Guidelines-based treatment associated with improved economic outcomes in nontuberculous mycobacterial lung disease
Bibliographic record
Abstract
Background: The prevalence of nontuberculous mycobacterial lung disease (NTMLD) in the US has increased; however, data characterizing the associated healthcare utilization and expenditure at the national level are limited.Objective: To examine associations between economic outcomes and the use of anti-Mycobacterium avium complex (MAC) guidelines-based treatment (GBT) for newly-diagnosed NTMLD in a US national managed care claims database (Optum® Clinformatics® Data Mart).Methods: NTMLD was defined as having ≥2 claims for NTMLD (ICD-9 031.0; ICD-10 A31.0) on separate occasions ≥30 days apart (between 2007 and 2016). The cohort included patients insured continuously over a period of at least 36 months (12 months before initial NTMLD diagnostic claim and for the subsequent 24 months). Treatment was classified as GBT (consistent with American Thoracic Society/Infectious Diseases Society of America guidelines), non-GBT, or untreated. All-cause hospitalization rates and total healthcare expenditures at Year 2 were assessed as outcomes of the treatment prescribed in Year 1 after NTMLD diagnosis.Results: A total of 1,039 patients met study criteria for NTMLD (GBT, n = 294; non-GBT, n = 298; untreated, n = 447). After adjustment for baseline characteristics, GBT was associated with a significantly lower all-cause hospitalization risk vs non-GBT (odds ratio [OR] = 0.53; 95% CI = 0.33–0.85, p = 0.008), and vs being untreated (OR = 0.57; 95% CI = 0.35–0.91, p = 0.020). Adjusted total healthcare expenditure in Year 2 with GBT ($69,691) was lower than that with non-GBT ($77,624) with a difference of −$7,933 (95% CI = −$14,968 to −$899; p = 0.03).Conclusions: Patients with NTMLD in a US managed care claims database who were prescribed GBT had lower hospitalization risk than those who were prescribed non-GBT or were untreated. GBT was associated with lower total healthcare expenditure compared with non-GBT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".