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Research Article |

Modelling Count Data for HIV-Positive Patients on Antiretroviral Treatment in Kenya

The Acquired Immunodeficiency Syndrome (AIDS), caused by the Human Immunodeficiency Virus (HIV), is a lentivirus that weakens a person's resistance to infection. The National AIDS Control Programme (NASCOP) guidelines advise patients to begin antiretroviral therapy (ART) when an individual`s CD4+ cell count is below 350 cells/ml or when they begin to exhibit symptoms of HIV infection, as defined by WHO stages I through IV. To achieve HIV viral suppression, antiretroviral drug adherence is essential. Measurements on a variable are gathered for each individual at several points in longitudinal research. Although variables with repeated measurements within an individual are correlated, the between individuals are typically presumed to pose independence, and this is a major characteristic of such longitudinal data. A Retrospective Longitudinal study of HIV-Positive patients enrolled on ART from 2018 to 2021 those above 9 years when they sign up for ART. In total, 1489 individuals were involved during research. Data was examined by descriptive statistics. A generalized linear mixed effect model was fitted which took into account the within and between variations due to its flexibility. The number of patients enrolled on ART increases by Age and Gender over the four years. In 2018, 2019, 2020, and 2021 ART coverage was 22.4%, 24.2%, 26.1%, and 27.3% respectively. The variables age, gender and year were found to be the significant predictors. The GLMM with negative binomial distribution was used to analyze the data due to overdispersion in the data and the fact that there was a random factor. The AIC was used as the model selection approach. A model considered as the baseline was built with all possible interactions and major effects, and the best fitting model was defined as the one with the lowest AIC.

Antiretroviral Treatment (ART), Generalized Linear Models (GLM), Generalized Linear Mixed Models (GLMM)

APA Style

Anna Nanjala Muricho, Thomas Mageto, Samuel Mwalili. (2023). Modelling Count Data for HIV-Positive Patients on Antiretroviral Treatment in Kenya. International Journal of Statistical Distributions and Applications, 9(3), 68-80. https://doi.org/10.11648/j.ijsd.20230903.11

ACS Style

Anna Nanjala Muricho; Thomas Mageto; Samuel Mwalili. Modelling Count Data for HIV-Positive Patients on Antiretroviral Treatment in Kenya. Int. J. Stat. Distrib. Appl. 2023, 9(3), 68-80. doi: 10.11648/j.ijsd.20230903.11

AMA Style

Anna Nanjala Muricho, Thomas Mageto, Samuel Mwalili. Modelling Count Data for HIV-Positive Patients on Antiretroviral Treatment in Kenya. Int J Stat Distrib Appl. 2023;9(3):68-80. doi: 10.11648/j.ijsd.20230903.11

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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