Evidence map›Paper›PMID 36621926›Full record

ArticleObesity (Silver Spring, Md.)2023

Explaining obesity disparities by urbanicity, 2006 to 2016: A decomposition analysis.

Emma Zang, Josefina Flores Morales, Liying Luo, Drishti Baid

Abstract read
In one paragraph

Article in Obesity (Silver Spring, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Emma ZangDepartment of Sociology, Yale University, New Haven, Connecticut, USA.ORCID 0000-0002-5700-3179
Josefina Flores MoralesDepartment of Sociology, University of California, Los Angeles, California, USA.ORCID 0000-0001-5370-1423
Liying LuoDepartment of Sociology and Criminology, Penn State University, University Park, Pennsylvania, USA.
Drishti BaidSol Price School of Public Policy, University of Southern California, Los Angeles, California, USA.

Funding

Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Denise Esserman · 2002 to 2026
$37.9M
The Population Research Institute: Development CoreP2CHD041025 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI Sarah Damaske · 2016 to 2026
$9.0M
A Multifactorial Approach to Evaluating Disparities in Outcomes after Major Surgery in Disadvantaged Older PersonsR01MD017298 · NIMHD · YALE UNIVERSITY · PI BECHER, ROBERT DAVID, GILL, THOMAS MICHAEL · 2022 to 2025
$2.6M
Heterogeneous Effects of Education on Alzheimers Disease and Related Dementia among Demographic Groups: A Multigenerational and Multilevel StudyR01AG078518 · NIA · PENNSYLVANIA STATE UNIVERSITY, THE · PI Liying Luo · 2022 to 2026
$1.4M
Linked Lives, Linked Health: Health Trajectories of Persons with Cognitive Impairment and Their Caregivers' HealthR21AG074238 · NIA · YALE UNIVERSITY · PI ZANG, EMMA · 2021 to 2022
$473k
NIA NIH HHS P30 AG021342NIA NIH HHS R01 AG078518NIA NIH HHS R21 AG074238NICHD NIH HHS P2C HD041025NIMHD NIH HHS R01 MD017298
6 · The paper itself

Abstract

objectiveA large, and potentially growing, disparity in obesity prevalence exists between large central metros and less urban United States counties. This study examines its key predictors.

methodsUsing a rich county-year data set spanning 2006 to 2016, the authors conducted a Gelbach decomposition to examine the relative importance of demographic, socioeconomic, environmental, and behavioral factors in shaping the baseline obesity gap and the growth rate over time between large central metros and other counties.

resultsPredictors included in this model explain almost the entire obesity gap between large central metros and other counties in the baseline year but can explain only ~32% of the growing gap. At baseline, demographic predictors explain more than half the obesity gap, and socioeconomic and behavioral predictors explain the other half. Behavioral and socioeconomic predictors explain more than half the growing gap over time whereas controlling for environmental and demographic predictors decreases the obesity gap by urbanicity over time.

conclusionsResults suggest policy makers should prioritize interventions targeting health behaviors of residents in non-large central metros to slow the growth of the obesity gap between large central metros and other counties. However, to fundamentally eliminate the obesity gap, in addition to improving health behaviors, policies addressing socioeconomic inequalities are needed.

Indexed as

Health BehaviorObesityHealth Status DisparitiesHumansPrevalenceSocioeconomic FactorsUnited States

Identifiers

PMID36621926
PMCPMC9877136

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.