Psychopathology and Resilience Following Strict COVID-19 Lockdowns in Hubei, China: Examining Person- and Context-Level Predictors for Longitudinal Trajectories

In Hubei, China, where the COVID-19 epidemic first emerged, the government has enforced strict quarantine and lockdown measures. Longitudinal studies suggest that the impact of adverse events on psychological adjustment is highly heterogenous. To better understand protective and risk factors that predict longitudinal psychopathology and resilience following strict COVID-19 lockdowns, this study used unsupervised machine learning to identify half-year longitudinal trajectories (April, June, August, and October, 2020) of three mental health outcomes (depression, anxiety, and PTSD) among a sample of Hubei residents (N = 326), assessed a broad range of person- and context-level predictors, and applied LASSO logistic regression, a supervised machine learning approach, to select best predictors for trajectory memberships of resilience and chronic psychopathology. Across outcomes, most individuals remained resilient. Models with both person- and context-level predictors showed excellent predictive accuracy, except for models predicting chronic anxiety. The person-level models showed either good or excellent predictive accuracy. The context-level models showed good predictive accuracy for depression trajectories but were only fair in predicting trajectories of anxiety and PTSD. Overall, the most critical person-level predictors were worry, optimism, fear of COVID, and coping flexibility, whereas important context-level predictors included features of stressful life events, community satisfaction, and family support. This study identified clinical patterns of response to COVID-19 lockdowns and used a combination of risk and protective factors to accurately differentiate these patterns. These findings have implications for clinical risk identifications and interventions in the context of potential trauma.

The Differential Impact of Multimorbidity Patterns and Subsequent Accumulation on Longitudinal Trajectories of Physical Function Decline in a Population-based Cohort of Older People

Background Although both the patterns and accumulation of multimorbidity are important for predicting physical function, studies have not simultaneously examined their impact on functional decline. This study aimed to associate multimorbidity patterns and subsequently developed conditions with longitudinal trajectories of functional decline, and it tested whether the effects of newly developed conditions on functional decline varied across distinct multimorbidity patterns. Methods We included 6,634 participants aged at least 60 years from the China Health and Retirement Longitudinal Survey. Latent class analysis identified multimorbidity patterns from 14 chronic conditions. Mixed negative binomial models estimated the changes in physical function measured across four waves as a function of multimorbidity patterns, subsequently developed conditions and their interactions. Results Five distinct patterns were identified three years before wave 1: stomach/arthritis (15.7%), cardiometabolic (6.7%), arthritis/hypertension (47.9%), hepatorenal/multi-system (18.3%), and lung/asthma (11.4%). The hepatorenal/multi-system and the lung/asthma pattern were associated with worse baseline physical function, and the hypertension/arthritis pattern was associated with greater decline of physical function. The effect of developing new conditions on decline of physical function over time was most evident for individuals from the cardiometabolic pattern. Discussion Considering both the combinations and progressive nature of multimorbidity is important for identifying individuals at greater risk of disability. Future studies are warranted to differentiate the factors responsible for the progression of chronic conditions in distinct multimorbidity patterns and investigate the potential implications for improved prediction of functional decline.