Current and Predicted Fertility using Poisson Regression Model: Evidence from 2008 Nigerian Demographic Health Survey

Adeniyi F. Fagbamigbe, Ayo S. Adebowale


Nigeria with persistent high growth rate is among top ten most populous countries. Monitoring key mechanisms of population dynamics particularly fertility in Nigeria is long overdue. Periodical availability of data on fertility and other demographic indices is scarce, hence this study. Our objective was to build a non-linear model to identify fertility determinants and predict fertility using women’s background characteristics.   We used 2008 Nigeria Demography and Health Survey dataset consisting of 33,385 women with 31.4% from urban area. Fertility was measured using children ever born (CEB) and fitted into multi-factors additive Poisson regression models. Respondents mean age was 28.64±9.59years, average CEB of 3.13±3.07 but higher among rural women than urban women (3.42±3.16 vs 2.53±2.79). Women aged 20-24years were about twice as likely to have higher CEB as those aged 15-19years (IRR=2.06, 95% CI: 1.95-2.18). Model with minimum deviance was selected and was used to predict CEB by the woman. (Afr J Reprod Health 2014; 18[1]: 71-83).


Keywords: Fertility, Incidence rate ratio, Poisson prediction, children ever born, Nigeria,




Le Nigeria avec un taux de croissance élevé et persistant est parmi les dix pays les plus peuplés. La surveillance des mécanismes clés de la dynamique des populations notamment la fécondité au Nigeria est attendue depuis longtemps. La disponibilité périodique des données sur la fécondité et d'autres indices démographiques sont rares, d'où cette étude. Notre objectif était de construire un modèle non - linéaire pour identifier les déterminants de la fécondité et de prédire la fécondité en utilisant les antécédents caractéristiques des femmes. Nous avons utilisé les données de l’Enquête nigériane démographique et de santé de 2008 qui comprenaient  33 385 femmes avec 31,4 % de la zone urbaine. La fécondité a été mesurée à l'aide des enfants déjà nés

(EDN) et installée dans les additifs multi-facteurs des modèles de la  régression  de Poisson. L’âge moyen des interrogées était de

28,64 ± 9,59 ans, la  moyenne des EDN était de 3,13 ± 3,07, mais plus élevé chez les femmes rurales que les femmes urbaines (3,42 ± 3,16 vs 2,53 ± 2,79). Les femmes âgées de 20 24 années étaient deux fois plus susceptibles d'avoir EDN plus que les femmes âgées de 15-19 ans (IRR = 2,06, IC 95%: 1,95 à 2,18). Un modèle avec la déviance minimum a été sélectionné et a été utilisé pour prédire la l’EDN chez la femme. (Afr J Reprod Health 2014; 18[1]: 71-83).


Mots-clés : fertilité,  rapport des taux d'incidence, prédiction de Poisson, enfants nés, Nigeria

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Cleland J, Onuoha N & Timaeus I. Fertility change in sub-Saharan Africa: a review of evidence. In: Locoh T, Hertrich V (eds) The onset of fertility transition in Sub-Saharan Africa. Ordinal editions, Liège 1-20 (1994).

Cohen, B. The emerging fertility transition in sub-Saharan Africa. World Develop 26, 1431-1461 (1998).

NPC. Nigeria Over 167 Million, (2013).

PRB. Population Reference Bureau: The World’s Women and Girls 2012 Datasheet, (2012).

Feyisetan, B. & Casterline, J., B,. Socio-Economic Status, Fertility and Contraceptive Change in Sub-Saharan Africa. African Population Studies 2, 1-24 (2000).

NDHS. Nigeria Demographic and Health Survey (NPC, Federal Republic of Nigeria Abuja, Nigeria, Measure DHS and Nigeria Population Commission (NPC), ICF Macro Calverton, Maryland, USA, 2008).

Adeboyejo A T & Onyeonoru I P. Residential Density and Adolescent Reproductive Health Problems in Ibadan, Nigeria. African Population Studies 18, 81-95 (2003).

Lacey L, Adeyemi V & Adewuyi A. A Tool for Monitoring the Performance of Family Planning Programs in the Public and Private Sectors: An Application in Nigeria. International Family Planning Perspectives 23, 162-167 (1997).

Makinwa-Adebusoye P K & Feyisetan B J. The quantum and Tempo of Fertility in Nigeria. In Fertility Trends and Determinants in Six African Countries. (Macro International Inc., Calverton, Maryland, USA, 1994).

Odimegwu C O. Family Planning Attitudes and Use in Nigeria: A Factor Analysis. International Family Planning Perspectives 25, 86-91 (1999).

Odusola, A. Poverty and Fertility Dynamics in Nigeria: A Micro Evidence, (2002).

Otoide V O, Oronsaye F & Okonofua F E. Why Nigerian Adolescents Seek Abortion Rather than Contraception: Evidence from Focus-Group Discussions. International Family Planning

Perspectives 27, 77-81 (2001).

Togunde D & Newman S. Value of Children, Child labor and Fertility Preferences in Urban Nigeria. West Africa Review 7 (2005).

Feyisetan B J & Bankole A. Fertility Transition in Nigeria: Trends and Prospects, (2002).

Oladosu M. Prospects for Fertility Decline in Nigeria: Comparative Analysis of the 1990 and 1999 NDHS Data. ( Population Division, Department of Economic and Social Affairs, United Nations Secretariat, New York, USA, 2001).

Caldwell J C & Caldwell P. The Fertility Transition in Sub-Saharan Africa, (2002).

McNicoll G. Population and Development: An

Introductory View. (2003).

Caldwell J C. Globalization of Fertility Behaviour. Population and Development Review, Supplement: Global Fertility Transition 27, 93-115 (2001).

El-Badry MA. Emerging Population Issues in Africa. International Statistical Review 60, 119-127 (1992).

UAPS. Union for African Population Studies Conference Announcement: Emerging issues on Population and Development in Africa. , (2007).

UNFPA. United Nations Population Fund: Population, Health and Socio-Economic Indicators/Policy Developments. Overview: Nigeria., (2007).

Birdsall N, Kelley A C & Sinding S. Population matters: demographic change, economic growth, and poverty in the developing world. (Oxford University Press, 2003).

Bongaarts, J. & Potter, R. G. Fertility biology and behavior: an analysis of the proximate determinants. (Academic, 1983).

Foote K A, Hill K H & Martin L G. Demographic change in sub-Saharan Africa population dynamics of subSaharan Africa. (National Academy Press, 1993).

Kazembe, L. N. Modelling individual fertility levels in Malawian women: a spatial semiparametric regression model. Stat Methods Appl 18, 237-255 (2009).

Shen C & Williamson J B. Maternal mortality, women’s status, and economic dependency in less developed countries: a cross national analysis. Soc Sci Med 49, 197-214 (1999).

Famoye F & Wang W. Modeling household fertility decisions with generalized Poisson regression. J Popul Econ 10, 273-283 (1997).

Olfa F & El-Lahga AR A socioeconomic analysis of fertility determinants with a count data models: the case of Tunisia. (2002).

Poston D L. The statistical modelling of the fertility of chinese women. J Modern Appl Stat Method 1, 387396 (2002).

Cameron A & Trivedi P. Regression analysis of count data. (Cambrigde University Press, 1998).

Fahrmeir L & Lang S. Bayesian inference for generalized additive mixed models based on Markov random field priors. Journal Royal Statistical Society (Series C) 50, 201-220 (2001).

Little, R. J. A. Generalized Linear Models for CrossClassified Data from the WFS. (1978).

Rogers, W. H. Poisson regression with rates. Vol. 1 (Stata Press, 1991).

Adebimpe, W. O., Asekun-Olarinmoye, E., Bamidele, J. O. & Abodunrin, O. Comparative study of sociodemographic determinants and fertility pattern among women and urban communities in south westernNigeria. Continental Journal of Medical Research 5, 32-40 (2011).

Famule, F. D. Regional levels and differentials of fertility in Osun State, Nigeria. Pacific Journal of Science and Technology 11, 449-454 (2010).

Alene, G. D. & Worku, A. Differentials of fertility in North and South Gondar zones, northwest Ethiopia: A comparative cross-sectional study. BMC Public Health 8, 397-398 (2008).


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