780. Incidence and Prevalence of Nontuberculous Mycobacterial Lung Disease in US Medicare, 2008–2015
Bibliographic record
Abstract
Abstract Background Previous research has reported nontuberculous mycobacterial lung disease (NTMLD) prevalence of 47 per 100,000 among Medicare beneficiaries ≥65 years in 2007, with an average increase of 8.2% annually between 1997 and 2007. In this study, we have evaluated NTMLD incidence and prevalence in Medicare between 2008 and 2015. Methods Patients diagnosed for NTMLD with an ICD9 031.0 were identified from the Medicare database (N≈30 million yearly), not including the Part C portion. Individuals who incurred at least 2 medical claims ≥30 days apart between 2007 and 2015 were considered as a positive NTMLD case, yielding 58,294 patients. All individuals fulfilling the case definition each calendar year were considered as prevalent cases. Incident cases included those meeting case criteria and who did not have a Medicare claim for NTMLD in the prior year. Poisson regression was used to estimate yearly confidence intervals. ARIMA models were used to forecast incidence and prevalence over 2016–2025. Results Patients with NTMLD in the Medicare database had a mean age of 74 (standard deviation: ±10) years. Sixty-nine percent were women and 89% white. Yearly NTMLD incidence increased from 20.7 (95% CI: 20.2–21.3) in 2008 to 28.1 (27.5–28.7) in 2013 per 100,000 Medicare beneficiaries and leveled to 27.6 (26.9–28.2) in 2014 and 25.9 (25.3–26.5) in 2015 per 100,000. Yearly NTMLD prevalence increased throughout the observation period from 41.6 (40.9–42.3) in 2008 to 63.1 (62.2–64.0) in 2015 per 100,000 Medicare beneficiaries. Incidence was 28.1 vs. 14.7 per 100,000 in 2015 in Medicare beneficiaries ≥65 years vs. those <65 years, respectively. Prevalence was 70.2 vs. 27.9 per 100,000 in 2015 in Medicare beneficiaries ≥65 years vs. those <65 years, respectively. In 2015, incidence and prevalence were higher in women than men (33.9 vs. 16.0/100,000 and 86.2 vs. 34.6/100,000, respectively) and among individuals of Asian origin compared with White (41.1 vs. 27.6/100,000 and 89.4 vs. 68.7/100,000, respectively). The 10-year incidence and prevalence forecasts were presented in figures. Conclusion In US Medicare beneficiaries, NTMLD incidence increased from 2008 through 2013 and leveled off in more recent years, while NTMLD prevalence continued to rise through 2015. Disclosures K. L. Winthrop, Insmed Incorporated: Scientific Advisor, Consulting fee and Research grant. T. Marras, Insmed Incorporated: Investigator, Consulting fee and Research grant, Horizon Pharmaceuticals: Consultant, Consulting fee, Red Hill: Consultant, Consulting fee, AstraZeneca: CME, Speaker honorarium. G. Eagle, Insmed Incorporated: Employee, Salary. R. Zhang, Insmed Incorporated: Consultant, Consulting fee. P. Wang, Insmed Incorporated: Employee, Salary. E. Chou, Insmed Incorporated: Employee, Salary. Q. Zhang, Insmed Incorporated: Employee, Salary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".