Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya

Mengich Kibichii Robert; Ann Mwangi; Gregory Kibet Kerich; Nyakundi Omwando Cornelious

doi:doi:10.11648/j.ijsd.20210702.12

| Peer-Reviewed

Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya

Mengich Kibichii Robert, Ann Mwangi, Gregory Kibet Kerich, Nyakundi Omwando Cornelious

Published in International Journal of Statistical Distributions and Applications (Volume 7, Issue 2)

Received: 17 May 2021 Accepted: 2 June 2021 Published: 15 June 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Survival modelling is a technique which exploits repeated measures of continuous covariates to predict explanatory variable’s effects on the response factor. The survival modelling helps design interventions in the health sector, which has seen one of its applications in the management of Human Immune Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS). However, despite improvement in Anti-Retroviral Therapy (ART) interventions over the years, the observed disease effects (morbidity, progression and mortality) remain high and varies across geographical borders. This study utilizes survival models to determine the predictors of survival among adult HIV/AIDS patients on ART in Moi Teaching and Referral Hospital (MTRH) Kenya. This is achieved by fitting a Cox proportional hazard regression model to adult HIV/AIDS patients data and determine predictors of survival amongst the study subjects. A retrospective study design was adopted where a target population of 10,038 patients who were on ART and were enrolled between January 2005 and January 2007 were investigated for a ten years follow-up period. The Cox proportional hazard regression model (CPHRM) was fitted to the data using log partial likelihood function. The log rank test and 95% confidence Interval (C.I) were used to analyze the significance of the hazard ratios of each variable. The results showed that HIV severity with unadjusted Hazard Ratio [UHR=0.729, p=0.032], level of education [lower UHR=0.952, p=0.019], and perfect adherence of antiretroviral drugs (ARV) [UHR=0.668, p=0.004] positively influenced patient survival time. Patient’s gender [male UHR=1.633, p< 0.001] showed negative effect on patient survival time. The adjusted hazard ratios for multivariate Cox model were, HIV severity [AHR1.18, p=0.735] age category between 30-40 in reference to age less than 30 [AHR=0.459, p=0.178] and age category above 40 years [AHR=0.644, p=0.447], Body Mass Index (BMI) less than 18.5kg/m² in reference to between 18.5-<25kg/m² [AHR=1.65, p=0.847] and BMI above 25 kg/m² [AHR=0.861, p=0.847], level of education [lower AHR=0.931, p=0.209], patients’ gender [male AHR=1.884, p=0.19] and ARV adherence [perfect AHR=1.393, p=0.498]. In conclusion, HIV severity, level of education, ARV adherence and patients' gender were significant predictors of survival time. In addition, none of the patient's characteristics predicted survival time in the multivariate Cox model. Therefore, this study recommends to the government of Kenya to spearhead the development of policy framework for the provision of regular screening services for the male population to avoid late diagnosis and interventions of HIV/AIDS disease.

Published in	International Journal of Statistical Distributions and Applications (Volume 7, Issue 2)
DOI	10.11648/j.ijsd.20210702.12
Page(s)	35-47
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), 2021. Published by Science Publishing Group

Keywords

Survival Modeling, Survival Time, Survival Analysis, Censoring Censored Observation

References

[1]	Abaasa, A. M., Todd, J., Ekoru, K., Kalyango, J. N., Levin, J., Odeke, E., & Karamagi, C. A. S. (2008). Good adherence to HAART and improved survival in a community HIV/AIDS treatment and care programme: The experience of the AIDS Support Organization (TASO), Kampala, Uganda. BMC Health Services Research, 8. https://doi.org/10.11-86/1472-6963-8-241.
[2]	Andrinopoulou, E.-R., Rizopoulos, D., Takkenberg, J. J. M., & Lesaffre, E. (2014). Joint modeling of two longitudinal outcomes and competing risk data. Statistics in Medicine, 33 (18), 3167–3178. https://doi.org/10.1002/sim.6158.
[3]	Birtukan, T. (2010). Predictors of Early Death in HIV Positive Individuals Enrolled for Chronic HIV Care in Jimma Zone, South Western Ethiopia. Addis Abeba, Ethiopia: University.
[4]	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B (Methodological), 34 (2), 187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x.
[5]	Dombrowski, J. C., Kitahata, M. M., Van Rompaey, S. E., Crane, H. M., Mugavero, M. J., Eron, J. J., Boswell, S. L., Rodriguez, B., Mathews, W. C., Martin, J. N., Moore, R. D., & Golden, M. R. (2013). High levels of antiretroviral use and viral suppression among persons in HIV care in the United States, 2010. Journal of Acquired Immune Deficiency Syndromes, 63 (3), 299–306. https://doi.org/10.1097/QAI.0b013e3182945bc7.
[6]	Ibrahim, N. (2007). Education of factor affecting the chance of survival/death status among hiv positive people under the antiretroviral treatment program: The Case of Adama Referral Hospital (Doctoral dissertation, M. Sc. Thesis). Addis Ababa, Ethiopia: Addis Ababa University.
[7]	Levy, B., Correia, H. E,. Chivore, F., Ronoh, M,. Abebe, A., Kgosimore, M.,…& White, K. J (2021). modelling the effect of HIV/AIDS stigma on HIV infection Dynamics in kenya. Bulletin of mathematical biology, 83 (5), 1-25.
[8]	Maposa, I. (2016). Survival Modelling and Analysis of Hiv/Aids Patients on Hiv Care And Antiretroviral Treatment to Determine Longevity Prognostic Factors. Cape Town, South Africa: University of the Western Cape. http://hdl.handle.net/11394/5444.
[9]	May, S., & Hosmer, D. W. (1998). A simplified method of calculating an overall goodness-of-fit test for the Cox proportional hazards model. Lifetime Data Analysis, 4 (2), 109-120. https://link.springer.com/article/10.1023/A:1009612305785.
[10]	Mecha, J. O., Kubo, E. N., Nganga, L. W., Muiruri, P. N., Njagi, L. N., Mutisya, I. N., Odionyi, J. J., Ilovi, S. C., Wambui, M., Githu, C., Ngethe, R., Obimbo, E. M., & Ngumi, Z. W. (2016). Trends in clinical characteristics and outcomes of Pre-ART care at a large HIV clinic in Nairobi, Kenya: a retrospective cohort study. AIDS Research and Therapy, 13 (1), 38. https://doi.org/10.1186/s12981-016-0122-y.
[11]	Mzileni, M. O., Longo-Mbenza, B., & Chephe, T. J. (2008). Mortality and causes of death in HIV-positive patients receiving antiretroviral therapy at Tshepang Clinic in Doctor George Mukhari Hospital. Pol Arch Med Wewn, 118 (10), 548-554.
[12]	National AIDS and STIs Control Programme (2016). Kenya AIDS response progress report: Progress report 2016.
[13]	Piulachs Lozada-Benavente, X., Alemany Leira, R., & Guillén, M. (2014). UB Riskcenter Working Paper Series A joint longitudinal and survival model with health care usage for insured elderly A joint longitudinal and survival model with health care usage for insured elderly. Bercelona, Spain: University of Bercelona. http://hdl.handle.net/2445/98450.
[14]	Shebeshi, D. (2011). Survival analysis of adult HIV/aids patients and stochastic modelling of aids disease progression: a case study of jimma. Addis Ababa, Ethiopia: jimma University Specialized Hospital.
[15]	Sika, A. M., Wools-Kaloustian, K., Mwangi, A. W., Kimaiyo, S. N., Diero, L. O., Ayuo, P. O.,... & Musick, B. (2010). Risk factors for death in HIV-infected adult african patients recieving anti-retroviral therapy. East African Medical Journa, 87 (11), 443-451.
[16]	Smith, A. D., Kimani, J., Kabuti, R., Weatherburn, P., Fearron, E., and Bourne, A. (2021) HIV burden and correlates of infection among transfeminine people and cisgender men who have sex with men in nairobi, kenya: an observational study. The lancent HIV, 8 (5), e274-e283.

Cite This Article

Plain Text BibTeX RIS

APA Style

Mengich Kibichii Robert, Ann Mwangi, Gregory Kibet Kerich, Nyakundi Omwando Cornelious. (2021). Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya. International Journal of Statistical Distributions and Applications, 7(2), 35-47. https://doi.org/10.11648/j.ijsd.20210702.12

Copy | Download

ACS Style

Mengich Kibichii Robert; Ann Mwangi; Gregory Kibet Kerich; Nyakundi Omwando Cornelious. Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya. Int. J. Stat. Distrib. Appl. 2021, 7(2), 35-47. doi: 10.11648/j.ijsd.20210702.12

Copy | Download

AMA Style

Mengich Kibichii Robert, Ann Mwangi, Gregory Kibet Kerich, Nyakundi Omwando Cornelious. Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya. Int J Stat Distrib Appl. 2021;7(2):35-47. doi: 10.11648/j.ijsd.20210702.12

Copy | Download

@article{10.11648/j.ijsd.20210702.12,
  author = {Mengich Kibichii Robert and Ann Mwangi and Gregory Kibet Kerich and Nyakundi Omwando Cornelious},
  title = {Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {2},
  pages = {35-47},
  doi = {10.11648/j.ijsd.20210702.12},
  url = {https://doi.org/10.11648/j.ijsd.20210702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210702.12},
  abstract = {Survival modelling is a technique which exploits repeated measures of continuous covariates to predict explanatory variable’s effects on the response factor. The survival modelling helps design interventions in the health sector, which has seen one of its applications in the management of Human Immune Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS). However, despite improvement in Anti-Retroviral Therapy (ART) interventions over the years, the observed disease effects (morbidity, progression and mortality) remain high and varies across geographical borders. This study utilizes survival models to determine the predictors of survival among adult HIV/AIDS patients on ART in Moi Teaching and Referral Hospital (MTRH) Kenya. This is achieved by fitting a Cox proportional hazard regression model to adult HIV/AIDS patients data and determine predictors of survival amongst the study subjects. A retrospective study design was adopted where a target population of 10,038 patients who were on ART and were enrolled between January 2005 and January 2007 were investigated for a ten years follow-up period. The Cox proportional hazard regression model (CPHRM) was fitted to the data using log partial likelihood function. The log rank test and 95% confidence Interval (C.I) were used to analyze the significance of the hazard ratios of each variable. The results showed that HIV severity with unadjusted Hazard Ratio [UHR=0.729, p=0.032], level of education [lower UHR=0.952, p=0.019], and perfect adherence of antiretroviral drugs (ARV) [UHR=0.668, p=0.004] positively influenced patient survival time. Patient’s gender [male UHR=1.633, p2 in reference to between 18.5-2 [AHR=1.65, p=0.847] and BMI above 25 kg/m2 [AHR=0.861, p=0.847], level of education [lower AHR=0.931, p=0.209], patients’ gender [male AHR=1.884, p=0.19] and ARV adherence [perfect AHR=1.393, p=0.498]. In conclusion, HIV severity, level of education, ARV adherence and patients' gender were significant predictors of survival time. In addition, none of the patient's characteristics predicted survival time in the multivariate Cox model. Therefore, this study recommends to the government of Kenya to spearhead the development of policy framework for the provision of regular screening services for the male population to avoid late diagnosis and interventions of HIV/AIDS disease.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya
AU  - Mengich Kibichii Robert
AU  - Ann Mwangi
AU  - Gregory Kibet Kerich
AU  - Nyakundi Omwando Cornelious
Y1  - 2021/06/15
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210702.12
DO  - 10.11648/j.ijsd.20210702.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 35
EP  - 47
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210702.12
AB  - Survival modelling is a technique which exploits repeated measures of continuous covariates to predict explanatory variable’s effects on the response factor. The survival modelling helps design interventions in the health sector, which has seen one of its applications in the management of Human Immune Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS). However, despite improvement in Anti-Retroviral Therapy (ART) interventions over the years, the observed disease effects (morbidity, progression and mortality) remain high and varies across geographical borders. This study utilizes survival models to determine the predictors of survival among adult HIV/AIDS patients on ART in Moi Teaching and Referral Hospital (MTRH) Kenya. This is achieved by fitting a Cox proportional hazard regression model to adult HIV/AIDS patients data and determine predictors of survival amongst the study subjects. A retrospective study design was adopted where a target population of 10,038 patients who were on ART and were enrolled between January 2005 and January 2007 were investigated for a ten years follow-up period. The Cox proportional hazard regression model (CPHRM) was fitted to the data using log partial likelihood function. The log rank test and 95% confidence Interval (C.I) were used to analyze the significance of the hazard ratios of each variable. The results showed that HIV severity with unadjusted Hazard Ratio [UHR=0.729, p=0.032], level of education [lower UHR=0.952, p=0.019], and perfect adherence of antiretroviral drugs (ARV) [UHR=0.668, p=0.004] positively influenced patient survival time. Patient’s gender [male UHR=1.633, p2 in reference to between 18.5-2 [AHR=1.65, p=0.847] and BMI above 25 kg/m2 [AHR=0.861, p=0.847], level of education [lower AHR=0.931, p=0.209], patients’ gender [male AHR=1.884, p=0.19] and ARV adherence [perfect AHR=1.393, p=0.498]. In conclusion, HIV severity, level of education, ARV adherence and patients' gender were significant predictors of survival time. In addition, none of the patient's characteristics predicted survival time in the multivariate Cox model. Therefore, this study recommends to the government of Kenya to spearhead the development of policy framework for the provision of regular screening services for the male population to avoid late diagnosis and interventions of HIV/AIDS disease.
VL  - 7
IS  - 2
ER  -

Copy | Download

Author Information

Mengich Kibichii Robert

Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya
Ann Mwangi

Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya
Gregory Kibet Kerich

Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya
Nyakundi Omwando Cornelious

Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Mengich Kibichii Robert, Ann Mwangi, Gregory Kibet Kerich, Nyakundi Omwando Cornelious. (2021). Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya. International Journal of Statistical Distributions and Applications, 7(2), 35-47. https://doi.org/10.11648/j.ijsd.20210702.12

Copy | Download

ACS Style

Mengich Kibichii Robert; Ann Mwangi; Gregory Kibet Kerich; Nyakundi Omwando Cornelious. Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya. Int. J. Stat. Distrib. Appl. 2021, 7(2), 35-47. doi: 10.11648/j.ijsd.20210702.12

Copy | Download

AMA Style

Mengich Kibichii Robert, Ann Mwangi, Gregory Kibet Kerich, Nyakundi Omwando Cornelious. Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya. Int J Stat Distrib Appl. 2021;7(2):35-47. doi: 10.11648/j.ijsd.20210702.12

Copy | Download

@article{10.11648/j.ijsd.20210702.12,
  author = {Mengich Kibichii Robert and Ann Mwangi and Gregory Kibet Kerich and Nyakundi Omwando Cornelious},
  title = {Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {2},
  pages = {35-47},
  doi = {10.11648/j.ijsd.20210702.12},
  url = {https://doi.org/10.11648/j.ijsd.20210702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210702.12},
  abstract = {Survival modelling is a technique which exploits repeated measures of continuous covariates to predict explanatory variable’s effects on the response factor. The survival modelling helps design interventions in the health sector, which has seen one of its applications in the management of Human Immune Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS). However, despite improvement in Anti-Retroviral Therapy (ART) interventions over the years, the observed disease effects (morbidity, progression and mortality) remain high and varies across geographical borders. This study utilizes survival models to determine the predictors of survival among adult HIV/AIDS patients on ART in Moi Teaching and Referral Hospital (MTRH) Kenya. This is achieved by fitting a Cox proportional hazard regression model to adult HIV/AIDS patients data and determine predictors of survival amongst the study subjects. A retrospective study design was adopted where a target population of 10,038 patients who were on ART and were enrolled between January 2005 and January 2007 were investigated for a ten years follow-up period. The Cox proportional hazard regression model (CPHRM) was fitted to the data using log partial likelihood function. The log rank test and 95% confidence Interval (C.I) were used to analyze the significance of the hazard ratios of each variable. The results showed that HIV severity with unadjusted Hazard Ratio [UHR=0.729, p=0.032], level of education [lower UHR=0.952, p=0.019], and perfect adherence of antiretroviral drugs (ARV) [UHR=0.668, p=0.004] positively influenced patient survival time. Patient’s gender [male UHR=1.633, p2 in reference to between 18.5-2 [AHR=1.65, p=0.847] and BMI above 25 kg/m2 [AHR=0.861, p=0.847], level of education [lower AHR=0.931, p=0.209], patients’ gender [male AHR=1.884, p=0.19] and ARV adherence [perfect AHR=1.393, p=0.498]. In conclusion, HIV severity, level of education, ARV adherence and patients' gender were significant predictors of survival time. In addition, none of the patient's characteristics predicted survival time in the multivariate Cox model. Therefore, this study recommends to the government of Kenya to spearhead the development of policy framework for the provision of regular screening services for the male population to avoid late diagnosis and interventions of HIV/AIDS disease.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Modelling of Survival Time Among Adult HIV/AIDS Patients Under Antiretroviral Therapy in Moi Teaching and Referral Hospital in Kenya
AU  - Mengich Kibichii Robert
AU  - Ann Mwangi
AU  - Gregory Kibet Kerich
AU  - Nyakundi Omwando Cornelious
Y1  - 2021/06/15
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210702.12
DO  - 10.11648/j.ijsd.20210702.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 35
EP  - 47
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210702.12
AB  - Survival modelling is a technique which exploits repeated measures of continuous covariates to predict explanatory variable’s effects on the response factor. The survival modelling helps design interventions in the health sector, which has seen one of its applications in the management of Human Immune Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS). However, despite improvement in Anti-Retroviral Therapy (ART) interventions over the years, the observed disease effects (morbidity, progression and mortality) remain high and varies across geographical borders. This study utilizes survival models to determine the predictors of survival among adult HIV/AIDS patients on ART in Moi Teaching and Referral Hospital (MTRH) Kenya. This is achieved by fitting a Cox proportional hazard regression model to adult HIV/AIDS patients data and determine predictors of survival amongst the study subjects. A retrospective study design was adopted where a target population of 10,038 patients who were on ART and were enrolled between January 2005 and January 2007 were investigated for a ten years follow-up period. The Cox proportional hazard regression model (CPHRM) was fitted to the data using log partial likelihood function. The log rank test and 95% confidence Interval (C.I) were used to analyze the significance of the hazard ratios of each variable. The results showed that HIV severity with unadjusted Hazard Ratio [UHR=0.729, p=0.032], level of education [lower UHR=0.952, p=0.019], and perfect adherence of antiretroviral drugs (ARV) [UHR=0.668, p=0.004] positively influenced patient survival time. Patient’s gender [male UHR=1.633, p2 in reference to between 18.5-2 [AHR=1.65, p=0.847] and BMI above 25 kg/m2 [AHR=0.861, p=0.847], level of education [lower AHR=0.931, p=0.209], patients’ gender [male AHR=1.884, p=0.19] and ARV adherence [perfect AHR=1.393, p=0.498]. In conclusion, HIV severity, level of education, ARV adherence and patients' gender were significant predictors of survival time. In addition, none of the patient's characteristics predicted survival time in the multivariate Cox model. Therefore, this study recommends to the government of Kenya to spearhead the development of policy framework for the provision of regular screening services for the male population to avoid late diagnosis and interventions of HIV/AIDS disease.
VL  - 7
IS  - 2
ER  -

Copy | Download