Kenya Population and Housing Census “Counting Our People for Implementation of Vision 2030” Volume VI Mortality

Abstract

The ever increasing demand for evidence based decision making requires data, and to this end, it is imperative that necessary data is collected and availed for use. Demographic data collected through censuses and other surveys produce indicators that assist the nation to evaluate its progress towards achievement of the Millennium Development Goals as well as Vision 2030. One of the objectives of the 2009 Kenya Population and Housing Census was to collect information that would be used to determine levels, trends and differentials in mortality. Mortality estimates derived from the Census data are expected to assist in the assessment of the country‟s progress towards the achievement of the 4th MDG target of reduction of under-five mortality by two thirds and the 5th MDG, to improve maternal health. The estimates will also be useful for assessing implementation of national population and health policies, especially in their attempt to put in place mechanisms to reduce infant, child and maternal mortality as well as improving newborn health. Indirect methods of estimation developed by William Brass (with various modifications) were used to estimate infant and child mortality indicators. However, due to the unreliable estimates derived from these methods, direct estimates were used to generate mortality estimates from the 2009 Census data. The estimates derived from this methods relate to a period five years prior to the Census, as opposed to the previous census estimates that related to a period of ten years prior to the Census. Whereas in the 1999 Census there were indications of an upward trend in childhood mortality estimates, the 2009 Census estimates indicate there has been a drastic reduction. However, the reduction has not been uniform throughout the country. The traditionally high child mortality counties such as Siaya, Kisumu, Homa Bay, Migori, Wajir and Mandera, continue to experience high mortality. Esimations of maternal mortality also show significant variations between the counties, with Mandera having the highest maternal mortality ratio and Elgeyo Marakwet the lowest. The results should however be intepreted with caution due to data quality issues alluded to in Chapter 2. With regard to adult mortality the results indicate that there have been improvements in survival chances nationally. However there are marked differences between counties, with Siaya County having the worst scenario. The results indicate that of those aged 15 in Siaya, 65 percent will die before reaching age 60. This calls for programmatic interventions to change the scenario.