Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration–response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM_2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM_2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: −10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM_2.5. Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires.

中文翻译：

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评估 2017 年加州野火期间因火灾引起的 PM2.5 暴露对健康的急性影响：对输入选择的敏感性

暴露于野火烟雾会增加呼吸系统和心血管疾病住院的风险。用于为决策过程提供信息的健康影响评估通过将预先存在的流行病学浓度-反应函数 (CRF) 与暴露估计值相结合来表征环境暴露对健康的影响。这两个关键输入影响估计的健康影响的幅度和不确定性，但对于与野火相关的影响评估，其影响程度在很大程度上是未知的。我们首先估算了由火灾引起的 PM _2.5导致的呼吸、心血管和哮喘住院人数2017 年 10 月野火期间加州中部的暴露，使用蒙特卡罗模拟量化暴露和流行病学输入的不确定性。我们接下来进行了敏感性分析，比较了火灾引起的 PM _{2.5 的}四种估计值和两个 CRF，特定于野火和非野火，以了解它们对估计超额入院和不确定性来源的影响。我们估计火灾导致了超过 240 人（95% CI：114、404）呼吸系统、68（95％CI：-10、159）心血管和 45（95％CI：18、81）哮喘入院，其中 56湾区录取的百分比。尽管影响评估方法之间的差异在统计上并不显着，但录取估计的大小对指定的 CRF 特别敏感，而不确定性对火灾引起的 PM _{2.5 的}估计最敏感. 不考虑暴露面的不确定性会导致对估计的健康影响的不确定性的低估。采用特定环境的 CRF 并使用结合多个数据集的准确暴露估计，可以对野火的急性健康影响产生更确定的估计。