Hierarchical Logistic Regression Model for Multilevel Analysis: An Application on Use of Contraceptives Among Women in Reproductive Age in Kenya
International Journal of Data Science and Analysis
Volume 4, Issue 5, October 2018, Pages: 58-78
Received: Jul. 19, 2018;
Accepted: Oct. 8, 2018;
Published: Nov. 16, 2018
Views 824 Downloads 166
Linda Vugutsa Luvai, Department of Applied Statistics and Actuarial Science, Maseno University, Kisumu, Kenya
Fred Ongango, Department of Applied Statistics and Actuarial Science, Maseno University, Kisumu, Kenya
Contraception allows women and couples to have the number of children they want, when they want them. This is everybody’s right according to the United Nations Declaration of Human Rights. Use of Contraceptive also reduces the need for abortion by preventing unwanted pregnancies. It therefore reduces cases of unsafe abortion, one of the leading causes of maternal death worldwide. According to Mohammed, in 2012 an estimated 464,000 induced abortions occurred in Kenya. This translates into an abortion rate of 48 per 1,000 women aged 15−49, and an abortion ratio of 30 per 100 live births. About 120,000 women received care for complications of induced abortion in health facilities. About half (49%) of all pregnancies in Kenya were unintended and 41% of unintended pregnancies ended in an abortion. The use of contraceptives in Kenya still remains a big challenge despite the presence of family planning programs through the government and other stake holders. In 2014 a household based cross-sectional study was conducted by Kenya National Bureau of Statistics on women of reproductive age to determine the country’s Contraceptive Prevalence Rate and Total Fertility Rate. This dataset is used to exemplify all aspects of working with multilevel logistic regression models, comparison between different estimates and investigation of the selected determinants of contraceptive usage using statistical software, since large surveys in demography and sociology often follow a hierarchical data structure. The appropriate approach to analyzing such survey data is therefore based on nested sources of variability which come from different levels of the hierarchy. When the variance of the residual errors is correlated between individual observations as a result of these nested structures, traditional logistic regression is inappropriate. These analysis showed that different regions have different effects that affect their contraception prevalence. The study also clearly revealed how single level modeling overestimates or underestimates the parameters in study and also helped to bring to understanding of the structure of required multilevel data and estimation of the model via the statistical package R 3.4.1.
Linda Vugutsa Luvai,
Hierarchical Logistic Regression Model for Multilevel Analysis: An Application on Use of Contraceptives Among Women in Reproductive Age in Kenya, International Journal of Data Science and Analysis.
Vol. 4, No. 5,
2018, pp. 58-78.
Achana, F. S., Bawah, A. A., Jackson, E. F., Welaga, P., Awine, T., AsuoMante, E., Oduro, A., Awoonor-Williams, J. K., and Phillips, J. F. (2015). Spatial and socio-demographic determinants of contraceptive use in the upper east region of ghana. Reproductive health, 12(1):29.
Bongaarts, J. (2011). Can family planning programs reduce high desired family size in sub-saharan africa? International Perspectives on Sexual and Reproductive Health, 37(4):209–216.
Christensen, R. (2006). Log-linear models and logistic regression. Springer Science & Business Media.
Darko, J. A. (2016). Reproductive and child health: contraceptive knowledge, use and factors affecting contraceptive use among female adolescents (15–19 years) in Ghana. PhD thesis, KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY.
Ettarh, R. R. and Kyobutungi, C. (2012). Physical access to health facilities and contraceptive use in kenya: Evidence from the 2008-2009 kenya demographic and health survey. African journal of reproductive health, 16(3).
Gelman, A. and Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.
Gilmour, A. R., Thompson, R., and Cullis, B. R. (1995). Average information reml: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics, pages 1440–1450.
Hair, J. F., Anderson, R. E., Babin, B. J., and Black, W. C. (2010). Multivariate data analysis: A global perspective, volume 7. Pearson Upper Saddle River, NJ.
Khan, H. R. and Shaw, E. (2011). Multilevel logistic regression analysis applied to binary contraceptive prevalence data.
Levandowski, B. A., Kalilani-Phiri, L., Kachale, F., Awah, P., Kangaude, G., and Mhango, C. (2012). Investigating social consequences of unwanted pregnancy and unsafe abortion in malawi: the role of stigma. International Journal of Gynecology & Obstetrics, 118(S2).
Makau, A., Waititu, A. G., and Mungï¿œatu, J. K. (2016). Multinomial logistic regression for modeling contraceptive use among women of reproductive age in kenya. American Journal of Theoretical and Applied Statistics, 5(4):242–251.
Malhotra, A., Schuler, S. R., et al. (2005). Womenï¿œs empowerment as a variable in international development. Measuring empowerment: Crossdisciplinary perspectives, pages 71–88.
Manlove, J., Welti, K., Barry, M., Peterson, K., Schelar, E., and Wildsmith, E. (2011). Relationship characteristics and contraceptive use among young adults. Perspectives on Sexual and Reproductive Health, 43(2):119–128.
Menard, S. (2002). Applied logistic regression analysis. Number 106. Sage.
Mohamed, S. F., Izugbara, C., Moore, A. M., Mutua, M., Kimani-Murage, E. W., Ziraba, A. K., Bankole, A., Singh, S. D., and Egesa, C. (2015). The estimated incidence of induced abortion in kenya: a cross-sectional study. BMC pregnancy and childbirth, 15(1):185.
Nsubuga, H., Sekandi, J. N., Sempeera, H., and Makumbi, F. E. (2016). Contraceptive use, knowledge, attitude, perceptions and sexual behavior among female university students in uganda: a cross-sectional survey. BMC women’s health, 16(1):6.
Ojakaa, D. (2008a). The fertility transition in kenya: patterns and determinants.
Ojakaa, D. (2008b). Trends and determinants of unmet need for family planning in kenya.
Oluwaseun, O. J., Babalola, B. I., and Gbemisola, A. Determinants of contraceptive use among female adolesents in nigeria.
ON, A., OF, U., and AMONG, C. (2017). Multilevel logistic regression model: An.
Sonfield, A., Hasstedt, K., Kavanaugh, M. L., and Anderson, R. (2013). The social and economic benefits of womenï¿œs ability to determine whether and when to have children.
Standard Reporter, K. (2015). 30 years on, myths and misconceptions still a barrier to contraceptive use. Read more at: https://www.standardmedia.co.ke/health/article/2000163756/30years-on-myths-and-misconceptions-still-a-barrier-to-contraceptive-use.
Tumlinson, K., Pence, B. W., Curtis, S. L., Marshall, S. W., and Speizer, I. S. (2015). Quality of care and contraceptive use in urban kenya. International perspectives on sexual and reproductive health, 41(2):69. Vaughn, B. K. (2008). Data analysis using regression and multilevel/hierarchical models, by gelman, a., & hill, j. Journal of Educational Measurement, 45(1):94–97.
Worku, A. G., Tessema, G. A., and Zeleke, A. A. (2015). Trends of modern contraceptive use among young married women based on the 2000, 2005, and 2011 ethiopian demographic and health surveys: a multivariate decomposition analysis. PloSone, 10(1):e0116525.