Food budget ratio as an equitable metric for food affordability and insecurity: a population-based cohort study of 121 remote Indigenous communities in Canada
Bibliographic record
Abstract
Abstract Background Food insecurity is a public health issue for many regions globally, and especially Indigenous communities. We propose food budget ratio (FBR)—the ratio of food spending to after-tax income—as an affordability metric that better aligns with health equity over traditional price-focused metrics. Existing census and inflation monitoring programs render FBR an accessible tool for future affordability research. Methods Public census and food pricing datasets from 2011 to 2021 were analyzed to evaluate food affordability for a cohort of 121 remote Indigenous communities in Canada (n = 80,354 persons as of March 2021). Trends in population-weighted versus community-weighted averages, inflation-adjusted mean price of the Revised Northern Food Basket (RNFB), and distributions of FBR, per-capita price of food, and per-capita after-tax income were calculated and compared to Canada at large. Results Population-weighted versus community-weighted mean price of the RNFB differed by < 5% for most points in time, peaking at 17%. Mean raw price of the RNFB was relatively stable, while mean inflation-adjusted price of the RNFB decreased 19%. Mean and standard deviation in FBR trended downwards from (0.40; 0.21) in 2011 to (0.25; 0.10) in 2021, while the mean for Canada held stable at 0.10 ± 0.01. Mean and standard deviation in inflation-adjusted per-capita price of food fell from ($5,621; $493) to ($4,510; $243), while the Canada-wide mean rose from $2,189 to $2,567; values for per-capita after-tax income increased from ($17,384; $7,816) to ($21,661; $9,707), while the Canada-wide mean remained between $24,443 and $26,006. Current Nutrition North Canada (NNC) subsidy rates correlate closely with distance to nearest transportation hub (σXY = 0.68 to 0.70) whereas food pricing, after-tax income, and FBR correlate poorly with distance (σXY = -0.22 to 0.03). Conclusions The FBR approach yields greater insights on food affordability compared to price-based results, while using readily available public datasets. Whereas 19% reductions in RNFB per-capita food price were observed, FBR decreased 63% yet remained 2.5 times the Canada-wide FBR. The reduction in FBR was driven both by the reduced price of food and a 25% increase in after-tax income. It is recommended that NNC consider FBR for performance measurement and setting subsidy rates.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".