Evidence map›Paper›PMID 31424169›Full record

ArticleObesity (Silver Spring, Md.)2019

Dynamic Empirically Based Model for Understanding Future Trends in US Obesity Prevalence in the Context of Social Influences.

Leah Frerichs, Ozgur M Araz, Larissa Calancie, Terry T-K Huang, Kristen Hassmiller Lich

Abstract read
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In one paragraph

Article in Obesity (Silver Spring, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
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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.

Leah FrerichsDepartment of Health Policy and Management, Gillings Global School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.ORCID 0000-0002-2507-3789
Ozgur M ArazCollege of Business, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Larissa CalancieCenter for Health Equity Research, University of North Carolina, Chapel Hill, North Carolina, USA.
Terry T-K HuangCenter for Systems and Community Design, Graduate School of Public Health & Health Policy, City University of New York, New York, New York, USA.
Kristen Hassmiller LichDepartment of Health Policy and Management, Gillings Global School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.

Funding

Identifying and Disentangling Social and Physical Environmental Effects on Physical Activity in Diverse Adolescent and Young Adult PopulationsK01HL138159 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FRERICHS, LEAH M. · 2017 to 2021
$780k
NHLBI NIH HHS K01 HL138159
6 · The paper itself

Abstract

objectiveThis study aimed to (1) identify mechanistic model structures that produced quality fit to historic obesity prevalence trends and (2) evaluate the sensitivity of future obesity prevalence to social transmission and nonsocial parameters.

methodsAn age- and gender-structured compartmental model was used to describe transitions between weight status groups. Four model structures with different combinations of social transmission and nonsocial mechanisms were calibrated to match historic time series and assessed for quality of fit. Projections of overall obesity prevalence to 2052 were simulated, and sensitivity analyses were conducted.

resultsThe model structure that included only nonsocial mechanisms indicated that the overall obesity prevalence in the United States has already stabilized and will increase little more; however, it underestimated observed obesity prevalence since 2013. If social transmission mechanisms influence obesity, the model estimated continued increases in obesity prevalence, reaching 48.0% to 55.1% by 2050. Obesity prevalence was most sensitive to changes in the adult social transmission parameters, especially among women.

conclusionsThe model projected that US obesity prevalence in the overall population will likely continue to increase for decades. The findings that obesity prevalence was most sensitive to adult parameters can be used to inform conversations about priorities for public health and health care programs and policies.

Indexed as

Models, StatisticalPeer InfluenceSocial ChangeAdolescentAdultBiobehavioral SciencesChildChild, PreschoolFemaleHumansInfantInfant, NewbornMaleMiddle AgedObesityPrevalence

Identifiers

PMID31424169
PMCPMC6756946

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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.