Testing and Treatment Interventions in Community Settings Key to Controlling a Recent Human Immunodeficiency Virus Outbreak Among People Who Inject Drugs in Glasgow: A Modeling Study.

BACKGROUND: A human immunodeficiency virus (HIV) outbreak was identified among people who inject drugs (PWID) in Glasgow in 2015, with >150 diagnoses by the end of 2019. The outbreak response involved scaling up HIV testing and improving HIV treatment initiation and retention. METHODS: We parameterized and calibrated a dynamic, deterministic model of HIV transmission among PWID in Glasgow to epidemiological data. We use this model to evaluate HIV testing and treatment interventions. We present results in terms of relative changes in HIV prevalence, incidence, and cases averted. RESULTS: If the improvements in both testing and treatment had not occurred, we predict that HIV prevalence would have reached 17.8% (95% credible interval [CrI], 14.1%-22.6%) by the beginning of 2020, compared to 5.9% (95% CrI, 4.7%-7.4%) with the improvements. If the improvements had been made on detection of the outbreak in 2015, we predict that peak incidence would have been 26.2% (95% CrI, 8.8%-49.3%) lower and 62.7% (95% CrI, 43.6%-76.6%) of the outbreak cases could have been averted. The outbreak could have been avoided if the improvements had already been in place. CONCLUSIONS: Our modeling suggests that the HIV testing and treatment interventions successfully brought the HIV outbreak in Glasgow under control by the beginning of 2020.

Predictors of engagement in screening for a hepatitis C virus (HCV) treatment trial in a rural Appalachian community.

An HCV treatment trial was initiated in September 2019 to address the opioid/hepatitis C virus (HCV) syndemic in rural Kentucky. The focus of the current analysis is on participation in diagnostic screening for the trial. Initial eligibility (≥18 years of age, county resident) was established by phone followed by in-person HCV viremia testing. 900 rural residents met the inclusion criteria and comprised the analytic sample. Generalized linear models were specified to estimate the relative risk of non-attendance at the in-person visit determining HCV eligibility. Approximately one-quarter (22.1%) of scheduled participants were no-shows. People who inject drugs were no more likely than people not injecting drugs to be a no-show; however, participants ≤35 years of age were significantly less likely to attend. While the median time between phone screening and scheduled in-person screening was only 2 days, each additional day increased the odds of no-show by 3% (95% confidence interval: 2%-3%). Finally, unknown HCV status predicted no-show even after adjustment for age, gender, days between screenings and injection status. We found that drug injection did not predict no-show, further justifying expanded access to HCV treatment among people who inject drugs. Those 35 years and younger were more likely to no-show, suggesting that younger individuals may require targeted strategies for increasing testing and treatment uptake. Finally, streamlining the treatment cascade may also improve outcomes, as participants in the current study were more likely to attend if there were fewer days between phone screening and scheduled in-person screening.

Cancer researchers' experiences with and perceptions of research data sharing: Results of a cross-sectional survey.

BACKGROUND: Despite wide recognition of the benefits of sharing research data, public availability rates have not increased substantially in oncology or medicine more broadly over the last decade. METHODS: We surveyed 285 cancer researchers to determine their prior experience with sharing data and views on known drivers and inhibitors. RESULTS: We found that 45% of respondents had shared some data from their most recent empirical publication, with respondents who typically studied non-human research participants, or routinely worked with human genomic data, more likely to share than those who did not. A third of respondents added that they had previously shared data privately, with 74% indicating that doing so had also led to authorship opportunities or future collaborations for them. Journal and funder policies were reported to be the biggest general drivers toward sharing, whereas commercial interests, agreements with industrial sponsors and institutional policies were the biggest prohibitors. We show that researchers' decisions about whether to share data are also likely to be influenced by participants' desires. CONCLUSIONS: Our survey suggests that increased promotion and support by research institutions, alongside greater championing of data sharing by journals and funders, may motivate more researchers in oncology to share their data.

Trends and factors associated with illicit drug use in South Africa: Findings from multiple national population-based household surveys, 2002-2017.

BACKGROUND: Illicit drug use results in considerable global morbidity, but there is little data on its trends and factors associated with it in sub-Saharan Africa. We consider these questions using national data from South Africa for 2002-2017. METHODS: We analysed data among individuals aged 15 years or older from five national population-based household surveys in South Africa (2002-2017; n = 89,113). Recent drug use was defined as the last three-months use of illicit drugs, i.e., any use of cannabis, cocaine, amphetamine, inhalants, sedatives, hallucinogens, opioids, and/or other illicit drugs. Time trends in recent drug use were assessed using logistic regression. Multivariable logistic regression assessed the association between recent drug use and socio-demographic factors and between drug use and sexual risk behaviours, HIV-related and other well-being variables. RESULTS: The prevalence of recent drug use increased from 1·5% to 10·0% from 2002 to 2017, driven by increases in cannabis use (1·5% to 7·8%) and use of opioids (0·01% to 1·6%), cocaine (0·02% to 1·8%), or amphetamines (0·1% to 1·5%). In adjusted analyses, male gender, younger age, living in urban areas, mixed-ancestry or white ethnicity (compared to black-African), and unemployment were positively associated with recent drug use. Recent drug use was associated with: multiple sexual partners (adjusted odds ratio [aOR] 2·13, 95% confidence interval [CI]: 1·80-2·51); sexual debut before 15 years old (aOR 1·70, 95%CI: 1·29-2·23); hazardous/harmful alcohol use (aOR 2·50, 95%CI: 2·14-2·93) or alcohol dependence (aOR 3·33, 95%CI 2·92-3·80); ever experiencing intimate partner violence (aOR 1·56, 95%CI 1·12-2·17); psychological distress (aOR 1·53, 95%CI: 1·28-1·82); and lower chance of ever testing for HIV (aOR 0·89, 95%CI 0·80-1·00). Recent drug use was not associated with HIV positivity, condom use or being on antiretroviral therapy. CONCLUSION: Illicit drug use has increased substantially in South Africa and is associated with numerous socio-demographic characteristics, higher sexual risk behaviours and other well-being variables.

Modeling the impact of the COVID-19 pandemic on achieving HCV elimination amongst young and unstably housed people who inject drugs in San Francisco.

BACKGROUND: Young adult (18-30 years) people who inject drugs (PWID) face high hepatitis C virus (HCV) prevalence. In San Francisco, where >60% of PWID lack stable housing, barriers hinder HCV treatment access. We assessed progress towards the World Health Organization's (WHO) HCV elimination goal of an 80% reduction in incidence over 2015-2030, focusing on young (YPWID) and unstably housed PWID in San Francisco. METHODS: We developed a dynamic HCV transmission model among PWID, parameterized and calibrated using bio-behavioural survey datasets from San Francisco. This included 2018 estimates for the antibody-prevalence among PWID (77%) and care cascade estimates for HCV for YPWID (72% aware of their status and 33% ever initiating treatment). Based on programmatic data, we assumed a 53.8% reduction in testing and 40.7% decrease in treatment from 2020 due to the COVID-19 pandemic, which partially rebounded from April 2021 with testing rates then being 31.1% lower than pre-pandemic rates and treatment numbers being 19.5% lower. We simulated different scenarios of how services changed after the pandemic to project whether elimination goals would be met. RESULTS: Continuing post-pandemic rates of testing and treatment, the model projects an 83.3% (95% credibility interval [95% CrI]:60.6-96.9%) decrease in incidence among PWID over 2015-2030 to 1.5/100pyrs (95% CrI:0.3-4.4) in 2030. The probability of achieving the elimination goal by 2030 is 62.0%. Among YPWID and unstably housed PWID, the probability of achieving the elimination goal by 2030 is 54.8 and 67.6%, respectively. Importantly, further increasing testing and treatment rates to pre-pandemic levels by 2025 only results in a small increase in the probability (67.5%) of the elimination goal being achieved among all PWID by 2030, while increased coverage of medication for opioid use disorder among YPWID and/or housing interventions results in the probability of achieving elimination increasing to over 75%. CONCLUSION: The COVID-19 pandemic impeded progress toward achieving HCV elimination. Our findings indicate that existing partial rebounds in HCV testing and treatment may achieve the elimination goal by 2030, with an additional scale-up of interventions aimed at YPWID or unstably housed PWID ensuring San Francisco is likely to achieve elimination by 2030.