Addressing Selection Biases within Electronic Health Record Data for Estimation of Diabetes Prevalence among New York City Young Adults: A Cross-Sectional Study.

ABSTRACT:

Introduction

There is growing interest in using electronic health records (EHRs) for chronic disease surveillance. However, these data are convenience samples of in-care individuals, which are not representative of target populations for public health surveillance, generally defined, for the relevant period, as resident populations within city, state, or other jurisdictions. We focus on using EHR data for estimation of diabetes prevalence among young adults in New York City, as rising diabetes burden in younger ages call for better surveillance capacity.

Methods

This article applies common nonprobability sampling methods, including raking, post-stratification, and multilevel regression with post-stratification, to real and simulated data for the cross-sectional estimation of

SUBMITTER: Conderino S 

PROVIDER: S-EPMC11578099 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature