Incidence and predictors of tuberculosis among HIV-positive adults on antiretroviral therapy at Debre Markos referral hospital, Northwest Ethiopia: a retrospective record review
Bibliographic record
Abstract
BACKGROUND: Tuberculosis is the leading cause of morbidity and mortality among people living with human immunodeficiency virus. Almost one-third of deaths among people living with human immunodeficiency virus are attributed to tuberculosis. Despite this evidence, in Ethiopia, there is a scarcity of information regarding the incidence and predictors of tuberculosis among people living with HIV. Thus, this study assessed the incidence and predictors of tuberculosis among HIV-positive adults on antiretroviral therapy. METHODS: This study was a retrospective record review including 544 HIV-positive adults on antiretroviral therapy at Debre Markos Referral Hospital between January 1, 2012 and December 31, 2017. The study participants were selected using a simple random sampling technique. The data extraction format was adapted from antiretroviral intake and follow-up forms. Cox-proportional hazards regression model was fitted and Cox-Snell residual test was used to assess the goodness of fit. Tuberculosis free survival time was estimated using the Kaplan-Meier survival curve. Both the bi-variable and multivariable Cox-proportional hazard regression models were used to identify predictors of tuberculosis. RESULTS: In the final analysis, a total of 492 HIV-positive adults were included, of whom, 83 (16.9%) developed tuberculosis at the time of follow-up. This study found that the incidence of tuberculosis was 6.5 (95% CI: 5.2, 8.0) per 100-person-years (PY) of observation. Advanced World Health Organization clinical disease stage (III and IV) (AHR: 2.1, 95% CI: 1.2, 3.2), being ambulatory and bedridden (AHR: 1.8, 95% CI: 1.1, 3.1), baseline opportunistic infections (AHR: 2.8, 95% CI: 1.7, 4.4), low hemoglobin level (AHR: 3.5, 95% CI: 2.1, 5.8), and not taking Isonized Preventive Therapy (AHR: 3.9, 95% CI: 1.9, 7.6) were found to be the predictors of tuberculosis. CONCLUSION: The study found that there was a high rate of tuberculosis occurrence as compared to previous studies. Baseline opportunistic infections, being ambulatory and bedridden, advanced disease stage, low hemoglobin level, and not taking Isonized Preventive Therapy were found to be the predictors of tuberculosis. Therefore, early detection and treatment of opportunistic infections like tuberculosis should get a special attention.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".