Jurisdiction Level Vulnerability Assessment Toolkit



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.

CIS uses the rankings of indicators within each jurisdiction to assess which counties (or other subdivisions) are most vulnerable to opioid overdoses and/or infections from non-sterile IDU. This method is less statistical than regression approaches which have more robust modeling and predictive power, but is very adaptable and works well in applied situations.

Composite Index Score Example:

For this example, we used the following category/indicator pairs, based on publicly available data:

  • Socioeconomics: Per Capita Income (2013-2017 American Community Survey)
  • Crime: Drug Arrest Rate (Uniform Crime Reporting Program, 2016)
  • Prescriptions: Per Capita Opioid Prescriptions (IQVIA Xponent, 2017)
  • Mortality: Drug Overdose Rate (CDC Wonder, 2013-2017)

To handle missing data and zeros, we made the following assumptions. We assumed that zero values were a legitimate reflection of zero events and assigned the nationwide median for missing (N/A) values, as a simple form of imputation. Assumptions regarding zero values and imputation methods could be adjusted based on local context and comfort with other imputation methods.

We ranked each indicator among the 3,412 counties and county equivalents, with a “higher is worse” categorization (see Table 2). We then took the average of the four ranks to calculate the overall CIS rank for each county. Analysis was performed using widely available spreadsheet software (Microsoft Excel).



Table 2. Examplae CIS for Select Indicators

Geography Drug Arrests
(Rate, Rank)
Opiod Prescriptions
(Rate, Rank)
Drug Overdose Deaths
(Rate, Rank)
Per Capita Income
(Value, Rank)
Average Rank
Bell County, Kentucky 113 (3063) 228 (3137) 60 (3126) 14754 (3104) 3107.5
Clay County, Tennessee 87 (2920) 191 (3125) 49 (3108) 17667 (3002) 3038.75
Grundy County, Tennessee 83 (2891) 141 (3046) 35 (3009) 15824 (3080) 3006.5
Logan County, West Virginia 109 (3046) 170 (3109) 70 (3137) 21074 (2522) 2953.25
Scott County, Tennessee 100 (3006) 154 (3075) 29 (2841) 18748 (2902) 2956


Figure 4 presents a map of the overall CIS rank from this illustrative example by county using Microsoft Power BI. Specific counties can then be further explored to understand the underlying factors contributing to their overall CIS rank as well as the different preventive services and interventions available in the community.



Figure 4. Composite Index Score Vulnerability

Figure 4. Composite Index Score Vulnerability

When interpreting the overall CIS rank, it may be helpful to compare it with an appropriate outcome metric for context. Jurisdictions who used the CIS approach often included or compared their CIS ranks with diagnosis data for HCV or HIV or administrative claims data for endocarditis, HCV or HIV.

When doing this type of contextual comparison, we offer the following considerations:

  1. Areas with high outcome metric and high overall CIS rank: these represent areas where additional resources may be needed. Health departments should look at available resources within these areas and see what steps are being taken, and could be taken, to improve prevention programs in those communities.
  2. Areas with low outcome metric and high CIS rank: these areas may have unmeasured protective factors such as syringe services programs, Naloxone availability, or programs by local public health officials. Conversely, such areas may reflect a lack of testing for the outcome metric, such as lack of capacity to test for HIV/HCV. Health departments should consider if available testing and prevention services are meeting local needs.
  3. Areas with high outcome metric and low CIS rank: these areas may reflect unmeasured exogenous factors, such as drug traffic patterns or unique population characteristics. When working with one state, a county identified with high HCV incidence was found to be a county that contained a major interstate highway and located near a large urban area in a neighboring state.



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 that includes 114 counties (115 when including the independent city of 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.