Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review.
Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review.
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DOI：
10.1371/journal.pntd.0005659
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发表时间：
2017-06
影响因子：
3.8
通讯作者：
Nichols GL
中科院分区：
文献类型：
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作者：
Lo Iacono G;Armstrong B;Fleming LE;Elson R;Kovats S;Vardoulakis S;Nichols GL
由不安全的供水、环境卫生和个人卫生引起的传染病（例如霍乱、钩端螺旋体病、贾第虫病）仍然是造成发病率和死亡率的一个重要原因，特别是在低收入国家。众所周知，气候和天气因素会影响传染病的传播和分布，正在不断发展统计和数学模型，以调查天气和气候对与水有关的疾病的影响。对方法论的批判性分析很少。我们的目标是审查和总结用于调查天气和气候对与水有关的传染病的影响的统计和建模方法，以便确定在开发新方法方面的局限性和知识差距。我们对2000年至2015年发表的英文论文进行了系统回顾。搜索词包括与水有关的疾病、天气和气候、统计、流行病学和建模方法有关的概念。我们找到了102篇符合我们标准的全文论文，并纳入了分析。最常用的方法分为两类：基于过程的模型（PBM）和时间序列和空间流行病学（TS-SE）。一般来说，当所研究的病原体（如霍乱弧菌）的生物物理机制相对清楚时，采用PBM方法；当具体的环境机制不清楚时（如弯曲杆菌），倾向于使用TS-SE。出现了重要的数据和方法挑战，对监测和控制与水有关的感染产生影响。最常见的限制包括：未纳入关键因素（如生物机制、人口异质性、人类行为）、报告偏倚、数据质量差和暴露共线性。此外，这些方法往往没有区分环境驱动因素/暴露与疾病检测之间的多重时滞来源（例如患者生理、报告偏差、医疗保健获取）。未来研究的关键领域包括：解开天气/气候对每个暴露-健康结果途径的复杂影响（例如人与人对环境对人），并将天气数据与个人病例纵向联系起来。不安全的供水、有限的环境卫生和不良的个人卫生仍然是传染病（如霍乱、钩端螺旋体病、贾第虫病）的重要原因，特别是在低收入国家。气候和天气影响传染病的传播和分布。因此，科学家们正在不断开发新的分析方法，以调查天气和气候对传染病的影响，特别是对与水有关的疾病的影响。由于这些方法是基于对现实世界的不完美表现，它们不可避免地受到许多挑战。基于对文献的系统回顾，我们确定了开发新分析方法的科学家面临的七个重要挑战。
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. Unsafe water supplies, limited sanitation and poor hygiene are still important causes of infectious disease (e.g. Cholera, Leptospirosis, Giardiasis), especially in low-income countries. Climate and weather affect the transmission and distribution of infectious diseases. Therefore, scientists are continuously developing new analysis methods to investigate the impacts of weather and climate on infectious disease, and particularly, on those associated with water. As these methods are based on an imperfect representation of the real world, they are inevitably subjected to many challenges. Based on a systematic review of the literature, we identified seven important challenges for scientists who develop new analysis methods.