Ranking the burden of infectious diseases in Ontario, Canada
Bibliographic record
Abstract
Background: Evidence-based priority-setting requires knowledge about the relative contributions of diseases to the loss of healthy life. The Global Burden of Disease method uses the Disability-Adjusted Life Year (DALY), a measure that incorporates the population burden of mortality and morbidity, to compare the relative contributions of diseases. We calculated DALYs for infectious pathogens in Ontario to inform the policies of the province's new public health agency. Methods: The DALY is a health gap measure comprising two components: 1) years of life lost due to premature mortality (YLL); and 2) years of “healthy” life lost due to disability (YLD). Health gap measures quantify the amount of “healthy” life lost by estimating the difference between actual population health and a specified ideal. We used an incidence-based approach and incorporated modeling techniques where possible to estimate the disease burden of various infectious disease agents. Data sources included census estimates, vital statistics, public health reportable disease data, health care utilization data, cancer registries, and epidemiological studies. Results: For most infectious diseases, mortality (YLLs) contributed more to DALYs than morbidity (YLDs). Overall, the infectious agents accounting for the greatest burden were: influenza, Streptococcus pneumoniae, hepatitis C virus (HCV), Escherichia coli, Gram negative bacteria excluding E. coli, HIV/AIDS, Staphylococcus aureus, human papilloma virus (HPV), hepatitis B virus (HBV), and other Gram positive bacteria. The DALYs for HPV and urinary tract infections were greater in females while the DALYs for HIV/AIDS, HCV, and HBV were greater in males. Conclusion: Strategic priorities for specific infectious diseases in Ontario should focus on those identified here to have the greatest burden. Further work is also required to improve the timeliness of data access and the quality of information available. While the prevention of some infectious diseases will require the development of novel interventions, much of Ontario's infectious disease burden could be reduced through better implementation of existing interventions. Abstracts for SupplementInternational Journal of Infectious DiseasesVol. 14Preview Full-Text PDF Open Archive
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".