Retrospective cohort study of lost to follow up predictors among TB patients in Yerevan, Armenia
Bibliographic record
Abstract
Background The early diagnosis and treatment of tuberculosis (TB) are essential to prevent TB related morbidity, mortality and transmission. Despite the efforts of making TB treatment available, patients still fail to complete the required treatment course. According to World Health Organization (WHO) a TB patient who did not start treatment or whose treatment was interrupted for 2 consecutive months or more is considered as lost to follow up (LTFU). The identification of factors influencing treatment interruption and LTFU of TB patients can guide designing appropriate strategies to promote treatment adherence. In the capital city of Armenia, Yerevan, outpatient TB care is provided by nine outpatient TB clinics and a prison hospital. This study aimed to investigate the predictors of LTFU among pulmonary TB patients in Yerevan, Armenia. Methods We conducted a retrospective cohort study among pulmonary TB patients from Yerevan whose treatment outcomes were recorded from 2013 to 2014 in National TB Control Center (NTCC). Patient information was extracted from the NTCC database and outpatient medical charts. The primary outcome of the study was LTFU treatment status of patients. Multivariable logistic regression was used to identify predictors associated with LTFU. Results There were 621 patients in the sample, 9.5% (n = 59) of whom were LTFU. The majority of patients interrupted their treatment during the outpatient phase of the treatment. On average they were LTFU after 7 months since the treatment started. Out of 59 patients who were LTFU, 18.6% were migrant workers and about 4% were alcoholics. Multivariable logistic regression revealed significant association of LTFU with being male (OR: 3.14, CI: 1.21-8.20, p = 0.019), being younger (OR: 0.98, CI: 0.96-0.99, p = 0.028) and having drug resistant (DR) type of TB (OR: 2.26, CI: 1.18-4.33, p = 0.014.) Conclusions Our study found that younger age, being male and having DR type of TB are predictors of LTFU treatment status. Key messages: The availability of and access to treatment is necessary but might not be enough to ensure every TB patient completes the treatment. Health care providers should focus on young men and patients with DR TB to make sure they complete the TB treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".