Department of Public Health and Clinical Medicine, Sustainable Health Section, Umeå University, Umea, Sweden
Eduardo Mondlane University, Maputo, Mozambique.
BMJ Open. 2024 Aug 19;14(8):e082503. doi: 10.1136/bmjopen-2023-082503.
This study aims to assess both socioeconomic and climatic factors of cholera morbidity in Mozambique considering both spatial and temporal dimensions.
An ecological longitudinal retrospective study using monthly provincial cholera cases from Mozambican Ministry of Health between 2000 and 2018. The cholera cases were linked to socioeconomic data from Mozambique Demographic and Health Surveys conducted in the period 2000-2018 and climatic data; relative humidity (RH), mean temperature, precipitation and Normalised Difference Vegetation Index (NDVI). A negative binomial regression model in a Bayesian framework was used to model cholera incidence while adjusting for the spatiotemporal covariance, lagged effect of environmental factors and the socioeconomic indicators.
Eleven provinces in Mozambique.
Over the 19-year period, a total of 153 941 cholera cases were notified to the surveillance system in Mozambique. Risk of cholera increased with higher monthly mean temperatures above 24°C in comparison to the reference mean temperature of 23°C. At mean temperature of 19°C, cholera risk was higher at a lag of 5-6 months. At a shorter lag of 1 month, precipitation of 223.3 mm resulted in an 57% increase in cholera risk (relative risk, RR 1.57 (95% CI 1.06 to 2.31)). Cholera risk was greatest at 3 lag months with monthly NDVI of 0.137 (RR 1.220 (95% CI 1.042 to 1.430)), compared with the reference value of 0.2. At an RH of 54%, cholera RR was increased by 62% (RR 1.620 (95% CI 1.124 to 2.342)) at a lag of 4 months. We found that ownership of radio RR 0.29, (95% CI 0.109 to 0.776) and mobile phones RR 0.262 (95% CI 0.097 to 0.711) were significantly associated with low cholera risk.
The derived lagged patterns can provide appropriate lead times in a climate-driven cholera early warning system that could contribute to the prevention and management of outbreaks.
本研究旨在评估莫桑比克霍乱发病率的社会经济和气候因素,同时考虑到时空维度。
这是一项基于生态的纵向回顾性研究,使用了 2000 年至 2018 年期间莫桑比克卫生部每月的省级霍乱病例数据。将霍乱病例与 2000-2018 年期间进行的莫桑比克人口与健康调查中的社会经济数据以及气候数据(相对湿度(RH)、平均温度、降水和归一化差异植被指数(NDVI))相关联。使用贝叶斯框架中的负二项回归模型来对霍乱发病率进行建模,同时调整时空协方差、环境因素的滞后效应以及社会经济指标。
莫桑比克的 11 个省。
在 19 年期间,向莫桑比克监测系统报告了总共 153941 例霍乱病例。与参考平均温度 23°C 相比,高于 24°C 的每月平均温度会增加霍乱的风险。在平均温度为 19°C 时,滞后 5-6 个月时霍乱风险更高。在更短的 1 个月滞后时间内,223.3mm 的降水量会导致霍乱风险增加 57%(相对风险,RR 1.57(95% CI 1.06 至 2.31))。与参考值 0.2 相比,NDVI 为 0.137(RR 1.220(95% CI 1.042 至 1.430))时,3 个月滞后的霍乱风险最大。在 RH 为 54%时,滞后 4 个月时,霍乱的 RR 增加了 62%(RR 1.620(95% CI 1.124 至 2.342))。我们发现,拥有收音机(RR 0.29,95% CI 0.109 至 0.776)和移动电话(RR 0.262,95% CI 0.097 至 0.711)与较低的霍乱风险显著相关。
推断出的滞后模式可以为气候驱动的霍乱早期预警系统提供适当的提前期,有助于预防和管理暴发。