Building a COVID-19 Vulnerability Index

Dave DeCaprio; Joseph A Gartner; Thadeus Burgess; Sarthak Kothari; Shaayaan Sayed; Carol J McCall

is the (which was not peer-reviewed) The copyright holder for this preprint -0.020 CCSR:RSP016 age X Pneumonia 0.010 n/a age X Other and ill-defined heart disease 0.003 n/a age X Heart failure 0.009 n/a age X Acute rheumatic heart disease 0.003 n/a age X Coronary atherosclerosis and other heart disease 0.011 n/a age X Pulmonary heart disease -0.000 n/a age X Chronic rheumatic heart disease -0.001 n/a age X Diabetes mellitus with complication 0.007 n/a age X Diabetes mellitus without complication 0.009 n/a age X Chronic obstructive pulmonary disease and bronchiectasis 0.013 n/a age X Other specified and unspecified lower respiratory disease 0.006 n/a To turn this into a model, we extract ICD-10 diagnosis codes from the claims and aggregate them using the CCSR categories. We create indicator features for the presence of any code in the CCSR category. The mapping between the CDC risk factors and CCSR codes is described in Table 2 . We start with these features as they give us an ability to quantify the portion of the at-risk population that are encapsulated by the high-level CDC recommendations. In addition to the conditions coming from the recommendations of the CDC, we will look at features that our other modeling efforts surfaced as important and avail those features to the model as well. We also provide gender and age in years, as well as an interaction term between age and the diagnostic features. This simple dataset is used to train a logistic regression model [10] . In addition to the CCSR codes, Table 2 includes the beta coefficients associated with these features in the logistic regression model.