ArticleSocial science & medicine (1982)2022
Using Electronic Health Records to understand the population of local children captured in a large health system in Durham County, NC, USA, and implications for population health research.
Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 18 citations in OpenAlex.
- Improving the Performance of Models Trained on Small EHR-Derived Samples by Leveraging External Data with Continual Learning Methods.medRxiv : the preprint server for health sciences · 2026Article
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- How representative are electronic health records? A record linkage study using individual-level census data.Social science & medicine (1982) · 2026Article
- Using clinical notes to identify children with speech-language delay and understand differences in diagnostic timing.JAMIA open · 2026Article
- Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records.Nature. Mental health · 2026Article
- Low uptake and disparities in therapeutic inertia of cardiorenal protective diabetes medications for patients with type 2 diabetes and above-target hemoglobin A1c.BMJ open diabetes research & care · 2026Article
- A comparative analysis estimating aspergillosis and histoplasmosis encounters among inpatients in the United States using multi-regional electronic health record and National Discharge Data, 2014-2020.AJE advances : research in epidemiology · 2026Article
- Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Discrete Time Neural Network Models to Address Time-Varying Predictor Importance: An Illustration in Predicting Mortality Over Different Time Horizons.IEEE journal of biomedical and health informatics · 2026Article
- Early life factors documented in electronic health records predict recurrent acute otitis media.PloS one · 2026Article
- Evaluating bias in electronic health record data: using agent-based models to examine whether geographic disparities in community-acquired methicillin-resistant Staphylococcus aureus are due to differential health care-seeking behaviors.American journal of epidemiology · 2025Article
- Estimating the observability of an outcome from an electronic health record data set using external data.American journal of epidemiology · 2025Article
- Neighborhood Environmental and Contextual Factors Improve Prediction of Childhood Body Mass Index: An Application of Novel Graph Neural Networks.AJE advances : research in epidemiology · 2025Article
- Poverty and Childhood Obesity: Current Evidence and Methodologies for Future Research.Current obesity reports · 2025Review
- Article
- Linking Electronic Health Record Prescribing Data and Pharmacy Dispensing Records to Identify Patient-Level Factors Associated With Psychotropic Medication Receipt: Retrospective Study.JMIR medical informatics · 2025Article
- A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis.Journal of biomedical informatics · 2024Article
- Interactive visualization tool to understand and monitor health disparities in diabetes care and outcomes.Journal of clinical and translational science · 2024Article
- Sex differences in the age of childhood autism diagnosis and the impact of co-occurring conditions.Autism research : official journal of the International Society for Autism Research · 2023Article
- Residential Structural Racism and Prevalence of Chronic Health Conditions.JAMA network open · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Although local policies aimed at reducing childhood health inequities can benefit from local data, sample size constraints in population representative health surveys often prevent rigorous evaluations of child health disparities and health care patterns at local levels. Electronic Health Records (EHRs) offer a possible solution as they contain large amounts of information on pediatric patients within a health system. In this paper, we consider the suitability of using EHRs from a large health system to study local children's health by evaluating the extent to which the EHRs capture the county's child population. First, we compare the demographic characteristics of Duke University Health System pediatric patients who live in Durham County, NC (USA) to the child population estimates in the 2015-2019 American Community Survey. We then examine geographic variation in census tract rates of children captured in the EHR data and estimate negative binomial models to assess how tract characteristics are associated with these rates. We also perform these analyses for the subset of pediatric patients who have a well-child encounter. We find that the demographic characteristics of pediatric patients captured by the EHRs are similar to those of the county's child population. Although the county rate of children captured in the EHRs is high, there is variation across census tracts. On average, census tracts with higher concentrations of non-Hispanic Black residents have lower capture rates and tracts with higher concentrations of poverty have higher capture rates, with the poorest tracts showing the largest racial gap in rates of children captured by EHRs. Our findings suggest that EHRs from a large health system can be used to assess children's population health, but that EHR-based evaluations of children's health disparities and health care patterns should account for differences in who is captured by the EHRs based on census tract characteristics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.