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A process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records.
Scott, Kenneth A; Davies, Sara Deakyne; Zucker, Rachel; Ong, Toan; Kraus, Emily McCormick; Kahn, Michael G; Bondy, Jessica; Daley, Matt F; Horle, Kate; Bacon, Emily; Schilling, Lisa; Crume, Tessa; Hasnain-Wynia, Romana; Foldy, Seth; Budney, Gregory; Davidson, Arthur J.
Afiliación
  • Scott KA; Denver Public Health Denver Health Denver Colorado USA.
  • Davies SD; Department of Epidemiology Colorado School of Public Health, University of Colorado Anschutz Medical Campus Aurora Colorado USA.
  • Zucker R; Analytics Research Center Children's Hospital Colorado Aurora Colorado USA.
  • Ong T; Adult and Child Consortium for Health Outcomes Research and Delivery Science (ACCORDS) University of Colorado Anschutz Medical Campus Aurora Colorado USA.
  • Kraus EM; Department of Pediatrics School of Medicine, University of Colorado Anschutz Medical Campus Aurora Colorado USA.
  • Kahn MG; Kraushold Consulting LLC Atlanta Georgia USA.
  • Bondy J; Department of Pediatrics School of Medicine, University of Colorado Anschutz Medical Campus Aurora Colorado USA.
  • Daley MF; Department of Biostatistics and Informatics Colorado School of Public Health, University of Colorado Anschutz Medical Campus Denver Colorado USA.
  • Horle K; Bacon Analytics, LLC Denver Colorado USA.
  • Bacon E; Institute for Health Research, Kaiser Permanente Colorado Aurora Colorado USA.
  • Schilling L; CORHIO Denver Colorado USA.
  • Crume T; Denver Public Health Denver Health Denver Colorado USA.
  • Hasnain-Wynia R; Bacon Analytics, LLC Denver Colorado USA.
  • Foldy S; Adult and Child Consortium for Health Outcomes Research and Delivery Science (ACCORDS) University of Colorado Anschutz Medical Campus Aurora Colorado USA.
  • Budney G; Division of General Internal Medicine, Department of Medicine University of Colorado Denver School of Medicine Aurora Colorado USA.
  • Davidson AJ; Department of Epidemiology Colorado School of Public Health, University of Colorado Anschutz Medical Campus Aurora Colorado USA.
Learn Health Syst ; 6(3): e10297, 2022 Jul.
Article en En | MEDLINE | ID: mdl-35860322
ABSTRACT

Introduction:

Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates.

Methods:

We operationalized a two-step deduplication process, leveraging health information exchange (HIE)-assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0-17 years) with at least one 2017 encounter in one of two geographically-proximate DDN partners. We assessed the extent of cross-system duplication and its effect on prevalence estimates.

Results:

We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute.

Conclusions:

We implemented an HIE-dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prevalence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Learn Health Syst Año: 2022 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prevalence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Learn Health Syst Año: 2022 Tipo del documento: Article