Prediction of the Effect of Demographic Characteristics on Parity Using Poisson Regression Model

Main Article Content

C. M. Gatwiri
M. M. Muraya
L. K. Gitonga

Abstract

There is growing interest among the public in demography since demographic change has become the subject of political debates in many countries. Statistics on demography are used to support policy-making and monitor demographic behaviour of political, economic, social and cultural perspectives. Most studies have used descriptive statistics to study demographic characteristics. Moreover, most of these studies investigate effects of individual character at a time. Therefore, there is a need to come up with more robust statistical methods, such as predictive models for demographic studies. The objective of this study was to predict the effect of demographic characteristics on parity using Poisson regression model. Secondary data on parity, age, marital status and education level was collected from Chuka and Embu hospital maternal units from 2013 to 2017. The data was analysed using R-statistical software. Three Poisson regression models (PRMs) were fitted. The likelihood ratio test of all the Poisson regression models had p-values < 0.05 indicating that all the models were statistically significant. Deviance test and Akaike Information Criterion (AIC) were used to assess the fit of Poisson regression models. The overall Poisson model had residual deviance of 184.23, which was the lowest of all other fitted PRM models, suggesting that it was the best fit. The AIC of the PRM with both education and marital status as the predictors had the lowest AIC value of 2078.620, indicating that it was the best fitted model. The dispersion test proved that PRM was not over-dispersed, confirming the model as a good fit of the data. The improved model can be used in prediction of population growth rates.

Keywords:
Prediction model, parity, demographic characteristics, Poisson regression model.

Article Details

How to Cite
Gatwiri, C. M., Muraya, M. M., & Gitonga, L. K. (2020). Prediction of the Effect of Demographic Characteristics on Parity Using Poisson Regression Model. Asian Journal of Probability and Statistics, 6(1), 55-63. https://doi.org/10.9734/ajpas/2020/v6i130154
Section
Original Research Article

References

Diekmann O, Heesterbeek JAP. Mathematical epidemiology of infectious diseases: Model building, Analysis and Interpretation, John Wiley & Sons. 2000;5.

Weegman MD, Bearhop S, Fox AD, Hilton GM, Walsh AJ, Mcdonald JL, Hodgson DJ. Integrated population modelling reveals a perceived source to be a cryptic sink. Journal of Animal Ecology. 2016;85(2):467–475.

Dahlgren G, Whitehead M. Policies and strategies to promote social equity in health. Background document to WHO-Strategy Paper for Europe (No. 2007: 20114) Stockholm: Institute for Future Studies; 1991.

Boyden J. Childhood and the policy makers: A comparative perspective on the globalization of childhood. In Constructing and Reconstructing Childhood Routledge. 2015;185-219.

Brantingham PJ, Brantingham PL. Patterns in Crime. New York: Macmillan; 1984.

Chesnais JC. The demographic transition: Stages, patterns and economic implications. Oxford University Press; 1992.

Noland RB, Oh L. The effect of infrastructure and demographic change on traffic-related fatalities and crashes: A case study of illinois county-level data. Accident Analysis & Prevention. 2004; 36(4):525-532.

Erkan G, Evkaya O, Türkan S. Determination of the affecting factors of the number of babies born alive in multiple pregnancies with poisson models. Türkiye Klinikleri Biyoistatistik. 2017;9(3):222-229.

Barakat B. Generalised count distributions for modelling parity. Demographic Research. 2017;36: 745-758.

Fagbamigbe AF, Adebowale AS. Current and predicted fertility using Poisson regression model: Evidence from 2008 Nigerian Demographic Health survey. African Journal of Reproductive Health. 2014;18(1):71-83.

López PO, Bréart G. Sociodemographic characteristics of mother’s population and risk of preterm birth in Chile. Reproductive Health. 2013;10(1):26.

Emelumadu OF, Ukegbu AU, Ezeama NN, Kanu OO, Ifeadike CO, Onyeonoro UU. Socio-demographic determinants of maternal health-care service utilization among rural women in Anambra State, South East Nigeria. Ann Med Health Sci Res. 2014;4(3):374–382.

Tarekegn SM, Lieberman LS, Giedraitis V. Determinants of maternal health service utilization in Ethiopia: Analysis of the 2011 Ethiopian demographic and health survey. BMC Pregnancy Childbirth. 2014;14(1):161-161.

Darlington RB, Hayes AF. Regression analysis and linear models: Concepts, applications and implementation. Guilford Publications; 2016.

Finer LB, Zolna MR. Declines in unintended pregnancy in the United States, 2008–2011. New England Journal of Medicine. 2016;374(9):843-852.

Creswell JW, Creswell JD. Research design: Qualitative, quantitative and mixed methods approaches. Sage Publications; 2017.

Mugenda O, Mugenda A. Research methods: Quantitative and qualitative approaches. Nairobi, Kenya; 2003.

Vanvoorhis CW, Morgan BL. Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology. 2007;3(2):43-50.

Impicciatore R, Dalla Zuanna G. The impact of education on fertility in Italy. Changes across cohorts and South–north Differences. Quality & Quantity. 2017;51(5):2293-2317.

Anker R, Knowles JC. Fertility determinants in developing countries: A case study of Kenya; 1982.

Phillips JA, Sweeney MM. Premarital cohabitation and marital disruption among white, black and Mexican American women. Journal of Marriage and Family. 2005;67(2):296-314.

Shapiro D, Gebreselassie T. Fertility transition in Sub-Saharan Africa: Falling and stalling. African Population Studies. 2013;23(1):3-23.

Cygan-Rehm K, Maeder M. The effect of education on fertility: Evidence from a compulsory schooling reform. Labour Economics. 2013;25:35-48.

Rindfuss RR, Parnell AM. The varying connection between marital status and childbearing in the United States. Population and Development Review. 1989;447-470.

Murithi MJ. W The Effect of Female Education on Fertility in Kenya; 1998.
Available:https://wol.iza.org/uploads/articles/228/female-education-and-its-impact-on-fertility

Andersen AMN, Wohlfahrt J, Christens P, Olsen J, Melbye M. Maternal age and fetal loss: Population-based register linkage study. British Medical Journal. 2000;320(7251):1708-1712.

Stephen EH, Chandra A. Declining estimates of infertility in the United States: 1982–2002. Fertility and Sterility. 2006;86(3):516-523.

Spiegelhalter DJ, Best NG, Carlin BP, Linde A. The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2014;76(3):485-493.

Koehler AB, Murphree ES. A comparison of the Akaike and Schwarz criteria for selecting model order. Applied Statistics. 1988;187-195.

Pan W. Akaike's information criterion in generalized estimating equations. Biometrics. 2001;57(1): 120-125.

Symonds MR, Moussalli A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike's Information Criterion. Behavioural Ecology and Socio-biology. 2011;65(1):13-21.

Patience EO, Osagie AM. Modelling the prevalence of malaria in Niger State: An application of Poisson regression and negative binomial regression models. International Journal of Physical Sciences. 2014;4:061-068.