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Data-Driven Longitudinal Model with Application to HIV Differentiated Care Pages 89-97

Stephen O. Weunda, Collins Odhiambo and Idah Orowe


Published: 25 November 2019

Abstract: Background: Differentiated care is a new innovative approach for managing HIV/AIDS where ART treatment services are customized by staggering patients’ visits for stable status while reducing unnecessary burdens on the health system. Through provision of differentiated care, the health providers are able to reallocate resources and time to patients in who are failing treatment.

Objective: 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 (µ) and g (µ) to be dependent on the type of response Yi. For a binary Yi, we consider f (µ) as Bernoulli distribution and g (µ) as the logit function, g (µ) = log [µ/1−µ] resulting to GLM is the logistic regression.

Results and Conclusion: 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 framework.

Keywords: Differentiated care, HIV/AIDS, Generalized Linear Models (GLM), Generalized Estimating Equations (GEE), and Generalized Linear Mixed Models (GLMM).


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