DIFFICULTY OF MEASURING DOSE RESPONSE DUE TO UNRELIABILITY OF DAYS SUPPLY IN ADMINISTRATIVE CLAIMS DATA
Author(s)
Zambelli-Weiner A, Via C, Yuen M, Weiner D, Bauserman R
Translational Technologies International, Hampstead, MD, USA
OBJECTIVES : To explore the reliability of days supply data in administrative claims datasets METHODS : Data on exposure to ondansetron, an antiemetic that may be prescribed to treat nausea and vomiting during early pregnancy, was obtained from Truven Health Analytics for all live births from 2000 to 2014. Data on ondansetron exposure was collected using NDC codes, and included the date it was prescribed and the days supplied associated with each prescription. Use of days supplied was explored to see if dose over the course of pregnancy could be extrapolated reliably. Qualitative interviews with obstetricians were further conducted to explore reliability of days supply found in the administrative claims data. RESULTS : Our dataset contained 1,149,145 live births from 1,004,175 mothers. Of these, 88,695 infants were exposed to ondansetron in-utero during the first trimester. The days supply associated with each prescription ranged from 1 to 3,400, with a mean of 17.67 (standard deviation of 23.44) and a median of 10 (interquartile range of 18). One or two days supply made up over 9% of all prescriptions. However, qualitative interviews with prescribing physicians further confirmed that there was no clinical scenario under which they would prescribe a 1 or 2 days supply. Further, unrealistic prescribing patters were found, such as considerable overlap in days supply between sequential prescriptions, further calling the reliability of days supply into question. CONCLUSIONS : Days supply may be unreliable in large administrative claims databases, which can make calculating dose of medication through a patient’s course of treatment difficult and calls into question the validity of dose-response assessments using administrative claims data.
Conference/Value in Health Info
2018-05, ISPOR 2018, Baltimore, MD, USA
Value in Health, Vol. 21, S1 (May 2018)
Code
PRM45
Topic
Real World Data & Information Systems
Topic Subcategory
Reproducibility & Replicability
Disease
Multiple Diseases