Concordance of opioid exposure in all‐payer claims databases with prescription drug monitoring program database using Arkansas as a case example

• Published in: Health services research Vol. 58; no. 4; pp. 938 - 947
• Main Authors: Mahashabde, Ruchira V., Shrikhande, Mriga A., Han, Xiaotong, Martin, Bradley C., ElHassan, Nahed O., Hayes, Corey J.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.08.2023; Health Research and Educational Trust
• Subjects: administrative claims data; all‐payer claims database; Analgesics, Opioid - therapeutic use; Analysis; Arkansas; CD-ROM catalog; CD-ROM database; Company systems management; Computer programs; Data collection; Data Management; Data sources; Databases, Factual; Drug dosages; Drugs; Drugstores; Exposure; Extraction; Humans; Medication Policy; Monitoring; Narcotics; Opioid abuse; opioid therapy; Opioids; pharmacoepidemiology; Pharmacy; Prescribing; prescription drug monitoring program; Prescription Drug Monitoring Programs; Prescription drugs; Prescriptions (Drugs); Sensitivity; Arkansas; United States > US; opioid therapy; administrative claims data; prescription drug monitoring program; all-payer claims database; pharmacoepidemiology
• ISSN: 0017-9124; 1475-6773; 1475-6773
• DOI: 10.1111/1475-6773.14117

Objective To 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 Setting Arkansas APCD and PDMP. Study Design Enrollees 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 Methods Arkansas 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 Findings The 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. Conclusions Claims data sources, such as APCDs, are fairly accurate in identifying opioid exposure and the level of opioid exposure among persons with continuous pharmacy coverage.