Lo Iacono Giovanni, Armstrong Ben, Fleming Lora E, Elson Richard, Kovats Sari, Vardoulakis Sotiris, Nichols Gordon L
Chemical and Environmental Effects Department, Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton, United Kingdom.
Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, United Kingdom.
PLoS Negl Trop Dis. 2017 Jun 12;11(6):e0005659. doi: 10.1371/journal.pntd.0005659. eCollection 2017 Jun.
Infectious diseases attributable to unsafe water supply, sanitation and hygiene (e.g. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. There have been little critical analyses of the methodological approaches. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We found 102 full text papers that met our criteria and were included in the analysis. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known (e.g. Vibrio cholerae); TS-SE tended to be used when the specific environmental mechanisms were unclear (e.g. Campylobacter). Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. The most common limitations comprised: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. Furthermore, the methods often did not distinguish among the multiple sources of time-lags (e.g. patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Key areas of future research include: disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), and linking weather data to individual cases longitudinally.
不安全的供水、环境卫生和个人卫生导致的传染病（如霍乱、钩端螺旋体病、贾第虫病）仍然是发病和死亡的重要原因，尤其是在低收入国家。已知气候和天气因素会影响传染病的传播和分布，统计和数学建模也在不断发展，以研究天气和气候对与水相关疾病的影响。目前对这些方法的批判性分析很少。我们的目标是回顾和总结用于研究天气和气候对与水相关传染病影响的统计和建模方法，以确定新方法开发中的局限性和知识空白。我们对2000年至2015年发表的英文论文进行了系统综述。检索词包括与水相关疾病、天气和气候、统计、流行病学和建模方法相关的概念。我们发现102篇全文论文符合我们的标准并纳入分析。最常用的方法分为两类：基于过程的模型（PBM）和时间序列与空间流行病学（TS-SE）。一般来说，当所研究病原体的生物物理机制相对清楚时（如霍乱弧菌），采用PBM方法；当具体环境机制不明确时（如弯曲杆菌），倾向于使用TS-SE方法。出现了重要的数据和方法挑战，对与水相关感染的监测和控制产生了影响。最常见的局限性包括：未纳入关键因素（如生物机制、人口统计学异质性、人类行为）、报告偏倚、数据质量差以及暴露因素的共线性。此外这些方法往往没有区分环境驱动因素/暴露因素与疾病检测之间多种时间滞后来源（如患者生理状况、报告偏倚、医疗服务可及性）。未来研究的关键领域包括：理清天气/气候对每条暴露-健康结果途径（如人传人 vs 环境传人）的复杂影响，以及纵向将天气数据与个体病例相联系。
