Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification.
Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification.
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DOI：
10.1371/journal.pgph.0000014
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发表时间：
2021-12-07
期刊：
影响因子：
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通讯作者：
Snow RW
中科院分区：
文献类型：
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作者：
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
疟疾高负担高影响战略鼓励各国利用多种现有数据来源，确定国家以下各级疟疾风险的脆弱性，包括寄生虫流行率。在这里，提供了肯尼亚、坦桑尼亚大陆和乌干达社区寄生虫调查数据的更新集合中对恶性疟原虫的模型化估计，并用于提供对该次区域2019年次国家疟疾流行分层的更现代的理解。这三个国家都收集了2010年1月至2020年6月期间进行的调查的疟疾流行数据。基于贝叶斯时空模型的方法被用来以精细的空间分辨率内插时空数据，以调整三个国家的人口、环境和生态协变量。总共收集了18940份时空年龄标准化和显微镜转换的调查，其中14170份(74.8%)是在2017年后确定的。经人口调整后的全国平均寄生虫流行率估计在肯尼亚为4.7%(95%贝叶斯可信区间2.6-36.9)，在坦桑尼亚大陆为10.6%(3.4-39.2)，在乌干达为9.5%(4.0-48.3)。2019年，超过1270万人居住在寄生虫流行率预计为≥30%的社区，其中分别占肯尼亚、坦桑尼亚大陆和乌干达人口的6.4%、12.1%和6.3%。相反，支持极低寄生虫流行率(1%)的地区居住着整个次区域约4620万人，分别占肯尼亚、坦桑尼亚大陆和乌干达人口的52.2%、26.7%和10.4%。总之，寄生虫流行率是国家和国家以下各级疾病分层的几个数据指标之一。为了更多地使用这一指标进行决策，有必要整合与疟疾有关的死亡率、疟疾病媒构成、杀虫剂抗药性和生物学、疟疾求医行为以及疟疾干预措施目前未得到满足的需求水平的其他数据层。
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.