Food Insecurity Negatively Impacts Gluten Avoidance and Nutritional Intake in Patients With Celiac Disease
Bibliographic record
Abstract
BACKGROUND: Food insecurity is a major public health challenge. For patients with celiac disease (CeD), food insecurity may be particularly detrimental as it threatens the cornerstone of their treatment: adoption of a gluten-free diet (GFD). We aimed to characterize the prevalence of food insecurity in patients with CeD and evaluate its impact on GFD adoption and nutritional intake. METHODS: We analyzed data from patients with CeD participating in the US National Health and Nutrition Examination Survey (NHANES) from 2009 to 2014. Food insecurity was defined using the US Department of Agriculture 18-Item Standard Food Security Survey Module. Survey-weighted logistic regression was used to assess differences in demographic characteristics of CeD patients living with food insecurity and the impact of food security on GFD adoption. Multivariable survey-weighted linear regression was used to evaluate the effect of food insecurity on nutritional intake of macronutrients and micronutrients. RESULTS: Overall, 15.9% (95% confidence interval: 10.6%, 23.1%) of patients with CeD in the United States [weighted N=2.9 million (95% confidence interval: 2.2, 3.5 million)] are food insecure. Food insecure patients with CeD were disproportionately younger, poorly educated, nonwhite, living in poverty, and were significantly less likely to adopt a GFD (24.1% vs. 67.9%, P =0.02). Food insecurity was associated with significantly lower consumption of protein, carbohydrates, fat, and most vitamins and minerals. CONCLUSIONS: One in 6 patients with CeD are food insecure, negatively impacting GFD adoption and the ability to meet recommended daily intake of most micronutrients. Less than one quarter of food insecure CeD patients adhere to a GFD.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".