The COVID-19 pandemic raised concerns about the mental health of an already burdened healthcare workforce. This study examined mental health trajectories among healthcare workers (HCWs) across the pandemic and identified personal and employment factors associated with different symptom patterns.

Longitudinal data were drawn from the NHS CHECK cohort, including clinical and non-clinical staff from 18 NHS Trusts in England (April 2020–April 2023). Growth curve and growth mixture models identified latent classes of HCWs characterized by distinct trajectories of probable common mental disorders. Secondary outcomes included anxiety, depression, alcohol misuse, and post-traumatic stress symptoms. Logistic regression examined associations between baseline personal and employment characteristics and class membership.

The analytical sample included 22,764 participants. For each outcome, growth mixture models identified two latent classes. Approximately 31% of HCWs experienced persistently high symptoms of probable common mental disorders, while 69% experienced persistently low symptoms. Similar patterns were observed for secondary outcomes, with small subgroups demonstrating worsening symptoms followed by improvement. Logistic regression analyses showed that being female, younger, single, working as a nurse, or having a pre-existing mental health diagnosis increased the odds of belonging to a high symptom class. Perceived support from colleagues and managers was protective.

While many HCWs reported consistently low mental health symptom levels, almost a third belonged to a latent class characterized by persistently high symptoms across all time points. These findings underscore the need for mental health support for vulnerable HCW groups, embedded within routine NHS practice rather than limited to crisis periods.

In times of repeated disaster events, including natural disasters and pandemics, public health workers must recover rapidly to respond to subsequent events. Understanding predictors of time to recovery and developing predictive models of time to recovery can aid planning and management.

We examined 681 public health workers (21-72 y, M(standard deviation [SD]) = 48.25(10.15); 79% female) 1 mo before (T1) and 9 mo after (T2) the 2005 hurricane season. Demographics, trauma history, social support, time to recover from previous hurricane season, and predisaster work productivity were assessed at T1. T2 assessed previous disaster work, initial emotional response, and personal hurricane injury/damage. The primary outcome was time to recover from the most recent hurricane event.

Multivariate analyses found that less support (T1; odds ratio [OR] = .74[95% confidence interval [CI] = .60-.92]), longer previous recovery time (T1; OR = 5.22[95%CI = 3.01-9.08]), lower predisaster work productivity (T1; OR = 1.98[95%CI = 1.08-3.61]), disaster-related personal injury/damage (T2; OR = 3.08[95%CI = 1.70-5.58]), and initial emotional response (T2; OR = 1.71[95%CI = 1.34-2.19]) were associated with longer recovery time (T2).

Recovery time was adversely affected in disaster responders with a history of longer recovery time, personal injury/damage, lower work productivity following prior hurricanes, and initial emotional response, whereas responders with social support had shorter recovery time. Predictors of recovery time should be a focus for disaster preparedness planners.