Risk factors for high anti-HHV-8 antibody titers (≥1:51,200) in black, HIV-1 negative South African cancer patients: a case control study
Bibliographic record
Abstract
BACKGROUND: Infection with human herpesvirus 8 (HHV-8), also known as Kaposi's sarcoma-associated herpesvirus (KSHV), is the necessary causal agent in the development of Kaposi's sarcoma (KS). Infection with HIV-1, male gender and older age all increase risk for KS. However, the geographic distribution of HHV-8 and KS both prior to the HIV/AIDS epidemic and with HIV/AIDS suggest the presence of an additional co-factor in the development of KS. METHODS: Between January 1994 and October 1997, we interviewed 2576 black in-patients with cancer in Johannesburg and Soweto, South Africa. Blood was tested for antibodies against HIV-1 and HHV-8 and the study was restricted to 2191 HIV-1 negative patients. Antibodies against the latent nuclear antigen of HHV-8 encoded by orf73 were detected with an indirect immunofluorescence assay. We examined the relationship between high anti-HHV-8 antibody titers (> or =1:51,200) and sociodemographic and behavioral factors using unconditional logistic regression models. Variables that were significant at p = 0.10 were included in multivariate analysis. RESULTS: Of the 2191 HIV-1 negative patients who did not have Kaposi's sarcoma, 854 (39.0%) were positive for antibodies against HHV-8 according to the immunofluorescent assay. Among those seropositive for HHV-8, 530 (62.1%) had low titers (1:200), 227 (26.6%) had medium titers (1:51,200) and 97 (11.4%) had highest titers (1:204,800). Among the 2191 HIV-1 negative patients, the prevalence of high anti-HHV-8 antibody titers (> or =1:51,200) was independently associated with increasing age (p-trend = 0.04), having a marital status of separated or divorced (p = 0.003), using wood, coal or charcoal as fuel for cooking 20 years ago instead of electricity (p = 0.02) and consuming traditional maize beer more than one time a week (p = 0.02; p-trend for increasing consumption = 0.05) although this may be due to chance given the large number of predictors considered in this analysis. CONCLUSIONS: Among HIV-negative subjects, patients with high anti-HHV-8 antibody titers are characterized by older age. Other associations that may be factors in the development of high anti-HHV-8 titers include exposure to poverty or a low socioeconomic status environment and consumption of traditional maize beer. The relationship between these variables and high anti-HHV-8 titers requires further, prospective study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".