de Schrijver Evan, Royé Dominic, Gasparrini Antonio, Franco Oscar H, Vicedo-Cabrera Ana M
Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Oeschger Center for Climate Change Research (OCCR), University of Bern, Bern, Switzerland.
Environ Res Health. 2023 Jun 1;1(2):025003-25003. doi: 10.1088/2752-5309/acab78. Epub 2023 Feb 14.
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. PM) 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.
与热和冷相关的死亡风险在不同地理区域差异很大，这表明国家之间和国家内部脆弱性因素的分布存在差异，这在一定程度上可能是由城乡差距导致的。识别这些风险驱动因素对于描述当地脆弱性以及设计针对性的公共卫生干预措施以提高人群对气候变化的适应能力至关重要。我们旨在评估瑞士城市、城郊和农村地区与热和冷相关的死亡风险如何变化，并识别和比较不同区域类型内部和之间与脆弱性增加相关的因素。我们使用病例时间序列设计和分布滞后非线性模型，对瑞士各城市1990 - 2017年期间的日平均气温和全因死亡序列进行分析，估计与热和冷相关的死亡率关联。然后，通过多变量meta回归，我们按类型（即城市/农村/城郊）得出汇总的热和冷死亡率关联，并在大量人口、社会经济、地形、气候、土地利用和其他环境数据中评估潜在的脆弱性因素。城市集群报告的汇总热相关死亡风险更大（在第99百分位数，相对于最低死亡率温度（MMT））（相对风险 = 1.17（95%CI：1.10；1.24），城郊为1.03（1.0%；1.06），农村为1.03（0.99；1.08）），但冷相关死亡风险相似（在第1百分位数，相对于MMT）（1.35（1.28；1.43），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集可以解释不同类型之间的差异风险模式。在城市集群中，主要是环境因素（即PM）导致热死亡率关联的差异，而对于城郊/农村集群，社会经济变量也很重要。对于冷，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是城郊/农村集群中更大脆弱性的其他重要驱动因素，关联方向存在异质性。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热的影响，并且不同的脆弱性因素集可能在每种类型中驱动这些关联。因此，未来的公共卫生适应策略应考虑当地和更具针对性的干预措施，而不是一刀切的方法。 一刀切的方法。
