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A Generalized Linear Model of HIV/AIDS Patients in Kenya: A Case Study of Nyeri County Referral Hospital

Received: 24 July 2024     Accepted: 13 August 2024     Published: 22 August 2024
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Abstract

With millions of new cases and deaths reported every year, HIV/AIDS is a significant worldwide health concern. Creating successful public health policies and interventions requires an understanding of the dynamics of HIV transmission and progression. WHO predicted that by the end of 2022 roughly 39 million individuals worldwide would be living with HIV, out of which 37.5 million are adults, whereas 1.5 million are children. Despite outstanding global gains in HIV/AIDS prevention, treatment and care, Kenya continues to struggle to effectively handle the HIV epidemic, particularly in areas like Nyeri County. Nyeri County Referral Hospital is a critical healthcare institution for HIV/AIDS patients in the region. However, there is still a lack of understanding about the epidemiological characteristics of HIV/AIDS in this particular population. This study’s aim was to use a GLM on HIV/AIDS data in Nyeri County Referral Hospital in Kenya. To determine the significance of model parameters, Likelihood Ratio Test was used whereas significance of regression coefficients was determined using Wald Chi- Square Test. Deviance was utilized to test for the goodness of fit. R software version 4.4.1 was utilized. This project may help health policymakers in developing or refining HIV/AIDS care programs. Findings from the study can help healthcare planners and policymakers allocate resources more efficiently to meet the requirements of HIV/AIDS patients. The fitted model showed that, only ART use was significant (p-value = 2.684562 × 10−13). Because some covariates were not significant, each of them was analyzed separately. Age was a significant predictor (p-value = 0.0001536103). The other variables were not significant. This finding is consistent with previous evidence, which stresses the relevance of ART in lowering viral load, enhancing immunological function, and extending the lives of people living with HIV. To build upon the current findings, future research should explore additional variables that may influence HIV status, for example cultural beliefs, and access to healthcare services. Again, future studies may involve the use of survival analysis through GLM in analyzing similar data.

Published in American Journal of Theoretical and Applied Statistics (Volume 13, Issue 4)
DOI 10.11648/j.ajtas.20241304.13
Page(s) 80-84
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Generalized Linear Model, HIV/AIDS, Logistic Regression, Likelihood Ratio Test

References
[1] Deeks, S. G., Lewin, S. R., & Havlir, D. V. (2013). The end of AIDS: HIV infection as a chronic disease. The lancet, 382(9903), 1525-1533.
[2] Granich, R., Gupta, S., Hersh, B., Williams, B., Montaner, J., Young, B., & Zuniga, J. M. (2015). Trends in AIDS deaths, new infections and ART coverage in the top 30 countries with the highest AIDS mortality burden; 1990-2013. PloS one, 10(7), e0131353.
[3] Hastie, T. J., & Pregibon, D. (2017). Generalized linear models. In Statistical models in S (pp. 195-247). Routledge.
[4] Hoosen, N. (2021). Interventions for Improving Adherence and Retention in HIV-Infected Women on ART During Antenatal and Postnatal Care: A Systematic Review.
[5] Keats, E. C., Macharia, W., Singh, N. S., Akseer, N., Ravishankar, N., Ngugi, A. K., ... & Bhutta, Z. A. (2018). Accelerating Kenya’s progress to 2030: understanding the determinants of under-five mortality from 1990 to 2015. BMJ global health, 3(3), e000655.
[6] Kearns, B., Stevenson, M. D., Triantafyllopoulos, K., & Manca, A. (2019). Generalized linear models for flexible parametric modeling of the hazard function. Medical Decision Making, 39(7), 867-878.
[7] Kiweewa, F., Esber, A., Musingye, E., Reed, D., Crowell, T. A., Cham, F., ... & Kibuuka, H. (2019). HIV virologic failure and its predictors among HIV-infected adults on antiretroviral therapy in the African Cohort Study. PloS one, 14(2), e0211344.
[8] Klatt, N. R., Chomont, N., Douek, D. C., & Deeks, S. G. (2013). Immune activation and HIV persistence: implications for curative approaches to HIV infection. Immunological reviews, 254(1), 326-342.
[9] McCullagh, P. (2019). Generalized linear models. Routledge.
[10] McCue, T., Carruthers, E., Dawe, J., Liu, S., Robar, A., & Johnson, K. (2008). Evaluation of generalized linear model assumptions using randomization. Unpublished manuscript. Retrieved from
[11] Neuhaus, J., & McCulloch, C. (2011). Generalized linear models. Wiley Interdisciplinary Reviews: Computational Statistics, 3(5), 407-413.
[12] Strickland, J. (2017). Logistic regression inside and out. Lulu.com
[13] Vicente, V., & Aguiar, P. (2023). Practical Epidemiology with Generalized Linear Models. Leya.
[14] Wanjiru, s. w. (2021). Efficacy of strategies that mitigate challenges faced by women infected with HIV/AIDS in Majengo urban informal settlement, Nyeri county, Kenya (doctoral dissertation, school of humanities and social sciences in partial fulfillment of the requirement for the degree of (Master of Arts) in (gender and development studies), Kenyatta university).
[15] World Health Organization. (2022). Health at a Glance: Asia/Pacific 2022 Measuring Progress Towards Universal Health Coverage: Measuring Progress Towards Universal Health Coverage. OECD publishing.
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    Ireri, S., Esekon, J., Kinyua, M. (2024). A Generalized Linear Model of HIV/AIDS Patients in Kenya: A Case Study of Nyeri County Referral Hospital. American Journal of Theoretical and Applied Statistics, 13(4), 80-84. https://doi.org/10.11648/j.ajtas.20241304.13

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    ACS Style

    Ireri, S.; Esekon, J.; Kinyua, M. A Generalized Linear Model of HIV/AIDS Patients in Kenya: A Case Study of Nyeri County Referral Hospital. Am. J. Theor. Appl. Stat. 2024, 13(4), 80-84. doi: 10.11648/j.ajtas.20241304.13

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    AMA Style

    Ireri S, Esekon J, Kinyua M. A Generalized Linear Model of HIV/AIDS Patients in Kenya: A Case Study of Nyeri County Referral Hospital. Am J Theor Appl Stat. 2024;13(4):80-84. doi: 10.11648/j.ajtas.20241304.13

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  • @article{10.11648/j.ajtas.20241304.13,
      author = {Sarah Ireri and Joseph Esekon and Margaret Kinyua},
      title = {A Generalized Linear Model of HIV/AIDS Patients in Kenya: A Case Study of Nyeri County Referral Hospital},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {13},
      number = {4},
      pages = {80-84},
      doi = {10.11648/j.ajtas.20241304.13},
      url = {https://doi.org/10.11648/j.ajtas.20241304.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20241304.13},
      abstract = {With millions of new cases and deaths reported every year, HIV/AIDS is a significant worldwide health concern. Creating successful public health policies and interventions requires an understanding of the dynamics of HIV transmission and progression. WHO predicted that by the end of 2022 roughly 39 million individuals worldwide would be living with HIV, out of which 37.5 million are adults, whereas 1.5 million are children. Despite outstanding global gains in HIV/AIDS prevention, treatment and care, Kenya continues to struggle to effectively handle the HIV epidemic, particularly in areas like Nyeri County. Nyeri County Referral Hospital is a critical healthcare institution for HIV/AIDS patients in the region. However, there is still a lack of understanding about the epidemiological characteristics of HIV/AIDS in this particular population. This study’s aim was to use a GLM on HIV/AIDS data in Nyeri County Referral Hospital in Kenya. To determine the significance of model parameters, Likelihood Ratio Test was used whereas significance of regression coefficients was determined using Wald Chi- Square Test. Deviance was utilized to test for the goodness of fit. R software version 4.4.1 was utilized. This project may help health policymakers in developing or refining HIV/AIDS care programs. Findings from the study can help healthcare planners and policymakers allocate resources more efficiently to meet the requirements of HIV/AIDS patients. The fitted model showed that, only ART use was significant (p-value = 2.684562 × 10−13). Because some covariates were not significant, each of them was analyzed separately. Age was a significant predictor (p-value = 0.0001536103). The other variables were not significant. This finding is consistent with previous evidence, which stresses the relevance of ART in lowering viral load, enhancing immunological function, and extending the lives of people living with HIV. To build upon the current findings, future research should explore additional variables that may influence HIV status, for example cultural beliefs, and access to healthcare services. Again, future studies may involve the use of survival analysis through GLM in analyzing similar data.},
     year = {2024}
    }
    

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