Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Unité d'Épidémiologie Populationnelle, Geneva University Hospitals, Geneva, Switzerland.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Epidemiology, Gillings School of Global Public Health, and University of North Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Lancet Glob Health. 2022 Jun;10(6):e831-e839. doi: 10.1016/S2214-109X(22)00007-9. Epub 2022 Apr 21.
Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality.
Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010-16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation).
24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding.
Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate.
US National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation.
在撒哈拉以南非洲（SSA），霍乱仍然是一个主要威胁，那里报告的病死率最高。了解整个非洲大陆在哪些月份和地方容易发生霍乱，可以帮助改善消除霍乱作为公共卫生关注的努力。然而，由于缺乏统一的大规模数据集，目前还没有全大陆范围的估计。在这项研究中，我们旨在估计 SSA 范围内的霍乱季节性，并探讨水文气象变量与霍乱季节性之间的相关性。
利用全球霍乱控制工作队的全球霍乱数据库，我们开发了统计模型，以综合跨时空尺度的数据，推断 SSA 疑似霍乱发生的过剩（定义为发病率高于 2010-16 年平均发病率）的季节性。我们开发了一个贝叶斯统计模型，以推断第一和第二行政级别过剩霍乱的每月风险。然后将季节性模式分组为空间聚类。最后，我们研究了季节性估计值与水文气象变量（每月平均洪水面积比例、每月平均空气温度和每月累计降水量）之间的关联。
在所研究的 34 个国家中，有 24 个（71%）国家存在过剩霍乱风险的季节性模式，约占 SSA 人口的 86%。在这 24 个国家中，有 12 个（50%）国家还存在季节性模式的国家级差异，区域之间的季节性强度差异很大。季节性模式聚类为两个大区（西非和萨赫勒地区与东部和南部非洲），这些大区由具有不同季节性程度的次区域聚类组成。探索性关联分析发现，霍乱季节性与降水之间存在最一致和积极的相关性，其次是与温度和洪水之间的相关性。
SSA 广泛存在的霍乱季节性为干预规划提供了机会。需要进一步研究霍乱与气候之间的关系。
美国国家航空航天局应用科学计划和比尔和梅琳达盖茨基金会。
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