首页> 外文会议>Joint annual meeting of the International Society of Exposure Science and the International Society for Environmental Epidemiology >Use of Geocoding to Understand Variation in Neighborhood Socioeconomic Status in a Nationwide Occupational Cohort across Time, Space and Demographic Characteristics
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Use of Geocoding to Understand Variation in Neighborhood Socioeconomic Status in a Nationwide Occupational Cohort across Time, Space and Demographic Characteristics

机译:使用地理编码了解全国职业队列中跨时空和人口统计学特征的邻里社会经济地位的变化

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Geocoding has been used in environmental epidemiology to understand geographically patterned exposures but has not been widely applied in occupational studies. We present the feasibility of geocoding a nationwide occupational cohort, the US Radiologic Technologists Study (USRT, N=146,021) to: (1) evaluate geocoding across time, (2) test precision of geocoding by rural (vs. urban) status and by neighborhood socioeconomic status (nSES), and (3) explore nSES distribution by some cohort demographic factors. We created a standardized nSES index, based on validated methods, for each census block-group in the US, at 3 time points, using 6 items from the U.S. Census (1990, 2000) and American Community Survey (2010): regionally-adjusted median household income and median housing value, % households with interest/income, % adults who completed high school, % adults who completed college, and % employed persons in managerial occupations. To link nSES to the cohort, we geocoded respondent addresses in 1992, 2003, 2010, to block-group of residence. Over 99% of addresses were geocoded; 76 to 86% were mapped to an address-specific block-group, while the rest were geocoded to a block-group of the zipcode centroid. Urban (vs. rural) addresses and those in the highest (vs. lowest) nSES quintile were more likely to be geocoded to an address-specific block-group (OR=4.6; 95%CI=4.5-4.7 and OR=6.4; 95%CI=6.1-6.7, respectively for 1992 addresses). Results of linear regression show higher nSES in 1990 was associated with USRT participants of female gender (3=0.22; 95%CI=0.20, 0.23), older age (3=0.003; 95%CI=0.002, 0.003), white race (3=0.37; 95%CI=0.35, 0.38) and more recent age-adjusted certification year (3= -0.018; 95%CI= -0.016, -0.019). Similar results were obtained for other years. This analysis indicates geocoding can describe neighborhood characteristics within the USRT which may be related to diseases linked to occupational exposures like cancer and cardiovascular disease.
机译:地理编码已用于环境流行病学中,以了解地理分布的暴露情况,但尚未广泛用于职业研究。我们介绍了对全国性职业队列进行地理编码的可行性,美国放射技术专家研究(USRT,N = 146,021)可以:(1)评估跨时间的地理编码,(2)通过农村(相对于城市)状况以及按地理区域测试地理编码的精度邻里社会经济地位(nSES),以及(3)通过一些队列人口统计学因素探讨nSES的分布。我们使用美国人口普查(1990,2000)和美国社区调查(2010)的6个项目,基于经过验证的方法,针对美国的每个人口普查组,在3个时间点创建了标准化的nSES指数:家庭收入中位数和房屋价值中位数,有兴趣/收入的家庭百分比,完成高中的成年人百分比,完成大学的成年人百分比以及从事管理职业的人员百分比。为了将nSES链接到同类群组,我们分别在1992年,2003年和2010年对受访者住址进行了地理编码。超过99%的地址已经过地理编码; 76%到86%的地址被映射到特定于地址的块组,其余的地址被地理编码到邮政编码重心的块组。城市(vs.农村)地址和最高(vs.最低)nSES五分位数的地址更有可能被地理编码到特定于地址的块组中(OR = 4.6; 95%CI = 4.5-4.7和OR = 6.4;对于1992年的地址,分别为95%CI = 6.1-6.7)。线性回归结果显示,1990年较高的nSES与女性的USRT参与者(3 = 0.22; 95%CI = 0.20,0.23),年龄较大的人群(3 = 0.003; 95%CI = 0.002,0.003),白人( 3 = 0.37; 95%CI = 0.35,0.38)和最近的经过年龄调整的认证年份(3 = -0.018; 95%CI = -0.016,-0.019)。其他年份也获得了类似的结果。该分析表明,地理编码可以描述USRT内的邻里特征,这可能与与职业暴露相关的疾病(如癌症和心血管疾病)有关。

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