Each of the three main approaches used in the JVAs has pros and cons. The CIS approach is very adaptable, works well in applied situations, and may be easier to communicate to broader audiences. However, it is less statistical, relies on existing subject matter expertise to identify the appropriate indicators, and may orient ‘vulnerability’ in the wrong direction if true associations differ from the expected direction based on subject matter expertise. Regression modeling utilizes dependent variables or outcomes, and independent variables or risk factors, similar to those in the CIS; however, regression has the advantage of employing quantitative methods, such as maximum likelihood, to determine the strengths and directions of associations between independent variables and outcomes. Moreover, validated statistical models can predict outcomes, whereas, CIS is limited to county rankings, based on vulnerability inputs. Yet, regression modeling requires more technical skills, can be challenging to communicate to general audiences, and may also inadvertently exclude important indicators. Spatial epidemiology is less focused on the relationship between variables and more on the spatial relationships between variables. Spatial epidemiology has the advantage of incorporating techniques to account for human geography, but is technically advanced and has limited application without using CIS or statistical modeling to inform the spatial epidemiology approach. The choice of approach ultimately depends on both the familiarity of the analyst with the data and method options, as well as the statistical literacy of the audience; an analyst must not only be familiar with the technique used, but also be able to explain the technique and results to others.