Multilevel approach to individual and organisational predictors of stress and fatigue among healthcare workers of a university hospital: a longitudinal study.

OBJECTIVE: Healthcare workers (HCWs) are at high risk of experiencing stress and fatigue due to the demands of their work within hospitals. Improving their physical and mental health and, in turn, the quality and safety of care requires considering factors at both individual and organisational/ward levels. Using a multicentre prospective cohort, this study aims to identify the individual and organisational predictors of stress and fatigue of HCWs in several wards from university hospitals. METHODS: Our cohort consists of 695 HCWs from 32 hospital wards drawn at random within four volunteer hospital centres in Paris-area. Three-level longitudinal analyses, accounting for repeated measures (level 1) across participants (level 2) nested within wards (level 3) and adjusted for relevant fixed and time-varying confounders, were performed. RESULTS: At baseline, the sample was composed by 384 registered nurses, 300 auxiliary nurses and 11 midwives. According to the three-level longitudinal models, some predictors were found in common for both stress and fatigue (low social support from supervisors, work overcommitment, sickness presenteeism and number of beds per ward). However, specific predictors for high level of stress (negative life events, low social support from colleagues and breaks frequently cancelled due to work overload) and fatigue (longer commuting duration, frequent use of interim staff in the ward) were also found. CONCLUSION: Our results may help identify at-risk HCWs and wards, where interventions to reduce stress and fatigue should be focused. These interventions could include manager training to favour better staff support and overall safety culture of HCWs.

Assessing the impact of preventive mass vaccination campaigns on yellow fever outbreaks in Africa: A population-level self-controlled case series study.

BACKGROUND: The Eliminate Yellow fever Epidemics (EYE) strategy was launched in 2017 in response to the resurgence of yellow fever in Africa and the Americas. The strategy relies on several vaccination activities, including preventive mass vaccination campaigns (PMVCs). However, to what extent PMVCs are associated with a decreased risk of outbreak has not yet been quantified. METHODS AND FINDINGS: We used the self-controlled case series (SCCS) method to assess the association between the occurrence of yellow fever outbreaks and the implementation of PMVCs at the province level in the African endemic region. As all time-invariant confounders are implicitly controlled for in the SCCS method, this method is an alternative to classical cohort or case-control study designs when the risk of residual confounding is high, in particular confounding by indication. The locations and dates of outbreaks were identified from international epidemiological records, and information on PMVCs was provided by coordinators of vaccination activities and international funders. The study sample consisted of provinces that were both affected by an outbreak and targeted for a PMVC between 2005 and 2018. We compared the incidence of outbreaks before and after the implementation of a PMVC. The sensitivity of our estimates to a range of assumptions was explored, and the results of the SCCS method were compared to those obtained through a retrospective cohort study design. We further derived the number of yellow fever outbreaks that have been prevented by PMVCs. The study sample consisted of 33 provinces from 11 African countries. Among these, the first outbreak occurred during the pre-PMVC period in 26 (79%) provinces, and during the post-PMVC period in 7 (21%) provinces. At the province level, the post-PMVC period was associated with an 86% reduction (95% CI 66% to 94%, p < 0.001) in the risk of outbreak as compared to the pre-PMVC period. This negative association between exposure to PMVCs and outbreak was robustly observed across a range of sensitivity analyses, especially when using quantitative estimates of vaccination coverage as an alternative exposure measure, or when varying the observation period. In contrast, the results of the cohort-style analyses were highly sensitive to the choice of covariates included in the model. Based on the SCCS results, we estimated that PMVCs were associated with a 34% (95% CI 22% to 45%) reduction in the number of outbreaks in Africa from 2005 to 2018. A limitation of our study is the fact that it does not account for potential time-varying confounders, such as changing environmental drivers of yellow fever and possibly improved disease surveillance. CONCLUSIONS: In this study, we provide new empirical evidence of the high preventive impact of PMVCs on yellow fever outbreaks. This study illustrates that the SCCS method can be advantageously applied at the population level in order to evaluate a public health intervention.

Monitoring sick leave data for early detection of influenza outbreaks.

BACKGROUND: Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks. METHODS: Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place. RESULTS: Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier. CONCLUSION: Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.

Identifying individual and organizational predictors of accidental exposure to blood (AEB) among hospital healthcare workers: A longitudinal study.

BACKGROUND: Accidental exposure to blood (AEB) poses a risk of bloodborne infections for healthcare workers (HCWs) during hospital activities. In this study, we identified individual behavioral and organizational predictors of AEB among HCWs. METHODS: The study was a prospective, 1-year follow-up cohort study conducted in university hospitals in Paris, France. Data were collected from the Stress at Work and Infectious Risk in Patients and Caregivers (STRIPPS) study. Eligible participants included nurses, nursing assistants, midwives, and physicians from 32 randomly selected wards in 4 hospitals. AEB occurrences were reported at baseline, 4 months, 8 months, and 12 months, and descriptive statistical and multilevel risk-factor analyses were performed. RESULTS: The study included 730 HCWs from 32 wards, predominantly nurses (52.6%), nursing assistants (41.1%), physicians (4.8%), and midwives (1.5%). The incidence rate of AEB remained stable across the 4 visits. The multilevel longitudinal analysis identified several significant predictors of AEB occurrence. Individual-level predictors included younger age, occupation as nurses or midwives, irregular work schedule, rotating shifts, and lack of support from supervisors. The use of external nurses was the most significant ward-level predictor associated with AEB occurrence. CONCLUSIONS: AEBs among HCWs are strongly associated with organizational predictors, highlighting the importance of complementing infection control policies with improved staff management and targeted training. This approach can help reduce AEB occurrences and enhance workplace safety for HCWs.

A statistical algorithm for outbreak detection in multisite settings: an application to sick leave monitoring.

Motivation: Public health authorities monitor cases of health-related problems over time using surveillance algorithms that detect unusually high increases in the number of cases, namely aberrations. Statistical aberrations signal outbreaks when further investigation reveals epidemiological significance. The increasing availability and diversity of epidemiological data and the most recent epidemic threats call for more accurate surveillance algorithms that not just detect aberration times but also detect locations. Sick leave data, for instance, can be monitored across companies to identify companies-related aberrations. In this context, we develop an extension to multisite surveillance of a routinely used aberration detection algorithm, the quasi-Poisson regression Farrington Flexible algorithm. The new algorithm consists of a negative-binomial mixed effects regression model with a random effects term for sites and a new reweighting procedure reducing the effect of past aberrations. Results: A wide range of simulations shows that, compared with Farrington Flexible, the new algorithm produces better false positive rates and similar probabilities of detecting genuine outbreaks, for case counts that exceed historical baselines by 3 SD. As expected, higher surges lead to lower false positive rates and higher probabilities of detecting true outbreaks. The new algorithm provides better detection of true outbreaks, reaching 100%, when cases exceed eight baseline standard deviations. We apply our algorithm to sick leave rates in the context of COVID-19 and find that it detects the pandemic effect. The new algorithm is easily implementable over a range of contrasting data scenarios, providing good overall performance and new perspectives for multisite surveillance. Availability and implementation: All the analyses are performed in the R statistical software using the package glmmTMB. The code for performing the analyses and for generating the simulations can be found online at the following link: https://github.com/TomDuchemin/mixed_surveillance. Contact: a.noufaily@warwick.ac.uk.

Distinct Microbiotas Are Associated with Different Production Lines in the Cutting Room of a Swine Slaughterhouse.

The microorganisms found on fresh, raw meat cuts at a slaughterhouse can influence the meat's safety and spoilage patterns along further stages of processing. However, little is known about the general microbial ecology of the production environment of slaughterhouses. We used 16s rRNA sequencing and diversity analysis to characterize the microbiota heterogeneity on conveyor belt surfaces in the cutting room of a swine slaughterhouse from different production lines (each associated with a particular piece/cut of meat). Variation of the microbiota over a period of time (six visits) was also evaluated. Significant differences of alpha and beta diversity were found between the different visits and between the different production lines. Bacterial genera indicative of each visit and production line were also identified. We then created random forest models that, based on the microbiota of each sample, allowed us to predict with 94% accuracy to which visit a sample belonged and to predict with 88% accuracy from which production line it was taken. Our results suggest a possible influence of meat cut on processing surface microbiotas, which could lead to better prevention, surveillance, and control of microbial contamination of meat during processing.

A new ranking index to identify the work-related psychosocial factors most impacting mental health: a cross-sectional study.

OBJECTIVES: In modern professional life, mental health prevention and promotion have become a major challenge for decision-makers. Devising appropriate actions requires better understanding the role played by each work-related psychosocial factor (WPSF). The objective of this study was to present a relevant tool to hierarchise WPSFs that jointly takes into account their importance (impact on mental health) and their prevalence (the proportion of the population exposed to WPSF). DESIGN: A cross-sectional study was conducted in March 2018 among 3200 French workers which are representative of the French working population. SETTING: France. PARTICIPANTS: Individuals aged 18-80 years who declared currently having a job (even a part-time job) whatever their occupation or status (employee or self-employed) were eligible. We excluded students, unemployed individuals, housewives/husbands and retired people. The mental health level was assessed using the General Health Questionnaire-28 and 44 items were gathered from theoretical models of WPSFs. We assessed two distinct multivariate methods for calculating WPSF importance: (1) weifila (weighted first last) method in a linear regression context and (2) random forests in a non-linear context. Both methods were adjusted on individual, health and job characteristics. RESULTS: The WPSF rankings obtained with the two methods to calculate importance are strongly consistent with each other (correlation coefficient=0.88). We highlighted nine WPSFs that are ranked high by both methods. In particular, irrespective of the chosen method, lack of communication, lack of social and hierarchy support and personal-professional life imbalance, emotional demands at work and dissatisfaction with the compensation received came out as top-ranking WPSFs. CONCLUSIONS: A total of nine WPSFs were identified as key for decision-making. The easy-to-use tools we propose can help decision-makers identify priority WPSFs and design effective strategies to promote mental health in the workplace.

Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.

BACKGROUND: High turnover among healthcare workers is an increasingly common phenomenon in hospitals worldwide, especially in intensive care units (ICUs). In addition to the serious financial consequences, this is a major concern for patient care (disrupted continuity of care, decreased quality and safety of care, increased rates of medication errors, …). OBJECTIVE: The goal of this article was to understand how the ICU-level nurse turnover rate may be explained from multiple covariates at individual and ICU-level, using data from 526 French registered and auxiliary nurses (RANs). METHODS: A cross-sectional study was conducted in ICUs of Paris-area hospitals in 2013. First, we developed a small extension of a multi-level modeling method proposed in 2007 by Croon and van Veldhoven and validated its properties using a comprehensive simulation study. Second, we applied this approach to explain RAN turnover in French ICUs. RESULTS: Based on the simulation study, the approach we proposed allows to estimate the regression coefficients with a relative bias below 7% for group-level factors and below 12% for individual-level factors. In our data, the mean observed RAN turnover rate was 0.19 per year (SD = 0.09). Based on our results, social support from colleagues and supervisors as well as long durations of experience in the profession were negatively associated with turnover. Conversely, number of children and impossibility to skip a break due to workload were significantly associated with higher rates of turnover. At ICU-level, number of beds, presence of intermediate care beds (continuous care unit) in the ICU and staff-to-patient ratio emerged as significant predictors. CONCLUSIONS: The findings of this research may help decision makers within hospitals by highlighting major determinants of turnover among RANs. In addition, the new approach proposed here could prove useful to researchers faced with similar micro-macro data.

Case series analysis for censored, perturbed, or curtailed post-event exposures.

A new method is developed for analyzing case series data in situations where occurrence of the event censors, curtails, or otherwise affects post-event exposures. Unbiased estimating equations derived from the self-controlled case series model are adapted to allow for exposures whose occurrence or observation is influenced by the event. The method applies to transient point exposures and rare nonrecurrent events. Asymptotic efficiency is studied in some special cases. A computational scheme based on a pseudo-likelihood is proposed to make the computations feasible in complex models. Simulations, a validation study, and 2 applications are described.

Modeling sickness absence data: A scoping review.

The identification of sick leave determinants could positively influence decision making to improve worker quality of life and to reduce consequently costs for society. Sick leave is a research topic of interest in economics, psychology, health and social behaviour. The question of choosing an appropriate statistical tool to analyse sick leave data can be challenging. In fact, sick leave data have a complex structure, characterized by two dimensions: frequency and duration, and involve numerous features related to individual and environmental factors. We conducted a scoping review to characterize statistical approaches to analyse individual sick leave data in order to synthesise key insights from the extensive literature, as well as to identify gaps in research. We followed the PRISMA methodology for scoping reviews and searched Medline, World of Science, Science Direct, Psycinfo and EconLit for publications using statistical modeling for explaining or predicting sick leave at the individual level. We selected 469 articles from the 5983 retrieved, dated from 1981 to 2019. In total, three types of model were identified: univariate outcome modeling using for the most part count models (438 articles), bivariate outcome modeling (14 articles), such as multistate models and structural equation modeling (22 articles). The review shows that there was a lack of evaluation of the models as predictive accuracy was only evaluated in 18 articles and the explanatory accuracy in 43 articles. Further research based on joint models could bring more insights on sick leave spells, considering both their frequency and duration.

Seasonal influenza vaccine and Guillain-Barré syndrome: A self-controlled case series study.

OBJECTIVE: To evaluate the risk of Guillain-Barré syndrome (GBS) following seasonal influenza vaccination based on French nationwide data. METHODS: All cases of GBS occurring in metropolitan France between September 1 and March 31 from 2010 to 2014 were identified from the French national health data system. Data were analyzed according to the self-controlled case series method. The risk period started 1 day after the patient received vaccine (D1) until 42 days after vaccination (D42). The incidence of GBS during this risk period was compared to that of the control period (D43-March 31). The incidence rate ratio (IRR) was estimated after adjusting for seasonality and presence or not of acute infections. RESULTS: Between September and March, of the 2010/2011 to 2013/2014 influenza vaccination seasons, 3,523 cases of GBS occurred in metropolitan France and were included in the study. Among them, 15% (527 patients) had received influenza vaccination. A total of 140 patients developed GBS during the 42 days following influenza vaccination. The crude risk of developing GBS was not significantly increased during the 42 days following influenza vaccination (IRR, 1.02; 95% confidence interval [CI], 0.83-1.25; p = 0.85). This result remained nonsignificant after adjustment for calendar months and the incidence of acute gastrointestinal and respiratory tract infections (IRR, 1.10; 95% CI, 0.89-1.37; p = 0.38). In contrast, the risk of GBS was fourfold higher after acute respiratory tract infection (IRR, 3.89; 95% CI, 3.52-4.30; p < 0.0001) or gastrointestinal infection (IRR, 3.64; 95% CI, 3.01-4.40; p < 0.0001). CONCLUSIONS: No association between seasonal influenza vaccination and GBS was shown during the 42 days following vaccination.

Work-related psychosocial risk factors and psychiatric disorders: A cross-sectional study in the French working population.

PURPOSE: The study estimates the prevalence of probable psychiatric disorder in the working population, determines the proportion of people presenting a probable psychiatric disorder among people exposed to work-related psychosocial risk factors (PSRFs), and identifies which PSRF has the strongest association with having a probable psychiatric disorder. METHODS: A cross-sectional study conducted in March 2018 involved a representative sample of the French working population. The General Health Questionnaire 28 (GHQ-28) was used to estimate the prevalence of probable psychiatric disorder and 44 items were gathered from theoretical models of PSRFs. We used multiple logistic regression to estimate the association of each PSRF with having a probable psychiatric disorder, adjusted on individual, health, and job confounders. RESULTS: This study involved 3200 French participants. The proportion of probable psychiatric disorder was 22.2% [20.6; 24.0]. Ten PSRFs were significantly associated with it. The strongest association was for having problems handling professional and personal responsibilities (reported by 15% of the study population) (OR = 1.97 [1.52; 2.54]), with 45% pathological GHQ-28 scores (potential psychiatric cases) for people exposed to this PSRF versus 18% non-exposed. The next strongest association was lack of support of colleagues (reported by 28%) (OR = 1.63 [1.29; 2.06]). The third strongest association was feeling sometimes afraid when doing the job (reported by 63%) (OR = 1.53, [1.21; 1.93]). CONCLUSIONS: Our study identified 10 PSRFs associated with psychiatric disorder, with substantial exposure rate among the population. The results of our research could help develop recommendations to improve work environment.

The methodology of self-controlled case series studies.

The self-controlled case series method is increasingly being used in pharmacoepidemiology, particularly in vaccine safety studies. This method is typically used to evaluate the association between a transient exposure and an acute event, using only cases. We present both parametric and semiparametric models using a motivating example on MMR vaccine and bleeding disorders. We briefly describe approaches for interferent events and a sequential version of the method for prospective surveillance of drug safety. The efficiency of the self-controlled case series method is compared to the that of cohort and case control studies. Some further extensions, to long or indefinite exposures and to bivariate counts, are described.

Hierarchizing Determinants of Sick Leave: Insights From a Survey on Health and Well-being at the Workplace.

OBJECTIVE: We hierarchized a range of individual and occupational factors impacting the occurrence of very short (1-3 days), short (4 days to 1 month), or long-term (more than a month) sick leave spells. METHODS: Data were collected from a repeated cross-sectional survey conducted in the French private sector over the period 2011 to 2017. Fifty one sick leave determinants were ranked using a conditional random forest approach. RESULTS: The main determinants of long-term sick leaves were mainly health-related characteristics, such as perceived health, but also work-related covariates such as supervisor acknowledgment. On the contrary, very short-term spells were mainly defined by sociodemographic covariates. CONCLUSION: These results could be useful for devising appropriate actions to prevent against sick leave at the workplace, particularly long-term spells. Random forest approach is a promising approach for ranking correlated covariates from large datasets.

Validation of the French 3-year prognostic score using the Canadian Cystic Fibrosis registry.

Studies of large CF populations using registry data are important to identify people at high risk for death. Nkam et al. published a prognostic score developed on French CF registry data to predict death or lung transplantation (LT) over a 3-year period in the adult CF population. The goal of our study was to validate the proposed tool using the Canadian CF registry. Using data between 2011 and 2014, a total of 2043 adult CF patients were included. We found that the French prognostic score was a good predictor of death or LT in the Canadian CF population (OR for each unit increase: 3.12, 95% CI: 2.74-3.55; p value < 0.001). The proposed prognostic score accurately categorizes patients when applied to an external dataset. This score provides an important tool for early identification of patients at high risk for death or LT, in whom specific therapeutic intervention can be proposed.

Impact of a multicomponent hand hygiene-related intervention on the infectious risk in nursing homes: A cluster randomized trial.

BACKGROUND: The aim of this study was to assess the impact of a multifaceted hand hygiene (HH) program on the infectious risk in nursing homes (NHs). METHODS: This was a 2-arm cluster randomized trial; French NHs were allocated randomly to the intervention (13 NHs) or control (13 NHs) groups. The intervention consisted of implementing a bundle of HH-related measures over 1 year, including increased availability of alcohol-based handrub, HH promotion, staff education, and local work groups. The primary end point was the incidence rate of acute respiratory infections and gastroenteritis reported in the context of clustered cases episodes. Secondary end points were mortality, hospitalization, and antibiotic prescription rates. RESULTS: Baseline characteristics did not differ between groups. The overall handrub consumption was higher in the intervention group over the 1-year intervention period. Because of underreporting, data on the primary end points were of insufficient quality for analysis. Hospitalizations did not differ between the 2 groups. However, the intervention group showed significantly lower mortality (2.10 vs 2.65 per 100 residents per month, respectively; P = .003) and antibiotic prescriptions (5.0 vs 5.8 defined daily doses per 100 resident days, respectively; P < .001). These results were confirmed by the longitudinal multivariate analysis adjusted for NH and resident characteristics and for seasonality (mortality rate ratio, 0.76). CONCLUSIONS: A multifaceted HH intervention may have a short-term impact on mortality in NHs. Nevertheless, other strategies may remain necessary to reduce morbidity.

How to Identify and Prioritize Psychosocial Factors Impacting Stress Level.

We develop a methodological approach to identify and prioritize psychosocial factors (stressors) requiring priority action to reduce stress levels. Data analysis was carried out on a random sample of 10 000 French employees who completed, during a routine interview with the occupational physician, a 25-item questionnaire about stress levels, as well as a questionnaire about 58 stressors grouped into 5 latent variables: job control, job context, relationships at work, tasks performed and recognition. Our method combines Importance-Performance Analysis, a valuable approach for prioritizing improvements in the quality of services, with Partial Least Squares-Path modeling, a Structural Equation Modeling approach widely applied in psychosocial research. Findings on our data suggest two areas worthy of attention: one with five stressors on which decision makers should concentrate, and another with five stressors that managers should leave alone when acting to reduce stress levels. We show that IPA is robust when answers to questions are dichotomized, as opposed to the initial 6-point Likert scale. We believe that our approach will be a useful tool for experts and decision-makers in the field of stress management and prevention.

Impact of hand hygiene on the infectious risk in nursing home residents: A systematic review.

BACKGROUND: In nursing homes, the infectious risk is high, making infection control using approaches such as hand hygiene (HH) a major issue. However, the effectiveness of HH in these settings is not well documented, and HH compliance is low. METHODS: We systematically searched PubMed, Scopus, Web of Science, and Cochrane Clinical Trials for studies in nursing homes that either described a HH-related intervention or assessed HH compliance and included a measured infectious outcome. Two reviewers independently performed the study selection. RESULTS: Fifty-six studies met the inclusion criteria and were reviewed. Most were outbreak reports (39%), followed by observational studies (23%), controlled trials (23%), and before-after intervention studies (14%). Thirty-five studies (63%) reported results in favor of HH on at least one of their outcome measures; in addition, the infection control success rate was higher when at least one HH-related intervention (eg, staff education on HH, increased availability of handrub solution) was included (70% vs 30% for no intervention). However, only 25% of randomized trials concluded that HH-related interventions led to a reduction in the infectious risk. CONCLUSION: The results of this systematic review suggest that more evidence on HH effectiveness in nursing homes is needed. Future interventional studies should enhance methodologic rigor using clearly defined outcome measures, standardized reporting of findings, and a relevant HH observation tool.