ABSTRACT
Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Bayes Theorem , Pandemics , Search Engine , Communicable Disease ControlABSTRACT
OBJECTIVES: Our goal was to estimate the relative importance assigned to health technology assessment (HTA) criteria by stakeholders involved in the HTA process. HTA is an increasingly common framework used in the appraisal of drugs for public reimbursement. It identifies clinical, economic, social, and organizational criteria to be considered. The criteria can vary across jurisdictions and are typically appraised by multidisciplinary expert committees. Guidance on the relative weighing of criteria is often absent. METHODS: We elicited stakeholders' preferences using a single-scenario discrete choice experiment and a best-worst scaling model with conviction scores to assess the weights assigned to selected criteria by HTA stakeholders. We recruited 111 HTA stakeholders across multiple jurisdictions, including members of expert committees, clinical and economic experts, patients, and public payer representatives. Each judged twelve hypothetical cancer drug profiles for suitability for public funding and identified which characteristics were best and worst. In addition to standard discrete choice experiment and best-worst scaling models, we estimated a hybrid model to obtain a ranking of criteria by importance they played in the appraisal. RESULTS: A strong clinical benefit proved the most important criterion, followed by cost considerations, presence of adverse events, and availability of other treatments. The importance of clinical benefit was moderated by unmet need, adverse events, and number of patients. CONCLUSION: Policymakers might want to consider providing an explicit weighing scheme, or moving to a 2-stage selection process with an assessment of the quality of clinical evidence as a gatekeeping step for a full HTA review.
