Flexible scan statistic with a restricted likelihood ratio for optimized COVID-19 surveillance.

IF 1 4区 医学 Q4 HEALTH CARE SCIENCES & SERVICES
Ernest Akyereko, Frank B Osei, Kofi M Nyarko, Alfred Stein
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引用次数: 0

Abstract

Disease surveillance remains important for early detection of new COVID-19 variants. For this purpose, the World Health Organization (WHO) recommends integrating of COVID-19 surveillance with other respiratory diseases. This requires knowledge of areas with elevated risk, which in developing countries is lacking from the routine analyses. Focusing on Ghana, this study employed scan-statistic cluster analysis to uncover the spatial patterns of incidence and Case Fatality Rates (CFR) of COVID-19 based on reports covering the four pandemic waves in Ghana between 12 March 2020 and 28 February 2022. Applying flexible spatial scan statistic with restricted likelihood ratio, we examined the incidence and CFR clusters before and after adjustment for covariates. We used distance to the epicentre, proportion of the population aged ≥ 65, male proportion of the population and urban proportion of the population as the covariates. We identified 56 significant spatial clusters for incidence and 26 for CFR for all four waves of the pandemic. The Most Likely Clusters (MLCs) of incidence occurred in the districts in south-eastern Ghana, while the CFR ones occurred in districts in the central and the northeastern parts of the country. These districts could serve as sites for sentinel or genomic surveillance. Spatial relationships were identified between COVID-19 incidence covariates and the CFR. We observed closeness to the epicentre and high proportions of urban populations increased COVID-19 incidence, whiles high proportions of those aged ≥ 65 years increased the CFR. Accounting for the covariates resulted in changes in the distribution of the clusters. Both incidence and CFR due to COVID-19 were spatially clustered, and these clusters were affected by high proportions of the urban population, high proportions of the male population, high proportions of the population aged ≥ 65 years and closeness to the epicentre. Surveillance should target districts with elevated risk. Long-term control measures for COVID-19 and other contagious diseases should consider improving quality healthcare access and measures to reduce growth rates of urban populations.

采用限制似然比的灵活扫描统计,优化 COVID-19 监测。
疾病监测对于早期发现 COVID-19 的新变种仍然非常重要。为此,世界卫生组织(WHO)建议将 COVID-19 监测与其他呼吸道疾病结合起来。这需要了解高风险地区的情况,而发展中国家的常规分析缺乏这方面的知识。本研究以加纳为重点,根据 2020 年 3 月 12 日至 2022 年 2 月 28 日期间加纳四次大流行的报告,采用扫描统计聚类分析来揭示 COVID-19 发病率和病死率(CFR)的空间模式。我们应用灵活的空间扫描统计与限制似然比,在对协变量进行调整之前和之后对发病率和病死率聚类进行了检验。我们将与震中的距离、65 岁以上人口比例、男性人口比例和城市人口比例作为协变量。在大流行的所有四波中,我们确定了 56 个重要的发病率空间集群和 26 个 CFR 空间集群。最有可能的发病集群(MLCs)出现在加纳东南部的地区,而 CFR 集群则出现在该国中部和东北部的地区。这些地区可作为哨点或基因组监测点。我们确定了 COVID-19 发病率协变量与 CFR 之间的空间关系。我们观察到,距离震中越近、城市人口比例越高,COVID-19 的发病率就越高,而年龄≥ 65 岁的人口比例越高,CFR 就越高。考虑协变量后,群集的分布发生了变化。COVID-19的发病率和CFR均呈空间集群分布,这些集群受到城市人口比例高、男性人口比例高、年龄≥65岁的人口比例高以及距离震中较近的影响。应针对风险较高的地区进行监测。针对 COVID-19 和其他传染病的长期控制措施应考虑提高医疗保健服务的质量,并采取措施降低城市人口的增长率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Geospatial Health
Geospatial Health 医学-公共卫生、环境卫生与职业卫生
CiteScore
2.40
自引率
11.80%
发文量
48
审稿时长
12 months
期刊介绍: The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.
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