Evidence map›Paper›PMID 29857248›Full record

ArticlePublic health2018

Obesity trend in the United States and economic intervention options to change it: A simulation study linking ecological epidemiology and system dynamics modeling.

H-J Chen, H Xue, S Liu, T T K Huang, Y C Wang, Y Wang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 5 pooled it
3.1field-weighted citation impact, top 8% 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

14 citing papers in PubMed, 5 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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  5. A System Dynamics Simulation Applied to Healthcare: A Systematic Review.International journal of environmental research and public health · 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

6 authors at 5 institutions in 3 countries.

H-J ChenInstitute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Johns Hopkins Global Center on Childhood Obesity, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
H XueSystems-Oriented Global Childhood Obesity Intervention Program, Fisher Institute of Health and Well-Being, College of Health, Ball State University, Muncie, IN, USA; Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
S LiuResearch Institute of Economics and Management, Southwestern University of Finance and Economics, #55 Guanghua Ave, Chengdu, Sichuan 610074, China.
T T K HuangCUNY School of Public Health, New York, NY, USA; Department of Health Promotion, Social and Behavioral Health, University of Nebraska Medical Center, Omaha, NE, USA.
Y C WangDepartment of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, NY, USA.
Y WangSystems-Oriented Global Childhood Obesity Intervention Program, Fisher Institute of Health and Well-Being, College of Health, Ball State University, Muncie, IN, USA; Department of Nutrition and Health Sciences, College of Health, Ball State University, Muncie, IN, USA. Electronic address: ywang26@bsu.edu.
Ball State University · USCUNY School of Law · USJohns Hopkins University · USSouthwestern University of Finance and Economics · CNVirginia Commonwealth University · US

Funding

MULTILEVEL SYSTEMS-ORIENTED CHILDHOOD OBESITY STUDY IN CHINAU54HD070725 · NICHD · JOHNS HOPKINS UNIVERSITY · PI GITTELSOHN, JOEL · 2011 to 2016
$15.9M
Causes and Interventions for Childhood Obesity:Innovative Systems AnalysisR01HD064685 · NICHD · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI WANG, YOUFA · 2010 to 2013
$1.2M
NICHD NIH HHS R01 HD064685NICHD NIH HHS U54 HD070725
6 · The paper itself

Abstract

objectivesTo study the country-level dynamics and influences between population weight status and socio-economic distribution (employment status and family income) in the US and to project the potential impacts of socio-economic-based intervention options on obesity prevalence. STUDY

designEcological study and simulation.

methodsUsing the longitudinal data from the 2001-2011 Medical Expenditure Panel Survey (N = 88,453 adults), we built and calibrated a system dynamics model (SDM) capturing the feedback loops between body weight status and socio-economic status distribution and simulated the effects of employment- and income-based intervention options.

resultsThe SDM-based simulation projected rising overweight/obesity prevalence in the US in the future. Improving people's income from lower to middle-income group would help control the rising prevalence, while only creating jobs for the unemployed did not show such effect.

conclusionsImproving people from low- to middle-income levels may be effective, instead of solely improving reemployment rate, in curbing the rising obesity trend in the US adult population. This study indicates the value of the SDM as a virtual laboratory to evaluate complex distributive phenomena of the interplay between population health and economy.

Indexed as

AdultAgedComputer SimulationEmploymentFemaleHumansIncomeLongitudinal StudiesMaleMiddle AgedModels, StatisticalObesityPrevalenceSystems AnalysisUnited StatesYoung AdultEmployment rateIncomeObesitySimulationSystem dynamics model

Identifiers

PMID29857248
PMCPMC6953626
OpenAlexW2806456963

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.