Volume 6, Issue 1, March 2020, Page: 44-52
Application of Bayesian Approach Survival Analysis of Under-five Pneumonia Patients in Tercha General Hospital, South West Ethiopia
Lema Abate, Department of Statistics, College of Natural and Computational Science, Mizan-Tepi University, Tepi, Ethiopia
Megersa Tadesse, Department of Statistics, College of Natural Science, Jimma University, Jimma, Ethiopia
Received: Dec. 9, 2019;       Accepted: Dec. 25, 2019;       Published: Jan. 16, 2020
DOI: 10.11648/j.ijdst.20200601.16      View  468      Downloads  124
Pneumonia is among the major killer diseases in under-five children in the world. In developing countries 3 million children die each year due to pneumonia. Ethiopia is one of the 15 pneumonia high burden countries. The aim of this study was to examine the risk factors of the survival time of under-five pneumonia patients using Bayesian approach analysis. Total of 281 under-five pneumonia patients included in this study. The parametric survival models such as Weibull, Lognormal and Log-logistic baseline distributions were used to fit the datasets by introducing prior distributions. The DIC value was used to compare the baseline distributions, and based on the DIC value the Weibull baseline distribution was selected as good model to fit under-five pneumonia dataset well. The results obtained from the Weibull survival model showed that patients from urban residence and patients who were admitted during patient nurse ratio (PNR) was small; were prolong timing death of under-five pneumonia patients, while patients who admitted during Spring and summer season, patients who suffer comorbidity and severe acute malnutrition (SAM) were shorten timing of death of under-five pneumonia patients. Factors such as sex, residence, Season of Diagnosis, Comorbidity, Severe Acute Malnutrition (SAM), Patient refer status and Patient to Nurse Ratio (PNR) were associated with the survival time of under-five pneumonia in this study. The concerned body should give attention for the factors identified in these study to prevent the mortality of under-five children due to pneumonia.
Pneumonia, Under-Five, Parametric Models, Risk Factors, Bayesian Approach, WinBUGs
To cite this article
Lema Abate, Megersa Tadesse, Application of Bayesian Approach Survival Analysis of Under-five Pneumonia Patients in Tercha General Hospital, South West Ethiopia, International Journal on Data Science and Technology. Vol. 6, No. 1, 2020, pp. 44-52. doi: 10.11648/j.ijdst.20200601.16
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
WHO.(2016). Pneumonia [online] Availableat: www.who.int/mediacentre/factsheets/fs331/en/.
WHO. (2012). Improving health and saving lives by ensuring access to priority medicines.
Jamison DT, B. J. (2006). Disease control priorities in developing countries. World Bank Publications.
Li Liu, H. L. (2012). Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet, 379: 2151–61.
Amare D. et al. (2007). Determinants of under-five mortality in Gilgel Gibe Field Research Center, Southwest Ethiopia,. Ethiop. J. Health Dev., 21 (2): 117-124].
Klein M. (2005). Survival analysis: techniques for censored and truncated data.
M. L. Calle et al. (2006). Bayesian survival analysis modeling applied to sensory shelf life of foods. Food Quality and Preference 17, 307–312.
Pascale S., M. W. (2014). Bayesian and Frequentist Comparison for Epidemiologists: The Open Epidemiology Journal, 7, 17-26, Beirut Lebanon.
Gelfand, A. E., & Mallick, B. K. (2005). Bayesian analysis of proportional hazards models built from monotone functions. Biometrics, 51 (3), 843–852.
Wioletta G. (2013). The Significance Of Prior Information In Bayesian Parametric Survival Models. Institute of Statistics and Demography, Warsaw School of Economics., 285.
Gelman A. et.al, C. J. (2000). Bayesian data analysis. Chapman.
Mantel, N. a. (1959). Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22, 719-748.
Christensen R. (2011). Bayesian Ideas and Data Analysis, for Scientists and Statisticians.
Ghassan H., R. M. (2013). Markov Chain Monte Carlo: an introduction for Epidemiologists,. International Journal of Epidemiology 42: 627–634 doi: 10.1093/ije/dyt043.
Dezfuli H. et al. (2009). Bayesian inference for probabilistic risk and reliability analysis.
Spiegelhalter D., A. (2004). Bayesian approaches to clinical trials and health-care evaluation.
Ibrahim J. et al. (2001). Bayesian Survival Analysis. Spring.
Muluneh S. et al. (2011). Factors Influencing the Intention Not to Use Contraceptives among Sexually Active Women in Ethiopia. Journals of statistical Society, vol 20.
Hakim E., U. M. (2009). Squamous cell carcinoma and keratoa canthoma of the lower lip associated with “Goza” and “Shisha” smoking. Int J Dermatol, 108-10.
Geir Storvik, H. L. (2014). Bayesian methods. Statistical Machine Learning.
Christa L, F. W. (2013). Childhood Pneumonia and Diarrhoea: Global burden of childhood pneumonia and diarrhoea. Lancet, 381: 1405–16.
Firaol B., M. S. (2017). Factors associated with outcomes of severe pneumonia in children aged 2 months to 59 months at JUSH.
Tariku T. (2017). Modelling Under-Five Mortality among Hospitalized Pneumonia Patients in Hawassa City: A Cross-Classified Multilevel Analysis. Ann. Data. Sci. doi: DOI 10.1007/s40745-017-0121-4.
Lieberman D and Porath A. (2005). Seasonal variation in community-acquired pneumonia in Southern Israel. Eur Respir J, 9 (12): 2630–2634.
Fischer W., (2013). Global burden of childhood pneumonia and diarrhea. Lancet, 381, 1405-1416.
Duke T, P. H. (2002). Chloramphenicol versus benzylpenicillin and gentamicin for the treatment of severe pneumonia in children in Papua New Guinea: Lancet, 359: 474–80.
Ellubey R. (2004). Pneumonia Case Fatality Rate In Children Under-Five.
Andrea D. (2017). The effect of nurse-to-patient ratios on nurse-sensitive patient outcomes in acute specialist units.
Ganjali M. and Baghfalaki T. (2012). Bayesian Analysis of Unemployment Duration Data in the Presence of Right and Interval Censoring. Journal of Reliability and Statistical Studies, 5 (1), 17-32.
Browse journals by subject