Survival Analysis to Determine the Significant Factors Associated with Birth Interval of Women in Ethiopia: Based on 2011 Ethiopian Demographic and Health Survey Data
Biomedical Statistics and Informatics
Volume 5, Issue 1, March 2020, Pages: 26-38
Received: Aug. 3, 2019;
Accepted: Oct. 31, 2019;
Published: Apr. 14, 2020
Views 528 Downloads 160
Yenefenta Wube Bayleyegne, Department of Statistics, Faculty of Natural and Computational Sciences, Woldia University, Woldia, Ethiopia
Zeytu Gashaw Asfaw, Department of Statistics, Collage of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia
Longer intervals between consecutive births decrease the number of children a woman can have. This results in beneficial effects on population size and on the health status of mothers and children. The general objective of this study was to model the birth intervals of adult women age 15-49 years old in Ethiopia and to identify the variable that affects the length of birth intervals of women. The study utilizes the data extracted from the 2011 Ethiopian Demographic and Health Survey (EDHS). In this study cox proportional hazards and shared gamma frailty models were adopted for the analysis to identify important demographic and socioeconomic factors that may affect the length of birth intervals and to analyze correlated birth intervals respectively. The result of the two models revealed that mother’s age, place of residence, mother education level, wealth index, mother age at first birth, childbirth order, survival status of the previous child, breast feeding status, and contraceptive use were found to have significant effect on the length of birth interval for Ethiopian women. The analysis with the frailty model shows that child birth order may not be an important covariate for analyzing birth intervals, especially when mother’s age at first birth is already in the model. Moreover, shared gamma frailty model have resulted in a minimum AIC as compared to cox proportional hazard model without frailty term in the model, suggesting that shared gamma frailty model is the most powerful one in predicting the birth intervals of women among regional states of Ethiopia. Hence, the setting of correlated observations, the cox frailty models are recommended for providing statistically valid estimates of the effects of proximate determinants after adjusting for the background variables and unobserved random effects.
Yenefenta Wube Bayleyegne,
Zeytu Gashaw Asfaw,
Survival Analysis to Determine the Significant Factors Associated with Birth Interval of Women in Ethiopia: Based on 2011 Ethiopian Demographic and Health Survey Data, Biomedical Statistics and Informatics.
Vol. 5, No. 1,
2020, pp. 26-38.
Rutstein SO. (2008). Further Evidence of the Effects of Preceding Birth Intervals on Neonatal, Infant, and Under-Five-Years Mortality and Nutritional Status in Developing Countries: Evidence from the Demographic and Health Surveys. In Demography and health research, Volume 41.
Smits L., Pedersen C., Mortensen P., van Jim OS. (2003). Association between short intervals and schizophrenia in the offspring. Schizophr Res, 70: 49–56.
Organization, W. H. (2005). Department of Making Pregnancy Safer. Switzerland: Technical Consultation report on Birth Spacing.
Conde-Agudelo A., Rosas-Bermudez A., Castano F., Norton MH. (2012). Effects of birth spacing on maternal, perinatal, infant, and child health: a systematic review of causal mechanisms. Stud Fam Plann, 43 (2): 93-114.
USAID. (2010). United States agency for International Development. Strengthening family planning policies and programs in developing countries.
Macro, C. [. (2011). Ethiopian Demographic and Health Survey preliminary report. Addis Ababa, Ethiopia and Calverton, Maryland, USA.
Upadhyay, U. D. and Setty-Venugopal, V. (2002). Birth Spacing: Three to Five Saves Lives. Johns Hopkins University Editor, Baltimore, 1-23.
Rodriguez G., Hobcraft J., McDonald J., Menken J., and Trussell J. (1984). A Comparative Analysis of Determinants of Birth Intervals, WFS comparative studies. International Statistical Institute.
Miller J., Trussell J., Pebley A., and Vaughan B. (1992). Birth spacing and child mortality in Bangladesh and the Philippines. Demography 29 (2), pp. 305–318.
Federal Democratic Republic of Ethiopia, M. (2010). Health Sector Development Programme IV 2010/11 – 2014/15.
N., S. (1998). Family-level clustering of childhood mortality risk in Northeast Brazil. Popul. Studies 51 (3), pp. 245–261.
Mahmood S., Zainabet B. and A. H. M. Mahbubnd. (2013). Frailty modeling for clustered survival data: an application to birth interval in Bangladesh. Journal of Applied Statistics.
Begna Z., Assegid s., Kassahun W., Mulusew G. (2013). Determinants of birth interval among married women living in rural pastoral communities of southern Ethiopia: a case control study. BMC Pregnancy and Child birth, 13: 116.
Yohannes S., Wondafrash M., and Abera M. and Girma E. (2011). Duration and determinants of birth interval among women of child bearing age in Southern Ethiopia. BMC Pregnancy and Child birth, 11: 38.
Munda M., Rotolo F., and Legrand C. (2012). Parametric Frailty Models in R. Journal of American Statistical Association, 55: 1-2.1.
Shayan Z., Mohammad S., Zare N., and Moradi F. (2014). Prognostic factors of first birth interval using the parametric survival models. Iranian Journal of Reproductive Medicine Vol. 12. No. 2, pp: 125-130.
Sharmin S., Shamal C., Ahbab M., Shahadat M. (n. d.). Determinants of Birth Spacing and Effect of Birth Spacing on Fertility in Bangladesh. Dhaka Univ. J. Sci. 61 (1), 105-110.
Guo G. and Rodriguez G. (1992). Estimating a multivariate proportional hazards model for clustered data using the EM algorithm, with an application to child survival in Guatemala. J. Am. Stat. Assoc. 87 (420), pp. 969–976.
Klembaum, G. (1996). Survival Analysis. A self learning text, 3rd edition, Springer.
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society Series, B 34, 187.220.
A., W. (2010). Frailty Models in Survival Analysis. Chapman & Hal and Boca Raton.
Abrahantes J., Legrand C., Burzykowski T., Janssen P., Ducrocq V., and Duchateau L. (2007). Comparison of Different Estimation Procedures for Proportional Hazards Model with Random Effects. Computational Statistics & Data Analysis., 51: 3919-3930.
Duchateau, L. and Janssen, P. (2008). The Frailty Model. New York: Springer.
Hougaard P. (1986). Survival Model for heterogeneous populations derived from stable distribution. Biometrika, Oxford Academic, pp387-396.
Flinn, C. J. and Heckman, J. J,. (1982). New methods for analyzing individual event.
Vaupel, J. W., Manton, K. G. and Stallard, E. (1979). The impact of heterogeneity on individual frailty on the dynamic of mortality. Demography, 16 (3), 439-454.
Hidayat R., Sumarno H. and Nugrahani, E. H. (2014). Survival Analysis in Modeling the Birth Interval of the First Child in Indonesia. Open Journal of Statistics, 198-206.
Ayenew., A. (2008). Proximate Determinants of Birth Interval Length in Amhara Region: The Case of Fagita Lekoma Wereda, Awi-Zone. Unpublished M. Sc. Thesis, Population Study Department, Addis Ababa University, Ethiopia.
Chowdhury A. and Karim A. (2013). Patterns and Differentials of Birth Intervals in Bangladesh. Global Journal Of Science Frontier Research Interdisciplinary, Volume 13, 2249-4626.
Amoako F., and Madise N.,. (2008). Examining the geographical Heterogeneity associated with risk of mistimed and unwanted pregnancy in Ghana. Journal of Biosocial Science, pp 1-19.
Asifa K. and Muhammad K. (2012). Determinants of Higher Order Birth Intervals in Pakistan. Journal of Statistics, Volume 19, pp. 54-82.
Singh SN, Singh N, Narendra RK. (2010). Demographic and Socioeconomic Determinants of Birth Interval Dynamics in Manipur: A Survival Analysis. Online J Health Allied Scs., 9 (4): 3.
Tessema G., Zeleke B. and Ayele. (2013). Birth interval and its predictors among married women in Dabat District, Northwest Ethiopia: A retrospective follow up study. Original Research Article.