MétaCan
Menu
Back to cohort
Record W3189688798 · doi:10.1016/s2542-5196(21)00151-0

Family policy and food insecurity: an observational analysis in 142 countries

2021· article· en· W3189688798 on OpenAlexaff
Aaron Reeves, Rachel Loopstra, Valerie Tarasuk

Bibliographic record

VenueThe Lancet Planetary Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Toronto
FundersWellcome Trust
KeywordsFood insecurityObservational studyScale (ratio)Multilevel modelGenerosityMalnutritionEnvironmental healthPovertySocioeconomicsEconomicsDemographic economicsGeographyDemographyEconomic growthPolitical scienceFood securityMedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Levels of child malnutrition and hunger across the world have decreased substantially over the past century, and this has had an important role in reducing mortality and improving health. However, progress has stalled. We examined whether family policies (eg, cash transfers from governments that aim to support households with children) are associated with reduced food insecurity. METHODS: In this observational analysis, we used a dataset of individual-level data that captured experience-based measures of food insecurity and sociodemographic characteristics collected by the Gallup World Poll in 142 countries for 2014-17. We then combined this dataset with indicators of the type and generosity of family policies in these countries, taken from the University of California, Los Angeles' World Policy Analysis Center. We used multilevel regression models to examine the association between the presence of family policies for households with children and the probability of reporting moderate or severe food insecurity or severe food insecurity (moderate or severe food insecurity was defined as a "yes" response to at least four of eight questions on the Gallup Food Insecurity Experience Scale, and severe food insecurity was defined as a "yes" response to at least seven questions). We controlled for multiple covariates, including individual-level measures of social position and country-level measures, such as gross domestic product. We further examined whether this association varied by household income level. FINDINGS: Using data from 503 713 households, we found that, on average, moderate or severe food insecurity is 4·09 percentage points (95% CI 3·50-4·68) higher in households with at least one child younger than 15 years than in households with no children and severe food insecurity is 2·20 percentage points (1·76-2·63) higher. However, the additional risk of food insecurity among households with children is lower in countries that provide financial support (either means-tested or universal) for families than for countries with little or no financial assistance. These policies not only reduce food insecurity on average, but they also reduce inequalities in food insecurity by benefiting the poorest households most. INTERPRETATION: In some countries, family policies have been cut back in the past decade and such retrenchment might expose low-income households to increased risk of food insecurity. By increasing investment in family policies, progress towards Sustainable Development Goal 2, zero hunger, might be accelerated and, in turn, improve health for all. FUNDING: Wellcome Trust.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.133
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.393
GPT teacher head0.483
Teacher spread0.089 · 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 teacher head, 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

Citations35
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Planetary HealthSame topicFood Security and Health in Diverse PopulationsFrench-language works237,207