Volume 7, Issue 5, September 2018, Page: 193-199
A Comparison of Poisson Model and Modified Poisson Model in Modelling Relative Risk of Childhood Diabetes in Kenya
Christine Gacheri Mutuura, Department of Statistics and Actuarial Sciences, School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
Anthony Kibira Wanjoya, Department of Statistics and Actuarial Sciences, School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
Isaiah Njoroge Mwangi, Centre for Respiratory Disease Research, Kenya Medical Research Institute, Nairobi, Kenya
Received: May 26, 2015;       Accepted: Jun. 7, 2015;       Published: Oct. 11, 2018
DOI: 10.11648/j.ajtas.20180705.15      View  277      Downloads  27
Abstract
This study models the relative risk of diabetes, taking obesity and malnutrition as the major risk factors to define exposure, using three different prevalence rates i.e. 3%, 7% and 11% (estimates and projections from various studies). Secondary data consisting of a sample population of 300 children from the Kenya Diabetes Management and Information Centre (DMI), a national central diabetes registry, databases is used. In this research project, the modified Poisson regression approach is used to directly estimate the relative risk of pediatric diabetes in age strata of patients aged between the ages of 0-14years inclusive and for the purpose of model comparison RR estimation is done using Poisson regression which will prove to be less desirable for assessment of risk in this study proving the modified Poisson model gives the best estimates. From the data used in this study it is evident that: exposure (being overweight or underweight) is not a risk factor for diabetes onset in children aged 0-14 years.
Keywords
Type 1 Diabetes (T1D), Relative Risk (RR), Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), Poisson Model and Modified Poisson Model
To cite this article
Christine Gacheri Mutuura, Anthony Kibira Wanjoya, Isaiah Njoroge Mwangi, A Comparison of Poisson Model and Modified Poisson Model in Modelling Relative Risk of Childhood Diabetes in Kenya, American Journal of Theoretical and Applied Statistics. Vol. 7, No. 5, 2018, pp. 193-199. doi: 10.11648/j.ajtas.20180705.15
Reference
[1]
World Health Organisation(2010), www.who.int/diabetes/publications/Definition%20and%20diagnosis%20of%20diabetes_new.pdf
[2]
World Diabetes Foundation (2012), Celebrating 10 years of making a difference.
[3]
Swai A. B, Lutale J. L, McLarty D. G, (1993), Prospective study of Incidence of Juvenile diabetes mellitus over 10 years in Dar es Salaam Tanzania. BioMedical Science journal, 306(6892): 1570-1572.
[4]
Barros J. D and Hirakata V, (2003), an empirical comparison of models that directly estimate the prevalence ratio. Bio Med Central Medical Research Methodology, 3: 21.
[5]
Parodi S, Bottarelli E. (2006) Poisson Regression model in Epidemiology, Ann. Fac. Medic. Vet. Al. Parma, XXVI: 25-44.
[6]
Gyula S, Paterson C, Dahlquist G, (2010), Diabetes in the Young: A Global Perspective International Diabetes Federation, Diabetes Atlas 4th Edition.
[7]
Yonas B, Waernbaum I, Lind T, Mollsten A and Dahlquist G, (2011), Thirty years of Prospective Nationwide Incidence of childhood type 1 diabetes. Diabetes Journal, 60: 577-581.
[8]
World Health Organisation (2010), Guidelines, www.who.int/mediacentre/factsheets/fs312/en/, 2013.
[9]
Zou, G. (2004), A Modified Poisson Regression Approach to Prospective Studies with Binary Data. American Journal of Epidemiology, 159:702-706.
[10]
Kleinbaum, D. G, Kupper, L. L, Muller, K. E and Nizam A, (1998). Applied Regression Analysis and other Multivariate Methods. Third Edition, Brooks/Cole Publishing Company, Duxbury. Press, Pacific Grove (CA).
[11]
McNutt, L. A, Wu C, Xue X, (2003), Estimating the Relative Rise in Cohort Studies and Clinical Trials of Common Outcomes. American Journal of epidemiology, 157:940-943.
[12]
Zochetti C, Consonni D, Berlazzi P. A, (1995). Estimation of Prevalence Rate and Ratios from Cross-sectional Data. International Journal of Epidemiology, 24:1064-1065.
[13]
Royal, R. M, (1986). Model Robust Confidence Intervals using Maximum Likelihood Estimates. International Statistic review, 54: 221-226.
[14]
Gale, E. A. M, Environmental factors [www.diapedia.org/type-1-diabetes-mellitus/environmental-factors], 2014 Aug 13; Diapedia 21040851139 rev. no. 45. Available from: http://dx.doi.org/10.14496/dia.21040851139.45.
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