Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification
The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community p...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Journal article |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2021
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| _version_ | 1826266191954444288 |
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| author | Alegana, VA Macharia, PM Muchiri, S Mumo, E Oyugi, E Kamau, A Chacky, F Thawer, S Molteni, F Rutazanna, D Maiteki-Sebuguzi, C Gonahasa, S Noor, AM Snow, RW |
| author_facet | Alegana, VA Macharia, PM Muchiri, S Mumo, E Oyugi, E Kamau, A Chacky, F Thawer, S Molteni, F Rutazanna, D Maiteki-Sebuguzi, C Gonahasa, S Noor, AM Snow, RW |
| author_sort | Alegana, VA |
| collection | OXFORD |
| description | The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6–36.9) in Kenya, 10.6% (3.4–39.2) in mainland Tanzania, and 9.5% (4.0–48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.
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| first_indexed | 2024-03-06T20:35:10Z |
| format | Journal article |
| id | oxford-uuid:3264e5c9-4a26-4c4e-a955-6dd0de914d92 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:35:10Z |
| publishDate | 2021 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:3264e5c9-4a26-4c4e-a955-6dd0de914d922022-03-26T13:13:42ZPlasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratificationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3264e5c9-4a26-4c4e-a955-6dd0de914d92EnglishSymplectic ElementsPublic Library of Science2021Alegana, VAMacharia, PMMuchiri, SMumo, EOyugi, EKamau, AChacky, FThawer, SMolteni, FRutazanna, DMaiteki-Sebuguzi, CGonahasa, SNoor, AMSnow, RWThe High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6–36.9) in Kenya, 10.6% (3.4–39.2) in mainland Tanzania, and 9.5% (4.0–48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions. |
| spellingShingle | Alegana, VA Macharia, PM Muchiri, S Mumo, E Oyugi, E Kamau, A Chacky, F Thawer, S Molteni, F Rutazanna, D Maiteki-Sebuguzi, C Gonahasa, S Noor, AM Snow, RW Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification |
| title | Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification |
| title_full | Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification |
| title_fullStr | Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification |
| title_full_unstemmed | Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification |
| title_short | Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification |
| title_sort | plasmodium falciparum parasite prevalence in east africa updating data for malaria stratification |
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