COMMA: A microsimulation model investigating the impact of COVID-19 lockdowns on depression

Abstract

Abstract Background The COVID-19 pandemic has highlighted the importance of tailored preparedness plans to cope with potential disease outbreaks. During the pandemic, a major challenge for policymakers was finding the balance between containing the disease and supporting mental health and well-being. Although COVID-19 lockdowns and accompanying restrictions mitigated the spread of the virus, many countries witnessed significant increasing rate of depression during these periods. It is not clear the extent to which different lockdown scenarios impacted depression rates, and whether these effects might be felt in the future. Methods In this study, a microsimulation model, COMMA (COvid Mental-health Model with Agents), was developed to simulate behaviors of individuals under different lockdown scenarios, both actual and hypothetical, and of varying duration, sequence and severity. COMMA helps to understand how the prevalence of depression changed in each scenario. The characteristics of simulated population were synthesized based on health cohort data from Lifelines. The data included participants living in the Dutch city of Groningen during the pandemic. Results Preliminary results show that hard lockdowns resulted in increased rates of depression. Light lockdowns had smaller effects on depression rates. In all simulations with lockdowns, depression rates had almost rebounded to pre-COVID levels, although they remained slightly higher than in simulations with no lockdowns at all. Conclusions COMMA is the first microsimulation model to examine COVID-19 lockdowns’ impact on mental health, and offers important insights into the actual, hypothetical and potential effects of lockdowns on mental health. Key messages • Simulations showed that lockdown severity mattered, with hard lockdowns resulting in elevated rates of depression. • This study highlights that lockdowns are not without secondary health consequences, which may be important for policymakers to consider during future pandemics.