探索瑞士城市和农村人口易受冷热影响的情况。

Environmental research, health : ERH Pub Date : 2023-06-01 Epub Date: 2023-02-14 DOI:10.1088/2752-5309/acab78
Evan de Schrijver, Dominic Royé, Antonio Gasparrini, Oscar H Franco, Ana M Vicedo-Cabrera
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引用次数: 0

摘要

与热和冷相关的死亡风险在不同地理区域之间存在很大差异,这表明国家之间和国家内部的脆弱性因素分布不同,其部分原因可能是城市与农村之间的差异。确定这些风险驱动因素对于描述当地的脆弱性和设计有针对性的公共卫生干预措施以改善人口对气候变化的适应至关重要。我们的目标是评估瑞士城市、城郊和农村地区的高温和严寒死亡风险是如何变化的,并识别和比较不同地区类型内部和之间与脆弱性增加相关的因素。我们采用病例时间序列设计和分布式滞后非线性模型,对 1990-2017 年间瑞士各城市的日平均气温和全因死亡率序列进行了热与冷相关死亡率的估算。然后,通过多元元回归,我们得出了按类型(即城市/农村/城郊)划分的集合高温和严寒死亡率关联,并评估了大量人口、社会经济、地形、气候、土地利用和其他环境数据中的潜在脆弱性因素。城市群报告了更大的与热相关的集合死亡风险(第 99 百分位数,与最低死亡温度(MMT)相比)(相对风险=1.17(95%CI:1.10;1.24, vs 城郊为 1.03(1.00;1.06), 农村为 1.03 (0.99;1.08) ),但与寒冷相关死亡风险(第 1 百分位数,与最低死亡温度相比)相似(1.35(1.28;1.43), vs 农村为 1.28(1.14;1.44), 城郊为 1.39 (1.27-1.53))。我们发现不同的脆弱性因素可以解释不同类型的风险模式。在城市群中,主要是环境因素(即 PM2.5)导致了高温与死亡之间的关联差异,而在城郊/农村群中,社会经济变量也很重要。就寒冷而言,社会经济变量驱动了所有类型的脆弱性变化,而环境因素和老龄化则是城郊/农村集群脆弱性增加的其他重要驱动因素,但两者的关联方向存在差异。我们的研究结果表明,与农村地区相比,瑞士的城市人口可能更容易受到高温的影响,而在每种类型中,不同的脆弱性因素可能会驱动这些关联。因此,未来的公共卫生适应战略应考虑因地制宜的干预措施,而不是一刀切。
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Exploring vulnerability to heat and cold across urban and rural populations in Switzerland.

Heat- and cold-related mortality risks are highly variable across different geographies, suggesting a differential distribution of vulnerability factors between and within countries, which could partly be driven by urban-to-rural disparities. Identifying these drivers of risk is crucial to characterize local vulnerability and design tailored public health interventions to improve adaptation of populations to climate change. We aimed to assess how heat- and cold-mortality risks change across urban, peri-urban and rural areas in Switzerland and to identify and compare the factors associated with increased vulnerability within and between different area typologies. We estimated the heat- and cold-related mortality association using the case time-series design and distributed lag non-linear models over daily mean temperature and all-cause mortality series between 1990-2017 in each municipality in Switzerland. Then, through multivariate meta-regression, we derived pooled heat and cold-mortality associations by typology (i.e. urban/rural/peri-urban) and assessed potential vulnerability factors among a wealth of demographic, socioeconomic, topographic, climatic, land use and other environmental data. Urban clusters reported larger pooled heat-related mortality risk (at 99th percentile, vs. temperature of minimum mortality (MMT)) (relative risk=1.17(95%CI:1.10;1.24, vs peri-urban 1.03(1.00;1.06), and rural 1.03 (0.99;1.08)), but similar cold-mortality risk (at 1st percentile, vs. MMT) (1.35(1.28;1.43), vs rural 1.28(1.14;1.44) and peri-urban 1.39 (1.27-1.53)) clusters. We found different sets of vulnerability factors explaining the differential risk patterns across typologies. In urban clusters, mainly environmental factors (i.e. PM2.5) drove differences in heat-mortality association, while for peri-urban/rural clusters socio-economic variables were also important. For cold, socio-economic variables drove changes in vulnerability across all typologies, while environmental factors and ageing were other important drivers of larger vulnerability in peri-urban/rural clusters, with heterogeneity in the direction of the association. Our findings suggest that urban populations in Switzerland may be more vulnerable to heat, compared to rural locations, and different sets of vulnerability factors may drive these associations in each typology. Thus, future public health adaptation strategies should consider local and more tailored interventions rather than a one-size fits all approach. size fits all approach.

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