Gender Disparities in Food Security, Dietary Intake, and Nutritional Health in the United States
Bibliographic record
Abstract
INTRODUCTION: Food insecurity is associated with negative nutritional outcomes and is experienced differently by women vs men. We evaluated the effects of gender on food insecurity and dietary intake in the United States. METHODS: Data from the National Health and Nutrition Examination Survey (2007-2016) were analyzed. Survey-weighted linear and logistic regression models were used to evaluate predictors of food security and the effect of food security on dietary consumption and body anthropometrics. Gender was modeled as a covariable and as an effect modifier. RESULTS: A total of 30,251 respondents were included. Approximately 15.1% (95% confidence interval [CI]: 14.1%-16.1%) of participants were food insecure. This increased over time from 11.7% in 2007-2008 to 18.2% in 2015-2016. A higher proportion of women experienced food insecurity compared with men (53.3% vs 46.7%, P = 0.02), although this was not significant after adjusting for poverty and other confounders (adjusted odds ratio 1.01; 95% CI: 0.93-1.09; P = 0.81). Among food insecure women, 32.4% (95% CI: 30.0%-34.9%) received emergency food assistance and 75.0% (95% CI: 71.5%-78.2%) received supplemental nutrition assistance benefits. Relative to men, food insecure women were less likely to meet the recommended dietary allowance of most macronutrients and micronutrients. They were also significantly more likely to be obese, have a wider waist circumference, and have higher total body fat percentage (P interaction all <0.001). DISCUSSION: Food insecurity represents a substantial public health challenge in the United States that differentially affects women compared with men. Alternative strategies may be required to meet the nutritional requirements for food insecure women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".