Benjamin D. Trump, A. Jin, S. Galaitsi, Christopher Cummings, H. Jarman, S. Greer, Vidur Sharma, I. Linkov
{"title":"Equitable Response in Crisis: Methodology and Application for COVID-19","authors":"Benjamin D. Trump, A. Jin, S. Galaitsi, Christopher Cummings, H. Jarman, S. Greer, Vidur Sharma, I. Linkov","doi":"10.1115/1.4062683","DOIUrl":null,"url":null,"abstract":"\n Equitable allocation and distribution of the COVID-19 vaccine have proven to be a major policy challenge exacerbated by incomplete pandemic risk data. To rectify this shortcoming, a three-step data visualization methodology was developed to assess COVID-19 vaccination equity in the United States using state health department, U.S. Census, and CDC data. Part one establishes an equitable pathway deviation index to identify populations with limited vaccination. Part two measures perceived access and public intentions to vaccinate over time. Part three synthesizes these data with the social vulnerability index to identify areas and communities at particular risk. Results demonstrate significant equity differences at a census-tract level, and across demographic and socioeconomic population characteristics. Results were used by various federal agencies to improve coordinated pandemic risk response and implement a commitment to equity as defined by the Executive Order regarding COVID-19 vaccination and booster policy. This methodology can be utilized in other fields where addressing the difficulties of promoting health equity in public policy is essential.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4062683","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Equitable allocation and distribution of the COVID-19 vaccine have proven to be a major policy challenge exacerbated by incomplete pandemic risk data. To rectify this shortcoming, a three-step data visualization methodology was developed to assess COVID-19 vaccination equity in the United States using state health department, U.S. Census, and CDC data. Part one establishes an equitable pathway deviation index to identify populations with limited vaccination. Part two measures perceived access and public intentions to vaccinate over time. Part three synthesizes these data with the social vulnerability index to identify areas and communities at particular risk. Results demonstrate significant equity differences at a census-tract level, and across demographic and socioeconomic population characteristics. Results were used by various federal agencies to improve coordinated pandemic risk response and implement a commitment to equity as defined by the Executive Order regarding COVID-19 vaccination and booster policy. This methodology can be utilized in other fields where addressing the difficulties of promoting health equity in public policy is essential.