New COVID-19 Study Findings Have Been Reported from Kaiser Permanente Washington Health Research Institute (Empirical Application of Missing Data Methods To Address Confounding In Health Insurance Claims-based Analyses Using Electronic Health …): Coronavirus – COVID-19

2026 OCT 16 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx COVID-19 Daily -- Investigators discuss new findings in Coronavirus - COVID-19. According to news reporting originating in Seattle, Washington, by NewsRx journalists, research stated, “Health insurance claims-based analyses inform many large epidemiologic studies but may have unmeasured confounding. Electronic health records (EHRs) have more detailed health information, but data may be missing on some individuals.”

Financial support for this research came from US Food & Drug Administration (FDA).

The news reporters obtained a quote from the research from Kaiser Permanente Washington Health Research Institute, “Informed by recent work comparing approaches to address missing data, we selected and applied generalized raking (GR) and multiple imputation (MI) to illustrate how to efficiently combine data from these 2 sources. We considered a previously conducted claims-based study comparing 90-day arterial thromboembolism (ATE) risk among patients hospitalized with COVID-19 versus influenza, for which body mass index (BMI), a potential confounder, was unavailable. Using linked claims-EHR data, we adjusted for the same covariates as the original study and used GR and MI to additionally control for EHR-ascertained BMI, available for a subset. We included 912 hospitalized Kaiser Permanente Washington patients, 449 with COVID-19 and 463 with influenza (31.0% and 38.5% had EHR-ascertained BMI in the prior 90 days, respectively). Adjusted hazard ratios of ATE were similar with and without adjustment for BMI, in GR and MI analyses.”

According to the news reporters, the research concluded: “In the setting of a claims-EHR-based study with missing data on a potential confounder, we describe our selection of the missing data approach, and its implementation, to demonstrate a broadly applicable process.”

This research has been peer-reviewed.

For more information on this research see: Empirical Application of Missing Data Methods To Address Confounding In Health Insurance Claims-based Analyses Using Electronic Health Records Available On a Subset: an Example From the Food and Drug Administration’s Sentinel System. American Journal of Epidemiology, 2026. American Journal of Epidemiology can be contacted at: Oxford Univ Press Inc, Journals Dept, 2001 Evans Rd, Cary, NC 27513, USA. (Oxford University Press - http://www.oup.com/; American Journal of Epidemiology - aje.oxfordjournals.org)

Our news correspondents report that additional information may be obtained by contacting Gaia Pocobelli, Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, United States. Additional authors for this research include Noorie Hyun, Pamela A. Shaw, Laura B. Harrington, Arvind Ramaprasan, Susan M. Shortreed, John G. Connolly, Sengwee Toh, Rishi J. Desai, Sarah K. Dutcher, Hana Lee, Yan Li, Mingfeng Zhang and Jummai Apata.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1093/aje/kwag198. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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