P1-S3.02 The economic burden of chlamydia and gonorrhoea in Canada
Bibliographic record
Abstract
Background The latest estimate of the economic burden of chlamydia and gonorrhoea in Canada was modelled with data from 1990 and was published in 1992 by Ron Goeree and Paul Gully. Given the changes that have occurred since in diagnostic testing technology, the availability of new data sources and increasing rates of the two infections, a new model using updated data from 2000 is called for. Methods Diagnostic test costs were estimated using provincial laboratory data on the number of diagnostic tests performed forChlamydia trachomatis(CT) andNeisseria gonorrhoeae(GC) for the year 2000. Direct costs of CT and GC from drugs, hospitals, and physician billings were estimated using the Economic Burden of Illness in Canada (EBIC) data. Direct costs of associated sequelae of each infectious disease were also included in the model. Indirect costs estimated in the model included production losses from both infectious diseases and their associated sequelae. Sensitivity analyses were conducted to provide upper, base and lower-bound estimates of the total cost. Results The preliminary combined estimate for both direct and indirect costs of CT and GC (in 2000 dollars) ranges from approximately $31.5 to $178.4 million (CAD). Conclusions Further work is required to improve data access and estimates of the burden of infection in Canada. A top priority should be to improve the data infrastructure by expanding data linkages within and among provinces around laboratory tests. The majority of costs related to CT and GC are attributable to drug, hospital and physician costs, suggesting that much of the burden of these two infections can be reduced through implementation of effective prevention programs. The number of CT infections has increased exponentially since 2000, warranting further modelling considering current incidence rates and inflation costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 0.007 |
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".