Global perspectives on tuberculosis in prisons and incarceration centers - Risk factors, priority needs, challenges for control and the way forward
Bibliographic record
Abstract
Tuberculosis (TB) remains a prominent cause of illness and mortality worldwide. Prisons are hotspots for TB transmission worldwide. We reviewed the literature on TB in prisons worldwide, including TB risk factors, delays in diagnosis including drug resistance, the treatment accorded, and operational and logistical issues of TB care in prison. The quantity and quality of data on TB in prisons varies worldwide. The TB incidence rate in prisons varies by World Health Organization region, with African countries having the highest rates of TB and TB/HIV co-infection. Its incidence rate among inmates is about 10 times higher than that of the general population. The growing prevalence of multidrug-resistant TB is particularly concerning, as it may affect high-risk settings and disproportionately affects vulnerable populations, such as prisoners and incarcerated individuals who go undiagnosed for extended periods of time. Factors that drive the high TB rates in prisons include limited access to health services such as TB care, overcrowding, poor ventilation, malnutrition, HIV, alcohol use disorders, illegal drug use, smoking, and other comorbidities, compounded by limited access to healthcare. Addressing TB in prisons requires a multifaceted approach, that includes improving living conditions, enhancing healthcare services, and developing innovative detection methods. The ongoing conflicts in Europe, the Middle East, Asia, and Africa further complicated TB prevention and control efforts in prisons, emphasizing the need for targeted interventions to address TB in these high-risk settings. Structured interventions tailored to the specific risk factors present in each environment should be investigated to effectively focus measures aimed at diminishing the overall burden of TB in prisons. Electronic record-keeping worldwide will allow for accurate data to be collected and shared.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".