Homelessness and HIV: A Combination Predictive of Poor Tuberculosis Treatment Outcomes and in Need of Innovative Strategies to Improve Treatment Completion
Bibliographic record
Abstract
Antioquia Department is the state with the highest burden of tuberculosis (TB) in Colombia. Our aim was to determine the risk factors associated with unsuccessful TB treatment in HIV-seropositive and homeless persons, compared with non-HIV-infected and non-homeless persons with TB. We conducted a retrospective cohort study using observational, routinely collected health data from all drug-susceptible TB cases in homeless and/or HIV-seropositive individuals in Antioquia from 2014 to 2016. Unsuccessful TB treatment was defined as individuals having been lost to follow-up, having died, or treatment failure occurrence during the study period. Successful treatment was defined as cure of TB or treatment completion according to the WHO definitions. We identified 544 homeless persons with TB (432 HIV- and 112 HIV+), 835 HIV+ persons with TB and non-homeless, and 5,086 HIV-/non-homeless people with TB. Unsuccessful treatment rates were 19.3% in HIV-/non-homeless persons, 37.4% in non-homeless HIV+ patients, 61.5% in homeless HIV- patients, and 70.3% in homeless HIV+ patients; all rates fall below End TB strategy targets. More than 50% of homeless patients were lost to follow-up. Risk factors associated with unsuccessful treatment were HIV seropositivity, homelessness, male gender, age ≥ 25 years, noncontributory-type health insurance, TB diagnosis made during hospitalization, and previous treatment for TB. These results highlight the challenge of treating TB in the homeless population. These findings should put an onus on TB programs, governments, clinicians, and others involved in the collaborative care of TB patients to pursue innovative strategies to improve treatment success in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".