PrEP Use
New York State

In 2022, there were 50,558 PrEP recipients in New York State, a 15% increase from 2021


  • Data Source: IDV® (Integrated Dataverse) from Symphony Health and the NYS Medicaid Data Warehouse (MDW)
  • PrEP use defined as persons who filled at least one PrEP prescription during a 6 month/12 month time period.
  • NYS uses the IDV® (Integrated Dataverse) from Symphony Health data to extract prescription data for all payors with the exception of Medicaid. The Medicaid data information is pulled separately from the NYS Medicaid Data Warehouse (MDW).
  • IDV® (Integrated Dataverse) from Symphony Health contains longitudinal patient data sources that capture adjudicated prescription, medical, and hospital claims across the United States for all payment types, including commercial plans, Medicare Part D, cash, assistance programs, and Medicaid. The IDV contains over 10 billion prescriptions claims linked to over 280 million unique prescription patients with an average of 5 years of prescription drug history. Claims from hospital and physician practices include over 190 million patients with CPT/HCPCS medical procedure history as well as ICD-9/10 diagnosis history of which nearly 180 million prescription drug patients are linked to a diagnosis. The overall sample represents over 65,000 pharmacies, 1,500 hospitals, 800 outpatient facilities, and 80,000 physician practices.
  • While IDV® (Integrated Dataverse) from Symphony Health data contains over 90% of prescriptions dispensed nationally, this does not mean that the completeness of the PrEP prescriptions filled is at 90%. This is due to a lower level of completeness of medical claim information in Symphony, which the NYS PrEP algorithm relies on for excluding prescriptions for individuals with other conditions (HIV, HBV).
  • Age/gender/race/region were calculated as of the first half of 2017 until January-June 2017 report, and after that they were based on the latest information.
  • Region is based on residence of individual.
  • Claims and encounter data are subject to errors and omissions, specifically with regard to the race/ethnicity of individuals. Interpret with caution.
  • Data not shown is suppressed due to small cell sizes.

Additional Notes