Evidence map›Paper›PMID 38326799›Full record

ArticleBMC public health2024

Relative predictive value of sociodemographic factors for chronic diseases among All of Us participants: a descriptive analysis.

Ansley J Kunnath, Daniel E Sack, Consuelo H Wilkins

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
5.3field-weighted citation impact, top 4% of its field
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

11 citing papers in PubMed, 10 citations in OpenAlex.

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  6. Letter to the editor: Reassessing predictive models for hypertension control in resource-constrained settings.International journal of cardiology. Cardiovascular risk and prevention · 2025
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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

3 authors at 2 institutions in 1 country.

Ansley J KunnathVanderbilt University Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN, USA.
Daniel E SackVanderbilt University Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN, USA.
Consuelo H WilkinsDepartment of Medicine, Vanderbilt University Medical Center, 2525 West End, Suite 600, Nashville, TN, 37203, USA. consuelo.h.wilkins@vumc.org.
Vanderbilt University · USVanderbilt University Medical Center · US

Funding

Vanderbilt Institute for Clinical and Translational Research (VICTR) -Identifying correlates of functional immunity in SARS-CoV-2 convalescent plasmaUL1TR002243 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Paul A. Harris, Wesley H Self · 2017 to 2026
$130.7M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM007347 · NIGMS · VANDERBILT UNIVERSITY · PI WILLIAMS, CHRISTOPHER S. · 1985 to 2023
$26.3M
Southeast Collaborative for Innovative Solutions to Chronic DiseasesP50MD017347 · NIMHD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI WILKINS, CONSUELO HOPKINS · 2021 to 2025
$15.5M
Medical Scientist Training ProgramT32GM152284 · NIGMS · VANDERBILT UNIVERSITY · PI Christopher S. Williams · 2024 to 2026
$4.8M
Multisensory Integration and Cortical Plasticity in Cochlear Implant UsersF30DC020917 · NIDCD · VANDERBILT UNIVERSITY · PI KUNNATH, ANSLEY · 2022 to 2025
$153k
A Couples-Based Intervention and Postpartum Contraceptive Uptake in Zambézia Province, MozambiqueF30MH123219 · NIMH · VANDERBILT UNIVERSITY · PI SACK, DANIEL ELI · 2020 to 2022
$105k
NCATS NIH HHS UL1 TR002243NIDCD NIH HHS F30 DC020917NIDCD NIH HHS F30DC020917NIGMS NIH HHS T32 GM007347NIGMS NIH HHS T32GM007347NIGMS NIH HHS T32 GM152284NIH HHS P50MD017347NIMHD NIH HHS P50 MD017347NIMH NIH HHS F30 MH123219NIMH NIH HHS F30MH123219
6 · The paper itself

Abstract

backgroundAlthough sociodemographic characteristics are associated with health disparities, the relative predictive value of different social and demographic factors remains largely unknown. This study aimed to describe the sociodemographic characteristics of All of Us participants and evaluate the predictive value of each factor for chronic diseases associated with high morbidity and mortality.

methodsWe performed a cross-sectional analysis using de-identified survey data from the All of Us Research Program, which has collected social, demographic, and health information from adults living in the United States since May 2018. Sociodemographic data included self-reported age, sex, gender, sexual orientation, race/ethnicity, income, education, health insurance, primary care provider (PCP) status, and health literacy scores. We analyzed the self-reported prevalence of hypertension, coronary artery disease, any cancer, skin cancer, lung disease, diabetes, obesity, and chronic kidney disease. Finally, we assessed the relative importance of each sociodemographic factor for predicting each chronic disease using the adequacy index for each predictor from logistic regression.

resultsAmong the 372,050 participants in this analysis, the median age was 53 years, 59.8% reported female sex, and the most common racial/ethnic categories were White (54.0%), Black (19.9%), and Hispanic/Latino (16.7%). Participants who identified as Asian, Middle Eastern/North African, and White were the most likely to report annual incomes greater than $200,000, advanced degrees, and employer or union insurance, while participants who identified as Black, Hispanic, and Native Hawaiian/Pacific Islander were the most likely to report annual incomes less than $10,000, less than a high school education, and Medicaid insurance. We found that age was most predictive of hypertension, coronary artery disease, any cancer, skin cancer, diabetes, obesity, and chronic kidney disease. Insurance type was most predictive of lung disease. Notably, no two health conditions had the same order of importance for sociodemographic factors.

conclusionsAge was the best predictor for the assessed chronic diseases, but the relative predictive value of income, education, health insurance, PCP status, race/ethnicity, and sexual orientation was highly variable across health conditions. Identifying the sociodemographic groups with the largest disparities in a specific disease can guide future interventions to promote health equity.

Indexed as

Coronary Artery DiseaseDiabetes MellitusHypertensionLung DiseasesPopulation HealthRenal Insufficiency, ChronicSkin NeoplasmsAdultChronic DiseaseCross-Sectional StudiesFemaleHealth PromotionHumansMaleMiddle AgedObesityAll of Us Research ProgramChronic diseasesSocial determinants of health

Identifiers

PMID38326799
PMCPMC10851469
OpenAlexW4392732738

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.