Key points covered in risk adjusting the U.S.
population for poor outcomes related to COVID-19 infection
Total Population
2010 U.S. Census counts projectedforward to 2020 counts (by age and gender cohorts) for each 3-digit zip code (ZIP3) region
Risk-Adjustment Methodology
How anonymized patient-level claims data
mapped to CMS HCCs risk-adjusts population
for unfavorable underlying conditions
PurpleLab Claims Repository
~70% census of annual U.S. medical claims anonymized patient level data (de-identified according to HIPPA safe harbor rules)
HealthNexus
PurpleLab’s medical terminology platform has ICD9 CM & ICD10 CM codes mapped to CMS Hierarchical Condition Categories (HCCs)
Risk Adjusted Population
Projected 2020 population counts of Low, Moderate, High and Severe risk cohorts for each ZIP3 region (by age and gender cohorts)
Capacity-to-Treat Methodology
Counts as basis for risk-adjusted model of “demand” for hospitalization, ventilation and risk of mortality relative to “supply” of capacity as measured across 4 measures of capacity: (i) Hospital Total Beds; (ii) Hospital ICU Beds; (iii) Physicians with experience in caring for ventilator dependent patients; and (iv) Respiratory Therapists
Interventions
Sensitivity analyses for increases in capacity-to-treat. Unretiring physicians (+15%). Adding beds (+10%, 25% and 50%). Adding ventilatory capacity (really adding ICU beds +25%, 50% and 100%).
