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Household Food Insecurity is Associated with Respiratory Infections Among 6–11-Month Old Infants in Rural Ghana

2015· article· en· W293404991 on OpenAlexaff
Agartha Ohemeng, Grace S. Marquis, Anna Lartey

Bibliographic record

VenueThe Pediatric Infectious Disease Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsSte. Anne's HospitalMcGill University
Fundersnot available
KeywordsOdds ratioConfidence intervalFood securityLogistic regressionMedicinePsychological interventionEnvironmental healthOddsDemographyRural areaPediatricsAgricultureGeography

Abstract

fetched live from OpenAlex

BACKGROUND: To determine the relationship between household food insecurity (HHFI) and symptoms of respiratory infections among infants in rural Ghana. METHODS: The study was cross-sectional. The outcome variables were symptoms of respiratory infections (cough and nasal discharge) in infants. HHFI was measured using a 15-item modified U.S. Department of Agriculture (USDA) household food security module. Households were classified as food insecure if they had an affirmative answer for at least 1 item. Associations were examined using multiple logistic regression analysis. Data were collected in 32 communities located in 3 rural subdistricts in the Upper Manya Krobo district of the Eastern region of Ghana. The sample included 367 infants aged 6-11 months who attended a community-based growth monitoring session. RESULTS: Overall, 20.5% of households reported experiencing food insecurity in the last month. Compared with infants in food secure households, infants living in food insecure households were about twice as likely to experience cough (adjusted odds ratio: 2.25, 95% confidence intervals: 1.25, 4.04) and nasal discharge (adjusted odds ratio: 1.87, 95% confidence intervals: 1.05, 3.36). CONCLUSION: Infants living in food insecure households are at an increased risk of respiratory tract morbidity. Interventions that address HHFI might be important to improve infant health in rural Ghana.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.098
GPT teacher head0.357
Teacher spread0.259 · 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.

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

Citations15
Published2015
Admission routes1
Has abstractyes

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