Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya

Abstract

BackgroundSpatial 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.MethodsPlasmodium 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).Results5020 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%.ConclusionsKenya 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.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2489-9) contains supplementary material, which is available to authorized users.

Genetic diversity and population structure of Plasmodium falciparum in Kenyan–Ugandan border areas

“…The level of multilocus non-random association in this study is lower than previously reported in areas with intense transmission, including Kenya [13,14,20,22,38]. The recent increase (2011-2014) and subsequent decline (from 2015 on) of malaria prevalence are likely to have altered the frequency of minor alleles [4,5], thus contributing to the lower LD observed here. This is demonstrated by significant multilocus LD in Asembo, western Kenya before deployment of ITNs in 1996, followed by non-significant and significant multilocus LDs in 2001 and 2007, respectively [13,14].…”

“…This was a slight reduction from the ≥ 80% prevalence reported by previous studies . Since the prevalence of polyclonal infections is proxies of malaria transmission, our findings suggest that malaria transmission intensity has decreased in line with the lower malaria prevalence in Kenya over the last three decades . However, this trend has not been observed in the genetic diversity of the Kenyan P. falciparum population as exemplified in the present and in previous studies .…”

“…This is demonstrated by significant multilocus LD in Asembo, western Kenya before deployment of ITNs in 1996, followed by non‐significant and significant multilocus LDs in 2001 and 2007, respectively . The lower parasite prevalence in coastal (10–29%) and western (≥30%) Kenya offers an alternative explanation for the observed significant multilocus LD in Msambweni, which was not seen in Busia and Nyando . Apparently, parasite inbreeding increases with a reduction transmission .…”

Malaria Micro-stratification Using Routine Surveillance Data in Western Kenya

“…Such an approach does not adjust for a) the spatial and temporal heterogeneities in the data at a more granular scale; b) the populations who would use health facilities at the borders of administrative units; or c) missingness of the reported data by health facility. Importantly, data use rarely considers uncertainty thresholds which are important metrics for decision making when choosing between malaria strategies [12,[14][15][16][17][18].…”

Assessment of the relationship between gametocyte density, multiplicity of infection and mosquito infectivity in Plasmodium falciparum infections

“…This involved the introduction and scaling up of interventions such as long-lasting insecticide net (LLIN), rapid diagnostic test (RDT), and artemisinin-based combination therapy (ACT) [5,6]. The implementation of these interventions have resulted to a decline in malaria transmission in many parts of the country [7]. Nevertheless, the coastal part of the country and areas along the shores of Lake Victoria continue to face high malaria transmission [8].…”