National Center for Advancing Translational Sciences of the National Institutes of Health.

People who inject drugs (PWID) are at risk for infective endocarditis (IE). Hospitalization rates related to misuse of prescription opioids and heroin have increased in recent years, but there are no recent investigations into rates of hospitalizations from injection drug use-related IE (IDU-IE). Using the Health Care and Utilization Project National Inpatient Sample (HCUP-NIS) dataset, we found that the proportion of IE hospitalizations from IDU-IE increased from 7% to 12.1% between 2000 and 2013. Over this time period, we detected a significant increase in the percentages of IDU-IE hospitalizations among 15- to 34-year-olds (27.1%–42.0%; P < .001) and among whites (40.2%–68.9%; P < .001). Female gender was less common when examining all the IDU-IE (40.9%), but it was more common in the 15- to 34-year-old age group (53%). Our findings suggest that the demographics of inpatients hospitalized with IDU-IE are shifting to reflect younger PWID who are more likely to be white and female than previously reported. Future studies to investigate risk behaviors associated with IDU-IE and targeted harm reduction strategies are needed to avoid further increases in morbidity and mortality in this rapidly growing population of young PWID.

Breakdowns in the HCV care continuum may have adverse effects on HCV-treatment readiness and willingness. Improved public health and practice approaches are needed to address these barriers to effectively engage young PWID in care.

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This cohort study was designated as not human subjects research by the Mass General Brigham institutional review board because it used anonymous, publicly available data; thus, informed consent was not sought. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. We compiled weekly SARS-CoV-2 molecular testing data from the Massachusetts Department of Public Health and Boston Public Health Commission for the period May 27 to October 14, 2020, following the initial COVID-19 wave. The Boston Public Health Commission reported tests of unique Boston residents, whereas the Massachusetts Department of Public Health reported total tests, including repeat testing of individuals. Consequently, we performed separate analyses for Massachusetts (351 cities and towns) and for Boston (15 neighborhoods).We defined testing intensity as the number of SARS-CoV-2 tests performed weekly per 100 000 population and epidemic intensity as weekly test positivity. We considered optimal alignment of testing resources to be matching community ranks of testing and positivity. In communities with a testing gap (ie, the testing rank was lower than the positivity rank) in a given week, the gap was calculated as additional testing required to achieve matching ranks. For example, the testing gap for a community with the third highest positivity is the difference between its testing rate and that of the community with the third highest testing intensity.Responses from the American Community Survey (2014-2018) 5 were aggregated to characterize communities. Negative binomial models using robust sandwich estimators to account for repeated measures at the community level were fit to assess associations of the magnitude of the weekly testing gap with time (linearly by week), selected Centers for Disease Control and Prevention Social Vulnerability Index 6 domains (eg, Socioeconomic Status and Minority Status/Language), and large university student population (>10% of residents). Owing to collinearity, the model of Boston neighborhoods only assessed associations with time and socioeconomic vulnerability. Two-sided Wald tests were used to assess significance at a threshold of P < .05. Data analysis was performed using R statistical software version 3.6.1 (R Project for Statistical Computing). ResultsDuring the observation period, 4 262 000 tests were reported. COVID-19 incidence (median [range], 339 [0-6670] cases per 100 000) and testing intensity (median [range], 41 000 [5350-274 000] tests per 100 000) varied considerably between communities, with observed increased testing in Author affiliations and article information are listed at the end of this article.