Show simple item record

dc.contributor.authorOdhiambo, Collins
dc.contributor.authorWeunda, Stephen
dc.descriptionPaper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenyaen_US
dc.description.abstractDifferentiated 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.en_US
dc.description.sponsorshipInstitute of Mathematical Sciences, Strathmore University, Nairobi, Kenya. School of Mathematics, University of Nairobi, Nairobi, Kenya.en_US
dc.publisherStrathmore Universityen_US
dc.subjectDifferentiated careen_US
dc.titleData driven longitudinal model with application to HIV differentiated careen_US

Files in this item


This item appears in the following Collection(s)

  • SIMC 2019 [99]
    5th Strathmore International Mathematics Conference (August 12 – 16, 2019)

Show simple item record