Costs Associated with Nontuberculous Mycobacteria Infection, Ontario, Canada, 2001–2012
Bibliographic record
Abstract
P ulmonary disease (PD) caused by nontuberculous mycobacteria (NTM) is an emerging public health threat (1).As identification of persons with NTM-PD increases (2,3), information about the economic burden of NTM-PD can help decision makers set funding priorities.NTM-PD accounts for most NTM infections and poses particular challenges (4).One challenge is that NTM-PD often occurs in patients with preexisting conditions such as cystic fibrosis, lung cancer, and chronic obstructive pulmonary disease (5,6); these conditions affect the spectrum and severity of signs and symptoms (7).Treatment of NTM-PD is complex, typically involving use of >3 antimicrobial agents for >18 months (5).Eradication of the organism is challenging; a recent meta-analysis demonstrated a success rate of 60% for eradication of Mycobacterium avium complex PD (MAC-PD) (8).Furthermore, recurrence after completed treatment for MAC-PD is extremely common; recurrence rates are 30% at 14 months (9) and ≈50% at 4 years (10).During 1998-2010, the prevalence of NTM-PD in Ontario, Canada, increased; the 5-year prevalence increased from 29.3 (1998-2002) to 41.3 (2003-2007) cases/100,000 persons (2).The costs of NTM-PD in Canada are not well established; to the best of our knowledge, no studies have used population-based data to estimate associated costs.One study conducted in a tertiary care center in Toronto, Ontario, estimated the median monthly cost per NTM-PD patient to be approximately CAD $500; most of the costs were associated with medications (11).Although informative, that study considered only 1 clinic and did not account for potential long-term costs.Our objective with this study was to determine the costs associated with NTM-PD and with pulmonary isolation of NTM from patients without disease (NTM-PI), from the healthcare payer perspective, in Ontario, Canada, during 2001-2012.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".