Incidence and Prevalence of Nontuberculous Mycobacterial Lung Disease in a Large U.S. Managed Care Health Plan, 2008–2015
Bibliographic record
Abstract
Abstract Rationale Estimating the annual incidence and prevalence of nontuberculous mycobacterial (NTM) lung disease may assist in improving understanding of the public health and economic impacts of this disease and its treatment. Objective To estimate the yearly incidence and prevalence of administrative claims–based NTM lung disease between 2008 and 2015 in a U.S. managed care claims database. Methods We used a national managed care claims database (Optum Clinformatics Data Mart) representing a geographically diverse population of approximately 27 million members annually. All medical claims from January 1, 2007, to June 30, 2016, were scanned for diagnosis codes for NTM lung disease (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 031.0 or ICD-10-CM code A31.0). We defined a case of NTM lung disease as having at least two medical claims with a code of 031.0 or A31.0 that were dated at least 30 days apart. Annual incidence and prevalence were estimated for each calendar year from 2008 to 2015. Results From 2008 to 2015, the annual incidence of NTM lung disease increased from 3.13 (95% confidence interval [CI], 2.88–3.40) to 4.73 (95% CI, 4.43–5.05) per 100,000 person-years, and the annual prevalence increased from 6.78 (95% CI, 6.45–7.14) to 11.70 (95% CI, 11.26–12.16) per 100,000 persons. The average annual changes in incidence and prevalence were +5.2% (95% CI, 4.0–6.4%; P < 0.01) and +7.5% (95% CI, 6.7–8.2%; P < 0.01), respectively. For women, the annual incidence increased from 4.16 (95% CI, 3.76–4.60) to 6.69 (95% CI, 6.19–7.22) per 100,000 person-years, and the annual prevalence increased from 9.63 (95% CI, 9.08–10.22) to 16.78 (95% CI, 16.04–17.55) per 100,000 persons. For individuals aged 65 years or older, the annual incidence increased from 12.70 (95% CI, 11.46–14.07) to 18.37 (95% CI, 16.98–19.87) per 100,000 person-years, and the annual prevalence increased from 30.27 (95% CI, 28.41–32.24) to 47.48 (95% CI, 45.37–49.67) per 100,000 persons. The incidence and prevalence of NTM lung disease increased in most U.S. states and overall at the national level. Conclusions The incidence and prevalence of NTM lung disease appears to be increasing in the United States, particularly among women and older age groups.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".