The rise of hunger among low-income households: an analysis of the risks of food insecurity between 2004 and 2016 in a population-based study of UK adults
Bibliographic record
Abstract
BACKGROUND: Rising food bank use in the past decade in the UK raises questions about whether food insecurity has increased. Using the 2016 Food and You survey, we describe the magnitude and severity of the problem, examine characteristics associated with severity of food insecurity, and examine how vulnerability has changed among low-income households by comparing 2016 data to the 2004 Low Income Diet and Nutrition Survey. METHODS: The Food and You survey is a representative survey of adults living in England, Wales, and Northern Ireland (n=3118). Generalised ordered logistic regression models were used to examine how socioeconomic characteristics related to severity of food insecurity. Coarsened exact matching was used to match respondents to respondents in the 2004 survey. Logistic regression models were used to examine if food insecurity rose between survey years. RESULTS: 20.7% (95% CI 18.7% to 22.8%) of adults experienced food insecurity in 2016, and 2.72% (95% CI 2.07% to 3.58%) were severely food insecure. Younger age, non-white ethnicity, low education, disability, unemployment, and low income were all associated with food insecurity, but only the latter three characteristics were associated with severe food insecurity. Controlling for socioeconomic variables, the probability of low-income adults being food insecure rose from 27.7% (95% CI 24.8% to 30.6 %) in 2004 to 45.8% (95% CI 41.6% to 49.9%) in 2016. The rise was most pronounced for people with disabilities. CONCLUSIONS: Food insecurity affects economically deprived groups in the UK, but unemployment, disability and low income are characteristics specifically associated with severe food insecurity. Vulnerability to food insecurity has worsened among low-income adults since 2004, particularly among those with disabilities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".