Association between household food insecurity and annual health care costs
Bibliographic record
Abstract
BACKGROUND: Household food insecurity, a measure of income-related problems of food access, is growing in Canada and is tightly linked to poorer health status. We examined the association between household food insecurity status and annual health care costs. METHODS: We obtained data for 67 033 people aged 18-64 years in Ontario who participated in the Canadian Community Health Survey in 2005, 2007/08 or 2009/10 to assess their household food insecurity status in the 12 months before the survey interview. We linked these data with administrative health care data to determine individuals' direct health care costs during the same 12-month period. RESULTS: Total health care costs and mean costs for inpatient hospital care, emergency department visits, physician services, same-day surgeries, home care services and prescription drugs covered by the Ontario Drug Benefit Program rose systematically with increasing severity of household food insecurity. Compared with total annual health care costs in food-secure households, adjusted annual costs were 16% ($235) higher in households with marginal food insecurity (95% confidence interval [CI] 10%-23% [$141-$334]), 32% ($455) higher in households with moderate food insecurity (95% CI 25%-39% [$361-$553]) and 76% ($1092) higher in households with severe food insecurity (95% CI 65%-88% [$934-$1260]). When costs of prescription drugs covered by the Ontario Drug Benefit Program were included, the adjusted annual costs were 23% higher in households with marginal food insecurity (95% CI 16%-31%), 49% higher in those with moderate food insecurity (95% CI 41%-57%) and 121% higher in those with severe food insecurity (95% CI 107%-136%). INTERPRETATION: Household food insecurity was a robust predictor of health care utilization and costs incurred by working-age adults, independent of other social determinants of health. Policy interventions at the provincial or federal level designed to reduce household food insecurity could offset considerable public expenditures in health care.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".