Addressing selection biases within electronic health record data for estimation of diabetes prevalence among New York City young adults: a cross-sectional study
While EHRs offer potential to innovate on chronic disease surveillance, care is needed when estimating prevalence for small geographies or when selection is non-ignorable.
Authors
- Sarah Conderino (ORCID: https://orcid.org/0000-0003-1762-9262)
- Jasmin Divers (ORCID: https://orcid.org/0000-0003-0120-7620)
- Rebecca Anthopolos (ORCID: https://orcid.org/0000-0002-7251-7792)
- Shannon M. Farley (ORCID: https://orcid.org/0000-0003-0321-5974)
- Sandra S. Albrecht (ORCID: https://orcid.org/0000-0003-3444-2192)
- Lorna E. Thorpe (ORCID: https://orcid.org/0000-0002-5535-2674)
- David C. Lee
Institutions
- Long Island University (US)
- ICAP Global Health
- New York University (US)
- Columbia University (US)
Publication Details
- Journal
- BMJ Public Health
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1136/bmjph-2024-001666
- Citations
- 2
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.77
Funders
- Centers for Disease Control and Prevention