Background: In nonexperimental comparative effectiveness research using health care databases, outcome measurements must be validated to evaluate and potentially adjust for misclassification bias. We aimed to validate claims-based myocardial infarction (MI) algorithms in a Medicaid population using an HIV clinical cohort as the gold standard.
Methods: Medicaid administrative data were obtained for the years 2002–2008 and linked to the UNC CFAR HIV Clinical Cohort based on social security number, first name, and last name and MI were adjudicated. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated.
Results: There were 1063 individuals included in the study. Over a median observed time of 2.5 years, 17 had an MI. Specificity ranged from 0.979 to 0.993 with the highest specificity obtained using the ICD-9 code 410.xx in the primary or secondary position and a length of stay >3 days. Sensitivity of MI ascertainment varied from 0.588 to 0.824 depending on algorithm.
Conclusions: Specificities of varying claims-based MI ascertainment criteria are high but small changes impact positive predictive value in a cohort with low incidence. Sensitivities vary based on ascertainment criteria. Type of algorithm used should be prioritized based on study question and maximization of specific validation parameters that will minimize bias while also considering precision.
*Department of Epidemiology, Gillings School of Global Public Health
†Department of Medicine, Division of Infectious Diseases, University of North Carolina, Chapel Hill, NC
‡College of Pharmacy, Institute for Pharmaceutical Outcomes and Policy, University of Kentucky, Lexington, KY
§Department of Medicine, Division of Cardiology, University of North Carolina, Chapel Hill, NC
Supported in part by Grants M01RR00046 and UL1RR025747 from the National Center of Research Resources, National Institutes of Health and the BIRCWH grant (#K12 DA035150) from OWHR, NIDA, and the NIH. The project was also supported by National Institutes of Health Grants P30 AI590410, 5T32AI07001-35 and R01 AG023178 and the Agency for Healthcare Research and Quality Grant R01 HS018731.
Preliminary results were presented at the 27th International Conference on Pharmacoepidemiology and Therapeutic Risk Management, Chicago, IL, August 15, 2011.
J.E. has received consultancies or honoraria from GlaxoSmithKline/ViiV, Bristol Myers Squibb, Merck, Gilead and Tibotec/Janssen. T.S. has existing or pending grants with GlaxoSmithKline, Merck and Sanofi. The remaining authors have no conflicts of interest to declare.
Reprints: Emily S. Brouwer, MPH, PharmD, PhD, Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky, 789 South Limestone, Room 243, Lexington, KY 40536-0596. E-mail: firstname.lastname@example.org.