Facilitating research on racial and ethnic disparities and inequities in transportation: Application and evaluation of the Bayesian Improved Surname Geocoding (BISG) algorithm.
Facilitating research on racial and ethnic disparities and inequities in transportation: Application and evaluation of the Bayesian Improved Surname Geocoding (BISG) algorithm.
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DOI：
10.1080/15389588.2021.1955109
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发表时间：
2021
影响因子：
2
通讯作者：
Curry AE
中科院分区：
文献类型：
--
作者：
Sartin EB;Metzger KB;Pfeiffer MR;Myers RK;Curry AE
交通安全研究记录了种族和民族差异和/或不平等现象。然而，种族/民族数据通常不会被纳入人口层面的交通安全数据库，这限制了该领域全面研究交通结果中种族/民族差异的能力，以及我们减轻这种差异的能力。为了克服这一限制，我们探索了使用贝叶斯改进姓氏地理编码（BISG）算法在新泽西州安全与健康结果（NJ-SHO）数据仓库中估计驾驶员种族和民族的效用。此外，我们还总结了一些重要的建议，以指导研究人员开展和实施种族和民族差异研究。我们应用BISG来估计2017年新泽西州司机的人口水平种族/民族，并使用每个种族/民族类别内接收者操作符曲线（AUC）下的面积评估综合行政来源（例如，医院记录）中可用的报告值与BISG概率分布之间的一致性。总体AUC是通过对每个报告类别的人口数量加权每个AUC值来计算的。在使用2017年车祸数据的范例分析中，我们使用NJ-SHO和BISG的种族/族裔值集对2017年警察每月报告的平均车祸率进行了分析，以比较它们的输出。我们发现白人、西班牙裔、黑人和亚洲/太平洋岛民司机报告的种族/民族与BISG概率之间存在极好的或显著的一致性（AUC≥0.86）。我们发现美洲印第安人/阿拉斯加土著司机的一致性较差（AUC= 0.65），而多种族司机的一致性并不比随机分配好（AUC= 0.52）。在白人、西班牙裔、亚洲/太平洋岛民和美洲印第安人/阿拉斯加本土司机中，使用NJ-SHO报告的种族/民族值和BISG概率计算的月碰撞率相似。黑人司机的月撞车率相差11%，多种族司机的月撞车率相差200%以上。白人、西班牙裔、黑人和亚洲/太平洋岛民司机（该样本中所有司机的98.9%）的种族/民族和BISG概率之间的碰撞率非常一致或非常相似，这表明BISG在研究交通差异和不平等方面具有潜在的效用。对于美洲印第安人/阿拉斯加原住民和多种族司机来说，种族/民族价值观之间的一致性是不可接受的，这与BISG之前的应用和评估相似。未来的工作需要确定BISG在交通安全环境中的应用程度。
Racial and ethnic disparities and/or inequities have been documented in traffic safety research. However, race/ethnicity data are often not captured in population-level traffic safety databases, limiting the field’s ability to comprehensively study racial/ethnic differences in transportation outcomes, as well as our ability to mitigate them. To overcome this limitation, we explored the utility of estimating race and ethnicity for drivers in the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse using the Bayesian Improved Surname Geocoding (BISG) algorithm. In addition, we summarize important recommendations established to guide researchers developing and implementing racial and ethnic disparity research. We applied BISG to estimate population-level race/ethnicity for New Jersey drivers in 2017 and evaluated the concordance between reported values available in integrated administrative sources (e.g., hospital records) and BISG probability distributions using an area under the receiver operator curve (AUC) within each race/ethnicity category. Overall AUC was calculated by weighting each AUC value by the population count in each reported category. In an exemplar analysis using 2017 crash data, we conducted an analysis of average monthly police-reported crash rates in 2017 by race/ethnicity using the NJ-SHO and BISG sets of race/ethnicity values to compare their outputs. We found excellent or outstanding concordance (AUC ≥0.86) between reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers. We found poor concordance for American Indian/Alaskan Native drivers (AUC= 0.65), and concordance was no better than random assignment for Multiracial drivers (AUC = 0.52). Among White, Hispanic, Asian/Pacific Islander, and American Indian/Alaskan native drivers, monthly crash rates calculated using both NJ-SHO reported race/ethnicity values and BISG probabilities were similar. Monthly crash rates differed by 11% for Black drivers, and by more than 200% for Multiracial drivers. Findings of excellent or outstanding concordance between and mostly similar crash rates derived from reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers (98.9% of all drivers in this sample) demonstrate the potential utility of BISG in enabling research on transportation disparities and inequities. Concordance between race/ethnicity values were not acceptable for American Indian/Alaskan Native and Multiracial drivers, which is similar to previous applications and evaluations of BISG. Future work is needed to determine the extent to which BISG may be applied to traffic safety contexts.
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