首页> 外文会议>Joint annual meeting of the International Society of Exposure Science and the International Society for Environmental Epidemiology >Long-Term Exposure to Low Level Air Pollution in Sydney and Mortality and Hospital Admission Using the '45 and Up' Cohort: Methodological Challenges
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Long-Term Exposure to Low Level Air Pollution in Sydney and Mortality and Hospital Admission Using the '45 and Up' Cohort: Methodological Challenges

机译:使用'45和UP'队列的悉尼和死亡率和医院入学的长期暴露在悉尼和死亡率和医院入学中:方法论挑战

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We used the '45 and Up' cohort study of older (45+ years) residents of New South Wales, Australia, to investigate the health efffects of long term air pollution exposure. Individual level data was collected on 267,153 subjects via baseline questionnaire mostly completed in 2008 and these data were linked to routinely collected death registrations data (up to 2015) and hospital admissions data (up to 2014) via personal identifiers (including age, sex, residential address). Neighbourhood level socio-economic status data was liked to study subjects residential address. Subjects residential address was used to estimated annual average NO2 exposure using a satellite based land use regression model for 2007 for all study subjects, while Chemical Transport Model data blended with fixed site monitor data for 2010/11 was used to estimate annual average PM2.5 exposure for subjects in the Sydney metropolitan area. We implemented Cox proportional hazards models to assess the associations between air pollution exposure and all-cause mortality, and respiratory hospital admissions while adjusting for individual level socio-demographic and behavioral factors, as well as area-level factors. Annual average PM2.5 and NO2 exposure was 4.5 μg/m3 and 17.5 μg/m3 respectively. Covariate adjusted models found that mortality and respiratory hospital admissions were associated with PM2.5 and NO2 (mortality Hazard Ratios (HR)=1.06 (95% CI: 1.00-1.11), asthma admissions HR=1.08 (95% CI: 0.89 -1.30), bronchitis admissions HR=1.32 (95%CI: 0.96 -1.80) per 1 u.g/m3 increase in PM2.5 respectively). Increasing model complexity generally reduced the magnitude of the HR point estimates and the strength of the associations but may have resulted in over control for covariate effects. Even at the relatively low level long term air pollution exposures generally seen in Sydney, and with limited follow up, air pollution exposure within our cohort had a detrimental effect on mortality and hospitalisation.
机译:我们使用了澳大利亚新南威尔士州的较老年(45岁)居民的“45和Up”队列研究,探讨了长期空气污染暴露的健康效果。通过2008年主要完成的基线调查问卷收集了个人级别数据,这些数据与经常收集的死亡登记数据(最多2015年)和医院招生数据(高达2014年)通过个人标识符(包括年龄,性别,性别,住宅地址)。邻里级别社会经济地位数据被人喜欢研究科目的住宿地址。对于所有研究科目,使用2007年基于卫星的土地利用回归模型来估计年平均No2曝光的年平均No2曝光,而2010/11的固定网站监测数据混合的化学传输模型数据用于估计年平均PM2.5悉尼大都市区受试者的暴露。我们实施了Cox比例危险模型,以评估空气污染暴露和全因死亡率之间的协会,以及调整个人级别社会人口和行为因素的同时,以及地区级别因素。年平均PM2.5和NO2暴露分别为4.5μg/ m3和17.5μg/ m3。调节模型发现,死亡率和呼吸医院入院与PM2.5和NO2有关(死亡危害比(HR)= 1.06(95%CI:1.00-1.11),哮喘录取HR = 1.08(95%CI:0.89 -1.30 ),支气管炎入院HR = 1.32(95%CI:0.96 -1.80)每1 UG / M3分别增加PM2.5)。模型复杂性的增加通常降低了HR点估计的大小和关联的强度,但可能导致对协变量的控制进行控制。即使在悉尼普遍看过的相对较低的长期空气污染暴露,并且随着有限的跟进,我们的队列内的空气污染暴露对死亡率和住院都有不利影响。

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