Evidence map›Paper›PMID 32162715›Full record

ArticlePaediatric and perinatal epidemiology2020

Effects of intergenerational exposure interventions on adolescent outcomes: An application of inverse probability weighting to longitudinal pre-birth cohort data.

Yu-Han Chiu, Sheryl L Rifas-Shiman, Ken Kleinman, Emily Oken, Jessica G Young

Open access · greenAbstract read
In one paragraph

Article in Paediatric and perinatal epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

9 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Cohort Profile Update: Project Viva Offspring.International journal of epidemiology · 2024
    Article
  5. Cohort Profile Update: Project Viva mothers.International journal of epidemiology · 2023
    Article
  6. Article
  7. Review
  8. Article
  9. 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

5 authors at 2 institutions in 1 country.

Yu-Han ChiuDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.ORCID 0000-0002-0904-7855
Sheryl L Rifas-ShimanDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Ken KleinmanDepartment of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA.
Emily OkenDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Jessica G YoungDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Harvard University · USUniversity of Massachusetts Amherst · US

Funding

Prenatal environmental determinants of health in young adulthood: a lifecourse approachR01HD034568 · NICHD · HARVARD PILGRIM HEALTH CARE, INC. · PI Marie-France Hivert, Emily Oken · 1998 to 2026
$20.6M
Common and distinct early environmental influences on cardiometabolic and respiratory health: Mechanisms and methodsUH3OD023286 · OD · HARVARD PILGRIM HEALTH CARE, INC. · PI OKEN, EMILY · 2018 to 2022
$11.7M
NICHD NIH HHS R01 HD 034568NICHD NIH HHS R01 HD034568NIH HHS UH3 OD023286NIH HHS UH3OD023286
6 · The paper itself

Abstract

backgroundThere is great interest in understanding whether interventions on sugar-sweetened beverage (SSB) consumption through pregnancy and early childhood affect adolescent body mass index (BMI). Without data from randomised trials, unbiased estimation of such effects might be achieved with observational data given sufficient and appropriate adjustment for both baseline and time-varying confounders.

objectivesTo illustrate the use of inverse probability (IP) weighting of marginal structural models (MSM) for estimating the effects of SSB consumption through pregnancy and early childhood on the mean early adolescent BMI z-score.

methodsOur baseline sample consisted of 1584 pregnant women from a pre-birth cohort. We defined 6 intervention intervals: early pregnancy, late pregnancy, 3, 4, 5, and 6 years. We fitted a MSM via a weighted linear regression with IP exposure and censoring weights to estimate the mean difference in BMI z-score under interventions: "maintain SSB consumption below (vs above) 0.5 servings/day in all intervals."

resultsThe estimated difference in mean BMI z-score under interventions maintaining SSB consumption at or below (vs above) 0.5 servings/day from pregnancy to 6 years was -0.94 (95% confidence interval [CI] -1.52, -0.08). The effect estimate in pregnancy, while fixing the exposure range in childhood, was -0.05 (95% CI -0.34, 0.23), and in early childhood, while fixing the range in pregnancy was -0.89 (95% CI -1.46, -0.11). The effect estimates were largely unchanged under sensitivity analyses to different implementation choices except for the choice of time interval length.

conclusionsUnder assumptions that include no unmeasured confounding and selection bias, and no model misspecification, results of this IP weighting application are in line with a lower mean BMI z-score in early adolescence under interventions ensuring lower, vs greater, SSB consumption in early life. This application provides a resource for researchers working with longitudinal birth cohort studies and interested in similar causal questions.

Indexed as

Pediatric ObesityPrenatal Nutritional Physiological PhenomenaSugar-Sweetened BeveragesAdolescentBody Mass IndexCausalityChild, PreschoolFemaleHumansMalePregnancyPrenatal Exposure Delayed EffectsProbabilityRegression AnalysisRisk AssessmentRisk Factorschildhood obesityInverse probability weightingmarginal structural modelsrepresentative interventionssensitive windowsugar-sweetened beverages

Identifiers

PMID32162715
PMCPMC7371449
OpenAlexW3011104489

What OpenQuestion holds

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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.