TITLE:
Modelling Key Population Attrition in the HIV and AIDS Programme in Kenya Using Random Survival Forests with Synthetic Minority Oversampling Technique-Nominal Continuous
AUTHORS:
Evan Kahacho, Charity Wamwea, Bonface Malenje, Gordon Aomo
KEYWORDS:
Random Survival Forests, Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC), Key Population, Female Sex Workers (FSW), Men Who Have Sex with Men (MSM), People Who Inject Drugs (PWID)
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.1,
January
31,
2023
ABSTRACT: HIV and AIDS has continued to be a major public
health concern, and hence one of the epidemics that the world resolved to end
by 2030 as highlighted in sustainable development goals (SDGs). A colossal
amount of effort has been taken to reduce new HIV infections, but there are
still a significant number of new infections
reported. HIV prevalence is more skewed towards the key population who
include female sex workers (FSW), men who have sex with men (MSM), and people
who inject drugs (PWID). The study design was retrospective and focused on key
population enrolled in a comprehensive HIV and AIDS programme by the Kenya Red
Cross Society from July 2019 to June 2021. Individuals who were either lost to
follow up, defaulted (dropped out, transferred out, or relocated) or died were
classified as attrition; while those who were active and alive by the end of
the study were classified as retention. The
study used density analysis to determine the spatial differences of key population attrition in the 19 targeted counties, and used Kilifi county as an example to map attrition cases in smaller
administrative areas (sub-county level). The study used synthetic
minority oversampling technique-nominal continuous (SMOTE-NC) to balance the datasets since the cases of
attrition were much less than retention. The random survival forests model was
then fitted to the balanced dataset. The model correctly identified attrition
cases using the predicted ensemble mortality and their survival time using the
estimated Kaplan-Meier survival function. The predictive performance of the
model was strong and way better than random chance with concordance indices
greater than 0.75.