Risk Adjusted Population

Projected 2020 population counts for Low, Moderate, High and Severe risk cohorts for each ZIP3 region.

Unlike Ajao et al. [1], PurpleLab decided to risk adjust the population to understand the distribution of underlying chronic conditions and comorbidities that are likely to adversely affect outcomes in the COVID-19 population. Patients with underlying chronic conditions and comorbidities that COVID-19 is selective for are unevenly distributed throughout populations in various geographies across the U.S.

To measure the population counts with underlying chronic conditions and comorbidities of interest within each 3-digit Zip code region (ZIP3) of interest, PurpleLab incorporated the CMS HCCs. Originally, this HCC model was developed to adjust capitation payments by estimating a risk score of future medical cost for each beneficiary based on demographic information (primarily age and gender) and patient health status (based on observed ICD9 CM and ICD10 CM codes within patient claims histories). CMS HCCs map diagnoses into unique categories that represent comorbidities with similar healthcare costs. Higher categories represent higher predicted healthcare costs. CMS HCCs have been shown to outperform both Charlson and Elixhauser risk adjustment models in predicting mortality rates (independent of COVID-19).

PurpleLab highlighted the HCCs for which COVID-19 would likely have selectively worse outcomes in the presence of, which include those HCCs representative of: (1) immune-compromise; and/or (2) cardio-respiratory compromise. Those HCCs are represented in the table below:

Anonymized patients in PurpleLab’s Claims Repository were evaluated for whether or not they have an ICD9 CM or ICD 10 CM code that maps to a CMS HCC. Overlapping chronic conditions represent comorbidities. Since there are 86 current and 4 historical CMS HCCs that ICD9 CM and ICD10 CM codes map to, using the CMS HCC model implies a 90 by 90 intersection which is too complex to visualize. For this reason, we have developed the following coding scheme and ultimate risk classification for the project population within each 3-digit Zip code region (ZIP3):

1. If a patient appears in the Claims Repository but does not have ICD9 or ICD10 CM codes that map to a CMS HCC AND their age is less than 60 years old, then the patient is classified as having Low Risk.

2a. If a patient appears in the Claims Repository but does not have ICD9 or ICD10 CM codes that map to a CMS HCC AND their age is greater than 60 years old, then the patient is classified as having Moderate Risk.

2b. If a patient appears in the Claims Repository and has an ICD9 or ICD10 CM code that maps to one CMS HCC that is NOT indicative of either (i) immuno-compromise; and/or (ii) cardio-respiratory compromise. Then the patient is classified as having Moderate Risk.

3a. If a patient appears in the Claims Repository and has an ICD9 or ICD10 CM code that maps to more than one CMS HCC that is NOT indicative of either (i) immuno-compromise; and/or (ii) cardio-respiratory compromise. Then the patient is classified as having High Risk.

3b. If a patient appears in the Claims Repository and has an ICD9 or ICD10 CM code that maps to a single CMS HCC indicative of either: (i) immuno-compromise; and/or (ii) cardio-respiratory compromise. Then the patient is classified as High Risk.

4. If a patient appears in the Claims Repository and has an ICD9 or ICD10 CM code that maps to a more than one CMS HCC indicative of either: (i) immuno-compromise; and/or (ii) cardio-respiratory compromise. Then, the patient is classified as having Severe Risk.

Ultimately the maps represent ratios of risk adjustment “demand” by measures of Low, Medium, High and Sever risk (or combinations thereof), relative to “supply” by measures of Total Beds, ICU Beds and Physician Intensivists. Those ratios indicate areas of the U.S. with severe limitations of capacity relative to the abundance of populations with underlying complications and comorbid conditions for each ZIP3 region.