Evidence map›Paper›PMID 28079710›Full record

ArticleMedical care2017

Electronic Health Record Data Versus the National Health and Nutrition Examination Survey (NHANES): A Comparison of Overweight and Obesity Rates.

Luke M Funk, Ying Shan, Corrine I Voils, John Kloke, Lawrence P Hanrahan

Abstract readComparative Study
In one paragraph

Article in Medical care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
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  16. Beyond insurance: race-based disparities in the use of metabolic and bariatric surgery for the management of severe pediatric obesity.Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery · 2020
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  19. US Pediatrician Practices on Early Nutrition, Feeding, and Growth.Journal of nutrition education and behavior · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Luke M Funk*William S. Middleton VA Hospital †Department of Surgery, Wisconsin Surgical Outcomes Research Program (WiSOR), University of Wisconsin-Madison, Madison, WI ‡Center for Health Services Research in Primary Care, Veterans Affairs Medical Center §Department of Medicine, Duke University Medical Center, Durham, NC Departments of ∥Biostatistics and Medical Informatics ¶Family Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Ying Shan
Corrine I Voils
John Kloke
Lawrence P Hanrahan

Funding

Identifying and Addressing Barriers to Bariatric Surgery within VAIK2HX002064 · VA · WM S. MIDDLETON MEMORIAL VETERANS HOSP · PI FUNK, LUKE M · 2018 to 2020
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HSRD VA IK2 HX002064VA RCS 14-443
6 · The paper itself

Abstract

backgroundEstimating population-level obesity rates is important for informing policy and targeting treatment. The current gold standard for obesity measurement in the United States-the National Health and Nutrition Examination Survey (NHANES)-samples <0.1% of the population and does not target state-level or health system-level measurement.

objectiveTo assess the feasibility of using body mass index (BMI) data from the electronic health record (EHR) to assess rates of overweight and obesity and compare these rates to national NHANES estimates. RESEARCH

designUsing outpatient data from 42 clinics, we studied 388,762 patients in a large health system with at least 1 primary care visit in 2011-2012. MEASURES: We compared crude and adjusted overweight and obesity rates by age category and ethnicity (white, black, Hispanic, Other) between EHR and NHANES participants. Adjusted overweight (BMI≥25) and obesity rates were calculated by a 2-step process. Step 1 accounted for missing BMI data using inverse probability weighting, whereas step 2 included a poststratification correction to adjust the EHR population to a nationally representative sample.

resultsAdjusted rates of obesity (BMI≥30) for EHR patients were 37.3% [95% confidence interval (95% CI), 37.1-37.5] compared with 35.1% (95% CI, 32.3-38.1) for NHANES patients. Among the 16 different obesity class, ethnicity, and sex strata that were compared between EHR and NHANES patients, 14 (87.5%) contained similar obesity estimates (ie, overlapping 95% CIs).

conclusionsEHRs may be an ideal tool for identifying and targeting patients with obesity for implementation of public health and/or individual level interventions.

Indexed as

Data AccuracyElectronic Health RecordsNutrition SurveysAdultDatabases, FactualFeasibility StudiesFemaleHumansMaleMiddle AgedObesityUnited StatesYoung Adult

Identifiers

PMID28079710
PMCPMC6985906

What OpenQuestion holds

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Registered trials

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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.