The impact of tuberculosis disease and infection: estimating patient-reported health-related quality of life and health utility among persons screened and treated for tuberculosis in Montreal, Canada
Bibliographic record
Abstract
Treatment for the respiratory disease tuberculosis (TB) and latent TB infection (LTBI) can be long and complex with potential for negative side effects. Canada has a low incidence of active TB disease (4.8 cases per 100,000 persons reported in 2012), with foreign-born persons representing the majority of reported cases. Immigrants and refugees may face considerable barriers to accessing consistent health care required for treatment. It is not clear that the aggregate health burden incurred by treating many people with LTBI is less than aggregate health gains of preventing relatively few cases of active TB disease. Patient-reported health utility and health-related quality of life (HRQOL) are key data required to analyze this tradeoff. This manuscript-based thesis focused on measuring and analyzing health utility and HRQOL reported by individuals screened for TB in Montreal, Canada.First, we conducted a systematic review of the literature on quantitative measures of health utility and HRQOL in the TB patient population. From over 15,000 abstracts retrieved, 76 full-text articles were reviewed, representing 28 unique cohorts (6,028 respondents) reporting health utility or HRQOL among persons diagnosed with active TB disease. Across different studies and settings, persons with active TB consistently reported poorer HRQOL than persons treated for LTBI.The second and third manuscripts estimate health utility and HRQOL, using the Standard Gamble instrument and the Short Form 36 questionnaire, version 2 (SF-36), respectively, among persons diagnosed and treated for active TB disease, LTBI, and healthy control participants over the year following diagnosis. Participants were recruited at two hospitals in Montreal (2008–2011) and completed questionnaires within two weeks of treatment initiation/initial assessment and at 1, 2, 4, 6, 9, and 12 months thereafter. Linear mixed models were used to compare mean scores at each evaluation and changes in scores over consecutive evaluations, among participants treated for active TB disease and those treated for LTBI, as compared to the control group. Of the 263 participants, 48 were treated for active TB disease, 105 were treated for LTBI, and 110 were control participants; 54% were women, mean age was 35 years, and 90% were foreign-born. Participants treated for active TB disease reported significantly worse mean HRQOL and health utility scores within two weeks of treatment initiation, compared to control participants. HRQOL and health utility scores were comparable among those treated for LTBI and the control group. Lastly, we performed a secondary analysis of potential response shift of general health (GH) evaluated using the SF-36 among participants of the longitudinal cohort study described above. Centered residual values from a predictive linear mixed model of GH were used in a group-based trajectory model to detect potential response shift at the individual level. Of the original 263 participants, 216 (82%) were included in this analysis. Thirty-one participants (14%) reported worse GH than expected (i.e. negative response shift) during the year following TB screening assessment/treatment initiation.Key contributions of this research include: (1) the first meta-analysis of health utility and HRQOL scores reported by respondents treated for TB, (2) estimates of patient-reported health utility and HRQOL at each milestone of TB care, as compared to a group of healthy individuals facing similar stressors as treated participants, and (3) the first analysis of response shift at the individual patient level in a TB patient population. Future research should address social and behavioral health determinants which may also affect health utility and HRQOL, and assess health utility and HRQOL in sub-groups of patients that differ from those included in our longitudinal study. We encourage comparison our findings of response shift to studies using alternative approaches.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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, 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".