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Validation of algorithms to estimate gestational age at birth in the Medicaid Analytic eXtract—Quantifying the misclassification of maternal drug exposure during pregnancy
Author(s) -
Zhu Yanmin,
Hampp Christian,
Wang Xi,
Albogami Yasser,
Wei YuJung Jenny,
Brumback Babette A.,
RoussosRoss Dikea,
Winterstein Almut G.
Publication year - 2020
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.5126
Subject(s) - medicine , gold standard (test) , medicaid , pregnancy , gestation , gestational age , obstetrics , pharmacoepidemiology , algorithm , medical prescription , mathematics , health care , biology , economics , pharmacology , genetics , economic growth
Purpose Accurate ascertainment of gestational age (GA) has been a challenge in perinatal epidemiologic research. To date, no study has validated GA algorithms in Medicaid Analytic eXtract (MAX). Methods We linked livebirths of mothers enrolled in Medicaid ≥30 days after delivery in 1999‐2010 MAX to state birth certificates. We used clinical/obstetric estimate of gestation on the birth certificates as gold standard to validate claims‐based GA algorithms. We calculated the proportions of deliveries with algorithm‐estimated GA within 1−/2‐weeks of the gold standard, the sensitivity, specificity, and positive/negative predictive value (PPV/NPV) of exposure to select medications during specific gestation windows, and quantified the impact of exposure misclassification on hypothetical relative risk (RR) estimates. Results We linked 1 336 495 eligible deliveries. Within 1‐week agreement was 77%‐80% overall and 47%‐56% for preterm deliveries. The trimester‐specific drug exposure status had high sensitivities and PPVs (88.5%‐98.5%), and specificities and NPVs (>99.0%). Assuming a hypothetical RR of 2.0, bias associated with exposure misclassification during first trimester ranged from 10% to 40% under non‐differential/differential misclassification assumptions. Conclusions Claims‐based GA algorithms had good agreement with the gold standard overall, but lower agreement among preterm deliveries, potentially resulting in biased risk estimated for pregnancy exposure evaluations.