Mental health and wellbeing of health and aged care workers in Australia, May 2021 - June 2022: a longitudinal cohort study.

OBJECTIVES: To assess the mental health and wellbeing of health and aged care workers in Australia during the second and third years of the coronavirus disease 2019 (COVID-19) pandemic, overall and by occupation group. DESIGN, SETTING, PARTICIPANTS: Longitudinal cohort study of health and aged care workers (ambulance, hospitals, primary care, residential aged care) in Victoria: May-July 2021 (survey 1), October-December 2021 (survey 2), and May-June 2022 (survey 3). MAIN OUTCOME MEASURES: Proportions of respondents (adjusted for age, gender, socio-economic status) reporting moderate to severe symptoms of depression (Patient Health Questionnaire-9, PHQ-9), anxiety (Generalized Anxiety Disorder scale, GAD-7), or post-traumatic stress (Impact of Event Scale-6, IES-6), burnout (abbreviated Maslach Burnout Inventory, aMBI), or high optimism (10-point visual analogue scale); mean scores (adjusted for age, gender, socio-economic status) for wellbeing (Personal Wellbeing Index-Adult, PWI-A) and resilience (Connor Davidson Resilience Scale 2, CD-RISC-2). RESULTS: A total of 1667 people responded to at least one survey (survey 1, 989; survey 2, 1153; survey 3, 993; response rate, 3.3%). Overall, 1211 survey responses were from women (72.6%); most respondents were hospital workers (1289, 77.3%) or ambulance staff (315, 18.9%). The adjusted proportions of respondents who reported moderate to severe symptoms of depression (survey 1, 16.4%; survey 2, 22.6%; survey 3, 19.2%), anxiety (survey 1, 8.8%; survey 2, 16.0%; survey 3, 11.0%), or post-traumatic stress (survey 1, 14.6%; survey 2, 35.1%; survey 3, 14.9%) were each largest for survey 2. The adjusted proportions of participants who reported moderate to severe symptoms of burnout were higher in surveys 2 and 3 than in survey 1, and the proportions who reported high optimism were smaller in surveys 2 and 3 than in survey 1. Adjusted mean scores for wellbeing and resilience were similar at surveys 2 and 3 and lower than at survey 1. The magnitude but not the patterns of change differed by occupation group. CONCLUSION: Burnout was more frequently reported and mean wellbeing and resilience scores were lower in mid-2022 than in mid-2021 for Victorian health and aged care workers who participated in our study. Evidence-based mental health and wellbeing programs for workers in health care organisations are needed. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12621000533897 (observational study; retrospective).

Mental Health Outcomes in Australian Healthcare and Aged-Care Workers during the Second Year of the COVID-19 Pandemic.

OBJECTIVE: the COVID-19 pandemic has incurred psychological risks for healthcare workers (HCWs). We established a Victorian HCW cohort (the Coronavirus in Victorian Healthcare and Aged-Care Workers (COVIC-HA) cohort study) to examine COVID-19 impacts on HCWs and assess organisational responses over time. METHODS: mixed-methods cohort study, with baseline data collected via an online survey (7 May-18 July 2021) across four healthcare settings: ambulance, hospitals, primary care, and residential aged-care. Outcomes included self-reported symptoms of depression, anxiety, post-traumatic stress (PTS), wellbeing, burnout, and resilience, measured using validated tools. Work and home-related COVID-19 impacts and perceptions of workplace responses were also captured. RESULTS: among 984 HCWs, symptoms of clinically significant depression, anxiety, and PTS were reported by 22.5%, 14.0%, and 20.4%, respectively, highest among paramedics and nurses. Emotional exhaustion reflecting moderate-severe burnout was reported by 65.1%. Concerns about contracting COVID-19 at work and transmitting COVID-19 were common, but 91.2% felt well-informed on workplace changes and 78.3% reported that support services were available. CONCLUSIONS: Australian HCWs employed during 2021 experienced adverse mental health outcomes, with prevalence differences observed according to occupation. Longitudinal evidence is needed to inform workplace strategies that support the physical and mental wellbeing of HCWs at organisational and state policy levels.

Understanding how Victoria, Australia gained control of its second COVID-19 wave.

During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria's second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2-56.4%) and of face coverings of 45.9% (95%CrI 32.9-55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important.

Sustaining effective COVID-19 control in Malaysia through large-scale vaccination.

INTRODUCTION: As of 3rd June 2021, Malaysia is experiencing a resurgence of COVID-19 cases. In response, the federal government has implemented various non-pharmaceutical interventions (NPIs) under a series of Movement Control Orders and, more recently, a vaccination campaign to regain epidemic control. In this study, we assessed the potential for the vaccination campaign to control the epidemic in Malaysia and four high-burden regions of interest, under various public health response scenarios. METHODS: A modified susceptible-exposed-infectious-recovered compartmental model was developed that included two sequential incubation and infectious periods, with stratification by clinical state. The model was further stratified by age and incorporated population mobility to capture NPIs and micro-distancing (behaviour changes not captured through population mobility). Emerging variants of concern (VoC) were included as an additional strain competing with the existing wild-type strain. Several scenarios that included different vaccination strategies (i.e. vaccines that reduce disease severity and/or prevent infection, vaccination coverage) and mobility restrictions were implemented. RESULTS: The national model and the regional models all fit well to notification data but underestimated ICU occupancy and deaths in recent weeks, which may be attributable to increased severity of VoC or saturation of case detection. However, the true case detection proportion showed wide credible intervals, highlighting incomplete understanding of the true epidemic size. The scenario projections suggested that under current vaccination rates complete relaxation of all NPIs would trigger a major epidemic. The results emphasise the importance of micro-distancing, maintaining mobility restrictions during vaccination roll-out and accelerating the pace of vaccination for future control. Malaysia is particularly susceptible to a major COVID-19 resurgence resulting from its limited population immunity due to the country's historical success in maintaining control throughout much of 2020.

Modelling direct and herd protection effects of vaccination against the SARS-CoV-2 Delta variant in Australia.

OBJECTIVES: To analyse the outcomes of COVID-19 vaccination by vaccine type, age group eligibility, vaccination strategy, and population coverage. DESIGN: Epidemiologic modelling to assess the final size of a COVID-19 epidemic in Australia, with vaccination program (Pfizer, AstraZeneca, mixed), vaccination strategy (vulnerable first, transmitters first, untargeted), age group eligibility threshold (5 or 15 years), population coverage, and pre-vaccination effective reproduction number ( Reffv¯ ) for the SARS-CoV-2 Delta variant as factors. MAIN OUTCOME MEASURES: Numbers of SARS-CoV-2 infections; cumulative hospitalisations, deaths, and years of life lost. RESULTS: Assuming Reffv¯ = 5, the current mixed vaccination program (vaccinating people aged 60 or more with the AstraZeneca vaccine and people under 60 with the Pfizer vaccine) will not achieve herd protection unless population vaccination coverage reaches 85% by lowering the vaccination eligibility age to 5 years. At Reffv¯ = 3, the mixed program could achieve herd protection at 60-70% population coverage and without vaccinating 5-15-year-old children. At Reffv¯ = 7, herd protection is unlikely to be achieved with currently available vaccines, but they would still reduce the number of COVID-19-related deaths by 85%. CONCLUSION: Vaccinating vulnerable people first is the optimal policy when population vaccination coverage is low, but vaccinating more socially active people becomes more important as the Reffv¯ declines and vaccination coverage increases. Assuming the most plausible Reffv¯ of 5, vaccinating more than 85% of the population, including children, would be needed to achieve herd protection. Even without herd protection, vaccines are highly effective in reducing the number of deaths.

Vaccines and variants: Modelling insights into emerging issues in COVID-19 epidemiology.

Mathematical modelling has played a pivotal role in understanding the epidemiology of and guiding public health responses to the ongoing coronavirus disease of 2019 (COVID-19) pandemic. Here, we review the role of epidemiological models in understanding evolving epidemic characteristics, including the effects of vaccination and Variants of Concern (VoC). We highlight ways in which models continue to provide important insights, including (1) calculating the herd immunity threshold and evaluating its limitations; (2) verifying that nascent vaccines can prevent severe disease, infection, and transmission but may be less efficacious against VoC; (3) determining optimal vaccine allocation strategies under efficacy and supply constraints; and (4) determining that VoC are more transmissible and lethal than previously circulating strains, and that immune escape may jeopardize vaccine-induced herd immunity. Finally, we explore how models can help us anticipate and prepare for future stages of COVID-19 epidemiology (and that of other diseases) through forecasts and scenario projections, given current uncertainties and data limitations.

Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study.

BACKGROUND: COVID-19 initially caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with many high-income countries, possibly because of differing demographics, socioeconomics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak. METHODS: We applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a "Minimum Health Standards" policy, MHS) to represent the first wave of the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions. FINDINGS: Population age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. The model indicated that MHS reduced the probability of transmission per contact by 13-27%. The February 2021 case detection rate was estimated at ~8%, population recovered at ~9%, and scenario projections indicated high sensitivity to MHS adherence. INTERPRETATION: COVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence. Continued compliance with low-cost MHS should help the Philippines control the epidemic until vaccines are widely distributed, but disease resurgence may be occurring due to a combination of low population immunity and detection rates and new variants of concern.

Modelling insights into the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R0 (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. †Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.

Role of modelling in COVID-19 policy development.

Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Early projections of international spread influenced travel restrictions and border closures. Model projections based on the virus's infectiousness demonstrated its pandemic potential, which guided the global response to and prepared countries for increases in hospitalisations and deaths. Tracking the impact of distancing and movement policies and behaviour changes has been critical in evaluating these decisions. Models have provided insights into the epidemiological differences between higher and lower income countries, as well as vulnerable population groups within countries to help design fit-for-purpose policies. Economic evaluation and policies have combined epidemic models and traditional economic models to address the economic consequences of COVID-19, which have informed policy calls for easing restrictions. Social contact and mobility models have allowed evaluation of the pathways to safely relax mobility restrictions and distancing measures. Finally, models can consider future end-game scenarios, including how suppression can be achieved and the impact of different vaccination strategies.