Evidence map›Paper›PMID 35762192›Full record

ArticlePediatric obesity2022

Evaluating the impact of community interventions on childhood obesity in populations living in low-income households in Los Angeles: A simulation study.

Roch A Nianogo, Megan P Mueller, Bryce Keeler, Kurt Kreuger, Lilly A Nhan, Tabashir Z Nobari, Catherine M Crespi, Nathaniel Osgood, Tony Kuo, Michael Prelip and 1 more

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

Article in Pediatric obesity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Health Related Social Needs Screening and Referral Fulfillment: Toward a Complex Model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
  3. 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

11 authors at 5 institutions in 2 countries.

Roch A NianogoDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, California, USA.ORCID 0000-0001-5932-6169
Megan P MuellerDepartment of Food Science and Human Nutrition, Colorado State University, Fort Collins, Colorado, USA.
Bryce KeelerDepartment of Computer Science, University of Saskatchewan, Saskatoon, Canada.
Kurt KreugerDepartment of Computer Science, University of Saskatchewan, Saskatoon, Canada.
Lilly A NhanDepartment of Community Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, USA.
Tabashir Z NobariDepartment of Community Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, USA.
Catherine M CrespiDepartment of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, California, USA.
Nathaniel OsgoodDepartment of Computer Science, University of Saskatchewan, Saskatoon, Canada.
Tony KuoDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, California, USA.ORCID 0000-0002-4120-8559
Michael PrelipDepartment of Community Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, USA.
May C WangCalifornia Center for Population Research, UCLA, Los Angeles, California, USA.
University of California, Los Angeles · USUniversity of Saskatchewan · CACalifornia State University, Fullerton · USColorado State University · USLos Angeles County Department of Public Health · US

Funding

The impact of natural experiments on child obesity: A systems science approachR01HD072296 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PRELIP, MICHAEL LEE, WANG, MAY C · 2013 to 2017
$2.4M
NICHD NIH HHS R01 HD072296
6 · The paper itself

Abstract

backgroundThe complex multifactorial nature of childhood obesity makes community interventions difficult to evaluate using traditional approaches; innovative methods are needed.

objectiveTo evaluate the impact of various interventions targeting childhood obesity-related behaviours, and classified as using a micro-level (e.g., home visitation programs) or macro-level (e.g., business practices) strategy, on obesity among children enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).

methodsWe simulated a population of 1500 children enrolled in WIC, with specific diet, physical activity, breastfeeding behaviours and body mass index z-scores (BMIz), following them from age 2 to 5 years.

resultsCombined interventions targeting breastfeeding appeared to be moderately effective, reducing BMIz by 0.03 (95% CI -005, -0.01). Two strategy-specific interventions, home visitation programs and business practices targeting obesity-related behaviours, appeared to be moderately effective at reducing BMIz by 0.04 (95% CI -0.06, -0.02) and 0.02 (95% CI -0.04, 0.00), respectively. Contrary to expectation, combining all micro and macro interventions appeared to have no impact or moderately increased the proportion of obesity/overweight among children.

conclusionInterventions targeting breastfeeding behaviour were most effective when both micro and macro strategies were implemented. Interventions targeting obesity-related behaviours in general were effective for two strategies, home visitation and business practices.

Indexed as

Pediatric ObesityBreast FeedingChildChild, PreschoolDietFemaleHumansInfantLos AngelesPovertyadipositychildhood obesitycommunityinterventionssimulation

Identifiers

PMID35762192
PMCPMC10754058
OpenAlexW4283640751

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.