ArticleBMC medical informatics and decision making2014
Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis.
Article in BMC medical informatics and decision making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature.Journal of medical systems · 2018Pooled it
- Automated extraction of weight, height, and obesity in electronic medical records are highly valid.Obesity science & practice · 2024Article
- Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.Journal of clinical and translational science · 2024Article
- Natural language processing to identify social determinants of health in Alzheimer's disease and related dementia from electronic health records.Health services research · 2023Article
- Characterization of an obese population: a retrospective longitudinal study from real-world data in northern Portugal.BMC primary care · 2023Article
- Automating data collection methods in electronic health record systems: a Social Determinant of Health (SDOH) viewpoint.Health systems (Basingstoke, England) · 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
- An electronic medical records study of population obesity prevalence in El Paso, Texas.BMC medical informatics and decision making · 2022Article
- "Engaging stakeholders in integrating social determinants of health into electronic health records: a scoping review".International journal of circumpolar health · 2021Article
- 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
- Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data.International journal of obesity (2005) · 2020Article
- Weight Loss for Patients With Obesity: An Analysis of Long-Term Electronic Health Record Data.Medical care · 2020Observational
- Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review.JMIR public health and surveillance · 2019Review
- Progress towards using community context with clinical data in primary care.Family medicine and community health · 2019Article
- Implementation of a Regional Perinatal Data Repository from Clinical and Billing Records.Maternal and child health journal · 2018Article
- Article
- Utilizing patient geographic information system data to plan telemedicine service locations.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Electronic Health Record Data Versus the National Health and Nutrition Examination Survey (NHANES): A Comparison of Overweight and Obesity Rates.Medical care · 2017Article
- Factors associated with overweight: are the conclusions influenced by choice of the regression method?BMC public health · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity and overweight are multifactorial diseases that affect two thirds of Americans, lead to numerous health conditions and deeply strain our healthcare system. With the increasing prevalence and dangers associated with higher body weight, there is great impetus to focus on public health strategies to prevent or curb the disease. Electronic health records (EHRs) are a powerful source for retrospective health data, but they lack important community-level information known to be associated with obesity. We explored linking EHR and community data to study factors associated with overweight and obesity in a systematic and rigorous way.
methodsWe augmented EHR-derived data on 62,701 patients with zip code-level socioeconomic and obesogenic data. Using a multinomial logistic regression model, we estimated odds ratios and 95% confidence intervals (OR, 95% CI) for community-level factors associated with overweight and obese body mass index (BMI), accounting for the clustering of patients within zip codes.
results33, 31 and 35 percent of individuals had BMIs corresponding to normal, overweight and obese, respectively. Models adjusted for age, race and gender showed more farmers' markets/1,000 people (0.19, 0.10-0.36), more grocery stores/1,000 people (0.58, 0.36-0.93) and a 10% increase in percentage of college graduates (0.80, 0.77-0.84) were associated with lower odds of obesity. The same factors yielded odds ratios of smaller magnitudes for overweight. Our results also indicate that larger grocery stores may be inversely associated with obesity.
conclusionsIntegrating community data into the EHR maximizes the potential of secondary use of EHR data to study and impact obesity prevention and other significant public health issues.
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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.