Abstract:
Background: Spatial and temporal malaria risk maps are essential tools to monitor the
impact of control, evaluate priority areas to reorient intervention approaches and
investments in malaria endemic countries. Here, the analysis of 36 years data on
Plasmodium falciparum prevalence is used to understand the past and chart a future for
malaria control in Kenya by confidently highlighting areas within important policy
relevant thresholds to allow either the revision of malaria strategies to those that support
pre-elimination or those that require additional control efforts.
Methods: Plasmodium falciparum parasite prevalence (PfPR) surveys undertaken in
Kenya between 1980 and 2015 were assembled. A spatio-temporal geostatistical model
was fitted to predict annual malaria risk for children aged 2-10 years (PfPR2-10) at 1 × 1
km spatial resolution from 1990 to 2015. Changing PfPR2-10 was compared against
plausible explanatory variables. The fitted model was used to categorize areas with
varying degrees of prediction probability for two important policy thresholds PfPR2-10 <
1% (non-exceedance probability) or ≥ 30% (exceedance probability).
Results: 5020 surveys at 3701 communities were assembled. Nationally, there was an
88% reduction in the mean modelled PfPR2-10 from 21.2% (ICR: 13.8-32.1%) in 1990
to 2.6% (ICR: 1.8-3.9%) in 2015. The most significant decline began in 2003. Declining
prevalence was not equal across the country and did not directly coincide with scaled
vector control coverage or changing therapeutics. Over the period 2013-2015, of Kenya's
47 counties, 23 had an average PfPR2-10 of < 1%; four counties remained ≥ 30%. Using
a metric of 80% probability, 8.5% of Kenya's 2015 population live in areas with PfPR2-
10 ≥ 30%; while 61% live in areas where PfPR2-10 is < 1%.
Conclusions: Kenya has made substantial progress in reducing the prevalence of malaria
over the last 26 years. Areas today confidently and consistently with < 1% prevalence
require a revised approach to control and a possible consideration of strategies that
support pre-elimination. Conversely, there remains several intractable areas where
current levels and approaches to control might be inadequate. The modelling approaches
presented here allow the Ministry of Health opportunities to consider data-driven model
certainty in defining their future spatial targeting of resources.