Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections

Edidiong Michael Udofia; Edith Uzoma Umeh; Chrisogonus Kelechi Onyekwere

doi:doi:10.11648/j.ajtas.20211006.12

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Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections

Edidiong Michael Udofia, Edith Uzoma Umeh, Chrisogonus Kelechi Onyekwere

Published in American Journal of Theoretical and Applied Statistics (Volume 10, Issue 6)

Received: 21 July 2021 Accepted: 4 August 2021 Published: 12 November 2021

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Abstract

Globally, there are many people living with Human Immune Deficiency Virus (HIV), and the rate increases every day. Research has shown that Nigeria is the second largest country with HIV epidemic, as many are living with advanced HIV. People with advanced stage of HIV infection are vulnerable to secondary infections and malignancies, generally termed Opportunistic Infections (OIs). This is because, these infections take advantage of the opportunity offered by a weakened immune system, thereby causing complications in HIV infected persons and causing harm to individuals. The aim of this work is to investigate and model the survival, by stages of immune suppression and opportunistic infections on patients undergoing Antiretroviral Therapy (ART), in a population in South-South Nigeria. 221 Human Immune Deficiency Virus (HIV) patients data obtained from St. Luke’s Hospital, Anua, for the period of 2008 to 2017 were used. Four different parametric models, the extreme, lognormal, logistics, log-logistics distributions and nonparametric Kaplan-Meier method were considered in order to carry out modeling of survival, and survival of patients respectively. The models were subjected to life application using lifetime datasets and a test of goodness of fit was made using Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) criteria. From the results obtained, extremedistribution had the lowest AIC and BIC value, indicating that it is the best parametric model for modeling survival of HIV patients in the hospital. Also, the Kaplan-Meier method indicates that the survival experience of female patients were favorable than male patients.

Published in	American Journal of Theoretical and Applied Statistics (Volume 10, Issue 6)
DOI	10.11648/j.ajtas.20211006.12
Page(s)	233-242
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

Survival Models, Parametric Models, Kaplan-Meier, Extreme, Lognormal, Logistics, Log-logistics Distributions

References

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[2]	Lifson, A. R., and Rutherford, G. W and Jaffe, H. W. (1988). The natural history of human immunodeficiency virus infection.‘Journal of infectious Disease, 158: 1360-1412.
[3]	Tansuphasawadikul, S., Amornkul, P. N., Tanchanpong, C., Limpakarnjanarat, K., Kaewkungwal, J., Likanonsakul, S. (1999). Clinical presentation of hospitalized adult patients with HIV infection and AIDS in Bangkok, Thailand. Journal of Acquired Immune Deficiency Syndrome, 21: 326-329.
[4]	Salami, A. K., Olatunji, P. O., Oluboyo, P. O., (2006). Spectrum and prognostic significance of opportunistic diseases in HIV/AIDS patients in Ilorin, Nigeria. West Africal Journal of Medicine, 25: 52-61.
[5]	Yazdanpanah, Y., Chene, G., Losina, E., Goldie, S. J., Merchadou, L. D., Alfandari, S. (2001). Incidence of primary opportunistic infections in two human immuno deficiency virus-infected French clinical cohorts. International Journal of Epidemic, 30: 864-870.
[6]	NACA (2017), ‘National Strategic Framework on HIV and AIDS, 2017-2021’ (PDF).
[7]	UNAIDS (2017), Data Book (PDF).
[8]	NACA (2015), ‘Nigeria GARPR 2015’ (PDF).
[9]	Vajpayee, M., Kauswal, S., Seth, P., Wig, N. (2005). Spectrum of opportunistic infections and profile of CD4 counts among AIDS patients in north india, 31: 336-364.
[10]	Sadraei, J, Rizvi, M. A., Baveja, U. K., (2005). Diarrhea CD4 cell counts and opportunistic protozoa in Indian HIV-infected patients. Parasitol Research, 97: 270-302.
[11]	Iroezindu, M. O., Ofondu, E. O., Hansler, H., Van, W. B (2013). Prevalence and Risk Factors for Opportunistic Infections in HIV patients receiving Antiretroviral Therapy in a Resource-limited Setting in Nigeria. Journal of AIDS Clinical Research, 10: 2155-6113.
[12]	Kleinbaum, D. G and Klein M. (2012). Survival Analysis: A Self Learning Text, Third Edition. New York: Springer-Verlag.
[13]	Kaplan, E. L and Meier, P. (1958). Estimation from Incomplete observations. Journal of Annual Statistical Association, 58: 457-481.
[14]	Xian, L. (2012). Survival Analysis Models and Applications, Uniformed Services, University of the Health Sciences and WalSter Reed Nationality Military Center, USA. John Wiley and Sons.
[15]	Schwarz, G. E. (1978). Estimating the dimension of a model, Annua. Statistics, 6: 461-464.
[16]	Jaber, J. (2017). Credit Risk Assessment Using Survival for Progressive Right-Censored Data: A Case Study in Jordan. Journal of Internet Banking and Commerce, Vol. 22: 1-18.
[17]	Eman, A (2015). Survival Analysis Approaches for Prostate Cancer, Master’s thesis in Computational Sciences, Laurentian University, Sudbury, Ontario, Canada.
[18]	Dinberu, S., Degryse, J., Kifle, Y., Taye, A., Tadesse, M and Birle, B., (2017), Risk Factors for Mortality among Adult HIV/AIDS Patients following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models. International Journal of Environmental Research of Public Health, 14: 296-300.
[19]	Tamam, D. (2008). Some statistical conclusion inferences for Weibull distribution in the complete and censored sample cases, Master’s thesis, Ankara University, Institute of Science, Ankara.

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APA Style

Edidiong Michael Udofia, Edith Uzoma Umeh, Chrisogonus Kelechi Onyekwere. (2021). Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections. American Journal of Theoretical and Applied Statistics, 10(6), 233-242. https://doi.org/10.11648/j.ajtas.20211006.12

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ACS Style

Edidiong Michael Udofia; Edith Uzoma Umeh; Chrisogonus Kelechi Onyekwere. Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections. Am. J. Theor. Appl. Stat. 2021, 10(6), 233-242. doi: 10.11648/j.ajtas.20211006.12

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AMA Style

Edidiong Michael Udofia, Edith Uzoma Umeh, Chrisogonus Kelechi Onyekwere. Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections. Am J Theor Appl Stat. 2021;10(6):233-242. doi: 10.11648/j.ajtas.20211006.12

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@article{10.11648/j.ajtas.20211006.12,
  author = {Edidiong Michael Udofia and Edith Uzoma Umeh and Chrisogonus Kelechi Onyekwere},
  title = {Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {6},
  pages = {233-242},
  doi = {10.11648/j.ajtas.20211006.12},
  url = {https://doi.org/10.11648/j.ajtas.20211006.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211006.12},
  abstract = {Globally, there are many people living with Human Immune Deficiency Virus (HIV), and the rate increases every day. Research has shown that Nigeria is the second largest country with HIV epidemic, as many are living with advanced HIV. People with advanced stage of HIV infection are vulnerable to secondary infections and malignancies, generally termed Opportunistic Infections (OIs). This is because, these infections take advantage of the opportunity offered by a weakened immune system, thereby causing complications in HIV infected persons and causing harm to individuals. The aim of this work is to investigate and model the survival, by stages of immune suppression and opportunistic infections on patients undergoing Antiretroviral Therapy (ART), in a population in South-South Nigeria. 221 Human Immune Deficiency Virus (HIV) patients data obtained from St. Luke’s Hospital, Anua, for the period of 2008 to 2017 were used. Four different parametric models, the extreme, lognormal, logistics, log-logistics distributions and nonparametric Kaplan-Meier method were considered in order to carry out modeling of survival, and survival of patients respectively. The models were subjected to life application using lifetime datasets and a test of goodness of fit was made using Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) criteria. From the results obtained, extremedistribution had the lowest AIC and BIC value, indicating that it is the best parametric model for modeling survival of HIV patients in the hospital. Also, the Kaplan-Meier method indicates that the survival experience of female patients were favorable than male patients.},
 year = {2021}
}

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TY  - JOUR
T1  - Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections
AU  - Edidiong Michael Udofia
AU  - Edith Uzoma Umeh
AU  - Chrisogonus Kelechi Onyekwere
Y1  - 2021/11/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211006.12
DO  - 10.11648/j.ajtas.20211006.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 233
EP  - 242
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211006.12
AB  - Globally, there are many people living with Human Immune Deficiency Virus (HIV), and the rate increases every day. Research has shown that Nigeria is the second largest country with HIV epidemic, as many are living with advanced HIV. People with advanced stage of HIV infection are vulnerable to secondary infections and malignancies, generally termed Opportunistic Infections (OIs). This is because, these infections take advantage of the opportunity offered by a weakened immune system, thereby causing complications in HIV infected persons and causing harm to individuals. The aim of this work is to investigate and model the survival, by stages of immune suppression and opportunistic infections on patients undergoing Antiretroviral Therapy (ART), in a population in South-South Nigeria. 221 Human Immune Deficiency Virus (HIV) patients data obtained from St. Luke’s Hospital, Anua, for the period of 2008 to 2017 were used. Four different parametric models, the extreme, lognormal, logistics, log-logistics distributions and nonparametric Kaplan-Meier method were considered in order to carry out modeling of survival, and survival of patients respectively. The models were subjected to life application using lifetime datasets and a test of goodness of fit was made using Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) criteria. From the results obtained, extremedistribution had the lowest AIC and BIC value, indicating that it is the best parametric model for modeling survival of HIV patients in the hospital. Also, the Kaplan-Meier method indicates that the survival experience of female patients were favorable than male patients.
VL  - 10
IS  - 6
ER  -

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Author Information

Edidiong Michael Udofia

Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
Edith Uzoma Umeh

Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
Chrisogonus Kelechi Onyekwere

Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria

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APA Style

Edidiong Michael Udofia, Edith Uzoma Umeh, Chrisogonus Kelechi Onyekwere. (2021). Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections. American Journal of Theoretical and Applied Statistics, 10(6), 233-242. https://doi.org/10.11648/j.ajtas.20211006.12

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ACS Style

Edidiong Michael Udofia; Edith Uzoma Umeh; Chrisogonus Kelechi Onyekwere. Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections. Am. J. Theor. Appl. Stat. 2021, 10(6), 233-242. doi: 10.11648/j.ajtas.20211006.12

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AMA Style

Edidiong Michael Udofia, Edith Uzoma Umeh, Chrisogonus Kelechi Onyekwere. Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections. Am J Theor Appl Stat. 2021;10(6):233-242. doi: 10.11648/j.ajtas.20211006.12

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@article{10.11648/j.ajtas.20211006.12,
  author = {Edidiong Michael Udofia and Edith Uzoma Umeh and Chrisogonus Kelechi Onyekwere},
  title = {Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {6},
  pages = {233-242},
  doi = {10.11648/j.ajtas.20211006.12},
  url = {https://doi.org/10.11648/j.ajtas.20211006.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211006.12},
  abstract = {Globally, there are many people living with Human Immune Deficiency Virus (HIV), and the rate increases every day. Research has shown that Nigeria is the second largest country with HIV epidemic, as many are living with advanced HIV. People with advanced stage of HIV infection are vulnerable to secondary infections and malignancies, generally termed Opportunistic Infections (OIs). This is because, these infections take advantage of the opportunity offered by a weakened immune system, thereby causing complications in HIV infected persons and causing harm to individuals. The aim of this work is to investigate and model the survival, by stages of immune suppression and opportunistic infections on patients undergoing Antiretroviral Therapy (ART), in a population in South-South Nigeria. 221 Human Immune Deficiency Virus (HIV) patients data obtained from St. Luke’s Hospital, Anua, for the period of 2008 to 2017 were used. Four different parametric models, the extreme, lognormal, logistics, log-logistics distributions and nonparametric Kaplan-Meier method were considered in order to carry out modeling of survival, and survival of patients respectively. The models were subjected to life application using lifetime datasets and a test of goodness of fit was made using Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) criteria. From the results obtained, extremedistribution had the lowest AIC and BIC value, indicating that it is the best parametric model for modeling survival of HIV patients in the hospital. Also, the Kaplan-Meier method indicates that the survival experience of female patients were favorable than male patients.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Modeling of Survival of HIV Patients by Stages of Immune Suppression and Opportunisic Infections
AU  - Edidiong Michael Udofia
AU  - Edith Uzoma Umeh
AU  - Chrisogonus Kelechi Onyekwere
Y1  - 2021/11/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211006.12
DO  - 10.11648/j.ajtas.20211006.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 233
EP  - 242
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211006.12
AB  - Globally, there are many people living with Human Immune Deficiency Virus (HIV), and the rate increases every day. Research has shown that Nigeria is the second largest country with HIV epidemic, as many are living with advanced HIV. People with advanced stage of HIV infection are vulnerable to secondary infections and malignancies, generally termed Opportunistic Infections (OIs). This is because, these infections take advantage of the opportunity offered by a weakened immune system, thereby causing complications in HIV infected persons and causing harm to individuals. The aim of this work is to investigate and model the survival, by stages of immune suppression and opportunistic infections on patients undergoing Antiretroviral Therapy (ART), in a population in South-South Nigeria. 221 Human Immune Deficiency Virus (HIV) patients data obtained from St. Luke’s Hospital, Anua, for the period of 2008 to 2017 were used. Four different parametric models, the extreme, lognormal, logistics, log-logistics distributions and nonparametric Kaplan-Meier method were considered in order to carry out modeling of survival, and survival of patients respectively. The models were subjected to life application using lifetime datasets and a test of goodness of fit was made using Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) criteria. From the results obtained, extremedistribution had the lowest AIC and BIC value, indicating that it is the best parametric model for modeling survival of HIV patients in the hospital. Also, the Kaplan-Meier method indicates that the survival experience of female patients were favorable than male patients.
VL  - 10
IS  - 6
ER  -

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