The National Institute on Drug Abuse, or NIDA, considers data harmonization in substance use disorder, human immunodeficiency virus (HIV), and hepatitis C virus (HCV) research a high-priority. Data harmonization allows investigators to synthesize data across independent studies and promote more rigorous and comparable analyses of an intervention, program, or policy. Harmonizing data also ensures that economic evaluations of treatment remain consistent with current cost-effectiveness and cost-benefit guidelines such as the recommendations of the Second Panel on Cost-Effectiveness in Health and Medicine.
Researchers affiliated with the Center for Health Economics of Treatment Interventions for Substance Use Disorder, HCV, and HIV (CHERISH) co-authored a study that demonstrates the feasibility of standardizing health services data collection and analysis to evaluate economic costs. “Harmonization and standardization of healthcare resource utilization measures in substance use disorder research are feasible, and the resulting data allows for more accurate outcome comparisons across studies, as well as economic analyses,” shares Michelle Papp, primary author of the study and assistant research coordinator at Population Health Sciences at Weill Cornell Medicine. Findings also support ongoing data harmonization efforts including those by the Helping to End Addiction Long-term Initiative, which integrates studies to develop, test, and validate evidence-based approaches to prevent and reduce opioid use and overdose deaths.
The authors examined self-reported baseline data from 3 unique studies implementing and testing the Seek, Test, Treat, and Retain (STTR) model of care that engages high-risk and hard-to-reach people who use substances in HIV testing. To harmonize the data, they:
- Identified common healthcare resource utilization measures that could potentially be used for economic analyses
- Categorized healthcare resource utilization measures as either general medical care, substance use disorder treatment, or medications
- Normalized baseline recall timeframes to 30 days
- Calculated costs by multiplying the healthcare measures with monetary conversion factors that represented cost per unit of care
The authors concluded that measures including both units of services and specific recall periods in a single question yielded the fewest missing data or blank responses. For example, “How many hospital-based emergency department visits did you make in the last 6 months?” reflects a single-question measure with units of services (e.g. number of emergency department visits) and specific recall period (e.g. 6-month timeline).
Outcomes of the study encourage more data harmonization efforts in research focusing on HIV, HCV, and substance use disorder. Papp shares,