Bayesian Modelling on Incidence of Pregnancy among HIV/AIDS Patient Women at Adare Hospital, Hawassa, Ethiopia
American Journal of Life Sciences
Volume 6, Issue 6, December 2018, Pages: 80-88
Received: Dec. 4, 2018;
Accepted: Jan. 9, 2019;
Published: Jan. 28, 2019
Views 211 Downloads 34
Yenesew Fentahun Gebrie, Department of Statistics, Debre Markos University, Debre Markos, Ethiopia
Ayele Taye Goshu, School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia
HIV/AIDS is the most serious diseases human kind has ever faced and a public problem, particularly, for women of childbearing age. For HIV infected women, the prospects of getting pregnant and having an HIV negative baby could be significantly improved with the increasing of the availability of Antiretroviral Therapy (ART). Even though, ART treatment has shown significant effect of clinical importance to reduce the risk of mother to child transmission of HIV but, HIV infected Women remain poorly understood or they fear to be pregnant and having HIV negative child. To the authors’ knowledge, no study examined incidence of pregnancy among women on ART follow-up in Ethiopia. In response, we conducted a study to explore the incidence and potential predictors of pregnancy. The objective of this study was to investigate the incidence of pregnancy among HIV/AIDS patient women under ART follow-up. A retrospective cohort study was conducted based on secondary data that reviews or visits medical chart of HIV/ADIS patient women aged 15-49 years under ART follow-up from April 2008 to February 2015. Out of 720 total patient women, a sample of size 328 was selected by using simple random sampling technique. Bayesian estimation were used for binary logistic regression model to identify the significant factors of incidence of pregnancy. The Gibbs sampler algorithm was implemented by WinBUGS software to solve approximate properties of the marginal posterior distributions for each parameter in Bayesian estimation. The results of this study revealed 21.3% of women got pregnancy during the follow-up. From Bayesian logistic regression analysis, significant predictors of incidence of pregnancy were: WHO clinical stage, marital status, contraception use, number of child alive before ART follow-up, CD4 cell count, time of Antiretroviral Therapy (ART) follow-up, educational level, spouse's HIV status, occupation and age (at p=0.05). In this study, when age of the women increased, the probability of becoming pregnant was decreased and advanced WHO clinical stage were associated with decreased incidence of pregnancy. Time on ART was a strong predictor of becoming pregnant: longer time on ART was associated with increased probability of becoming pregnant. Educational level of women was positively related with incidence of pregnancy that is, women who had college and above educational level was more likely to become pregnant. When CD4 count increased, incidence of pregnancy also increased and married women had more chance to become pregnant. The predictors identified in this study can be used to care for those HIV/AIDS patient women who want to have baby.
Yenesew Fentahun Gebrie,
Ayele Taye Goshu,
Bayesian Modelling on Incidence of Pregnancy among HIV/AIDS Patient Women at Adare Hospital, Hawassa, Ethiopia, American Journal of Life Sciences.
Vol. 6, No. 6,
2018, pp. 80-88.
Cooper, D., Harries J., Myer, L., Orner, P., Bracken, H. (2007). ““Life is Still Going on”: Reproductive Intentions among HIV-Positive Women and Men in South Africa,” Social Science and Medicine, vol. 65, no. 2, pp. 274–283.
UNAIDS (2013). Report on the Global AIDS Epidemic.
UNICEF (2013). Prevention of Mother to Child Transmission, Thematic Briefing Note , Media and External Relations Section UNICEF Ethiopia, July 2013
Godana, W., Atta, A. (2013). Prevalence of HIV/AIDS and its Associated Factors among Prevention of Mother-to-Child Transmission (PMTCT) Service Users in Jinka Town Health Institutions, South Omo Zone, South Ethiopia, Vol. 1, No. 3, 2013, pp. 125-130.
Siegfried, N., Merwe, V., Brocklehurst, P., Sint, T. T. (2011). Antiretrovirals for Reducing the Risk of Mother-to-Child Transmission of HIV Infection. Cochrane Database Syst Rev 2011, CD003510.
Makumbi, F. E, Nakigozi, G., Reynolds, S. J., Ndyanabo, A., Tom L., Serwada, D. Nalugoda, F., Wawer, M., and Gray, R. (2010). Associations between HIV Antiretroviral Therapy and the Prevalence and Incidence of Pregnancy in Rakai, Uganda, Vol. 2011.
Tweya, H., Feldacker, C., Breeze, E., Jahn, A., Haddad, L. B., Ben-Smith, A., Chaweza, T., Phiri, S. (2012). Incidence of Pregnancy among Women Accessing Antiretroviral Therapy in Urban Malawi: A Retrospective Cohort Study, AIDS Behav (2013) 17:471–478.
Westreich, D., Maskew, M., Rubel, D., Mac, P. D., Jaffray, I., Majuba, P. (2012). Incidence of Pregnancy after Initiation of Antiretroviral Therapy in South Africa: A Retrospective Clinical Cohort Analysis, Volume 2012, Article ID 917059, 7pages.
Kabami, J., Turyakira, E., Biraro, S., Bajunirwe, F. (2014). Increasing Incidence of Pregnancy among Women Receiving HIV Care and Treatment at a Large Urban Facility in Western Uganda, 11:81 doi: 10.1186/1742-4755-11-81.
Ruth, K. F., Francisco, I. B. (2010). Pregnancy Rates and Predictors in Women with HIV/AIDS in Rio de Janeiro, Southeastern Brazil, Rev Saúde Pública 2011;45(2):373-81.
Henry D. (2013).Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm. Journal of Social and Development Sciences, Vol. 4, No. 4, pp. 193-197, Apr 2013 (ISSN 2221-1152).
Rashwan, N. I., Eldereny, M. (2012). The Comparison between Results of Application Bayesian and Maximum Likelihood Approaches on Logistic Regression Model for Prostate Cancer Data. Journal of Applied Mathematical Sciences, Vol. 6, no. 23, p.1143 – 1158.
Aparecida, D. P., Helio S. M. (2004). Bayesian Binary Regression Model: An Application to In-Hospital Death after AMI Prediction. Pesquisa Operacional, v.24, n.2, p.253-267.
McCullagh, P., Nelder, J. A. (1989). Generalized Linear Models. 2nd Edition. Chapman and Hall, New York, USA.
Cox, D. R., and Snell, E. J. (1989). Analysis of Binary Data. London: Chapman and Hall.
Hosmer, D. and Lemeshow, S. (2000). Applied Logistic Regression, Second Edition, John Wiley & Sons Inc., New York.
Gelman, A., Carlin, J. C., Stern, H. and Rubin, D. B. (1984). Bayesian Data Analysis. Chapman and Hall, New York.
Muluneh, S., Emmanuel, G., (2011). Factors Influencing the Intention Not to Use Contraceptives among Sexually Active Women in Ethiopia, vol. 20.
Merkle, E., Zandt, T. V. (2005). WinBUGS Tutorial Outline.
Brooks, S. P. and Gelman, A. (1998). Alternative Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics.7, 434-455.