The Human Immunodeficiency Virus (HIV) pandemic still remains a major public
health concern worldwide. The World Health Organization (WHO) estimates that
approximately over 70% of people living with HIV in the world are in sub-Saharan
region. Malawi is one of the worst affected countries in sub-Saharan Africa with
prevalence reaching up to 16% in some areas. Recent study reports, largely in
Africa, comparing outcomes for HIV patients with Kaposi’s sarcoma (HIV/KS)
and HIV patients without KS indicate poor prognosis and poor health outcomes
amongst HIV patients with KS. While efforts are being made to improve the
management and care for the HIV/KS patient group, there is also need for continued
efforts to better understand the survival patterns in this patients. The work
presented in this thesis attempts to investigate the survival patterns in different
patient subgroups in HIV cohorts in Malawi by using advanced and novel statistical
techniques with an ultimate aim of informing targeted patient treatment and
management practices.
In this thesis, we aim to address the following four objectives; (1) to identify
risk factors for mortality among HIV patients diagnosed with Kaposi’s sarcoma
during routine initiation of ART, (2) to model the survival pattern among HIV
patients diagnosed with KS, (3) to model local geographical variations in survival
among HIV patients on ART, (4) to quantify transition dynamics in HIV and TB
co-infection using multi-state modelling.
For the first two objectives, we considered extended Cox models and parametric
models. We also used a novel approach of accounting for high attrition in cohorts in
which we used a ’gold-standard’ data to compare survival in our cohort. Sensitivity
analyses indicated consistencies in our approach providing an insight into how
model results change when using this comparison approach. Overall We noted
an early mortality with most patients dying in the first five months after starting
HIV treatment. Patients with TB and the patients who started in the early era
of ART were significantly at risk of dying. The model diagnostics indicated that
(i) a random effects Cox/Log-Gaussian frailty model and (ii) a flexible parametric
proportional hazards model, describe the risk of mortality in the HIV/KS patients
well.
For the third objective, spatial survival models were considered. The study showed
existence of possible residual spatial variation in survival after adjusting for age,
sex, KS status, TB status and unobserved individual frailties. To further aid our
understanding, we used the choropleth maps to indicate areas with substantially
high probability of mortality risk at different cut-off values. These results highlight
the local geographical variations in survival in HIV populations, an element more
often ignored in most studies on HIV data.
For the last objective, we considered the homogeneous continuous time multistate
Markov models. In this study we found that patients in TB free status had a
relatively higher probability of transitioning to being diagnosed with TB compared
to dying while in TB free status. However, the cumulative transition hazards for
the ’TB free ! death’ transitions compared to the "TB free ! TB infection"
transitions were only higher during the early days of HIV treatment. This result
emphasize how early periods after starting HIV treatment is crucial to ensure
better prognosis. We also noted significant gender differences in the ’TB-free !
death’ transitions.
It is anticipated that the findings in this thesis will help to inform treatment and
management practices of HIV patients. The findings provide clear outcome pathways
taken by HIV/TB patients before experiencing a terminal outcome. More importantly, the findings could help inform policies aimed at improving overall
survival in HIV cohorts by establishing targeted patient management and treatment
strategies and also formulating a more efficient triage system for care and
treatment of particular group of patients.