Use of real‐world evidence from healthcare utilization data to evaluate drug safety during pregnancy

Abstract

Purpose Because preapproval clinical trials typically exclude pregnant women, the evidence on drug safety during pregnancy required to inform drug labeling must come from postapproval controlled observational studies. Common designs have included pregnancy registries and case‐control studies. Recently, pregnancy cohorts nested within healthcare utilization databases are increasingly being used. Despite clear advantages, these databases share some important limitations that may threaten the validity of studies emerging from them. Methods This paper describes the distinctive methodological aspects of conducting drug safety studies in healthcare utilization databases with special emphasis on design and analytic approaches to minimize biases. Results We describe considerations for study design, cohort definition, and follow‐up. We then address issues related to exposure ascertainment based on prescription fills, including the importance of the etiologically relevant window and of properly accounting for preterm births. This is followed by a discussion of advantages and challenges when ascertaining maternal and infant outcomes based on secondary data. We then explore useful approaches to address confounding within the context of pregnancy research and of the potential for selection bias when restricting the cohort to live births. Finally, we consider issues related to external validity and statistical significance. The examples are mainly drawn from a pregnancy cohort nested in the Medicaid Analytic Extract. Conclusions The approaches presented provide guidance regarding the important methodological considerations that need to be attended to in order to generate valid, minimally biased risk when using large healthcare utilization databases for drug safety surveillance in pregnancy.

Identifying pregnancies in insurance claims data: Methods and application to retinoid teratogenic surveillance

“…For example, in our cohort, approximately 35 000 pregnancies had codes from conflicting outcome types, and thus, specialized algorithms were needed to assign the final pregnancy outcome. Previous studies have varied widely on this assignment . We developed our algorithms based on prior literature, claims reviews of a sample of pregnancies from each definition, further cycles of optimization, and finally removal of improbable or unclassifiable pregnancies.…”

“…Within a population of women ages 12 to 55, with nonmissing enrollee IDs, and inpatient or outpatient claims between January 1, 2011 and September 30, 2015, we searched inpatient and outpatient files for International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) diagnosis, ICD‐9‐CM procedure, Current Procedural Terminology (CPT), Healthcare Common Procedure Coding System (HCPCS), and Diagnosis Related Group (DRG) codes relating to the end of pregnancy (e‐Appendix A). End‐of‐pregnancy codes were codes that signaled that the pregnancy had ended (eg, “normal delivery”) and were categorized into one of eight outcomes (livebirth, stillbirth, mixed birth [at least one infant is a livebirth and at least one is a stillbirth], spontaneous abortion [SAB], elective termination, unspecified abortion, ectopic or molar pregnancy, and unclassified deliveries). Codes that signaled a delivery but did not indicate live or stillbirth status (eg, “caesarean delivery”) were classified as “unclassified delivery” codes.…”

“…Increasingly, administrative databases are being used for postmarketing surveillance in pregnancy. 1 An alternative to traditional pregnancy registries, these databases are often large, have prospective prescription recording, and are less costly for research than ad hoc registries. 2 However, administrative databases can be prone to misclassification and missing information.…”

“…3 Specifically, in pregnancy studies, identifying early losses and dates of conception can be challenging. [4][5][6][7] Indeed, limitations of prior work include missing mother-infant linkages, 5,[8][9][10] exclusion of pregnancy losses, 9,11,12 crude classification of pregnancy types, 1,8,13 and arbitrary gestational age assignments. 8,10,13,14 The development and validation of algorithms to identify pregnancies in administrative data is needed to improve the validity of these cohorts for future research.…”

“…and Diagnosis Related Group (DRG) codes relating to the end of pregnancy (e-Appendix A). 1,[6][7][8][11][12][13]31,32 End-of-pregnancy codes were codes that signaled that the pregnancy had ended (eg, "normal delivery") and…”