ArticleSubstance abuse treatment, prevention, and policy2025
A spatiotemporal analysis of opioid prescriptions in Indiana from 2015 to 2019.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.