Abstract Background Vaccine effectiveness (VE) studies are necessary to understand how well vaccines work in the real world. Many VE studies rely on health records to capture underlying medical conditions (UMCs) from a single acute respiratory illness- (ARI) associated encounter, which may bias VE if UMCs are not fully captured. We assessed capture of UMCs from a single acute encounter and a lookback period. Methods Data were used from MarketScan® Treatment Pathways, a healthcare claims dataset, between September 1, 2023 – August 31, 2024. We included beneficiaries aged ≥18+ years with ≥1 inpatient or emergency department (ED) claim containing an ICD-10 code for ARI who had 3 years of continuous enrollment in a participating insurance plan prior to the date of their first ARI claim (i.e., index encounter). The prevalence of UMCs was calculated using ICD-10 codes from 1) the index encounter, and 2) the 1-year lookback period; and the difference was reported. Negative predictive value (NPV) with 95% exact binomial confidence intervals was calculated for identification of UMCs on the index encounter date, using the 1-year lookback period to define true negatives. Results were stratified by age group and encounter setting. Results Among 65,056 beneficiaries with ≥1 inpatient ARI event and 162,943 with ≥1 ED ARI event, the most prevalent UMC categories were cardiovascular, endocrine/metabolic, and respiratory (Tables 1-4). Among beneficiaries aged 18–64 years, NPV was 80% for cardiovascular, endocrine/metabolic, and obesity categories; median difference in prevalence was 9.5 percentage points (pp) (min=0, max=30) (Tables 1 and 3). Among beneficiaries aged ≥65+ years, NPV was ≤80% for respiratory, cardiovascular, neurological and musculoskeletal, endocrine/metabolic, renal, and obesity categories; median difference in prevalence was 13.5 pp (min=0, max=52) (Tables 2 and 4). NPV, regardless of age, was 90% for UMC categories with the lowest overall prevalence (i.e., cerebrovascular, hematologic, and underweight categories) (Tables 1-4). Conclusion NPV was 80% for common UMCs when identified using a single ARI encounter compared to a 1-year lookback period. Misclassification may influence VE estimates if UMCs are confounders in VE studies. Disclosures Sara Y. Tartof, PhD, MPH, Centers for Disease Control and Prevention: Grant/Research Support Karthik Natarajan, PhD, Centers for Disease Control and Prevention: Grant/Research Support Stephanie Irving, MHS, Westat: Grant/Research Support Nicola P. Klein, MD, PhD, AstraZeneca: Grant/Research Support|Centers for Disease Control and Prevention: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Janssen: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support|Seqirus: Grant/Research Support Shaun J. Grannis, MD, MS, Centers for Disease Control and Prevention: Grant/Research Support|National Institutes of Health NCATS: Grant/Research Support|National Institutes of Health NIMH: Grant/Research Support Toan Ong, PhD, Centers for Disease Control and Prevention via Westat: Grant/Research Support|Patent Title: Systems and Methods For Record Linkage: Patent Number: PCT/US2018/047961|PCORI: Travel Support|Regenstrief Institute: Advisor/Consultant|Regenstrief Institute: Travel Support Sarah W. Ball, MPH, ScD, Centers for Disease Control and Prevention, Contract #200-2019-F-06819: Grant/Research Support|Centers for Disease Control and Prevention, Contract #75D30121D12779: Grant/Research Support|Novavax: Grant/Research Support Malini B. DeSilva, MD, MPH, Centers for Disease Control and Prevention Vaccine Safety Datalink: Grant/Research Support|Westat: Grant/Research Support Ryan E. Wiegand, PhD, Merck & Co., Inc.: Stocks/Bonds (Public Company)|Sanofi S.A.: Stocks/Bonds (Public Company)
Kautz et al. (Thu,) studied this question.