# A spatiotemporal analysis of opioid prescriptions in Indiana from 2015 to 2019

## TL;DR

This study examines how opioid prescriptions changed over time in Indiana, finding that urban areas had higher prescription rates and that disparities between rural and urban areas persisted despite state efforts.

## Contribution

The study provides a detailed spatiotemporal analysis of opioid prescriptions in Indiana, highlighting demographic and geographic trends.

## Key findings

- Older, female, White, and urban residents were more likely to receive opioid prescriptions.
- Rural areas saw a faster decline in opioid prescriptions compared to urban areas.
- Significant prescription clusters shifted from rural to urban areas over time.

## Abstract

People living in rural communities are more likely to receive opioid prescriptions, partly due to job-related injuries. State-level interventions have reduced opioid prescribing; however, rural/urban disparities persist due to differences in demographics and prescribing practices, particularly in states with large rural populations like Indiana. While spatiotemporal analyses have explored aspects of the opioid crisis, spatiotemporal patterns of opioid prescribing have not been sufficiently studied. This study utilizes a sample of Medicaid claims data from the Indiana Family and Social Services Administration from 2015 to 2019 to analyze spatiotemporal patterns of patients receiving at least one opioid prescription across Indiana. The goal was to analyze patient demographics and track prescription hotspot movement over time in rural and urban areas. We analyzed data for 107,574 Medicaid enrollees who received opioid prescriptions during the study period. We found that most patients in the cohort resided in urban areas, with the number of patients who were prescribed opioids and resided in rural areas decreasing at a faster rate. We conducted a negative binomial regression analysis to examine the relationship between various demographics (sex, age, race/ethnicity, and urban/rural classification) and the number of patients receiving at least one opioid prescription over time. Our findings indicate that older patients, patients identifying as females, patients who identify as White, and patients living in urban areas, are more likely to receive at least one opioid prescription. Additionally, the interaction effects revealed that patients from all demographic groups were more likely to receive at least one opioid prescription if they lived in urban areas, compared to those living in rural areas. Using Local Moran’s I as a local spatial autocorrelation measure, we identified clusters highlighting transitions from rural to urban areas over time. In 2015–2016, three significant clusters emerged within rural-surrounded 3-digit ZIP codes (472, 474, 476), based on the Rural-Urban Commuting Area Codes. Over time, significant clusters shifted towards urban or mixed areas, possibly influenced by state guidelines and legislation. These findings enhance the understanding of opioid prescription dynamics and identify patterns in opioid prescribing rates in terms of the proportion of patients receiving opioid prescriptions among urban vs. rural communities in Indiana.

The online version contains supplementary material available at 10.1186/s13011-025-00664-8.