School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States.
School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States; New York State Department of Health, Cancer Registry, Riverview Center, Menands, NY 12204, United States.
Spat Spatiotemporal Epidemiol. 2020 Feb;32:100322. doi: 10.1016/j.sste.2019.100322. Epub 2019 Dec 13.
Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM) and respiratory hospitalizations in New York State (2000-2005). We observed non-significantly higher PM exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (-0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.
在健康记录中推断缺失的空间属性可能有助于将其与地理参考环境暴露相关联,但很少有研究评估地理推断对流行病学推断的影响。我们在纽约州(2000-2005 年)进行的细颗粒物 (PM) 和呼吸道住院的病例交叉分析中推断了患者的人口普查区。我们观察到 PM 暴露量略高,但无统计学意义,二元暴露分配的准确性很高(89 至 99%),危险比 (HR) 略有不同(-0.2 至 0.7%)。在城市与农村地区,HR 差异更大。鉴于其效率和对暴露分类准确性和关联度量的名义影响,地理推断是解决健康研究中缺失空间属性的候选方法。