Two distant Counties have emerged as having Kenya’s highest life expectancy with the women living on average 22 years longer than those in the worst performing county in the country. At Birth, the life expectancy for females in both Garissa County to the east and Bungoma to the west is 77 years, whereas life expectancy at the Northern County of Wajir is 55 years of age.
The Census data further shows shocking differences amongst the urban areas characterized by high standards of living and good health facilities versus the counties that would be considered to be underdeveloped in Kenya, namely areas inhabited by pastoralist communities. Life expectancy at 60 for females in the highly developed capital of Nairobi is 79 years and an even lower 75 years in Mombasa, in the same age bracket, women are expected to live to 83 years in Garissa and to an impressive 88 years in Samburu.
While there is no clear explanation yet for the differences between the urban and the rural counties, we suspect that this can be attributed to the high prevalence of lifestyle diseases in the cities. People in the far flung villages tend to be more active, smoke or drink less often and are not used to fast foods that have contributed to an obesity epidemic in the urban areas.
However, location alone cannot be the only factor that affects the lifespan across Kenya. Either by causation or through coincidence some neighboring counties appear to exhibit very different trends in life expectancy. One clear example is Samburu which share a border with Laikipia County but have a wide gap of ten years between them amongst women. It is probably not surprising that a farmer will outlive a pastoralist living next door, what may surprise most people is how big the gap is.
Overall, at the time of Birth, Kenyan men are expected to live to 65 years and their female counterparts are expected to live for an extra two years. A Kenyan man who is lucky to hit 60 can expect to live for an extra 17 years, as his wife out-lives him by one year.
As devolution takes shape in the counties, less marginalization is expected due to more equitable sharing of the national cake and as a result a levelling of the life expectancy across the country. We can only hope that the early deaths and their causal factors will not be passed from the urban populations to the village folk. Meanwhile, more actions needs to be taken to address the wide gap in life expectancy in Kenya.
Steven Ireri is the Author of this post. He is an Open Data Fellow at the ICT Authority and hoping to find the right questions to ask from data. Write to info [at] opendata [dot] go [dot] ke or you can find me on github