Gender-related mortality for HIV-infected patients on highly active antiretroviral therapy (HAART) in rural Uganda
Bibliographic record
Abstract
Arif Alibhai1, Walter Kipp1, L Duncan Saunders1, Ambikaipakan Senthilselvan1, Amy Kaler2, Stan Houston3, Joseph Konde-Lule4, Joa Okech-Ojony5, Tom Rubaale51Department of Public Health Sciences, 2Department of Sociology, 3Department of Medicine, University of Alberta, Edmonton, Canada; 4School of Public Health, Makerere University, Kampala, Uganda; 5Kabarole Health Department, Fort Portal, Uganda Abstract: The purpose of this study was to examine gender differences in mortality for human immunodeficiency virus (HIV) patients in rural Western Uganda after six months of highly active antiretroviral therapy (HAART). Three hundred eighty five patients were followed up for six months after initiating HAART. Statistical analysis included descriptive, univariate and multivariate methods, using Kaplan–Meier estimates of survival distribution and Cox proportional hazards regression. Mortality in female patients (9.0%) was lower than mortality in males (13.5%), with the difference being almost statistically significant (adjusted hazard ratio for females 0.55; 95% confidence interval [CI]: 0.28–1.07; P = 0.08). At baseline, female patients had a significantly higher CD4+ cell count than male patients (median 147 cells/μL vs 120 cells/μL; P < 0.01). A higher CD4+ cell count and primary level education were strongly associated with better survival. The higher CD4+ cell count in females may indicate that they accessed HAART services at an earlier stage of their disease progression than males. A borderline statistically significant lower mortality rate in females shows that females fare better on treatment in this context than males. The association between lower mortality and higher CD4+ levels suggest that males are not accessing treatment early enough and that more concerted efforts need to be made by HAART programs to reach male HIV patients.Keywords: antiretroviral treatment, gender, rural, Uganda
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".