Mistry Malcolm N, Gasparrini Antonio
Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Department of Economics, Ca' Foscari University of Venice, Venice, Italy.
Environ Res Health. 2024 Jul 18;2(3):035011. doi: 10.1088/2752-5309/ad5f51.
The development of innovative tools for real-time monitoring and forecasting of environmental health impacts is central to effective public health interventions and resource allocation strategies. Though a need for such generic tools has been previously echoed by public health planners and regional authorities responsible for issuing anticipatory alerts, a comprehensive, robust and scalable real-time system for predicting temperature-related excess deaths at a local scale has not been developed yet. Filling this gap, we propose a flexible operational framework for coupling publicly available weather forecasts with temperature-mortality risk functions specific to small census-based zones, the latter derived using state-of-the-art environmental epidemiological models. Utilising high-resolution temperature data forecast by a leading European meteorological centre, we demonstrate a real-time application to forecast the excess mortality during the July 2022 heatwave over England and Wales. The output, consisting of expected temperature-related excess deaths at small geographic areas on different lead times, can be automated to generate maps at various spatio-temporal scales, thus facilitating preventive action and allocation of public health resources in advance. While the real-case example discussed here demonstrates an application for predicting (expected) heat-related excess deaths, the framework can also be adapted to other weather-related health risks and to different geographical areas, provided data on both meteorological exposure and the underlying health outcomes are available to calibrate the associated risk functions. The proposed framework addresses an urgent need for predicting the short-term environmental health burden on public health systems globally, especially in low- and middle-income regions, where rapid response to mitigate adverse exposures and impacts to extreme temperatures are often constrained by available resources.
开发用于实时监测和预测环境健康影响的创新工具，是有效的公共卫生干预措施和资源分配策略的核心。尽管公共卫生规划者和负责发布预警的地区当局此前曾表达过对这类通用工具的需求，但尚未开发出一个全面、强大且可扩展的用于在地方层面预测与温度相关的超额死亡人数的实时系统。为填补这一空白，我们提出了一个灵活的操作框架，将公开可用的天气预报与基于小普查区特定的温度-死亡率风险函数相结合，后者是使用最先进的环境流行病学模型得出的。利用欧洲一家领先气象中心预测的高分辨率温度数据，我们展示了一个实时应用案例，用于预测2022年7月英格兰和威尔士热浪期间的超额死亡率。输出结果包括不同提前期在小地理区域内与温度相关的预期超额死亡人数，可自动生成各种时空尺度的地图，从而便于提前采取预防行动和分配公共卫生资源。虽然这里讨论的实际案例展示了预测（预期）与高温相关的超额死亡人数的应用，但只要有气象暴露和潜在健康结果的数据来校准相关风险函数，该框架也可适用于其他与天气相关的健康风险以及不同的地理区域。所提出的框架满足了全球公共卫生系统预测短期环境健康负担的迫切需求，特别是在低收入和中等收入地区，这些地区对减轻极端温度的不利暴露和影响的快速反应往往受到可用资源的限制。
