Identification of pregnancies and infants within a United States commercial healthcare administrative claims database

Use of the Sentinel System to Examine Medical Product Use and Outcomes During Pregnancy

While many pregnant individuals use prescription medications, evidence supporting product safety during pregnancy is often inadequate. Existing electronic healthcare data sources provide large, diverse samples of health plan members to allow for the study of medical product utilization during pregnancy, as well as pregnancy, maternal, and infant outcomes. The Sentinel System is a national medical product surveillance system that includes administrative claims and electronic health record databases from large national and regional health insurers. In addition to these data sources, Sentinel develops and maintains a sizeable selection of analytic tools to facilitate epidemiologic analyses in a way that protects patient privacy and health system autonomy. In this article, we provide an overview of Sentinel System infrastructure, including the Mother-Infant Linkage Table, parameterizable analytic tools, and algorithms to estimate gestational age and identify pregnancy outcomes. We also describe past and future Sentinel work that contributes to our understanding of the way medical products are used and the safety of these products during pregnancy.

Association of Traditional and Nontraditional Risk Factors in the Development of Strokes Among Young Adults by Sex and Age Group: A Retrospective Case-Control Study

BACKGROUND

Despite women having fewer traditional risk factors (eg, hypertension, diabetes), strokes are more common in women than men aged ≤45 years. This study examined the contributions of traditional and nontraditional risk factors (eg, migraine, thrombophilia) in the development of strokes among young adults.

METHODS

This retrospective case-control study used Colorado's All Payer Claims Database (2012-2019). We identified index stroke events in young adults (aged 18-55 years), matched 1:3 to stroke-free controls, by (1) sex, (2) age±2 years, (3) insurance type, and (4) prestroke period. All traditional and nontraditional risk factors were identified from enrollment until a stroke or proxy-stroke date (defined as the prestroke period). Conditional logistic regression models stratified by sex and age group first assessed the association of stroke with counts of risk factors by type and then computed their individual and aggregated population attributable risks.

RESULTS

We included 2618 cases (52% women; 73.3% ischemic strokes) and 7827 controls. Each additional traditional and nontraditional risk factors were associated with an increased risk of stroke in all sex and age groups. In adults aged 18 to 34 years, more strokes were associated with nontraditional (population attributable risk: 31.4% men and 42.7% women) than traditional risk factors (25.3% men and 33.3% women). The contribution of nontraditional risk factors declined with age (19.4% men and 27.9% women aged 45-55 years). The contribution of traditional risk factors peaked among patients aged 35 to 44 years (32.8% men and 39.7% women). Hypertension was the most important traditional risk factor and increased in contribution with age (population attributable risk: 27.8% men and 26.7% women aged 45 to 55 years). Migraine was the most important nontraditional risk factor and decreased in contribution with age (population attributable risk: 20.1% men and 34.5% women aged 18-35 years).

CONCLUSIONS

Nontraditional risk factors were as important as traditional risk factors in the development of strokes for both young men and women and have a stronger association with the development of strokes in adults younger than 35 years of age.

Development and evaluation of an algorithm to link mothers and infants in two US commercial healthcare claims databases for pharmacoepidemiology research

BACKGROUND

Administrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair identification. Further, existing algorithms are not transportable across data sources. We developed a transportable mother-infant linkage algorithm and evaluated it in two, large US commercially insured populations.

METHODS

We used two US commercial health insurance claims databases during the years 2000 to 2021. Mother-infant links were constructed where persons of female sex 12-55 years of age with a pregnancy episode ending in live birth were associated with a person who was 0 years of age at database entry, who shared a common insurance plan ID, had overlapping insurance coverage time, and whose date of birth was within ± 60-days of the mother's pregnancy episode live birth date. We compared the characteristics of linked vs. non-linked mothers and infants to assess similarity.

RESULTS

The algorithm linked 3,477,960 mothers to 4,160,284 infants in the two databases. Linked mothers and linked infants comprised 73.6% of all mothers and 49.1% of all infants, respectively. 94.9% of linked infants' dates of birth were within ± 30-days of the associated mother's pregnancy episode end dates. Characteristics were largely similar in linked vs. non-linked mothers and infants. Differences included that linked mothers were older, had longer pregnancy episodes, and had greater post-pregnancy observation time than mothers with live births who were not linked. Linked infants had less observation time and greater healthcare utilization than non-linked infants.

CONCLUSIONS

We developed a mother-infant linkage algorithm and applied it to two US commercial healthcare claims databases that achieved a high linkage proportion and demonstrated that linked and non-linked mother and infant cohorts were similar. Transparent, reusable algorithms applied to large databases enable large-scale research on exposures during pregnancy and pediatric outcomes with relevance to drug safety. These features suggest studies using this algorithm can produce valid and generalizable evidence to inform clinical, policy, and regulatory decisions.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C)

Objectives

To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).

Materials and Methods

We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics.

Results

We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy.

Discussion

HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence.

Conclusion

We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

Distribution of gestational age at birth by maternal and infant characteristics in U.S. birth certificate data: Informing gestational age assumptions when clinical estimates are not available

PURPOSE

We aimed to describe the distribution of gestational age at birth (GAB) to inform the estimation of GAB when clinical or obstetric estimates are not available for perinatal pharmacoepidemiology studies.

METHODS

We estimated GAB (median, mode, mean, and standard deviation) and percentage born at each gestational week in groups based on plurality and other variables for live births in CDC's U.S. birth data.

RESULTS

In 2020, 3 617 213 newborns had birth certificates with nonmissing GAB. Among singletons (3 501 693), median and mode GAB were both 39 weeks. Births with lower median GAB were from women with eclampsia (37 weeks) or receiving intensive care (37 weeks); newborns receiving intensive care (37 weeks); newborns with birth weight <2500 g (35 weeks), <1500 g (28 weeks), or <1000 g (25 weeks); and newborns not discharged alive (23 weeks). Among twins (112 633), median GAB was 36 weeks (mode, 37 weeks). Additional noteworthy groups were women with 0-6 prenatal visits (median, 34 weeks) or 7-8 prenatal visits (median, 35 weeks) or aged 15-19 years (median, 35 weeks).

CONCLUSIONS

Some maternal and infant groups had distinct GAB distributions in the United States. This information can be useful in estimating GAB when individual-level clinical estimates are not available, such as in database studies of medication use during pregnancy.

Duration of medication for opioid use disorder during pregnancy and postpartum by race/ethnicity: Results from 6 state Medicaid programs

BACKGROUND

Medication for opioid use disorder (MOUD) is evidence-based treatment during pregnancy and postpartum. Prior studies show racial/ethnic differences in receipt of MOUD during pregnancy. Fewer studies have examined racial/ethnic differences in MOUD receipt and duration during the first year postpartum and in the type of MOUD received during pregnancy and postpartum.

METHODS

We used Medicaid administrative data from 6 states to compare the percentage of women with any MOUD and the average proportion of days covered (PDC) with MOUD, overall and by type of MOUD, during pregnancy and four postpartum periods (1-90 days, 91-180 days, 181-270 days, and 271-360 days postpartum) among White non-Hispanic, Black non-Hispanic, and Hispanic women diagnosed with OUD.

RESULTS

White non-Hispanic women were more likely to receive any MOUD during pregnancy and all postpartum periods compared to Hispanic and Black non-Hispanic women. For all MOUD types combined and for buprenorphine, White non-Hispanic women had the highest average PDC during pregnancy and each postpartum period, followed by Hispanic women and Black non-Hispanic women (e.g., for all MOUD types, 0.49 vs. 0.41 vs. 0.23 PDC, respectively, during days 1-90 postpartum). For methadone, White non-Hispanic and Hispanic women had similar average PDC during pregnancy and postpartum, and Black non-Hispanic women had substantially lower PDC.

CONCLUSIONS

There are stark racial/ethnic differences in MOUD during pregnancy and the first year postpartum. Reducing these inequities is critical to improving health outcomes among pregnant and postpartum women with OUD.

Validation of an ICD-9-Based Algorithm to Identify Stillbirth Episodes from Medicaid Claims Data

INTRODUCTION

In administrative data, accurate timing of exposure relative to gestation is critical for determining the effect of potential teratogen exposure on pregnancy outcomes.

OBJECTIVE

To develop an algorithm for identifying stillbirth episodes in the ICD-9-CM era using national Medicaid claims data (1999-2014).

METHODS

Unique stillbirth episodes were identified from clusters of medical claims using a hierarchy that identified the encounter with the highest potential of including the actual stillbirth delivery and that delineated subsequent pregnancy episodes. Each episode was validated using clinical detail on retrieved medical records as the gold standard.

RESULTS

Among 220 retrieved records, 197 were usable for validation of 1417 stillbirth episodes identified by the algorithm. The positive predictive value (PPV) was 64.0% (57.3-70.7%) overall, 80.4% (73.8-87.1%) for inpatient episodes, 28.2% (14.1-42.3%) for outpatient-only episodes, and 20.0% (2.5-37.5%) for outpatient episodes with overlapping hospitalizations. The absolute difference between the dates of the algorithm-specified stillbirth delivery and the medical record-based event was 4.2 ± 24.3 days overall, 1.7 ± 7.7 days for inpatient episodes, 14.3 ± 51.4 days for outpatient-only episodes, and 1.0 ± 2.0 days for outpatient episodes that overlapped with a hospitalization. Excluding all outpatient episodes, as well as pregnancies involving multiple births, the PPV increased to 82.7% (76.8-89.8%).

CONCLUSIONS

Our algorithm to identify stillbirths from administrative claims data had a moderately high PPV. Positive predictive value was substantially increased by restricting the setting to inpatient episodes and using only input diagnostic codes for singleton stillbirths.

Development and Validation of ICD-10-CM-based Algorithms for Date of Last Menstrual Period, Pregnancy Outcomes, and Infant Outcomes

INTRODUCTION AND OBJECTIVE

Validation studies of algorithms for pregnancy outcomes based on International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes are important for conducting drug safety research using administrative claims databases. To facilitate the conduct of pregnancy safety studies, this exploratory study aimed to develop and validate ICD-10-CM-based claims algorithms for date of last menstrual period (LMP) and pregnancy outcomes using medical records.

METHODS

Using a mother-infant-linked claims database, the study included women with a pregnancy between 2016-2017 and their infants. Claims-based algorithms for LMP date utilized codes for gestational age (Z3A codes). The primary outcomes were major congenital malformations (MCMs) and spontaneous abortion; additional secondary outcomes were also evaluated. Each pregnancy outcome was identified using a claims-based simple algorithm, defined as presence of ≥ 1 claim for the outcome. Positive predictive values (PPV) and 95% confidence intervals (CI) were calculated.

RESULTS

Overall, 586 medical records were sought and 365 (62.3%) were adjudicated, including 125 records each for MCMs and spontaneous abortion. Last menstrual period date was validated among maternal charts procured for pregnancy outcomes and fewer charts were adjudicated for the secondary outcomes. The median difference in days between LMP date based on Z3A codes and adjudicated LMP date was 4.0 (interquartile range: 2.0-10.0). The PPV of the simple algorithm for spontaneous abortion was 84.7% (95% CI 78.3, 91.2). The PPV for the MCM algorithm was < 70%. The algorithms for the secondary outcomes pre-eclampsia, premature delivery, and low birthweight performed well, with PPVs > 70%.

CONCLUSIONS

The ICD-10-CM claims-based algorithm for spontaneous abortion performed well and may be used in pregnancy studies. Further algorithm refinement for MCMs is needed. The algorithms for LMP date and the secondary outcomes would benefit from additional validation in a larger sample.

Changes in Medication Use During Pregnancy for Women with Chronic Conditions: An Analysis of Claims Data

PURPOSE

Evaluation of drug safety during pregnancy is dependent on the number of exposed women during routine clinical practice with data available for analysis. We examined medication fills in pregnant and nonpregnant women within select disease cohorts: general population, migraine, diabetes, and hyperlipidemia to explore the potential use of claims data to assess medication use and safety during pregnancy.

METHODS

This cohort study, using IBM MarketScan® Research Databases claims data, included women 10-54 years of age with pregnancy resulting in a liveborn infant between January 2010 and September 2015 and matched nonpregnant women. Medication use (antidepressants, antihypertensives, sedatives, glucose-lowering medications, antiepileptics, antipsychotics, lipid-lowering medications) was abstracted from pharmacy claims 180 days before last menstrual period through 180 days postdelivery.

RESULTS

Among 753,760 women in the general pregnancy population (including 73,268 migraine, 50,155 hyperlipidemia, and 8361 diabetes; non-exclusive cohorts), antidepressants, antihypertensives, and sedatives were the most commonly used medications during pregnancy. Medications of interest were less commonly used in the pregnancy cohort than in the matched nonpregnant cohort within each time period (e.g., 3.7% vs 13.1% antidepressant use in 1st trimester). Most prescription fills were less common during pregnancy then pre-pregnancy. Post-pregnancy, prescription fills increased to or exceeded pre-pregnancy levels, except antihypertensive and glucose-lowering medications, which increased during pregnancy.

CONCLUSIONS

Medication use among pregnant women was low and different from that among matched nonpregnant women. The underlying size of large commercial claims databases offer opportunities for efficient evaluation of potential safety concerns, particularly for rare drug exposures, compared to traditional pregnancy registries.