Substance Abuse Treatment, Prevention, and Policy (Apr 2021)

Harmonizing healthcare and other resource measures for evaluating economic costs in substance use disorder research

  • Michelle A. Papp,
  • Jared A. Leff,
  • Sean M. Murphy,
  • April Yang,
  • Heidi M. Crane,
  • Lisa R. Metsch,
  • Carlos Del Rio,
  • Daniel J. Feaster,
  • Josiah D. Rich,
  • Bruce R. Schackman,
  • Kathryn E. McCollister

DOI
https://doi.org/10.1186/s13011-021-00356-z
Journal volume & issue
Vol. 16, no. 1
pp. 1 – 11

Abstract

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Abstract Background Standardization and harmonization of healthcare resource utilization data can improve evaluations of the economic impact of treating people with substance use disorder (SUD), including reductions in use of expensive hospital and emergency department (ED) services, and can ensure consistency with current cost-effectiveness and cost-benefit analysis guidelines. Methods We examined self-reported healthcare and other resource utilization data collected at baseline from three National Institute on Drug Abuse (NIDA)-funded Seek, Test, Treat, and Retain intervention studies of individuals living with/at risk for HIV with SUD. Costs were calculated by multiplying mean healthcare resource utilization measures by monetary conversion factors reflecting cost per unit of care. We normalized baseline recall timeframes to past 30 days and evaluated for missing data. Results We identified measures that are feasible and appropriate for estimating healthcare sector costs including ED visits, inpatient hospital and residential facility stays, and outpatient encounters. We also identified two self-reported measures to inform societal costs (days experiencing SUD problems, participant spending on substances). Missingness was 8% or less for all study measures and was lower for single questions measuring utilization in a recall period. Conclusions We recommend including measures representing units of service with specific recall periods (e.g., 6 months vs. lifetime), and collecting healthcare resource utilization data using single-question measures to reduce missingness.

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