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. 2017 Mar;55(3):291-298.
doi: 10.1097/MLR.0000000000000676.

An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program

Gerald Cochran  1 , Adam J Gordon, Wei-Hsuan Lo-Ciganic, Walid F Gellad, Winfred Frazier, Carroline Lobo, Chung-Chou H Chang, Ping Zheng, Julie M Donohue
Affiliations

An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program

Gerald Cochran et al. Med Care. 2017 Mar.

Abstract

Background: Health systems may play an important role in identification of patients at-risk of opioid medication overdose. However, standard measures for identifying overdose risk in administrative data do not exist.

Objective: Examine the association between opioid medication overdose and 2 validated measures of nonmedical use of prescription opioids within claims data.

Research design: A longitudinal retrospective cohort study that estimated associations between overdose and nonmedical use.

Subjects: Adult Pennsylvania Medicaid program 2007-2012 patients initiating opioid treatment who were: nondual eligible, without cancer diagnosis, and not in long-term care facilities or receiving hospice.

Measures: Overdose (International Classification of Disease, ninth edition, prescription opioid poisonings codes), opioid abuse (opioid use disorder diagnosis while possessing an opioid prescription), opioid misuse (a composite indicator of number of opioid prescribers, number of pharmacies, and days supplied), and dose exposure during opioid treatment episodes.

Results: A total of 372,347 Medicaid enrollees with 583,013 new opioid treatment episodes were included in the cohort. Opioid overdose was higher among those with abuse (1.5%) compared with those without (0.2%, P<0.001). Overdose was higher among those with probable (1.8%) and possible (0.9%) misuse compared with those without (0.2%, P<0.001). Abuse [adjusted rate ratio (ARR), 1.52; 95% confidence interval (CI), 1.10-2.10), probable misuse (ARR, 1.98; 95% CI, 1.46-2.67), and possible misuse (ARR, 1.76; 95% CI, 1.48-2.09) were associated with significantly more events of opioid medication overdose compared with those without.

Conclusions: Claims-based measures can be used by health systems to identify individuals at-risk of overdose who can be targeted for restrictions on opioid prescribing, dispensing, or referral to treatment.

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Figures

Figure 1
Figure 1. Overdose Rate Based on Number of Opioid Treatment Episodes per Person (N=372,347) a
This figure contains overdose rates for patients within the cohort by the number of individual opioid treatment episodes they have.
Figure 2
Figure 2. Predicted Overdose Rate with 95% Confidence Intervals by Abuse, Misuse, and Daily Morphine Milligram Equivalents (MMEs/day) Adjusted for All Covariates and Offset by Log Length of Episode a,b
This figure contains the adjusted predicted overdose rates for opioid abuse, misuse, and daily morphine milligram equivalents.
See this image and copyright information in PMC

References

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