PROMISE (Program Refinements to Optimize Model Impact and Scalability based on Evidence): A cluster-randomized, stepped-wedge trial assessing effectiveness of the revised versus original Ryan White Part A HIV Care Coordination Program for patients with barriers to treatment
Authors: Irvine Mary K. , Levin Bruce , Robertson McKaylee , Penrose Katherine , Carmona Jennifer , Harriman Graham , Braunstein Sarah , Nash Denis 
Published: November 2019

AbstractIntroductionGrowing evidence supports combining social, behavioral and biomedical strategies to strengthen the HIV care continuum. However, combination interventions can be resource-intensive and challenging to scale up. Research is needed to identify intervention components and delivery models that maximize uptake, engagement and effectiveness. In New York City (NYC), a multi-component Ryan White-funded medical case management intervention called the Care Coordination Program (CCP) was launched at 28 agencies in 2009 to address barriers to care and treatment. Effectiveness estimates based on >7,000 clients enrolled by April 2013 and their controls indicated modest CCP benefits over ‘usual care’ for short- and long-term viral suppression, with substantial room for improvement.Methods and analysisIntegrating evaluation findings and CCP service-provider and community-stakeholder input on modifications, the NYC Health Department packaged a Care Coordination Redesign (CCR) in a 2017 request for proposals. Following competitive re-solicitation, 17 of the original CCP-implementing agencies secured contracts. These agencies were randomized within matched pairs to immediate or delayed CCR implementation. Data from three nine-month periods (pre-implementation, partial implementation and full implementation) will be examined to compare CCR versus CCP effects on timely viral suppression (TVS, within four months of enrollment) among individuals with unsuppressed HIV viral load newly enrolling in the CCR/CCP. Based on estimated enrollment (n=824) and the pre-implementation outcome probability (TVS=0.45), the detectable effect size with 80% power is an odds ratio of 2.90 (relative risk: 1.56).Ethics and disseminationThis study was approved by the NYC Department of Health and Mental Hygiene Institutional Review Board (IRB, Protocol 18-009) and the City University of New York Integrated IRB (Protocol 018-0057) with a waiver of informed consent. Findings will be disseminated via publications, conferences, stakeholder meetings, and Advisory Board meetings with implementing agency representatives.Trial registrationRegistered withClinicalTrials.govunder identifier:NCT03628287, Version 2, 25 September 2019; pre-results.ARTICLE SUMMARYStrengths and limitations of this studyThe PROMISE trial, conducted in real-world service settings, leverages secondary analyses of programmatic and surveillance data to assess the effectiveness of a revised (CCR) versus original HIV care coordination program to improve viral suppression.To meet stakeholder expectations for rapid completion of the CCR rollout, the study applies a stepped-wedge design with a nine-month gap between implementation phases, prompting use of a short-term (four-month) outcome and a brief (five-month) lead-in time for enrollment accumulation.Randomization is performed at the agency level to minimize crossover between the intervention conditions, since service providers would otherwise struggle logistically and ethically with simultaneously delivering the two different intervention models to different sets of clients, especially given common challenges related to reaching agreement on clinical equipoise.1–3The use of agency matching, when followed by randomization within matched pairs, offers advantages akin to those of stratified random assignment: increasing statistical power in a situation where the number of units of randomization is small, by maximizing equivalency between the intervention and control groups on key observable variables, thus helping to isolate the effects of the intervention.3In addition, nuisance parameters are removed through the conditional analytic approach, which accounts and allows for the unavoidably imperfect matching of agencies and arbitrary variation of period effects across agency pairs.4