The Prevalence and Drug Sensitivity of Tuberculosis among Patients Dying in Hospital in KwaZulu-Natal, South Africa: A Postmortem Study
Bibliographic record
Abstract
BACKGROUND: Tuberculosis is the leading cause of death in South Africa by death notification, but accurate diagnosis of tuberculosis is challenging in this setting of high HIV prevalence. We conducted limited autopsies on young adults dying in a single public hospital in the province of KwaZulu-Natal between October 2008 and August 2009 in order to estimate the magnitude of deaths attributable to tuberculosis. METHODS AND FINDINGS: We studied a representative sample of 240 adult inpatients (aged 20-45 years) dying after admission to Edendale Hospital. Limited autopsies included collection of respiratory tract secretions and tissue by needle core biopsies of lung, liver, and spleen. Specimens were examined by fluorescent microscopy for acid-fast bacilli and cultured in liquid media; cultures positive for M. tuberculosis were tested for drug susceptibility to first- and second-line antibiotics. Ninety-four percent of our study cohort was HIV seropositive and 50% of decedents had culture-positive tuberculosis at the time of death. Fifty percent of the participants were on treatment for tuberculosis at the time of death and 58% of these treated individuals remained culture positive at the time of death. Of the 50% not receiving tuberculosis treatment, 42% were culture positive. Seventeen percent of all positive cultures were resistant to both isoniazid and rifampin (i.e., multidrug resistant); 16% of patients dying during the initiation phase of their first ever course of tuberculosis treatment were infected with multidrug-resistant bacilli. CONCLUSIONS: Our findings reveal the immense toll of tuberculosis among HIV-positive individuals in KwaZulu-Natal. The majority of decedents who remained culture positive despite receiving tuberculosis treatment were infected with pan-susceptible M. tuberculosis, suggesting that the diagnosis of tuberculosis was made too late to alter the fatal course of the infection. There is also a significant burden of undetected multidrug-resistant tuberculosis among HIV-coinfected individuals dying in this setting. New public health approaches that improve early diagnosis of tuberculosis and accelerate the initiation of treatment are urgently needed in this setting. Please see later in the article for the Editors' Summary.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".