Prevalence and Predictors of HIV among Chinese Tuberculosis Patients by Provider-Initiated HIV Testing and Counselling (PITC): A Multisite Study in South Central of China
Bibliographic record
Abstract
Background Tuberculosis (TB) and HIV are two worldwide public health concerns. Co-infection of these two diseases has been considered to be a major obstacle for the global efforts in reaching the goals for the prevention of HIV and TB. Method A comprehensive cross-sectional study was conducted to recruit TB patients in three provinces (Guangxi, Henan and Sichuan) of China between April 1 and September 30, 2010. Results A total of 1,032 consenting TB patients attended this survey during the study period. Among the participants, 3.30% were HIV positive; about one quarter had opportunistic infections. Nearly half of the participants were 50 years or older, the majority were male and about one third were from minority ethnic groups. After adjusting for site, gender and areas of residence (using the partial/selective Model 1), former commercial plasma donors (adjusted OR [aOR] = 33.71) and injecting drug users(aOR = 15.86) were found to have significantly higher risk of being HIV-positivity. In addition, having extramarital sexual relationship (aOR = 307.16), being engaged in commercial sex (aOR = 252.37), suffering from opportunistic infections in the past six months (aOR = 2.79), losing 10% or more of the body weight in the past six months (aOR = 5.90) and having abnormal chest X-ray findings (aOR = 20.40) were all significantly associated with HIV seropositivity (each p<0.05). Conclusions HIV prevalence among TB patients was high in the study areas of China. To control the dual epidemic, intervention strategies targeting socio-demographic and behavioral factors associated with higher risk of TB-HIV co-infection are urgently called for.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".