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Record W2518944229 · doi:10.1136/jech-2016-208064.207

P110 Evaluation of trajectories in maternal mental health according to food security status: combined analysis of routine and cohort data

2016· article· en· W2518944229 on OpenAlexaboutno aff
Eleonora Uphoff, MS Power, Kate E. Pickett

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityMental healthPoisson regressionCohortDemographyFood insecurityOddsMedicineIncidence (geometry)Environmental healthEthnic groupCohort studyLogistic regressionGeographyPopulationPsychiatry

Abstract

fetched live from OpenAlex

Background Literature from Canada and the United States indicates that household food insecurity is associated with poorer mental health, and chronic mental health conditions increase the odds of household food insecurity, independent of household socio-demographic characteristics. However, there is no published work on the longitudinal relationship between mental health and food insecurity in the UK. The objective of this paper was to assess the mental health trajectories of women who report food insecurity compared to those who report no food insecurity around 12 months after giving birth. Methods We analysed linked data from three sources: the Born in Bradford (BiB) baseline questionnaire, data from the BiB1000 study, and GP records. BiB is a birth cohort that examines the impact of environmental, psychological and genetic factors on maternal and child health. BiB1000 is a nested cohort of BiB with 1735 mothers, aimed at identifying risk factors for obesity. The food insecurity questionnaire, derived from the USDA Food Security Survey 1995, was completed when babies were approximately 12 months old (N = 1297). Primary care records were matched to BiB data using NHS numbers (90.8% matched), and CMD was defined according to a standardised method. We calculated incidence rates of CMD per 1000 Patient Years At Risk (PYAR) among food secure compared to food insecure women in ten six-month periods. We used Poisson regression to calculate Incidence Ratio Ratios (IRR) adjusted for ethnicity, age of the mother, partner’s occupation and exposure. Results 22.1% of the sample classified as food insecure, and the incidence rate of CMD ranged from 29 to 118 per 1000 PYAR for the food secure group compared to 57 to 167 per 1000 PYAR for the food insecure group, with the lowest rates found prenatally. Incidence rates of CMD were higher for food insecure compared to food secure women in all periods. Adjusted IRRs suggest increased vulnerability both prenatally (IRR 1.46, 95% CI 0.99–2.14, p = 0.055) and postnatally (IRR 1.36, 95% CI 1.06–1.73, p = 0.014). Conclusion This study found that women who report food insecurity seem to be at higher risk of CMD during and after pregnancy. The prevention of mental illness and food insecurity involves understanding and tackling the root causes, and it appears we are currently ignoring part of the opportunities for reducing the burden of ill health and social disadvantage.

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)0.009
metaresearch head score (Gemma)0.015
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
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.260
GPT teacher head0.498
Teacher spread0.238 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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