Psychological distress among hospital caregivers during and after the first wave of COVID-19: Individual factors involved in the severity of symptoms expression.

Coronavirus disease 2019 has spread rapidly over the globe and has put an unprecedent psychological pressure on health care workers (HCWs). The present study aimed at quantifying the psychological consequences of the COVID-19 pandemic on HCWs during and after the first wave and identify sociodemographic, situational, and psychological risk/protective factors for symptoms severity. An online survey was sent by e-mail to all nurses and physicians employed by a teaching hospital in Brussels, Belgium. 542 (20,62%) completed the survey. 47%, 55%, 32% and 52% of participants reported posttraumatic stress, anxiety, depression and insomnia symptoms, respectively, during the peak. Two to three months later, posttraumatic symptoms emerged de novo in 54% of HCWs. It persisted in 89% of those presenting severe symptoms initially. Neuroticism was the strongest predictor of posttraumatic stress, anxiety, and insomnia. Work overload was the strongest predictor of depression and second predictor of posttraumatic stress, anxiety, and insomnia. Other significant predictors included being a nurse, the number of past traumatic experiences, avoidant coping style, and expressive suppression of emotions.

Case study: Developing a strategy combining human and empirical interventions to support the resilience of healthcare workers exposed to a pandemic in an academic hospital.

The COVID-19 pandemic has put healthcare workers under important psychological pressure. Concerns have been raised regarding the mental health and psychological status of healthcare workers and have underlined the need for institutions to develop long-term interventions to support their resilience. The current case study presents the way a large university hospital in Brussels, Belgium, has evolved to deal with this health crisis and support its workers. Initiatives were multiple and complementary, as it was decided to combine different forms of clinical interventions that were developed by psychologists, psychiatrists, and human resources, to an empirical approach including a large survey that permitted to reach a much larger audience (the results of the study have been published previously). We describe the initially proposed measures of psychological support, including the creation of a telephone hotline, the presence of psychologists among teams of dedicated COVID-19 units, discussion groups, and individualized follow-ups, and their consequences on healthcare workers. Second, we address how these initial measures of support were modified to tailor in the best way possible the needs of healthcare workers, using a research action project that used a survey to measure and address the psychological distress of healthcare workers. We explain how, through different objectives (screening of distress, adaptation of initial measures based on reported needs, active reinforcement of individual and collective resilience, reminder of availability of help, and normalization of distress), a research action project can be a form of support and is an effective way for an institution to show its pre-occupation for the mental health of its teams. The current case study highlights how an institution can provide support and the importance of the use of a combined strategy to limit the consequences of a major health crisis on the mental health of its healthcare workers. Improving the resilience of healthcare workers both in the short and long term is of the essence to maintain optimal care of patients.

Modelling the regional variability of the probability of high trihalomethane occurrence in municipal drinking water.

The regional variability of the probability of occurrence of high total trihalomethane (TTHM) levels was assessed using multilevel logistic regression models that incorporate environmental and infrastructure characteristics. The models were structured in a three-level hierarchical configuration: samples (first level), drinking water utilities (DWUs, second level) and natural regions, an ecological hierarchical division from the Quebec ecological framework of reference (third level). They considered six independent variables: precipitation, temperature, source type, seasons, treatment type and pH. The average probability of TTHM concentrations exceeding the targeted threshold was 18.1%. The probability was influenced by seasons, treatment type, precipitations and temperature. The variance at all levels was significant, showing that the probability of TTHM concentrations exceeding the threshold is most likely to be similar if located within the same DWU and within the same natural region. However, most of the variance initially attributed to natural regions was explained by treatment types and clarified by spatial aggregation on treatment types. Nevertheless, even after controlling for treatment type, there was still significant regional variability of the probability of TTHM concentrations exceeding the threshold. Regional variability was particularly important for DWUs using chlorination alone since they lack the appropriate treatment required to reduce the amount of natural organic matter (NOM) in source water prior to disinfection. Results presented herein could be of interest to authorities in identifying regions with specific needs regarding drinking water quality and for epidemiological studies identifying geographical variations in population exposure to disinfection by-products (DBPs).

Impact of catchment geophysical characteristics and climate on the regional variability of dissolved organic carbon (DOC) in surface water.

Dissolved organic carbon (DOC) is a recognized indicator of natural organic matter (NOM) in surface waters. The aim of this paper is twofold: to evaluate the impact of geophysical characteristics, climate and ecological zones on DOC concentrations in surface waters and, to develop a statistical model to estimate the regional variability of these concentrations. In this study, multilevel statistical analysis was used to achieve three specific objectives: (1) evaluate the influence of climate and geophysical characteristics on DOC concentrations in surface waters; (2) compare the influence of geophysical characteristics and ecological zones on DOC concentrations in surface waters; and (3) develop a model to estimate the most accurate DOC concentrations in surface waters. The case study involved 115 catchments from surface waters in the Province of Quebec, Canada. Results showed that mean temperatures recorded 60 days prior to sampling, total precipitation 10 days prior to sampling and percentages of wetlands, coniferous forests and mixed forests have a significant positive influence on DOC concentrations in surface waters. The catchment mean slope had a significant negative influence on DOC concentrations in surface waters. Water type (lake or river) and deciduous forest variables were not significant. The ecological zones had a significant influence on DOC concentrations. However, geophysical characteristics (wetlands, forests and slope) estimated DOC concentrations more accurately. A model describing the variability of DOC concentrations was developed and can be used, in future research, for estimating DBPs in drinking water as well evaluating the impact of climate change on the quality of surface waters and drinking water.

Using penalty functions to evaluate aggregation models for environmental indices.

The purpose of indices is to summarize a large volume of information into a single number that is easy to understand and interpret. Environmental indices provide a composite picture of an environmental condition derived from a series of observed measurements and parameters. They are used as communication tools by regulatory agencies to characterize the state of a specific environmental system (air, water, and sediments) and to study the impact of regulatory policies on various environmental management practices. In the development of environmental indices, a few issues and problems have been encountered arising as a result of the abstraction of information and data. These problems are referred to as characteristic properties that include ambiguity, eclipsing, compensation and rigidity. These characteristic properties have long been identified and interpreted in Boolean (e.g., Yes/No) or qualitative (e.g., low, medium, high) terms. In this paper, we propose a new approach to describe the above stated characteristic properties on a continuous scale to evaluate and compare the behavior of various aggregation models. Our approach is based on developing penalty functions for each characteristic property. A water quality index example by Swamee and Tyagi (2000) is used to explain our approach. A detailed case study for a developing microbial risk index is also provided to show how the proposed approach can be extended to complex hierarchical systems. Results show that it is possible to improve aggregation models for index development. Future research directions to improve index development are also discussed.

Using multi-criteria decision analysis to assess the vulnerability of drinking water utilities.

Outbreaks of microbiological waterborne disease have increased governmental concern regarding the importance of drinking water safety. Considering the multi-barrier approach to safe drinking water may improve management decisions to reduce contamination risks. However, the application of this approach must consider numerous and diverse kinds of information simultaneously. This makes it difficult for authorities to apply the approach to decision making. For this reason, multi-criteria decision analysis can be helpful in applying the multi-barrier approach to vulnerability assessment. The goal of this study is to propose an approach based on a multi-criteria analysis method in order to rank drinking water systems (DWUs) based on their vulnerability to microbiological contamination. This approach is illustrated with an application carried out on 28 DWUs supplied by groundwater in the Province of Québec, Canada. The multi-criteria analysis method chosen is measuring attractiveness by a categorical based evaluation technique methodology allowing the assessment of a microbiological vulnerability indicator (MVI) for each DWU. Results are presented on a scale ranking DWUs from less vulnerable to most vulnerable to contamination. MVI results are tested using a sensitivity analysis on barrier weights and they are also compared with historical data on contamination at the utilities. The investigation demonstrates that MVI provides a good representation of the vulnerability of DWUs to microbiological contamination.