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The Economic Burden of the Opioid Epidemic on States: The Case of Medicaid
Douglas L. Leslie, PhD; Djibril M. Ba, MPH; Edeanya Agbese, MPH; Xueyi Xing, PhD; and Guodong Liu, PhD
Considering the Child Welfare System Burden From Opioid Misuse: Research Priorities for Estimating Public Costs
Daniel Max Crowley, PhD; Christian M. Connell, PhD; Damon Jones, PhD; and Michael W. Donovan, MA
The Opioid Epidemic, Neonatal Abstinence Syndrome, and Estimated Costs for Special Education Services
Paul L. Morgan, PhD; and Yangyang Wang, MA
Opioid Misuse, Labor Market Outcomes, and Means-Tested Public Expenditures: A Conceptual Framework
Joel E. Segel, PhD; Yunfeng Shi, PhD; John R. Moran, PhD; and Dennis P. Scanlon, PhD
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Preventing the Next Crisis: Six Critical Questions About the Opioid Epidemic That Need Answers
Dennis P. Scanlon, PhD; and Christopher S. Hollenbeak, PhD
Beyond Rescue, Treatment, and Prevention: Understanding the Broader Impact of the Opioid Epidemic at the State Level
Laura Fassbender, BPH; Gwendolyn B. Zander, Esq; and Rachel L. Levine, MD
The Cost of the Opioid Epidemic, In Context
Sarah Kawasaki, MD; and Joshua M. Sharfstein, MD
The Opioid Epidemic: The Cost of Services Versus the Cost of Despair
Alonzo L. Plough, PhD, MPH

The Economic Burden of the Opioid Epidemic on States: The Case of Medicaid

Douglas L. Leslie, PhD; Djibril M. Ba, MPH; Edeanya Agbese, MPH; Xueyi Xing, PhD; and Guodong Liu, PhD
For this analysis, we used data from MAX files, which is a set of person-level data files with information on Medicaid eligibility, service utilization, and payments that was developed by Centers for Medicare and Medicaid Services (CMS) to support research and policy analysis about Medicaid populations. The claims data contain both fee-for-service and Medicaid managed care claims. Managed care contracts have become increasingly common in Medicaid, and managed care encounter claims have been shown to be complete and of comparable quality with fee-for-service claims.41 Until recently, information about treatments for substance use disorders was not available from Medicaid databases after the redaction of such claims under federal law.42 When this rule was changed in 2017, it allowed CMS to include substance use disorder claims in MAX data for every year.43,44 MAX data are available through the Pennsylvania State University Virtual Research Data Center. Seventeen states (California, Georgia, Idaho, Iowa, Louisiana, Michigan, Minnesota, Mississippi, Missouri, New Jersey, Pennsylvania, South Dakota, Tennessee, Utah, Vermont, West Virginia, and Wyoming) had complete MAX data from 1999 to 2013 that were available for the analysis.

The analytic sample included individuals with a diagnosis of OUD. Following previous studies,45 we used a broad definition of OUD that included any inpatient or outpatient visit with a diagnosis of opioid (prescription pain medications or heroin) abuse, dependence, poisoning, or adverse effects (International Classification of Diseases, Ninth Revision codes 304.0, 304.7, 305.5, 965.0, E850.0-E850.2, E935.0-E935.2) but excluded self-inflicted poisoning (E950.0-E950.5) and assault by poisoning (E962.0). We also identified a comparison group of individuals without a diagnosis of OUD, matched 1-to-1 with the OUD sample by state, age, and sex.

For both the OUD and comparison cohorts, Medicaid expenditures were computed per individual per year by adding the “Medicaid payment amount” variable across all claims (inpatient, long-term care, outpatient, and prescription drug) during the year. The “Medicaid payment amount” indicates the total amount of money paid by Medicaid for the service. Medicaid expenditures for OUD treatment were computed by adding 1) the Medicaid payment amount across all claims (inpatient, long-term care, and outpatient) that had an associated diagnosis code of OUD, and 2) prescription drug claims for medications used to treat OUD (methadone, buprenorphine, and long-acting injectable naltrexone). Some managed care claims for OUD rehabilitation services were set to zero because Medicaid managed care plans are paid a capitated amount per enrollee rather than per service provided, as in a fee-for-service plan. We replaced the zero cost of these claims with the average payments among the fee-for-service claims. Because patients with OUD may also have higher healthcare costs for other conditions (eg, infections or poor adherence to treatment for chronic conditions), we also computed Medicaid expenditures for non-OUD services for both the OUD and comparison cohorts. Total OUD-related Medicaid expenditures were then defined as the sum of the OUD treatment costs and the excess non-OUD costs (non-OUD costs in the OUD group minus the non-OUD costs in the comparison group). Expenditures were then Winsorized at the first and 99th percentiles to reduce the influence of outliers.46 All expenditures were adjusted for inflation using the Medical Care component of the Consumer Price Index and are reported in 2017 US dollars.

As shown in Figure 2, the number of patients with OUD increased substantially over time in our 17-state sample, from 39,109 in 1999 to 186,979 in 2013; this is an increase of 378%. Average annual Medicaid expenditures per patient for patients with OUD and the matched comparison group of patients without OUD are presented in the Table, and total Medicaid expenditures are presented in Figure 3. The total OUD-related Medicaid expenditures (the sum of OUD treatment costs and excess non-OUD costs) had an increase of 246%, from $919 million in 1999 to $3.18 billion in 2013. OUD treatment expenditures increased 118%, from $438 million in 1999 to $952 million in 2013. Excess non-OUD costs increased more (363%) from $482 million in 1999 to $2.23 billion in 2013. In 1999, OUD treatment expenditures represented 47.6% of total OUD-related Medicaid expenditures, but by 2013, this percentage had fallen to 29.9%, indicating that the burden of non-OUD expenditures for patients with OUD grew over time.

We used the results from our sample states to extrapolate to national estimates. For each of the 17 states in our sample, we created a sampling weight equal to the inverse of the ratio of the number of Medicaid enrollees in the state to the total US Medicaid enrollment. Based on these weights, we estimate that nationally, the number of individuals with OUD who were treated in state Medicaid programs increased 440%, from approximately 91,613 in 1999 to 494,569 in 2013. Total OUD-related Medicaid expenditures for these patients nearly quadrupled, from $2.15 billion in 1999 to $8.42 billion (or 3.2% of total Medicaid spending) in 2013.


Our analysis shows that costs to state Medicaid programs pertaining to the opioid epidemic have increased considerably over the past 15 years and reached $8.42 billion in 2013, the most recent year of data available at the time of the study. However, the results must be considered in the context of the study’s limitations. The most significant limitation is that complete MAX data were available for only 17 states and were limited to the period from 1999 to 2013. If Medicaid data were obtained directly from the states (or a selection of states) instead of from CMS, more recent cost estimates could be computed and patterns of treatment and costs over a long period of time could be examined. In addition, there may also be other costs to the state Medicaid programs that are attributable to OUD that we are not able to observe. For example, children of parents with an OUD may be more likely to become undernourished, suffer from chronic conditions, or become victims of accidents and injuries. Because we are not able to link family members in the MAX database, we cannot identify the children of parents with OUD and are not able to include these costs in the analysis.

Finally, the study is limited to Medicaid expenditures. States also incur costs related to the opioid epidemic among their employees and retirees. Although we are not aware of studies that specifically focus on state employees and retirees, there are studies of privately insured individuals. OUD greatly affects the working-age population, and studies report the highest rates of nonmedical use of opioids and overdose deaths in the group of adults aged 18 to 49 years.13,47 Rice et al estimated the incremental annual healthcare cost of OUD to an employer to be $10,627 per patient. In addition, an employee with OUD had $1244 excess annual work-loss costs.48

Future Directions

The analyses presented provide a general overview of the cost of the opioid epidemic to state Medicaid plans. A more robust analysis would involve developing cost models that control for state-level and patient characteristics. In addition, future studies should explore factors that may be related to the increase in OUD costs. For example, data on promotional activities by pharmaceutical firms, both direct-to-consumer and provider-targeted, could be included,49,50 which would allow for the estimation of the potential effects of industry behavior on Medicaid expenditures for the opioid epidemic.

In addition to the enhancements of the Medicaid analysis, future studies could examine insurance costs for state employees and retirees. The analyses described here could be applied to private health insurance claims data to estimate the cost to private insurers of the opioid epidemic and determine an annual cost per enrollee. As state employees are likely to yield results similar to those of other privately insured individuals, the estimates could be used to derive the cost associated with OUD to the states among state employees, to develop cost models and to estimate the effects of industry behaviors, as in the Medicaid analyses.

As the current analysis shows, the states’ economic burden from the opioid epidemic is considerable. However, the results likely underestimate this burden. Future studies could further refine our estimates to include non-Medicaid expenditures, and they could estimate the burden on infants, children, and adolescents associated with having parents with an OUD. Understanding these costs is important for developing targeted prevention and treatment programs and policies to help mitigate this public health crisis.

Author affiliations: Penn State College of Medicine (DLL, EA, DB, GL); Center for Applied Studies in Health Economics (DB, DLL, EA, GL); the Pennsylvania State University College of Health and Human Development (DLL,EA,GL,XX).
Funding: This project was supported by the Commonwealth of Pennsylvania under the project "Estimation of Societal Costs to States Due to Opioid Epidemic" as well as by a Strategic Planning Implementation award from the Penn State University Office of the Provost, "Integrated Data Systems Solutions for Health Equity."
Authorship information: Concept and design (DB, DLL, EA, GL); acquisition of data (DB, XX); analysis and interpretation of data (DB, DLL, GL, XX); drafting of the manuscript (DLL, EA); critical revision of the manuscript for important intellectual content (EA, GL); statistical analysis (DB, DLL, GL, XX); administrative, technical, or logistic support (EA, XX); supervision (DLL).
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