<i>Plasmodium falciparum</i> 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 <i>Plasmodium falciparum</i> from an updated asse...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-12-01
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| Series: | PLOS Global Public Health |
| Online Access: | https://doi.org/10.1371/journal.pgph.0000014 |
| _version_ | 1797695267679502336 |
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| author | Victor A Alegana Peter M Macharia Samuel Muchiri Eda Mumo Elvis Oyugi Alice Kamau Frank Chacky Sumaiyya Thawer Fabrizio Molteni Damian Rutazanna Catherine Maiteki-Sebuguzi Samuel Gonahasa Abdisalan M Noor Robert W Snow |
| author_facet | Victor A Alegana Peter M Macharia Samuel Muchiri Eda Mumo Elvis Oyugi Alice Kamau Frank Chacky Sumaiyya Thawer Fabrizio Molteni Damian Rutazanna Catherine Maiteki-Sebuguzi Samuel Gonahasa Abdisalan M Noor Robert W Snow |
| author_sort | Victor A Alegana |
| collection | DOAJ |
| 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 <i>Plasmodium falciparum</i> 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. |
| first_indexed | 2024-03-12T03:09:00Z |
| format | Article |
| id | doaj.art-248514bd4bb14d7182713ff529bd27ce |
| institution | Directory Open Access Journal |
| issn | 2767-3375 |
| language | English |
| last_indexed | 2024-03-12T03:09:00Z |
| publishDate | 2021-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Global Public Health |
| spelling | doaj.art-248514bd4bb14d7182713ff529bd27ce2023-09-03T14:29:03ZengPublic Library of Science (PLoS)PLOS Global Public Health2767-33752021-12-01112e000001410.1371/journal.pgph.0000014<i>Plasmodium falciparum</i> parasite prevalence in East Africa: Updating data for malaria stratification.Victor A AleganaPeter M MachariaSamuel MuchiriEda MumoElvis OyugiAlice KamauFrank ChackySumaiyya ThawerFabrizio MolteniDamian RutazannaCatherine Maiteki-SebuguziSamuel GonahasaAbdisalan M NoorRobert W SnowThe 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 <i>Plasmodium falciparum</i> 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.https://doi.org/10.1371/journal.pgph.0000014 |
| spellingShingle | Victor A Alegana Peter M Macharia Samuel Muchiri Eda Mumo Elvis Oyugi Alice Kamau Frank Chacky Sumaiyya Thawer Fabrizio Molteni Damian Rutazanna Catherine Maiteki-Sebuguzi Samuel Gonahasa Abdisalan M Noor Robert W Snow <i>Plasmodium falciparum</i> parasite prevalence in East Africa: Updating data for malaria stratification. PLOS Global Public Health |
| title | <i>Plasmodium falciparum</i> parasite prevalence in East Africa: Updating data for malaria stratification. |
| title_full | <i>Plasmodium falciparum</i> parasite prevalence in East Africa: Updating data for malaria stratification. |
| title_fullStr | <i>Plasmodium falciparum</i> parasite prevalence in East Africa: Updating data for malaria stratification. |
| title_full_unstemmed | <i>Plasmodium falciparum</i> parasite prevalence in East Africa: Updating data for malaria stratification. |
| title_short | <i>Plasmodium falciparum</i> parasite prevalence in East Africa: Updating data for malaria stratification. |
| title_sort | i plasmodium falciparum i parasite prevalence in east africa updating data for malaria stratification |
| url | https://doi.org/10.1371/journal.pgph.0000014 |
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