MétaCan
Menu
← Back to cohort

Abstract 033: Does Persistent Poverty Elevate Risk for Obesity More Than Occasional Poverty in Youth? Evidence From a Quebec Birth Cohort

2012· article· en· W4253385938 on OpenAlexaffabout
Lisa Kakinami, Marie Lambert, Lise Gauvin, Louise Séguin, Béatrice Nikièma, Gilles Paradis

Bibliographic record

VenueCirculation · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineMcGill University
Fundersnot available
KeywordsMedicineOverweightDemographyPercentileBody mass indexPovertyCohortObesityLogistic regressionChildhood obesityCohort studyGerontologyStatisticsEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Childhood poverty heightens the risk of obesity in adulthood, but its effect during childhood is poorly understood. We analyzed the relationship between poverty trajectories across the ages of 6, 8, 10, and 12 years with BMI Z-scores and the risk of being overweight in a birth cohort of children. Methods: Data were from 703 participants in the 1998-2010 ″Quebec Longitudinal Study of Child Development″ (n=2,120) birth cohort. Household income was measured annually with poverty defined as income below the low-income thresholds established by Statistics Canada adjusted for household size and geographic region. Children’s height and weight at ages 6, 8, 10, and 12 years were measured by trained study staff. Body mass index (BMI) was converted to age- and sex- standardized BMI Z-scores and percentiles and were classified as overweight or obese (BMI percentile > 85th) based on CDC growth curves. Trajectories of poverty across the ages of 6, 8, 10, and 12 years were characterized with a latent class group analysis using maximum likelihood in a semiparametric mixture model. Multivariable linear regressions predicted BMI Z-scores at different ages, and logistic regression predicted the risk of being overweight or obese based on poverty trajectories after adjusting for sex. Because all children at ages 6 and 8 years were pre-pubertal, and all children at age 12 were in puberty, only the model for BMI at age 10 adjusted for puberty. Results: Poverty trajectories were fairly stable across time and fell into 1 lower exposure category (consistently low exposure (approximately 70%, n=487)) and 3 higher exposure categories (increasing: 8%, n=55; decreasing: 10%, n=70; or consistently high exposure: 13%, n=91)). After adjusting for covariates, compared to children experiencing lower exposure to poverty, BMI Z-scores of children with consistently high exposure to poverty were 0.05 (p=NS), 0.12 (p=NS), 0.37 (p=0.02), and 0.42 (p=0.003) higher at ages 6, 8, 10, and 12 years, respectively. After adjustment, children experiencing consistently high exposure to poverty were at a significantly increased risk for being overweight or obese at age 8 (OR: 2.0, 95% CI: 1.2-3.3, p=0.01), age 10 (OR: 2.1, CI: 1.2-3.5, p=0.005), and at age 12 years (OR: 2.8, CI: 1.7-4.7, p<0.001) compared to children experiencing lower exposure to poverty. Children experiencing decreasing exposure to poverty at all ages, or increasing exposure at age 10 and 12 years were at an increased risk for being overweight or obese, but the results were not statistically significant. Conclusion: Findings suggest that there is a latency period for the detrimental effects of poverty on weight, but that previous exposure can still impact future weight even at a young age. Whether the disparity in weight status according to poverty trajectories widens as the children continue to age should be investigated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.279
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueCirculation→Same topicCardiovascular Health and Risk Factors→French-language works237,207→