Healthcare Utilization After Respiratory Tuberculosis: A Controlled Interrupted Time Series Analysis
Bibliographic record
Abstract
BACKGROUND: Despite data suggesting elevated morbidity and mortality among people who have survived tuberculosis disease, the impact of respiratory tuberculosis on healthcare utilization in the years following diagnosis and treatment remains unclear. METHODS: Using linked health administrative data from British Columbia, Canada, we identified foreign-born individuals treated for respiratory tuberculosis between 1990 and 2019. We matched each person with up to four people without a tuberculosis diagnosis from the same source cohort using propensity score matching. Then, using a controlled interrupted time series analysis, we measured outpatient physician encounters and inpatient hospital admissions in the 5 years following respiratory tuberculosis diagnosis and treatment. RESULTS: We matched 1216 individuals treated for respiratory tuberculosis to 4864 non-tuberculosis controls. Immediately following the tuberculosis diagnostic and treatment period, the monthly rate of outpatient encounters in the tuberculosis group was 34.0% (95% confidence interval [CI]: 30.7%, 37.2%) higher than expected, and this trend was sustained for the duration of the post-tuberculosis period. The excess utilization represented an additional 12.2 (95% CI: 10.6, 14.9) outpatient encounters per person over the post-tuberculosis period, with respiratory morbidity a large contributor to the excess healthcare utilization. Results were similar for hospital admissions, with an additional 0.4 (95% CI: .3, .5) hospital admissions per person over the post-tuberculosis period. CONCLUSIONS: Respiratory tuberculosis appears to have long-term impacts on healthcare utilization beyond treatment. These findings underscore the need for screening, assessment, and treatment of post-tuberculosis sequelae, as it may provide an opportunity to improve health and reduce resource use.
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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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".