FUW TRENDS IN SCIENCE & TECHNOLOGY JOURNAL
(A Peer Review Journal)
e–ISSN: 2408–5162; p–ISSN: 2048–5170
keywords: HIV, CD4, ART, Model
The Human Immunodeficiency Virus and Acquired Immune Deficiency Syndrome (HIV/AIDS) is posing a challenge as it has become drug resistant in some patients. Consequently, treatment failure and spread of drug resistant HIV/AIDS results. This compromises the effectiveness of the limited therapeutic options like the antiretroviral therapy (ART). It therefore becomes necessary to assess the future progress as well as predict the efficacy of ART treatment. To this end, a Markov chain model for this assessment and prediction of treatment efficacy was formulated using the CD4 counts of a sample of 1,418 patients, receiving treatment every six (6) month at the HIV Counselling and Testing (HCT) unit of the general hospital Wukari, Taraba State. Taraba state is one of the states with high prevalence rate of HIV in North-eastern Nigeria. This methodology is considered appropriate as it can be applied in assessing and predicting treatment performance on a group of HIV patients or a cohort study. The progression of patients response to the therapy was assessed from one CD4 count state to another using a transition probability matrix. The efficacy of the therapy which is the maximum response of patients to treatment was evaluated using the long run (steady state) chances of patients in each CD4 count state and the mean recurrence time of each CD4 count state. The CD4 count states adopted in the study are; CD4 cell counts 500 cells/L (state 1), CD4 cell counts in the range of 200 - 499 cell/L (state 2) and CD4 cell count < 200 cells/L (state 3) representing the Good, Moderate and Poor health states of patents respectively. The model predicts that at the long run, there is a 40, 44 and 16% chance that a patient will attain a Good, Moderate and Poor health state, respectively, with respective mean recurrence time of 1.24, 1.13 and 3.21 years. The study concludes that the difference in the chances of the health state of patients might be due to antiretroviral drug resistance among other factors. The authors recommend that these factors should be identified and considered when administering ART to ensure very high chances of the Good and Moderate health states.
Anderson RM, Medley GF, May RM & Johnson AM 1986. A preliminary study of the transition dynamics of the human immunodeficiency virus (HIV), the causative agents of AIDS. IMA J. Maths., Appl. Med. and Bio., 3(4): 229-263. Andrew J, Schecter M and Roberts S. 2005. Modelling medical treatment using Markov decision processes. Research Paper from Center for Research on Health Care, University of Pittsburg PA 15261; Operation Research and Healthcare, 4: 29-37. Erhabor O, Ejele OA & Nwauche CA 2006. The effects of highly active antiretroviral therapy (HAART) of stavudine, lamivudine and nevirapine on the CD4 lymphocyte count of HIV-infected Africans: The Nigerian experience. Nigerian Journal of Clinical Practice, 9(2): 128-133. Fidelis M 2007. Why Taraba Leads North-East in HIV/AIDS Prevalence. Daily Trust Newspaper, 17th November, 2007, Lagos, Nigeria, http://allafrica.com/stories/200711180160.html. Grover G, Gadpayle AK, Swain PK & Deka B 2013. A multistate Markov model based on CD4 cell count for HIV/AIDS patients on antiretroviral therapy (ART). Int. J. Stat. Med. Res., 2: 144-151. Kanti PJ & Adeyi O 2006. AIDS in Nigeria: A Nation on the Threshold’. Chapter 1: Introduction. Havard Center for Population and Development Studies. Lee S, Ko J, Tan X, Patel I, Balkrishnan R & Chang J 2014. Markov chain modelling analysis of HIV/AIDS progression: A race-based forecast in the United States. Indian J. Pharmac. Sci., 76(2): 107-115. Nwosu CA 2015. Determining the life expectancy of HIV/AIDS patients in Anambra State using stationary and non-stationary Markov chain models. ABACUS, 42(2): 93-107. Oruonye ED 2011. An Assessment of the Response to the HIV/AIDS Pandemic in Taraba State, Nigeria. The J. Geogr. & Regional Planning, 4(2): 104-109. Udom, AU 2010. Elements of Applied Mathematical Statistics. ICIRD Publishing House, Akwa-Ibom, Nigeria. UNAIDS 2004. Epidemiological Facts Sheets on HIV/AIDS on Sexually Transmitted Diseases in Nigeria. UNAIDS 2006. AIDS Endemic Update: Geneva. World Health Organization 2007. WHO Case Definitions of HIV for Surveillance and Revised Clinical Staging and Immunological Classification of HIV-Related Disease in Adults and Children. World Health Organization 2010. Antiretroviral Therapy for HIV infection in adult and Adolescent Recommendation for public health approach, revision.
