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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.
Zhu, Yanmin; Hampp, Christian; Wang, Xi; Albogami, Yasser; Wei, Yu-Jung Jenny; Brumback, Babette A; Roussos-Ross, Dikea; Winterstein, Almut G.
Afiliação
  • Zhu Y; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
  • Hampp C; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA.
  • Wang X; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
  • Albogami Y; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
  • Wei YJ; King Saud University, Riyadh, Saudi Arabia.
  • Brumback BA; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
  • Roussos-Ross D; Department of Biostatistics, College of Public Health & Health Professions and College of Medicine, Gainesville, Florida, USA.
  • Winterstein AG; Department of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, Florida, USA.
Pharmacoepidemiol Drug Saf ; 29(11): 1414-1422, 2020 11.
Article em En | MEDLINE | ID: mdl-32909348
ABSTRACT

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Preparações Farmacêuticas / Idade Gestacional Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Female / Humans / Newborn / Pregnancy País/Região como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Preparações Farmacêuticas / Idade Gestacional Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Female / Humans / Newborn / Pregnancy País/Região como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article