Since December 2019 the world has been shaken with an enormous global threat: the COVID-19 pandemic. The pandemic has generated an unprecedented impact both on the general population and on the healthcare systems in most countries. Based on the literature to date, the impact of the COVID-19 pandemic has been significantly larger than previous pandemics in terms of the number of affected people worldwide, its spread across countries, its impact on health care systems, and the severity of measures that have been taken by governments. Several social and economic consequences have already been reported (e.g., spiking unemployment rates), while others are expected to occur in the following months, increasing social divides and inequalities across the world. These consequences have also been evident in the health care system.
Given the global nature of the pandemic, comparisons across countries are warranted. However, there is a critical lack of data from low-and-middle income countries (LMICs) and low-resourced areas where the pandemic has been devastating in its effects. Cross-country and cross-cultural variations and similarities may elucidate whether sources of variation are associated with the mental health of health workers across diverse country settings. Accordingly, we present here HEROES, an ongoing, multisite prospective cohort study aimed to evaluate the impact of the COVID-19 pandemic on the mental health of health care workers (HCWs) in 28 countries across 5 continents using uniform methodology. HEROES encompasses a wide variety of academic institutions in 19 LMICs and 9 high income countries, in partnership with the Pan American Health Organization (PAHO) and with support from the World Health Organization (WHO).
We specifically aimed to (1) investigate the impact of COVID-19 related exposures on mental health symptoms (e.g. anxiety, depression) and disorder (e.g. PTSD) among HCWs within and across countries; (2) examine the associations between particular demographic (E.g., gender), occupational, and workplace exposures (e.g., being exposed to patients with confirmed and suspected COVID-19 and availability of personal protective equipment) and mental health outcomes among HCWs at different phases of the pandemic within and across countries; and (3) explore the role of country- and region-level measures, including infection, death, and hospitalization rates, in the relationship between COVID-19 exposures and mental health outcomes among HCWs.
We are using a prospective, open, cohort design including any kind of HCWs from pre-selected health facilities in the participating countries as noted below. The study considers follow up assessments at 6, 12, and 24 months. An on-line questionnaire based on standardized measures plus ad-hoc items is being employed. The questionnaire is self-administered and takes, on average, 15-20 minutes to complete. It includes the General Health Questionnaire (GHQ-12), the Patient Health Questionnaire (PHQ-9), the Columbia Suicide Severity Rating Scale (C-SSRS), and the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5), as well as a series of items on workplace, family, and social challenges related to the COVID-19 pandemic.
Participants include clinical and non-clinical HCWs at different types of health facilities, from emergency services in hospitals to primary care clinics. The inclusion criteria for potential participants include being of legal age, to work at one of the pre-selected health facilities, to work in a health facility that provides care to suspected or confirmed cases of COVID-19, and to have an internet connection needed to complete the on-line questionnaire. Most participating countries have identified several health facilities centers where they can identify denominators and ensure sufficient response rates (e.g., 30-50%).
Local teams are able to identify how many people work in those facilities and, when possible, the distribution of workers by type of occupation (e.g., doctors, nurses, ancillary services, technicians). To facilitate comparison between sites, we are focusing on large facilities (e.g., hospitals) and in each country we have included, when possible, one facility from an area with high rates of cases/deaths vs. an area with low cases/deaths. We have included a mandatory item asking for “which center do you work at/which institution are you affiliated to?”, in order to record the workplace of individuals and calculate response rates. If data on denominators for response rates are not available, cooperation rates will be calculated instead (number of completed surveys/number of emails sent).
Potential outcomes include mental health symptoms and disorders, as well as behavioral (e.g., resilience) and social (e.g., trust) outcomes. In order to provide examples for data analysis, we focus here on three primary outcomes of interest: mental health symptoms and disorders; resilience; and trust in the workplace and the government regarding actions to manage the pandemic. The on-line questionnaire collects data on several covariates including sociodemographics, employment, testing for COVID-19, fears and concerns related to COVID-19, stigma at work, COVID-19 training and prioritization, other mental health symptoms, formal and informal supports, and prior conditions (e.g., prior mental, physical, and substance use conditions).
Descriptive statistics will provide an overview of the prevalence of primary exposures and outcomes at baseline and over time. Bivariate analyses will be conducted to explore associations between primary outcomes and covariates. Potential confounders, moderators, and effect modifiers will be identified and examined based on prior literature and data. Furthermore, two type of analysis will be proposed for these exposures and outcomes: within and across countries. For the within countries analyses, we will study the association between the exposures and the outcomes of interest (e.g., mental health, behavioral, and social outcomes) between areas with low and high COVID-19 case and death rates. Additionally, we will examine changes in primary outcomes over time as rates change and across phases of pandemic. For the across countries analyses, we will use a similar approach as the one described above but adding another level to our multi-level models. Moreover, we hope to use country-level data, which has been well documented in most countries, on contextual factors such as when each country declared a national emergency due to the COVID-19 pandemic, restriction measures issued by governments and other entities to control the spread of the virus, and country economic development (e.g., LMICs vs HICs).