Sign In
Register
eISSN:2278-5299

International Journal of Latest Research in Science and Technology

DOI:10.29111/ijlrst   ISRA Impact Factor:3.35,  Peer-reviewed, Open-access Journal

A News Letter Sign UP!
STATISTICAL ANALYSIS OF RISK FACTORS OF MALARIA RELATED IN-HOSPITAL MORTALITY: A CASE STUDY AT BUSHULO MAJOR HEALTH CENTER, HAWASSA

Research Paper Open Access

International Journal of Latest Research in Science and Technology Vol.4 Issue 5, pp 32-42,Year 2015

STATISTICAL ANALYSIS OF RISK FACTORS OF MALARIA RELATED IN-HOSPITAL MORTALITY: A CASE STUDY AT BUSHULO MAJOR HEALTH CENTER, HAWASSA

Chala Gemechu Gute , Dr.O.Chandrasekhara Reddy,Ayele Taye, Dr.K.Vasudeva Rao

Correspondence should be addressed to :

Received : 22 September 2015; Accepted : 05 October 2015 ; Published : 31 October 2015

Share
Download 125
View 184
Article No. 10566
Abstract

Malaria is a major challenge to public health and socio-economic development worldwide and in sub –Saharan Africa in particular. It causes an estimated 300 to 500 million cases and 1.5 to 2.7 million deaths worldwide each year, of which 80% of the cases and 90% of the deaths occur in Sub-Saharan Africa. The main objective of this study was to identify the risk factors of malaria related in-hospital mortality; so that modeling and simulating the related risk factors. The data were taken from hospital records at Bushulo major health center from June 2009 to June 2012, Hawassa. From a total of 6594 laboratory confirmed malaria positive in the health center, a sample of 539 patients were selected using stratified random sampling technique. The data were analyzed using the classical logistic regression and Bayesian logistic regression approaches. In this effort the two approaches were compared using standard errors of model parameters. The results of the study showed that 78.5% of malaria patients were found to be discharged while the rest 21.5% died of malaria in the health center. From Bayesian simulation analysis age, residence, time from symptom onset to diagnoses, type of malaria species diagnosed, body temperature in the last diagnoses, malaria complicated, pregnancy cases, total length of stay in hospitalization, referral status and season when patient diagnosed, were found to be statistically significant at 5% level of significance. The classical logistic regression analysis could select the first eight of these as significant predictors. Model comparison also revealed that, the Bayesian modeling approach has given estimates of the parameters with smaller standard error values showing that, with appropriate choice of prior distributions, the Bayesian modeling approach might lead to a more accurate estimation than the classical approach. It can be concluded from this study that the most contributing risk factors of malaria related in-hospital mortality in the health center are under five age group, rural resident, P.falciprum type of malaria species, having malaria complicated cases, longer time (days) from symptoms onset to diagnoses, wet season, high body temperature in the last diagnoses, women of pregnancy cases, shorter time (days) of stay in hospitalization.

Key Words   
Bayesian, Logistic regression, Malaria, Risk factors, In-hospital mortality Bushulo

Copyright
References
  1. Agresti, A. (2012). Bayesian Inference for Categorical Data Analysis. Department of Statistics, University of Florida, Gainesville, Florida, USA 32611-8545.
  2. Albert, J. (2010). Bayesian Computation with R. Second Edition. Department of Mathematics and Statistics Bowling Green State University, 43403-0221 PP.117-122.
  3. Alexander, R. and Patrick, K. (2012). Using Health Facility-Based Data to Evaluate the Health Impact of Malaria Control Efforts in Africa. Malaria Journal 2009: Doi: 10.1186/1475-2875-8-209.
  4. Bachou, H., Slutsker, L.,Taylor, T. and Wirima, J. (2011). Malaria Patients Admitted to Mulago Hospital, Uganda: Mortality and Associated Factors. In Centre for International Health Bergen, Norway, Centre for International Health, University of Bergen.
  5. Clark, T., Hall, G. and Griffiths, R. (2002). Bayesian Logistic Regression Using a Perfect Phylogeny. Department of Epidemiology and Puplic Health Imperial College. London, UK.
  6. Cochran, W.G. (2007). Sampling Techniques. 3rd Edition. John Wiley & Sons Inc., New York.
  7. Cowles, MK., Carlin, BP. (1996). Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review. Journal of the American Statistical Association, 91, 883.
  8. Draper, D. (2008). Assessment and Propagation of Model Uncertainty (with Discussion).
  9. Journal of the Royal Statistical Society: Series B, 57(1), 45–97.
  10. Federal Ministry of Health (FMOH) and Ethiopian National Malaria Indicator Survey (ENMIS) (2011). Report on Malaria Situation in Ethiopia.
  11. Gelman, A. (2005). Alternative Methods for Monitoring Convergence of Markov Chain Monte Carlo Iterative Simulations. Journal of Computational and Graphical Statistics. 7, 434-455.
  12. Geman, S. and Geman, D. (2009). Stochastic Relaxation, Gibbs Distribution and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6):721-741.
  13. Gilks, W., Richardson, S. and Spiegelhalter, J. (2011). Markov Chain Monte Carlo in Practice. Chapman and Hall, London, UK.
  14. Guyatt, M., Hay, S.I., Noor, A.M., & Ochola, S.A. (2010). Clinical Epidemiology of Malaria in the Highlands of Western Kenya. Emerging Infectious Diseases 8, 543-548.
  15. Hosmer, D. and Lemeshow, S. (2011). Applied Logistic Regression (5th Edition). New York: John Wiley and Sons.
  16. Ioannis, N. (2009). Bayesian Modeling Using Win BUGS. Department of Statistics Athens University of Economics and Business Athens, Greece.
  17. Lawrence, N., Kazembe, L. and Tobias, F. (2011). Applications of Bayesian Approach in Modeling Risk of Malaria-Related Hospital Mortality in Zomba, Malawi.
  18. Lederberg, J. and Oaks, S. (2012). Emerging Malaria Infections Disease: Microbial Threats to Health in Sub-Saharan Africa. The National Academies Press. 312 p.
  19. Lee, P. (2010). Bayesian Statistics: An Introduction, 2nd edition, Arnold, London.
  20. Merkle, E., Sheu, C. and Trisha, G. (2011). Simulation-Based Bayesian Inference Using WinBUGS. WinBUGS Tutorial Outline:http://www.bu.cam.uk/winbugs/cont.shml.
  21. Mesganaw Fantahun, Wakgari Deressa and Ahmed Ali (2011). Malaria-Related Mortality at Butajira Hospital, Ethiopia. Malaria Journal 2011, Doi: 10.1186/1475-2875-6-49.
  22. Mila, A.L., Yang, X.B. and Carriquiry, A.L. (2003). Bayesian Logistic Regression of Clinical Epidemiology for Uncertainty in Parameter Estimation. Basic Science for Clinical Medicine: Little, Brown and Company, Boston.
  23. Ministry of Health (MOH) (2009). National Strategic Plan for Malaria Prevention, and Control  in Ethiopia 2010–2015, Ministry of Health, Addis Ababa.
  24. Newman, R.D, Jimma, D., Degifie, A., and Rietveld, A.(2008) Burden of Malaria for Pregnant in Ethiopia During a Non Epidemic Year. Journal Inf. Dis 2008; 188: 1259-61.
  25. Shargie E, Gebre T, Graves P and Teferi T. (2010). Evaluation of Light Microscopy and Rapid Diagnostic Test for the Detection of Malaria under Operational Field Conditions: Malaria Journal 2008, 7(1):118. Doi: 110.1186/1475-2875-1187-1118
  26. Stephenson, B. (2010). Binary Response and Logistic Regression Analysis. www. public state.edu / stephenson /stat 415)
  27. Tanner, M.A. (2011). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, New York: Springer-Verlag.
  28. World Health Organization (WHO) (2010). World Malaria Report 2010: Report Series: WHO/HTM/MAL/2010. Bull; 88:305–11.
  29. Zungu, M., Howard, V. and Watkins, N. (2011). Identification and Analysis of Malaria Related Patient Diagnosed in St. Augustines Hospital, South Africa, A J Trop Med Hyg. 61:13140.
To cite this article

Chala Gemechu Gute , Dr.O.Chandrasekhara Reddy,Ayele Taye, Dr.K.Vasudeva Rao , " Statistical Analysis Of Risk Factors Of Malaria Related In-hospital Mortality: A Case Study At Bushulo Major Health Center, Hawassa ", International Journal of Latest Research in Science and Technology . Vol. 4, Issue 5, pp 32-42 , 2015

Responsive image

MNK Publication was founded in 2012 to upholder revolutionary ideas that would advance the research and practice of business and management. Today, we comply with to advance fresh thinking in latest scientific fields where we think we can make a real difference and growth now also including medical and social care, education,management and engineering.

Responsive image

We offers several opportunities for partnership and tie-up with individual, corporate and organizational level. We are working on the open access platform. Editors, authors, readers, librarians and conference organizer can work together. We are giving open opportunities to all. Our team is always willing to work and collaborate to promote open access publication.

Responsive image

Our Journals provide one of the strongest International open access platform for research communities. Our conference proceeding services provide conference organizers a privileged platform for publishing extended conference papers as journal publications. It is deliberated to disseminate scientific research and to establish long term International collaborations and partnerships with academic communities and conference organizers.