Evidence map›Paper›PMID 40781687›Full record

ArticleSubstance abuse treatment, prevention, and policy2025

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

Paula A Jaimes-Buitron, Nicole Adams, Nan Kong, Carolina Vivas-Valencia

Abstract read
In one paragraph

Article in Substance abuse treatment, prevention, and policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Paula A Jaimes-BuitronDepartment of Biomedical Engineering and Chemical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA.
Nicole AdamsSchool of Nursing, Purdue University, West Lafayette, IN, USA.
Nan KongWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Carolina Vivas-ValenciaDepartment of Biomedical Engineering and Chemical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA. carolina.vivasvalencia@utsa.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Analgesics, OpioidDrug PrescriptionsPractice Patterns, Physicians'Rural PopulationAdolescentAdultFemaleHumansIndianaMaleMedicaidMiddle AgedSpatio-Temporal AnalysisUnited StatesUrban PopulationYoung AdultAnalgesics, OpioidGeospatial analysisIndianaMoran's IOpioid prescribing ratesOpioid prescriptionsUrbanicity

Identifiers

PMID40781687
PMCPMC12335077

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.