Assessing Complexity Among Patients With Tuberculosis in California, 1993–2016
Bibliographic record
Abstract
Abstract Background Although the number of patients with active tuberculosis (TB) has decreased in the last 25 years, anecdotal reports suggest that the complexity of these patients has increased. However, this complexity and its components have never been quantified or defined. We therefore aimed to describe the complexity of patients with active TB in California during 1993–2016. Methods We analyzed data on patient comorbidities, clinical features, and demographics from the California Department of Public Health TB Registry. All adult patients who were alive at the time of TB diagnosis in California during 1993–2016 were included in the analyses. Factors deemed by an expert panel to increase complexity (ie, increased resources or expertise requirement for successful management) were analyzed and included the following: age >75 years, HIV infection, multidrug resistance (MDR), and extrapulmonary TB disease. Second, using additional information on other comorbidities available starting in 2010, we performed exploratory factor analysis on 25 variables in order to define the dimensions of complexity. Results Among the 67 512 patients analyzed, the proportion of patients with extrapulmonary disease, age >75 years, or MDR-TB each increased over the study period (P < .001), while the proportion of patients with HIV decreased. Furthermore, the proportion of patients with at least 1 factor of those increased, rising from 38.8% to 45.3% (P < .001) from 1993 to 2016. Dimensions of complexity identified in the exploratory factor analysis included the following: race/immigration, social features, elderly/institutionalized, advanced TB, comorbidity, and drug resistance risk. Conclusions In this first description of complexity in the setting of TB, we found that the complexity of patients with active TB has risen over the last 25 years in California. These findings suggest that despite the overall decline in active TB cases, effective management of more complex patients may require additional attention and resource investment.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".