Preserving equipoise and performing randomised trials for COVID-19 social distancing interventions

The Ethical Obligation for Research During Public Health Emergencies: Insights From the COVID-19 Pandemic

In times of crises, public health leaders may claim that trials of public health interventions are unethical. One reason for this claim can be that equipoise-i.e. a situation of uncertainty and/or disagreement among experts about the evidence regarding an intervention-has been disturbed by a change of collective expert views. Some might claim that equipoise is disturbed if the majority of experts believe that emergency public health interventions are likely to be more beneficial than harmful. However, such beliefs are not always justified: where high quality research has not been conducted, there is often considerable residual uncertainty about whether interventions offer net benefits. In this essay we argue that high-quality research, namely by means of well-designed randomized trials, is ethically obligatory before, during, and after implementing policies in public health emergencies (PHEs). We contend that this standard applies to both pharmaceutical and non-pharmaceutical interventions, and we elaborate an account of equipoise that captures key features of debates in the recent pandemic. We build our case by analyzing research strategies employed during the COVID-19 pandemic regarding drugs, vaccines, and non-pharmaceutical interventions; and by providing responses to possible objections. Finally, we propose a public health policy reform: whenever a policy implemented during a PHE is not grounded in high-quality evidence that expected benefits outweigh harms, there should be a planned approach to generate high-quality evidence, with review of emerging data at preset time points. These preset timepoints guarantee that policymakers pause to review emerging evidence and consider ceasing ineffective or even harmful policies, thereby improving transparency and accountability, as well as permitting the redirection of resources to more effective or beneficial interventions.

Randomized trials on non-pharmaceutical interventions for COVID-19: a scoping review

OBJECTIVE

We aimed at providing a systematic overview of randomised trials assessing non-pharmaceutical interventions (NPIs) to prevent COVID-19.

DESIGN

Scoping review.

METHODS

We included all randomised trials assessing NPIs to prevent COVID-19 in any country and setting registered in ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform using the COVID-evidence platform (until 17 August 2021). We searched for corresponding publications in MEDLINE/PubMed, Google Scholar, the Living Overview of Evidence platform, and the Cochrane COVID-19 registry as well as for results posted in registries (until 14 November 2021). Descriptive statistics using numbers and percentages were used in the narrative synthesis of the results.

RESULTS

We identified 41 randomised trials. Of them, 12 were completed (29.3%) including 9 with published results. The 41 trials planned to recruit a median of 1700 participants (IQR 588-9500, range 30-35 256 399) with a median planned duration of 8 months (IQR 3-14, range 1-24). Most came from the USA (n=11, 26.8%). The trials mostly assessed protective equipment (n=11, 26.8%), COVID-19-related information and education programmes (n=9, 22.0%), access to mass events under specific safety measures (n=5, 12.2%), testing and screening strategies (n=5, 12.2%) and hygiene management (n=5, 12.2%).

CONCLUSIONS

Worldwide, 41 randomised trials assessing NPIs have been initiated with published results available to inform policy decisions for only 9 of them. A long-term research agenda including behavioural, environmental, social and systems level interventions is urgently needed to guide policies and practices in the current and future public health emergencies.

Aggressive measures, rising inequalities, and mass formation during the COVID-19 crisis: An overview and proposed way forward

A series of aggressive restrictive measures were adopted around the world in 2020-2022 to attempt to prevent SARS-CoV-2 from spreading. However, it has become increasingly clear the most aggressive (lockdown) response strategies may involve negative side-effects such as a steep increase in poverty, hunger, and inequalities. Several economic, educational, and health repercussions have fallen disproportionately on children, students, young workers, and especially on groups with pre-existing inequalities such as low-income families, ethnic minorities, and women. This has led to a vicious cycle of rising inequalities and health issues. For example, educational and financial security decreased along with rising unemployment and loss of life purpose. Domestic violence surged due to dysfunctional families being forced to spend more time with each other. In the current narrative and scoping review, we describe macro-dynamics that are taking place because of aggressive public health policies and psychological tactics to influence public behavior, such as mass formation and crowd behavior. Coupled with the effect of inequalities, we describe how these factors can interact toward aggravating ripple effects. In light of evidence regarding the health, economic and social costs, that likely far outweigh potential benefits, the authors suggest that, first, where applicable, aggressive lockdown policies should be reversed and their re-adoption in the future should be avoided. If measures are needed, these should be non-disruptive. Second, it is important to assess dispassionately the damage done by aggressive measures and offer ways to alleviate the burden and long-term effects. Third, the structures in place that have led to counterproductive policies should be assessed and ways should be sought to optimize decision-making, such as counteracting groupthink and increasing the level of reflexivity. Finally, a package of scalable positive psychology interventions is suggested to counteract the damage done and improve humanity's prospects.

Pandemics, policy, and the power of paradigm: will COVID-19 lead to a new scientific revolution?

Critical historical analysis of the 19th-century cholera and 21st-century coronavirus-19 (COVID-19) pandemics suggests that in conflicts over pandemic-mitigation policies, the professional backgrounds of principal opponents reveal dominant and minority scientific paradigms, presaging possible epistemological shifts. Epistemic conflict over cholera helped spur biomedical expertise as the dominant paradigm for U.S. public health science and policy beginning in the 20th century. This paradigm was reflected in federal government reliance on infectious disease physicians as the primary scientific decision makers in the COVID-19 pandemic. Similarly, epistemic conflict over challenges to behavioral and social well-being in 2020 may highlight discordance between the dominant biomedical paradigm used in making federal policy and the inherently holistic impact of that policy on population health, suggesting need for a new paradigm of multidisciplinary scientific engagement. Because population-wide public health initiatives affect many aspects of health-physiological, psychological, behavioral, and social-that are best measured and interpreted by experts in these respective fields, multidisciplinary scientific engagement would facilitate optimal, holistic evaluation of policy benefits and harms. This multidisciplinary approach, analogous to that currently recommended in medical management of chronic disease, would advance epidemiological research to inform evidence-based policy for public health crises in which U.S. population-wide interventions are contemplated.

COVID-19: A Catalyst for Transforming Randomized Trials

The coronavirus disease 2019 (COVID-19) pandemic incited a global clinical trial research agenda of unprecedented speed and high volume. This expedited research activity in a time of crisis produced both successes and failures that offer valuable learning opportunities for the scientific community to consider. Successes include the implementation of large adaptive and pragmatic trials as well as burgeoning efforts toward rapid data synthesis and open science principles. Conversely, notable failures include: (1) inadequate study design and execution; (2) data reversal, fraud, and retraction; and (3) research duplication and waste. Other challenges that became highlighted were the need to find unbiased designs for investigating complex, nonpharmaceutical interventions and the use of routinely collected data for outcomes assessment. This article discusses these issues juxtaposing the COVID-19 trials experience against trials in anesthesiology and other fields. These lessons may serve as a positive catalyst for transforming future clinical trial research.

Pearls on science, collaboration, and mentorship in health research: A masterclass conversation with Dr. John Ioannidis

Effective collaboration and mentorship are essential to success in a career of health research. We summarize our conversation with Dr. John Ioannidis, professor at Stanford University, author of the most accessed manuscript in the history of the Public Library of Science, and one of the most cited scientists in history. Dr. Ioannidis was invited for a question and answer session as part of a graduate-level course on biostatistical collaboration hosted at McMaster University in December 2020. This text provides insight into the experiences and pearls he shared, that we hope will inspire and guide other researchers early or junior in their careers. He emphasized the importance of passion, enthusiasm and a sincere pursuit for high quality research as being the cornerstones to success and continued productivity in this field.

Benefit of COVID-19 vaccination accounting for potential risk compensation

People receiving COVID-19 vaccines may subsequently markedly increase their previously suppressed exposure risk. A simple model can evaluate the benefit of vaccination to the vaccinated (index) person and others exposed to that person; and calculate the amount of risk compensation required to eliminate all the benefits or to halve the benefit. As shown, 2.5-fold increase in exposure will eliminate the benefit of a vaccine of moderate efficacy (E = 0.6) unless the probability of infection in the population of interest is very high. With very high vaccine efficacy (E = 0.95), substantial benefit is maintained except in situations where there is a very low probability of infection in the population. If the vaccine efficacy decreases to 0.8, the benefit gets eroded easily with modest risk compensation. Risk compensation may markedly affect the benefit of COVID-19 vaccination, especially if vaccine efficacy in real-life or specific high-risk populations (e.g., nursing home residents) is not very high.

Challenges and Lessons Learned From COVID-19 Trials: Should We Be Doing Clinical Trials Differently?

The COVID-19 crisis led to a flurry of clinical trials activity. The COVID-evidence database shows 2814 COVID-19 randomized trials registered as of February 16, 2021. Most were small (only 18% have a planned sample size > 500) and the rare completed ones have not provided published results promptly (only 283 trial publications as of February 2021). Small randomized trials and observational, nonrandomized analyses have not had a successful track record and have generated misleading expectations. Different large trials on the same intervention have generally been far more efficient in producing timely and consistent evidence. The rapid generation of evidence and accelerated dissemination of results have led to new challenges for systematic reviews and meta-analyses (eg, rapid, living, and scoping reviews). Pressure to regulatory agencies has also mounted with massive emergency authorizations, but some of them have had to be revoked. Pandemic circumstances have disrupted the way trials are conducted; therefore, new methods have been developed and adopted more widely to facilitate recruitment, consent, and overall trial conduct. On the basis of the COVID-19 experience and its challenges, planning of several large, efficient trials, and wider use of adaptive designs might change the future of clinical research. Pragmatism, integration in clinical care, efficient administration, promotion of collaborative structures, and enhanced integration of existing data and facilities might be several of the legacies of COVID-19 on future randomized trials.

Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent

Objective

To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models.

Study design and setting

We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons.

Results

While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons.

Conclusion

Inferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model-dependent.

Precision shielding for COVID-19: metrics of assessment and feasibility of deployment

The ability to preferentially protect high-risk groups in COVID-19 is hotly debated. Here, the aim is to present simple metrics of such precision shielding of people at high risk of death after infection by SARS-CoV-2; demonstrate how they can estimated; and examine whether precision shielding was successfully achieved in the first COVID-19 wave. The shielding ratio, S, is defined as the ratio of prevalence of infection among people in a high-risk group versus among people in a low-risk group. The contrasted risk groups examined here are according to age (≥70 vs <70 years), and institutionalised (nursing home) setting. For age-related precision shielding, data were used from large seroprevalence studies with separate prevalence data for elderly versus non-elderly and with at least 1000 assessed people≥70 years old. For setting-related precision shielding, data were analysed from 10 countries where information was available on numbers of nursing home residents, proportion of nursing home residents among COVID-19 deaths and overall population infection fatality rate (IFR). Across 17 seroprevalence studies, the shielding ratio S for elderly versus non-elderly varied between 0.4 (substantial shielding) and 1.6 (substantial inverse protection, that is, low-risk people being protected more than high-risk people). Five studies in the USA all yielded S=0.4-0.8, consistent with some shielding being achieved, while two studies in China yielded S=1.5-1.6, consistent with inverse protection. Assuming 25% IFR among nursing home residents, S values for nursing home residents ranged from 0.07 to 3.1. The best shielding was seen in South Korea (S=0.07) and modest shielding was achieved in Israel, Slovenia, Germany and Denmark. No shielding was achieved in Hungary and Sweden. In Belgium (S=1.9), the UK (S=2.2) and Spain (S=3.1), nursing home residents were far more frequently infected than the rest of the population. In conclusion, the experience from the first wave of COVID-19 suggests that different locations and settings varied markedly in the extent to which they protected high-risk groups. Both effective precision shielding and detrimental inverse protection can happen in real-life circumstances. COVID-19 interventions should seek to achieve maximal precision shielding.