Description
There are approximately 1.2 million people living with HIV in the United States among whom only 40% are engaged in HIV medical care and 30% have reached viral suppression. The substantial proportion of persons who are not engaged in care has important individual and public health implications because over 90% of new HIV infections are transmitted from persons with HIV who are not fully retained in medical care. Re-engaging out-of-care persons with HIV back into care, confers important individual-level health benefits and population-level prevention benefits. Use of HIV surveillance data to identify out-of-care persons, is one strategy for identifying and re-engaging out-of-care persons in care. Using surveillance data (i.e., CD4 and HIV viral load test results reported to the health department) to identify out-of-care persons and to re-engage these persons in care is called Data to Care or D2C. In the current D2C model, there is a delay in the identification of out-of-care persons due to the time interval between recommended monitoring tests (i.e., every 3-6 months) and the subsequent reporting of these tests to surveillance. Thus, the current D2C model identifies a failure to retain in care rather than identify persons at risk for dropping out of care and the model cannot intervene in the time between a gap in care and identification of being out of care. More real-time data is required to identify persons at risk of dropping out of care and to intervene prior to a gap in care or loss to care. Pharmacy prescription refill (claims) data are a source to identify HIV-infected persons, who have stopped filling antiretroviral (ARV) medications, and who are at risk for becoming out of care. Patients may elect to fill prescriptions at one of potentially many pharmacies that accept their insurance plan. Within one pharmacy network, prescriptions can be tracked across partnered pharmacies (e.g. chain drug stores). However, if a patient switches pharmacies (to another chain or independent pharmacy) the prior refill history remains with the first pharmacy. Pharmacy claims are adjudicated for an insurance company by a third-party claims processor or a pharmacy-benefit management (PBM) company. All pharmacy claims that are billed to an insurance company, for an individual patient, can be tracked through the PBM, regardless of where a patient filled the prescription. PBMs manage pharmacy benefits for ~85% of all people with prescription benefits. Because most ARVs are prescribed as a 30-day supply of medication, data from PBMs, can be used to identify persons who are not filling their medications on a monthly basis. Tracking ARV refill data can, therefore, be a more real-time indicator of poor adherence and can act as a harbinger of potential poor retention in care. Given that ~30% of persons on ARV are non-adherent, using real time pharmacy data to identify persons who fail to fill ARV prescriptions and to intervene could have a significant impact on adherence and potentially on retention in care. The purpose of the cooperative-agreement is to develop and implement a model using real-time pharmacy data to identify persons who fail to pick up prescribed ARVs, and who are at risk for poor retention in care, and to use this information for targeted adherence and retention interventions and to re-engage persons in care. The grantee, along with the Project Team, will develop and implement a 1st line intervention for persons who fail to pick up prescribed ARVs within 30 days, a 2nd line adherence /retention intervention to be conducted at the prescribing clinic or filling pharmacy for persons who fail to pick up prescribed ARVs within 60 days and a 3rd line intervention for the health department to locate persons who fail to pick up prescribed ARVS within 90 days and to re-link these individuals to the clinic or to the pharmacy. The expected outcomes of the model program are increased retention in HIV care, re-linkage to care, adherence to ARV therapy and viral load suppression. Specific activities, of the cooperative agreement, are broadly categorized into three areas: (1) develop a model to use pharmacy claims data to identify persons who fail to pick up prescribed ARVs and to target these individuals for progressive adherence interventions (2) implement the model (3) evaluate project outcomes and disseminate results.