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Food Insecurity in the Global Youth: an Analysis of Income‐Related Inequalities

2017· article· en· W3124792214 on OpenAlexaff
Ekta Dilip Amarnani, Arijit Nandi, Hugo Melgar‐Quiñonez

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMcGill University Health CentreMcGill UniversitySte. Anne's Hospital
Fundersnot available
KeywordsFood securityFood insecurityOddsInequalityEconomic inequalityPopulationUnemploymentLogistic regressionDemographyEconomicsSocioeconomicsGeographyAgricultureEconomic growthMedicineSociology

Abstract

fetched live from OpenAlex

Food security is closely related to household income. Because youth (15–24 year olds) are more susceptible to income volatility, lower income, and unemployment, it is important to understand the extent and significance of income‐related food insecurity inequalities in this age group and how it compares to adults (25–65 year olds). The main objectives of this study are (1) to determine the prevalence of food insecurity among the global youth, and (2) to investigate income‐related inequalities that contribute to youth food insecurity on a region‐by‐region basis. This cross‐sectional study uses data from the 2014 Gallup World Poll. The survey, administered in over 150 countries, includes nationally representative samples of the population 15 years and older. Food security status was measured using the Food Insecurity Experience Scale, developed and validated worldwide by the Food and Agriculture Organization. Per capita household income was standardized into International Dollars using the World Bank's Purchasing Power Parity conversion factor to allow for global comparisons. Logistic regressions, carried out using STATA 14, were used to compute odds ratios (OR) for youth food insecurity. Income‐related inequalities in food insecurity were analyzed using absolute concentration indices (ACI). Logistic regressions and ACI were computed and compared for each region using a statistical significance level of p ≤ 0.05. Globally, youth had higher odds of being food insecure than adults (OR 1.185; p <0.000), however, great heterogeneity was found between regions. Youth were more likely than adults to be food insecure in North America (NA) (OR 1.579; p=0.011) and Sub‐Saharan Africa (SSA) (OR 1.055; p = 0.049), but less likely than adults to be food insecure in Europe and Central Asia (ECA) (OR 0.897; p <0.000), Latin America and the Caribbean (LAC) (OR 0.777; p <0.000), the Middle East and North Africa (MENA) (OR 0.736; p =0.000) and South Asia (SA) (OR 0.792; p <0.000). No significant differences in food security outcome by age group were found in East Asia and the Pacific (EAP) (OR 1.089; p = 0.068). Outcomes in individuals 15 and older showed significant income‐related inequalities in food security in all regions, meaning that globally, the poorer accounted for a greater share of the food insecure population ( p <0.000). ECA showed the greatest degree of income‐related food insecurity inequality (ACI= −0.314); the least inequality was found in SSA (ACI= −0.1147). When separated by age group, in all regions except SA and MENA, household income was less closely associated with food security outcome in youth than in adults. The extent and significance of differences in income‐related food insecurity inequalities between youth and adults varied substantially by region, ranging from −0.012 (South Asia) to 0.130 (Europe and Central Asia). To our knowledge, this study is the first to explore the food security status of youth on a global scale. The findings demonstrate that for youth, income may be a stronger or weaker determinant of food security depending on the regional context, and promotes the need for youth‐specific policies that address food security issues beyond income. The results of the study establish a need for further research exploring what mechanisms play a role in protecting youth against food insecurity.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.249
GPT teacher head0.468
Teacher spread0.219 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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