AccScience Publishing / IJPS / Online First / DOI: 10.36922/ijps.1837

Impact of rural-urban migration on indirect child mortality estimation in Kenya

Alfred M. Kathare1* Kimani Murungaru1 Alfred O.T. Agwanda1
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1 Department of Geography, Population and Environmental Studies, Faculty of Social Sciences, University of Nairobi, Nairobi, Kenya
Submitted: 14 September 2023 | Accepted: 26 February 2024 | Published: 10 July 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( )

When estimating child mortality rates using the indirect method, it is assumed that all reported births and deaths occurred in the place where the mothers resided at the time of the survey. However, the migration of women can result in transferring data about deceased children from one place to another. In many developing countries, substantial migration happens between rural and urban regions, where child mortality disparities are significant. This migration creates challenges as child mortality estimates for rural and urban areas computed under the assumption of non-migration are likely to be erroneous. Our study pooled data from six Kenya Demographic and Health Surveys between 1989 and 2014. The study aimed to establish statistical evidence of the impact of rural-urban migration on indirect child mortality estimates. Our findings indicate that the inclusion of deceased children born to women who migrated from rural to urban regions led to a significant overestimation of infant, one-to-four, and under-five mortality rates in urban areas. On average, the overestimation of infant mortality rates ranged from 2.5% to 21.7%, while one-to-four mortality rate overestimation ranged from 4.0% to 41.2%. The average overestimation of under five mortality rate was between 3.0% and 26.8%. Based on these results, future indirect estimates of child mortality for rural and urban regions should be adjusted to account for the impacts of migration between these areas. Furthermore, it is essential to consider re-estimating trends of child mortality for rural and urban regions in Kenya to better understand the timing of mortality convergence between these regions.

Child mortality rate
Brass indirect method
Rural region
Urban region
Migration impact

Andersson, G., & Drefahl, S. (2017). Long-distance migration and mortality in Sweden: Testing the salmon bias and healthy migrant hypotheses. Population, Space and Place, 23(4):e2032.


Arthur, W.B., & Stoto, M.A. (1983). An analysis of indirect mortality estimation. Population Studies (Camb), 37(2):301-314.


Ayele, D.G., Zewotir, T., & Mwambi, H. (2016). Indirect child mortality estimation technique to identify trends of under-five mortality in Ethiopia. African Health Sciences, 16(1):18-26.


Bangura, A.H., Ozonoff, A., Citrin, D., Thapa, P., Nirola, I., Maru, S., et al. (2016). Practical issues in the measurement of child survival in health systems trials: Experience developing a digital community-based mortality surveillance programme in rural Nepal. BMJ Global Health, 1(4):e000050.


Bocquier, P., Madise, N.J., & Zulu, E.M. (2011). Is there an urban advantage in child survival in sub-Saharan Africa? Evidence from 18 countries in the 1990s. Demography, 48(2):531-558.


Brass, W., & Coale, A. (1968). Method of analysis and estimation. In: The Demography of Tropical Africa. Princeton: Princeton University Press, p.88-139.


Brockerhoff, M. (1994). The impact of rural-urban migration on child survival. Health Transition Review, 4(2):127-149.


Coale, A.J., & Demeny, P. (1966). Regional Model Life Tables and Stable Populations. New Jersey: Princeton University Press.


Etikan, I., Babatope, O., Bala, K., & Ilgi, S. (2019). Child mortality: A comparative study of some developing countries in the world. International Journal of Clinical Biostatistics and Biometrics, 5(2):022.


Government of Kenya. (1996). Kenya Economic Reforms for 1996- 1998: The Policy Framework Paper. Kenya: Government of Kenya.


Govindasamy, P., Stewart, M.K., Rutstein, S.O., Boerma, J.T., & Sommerfelt, A.E. (1993). High Risk Births and Maternal Care. Columbia MD: Macro International Inc.


Hallett, T.B., Gregson, S., Kurwa, F., Garnett, G.P., Dube, S., Chawira, G., et al. (2010). Measuring and correcting biased child mortality statistics in countries with generalized epidemics of HIV infection. Bulletin of the World Health Organization, 88(10):761-768.


IBM. (2020). Statistical Package for the Social Sciences (Version 26) [Computer Software]. United States: IBM Corporation.


Issaka, A.I., Agho, K.E., & Renzaho, A.M.N. (2017). Correction: The impact of internal migration on under-five mortality in 27 sub-Saharan African countries. PLoS One, 12(2):e0171766.


Kenya National Bureau of Statistics (KNBS), & International Classification Function (ICF), Macro. (2015). Kenya Demographic and Health Survey 2014. Maryland, USA: KNBS and Macro.


Kenya National Bureau of Statistics (KNBS), & International Classification Function (ICF), Macro. (2015). Kenya Demography and Health Survey 2014. Maryland, USA: KNBS and Macro, p.111-112.


Kimani-Murage, E.W., Fosto, J.C., Egondi, T., Abuya, B., Elungata, P., Ziraba, A.K., et al. (2014). Trends in childhood mortality in Kenya: The urban advantage has seemingly been wiped out. Health and Place, 29:95-103.


Madise, N.J., Banda, E.M., & Benaya, K.W. (2003). Infant mortality in Zambia: Socioeconomic and demographic correlates. Social Biology, 50(1-2):148-166.


Neupert, R., Fernandez Menjivar, R.E., & Fernandez Castilla, R.E. (2019). Indirect estimation of infant mortality in small areas. Revista Brasileira de Estudos de População, 36:1-37.


Otieno Onyango, E.B., Khasakhala, A., Agwanda, A.T., Kimani, M., & K’Oyugi, B. (2011). Effect of mother’s migration on under-two mortality in Kenya. African Population Studies, 25(2):534-555.


Rajaratnam, J.K., Tran, L.N., Lopez, A.D., & Murray, C.J.L. (2010). Measuring under-five mortality: Validation of new low-cost methods. PLoS Medicine, 7(4):e1000253.


Schmertmann, C.P., & Sawyer, D.O. (1996). Migration bias in indirect estimates of regional childhood mortality levels. Mathematical Population Studies, 6(2):69-93.


United Nations. (1983). Manual X: Indirect Techniques for Demographic Estimations. New York: United Nations Publications, p.1-81.


United Nations. (2013). MORTPAK for Windows (4.3) [Computer Software]. New York: United Nations.


United Nations. (2019). Word Urbanization Prospects; The 2018 Revision. New York: Department of Economic and Social Affairs.


Van De Poel, E., O’Donnell, O.A., & Van Doorslaer, E. (2007). Are urban children really healthier? SSRN Electronic Journal.


Verhulst, A. (2016). Child mortality estimation: An assessment of summary birth history methods using microsimulation. Demographic Research, 34(39):1075-1128.


Walker, N., Hill, K., & Zhao, F. (2012). Child mortality estimation: Methods used to adjust for bias due to aids in estimating trends in under-five mortality. PLoS Med, 9(8):e1001298.


Yadava, R.C., & Tiwari, A.K. (2003). An indirect technique for estimations of infant and child mortality: Data analysis from India and Bangladesh. Health and Population Perspectives and Issues, 26(2):67-73.


Yaya, S., Uthman, O.A., Okonofua, F., & Bishwajit, G. (2019). Decomposing the rural-urban gap in the factors of under-five mortality in sub-Saharan Africa? Evidence from 35 countries. BMC Public Health, 19(1):616.

Conflict of interest
The authors declare that they have no competing interests.
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International Journal of Population Studies, Electronic ISSN: 2424-8606 Print ISSN: 2424-8150, Published by AccScience Publishing