Alegana Victor A, Macharia Peter M, Muchiri Samuel, Mumo Eda, Oyugi Elvis, Kamau Alice, Chacky Frank, Thawer Sumaiyya, Molteni Fabrizio, Rutazanna Damian, Maiteki-Sebuguzi Catherine, Gonahasa Samuel, Noor Abdisalan M, Snow Robert W
Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.
Geography and Environmental Science, University of Southampton, Southampton, United Kingdom.
PLOS Glob Public Health. 2021 Dec 7;1(12):e0000014. doi: 10.1371/journal.pgph.0000014.
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 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.
疟疾的高负担高影响（HBHI）战略鼓励各国利用多种现有数据来源来确定国家以下地区疟疾风险的脆弱性，包括寄生虫流行率。在此，给出了根据肯尼亚、坦桑尼亚大陆和乌干达社区寄生虫调查数据的更新汇编得出的估计值，并用于更全面地了解2019年该次区域国家以下地区的疟疾流行分层情况。收集了2010年1月至2020年6月期间这三个国家开展的调查中的疟疾流行数据。基于贝叶斯时空模型的方法用于在精细空间分辨率下对时空数据进行插值，并针对这三个国家的人口、环境和生态协变量进行调整。总共收集了18940次时空年龄标准化且经显微镜检查转换的调查，其中14170次（74.8%）是在2017年之后确定的。估计肯尼亚经全国人口调整后的寄生虫流行率后验均值为4.7%（95%贝叶斯可信区间2.6 - 36.9），坦桑尼亚大陆为10.6%（3.4 - 39.2），乌干达为9.5%（4.0 - 48.3）。2019年，超过1270万人居住在预测寄生虫流行率≥30%的社区，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，寄生虫流行率极低（<1%）的地区居住着该次区域约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层面疾病分层的几个数据指标之一。为了增加这一指标在决策中的应用，有必要整合其他数据层面，包括与疟疾相关的死亡率、疟疾媒介组成、杀虫剂抗性和生物特性、疟疾就医行为以及当前疟疾干预未满足需求的水平。
