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:使用地理编码来了解跨时间,空间和人口特征在全国范围的职业队列中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:地理编码已在环境流行病学被用来理解地理图形化风险暴露,但尚未在职业研究中广泛应用。我们提出地理编码在全国范围内的职业群体,美国放射技师研究(USRT,N = 146021),以可行性:(1)评估地理编码跨越时间,(2)农村(城市对)状态且通过地理编码的测试精度附近社会经济地位(NSES),和(3)通过一些队列人口因素探索的NSE分布。我们创建了一个标准化的NSE指数的基础上,经过验证的方法,在美国每个人口普查区块组,在3个时间点,使用6个项目来自美国人口普查局(1990年,2000年)和美国社区调查(2010年):区域调整中位数家庭收入中位数和住房价值,%的家庭有利息/收入,%成年人谁完成高中学业,%的成年人谁完成了大学学业,并在管理职业%就业人口。要链接到网络搜索引擎的人群,我们在1992年,2003年,2010地理编码的受访者地址,以块组居住地。地址的超过99%是地理编码; 76至86%被映射到一个特定地址块的基团,而其余的被地理编码到一个块组的邮政编码质心。城市(相对于农村)地址和那些在最高(相对于最低)的五分之一更可能被地理编码到一个特定地址块的基团(OR = 4.6的NSE; 95%CI = 4.5-4.7和OR = 6.4; 95%CI = 6.1-6.7,分别为1992点的地址)。线性回归的结果在1990显示出更高的NSE与女性性别的USRT参与者相关联的(3 = 0.22; 95%CI = 0.20,0.23),年龄(3 = 0.003; 95%CI = 0.002,0.003),白色人种( 3 = 0.37; 95%CI = 0.35,0.38)和更近的年龄调整的认证年(3 = -0.018; 95%CI = -0.016,-0.019)。其他年份均得到了类似的结果。此分析表明地理编码可以其可能与连接于职业暴露,如癌症和心血管疾病的疾病的USRT内描述附近的特性。

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