Increasing representation of black communities in SARS-CoV-2 serosurveys by understanding barriers and motivations for participation

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ABSTRACT Injection is an increasingly common route of administration for opioids and other drugs in the U.S. Unsafe injection drug use (IDU) behavior threatens recent progress made in reducing HIV and hepatitis C virus (HCV) infections among persons who inject drugs (PWID), which is a major impediment to achieving ambitious national goals for reducing new HIV infections. During the past decade, New York State has experienced growing rates of opioid use disorder and, consequently, increases in IDU-related infectious diseases. New York has strong political will to reduce the burden of these infections among PWID and is the first U.S. state to commit to both “Ending the AIDS Epidemic” and HCV elimination strategies. However, New York, like other states, does not have a current, statewide estimate of how many PWID need infectious disease prevention services. Robust estimates of PWID population sizes are needed in New York and elsewhere to facilitate optimal allocation of scarce resources, measure risk-specific infectious disease burden among PWID, and assess coverage of prevention interventions. We propose to estimate PWID population size and associated risk-specific disease and prevention intervention coverage rates in New York using multiple systems estimation (MSE) with a combination of extant administrative and PWID survey data. MSE is an application of capture- recapture methods that allows estimation of underlying population size using joint probabilities of observing individuals in linked administrative datasets vis-à-vis their receipt of a service or diagnosis. We will apply MSE methods to linked, statewide datasets in New York including claims data, inpatient and emergency room electronic hospital records, drug treatment program data, and infectious disease surveillance data. We will also improve the rigor of MSE methods by addressing potential violations of key statistical assumptions through augmented estimation models, partially informed by survey data currently being collected by the study team. Our Specific Aims are: (1)To estimate PWID population size in New York State using MSE with indications of current IDU behavior from linked administrative datasets, overall and by region, sex, and age; (2) To create refined PWID population size estimates accounting for bias due to unequal probabilities of observing individuals in datasets; (3) To allocate state PWID population size to all 62 counties using a standardization modeling approach; (4) To compute key epidemiologic indicators needed to measure infectious disease risk among PWID: risk-specific HIV and HCV diagnosis rates, syringe service program utilization rates, and number of PWID initiating IDU during past year; (5) To disseminate estimates and methodology using the AIDSVu data visualization platform. The national impact of this work, using New York as a model, will be to establish a robust, replicable method for producing estimates that can guide efforts to improve PWID health and reduce the burden of HIV and HCV in this high-risk, under-studied group.