Risk-Based Monitoring in Clinical Trials: 2021 Update.

Clinical trial quality depends on ensuring participant safety and data integrity, which require careful management throughout the trial lifecycle, from protocol development to final data analysis and submission. Recent developments-including new regulatory requirements, emerging technologies, and trial decentralization-have increased adoption of risk-based monitoring (RBM) and its parent framework, risk-based quality management (RBQM) in clinical trials. The Association of Clinical Research Organizations (ACRO), recognizing the growing importance of these approaches, initiated an ongoing RBM/RBQM landscape survey project in 2019 to track adoption of the eight functional components of RBQM. Here we present results from the third annual survey, which included data from 4889 clinical trials ongoing in 2021. At least one RBQM component was implemented in 88% of trials in the 2021 survey, compared with 77% in 2020 and 53% in 2019. The most frequently implemented components in 2021 were initial and ongoing risk assessments (80 and 78% of trials, respectively). Only 7% of RBQM trials were Phase IV, while the proportions of Phase I-III trials ranged 27-36%. Small trials (< 300 participants) accounted for 60% of those implementing RBQM. The therapeutic areas with the largest number of RBQM trials were oncology (38%), neurology (10%), and infectious diseases (9%). The 2021 survey confirmed a pattern of increasing RBM/RBQM adoption seen in earlier surveys, with risk assessments, which have broad regulatory support, driving RBQM growth; however, one area requiring further development is implementation of centralized monitoring combined with reductions in source data verification (SDV) and source data review (SDR).

A Repeat Pattern of Founder Events for SARS-CoV-2 Variants in Alaska.

Alaska is a unique US state because of its large size, geographically disparate population density, and physical distance from the contiguous United States. Here, we describe a pattern of SARS-CoV-2 variant emergence across Alaska reflective of these differences. Using genomic data, we found that in Alaska, the Omicron sublineage BA.2.3 overtook BA.1.1 by the week of 27 February 2022, reaching 48.5% of sequenced cases. On the contrary, in the contiguous United States, BA.1.1 dominated cases for longer, eventually being displaced by BA.2 sublineages other than BA.2.3. BA.2.3 only reached a prevalence of 10.9% in the contiguous United States. Using phylogenetics, we found evidence of potential origins of the two major clades of BA.2.3 in Alaska and with logistic regression estimated how it emerged and spread throughout the state. The combined evidence is suggestive of founder events in Alaska and is reflective of how Alaska's unique dynamics influence the emergence of SARS-CoV-2 variants.

Pattern of SARS-CoV-2 variant B.1.1.519 emergence in Alaska.

Alaska has the lowest population density in the United States (US) with a mix of urban centers and isolated rural communities. Alaska's distinct population dynamics compared to the contiguous US may have contributed to unique patterns of SARS-CoV-2 variants observed in early 2021. Here we examined 2323 SARS-CoV-2 genomes from Alaska and 278,635 from the contiguous US collected from December 2020 through June 2021 because of the notable emergence and spread of lineage B.1.1.519 in Alaska. We found that B.1.1.519 was consistently detected from late January through June of 2021 in Alaska with a peak prevalence in April of 77.9% unlike the rest of the US at 4.6%. The earlier emergence of B.1.1.519 coincided with a later peak of Alpha (B.1.1.7) compared to the contiguous US. We also observed differences in variant composition over time between the two most populated regions of Alaska and a modest increase in COVID-19 cases during the peak incidence of B.1.1.519. However, it is difficult to disentangle how social dynamics conflated changes in COVID-19 during this time. We suggest that the viral characteristics, such as amino acid substitutions in the spike protein, likely contributed to the unique spread of B.1.1.519 in Alaska.

United States marijuana legalization and opioid mortality epidemic during 2010-2020 and pandemic implications.

BACKGROUND: The hypothesis that marijuana availability reduces opioid mortality merits more complete testing, especially in a country with the world's highest opioid death rate and 2nd highest cannabis-use-disorder prevalence. METHODS: The United States opioid mortality rate was compared in states and District of Columbia that had implemented marijuana legalization with states that had not, by applying joinpoint methodology to Centers for Disease Control and Prevention data. Variables included race/ethnicity and fentanyl-type opioids (fentanyls). RESULTS: After the same rates during 2010-2012, the opioid mortality rate increased more rapidly in marijuana-legalizing than non-legalizing jurisdictions (2010-2020 annual pairwise comparison p = 0.003 for all opioids and p = 0.0004 for fentanyls). During the past decade, all four major race/ethnicities in the U.S. had evidence for a statistically-significant greater increase in opioid mortality rates in legalizing than non-legalizing jurisdictions. Among legalizing jurisdictions, the greatest mortality rate increase for all opioids was in non-Hispanic blacks (27%/year, p = 0.0001) and for fentanyls in Hispanics (45%/year, p = 0.0000008). The greatest annual opioid mortality increase occurred in 2020, the first year of the COVID-19 pandemic, with non-Hispanic blacks having the greatest increase in legalizing vs. non-legalizing opioid-death-rate difference, from 32% higher in legalizing jurisdictions in 2019 to more than double in 2020. CONCLUSIONS: Instead of supporting the marijuana protection hypothesis, ecologic associations at the national level suggest that marijuana legalization has contributed to the U.S.'s opioid epidemic in all major races/ethnicities, and especially in blacks. If so, the increased use of marijuana during the 2020-2022 pandemic may thereby worsen the country's opioid crisis.

Risk-Based Monitoring in Clinical Trials: Increased Adoption Throughout 2020.

With the emergence of new technologies for data collection, the continued impact of the COVID-19 pandemic, and the increasing number of partially or fully decentralized clinical trials (DCTs), the importance of risk-based monitoring (RBM) and the larger risk-based quality management (RBQM) framework in clinical trial management is increasing. RBM and RBQM focus on the detection of events or trends that impact trial quality in terms of participant safety and data integrity. In 2019, the Association of Clinical Research Organizations (ACRO) began a landscape survey of RBM/RBQM implementation in ongoing clinical trials. Initial results of this survey, representing full-year data for 2019, were reported previously. Here, we present full-year landscape data for 2020 drawn from 5,987 clinical trials ongoing at the end of 2020, including 908 new studies started that year. Of these trials, 77% implemented at least one RBM/RBQM component, an increase from 47% for studies ongoing at the end of 2019. We also observed increased implementation for three of the five RBM components included in the survey. Centralized monitoring decreased nominally in 2020 compared with 2019. Although the percentages of 2020 trials incorporating reduced source data verification (SDV) and reduced source data review (SDR) increased from 2019 to 2020, these numbers are still low considering the large percentage of trials implementing at least one RBQM component. In the current clinical trial landscape, as more DCTs are launched and new data collection technologies are implemented, there remains a pressing need for greater use of centralized monitoring coupled with reductions in SDR/SDV and, ultimately, greater adoption of RBM and RBQM.

Risk-Based Monitoring in Clinical Trials: Past, Present, and Future.

Risk-based monitoring (RBM) is a powerful tool for efficiently ensuring patient safety and data integrity in a clinical trial, enhancing overall trial quality. To better understand the state of RBM implementation across the clinical trial industry, the Association of Clinical Research Organizations (ACRO) conducted a landscape survey among its member companies across 6,513 clinical trials ongoing at the end of 2019. Of these trials, 22% included at least 1 of the 5 RBM components: key risk indicators (KRIs), centralized monitoring, off-site/remote-site monitoring, reduced source data verification (SDV), and reduced source document review (SDR). The implementation rates for the individual RBM components ranged 8%-19%, with the most frequently implemented component being centralized monitoring and the least frequently implemented being reduced SDR. When the COVID-19 pandemic emerged in early 2020, additional data were collected to assess its impact on trial monitoring, focusing specifically on trials switching from on-site monitoring to off-site/remote-site monitoring. These mid-pandemic data show that the vast majority of monitoring visits were on-site in February 2020, but an even higher percentage were off-site in April, corresponding with the first peak of the pandemic. Despite this shift, similar numbers of non-COVID-related protocol deviations were detected from February through June, suggesting little or no reduction in monitoring effectiveness. The pre- and mid-pandemic data provide two very different snapshots of RBM implementation, but both support the need to promote adoption of this approach while also highlighting an opportunity to capitalize on the recent shift toward greater RBM uptake in a post-pandemic environment.