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

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 t...

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Main Authors: Peter M. Macharia, Emanuele Giorgi, Abdisalan M. Noor, Ejersa Waqo, Rebecca Kiptui, Emelda A. Okiro, Robert W. Snow
Format: Article
Language:English
Published: BMC 2018-09-01
Series:Malaria Journal
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12936-018-2489-9
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spelling doaj-2094916db5394cbfab56e0394bbdda642020-11-24T21:50:21ZengBMCMalaria Journal1475-28752018-09-0117111310.1186/s12936-018-2489-9Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in KenyaPeter M. Macharia0Emanuele Giorgi1Abdisalan M. Noor2Ejersa Waqo3Rebecca Kiptui4Emelda A. Okiro5Robert W. Snow6Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research ProgrammeLancaster Medical School, Lancaster UniversityGlobal Malaria Programme, World Health OrganizationNational Malaria Control Programme, Ministry of HealthNational Malaria Control Programme, Ministry of HealthPopulation Health Unit, Kenya Medical Research Institute-Wellcome Trust Research ProgrammePopulation Health Unit, Kenya Medical Research Institute-Wellcome Trust Research ProgrammeAbstract 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.http://link.springer.com/article/10.1186/s12936-018-2489-9Model-based geostatisticsMalariaKenyaPlasmodium falciparum
collection DOAJ
language English
format Article
sources DOAJ
author Peter M. Macharia
Emanuele Giorgi
Abdisalan M. Noor
Ejersa Waqo
Rebecca Kiptui
Emelda A. Okiro
Robert W. Snow
spellingShingle Peter M. Macharia
Emanuele Giorgi
Abdisalan M. Noor
Ejersa Waqo
Rebecca Kiptui
Emelda A. Okiro
Robert W. Snow
Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya
Malaria Journal
Model-based geostatistics
Malaria
Kenya
Plasmodium falciparum
author_facet Peter M. Macharia
Emanuele Giorgi
Abdisalan M. Noor
Ejersa Waqo
Rebecca Kiptui
Emelda A. Okiro
Robert W. Snow
author_sort Peter M. Macharia
title Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya
title_short Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya
title_full Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya
title_fullStr Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya
title_full_unstemmed Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya
title_sort spatio-temporal analysis of plasmodium falciparum prevalence to understand the past and chart the future of malaria control in kenya
publisher BMC
series Malaria Journal
issn 1475-2875
publishDate 2018-09-01
description 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.
topic Model-based geostatistics
Malaria
Kenya
Plasmodium falciparum
url http://link.springer.com/article/10.1186/s12936-018-2489-9
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