Nontuberculous Mycobacterial Infection in Wisconsin Adults and Its Relationship to Race and Social Disadvantage
Bibliographic record
Abstract
Abstract Rationale Population-based data on the epidemiology of nontuberculosis mycobacterial (NTM) infections are limited, particularly with respect to variation in NTM infection among racial groups and socioeconomic strata. Wisconsin is one of a handful of states where mycobacterial disease is notifiable, allowing large, population-based analyses of the epidemiology of NTM infection in this state. Objectives To estimate the incidence of NTM infection in Wisconsin adults, describe the geographic distribution of NTM infection across the state, identify the frequency and type of infection caused by different NTM species, and investigate associations between NTM infection and demographics and socioeconomic status. Methods We conducted a retrospective cohort study using laboratory reports of all NTM isolates from Wisconsin residents submitted to the Wisconsin Electronic Disease Surveillance System from 2011 to 2018. For the analyses of NTM frequency, multiple reports from the same individual were enumerated as separate isolates when nonidentical, collected from different sites or collected more than one year apart. Results A total of 8,135 NTM isolates from 6,811 adults were analyzed. Mycobacterium avium complex accounted for 76.4% of respiratory isolates. The M. chelonae-abscessus group was the most common species isolated from skin and soft tissue. The annual incidence of NTM infection was stable over the study period (from 22.1 per 100,000 to 22.4 per 100,000). The cumulative incidence of NTM infection among Black (224 per 100,000) and Asian (244 per 100,000) individuals was significantly higher compared with that among their White counterparts (97 per 100,000). Total NTM infections were significantly more frequent (P < 0.001) in individuals from disadvantaged neighborhoods, and racial disparities in the incidence of NTM infection generally remained consistent when stratified by measures of neighborhood disadvantage. Conclusions More than 90% of NTM infections were from respiratory sites, with the vast majority caused by M. avium complex. Rapidly growing mycobacteria predominated as skin and soft tissue pathogens and were important minor respiratory pathogens. We found a stable annual incidence of NTM infection in Wisconsin between 2011 and 2018. NTM infection occurred more frequently in non-White racial groups and in individuals experiencing social disadvantage, suggesting that NTM disease may be more frequent in these groups as well.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".