Evidence map›Paper›PMID 41954183›Full record

ArticlePaediatric and perinatal epidemiology2026

Exploring the Impact of Missing Death Data on Identification of Congenital Malformations in Insurance Claims Data.

Katelin B Nickel, Sascha Dublin, Sharon Fuller, Sarah S Osmundson, Ryan Colvin, Dustin Stwalley, Elyse O Kharbanda, Catherine A Panozzo, Sara S Procknow, Anne M Butler

Abstract read
In one paragraph

Article in Paediatric and perinatal epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Katelin B NickelDepartment of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St. Louis, Missouri, USA.
Sascha DublinKaiser Permanente Washington Health Research Institute, Seattle, Washington, DC, USA.
Sharon FullerKaiser Permanente Washington Health Research Institute, Seattle, Washington, DC, USA.
Sarah S OsmundsonDepartment of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID https://orcid.org/0000-0002-7626-9992
Ryan ColvinDepartment of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St. Louis, Missouri, USA.
Dustin StwalleyInstitute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA.
Elyse O KharbandaPregnancy and Child Health Research Center, HealthPartners Institute, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0003-1806-6502
Catherine A PanozzoEpiphany Consulting, LLC, Pawtucket, Rhode Island, USA.
Sara S ProcknowDepartment of Pediatrics, Division of Genetics & Genomic Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.
Anne M ButlerDepartment of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St. Louis, Missouri, USA.ORCID https://orcid.org/0000-0001-7307-6864

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Identifying Optimal Antibiotic Regimens to Treat Urinary Tract Infections During PregnancyR01HD107083 · NICHD · WASHINGTON UNIVERSITY · PI Anne Mobley Butler · 2022 to 2026
$3.4M
Eunice Kennedy Shriver National Institute of Child Health and Human Development R01HD107083NCATS NIH HHS UL1 TR002345NICHD NIH HHS R01 HD107083
6 · The paper itself

Abstract

backgroundAccurate algorithms are needed to support real-world studies of medication safety in pregnancy. Kharbanda et al. developed and validated algorithms for congenital malformations that incorporate death data and diagnosis codes from the ICD-9-CM and ICD-10-CM coding eras.

objectivesTo modify an EHR-based malformation algorithm for use with claims data and quantify the impact of missing death by calculating prevalence and sensitivity.

methodsUsing the MarketScan Commercial Database (2007-2022), we established a linked cohort of birthing parents and their liveborn infants, and a claims subcohort restricted to years with inpatient death information. We established a cohort of liveborn infants in Kaiser Permanente Washington (KPWA) integrated EHR/claims data (2007-2022) that included comprehensive death information. We applied the validated algorithm to identify 22 malformations in MarketScan and 7 in KPWA (those with a prevalence ≥ 10 per 10,000 live births in MarketScan). In MarketScan, we calculated malformation prevalence with and without death information. We assessed the contribution of death on malformation identification by calculating sensitivity (with death as the gold standard).

resultsAmong 2,203,328 infants in the MarketScan cohort, malformation prevalence was 201.3 per 10,000 live births. In the MarketScan subcohort (n = 1,287,384), prevalence was 198.2 and 199.1 per 10,000 live births without and with death information, respectively. Among the most prevalent malformations, estimated sensitivity ranged from 95.8% for severe cardiac defects to 100.0% for intestinal atresia or stenosis, pyloric stenosis and limb deficiency (claims/EHR cohort) and from 98.6% for severe cardiac defects to 100.0% for intestinal atresia or stenosis and pyloric stenosis (claims subcohort). Limitations include the use of an imperfect gold standard and a lack of chart review.

conclusionsWe adapted a validated malformation algorithm for use with claims data. Omitting death information did not meaningfully impact sensitivity, suggesting this algorithm can be applied to data sources lacking death information.

Indexed as

Congenital AbnormalitiesInsurance Claim ReviewAlgorithmsDatabases, FactualElectronic Health RecordsFemaleHumansInfant, NewbornInternational Classification of DiseasesMalePregnancyPrevalenceUnited StatesWashingtonadministrative claimsalgorithmscohort studiescongenital abnormalitieselectronic health recordshealthcareinternational classification of diseasesvalidation

Identifiers

PMID41954183
PMCPMC13126116

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.