Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
Infectious and Tropical Disease Unit, Cayenne Hospital, 97306 Cayenne, French Guiana.
Int J Environ Res Public Health. 2021 Jan 26;18(3):1077. doi: 10.3390/ijerph18031077.
This study examines the dynamics of malaria as influenced by meteorological factors in French Guiana from 2005 to 2019. It explores spatial hotspots of malaria transmission and aims to determine the factors associated with variation of hotspots with time. : Data for individual malaria cases came from the surveillance system of the Delocalized Centers for Prevention and Care (CDPS) ( = 17) from 2005-2019. Meteorological data was acquired from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) database. The Box-Jenkins autoregressive integrated moving average (ARIMA) model tested stationarity of the time series, and the impact of meteorological indices (issued from principal component analysis-PCA) on malaria incidence was determined with a general additive model. Hotspot characterization was performed using spatial scan statistics. : The current sample includes 7050 eligible ( = 4111) and ( = 2939) cases from health centers across French Guiana. The first and second PCA-derived meteorological components (maximum/minimum temperature/minimum humidity and maximum humidity, respectively) were significantly negatively correlated with total malaria incidence with a lag of one week and 10 days, respectively. Overall malaria incidence decreased across the time series until 2017 when incidence began to trend upwards. Hotspot characterization revealed a few health centers that exhibited spatial stability across the entire time series: Saint Georges de l'Oyapock and Antecume Pata for , and Saint Georges de l'Oyapock, Antecume Pata, Régina and Camopi for : This study highlighted changing malaria incidence in French Guiana and the influences of meteorological factors on transmission. Many health centers showed spatial stability in transmission, albeit not temporal. Knowledge of the areas of high transmission as well as how and why transmission has changed over time can inform strategies to reduce the transmission of malaria in French Guiana. Hotspots should be further investigated to understand other influences on local transmission, which will help to facilitate elimination.
本研究考察了 2005 年至 2019 年期间法属圭亚那的气象因素对疟疾动态的影响。探讨了疟疾传播的空间热点，并旨在确定与热点随时间变化相关的因素。
来自 2005-2019 年的 Delocalized Centers for Prevention and Care (CDPS) 监测系统的数据（=17）用于个别疟疾病例。气象数据来自美国宇航局戈达德地球科学数据与信息服务中心 (GES DISC) 数据库。Box-Jenkins 自回归综合移动平均 (ARIMA) 模型检验了时间序列的平稳性，并用广义加性模型确定了气象指数（来自主成分分析-PCA）对疟疾发病率的影响。使用空间扫描统计对热点进行了特征描述。
目前的样本包括来自法属圭亚那各地卫生中心的 7050 例合格病例（=4111）和 （=2939）。第一和第二 PCA 衍生的气象分量（最高/最低温度/最小湿度和最大湿度）与总疟疾发病率呈显著负相关，滞后时间分别为一周和十天。整个时间序列中疟疾发病率呈下降趋势，直到 2017 年发病率开始呈上升趋势。热点特征显示，少数卫生中心在整个时间序列中表现出空间稳定性：圣乔治德洛亚波克和安特库姆帕塔（=4111），以及圣乔治德洛亚波克、安特库姆帕塔、雷吉纳和卡莫皮（=2939）。
本研究强调了法属圭亚那疟疾发病率的变化以及气象因素对传播的影响。许多卫生中心的传播具有空间稳定性，尽管没有时间稳定性。了解高传播地区以及传播随时间变化的原因和方式，可以为减少法属圭亚那疟疾传播提供策略。还应进一步研究热点，以了解对当地传播的其他影响，这将有助于促进消除。
