(220) Identification of Pregnancies and Their Outcomes in Healthcare Claims Data, 2008-2019: An Algorithm

Publication Number: 551

Background: Pregnancy is a condition of broad interest across many medical and health services research domains, but one not easily identified in healthcare claims data.

Objectives: To establish an algorithm to identify pregnant women and their pregnancy outcomes and gestational ages at the end of pregnancy in claims data.

Methods: We identified pregnancy-related diagnosis, procedure, and diagnosis-related group codes, accounting for the transition to International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis and procedure codes in health encounter reporting on 10/1/2015. We selected women in IBM MarketScan commercial databases aged 15-49 years with pregnancy-related claims, and their infants, during 2008–2019. Pregnancies, pregnancy outcomes, and gestational ages at end of pregnancy were assigned using the constellation of service dates, code types, pregnancy outcomes, and infant linkage. We describe pregnancy outcomes and gestational ages, as well as maternal age, Census region, and health plan type. In sensitivity analyses, we assessed the impact of restricting pregnancy identification to diagnosis codes alone and of comparing our algorithm-assigned date of last menstrual period (LMP) to fertility procedure-based LMP (date of procedure + 14 days) among women with embryo transfer or insemination procedures.

Results: Among 5,766,952 identified pregnancies, most (78.5%) resulted in live births, followed by spontaneous abortions (15.6%); 3,268,992 (72.2%) live births could be linked to infants. Most pregnancies were among women 25-34 years (60.3%), living in the South (39.1%) or Midwest (22.4%), with large employer-sponsored insurance (51.9%). Outcome distributions were similar across ICD-9 and ICD-10 eras, though some variation in gestational age at end of pregnancy estimates was observed. Sensitivity analyses supported our algorithm’s framework. Reducing our pregnancy-related codes to diagnosis codes alone would have missed 300,000 pregnancies; algorithm- and fertility procedure-derived LMP estimates were comparable (mean difference: 1.3 days [IQR: -13 to 12 days]; n=89,750).

Conclusions: We have developed an algorithm to identify pregnancies, their gestational age, and outcomes, across ICD-9 and ICD-10 eras using administrative data. This algorithm may be useful to reproductive health researchers investigating a broad range of pregnancy and infant outcomes.