ArticleHealth services research2023
Concordance of opioid exposure in all-payer claims databases with prescription drug monitoring program database using Arkansas as a case example.
Article in Health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Compiling Comprehensive Databases for Opioid Overdose Monitoring in a Fragmented Health Care System.Health services research · 2026Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo assess the concordance between and benefit of adding prescription drug monitoring program (PDMP) data to all-payer claims database (APCD) data for identifying and classifying opioid exposure among insured individuals. DATA SOURCES AND STUDY
settingArkansas APCD and PDMP. STUDY
designEnrollees in APCD were classified as (1) true positives: if they received opioids in both databases, (2) false positives: if they only received opioids in APCD, (3) true negatives: if they had no opioid exposure in both databases, (4) false negatives: if they only received opioids in the PDMP database. Specificity, sensitivity, negative, and positive predictive values were calculated using PDMP as the "gold standard" database source. Subjects were also categorized as those who received any opioid, chronic opioid, high-dose opioid, or high-risk opioid therapies. DATA COLLECTION/EXTRACTION
methodsArkansas residents continuously enrolled with pharmacy coverage in 2016 were included. APCD and PDMP were linked using an encrypted enrollee identifier, gender, and year of birth. PRINCIPAL
findingsThe degree of concordance in opioid exposure between the two databases among 1,411,565 enrollees was high (sensitivity = 92.67%, specificity = 96.13%, positive predictive value = 91.60%, negative predictive value = 96.65%). Enrollees classified as having any opioid (APCD: 31.64% vs. PDMP: 31.26% vs. APCD+PDMP: 33.93%), chronic opioid (APCD: 7.81% vs. PDMP: 7.54% vs. APCD+PDMP: 8.24%), high-dose opioid (APCD: 10.60% vs. PDMP: 9.62% vs. APCD+PDMP: 11.33%), or high-risk opioid (APCD: 5.28% vs. PDMP: 5.33% vs. APCD+PDMP: 6.20%) therapies, were similar using only APCD versus PDMP versus the combined APCD and PDMP data sources.
conclusionsClaims data sources, such as APCDs, are fairly accurate in identifying opioid exposure and the level of opioid exposure among persons with continuous pharmacy coverage.
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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.