Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens.

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

Application of multi-criteria decision analysis techniques and decision support framework for informing plant select agent designation and decision making.

The United States Department of Agriculture (USDA) Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents (Select Agents List) that threaten crops of economic importance to the United States and regulates the procedures governing containment, incident response, and the security of entities working with them. Every 2 years the USDA DASAT reviews their select agent list, utilizing assessments by subject matter experts (SMEs) to rank the agents. We explored the applicability of multi-criteria decision analysis (MCDA) techniques and a decision support framework (DSF) to support the USDA DASAT biennial review process. The evaluation includes both current and non-select agents to provide a robust assessment. We initially conducted a literature review of 16 pathogens against 9 criteria for assessing plant health and bioterrorism risk and documented the findings to support this analysis. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for ensuring accuracy. Scoring criteria were adopted to ensure consistency. The MCDA supported the expectation that select agents would rank high on the relative risk scale when considering the agricultural consequences of a bioterrorism attack; however, application of analytical thresholds as a basis for designating select agents led to some exceptions to current designations. A second analytical approach used agent-specific data to designate key criteria in a DSF logic tree format to identify pathogens of low concern that can be ruled out for further consideration as select agents. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

Assessing the impact of COVID-19 on psychiatric clinical trials.

OBJECTIVE: COVID-19 and associated measures to control the spread of the COVID-19 has significantly impacted clinical research. This study aimed to determine the impact COVID-19 has had on psychiatric clinical trials and to assess whether certain trial areas or trial types were differentially affected. METHODS: We used information from ClinicalTrials.gov, the largest online database of clinical trial information, to examine changes in psychiatric clinical trials from January 2010-December 2020. RESULTS: Clinical trial initiation decreased in 2020, with a year-on-year percent change in trial initiation of -5.4% versus an expected percent change based on forecasting observed trends from 2010 to 2019 of 8.6%. When broken down by disease area, the distribution of trials observed in 2020 was significantly different from the predicted distribution (p < 0.00001). The greatest decrease in trial initiation was seen in Schizophrenia-specific trials, with an observed percent change of -29.2% versus an expected percent change of 3.2%. Conversely, anxiety trials saw a significant increase in trial initiation during 2020, with an observed percent change of 24.6% versus an expected percent change of 16.0%. When assessing interventional versus observational studies, data showed a significant increase in initiation of observational psychiatric clinical trials (p < 0.05), and a significant decrease in initiation of interventional psychiatric clinical trials (p < 0.01). When data was analyzed on a month-by-month time scale, 7/12 months in 2020 showed significant decreases when compared to initiation during matching months over prior years, and a single month, June, showed a significant increase. CONCLUSION: COVID-19 has had significant impacts on the initiation of psychiatric clinical trials over 2020, and this decrease in trial initiation may have long-term impacts on the development and assessment of psychiatric treatments and therapeutics.