Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification

被引:32
作者
Alegana, Victor A. [1 ,2 ]
Macharia, Peter M. [1 ]
Muchiri, Samuel [1 ,3 ,7 ]
Mumo, Eda [1 ,7 ]
Oyugi, Elvis [4 ]
Kamau, Alice [1 ]
Chacky, Frank [5 ]
Thawer, Sumaiyya [5 ,6 ]
Molteni, Fabrizio [5 ,6 ]
Rutazanna, Damian [8 ]
Maiteki-Sebuguzi, Catherine [8 ,9 ]
Gonahasa, Samuel [9 ]
Noor, Abdisalan M. [10 ]
Snow, Robert W. [1 ,10 ]
机构
[1] Kenya Govt Med Res Ctr, Populat Hlth Unit, Wellcome Trust Res Programme, Nairobi, Kenya
[2] Univ Southampton, Geog & Environm Sci, Southampton, England
[3] Univ Lancaster, Lancaster Med Sch, Ctr Hlth Informat Comp & Stat, Lancaster, England
[4] Minist Hlth, Div Natl Malaria Programme, Nairobi, Kenya
[5] Minist Hlth Community Dev Gender Elderly & Childre, Natl Malaria Control Programme, Dodoma, Tanzania
[6] Swiss Trop & Publ Hlth Inst, Basel, Switzerland
[7] Univ Basel, Basel, Switzerland
[8] Minist Hlth, Natl Malaria Control Div, Kampala, Uganda
[9] Infect Dis Res Collaborat, Kampala, Uganda
[10] Univ Oxford, Ctr Trop Med & Global Hlth, Nuffield Dept Med, Oxford, England
来源
PLOS GLOBAL PUBLIC HEALTH | 2021年 / 1卷 / 12期
基金
英国惠康基金;
关键词
TRANSMISSION; MODELS; RISK; INTENSITY; DISEASE; BURDEN;
D O I
10.1371/journal.pgph.0000014
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
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页数:21
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