Spatiotemporal distribution of cutaneous leishmaniasis in Sri Lanka and future case burden estimates.
Spatiotemporal distribution of cutaneous leishmaniasis in Sri Lanka and future case burden estimates.
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DOI：
10.1371/journal.pntd.0009346
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发表时间：
2021-04
影响因子：
3.8
通讯作者：
Zhou G
中科院分区：
文献类型：
--
作者：
Karunaweera ND;Senanayake S;Ginige S;Silva H;Manamperi N;Samaranayake N;Dewasurendra R;Karunanayake P;Gamage D;de Silva N;Senarath U;Zhou G
利什曼病是一种被忽视的热带媒介传播疾病，在斯里兰卡呈上升趋势。时空和危险因素分析有助于了解传播动态、空间聚集和预测未来疾病分布和趋势，以促进有效的感染控制。收集2001年至2019年全国临床确诊的皮肤利什曼病和气候资料。系统聚类法和时空互相关分析用于衡量传播的区域范围和局部(相邻地区之间)的同步性。建立了一个混合时空回归-自回归模型，研究气候、邻近地区扩散和感染携带变量对利什曼病流行动态和空间分布的影响。使用不含气候变量的相同模型预测斯里兰卡未来利什曼病病例的分布和趋势。2001-2019年，斯里兰卡共报告了19361例临床确诊的利什曼病病例。确定了三个阶段：低传播阶段(2001-2010年)、寄生虫种群积累阶段(2011-2017年)和暴发阶段(2018-2019年)。在空间上，根据时间动力学上的相似性将这些区域划分为三个组。各地区发病动态的全球平均相关系数为0.30(95%CI 0.25~0.35)，相邻地区间的局部平均相关系数为0.58(95%CI 0.42~0.73)。对发病率最高的7个区进行的风险分析表明，降雨量、邻区效应和感染携带效应与区级发病动态有显著相关性。除2018年暴发外，模型预测的发病动态和病例分布与观察结果吻合较好。模型预测2020年病例数约为5400例，传播加剧，高传区扩大。预计2022年病例数为9,115例，2025年为19212例。过去几年斯里兰卡利什曼病病例急剧增加是史无前例的，这与降雨量、局部感染的高负担和区域间传播密切相关。迫切需要有针对性的干预措施来阻止无法控制的疾病传播。与南亚其他地区的下降趋势形成对比的是，斯里兰卡的利什曼病呈上升趋势。时空分析和疾病风险因素有助于了解传播机制和预测未来的疾病分布，以便于控制。在这项研究中，我们分析了2001年至2019年斯里兰卡皮肤利什曼病病例的数据。我们就利什曼病强化传播背后的驱动力提出了三个重要问题：1)不同地区的传播动态是否同步？2)邻近地区的传播在形成传播动态中所起的作用是什么？3)气候变化在传播动态中的重要性有多大？我们使用了一个多步骤的方法来回答这些问题。除了交叉相关分析外，我们还建立了时空回归-自回归混合模型来分析危险因素，这在利什曼病研究中是独一无二的，因为简化的模型也有助于预测未来的疾病分布。我们发现，根据相似性，不同地区的发病动态可分为三个同步组。危险因素分析表明，降水、邻近地区扩散和局部感染携带在形成传播动态中起着重要作用。时空模型预测，随着病例数量的增加，传播加剧，高传播区扩大。迫切需要有针对性的干预措施来遏制疫情。
Leishmaniasis is a neglected tropical vector-borne disease, which is on the rise in Sri Lanka. Spatiotemporal and risk factor analyses are useful for understanding transmission dynamics, spatial clustering and predicting future disease distribution and trends to facilitate effective infection control. The nationwide clinically confirmed cutaneous leishmaniasis and climatic data were collected from 2001 to 2019. Hierarchical clustering and spatiotemporal cross-correlation analysis were used to measure the region-wide and local (between neighboring districts) synchrony of transmission. A mixed spatiotemporal regression-autoregression model was built to study the effects of climatic, neighboring-district dispersal, and infection carryover variables on leishmaniasis dynamics and spatial distribution. Same model without climatic variables was used to predict the future distribution and trends of leishmaniasis cases in Sri Lanka. A total of 19,361 clinically confirmed leishmaniasis cases have been reported in Sri Lanka from 2001–2019. There were three phases identified: low-transmission phase (2001–2010), parasite population buildup phase (2011–2017), and outbreak phase (2018–2019). Spatially, the districts were divided into three groups based on similarity in temporal dynamics. The global mean correlation among district incidence dynamics was 0.30 (95% CI 0.25–0.35), and the localized mean correlation between neighboring districts was 0.58 (95% CI 0.42–0.73). Risk analysis for the seven districts with the highest incidence rates indicated that precipitation, neighboring-district effect, and infection carryover effect exhibited significant correlation with district-level incidence dynamics. Model-predicted incidence dynamics and case distribution matched well with observed results, except for the outbreak in 2018. The model-predicted 2020 case number is about 5,400 cases, with intensified transmission and expansion of high-transmission area. The predicted case number will be 9115 in 2022 and 19212 in 2025. The drastic upsurge in leishmaniasis cases in Sri Lanka in the last few year was unprecedented and it was strongly linked to precipitation, high burden of localized infections and inter-district dispersal. Targeted interventions are urgently needed to arrest an uncontrollable disease spread. Leishmaniasis is on the rise in Sri Lanka in contrast to the declining trend in rest of South Asia. Spatiotemporal analysis and disease risk factors are useful for understanding transmission mechanisms and predicting future disease distribution to facilitate control. In this study we analyzed data on cutaneous leishmaniasis cases from Sri Lanka from 2001 to 2019. We asked three important questions regarding the driving forces behind the intensified leishmaniasis transmission: 1) Are the transmission dynamics in different areas synchronized? 2) What is the role of neighboring-area dispersal in shaping transmission dynamics? 3) How important is climatic variability in transmission dynamics? We used a multi-step approach to answer these questions. In addition to cross-correlation analysis, we built a mixed spatiotemporal regression-autoregression model to analyze risk factors, which is unique in leishmaniasis research because the simplified model was also useful for predicting future disease distribution. We found that the incidence dynamics in different districts could be divided into three synchronized groups based on similarity. Risk factor analysis indicated that precipitation, neighboring-district dispersal, and local infection carryover played important roles in shaping transmission dynamics. The spatiotemporal model predicted intensifying transmission with increasing case numbers, and expansion of high-transmission areas. Targeted interventions are urgently needed to stem the outbreak.
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