<p>Impact of the Transition from ICD–9–CM to ICD–10–CM on the Identification of Pregnancy Episodes in US Health Insurance Claims Data</p>

Abstract

Background: Before October 2015, pregnancy cohorts assembled from US health insurance claims have relied on medical encounters with International Classification of Diseases-ninth revision-clinical modification (ICD-9-CM) codes. We aimed to extend existing pregnancy identification algorithms into the ICD-10-CM era and evaluate performance. Methods: We used national private insurance claims data (2005-2018) to develop and test a pregnancy identification algorithm. We considered validated ICD-9-CM diagnosis and procedure codes that identify medical encounters for live birth, stillbirth, ectopic pregnancy, abortions, and prenatal screening to identify pregnancies. We then mapped these codes to the ICD-10-CM system using general equivalent mapping tools and reconciled outputs with literature and expert opinion. Both versions were applied to the respective coding period to identify pregnancies. We required 45 weeks of health plan enrollment from estimated conception to ensure the capture of all pregnancy endpoints. Results: We identified 7,060,675 pregnancy episodes, of which 50.1% met insurance enrollment requirements. Live-born deliveries comprised the majority (76.5%) of episodes, followed by abortions (20.3%). The annual prevalence for all pregnancy types was stable across the ICD transition period except for postterm pregnancies, which increased from 0.5% to 3.4%. We observed that ICD codes indicating gestational age were available for 86.8% of live-born deliveries in the ICD-10 era compared to 23.5% in the ICD-9 era. Patterns of prenatal tests remained stable across the transition period. Conclusion: Translation of existing ICD-9-CM pregnancy algorithms into ICD-10-CM codes provided reasonable consistency in identifying pregnancy episodes across the ICD transition period. New codes for gestational age can potentially improve the precision of conception estimates and minimize measurement biases.

Development and evaluation of standardized pregnancy identification and trimester distribution algorithms in U.S. IBM MarketScan® Commercial and Medicaid data

“…This discrepancy could occur due to differences in the methods of data collection; this study utilized insurance claims, which could potentially miss cases compared to the active and passive case reporting system used by the CDC. A similar trend has been seen in previous work using IBM MarketScan® data (Ailes et al, 2016; Sarayani et al, 2020), indicating that preterm birth estimates might be underestimated within insurance claim databases compared to other live birth outcomes, such as neonatal intensive care unit admission (Andrade et al, 2013) or possibly C‐section. C‐section is a surgical procedure that routinely is reimbursed by a health insurance company, whereas other live birth outcomes like preterm birth do not always have to be specified in an insurance claim.…”

“…A higher pregnancy loss percentage was observed in the IBM MarketScan® Commercial Claims and Encounters population compared to the Multi‐State Medicaid population. The Commercial population pregnancy loss percentage was overall similar to the CDC estimates reported in Table 3 (Jatlaoui et al, 2016; MacDorman & Gregory, 2015; Stulberg et al, 2014; Ventura et al, 2012) as well as those from previously published algorithms that used the IBM MarketScan® Commercial Claims and Encounters population (Ailes et al, 2016; MacDonald et al, 2019; Sarayani et al, 2020). In a prior study that compared pregnancy losses across both the IBM MarketScan® Commercial and Medicaid data sets, a lower pregnancy loss estimate was also observed in the Medicaid population (Matcho et al, 2018); however both estimates were a few percentage points higher than what was observed in this study.…”

“…In the last decade, many algorithms have attempted to create pregnancy algorithms for use in U.S. health insurance claims data (Ailes, Simeone, Dawson, Petersen, & Gilboa, 2016; Ellis et al, 2020; Hornbrook et al, 2007; Kharbanda et al, 2020; Li et al, 2013; MacDonald et al, 2019; Maric et al, 2019; Matcho et al, 2018; Palmsten et al, 2013; Palmsten, Huybrechts, Kowal, Mogun, & Hernandez‐Diaz, 2014; Sarayani et al, 2020; Taylor, Thelus Jean, Gordon, Fram, & Coster, 2015; Zhu et al, 2020). Some have assessed pregnancy losses in addition to live births (Ailes et al, 2016; Kharbanda et al, 2020; MacDonald et al, 2019; Matcho et al, 2018; Sarayani et al, 2020), enhanced capture of gestational age (MacDonald et al, 2019; Maric et al, 2019; Matcho et al, 2018; Zhu et al, 2020) or updated the algorithms to include the latest International Classification of Diseases, 10th Revision, Clinical Modification (ICD‐10‐CM) codes (Ellis et al, 2020; Kharbanda et al, 2020; Maric et al, 2019; Sarayani et al, 2020); however, only three of these studies created algorithms with ICD‐10‐CM codes specifically for IBM MarketScan® Commercial Claims and Encounters data (Ellis et al, 2020; Maric et al, 2019; Sarayani et al, 2020) and none have applied algorithms with the latest classification codes to IBM MarketScan® Multi‐State Medicaid data. IBM MarketScan® Multi‐State Medicaid data is representative of a more diverse population than Commercial data, which is a convenience sample of Americans with employer‐provided health insurance (IBM, 2021; Kaiser Family Foundation, 2021) and having an algorithm that can be applied to both databases provides a more representative picture of pregnancy and pregnancy outcomes in the United States.…”

A Pharmacoepidemiologic Approach to Evaluate Real-world Effectiveness of Hormonal Contraceptives in the Presence of Drug–drug Interactions

“…Conception was estimated via the pregnancy identification algorithm that uses medical encounters with ICD-9/10-CM, Current Procedural Terminology, and Healthcare Common Procedure Coding System codes to identify specific pregnancy endpoints, including live birth, ectopic pregnancies, stillbirth, terminations, and prenatal screening visits. 6 , 7 , 21 , 22 Once pregnancy episodes were identified, the algorithm estimated gestational age to calculate the last menstrual period (LMP). The conception date was assumed to be 14 days after the estimated LMP date.…”

Pregnancy and the Risk of In-Hospital Coronavirus Disease 2019 (COVID-19) Mortality

“… 2 A gestational age was assigned using the Z3A ICD-10-CM code. 24 If no Z3A code and no delivery procedure were coded, the patient was recategorized as nonpregnant. The remainder of patients were categorized as nonpregnant.…”