Inequality-related economic burden of communicable diseases in Canada
Bibliographic record
Abstract
BACKGROUND: Communicable diseases cause a significant burden on society in terms of health care expenditures and their health impact on individuals. Cost-of-illness studies estimate the total economic burden of illness and injury. OBJECTIVE: To identify the economic burden of illness for communicable diseases in Canada, and to derive the costs associated with inequalities based on income and hospital expenditures. METHODS: Data were derived from the Economic Burden of Illness in Canada (EBIC) database, for the year 2008. Data for communicable diseases were extracted and compared to the overall results. Data on income level was available for hospital expenditures, and was analyzed by income quintile. RESULTS: The total costs attributable to communicable diseases in Canada were $8.3 billion, which represented 9% of the total costs that could be attributed to a specific disease or diagnostic category. Indirect costs accounted for 44% of total communicable disease costs and represented a more significant proportion of the economic burden related to communicable diseases compared to non-communicable diseases. When hospital costs by income quintile were analyzed, a clear inverse relationship was found between income and hospital expenditures. The costs associated with this inequality in 2008 were $308 million. The current estimates are likely to be an underestimate due to the conservative assumptions made in the analysis. CONCLUSION: The cost of communicable disease in Canada is sizable and there is a clear correlation between lower income and higher hospital costs. Further research is needed to better account for co-morbid conditions and to better estimate the value of lost productivity related to disability arising from communicable diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".