Jurisdiction Level Vulnerability Assessment Toolkit

Assessment Approaches

There were three main approaches to conducting the assessments: Composite Index Scores, Statistical Modeling, and Spatial Epidemiology.

Composite Index Scores

The first main approach was the Composite Index Score (CIS) that uses methods similar to those developed for the Social Vulnerability Index (SVI) by the Geospatial Research, Analysis, and Services Program (GRASP). The SVI assesses the vulnerability of communities to disasters, such as earthquakes, hurricanes, and floods by ranking each census tract on fifteen factors and grouping them into four related themes. Each census tract receives a separate ranking for each of the four themes, as well as an overall ranking. The mechanics behind the CIS are similar. For this assessment, however, indicators relate to vulnerability, opioid overdose and/or to non-sterile IDU and are selected by subject matters experts. The rankings from the CIS then represent an indirect method of assessing risk for counties. An illustrative example is provided using a subset of the indicators identified in the National Vulnerability Assessment [1]: opioid prescriptions, drug arrests, drug overdose deaths, and per capita income. Similar to the SVI, if multiple variables on the same topic area are included, consider separate ranks for each topic or weighting the overall rank to account for the number of variables by topic area.

Missouri Vulnerability Assessment Project

Information provided by Becca Mickels, Chief, Bureau of Reportable Disease Informatics Missouri Department of Health and Senior Services

Missouri’s Opioid 2019 JVA project focused on the development and communication of an approach similar to the Social Vulnerability Index (SVI) by aggregating multiple factors related to opioid-related disease risk, and relating this risk across a geography including 114 counties (115 when including St. Louis). The primary intent of Missouri’s selected approach was to manage time and to offer simplicity in communication with the public. Overall, the results were a success and the methodology they employed seemed to be more transparent and easier to share.

Oregon Vulnerability Assessment Project

Information provided by Pickle, 2019. Viral Hepatitis Program Acute and Communicable Diseases.

Oregon’s County-Level JVA provided a thorough example of spatial epidemiology using a variety of choropleth map approaches, as introduced above. Oregon used multiple methodological approaches beginning with a high number of key indicator variables that were eventually reduced in a backward stepwise regression model until only significantly associated variables were included.

Illinois Vulnerability Assessment Project

Illinois undertook their JVA to inform work at the state level to conduct prevention activities, such as working with local health departments to develop jurisdictional response plans. The State leveraged national (e.g., Census Bureau) and local data sources (e.g., I-NEDSS; vital records) to derive 30 independent variables to test for association with the outcome of interest, HCV infections in individuals less than age 40.

Nebraska Vulnerability Assessment Project

Information provided by Felicia Quintana-Zinn

Nebraska’s Department of Health and Human Services (DHHS) sought to conduct a vulnerability assessment to both identify the high burden jurisdictions in the state, and to take a holistic approach to the entire state population. Nebraska has both rural and frontier areas, which despite having higher burdens of risk for opioid overdose and bloodborne disease transmission, lack the resources to address these risks. Public health jurisdictions had been expressing interest and the need for an assessment for several years, and collaborations and conversations that laid the groundwork for the assessment had begun more than two years ago.

Rhode Island Vulnerability Assessment Project

Rhode Island’s 2019 Opioid JVA project employed a more sophisticated statistical model approach featuring machine learning techniques. The model was proposed by Brown University partners as the best approach and ultimately included more than 300 variables which explored geographic units as far down as census tracts and ZIP codes.