas across the United States (US), with research indicating that these areas may be underserved and lack access to sufficient medication-assisted treatment (MAT) options. The objective of this study was to introduce a geospatial analytical framework for identifying sub-state priority areas to target federal allocation of MAT training and resources.

METHODS

We used a geospatial analytical framework, which integrated multiple substance use measures and layers of geographic information. Measures included estimates of illicit drug dependence and unmet treatment need from the National Survey on Drug Use and Health (NSDUH), opioid-related admissions from the Treatment Episode Data Set: Admissions (TEDs-A), and Drug Enforcement Agency (DEA) waiver practitioner data from the Substance Abuse and Mental Health Services Administration (SAMHSA). Analyses included standard deviation outlier mapping, local indicators of spatial autocorrelation (LISA), and map overlays.

RESULTS

We identified twenty-nine opioid dependence priority areas, eleven unmet treatment need priority areas, and seven low MAT capacity priority areas, located across the US, including southeastern Ohio, western Indiana, the District of Columbia, New England, and northern and southern California.

CONCLUSIONS

This study identified several areas across the US that have unmet need for MAT. Targeting these areas will allow for the most effective deployment of cost-effective MAT resources to aid the greatest number of patients with opioid use disorders.