The impact of buprenorphine prescriber data on geospatial access to treatment in HEALing Communities Study communities, 2022

Introduction

The location of buprenorphine treatment providers in the United States is pivotal to the understanding of regional factors associated with prescription and uptake. We evaluated how distinct data sources of treatment providers and their associated locations contribute to the differences observed when measuring buprenorphine accessibility.

Methods

We compared buprenorphine treatment provider data from the Drug Enforcement Administration (DEA) and data from the behavioral health treatment locator from the Substance Abuse and Mental Health Services Administration (SAMHSA) for July 2022. Both DEA and SAMHSA data, while similar in spirit, vary substantially in how and why each data set is collected. DEA registration was required by law, while SAMHSA data was an opt-in registry of provider-submitted details. Analyzing the underlying semantics of the data is important for understanding the contextual factors driving observable differences in analytical outputs. We measured accessibility using enhanced two-step floating catchment area (E2SFCA) analysis in three states participating in the HEALing Communities Study (Kentucky, Ohio, Massachusetts). Within communities, we compared decile rankings of accessibility per census tract using each data source. We linked prescribing data from Kentucky's prescription drug monitoring program (PDMP) to measure accessibility using providers prescribing buprenorphine. We explored the significance of localized rank exchanges using neighbor set local indicators of mobility association (LIMA).

Results

The number and rate of providers per capita differed substantially at the community level between data sources in the three states. These differences were less impactful in the spatial context of buprenorphine accessibility, which required both supply and demand in regions smaller than our intervention communities. Shifts did occur when measuring the intercommunity decile ranking of accessibility of census tracts, but LIMA results indicated that these rank exchanges were not significant.

Conclusions

When analyzing accessibility within a community using E2SFCA analyses, either DEA or SAMHSA data sources are acceptable; linkage to Kentucky's PDMP demonstrated that SAMHSA provider data is equally suitable to PDMP data for research studies involving spatial relationships with providers while being both significantly easier to obtain and less sensitive. When analyzing treatment provider rates per capita, results may vary substantially across these different data sources. Therefore, context must be considered when choosing an appropriate data source to use.