PNAS：研究揭示气候变化与儿童营养不良关系

营养不良严重威胁人类健康和社会发展。世界卫生组织和政府间气候变化委员会都指出，营养不良是气候变化对人类健康最显著的影响之一。但支持该观点的实验性证据来源于哪里?目前，有关气候对儿童营养不良影响的证据主要出自数量有限的异构研究，并且主要基于二手数据。另外，建立和验证复杂的混杂因素之间的因果路径仍为量化气候导致的营养不良的主要挑战。

为了应对这些挑战，德国海德堡大学公共卫生研究所的Revati K. Phalkey及其同事评议了15个探索气候/天气变化性、农作物产量和营养不良之间联系的研究。其中，12个研究报告了在天气变量与儿童期营养不良之间的一种显著联系，而8个研究报告了暴露在诸如干旱或洪水等极端天气事件与营养不良之间的显著相关性。另外，研究人员表示，得到研究的其他因素还包括温度、农田面积、农作物产量、家庭尺寸的效应、社会—经济状态和人口统计学变量。

作者说，尽管这些研究中有80%报告了一个或多个天气变量与营养不良之间具有一种相关性，但为了验证气候变化对儿童的健康和成长的影响，特别是依赖于自给农业的家庭，需要进行基于农业、环境、社会经济和健康相关因素等一系列因素的长期原始数据的进一步研究。

原文标题：Systematic review of current efforts to quantify the impacts of climate change on undernutrition

原文摘要：Malnutrition is a challenge to the health and productivity of populations and is viewed as one of the five largest adverse health impacts of climate change. Nonetheless, systematic evidence quantifying these impacts is currently limited. Our aim was to assess the scientific evidence base for the impact of climate change on childhood undernutrition (particularly stunting) in subsistence farmers in low- and middle-income countries. A systematic review was conducted to identify peer-reviewed and gray full-text documents in English with no limits for year of publication or study design. Fifteen manuscripts were reviewed. Few studies use primary data to investigate the proportion of stunting that can be attributed to climate/weather variability. Although scattered and limited, current evidence suggests a significant but variable link between weather variables, e.g., rainfall, extreme weather events (floods/droughts), seasonality, and temperature, and childhood stunting at the household level (12 of 15 studies, 80%). In addition, we note that agricultural, socioeconomic, and demographic factors at the household and individual levels also play substantial roles in mediating the nutritional impacts. Comparable interdisciplinary studies based on primary data at a household level are urgently required to guide effective adaptation, particularly for rural subsistence farmers. Systemization of data collection at the global level is indispensable and urgent. We need to assimilate data from long-term, high-quality agricultural, environmental, socioeconomic, health, and demographic surveillance systems and develop robust statistical methods to establish and validate causal links, quantify impacts, and make reliable predictions that can guide evidence-based health interventions in the future.

作者：秩名