Validation of algorithms to estimate gestational age at birth in the Medicaid Analytic eXtract-Quantifying the misclassification of maternal drug exposure during pregnancy.
Pharmacoepidemiol Drug Saf
; 29(11): 1414-1422, 2020 11.
Article
en 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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Preparaciones Farmacéuticas
/
Edad Gestacional
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
Límite:
Female
/
Humans
/
Newborn
/
Pregnancy
País como asunto:
America do norte
Idioma:
En
Año:
2020
Tipo del documento:
Article