The association between gender and HIV viral suppression on third-line therapy in Zambia: a retrospective cohort study.
Liana AndronescuPaul M ZuluSarah S JacksonLottie HachaambwaCassidy W ClaassenKristen Alyce StaffordPublished in: International journal of STD & AIDS (2019)
Patient's gender may impact pharmacokinetics and play a role in viral suppression. Existing literature has focused on treatment-naïve patients and produced inconclusive results, often implicating differences in adherence as the driver of gender-based outcome differences. The present analysis assessed whether viral suppression on third-line HIV treatment among a closely followed population differs by gender. A retrospective cohort study of patients on third-line HIV treatment was initiated at the HIV Advanced Treatment Centre in Lusaka, Zambia between January 2012 and December 2015. The association between gender and viral suppression was assessed using log binomial regression adjusted for core drug, number of drug mutations, and baseline viral load. Of the 80 included patients (56% female; median age: 40 years), 50 (62%) were virally suppressed at six months. After adjustment, females were less likely to be virologically suppressed at six months on third-line treatment compared to male HIV patients (relative risk 0.82, 95% confidence interval: 0.56, 1.20). Our data suggest that women were less likely to be suppressed following six months of third-line therapy compared to men; however, the difference was not statistically significant. Larger studies are needed to determine whether women are at increased risk of viral failure on third-line therapy compared to men.
Keyphrases
- sars cov
- hiv infected
- end stage renal disease
- antiretroviral therapy
- hiv positive
- human immunodeficiency virus
- hepatitis c virus
- chronic kidney disease
- hiv testing
- ejection fraction
- hiv aids
- newly diagnosed
- men who have sex with men
- patient reported outcomes
- emergency department
- skeletal muscle
- pregnant women
- adipose tissue
- deep learning
- machine learning
- bone marrow
- case report
- south africa
- artificial intelligence
- smoking cessation
- glycemic control
- cell therapy