Tuberculosis mortality in HIV-infected individuals: a cross-national systematic assessment
Bibliographic record
Abstract
Christopher Au-Yeung1, Steve Kanters1, Erin Ding1, Philippe Glaziou2, Aranka Anema1,3, Curtis L Cooper4, Julio SG Montaner1,3, Robert S Hogg1,5, Edward J Mills1,61BC Centre for Excellence in HIV/AIDS, Vancouver, Canada; 2Stop TB Department, World Health Organization, Geneva, Switzerland; 3Faculty of Medicine, University of British Columbia, Vancouver, Canada; 4The Ottawa Hospital Division of Infectious Diseases; 5Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada; 6Faculty of Health Sciences, University of Ottawa, Ottawa, CanadaObjective: Tuberculosis (TB) is a leading cause of death in human immunodeficiency virus (HIV)-positive individuals. We sought to compare mortality rates in TB/HIV co-infected individuals globally and by country/territory.Design: We conducted a cross-national systematic assessment.Methods: TB mortality rates in HIV-positive and HIV-negative individuals were obtained from the World Health Organization (WHO) Stop TB department for 212 recognized countries/territories in the years 2006–2008. Multivariate linear regression determined the impact of health care resource and economic variables on our outcome variable, and TB mortality rates.Results: In 2008, an estimated 13 TB/HIV deaths occurred per 100,000 population globally with the African region having the highest death rate ([AFRH] ≥4% adult HIV-infection rate) at 86 per 100,000 individuals. The next highest rates were for the Eastern European Region (EEUR) and the Latin American Region (LAMR) at 4 and 3 respectively per 100,000 population. African countries’ HIV-positive TB mortality rates were 29.9 times higher than non-African countries (95% confidence interval [CI]: 16.8–53.4). Every US$100 of government per capita health expenditure was associated with a 33% (95% CI: 24%–42%) decrease in TB/HIV mortality rates. The multivariate model also accounted for calendar year and did not include highly active antiretroviral therapy (HAART) coverage.Conclusions: Our results indicate that while the AFRH has the highest TB/HIV death rates, countries in EEUR and LAMR also have elevated mortality rates. Increasing health expenditure directed towards universal HAART access may reduce mortality from both diseases.Keywords: tuberculosis, HIV, antiretroviral therapy, mortality
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".