Evaluating the Effect of COVID-19 Pandemic Lockdown on Long-Term Care Residents' Mental Health: A Data-Driven Approach in New Brunswick.

ABSTRACT

Long-term care (LTC) residents, isolated because of the COVID-19 pandemic, are at increased risk for negative mental health outcomes. The purpose of our article is to demonstrate how the interRAI LTC facility (LTCF) assessment can inform clinical care and evaluate the effect of strategies to mitigate worsening mental health outcomes during the COVID-19 pandemic. We present a supporting analysis of the effects of lockdown in homes without COVID-19 outbreaks on depression, delirium, and behavior problems in a network of 7 LTC homes in New Brunswick, Canada, where mitigative strategies were deployed to minimize poor mental health outcomes (eg, virtual visits and increased student volunteers). This network meets regularly to review performance on risk-adjusted quality of care indicators from the interRAI LTCF and share learning through a community of practice model. We included 4209 assessments from 765 LTC residents between January 2017 to June 2020 and modeled the change within and between residents for depression, delirium, and behavioral problems over time with longitudinal generalized estimating equations. Though the number of residents who had in-person visits with family decreased from 73.2% before to 17.9% during lockdown (chi square, P < .001), the number of residents experiencing delirium (4.5%-3.5%, P = .51) and behavioral problems (35.5%-30.2%, P = .19) did not change. The proportion of residents with indications of depression decreased from 19.9% before to 11.5% during lockdown (P < .002). The final multivariate models indicate that the effect of lockdown was not statistically significant on depression, delirium, or behavioral problems. Our analyses demonstrate that poor mental health outcomes associated with lockdown can be mitigated with thoughtful intervention and ongoing evaluation with clinical information systems. Policy makers can use outputs to guide resource deployment, and researchers can examine the data to identify better management strategies for when pandemic strikes again.