Abdulsalam Fatima Ibrahim, Antunez Pablo, Yimthiang Supabhorn, Jawjit Warit
Environmental, Safety Technology and Health Program, School of Public Health, Walailak University, Thasala, Nakhon Si Thammarat, Thailand.
División de Estudios de Postgrado, Universidad de la Sierra Juárez, Ixtlán de Juárez, Oaxaca, México.
PLOS Glob Public Health. 2022 Apr 20;2(4):e0000188. doi: 10.1371/journal.pgph.0000188. eCollection 2022.
The 3-5year epidemic cycle of dengue fever in Thailand makes it a major re-emerging public health problem resulting in being a burden in endemic areas. Although the Thai Ministry of Public Health adopted the WHO dengue control strategy, all dengue virus serotypes continue to circulate. Health officers and village health volunteers implement some intervention options but there is a need to ascertain most appropriate (or a combination of) interventions regarding the environment and contextual factors that may undermine the effectiveness of such interventions. This study aims to understand the dengue-climate relationship patterns at the district level in the southern region of Thailand from 2002 to 2018 by examining the statistical association between dengue incidence rate and eight environmental patterns, testing the hypothesis of equal incidence of these. Data on environmental variables and dengue reported cases in Nakhon Si Thammarat province situated in the south of Thailand from 2002 to 2018 were analysed to (1) detect the environmental factors that affect the risk of dengue infectious disease; to (2) determine if disease risk is increasing or decreasing over time; and to (3) identify the high-risk district areas for dengue cases that need to be targeted for interventions. To identify the predictors that have a high and significant impact on reported dengue infection, three steps of analysis were used. First, we used Partial Least Squares (PLS) Regression and Poisson Regression, a variant of the Generalized Linear Model (GLM). Negative co-efficient in correspondence with the PLS components suggests that sea-level pressure, wind speed, and pan evaporation are associated with dengue occurrence rate, while other variables were positively associated. Using the Akaike information criterion in the stepwise GLM, the filtered predictors were temperature, precipitation, cloudiness, and sea level pressure with the standardized coefficients showing that the most influential variable is cloud cover (three times more than temperature and precipitation). Also, dengue occurrence showed a constant negative response to the average increase in sea-level pressure values. In southern Thailand, the predictors that have been locally determined to drive dengue occurrence are temperature, rainfall, cloud cover, and sea-level pressure. These explanatory variables should have important future implications for epidemiological studies of mosquito-borne diseases, particularly at the district level. Predictive indicators guide effective and dynamic risk assessments, targeting pre-emptive interventions.
泰国登革热3至5年的流行周期使其成为一个主要的重新出现的公共卫生问题，给流行地区带来负担。尽管泰国公共卫生部采用了世界卫生组织的登革热控制策略，但所有登革热病毒血清型仍在传播。卫生官员和乡村卫生志愿者实施了一些干预措施，但有必要确定与环境和背景因素相关的最适当（或组合）干预措施，这些因素可能会削弱此类干预措施的有效性。本研究旨在通过研究登革热发病率与八种环境模式之间的统计关联，检验这些模式发病率相等的假设，来了解2002年至2018年泰国南部地区县级层面的登革热与气候的关系模式。分析了泰国南部那空是拉差府2002年至2018年的环境变量和登革热报告病例数据，以（1）检测影响登革热传染病风险的环境因素；（2）确定疾病风险随时间是增加还是减少；以及（3）确定需要针对干预措施的登革热病例高风险地区。为了确定对报告的登革热感染有高度显著影响的预测因素，使用了三个分析步骤。首先，我们使用了偏最小二乘（PLS）回归和泊松回归，这是广义线性模型（GLM）的一种变体。与PLS成分对应的负系数表明，海平面气压、风速和蒸发皿蒸发量与登革热发病率相关，而其他变量呈正相关。在逐步GLM中使用赤池信息准则，筛选出的预测因素是温度、降水、云量和海平面气压，标准化系数表明最有影响的变量是云量（比温度和降水大三倍）。此外，登革热发病率对海平面气压值的平均增加呈持续负反应。在泰国南部，当地确定的驱动登革热发生的预测因素是温度（原文为“降雨”，根据前文逻辑此处应为“温度”）、降雨、云量和海平面气压。这些解释变量对蚊媒疾病的流行病学研究，特别是在县级层面，应该具有重要的未来意义。预测指标指导有效的动态风险评估，以进行先发制人的干预。
