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Modelling HIV-Free Survival Among Children Under Five-Year-Old from the Uganda Population-Based HIV Impact Assessment (UPHIA) 2016-2017 Survey

Received: Sep. 22, 2022    Accepted: Oct. 21, 2022    Published: Oct. 29, 2022
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Abstract

The Human Immunodeficiency Virus (HIV) pandemic is currently the most challenging public health matter that faces third world countries, especially those in Sub-Saharan Africa. Uganda, in East Africa, with a generalised and highly heterogeneous epidemic, is no exception, with HIV/AIDS affecting most sectors of the economy. The barrier HIV-stigma presents to the HIV treatment cascade is increasingly documented; however, no research has been done on the effects of HIV free survival of children under five-year olds in the presence of clustering. This study present models for analysing the effects of covariates on HIV free survival of children in Uganda in which tests for the association among children in different enumeration areas (EA) are carried out. The main objective of this study is to test for association between covariates and HIV free survival among under five-year old children born to HIV positive mothers in Uganda. This study uses the shared frailty model from the Uganda Population-Based HIV Impact Assessment (UPHIA 2016-2017) dataset. The study reviews the Cox proportional hazard model to include clustering within children from different EAs. The study first checks for proportional hazard assumption in the model then extend the Cox PH model to include clustering of children in different EA in Uganda using the shared frailty model. The basis for testing proportional hazard assumption in Cox regression analysis is to assess whether the effects of the covariates change overtime. When observations are clustered within groups or multiple event times are clustered within individuals, dependence between event times in a cluster is of interest. The study then uses the shared frailty model which is a random effect model which helps explain the unaccounted heterogeneity in the data.

DOI 10.11648/j.rd.20220304.13
Published in Research & Development ( Volume 3, Issue 4, December 2022 )
Page(s) 215-223
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

Cox Proportional Hazard Model, Frailty Model, Mother-To-Child Transmission, HIV-Free Survival

References
[1] Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34 (2), 187-202.
[2] Dessie, Z. G., Zewotir, T., Mwambi, H., & North, D. (2020). Modeling viral suppression, viral rebound and state-specific duration of HIV patients with CD4 count adjustment: parametric multistate frailty model approach. Infectious diseases and therapy, 9 (2), 367-388.
[3] García de Olalla, P., Knobel, H., Carmona, A., Guelar, A., López-Colomés, J. L., & Caylà, J. A. (2002). Impact of adherence and highly active antiretroviral therapy on survival in HIV-infected patients. Journal of acquired immune deficiency syndromes (1999), 30 (1), 105-110.
[4] Goethals, K., Janssen, P., & Duchateau, L. (2008). Frailty models and copulas: similarities and differences. Journal of Applied Statistics, 35 (9), 1071-1079.
[5] Hougaard, P. (2012). Analysis of multivariate survival data. Springer Science & Business Media.
[6] Joint United Nations Programme on HIV/AIDS (UNAIDS). (2019). Start free Stay free AIDS free-2019 report. Geneva: UNAIDS.
[7] Klein, J. P. (1992). Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics, 795-806.
[8] Lancaster, T., & Intrator, O. (1998). Panel data with survival: hospitalization of HIV-positive patients. Journal of the American statistical association, 93 (441), 46-53.
[9] Mbougua, J. B. T., Laurent, C., Ndoye, I., Delaporte, E., Gwet, H., & Molinari, N. (2013). Nonlinear multiple imputation for continuous covariate within semiparametric Cox model: application to HIV data in Senegal. Statistics in medicine, 32 (26), 4651-4665.
[10] Muttai, H., Guyah, B., Musingila, P., Achia, T., Miruka, F., Wanjohi, S.,... & Zielinski-Gutierrez, E. (2021). Development and Validation of a Sociodemographic and Behavioral Characteristics-Based Risk-Score Algorithm for Targeting HIV Testing Among Adults in Kenya. AIDS and Behavior, 25 (2), 297-310.
[11] Newell, M. L. (2003). Antenatal and perinatal strategies to prevent mother-to-child transmission of HIV infection. Transactions of the Royal Society of Tropical Medicine and Hygiene, 97 (1), 22-24.
[12] Rondeau, V., Gonzalez, J. R., Yassin Mazroui, A., Mauguen, A. D., Laurent, A., Lopez, M.,... & Rondeau, M. V. (2021). Package ‘frailtypack’.
[13] Sy, J. P., & Taylor, J. M. (2000). Estimation in a Cox proportional hazards cure model. Biometrics, 56 (1), 227-236.
[14] Therneau, T. M., & Lumley, T. (2015). Package ‘survival’. R Top Doc, 128 (10), 28-33.
[15] UBOS (2014). 2014 census - uganda bureau of statistics. https://www.ubos.org/2014-census/. (Accessed on 10/19/2022).
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  • APA Style

    Muriuki Francis Maina, Waititu Anthony, Wanjoya Anthony. (2022). Modelling HIV-Free Survival Among Children Under Five-Year-Old from the Uganda Population-Based HIV Impact Assessment (UPHIA) 2016-2017 Survey. Research & Development, 3(4), 215-223. https://doi.org/10.11648/j.rd.20220304.13

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    ACS Style

    Muriuki Francis Maina; Waititu Anthony; Wanjoya Anthony. Modelling HIV-Free Survival Among Children Under Five-Year-Old from the Uganda Population-Based HIV Impact Assessment (UPHIA) 2016-2017 Survey. Res. Dev. 2022, 3(4), 215-223. doi: 10.11648/j.rd.20220304.13

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    AMA Style

    Muriuki Francis Maina, Waititu Anthony, Wanjoya Anthony. Modelling HIV-Free Survival Among Children Under Five-Year-Old from the Uganda Population-Based HIV Impact Assessment (UPHIA) 2016-2017 Survey. Res Dev. 2022;3(4):215-223. doi: 10.11648/j.rd.20220304.13

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  • @article{10.11648/j.rd.20220304.13,
      author = {Muriuki Francis Maina and Waititu Anthony and Wanjoya Anthony},
      title = {Modelling HIV-Free Survival Among Children Under Five-Year-Old from the Uganda Population-Based HIV Impact Assessment (UPHIA) 2016-2017 Survey},
      journal = {Research & Development},
      volume = {3},
      number = {4},
      pages = {215-223},
      doi = {10.11648/j.rd.20220304.13},
      url = {https://doi.org/10.11648/j.rd.20220304.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.rd.20220304.13},
      abstract = {The Human Immunodeficiency Virus (HIV) pandemic is currently the most challenging public health matter that faces third world countries, especially those in Sub-Saharan Africa. Uganda, in East Africa, with a generalised and highly heterogeneous epidemic, is no exception, with HIV/AIDS affecting most sectors of the economy. The barrier HIV-stigma presents to the HIV treatment cascade is increasingly documented; however, no research has been done on the effects of HIV free survival of children under five-year olds in the presence of clustering. This study present models for analysing the effects of covariates on HIV free survival of children in Uganda in which tests for the association among children in different enumeration areas (EA) are carried out. The main objective of this study is to test for association between covariates and HIV free survival among under five-year old children born to HIV positive mothers in Uganda. This study uses the shared frailty model from the Uganda Population-Based HIV Impact Assessment (UPHIA 2016-2017) dataset. The study reviews the Cox proportional hazard model to include clustering within children from different EAs. The study first checks for proportional hazard assumption in the model then extend the Cox PH model to include clustering of children in different EA in Uganda using the shared frailty model. The basis for testing proportional hazard assumption in Cox regression analysis is to assess whether the effects of the covariates change overtime. When observations are clustered within groups or multiple event times are clustered within individuals, dependence between event times in a cluster is of interest. The study then uses the shared frailty model which is a random effect model which helps explain the unaccounted heterogeneity in the data.},
     year = {2022}
    }
    

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    AU  - Muriuki Francis Maina
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    AB  - The Human Immunodeficiency Virus (HIV) pandemic is currently the most challenging public health matter that faces third world countries, especially those in Sub-Saharan Africa. Uganda, in East Africa, with a generalised and highly heterogeneous epidemic, is no exception, with HIV/AIDS affecting most sectors of the economy. The barrier HIV-stigma presents to the HIV treatment cascade is increasingly documented; however, no research has been done on the effects of HIV free survival of children under five-year olds in the presence of clustering. This study present models for analysing the effects of covariates on HIV free survival of children in Uganda in which tests for the association among children in different enumeration areas (EA) are carried out. The main objective of this study is to test for association between covariates and HIV free survival among under five-year old children born to HIV positive mothers in Uganda. This study uses the shared frailty model from the Uganda Population-Based HIV Impact Assessment (UPHIA 2016-2017) dataset. The study reviews the Cox proportional hazard model to include clustering within children from different EAs. The study first checks for proportional hazard assumption in the model then extend the Cox PH model to include clustering of children in different EA in Uganda using the shared frailty model. The basis for testing proportional hazard assumption in Cox regression analysis is to assess whether the effects of the covariates change overtime. When observations are clustered within groups or multiple event times are clustered within individuals, dependence between event times in a cluster is of interest. The study then uses the shared frailty model which is a random effect model which helps explain the unaccounted heterogeneity in the data.
    VL  - 3
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Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Section