Trends in under-five mortality(U5M) in Kenya

Vital statistics remain the bedrock for population and social policies providing a country’s decision-makers with critical information for resource allocation, planning and health priorities. Yet, in 2021, in sub-Saharan Africa, few countries benefit from reliable and timely evidence on new births and information of who dies of what relying instead on estimates from census and surveys. Under-five mortality (U5M), the probability that a child will die before reaching the age of five, is a benchmark of a country’s health status and progress towards achievement of development goals. U5M has been used for decades as part of international development targets and has improved with time, with increasing data and improved methodologies. New methodologies have been applied to understand within-country temporal and spatial heterogeneity in child survival. However, examples of how U5M has changed at sub-national levels remains limited to only a few SSA countries and imperfectly described providing the basis of our work on mortality.

County Profiles

The progress towards improving child survival has been uneven across Kenyan counties, with high rates of reduction in some areas and slow progress in others. For example, while Kenya had 54 deaths per 1,000 live births in 2013, our work showed significant differences across the 47 counties since 1965, with U5M rates ranging between 32 to 121 per 1000 live births by 2013. Historically, the highest mortality rates are those in the coastal regions, the arid and semi-arid regions around Lake Turkana, and those around the Lake Victoria region. A range of factors was likely behind this. These included disparity in disease prevalence and the coverage of interventions such as the uptake of childhood immunizations, supplements, and breastfeeding practices as detailed in the county profiles.

Inequalities in achieving U5M targets

Child mortality-related inequalities have been documented across different socio-economic groupings and geographical units. The focus here was on spatial-temporal inequalities between counties from 1965 and 2013 because spatial inequality links health outcomes to characteristics of a place that poverty or urban/rural taxonomies cannot highlight. The inequality index increased in the 80s but started to decline in the early 1990s and sustained through to 2013. However, values recorded in 2013 show that high inequality continues to exist notably in Western and Nyanza provinces and that focusing on reducing U5M in these high burden areas will narrow the gap.

A scatter plot showing changes in mean under five mortality per 1000 live births (U5M) per county between 1965 and 2013. The provinces are differentiated by shapes; Coast (ӿ), North Eastern (♦), Eastern (+), Central (▲), Rift valley (●), Western (x), Nyanza (■) and Nairobi (−) and counties with colors (indexed in Table 2). The bold dark line shows inequality ratio calculated by dividing U5M of 40% of the counties with high U5M with the U5M of 10% of counties with low U5M between 1965 and 2013