ArticleAcademic pediatrics2021
Using Large Aggregated De-Identified Electronic Health Record Data to Determine the Prevalence of Common Chronic Diseases in Pediatric Patients Who Visited Primary Care Clinics.
Article in Academic pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Physical and Psychosocial Benefits of Sports Participation Among Children and Adolescents with Chronic Diseases: A Systematic Review.Sports medicine - open · 2024Pooled it
- The Neuroimmunology of Autism.Molecular neurobiology · 2025Review
- Definitions and operationalizations of pediatric chronic patients: a scoping review.European journal of pediatrics · 2025Article
- Integrating functional data analysis with case-based reasoning for hypertension prognosis and diagnosis based on real-world electronic health records.BMC medical informatics and decision making · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
objectiveWe used de-identified clinical data from multiple health care systems using different electronic health records (EHRs) to 1) quantify the prevalence of common pediatric chronic diseases, 2) investigate patent characteristics associated with common pediatric chronic diseases, and 3) compare the results of our methodology to determine chronic disease prevalence with traditional approaches.
methodsWe used a HIPAA-compliant and de-identified web application (Explorys; IBM Watson Health Explorys Inc.) to identify patients 17 years old or younger from multiple health care systems in the US who were seen in primary care clinics between 2016-2018 to determine the most common chronic conditions in this age group. The prevalence of chronic conditions was compared between different age groups, genders, races/ethnicities, and insurance; odds ratios and confidence intervals were calculated and reported.
resultsThe top 6 chronic conditions identified by prevalence were: obesity/overweight (36.7%), eczema (15.8%), asthma (12.7%), food allergies (4.7%), attention deficit-hyperactivity disorder (4.09%) and hypertension (4.07%). Chronic conditions were generally more prevalent among relatively older pediatric patients, males, and African-American and multiracial groups.
conclusionsApproximately 40% of children and adolescents have at least one chronic disease. Obesity/overweight, eczema, and asthma are the most common chronic diseases of childhood, in the US, among those seeking care in health care systems with EHRs. The work compiled herein demonstrates that aggregated, standardized, normalized and de-identified population-level EHR data can provide both sufficient insight and statistical power to calculate the prevalence of chronic diseases and the odds ratio of various risk factors.
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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.