ArticleAmerican journal of preventive medicine2015
Electronic health records and community health surveillance of childhood obesity.
Article in American journal of preventive medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature.Journal of medical systems · 2018Pooled it
- The Lifecycle of Electronic Health Record Data in HIV-Related Big Data Studies: Qualitative Study of Bias Instances and Potential Opportunities for Minimization.Journal of medical Internet research · 2025Article
- Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example.Online journal of public health informatics · 2024Article
- Postpartum Obesity Is Associated With Increases in Child Adiposity in Midchildhood in a Cohort of Black and Dominican Youth.Current developments in nutrition · 2024Article
- Addressing Selection Biases within Electronic Health Record Data for Estimation of Diabetes Prevalence among New York City Young Adults: A Cross-Sectional Study.BMJ public health · 2024Article
- Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review.Cureus · 2023Review
- Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records.Journal of the American Statistical Association · 2023Article
- Linking Electronic Health Records to the American Community Survey: Feasibility and Process.American journal of public health · 2022Article
- The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control.BMC medical informatics and decision making · 2022Article
- Article
- 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.Social science & medicine (1982) · 2022Article
- An electronic medical records study of population obesity prevalence in El Paso, Texas.BMC medical informatics and decision making · 2022Article
- Leveraging Data and Digital Health Technologies to Assess and Impact Social Determinants of Health (SDoH): a State-of-the-Art Literature Review.Online journal of public health informatics · 2021Article
- Moving Beyond Simple Risk Prediction: Segmenting Patient Populations Using Consumer Data.Frontiers in public health · 2021Article
- Small area estimation and childhood obesity surveillance using electronic health records.PloS one · 2021Article
- Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data.International journal of obesity (2005) · 2020Article
- Population-Based Registry Linkages to Improve Validity of Electronic Health Record-Based Cancer Research.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- Weight Loss for Patients With Obesity: An Analysis of Long-Term Electronic Health Record Data.Medical care · 2020Observational
- The effect of number of healthcare visits on study sample selection in electronic health record data.International journal of population data science · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundChildhood obesity remains a public health concern, and tracking local progress may require local surveillance systems. Electronic health record data may provide a cost-effective solution. PURPOSE: To demonstrate the feasibility of estimating childhood obesity rates using de-identified electronic health records for the purpose of public health surveillance and health promotion.
methodsData were extracted from the Public Health Information Exchange (PHINEX) database. PHINEX contains de-identified electronic health records from patients primarily in south central Wisconsin. Data on children and adolescents (aged 2-19 years, 2011-2012, n=93,130) were transformed in a two-step procedure that adjusted for missing data and weighted for a national population distribution. Weighted and adjusted obesity rates were compared to the 2011-2012 National Health and Nutrition Examination Survey (NHANES). Data were analyzed in 2014.
resultsThe weighted and adjusted obesity rate was 16.1% (95% CI=15.8, 16.4). Non-Hispanic white children and adolescents (11.8%, 95% CI=11.5, 12.1) had lower obesity rates compared to non-Hispanic black (22.0%, 95% CI=20.7, 23.2) and Hispanic (23.8%, 95% CI=22.4, 25.1) patients. Overall, electronic health record-derived point estimates were comparable to NHANES, revealing disparities from preschool onward.
conclusionsElectronic health records that are weighted and adjusted to account for intrinsic bias may create an opportunity for comparing regional disparities with precision. In PHINEX patients, childhood obesity disparities were measurable from a young age, highlighting the need for early intervention for at-risk children. The electronic health record is a cost-effective, promising tool for local obesity prevention efforts.
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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.