Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya
American Journal of Theoretical and Applied Statistics
Volume 6, Issue 2, March 2017, Pages: 123-128
Received: Feb. 20, 2017; Accepted: Mar. 1, 2017; Published: Mar. 21, 2017
Views 1585      Downloads 86
Authors
Kinyua Ann Muthoni, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Oscar Owino Ngesa, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Article Tools
Follow on us
Abstract
Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya using data from the 2014 Kenya Demographic and Health Survey (KDHS). Four models were used in this study namely the logistic regression model, the normal unstructured heterogeneity random effects model, ICAR (Integrated Conditional Autoregressive) spatial random effects model and the convolution model. A full Bayesian approach was used and the models were implemented using the Winbugs software version 1.4. Model selection was based on the DIC value where the model with the lowest DIC value was considered to be the best. The convolution model was the best model in this case and was used to map ARI across the different counties in Kenya. The national prevalence was 47.3%. The prevalence was found to be highest in the counties in the western part of Kenya. From the analysis, it’s clear that ARI is still a menace that need to be controlled. Proper planning and allocation of resources need to be put in place by the county governments in order to curb the rising cases of ARI.
Keywords
Prevalence, Spatial, Bayesian
To cite this article
Kinyua Ann Muthoni, Oscar Owino Ngesa, Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya, American Journal of Theoretical and Applied Statistics. Vol. 6, No. 2, 2017, pp. 123-128. doi: 10.11648/j.ajtas.20170602.18
Copyright
Copyright © 2017 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.
References
[1]
Broor, S., Parveen, S., Bharaj, P., Prasad, V. S., Srinivasulu, K. N., Sumanth, K. M., Kapoor, S. K., Fowler, K., Sullender, W. M.: A prospective three-year cohort study of the epidemiology and virology of acute respiratory infections of children in rural india. PloS one 2(6), 491 (2007).
[2]
Leowski, J.: [mortality from acute respiratory infections in children under 5 years of age: global estimates]. World health statistics quarterly. Rapport trimestriel de statistiques sanitaires mondiales 39(2), 138{44 (1986).
[3]
Murray CJL, Lopez AD, mathers CD, Stein C. The global burden of disease 2000 project: aims, methods and data sources. Geneva: World Heallth Organization; 2001.(Global Programme on Evidence for Health Policy, Discussion Paper No. 36, available at http://www3.who.int/whois/burden/papers/discussion%20paper%2036%20revised.doc).
[4]
Sikolia, D.: The prevalence of acute respiratory infections and the associated risk factors: a study of children under five years of age in kibera lindi village, nairobi, kenya. J. Natl. Inst. Public Health 51, 1 (2002).
[5]
Liu, L., Oza, S., Hogan, D., Perin, J., Rudan, I., Lawn, J. E.,… & Black, R. E. (2015). Global, regional, and national causes of childmortality in 2000-13, with projections to inform post-2015 priorities: an updated systematicanalysis. The Lancet, 385(9966), 430-440.
[6]
Onyango, D., Kikuvi, G., Amukoye, E., Omolo, J.: Risk factors of severe pneumonia among children aged 2-59 months in western kenya: a case control study. Pan African Medical Journal 13(1) (2012).
[7]
Kang, S. Y., Cramb, S. M., White, N. M., Ball, S. J., Mengersen, K. L.: Making the most of spatial information in health: a tutorial in bayesian disease mapping for areal data. Geospatial health 11(2) (2016).
[8]
Gupta N, Jain SK; Ratnesh, Chawla U, Hossain S, Venkatesh S. Indian J Pediatr. An Evaluation of diarrheal diseases and acute respiratory infections control programmes in a Delhi slum.2007 May; 74(5):471-6.
[9]
Koch, T.: Disease Maps: Epidemics on the Ground. University of Chicago Press, ? (2011).
[10]
Ujunwa, F., Ezeonu, C.: Risk factors for acute respiratory tract infections in under five children in enugu southeast nigeria. Annals of medical and health sciences research 4(1), 95{99 (2014).
[11]
Matu, M. N.: Risk factors and cost of illness for acute respiratory infections in children under five years of age attending selected health facilities in nakuru county, kenya. PhD thesis, Jomo Kenyatta University of Agriculture and Technology (2015).
[12]
Mishra, V.: Indoor air pollution from biomass combustion and acute respiratory illness in preschool age children in zimbabwe. International Journal of Epidemiology 32(5), 847{853 (2003).
[13]
Okiro, E. A., Ngama, M., Bett, A., Cane, P. A., Medley, G. F., James Nokes, D.: Factors associated with increased risk of progression to respiratory syncytial virus-associated pneumonia in young kenyan children. Tropical Medicine & International Health 13(7), 914{926 (2008).
[14]
Spiegelhalter, D., Thomas, A., Best, N., Lunn, D.: WinBUGS user manual. version (2003).
[15]
Gelman, A., Carlin, J. B., Stern, H.: Db rubin bayesian data analysis. Chapman Hall/CRC (2003).
[16]
Gelman, A., Hill, J.: Data Analysis Using Regression and Multilevel hierarchical Models vol. 1. Cambridge University Press New York, NY, USA, (2007).
[17]
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., Van Der Linde, A.: Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583{639 (2002).
[18]
Feng, C. X.: Models and methods for spatial data: applications in epidemiological, environmental and ecological studies. PhD thesis, Science: Department of Statistics and Actuarial Science (2011).
[19]
Prajapati, B., Talsania, N., Sonaliya, K.: A study on prevalence of acute respiratory tract infections (ari) in Under five children in urban and rural communities of ahmedabad district, gujarat. Natl J Community Med 2(2), 255{9 (2011).
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186