Data driven longitudinal model with application to HIV differentiated care
Date
2019-08
Authors
Odhiambo, Collins
Weunda, Stephen
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Differentiated care is a new innovative approach for managing HIV/AIDS where ART
treatment services are customized by staggering patient’s visits for stable status while
reducing unnecessary burdens on the health system. Through provision of differentiated
care, the health system can reallocate resources to patients most in need who are failing
treatment. The main objective of this study is to develop a data-driven longitudinal model
which is applicable to HIV differentiated care. Method: We used routine data of HIV
positive patients initiated to ART at the point of care from 4 medical facilities in Nairobi
in the year 2018. Since both the GLMM and GEE are extensions of the GLM, we start
with a brief overview of GEE then relooked at extensions of GLMM. We specify f (u)
and g(p) to be dependent on the type of response Yi . For a binary Yi, we consider f (u)
as Bernoulli distribution and g(p) as the logit function, g(u) — log resulting to GLM is
the logistic regression. Results show the binary response which was differentiated care
category fits well with GLMM. We also found TB-HIV co-infection to be the only
significant predictor of differentiated care under both GEE and GLMM.
Description
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenya
Keywords
GLMM, Differentiated care, HIV/AIDS