The Dynamics of Infectious Diseases Associated With Injection Drug Use in Lawrence and Lowell, Massachusetts.

BACKGROUND: There are a wide variety of infectious complications of injection drug use. Understanding the trajectory of these complications might inform the development of an early warning system for human immunodeficiency virus (HIV) outbreaks that occur regularly among people who inject drugs (PWID). METHODS: A distributed lag Poisson regression model in the Bayesian setting was used to examine temporal patterns in the incidence of injection-associated infectious diseases and their association with HIV cases in Lawrence and Lowell, Massachusetts between 2005 and 2018. RESULTS: Current-month HIV counts are associated with fatal overdoses approximately 8 months prior, cases of infective endocarditis 10 months prior, and cases of skin and soft tissue infections and incision and drainage procedures associated with these infections 12 months prior. CONCLUSIONS: Collecting data on these other complications associated with injection drug use by public health departments may be important to consider because these complications may serve as input to a sentinel system to trigger early intervention and avert potential outbreaks of HIV.

Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms.

Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers' allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods-bandit algorithms-similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.

Expanding access to methadone treatment in Ohio through federally qualified health centers and a chain pharmacy: A geospatial modeling analysis.

BACKGROUND: In the United States, methadone provision for opioid use disorder (OUD) occurs at opioid treatment programs (OTPs). Ohio recently enacted a policy to expand methadone administration to Federally Qualified Health Centers (FQHC). We compared how the provision of methadone at current OTPs or the proposed expansion to FQHCs and pharmacies meets the urban and rural need for OUD treatment. METHODS: Cross-sectional geospatial analysis of zip codes within Ohio with at least one 2017 opioid overdose death stratified by Rural-Urban Commuting Area codes. Our primary outcome was the proportion of need by zip code (using opioid overdose deaths as a proxy for need) within a 15- or 30- minute drive time of an OTP. RESULTS: Among 581 zip codes, sixty four percent of treatment need was within a 15-minute drive time and 81 %, within a 30-minute drive time. The proportion of need within a 15-minute drive decreased with increasing rural classification (urban 78 %, suburban 20 %, large rural 9%, and small rural 1%;p<.001). The portion of need within a 15-minute drive time increased with the addition of FQHCs (96 %) and the addition of chain pharmacies (99 %) relative to OTPs alone among all zip codes and for all urban-rural strata (p<.001). CONCLUSION: Over one-third of OUD treatment need was not covered by existing OTPs and coverage decreased with rural classification of zip codes. Most of the gap between supply and need could be mitigated with FQHC methadone provision, which would expand both urban and rural access.