Evidence map›Paper›PMID 39585694›Full record

ArticleJAMA network open2024

Modeling Health and Economic Outcomes of Eliminating Sex Disparities in Youth Physical Activity.

Kosuke Tamura, Marie F Martinez, Yangyang Deng, Jessie Heneghan, Colleen Weatherwax, Kavya Velmurugan, Kevin L Chin, Breanna Rogers, Mohammad Moniruzzaman, Meredith Whitley and 5 more

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Observational
  3. Review
  4. Article
  5. Article
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

15 authors.

Kosuke TamuraSocio-Spatial Determinants of Health Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland.
Marie F MartinezPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Yangyang DengSocio-Spatial Determinants of Health Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland.
Jessie HeneghanPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Colleen WeatherwaxPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Kavya VelmuruganPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Kevin L ChinPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Breanna RogersSocio-Spatial Determinants of Health Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland.
Mohammad MoniruzzamanSocio-Spatial Determinants of Health Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland.
Meredith WhitleyRuth S. Ammon College of Education and Health Sciences, Adelphi University, Garden City, New York.
Sarah M BartschPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Kelly J O'SheaPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Alexis DibbsPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Sheryl ScannellPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Bruce Y LeePublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health and Health Policy, New York, New York.

Funding

Project 4: Virtual Public Health Precision Nutrition LaboratoryU54TR004279 · NCATS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI Bruce Y Lee · 2022 to 2026
$5.4M
Regional Healthcare Ecosystem Analyst (RHEA) Modeling the Environment (MODE): SARS-CoV-2R01GM127512 · NIGMS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI LEE, BRUCE Y · 2020 to 2023
$2.1M
Socio-Spatial Determinants of Health (SSDH) ZIAMD000020 · NIMHD · NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES · PI TAMURA, KOSUKE · 2021 to 2025
$1.0M
AHRQ HHS R01 HS028165Intramural NIH HHS ZIA MD000020NCATS NIH HHS U54 TR004279NIGMS NIH HHS R01 GM127512
6 · The paper itself

Abstract

Importance: Sex disparities in physical activity (PA) and sports participation among US children and adolescents have been persistent. Quantifying the impact of reducing or eliminating these disparities may help determine how much to prioritize this problem and invest in interventions and policies to reduce them. Objective: To quantify what might happen if existing PA and sports participation disparities were reduced or eliminated between male and female children and adolescents. Design, Setting, and Participants: This simulation study used an agent-based model representing all children (aged 6 to 17 years) in the US, their PA and sports participation levels, and relevant physical and physiologic characteristics (eg, body mass index) as of 2023. Experiments conducted from April 5, 2024, to September 10, 2024, simulated what would happen during the lifetime of each cohort member if PA and sports participation levels for female participants were increased (to varying degrees) to match male participants in the same age group. Main Outcomes and Measures: Health outcomes, such as body mass index, incidence of weight-related conditions (eg, stroke, coronary heart disease, type 2 diabetes, and cancer), and economic outcomes (eg, direct medical costs and productivity losses). Results: This simulation study modeled 8 299 353 US children and adolescents (4 240 119 [51.1%] male and 4 059 234 [48.9%] female) aged 6 to 17 years. Eliminating PA sex disparities averted 28 061 (95% CI, 25 358-30 763) overweight and obesity cases per cohort by age 18 years, which in turn averted 4869 (95% CI, 4007-5732) weight-related disease cases during their lifetimes and resulted in recurring savings of $333.45 million (95% CI, $290.22 million to $376.68 million) in direct medical costs and $446.42 million (95% CI, $327.39 million to $565.44 million) in productivity losses (in 2024 US dollars) for every new cohort of 6- to 17-year-olds. Reducing PA disparities by 50% averted 9027 (95% CI, 6942-11 112) overweight and obesity cases. Eliminating sex disparities in sports participation averted 41 499 (95% CI, 37 874-45 125) cases of overweight and obesity and 8939 (95% CI, 8088-9790) weight-related disease cases during their lifetimes, generating recurring savings of $713.48 million (95% CI, $668.80 million to $758.16 million) in direct medical costs and $839.68 million (95% CI, $721.18 million to $958.18 million) in productivity losses. Conclusions and Relevance: In this simulation study of youth PA and sports participation, eliminating sex disparities could save millions of dollars for each new cohort of 6- to 17-year-olds, which could exceed the cost of programs and investments that could enable greater equity.

Indexed as

ExerciseAdolescentBody Mass IndexChildFemaleHealth Status DisparitiesHumansMaleSex FactorsSportsUnited States

Identifiers

PMID39585694
PMCPMC11589798

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.