*Article* **Effectiveness of Psychological Support to Healthcare Workers by the Occupational Health Service: A Pilot Experience**

**Guendalina Dalmasso 1,2 , Reparata Rosa Di Prinzio 2,3, Francesco Gilardi 4 , Federica De Falco 5 , Maria Rosaria Vinci 5 , Vincenzo Camisa 5 , Annapaola Santoro 5 , Daniela Casasanta 5 , Massimiliano Raponi 1 , Gabriele Giorgi 6 , Nicola Magnavita 2,3,† and Salvatore Zaffina 2,5,\* ,†**

	- Italian Ministry of Health, 00153 Roma, Italy<EMAIL_ADDRESS>
4

**Abstract:** Work-related stress is a significant risk for healthcare workers (HCWs). This study aims at evaluating the effectiveness of an individual psychological support programme for hospital workers. In all, 35 workers participated (*n*). A control group of 245 workers (7*n*) was set. Occupational distress was measured by the General Health Questionnaire, (GHQ-12), the quality of life by the Short Form-36 health survey, (SF-36), and sickness absence was recorded. Costs and benefits of the service were evaluated and the return on investment (ROI) was calculated. The level of distress was significantly reduced in the treated group at the end of the follow-up (*p* < 0.001). Quality of life had significantly improved (*p* < 0.003). A 60% reduction of sickness absence days (SADs) following the intervention was recorded. After the treatment, absenteeism in cases was significantly lower than in controls (*p* < 0.02). The individual improvement of mental health and quality of life was significantly correlated with the number of meetings with the psychologist (*p* < 0.01 and *p* < 0.03, respectively). The recovery of direct costs due to reduced sick leave absence was significantly higher than the costs of the programme; ROI was 2.73. The results must be examined with caution, given the very limited number of workers treated; this first study, however, encouraged us to continue the experience.

**Keywords:** work-related stress; workplace health promotion; well-being; sickness absence; quality of life; distress; return on investment

#### **1. Introduction**

In post-industrialized countries, traditional occupational diseases are decreasing, whereas nonspecific and multifactorial stress disorders are growing [1]. In European countries a share of 50–60% of all lost working days should be attributed to work-related stress (WRS) [2]. Nonetheless, only a minority of countries in the world include WRS among the risks to be prevented in the workplace [3]. Only about half of the employers inform their workers about psychosocial risks and their effects on health and safety, and less than a third of companies put in place procedures to deal with WRS [4].

Healthcare workers (HCWs) usually face high levels of WRS [5] as a persistent background associated with accident-related spikes [6], which may hinder the quality of the provided healthcare as well as the patient safety [7–9]. Epidemiological studies have

**Citation:** Dalmasso, G.; Di Prinzio, R.R.; Gilardi, F.; De Falco, F.; Vinci, M.R.; Camisa, V.; Santoro, A.; Casasanta, D.; Raponi, M.; Giorgi, G.; et al. Effectiveness of Psychological Support to Healthcare Workers by the Occupational Health Service: A Pilot Experience. *Healthcare* **2021**, *9*, 732. https://doi.org/10.3390/ healthcare9060732

Academic Editors: Alberto Modenese and Fabriziomaria Gobba

Received: 7 May 2021 Accepted: 10 June 2021 Published: 14 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

demonstrated that a high level of WRS is associated with an increased risk of cardiovascular and musculoskeletal diseases, as well as mental disturbances (anxiety, depression, and burnout) [10–18]. Many different well-being-promoting interventions for HCWs have been proposed [4,5,9,19]. Organization-directed psychological support is a sharable approach that has already shown its positive effects [7,20]. Individual interventions based on cognitive–behavioural therapy (CBT) have been shown to be effective at some extent [21]. These programmes aim at improving coping strategies, resilience, and control of emotions through various techniques (i.e., individual support, relaxation techniques, focused breath, meditation methods, and self-awareness training) [22,23].

Effectiveness evaluations of Workplace Health Promotion (WHP) programmes, especially psychological support interventions, are not so frequent. One of the most frequently measured objective indicators of HCWs' health and well-being is sickness absence [5]. Previous evidence showed that, contrary to physical-activity-focused WHP, psychological WHP methods were not considerably related to sickness absenteeism reduction [24]. Subsequently, another study demonstrated that anxiety and depression influence the impact of a perceived health-promotive workplace culture on employee presenteeism and, therefore, productivity [25]. However, an evaluation of the WHP's economic impact is rarely performed [26]. The importance of WHP economic assessment has also been acknowledged in a very recent systematic review, to the extent that knowing the cost–benefit of WHP interventions helps in defining better political and business solutions for healthier and safer workplaces [27]. In sight of this, the present study aims at evaluating the impact of psychological individual support on HCWs health. In particular, we intended to investigate the specific factors influencing the final result of the WHP in terms of psychological distress and quality of life.

#### **2. Materials and Methods**

#### *2.1. The Help Point (HP) Programme*

A WHP plan was developed in our hospital, including a "Help Point" (HP) programme, specifically designed to give psychological support for all employed healthcare personnel who need it. The HP programme was implemented as a part of improvement actions resulting from the assessment of WRS risk and is currently working.

The programme was addressed to all dependent HCWs of the hospital. Participation in the programme was voluntary. HP aimed at preventing work discomfort and, through active listening, making the worker able to analyse the working context, freely express thoughts to colleagues and superiors, and face stressful situations.

The HP path is currently in operation and is led by a multidisciplinary team, which comprises four occupational physicians, one psychologist, and one physician of the Health Directorate, and consists of the six following phases:

with respect to both individual status of health and working context and is gradually estimated by the psychologist.

All team members participate in all phases but the third phase, which is carried out by the psychologist alone. The occupational physicians share an in-depth clinical knowledge of workers and their professional risks, while the Health Directorate physician provides a detailed contextualization in the hospital framework. The coordinating occupational physician supervises the whole process. The final health outcome of the HP path is the improvement of mental health and, thus, of quality of life by gaining functional coping strategies.

#### *2.2. Study Design and Setting*

In the study period, 35 individuals joined the HP programme. They were mainly women (*n* = 31; 89%), and the mean age was 49.1 years (SD = 8.19), with an average seniority of 22.8 years (SD = 11.89). The prevalent job category was nurse (*n* = 24; 69%) followed by health technician (*n* = 7; 20%); other profiles were physician, biologist, dietician, and social health worker (overall *n* = 4; 11%). Married persons constituted 48.6% of the group, followed by single (22.8%) separated (20%) and divorced (8.6%). Overall 62.9% had children (mean = 1.3). On the whole, 20% performed night work shift and lived out of Rome. The most commonly underlying causes for joining the HP programme were acute distress syndrome (*n* = 25; 71.4%) and, to a lesser, extent work discomfort (*n* = 2; 5.7%).

The analysis was performed from September 2016 to June 2019 in the Bambino Gesù Paediatric Hospital in Rome.

For each of the subjects who asked to participate in the activities of the HP, the following were measured:

A control group (7*n* = 245) was also proportionally set through a random selection of dependent workers of the hospital. The inclusion criteria for controls were represented by the following: (1) being a dependent worker of the hospital; and (2) having the same age, gender, and job category of cases. Each case was uniquely matched to seven controls. For the control group, 89% were women and the mean age was 49.1 years (SD = 8.19). They were mainly nurses (7*n* = 168; 69%) and health technicians (7*n* = 49; 20%); physicians, biologists, dieticians, and social health workers were included too (7*n* = 7 for each category; overall, 11%). Married persons constituted 69.8%, followed by single (20.9%) and divorced (9.3%). Overall, 79.1% had children (mean = 2.1). On the whole, 27.8% performed night work shift, and 11.6% lived out of Rome. The mean seniority was 19.25 years (SD = 10.77). They did not have any psychological follow-up nor were they included in other WHP interventions.

#### *2.3. The Questionnaires*

The questionnaires were self-administered during the evaluation meetings at the beginning and at the end of the HP programme (i.e., the second and the fourth phases listed above).

#### 2.3.1. GHQ-12

The Goldberg's General Health Questionnaire (GHQ-12) is a 12-item self-administered screening tool used to detect minor psychiatric disorders for the general population [28]. GHQ-12 assesses the current mental state and asks whether that differs from the usual state. The questionnaire focuses on both lack of ability to carry out normal functions and appearance of new distressing phenomena. Each question is ranged on a four-point Likert scale and refers to the last two-week period. The total score can range from 0 to 36 points. Higher scores indicate greater impairment (Cronbach's alpha for Italian workers: 0.85 [29]); we assumed scores over 21 as needing intervention.

#### 2.3.2. SF-36 Questionnaire

The Short Form-36 Health Survey (SF-36) is a 36-item self-completed investigation of general health [30,31]. Each question is ranged on a five-point Likert scale. SF-36 investigates physical health, general health perception, and psychological–emotional health, and contains eight subscales (domains), each scored from 0 to 100 as a weighted sum of the correspondent questions; two indices are computed deriving from the subscales, synthesizing the overall physical and mental health. The higher the score, the better the perceived level of health (Cronbach's alpha: 0.88 [32]).

#### *2.4. Cost Analysis*

SAD-related direct costs were computed using the average per capita cost of a HCW working day (EUR 169.80), which was provided by the Human Resources Directorate of the hospital. The enhancement of savings on total absenteeism in 1 year was used for the definition of the return on investment (ROI) [33], computed as the ratio of the net profit and the investment cost for the HP programme management. In this respect, the weighted sum of the average hourly cost of each working group member was used, multiplied by the number of hours each professional has devoted to the specific activity.

#### *2.5. Statistical Analysis*

A descriptive analysis was carried out to define the characteristics of the treated population. Pre-treatment variables were compared to post-treatment variables using Student's paired *t*-test for normally distributed variables or Wilcoxon U test for non-normally distributed variables. Chi-square test was performed between qualitative variables. Twotailed *p*-value < 0.05 was considered statistically significant.

In order to understand which, among the numerous factors that can influence the outcome of a psychological support intervention, is more important in determining the improvement, we have built two multiple linear regression models using as a dependent variable the pre–post difference in GHQ-12 and, respectively, SF-36 scores and age, gender, job category, seniority, and number of meetings as predictors.

The improvement in GHQ-12 and SF-36 scores was divided at the median. By logistic regression, the association between demographic/social variables and score improvement higher/lower than the median was studied.

Finally, Student's paired t-test was used to compare SAR between cases and controls. Data were analysed using IBM Statistics Package for Social Sciences (SPSS) (version 25.0).

#### *2.6. Ethical Aspects*

Our study follows the principles of the Declaration of Helsinki. According to the guidelines on Italian observational retrospective studies, an independent Ethics Committee (EC) approved the study (protocol number 2000/2019). Moreover, as established by

the Italian legislation about the obligatory occupational surveillance and privacy management, confidentiality was safeguarded, and informed consent was obtained from all the participants.

#### **3. Results**

The HP programme lasted approximately 4 months per worker (median = 129 days, IQR = 106–154), distributed on average over eight meetings (median = 8, IQR = 6–13).

Participants in HP programme showed an improvement in the measured parameters. Both GHQ-12 and SF-36 post-test scores significantly improved compared to the pre-test scores (Table 1). All mean scores of the eight subscales of the SF-36 test were higher in post-test compared to the pre-test means. The highest increase was observed in subscale 7 (Emotive Role Limitations: +134.0%), whereas the lowest improvement was in subscale 1 (Physical Activity: +11.7%).

**Table 1.** GHQ-12 and SF-36 mean scores before and after treatment.

A stepwise multiple linear regression was performed to predict the improvement of mental and general health based on age, gender, job category, seniority, and number of meetings. The pre–post difference of GHQ-12 and of SF-36 scores was used as a dependent variable. The results of the regression indicated that seniority and number of meetings explained 61.5% of the variance of GHQ-12 difference (*p* < 0.003), while the 39.2% of the variance of SF-36 change was significantly predicted by the number of meetings (*p* < 0.03) (Table 2). A significant improvement of mental health was recorded after at least eight meetings (*p* = 0.005).

**Table 2.** Stepwise linear regression analysis. Relationship between demographic and intervention-related variables and improvement.

Variables excluded from the model: gender, age, job category.

After the intervention, SADs decreased by 19 days per worker on average in the 1-year period (−60.89%, *p* < 0.04), with a reduction rate (SAR) of 6.43. Absenteeism reduction in the 6-month period was not significantly different among cases. However, the comparison of SAR of the cases with the correspondent controls showed a statistically significant difference (*p* < 0.02) (Table 3).

**Table 3.** Changes in sickness absenteeism rate.

Moreover, from logistic regression analysis, it emerged that the improvement of quality of life (by SF-36 score) is significantly predicted by the comparison of SAD in the 1-year period (*p* = 0.05).

Regarding the cost analysis, the total amount of hours the working group devoted to HP-related activities was 647 h on average in a year (377 h for the psychologist and 54 h for each physician), which multiplied by the hourly cost of each professional, accounted for EUR 21,556.07 (total cost of investment). The total estimated cost saving related to absenteeism reduction in a year was EUR 80,485.20 (gross profit), the net profit was EUR 58,919.13 (calculated as the difference between the gross profit and the total cost of investment). The ROI was EUR 2.73 for each euro invested.

#### **4. Discussion**

Workers who voluntarily participated in the HP activities reported a significant reduction in work discomfort and an improvement of mental health status, with an associated reduction of absenteeism. In the treated subjects, SADs decreased by more than 60%, reaching levels lower than the general hospital absenteeism rate. Moreover, the quality of life significantly improved in treated workers. This effect was proportional to the number of meetings, which leads us to believe that the improvement was due to the psychological support interventions. In this respect, eight meetings were enough to realize a noteworthy enhancement of mental health. Additionally, seniority was found to be a predictor of the improvement of mental health. The improvement in productivity generated over EUR 80,000 of savings for the hospital, yielding an ROI of 2.73 for the service. These findings support the effectiveness of the HP activities.

The study confirms the results of the research on this topic. As a part of a comprehensive stress prevention programme existing in the hospital, the psychological support focuses on the individual ability of distress management. The HP programme runs in parallel with an organization-oriented stress intervention based on environmental, ergonomic, structural, and technological improvement measures [34]. Benefits of the HP programme include an outcome relevant for the individual (enhanced psychological and general health) as well as for the organization (reduced sickness absenteeism) [35].

On a clinical level, providing psychological care to employees generates a higher perceived workplace health support, which in turn positively influences productivity [36]. An educational and behavioural mixed approach is confirmed to positively influence cognitivefocused outcomes (such as job-related perceptions) [37]. Moreover, given the structure of the HP path, our study confirms the efficacy of individual engagement of the worker from the beginning of the path, throughout HP development and implementation [38]. In our studied population, age represents the most affecting factor of mental health improvement scores, as older age is associated with a higher risk of stress and emotional exhaustion [39], even if common mental disorders are an age-independent global disease burden [40]. Moreover, the prevalence of women is particularly higher in the studied population than in the whole hospital population (90.3% vs. 71.0%). Women are more susceptible to stress-related disorders than men and have a different neural processing of control [41]. Indeed, women are more prone to seek psychological support from primary care services (such as the occupational service), especially in the case of WRS [42,44]. Con-

sidering that different categories of psychiatric diseases are differently distributed among women and men [42], this composition bias could alter the picture of the mental status of workers in the hospital. Participation of male workers should be encouraged, for instance, by adding a fast-screening questionnaire for the most common male mental disorders during the sanitary surveillance. Finally, current evidence does not definitely assess the duration of CBT to be effective [21]. Our results suggest at least eight meetings on average for a considerably improved mental health condition.

At the organization level, sickness absenteeism is a reliable indicator of WRS and quality of life [5]. Our results proved that the HP programme notably lessened absenteeism, and this effect could last up to a year after the end of the programme. This finding supports sickness absence days to be a reliable objective indicator also in a medium-term period. As established in a previously evaluated WHP addressed to the same population [34,43], improved general and mental health leads to recovered working days, a remarkable economic saving for the hospital.

Furthermore, this study sharpens the specific role of two professional figures. On the one hand, the occupational physician, who embodies the figure of a professional mediator of the needs of workers and the organization and is in the meantime the solely responsible for the health of workers by the Italian law [44]. On the other hand, the psychologist, who is called to take care of the mental aspect of the workforce. Both these professionals need to raise the awareness of workers on the multilayered beneficial effects of the programme and the importance of steadiness in following the path throughout its duration as an individual empowerment strategy able to solve the underlying criticality.

To the best of our knowledge, this is the first effectiveness study of a hospital psychological support service conducted in Italy. The main strength is the accomplished effectiveness of a relatively short psychological support intervention on a medium-term period at the clinical and organization levels. The main limitation of the study is the small sample. The results need further confirmation through a larger number of observations possibly in a longer follow-up.

#### **5. Conclusions**

The psychological support programme showed a consistent effectiveness on mental health and quality of life as well as on productivity related to a decreased absenteeism in healthcare settings. It may be considered an effective and cost-saving approach able to mitigate the risk of WRS, a risk that, however, cannot be completely eradicated. The main future challenge in this field lies in confirming the validity of the preliminary results highlighted here on a wide-ranging workforce also in non-healthcare settings.

**Author Contributions:** Conceptualization, G.D., F.G., N.M., M.R.V., M.R., S.Z., V.C., A.S. and D.C.; methodology, F.G., R.R.D.P. and N.M.; data collection, G.D. and F.D.F.; formal analysis, G.D., F.D.F., F.G., N.M. and R.R.D.P.; writing—original draft preparation, F.G., G.D. and F.D.F.; writing—review and editing, N.M., R.R.D.P., M.R., M.R.V., G.G. and S.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Independent Ethics Committee of Bambino Gesù Children's Hospital (protocol code 2000/2019 approved on 12th of December 2019).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions.

**Conflicts of Interest:** As for F.G., medical director of Italian Ministry of Health, the expressed opinions and the contents of the article are solely the responsibility of the author, and they are not attributable in any way to the institutional and functional positions held by the same at the Italian Ministry of Health (Article 12, paragraph 6, of the Code of Conduct of the Italian Ministry of Health, adopted with D.M. 6 March 2015 and later). Other authors declare no conflict of interest.

#### **References**

## *Article* **Prevalence and Clustering of Cardiovascular Risk Factors among Medical Staff in Northeast China**

**Jianxing Yu, Huanhuan Jia, Zhou Zheng, Peng Cao and Xihe Yu \***

Social Medicine and Health Service Management, School of Public Health, Jilin University, Changchun 130021, China<EMAIL_ADDRESS>(J.Y<EMAIL_ADDRESS>(H.J<EMAIL_ADDRESS>(Z.Z<EMAIL_ADDRESS>(P.C.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-0431-85619431

**Abstract:** Background: The clustering of cardiovascular disease (CVD) risk factors has become a major public health challenge worldwide. Although many studies have investigated CVD risk factor clusters, little is known about their prevalence and clustering among medical staff in Northeast China. This study aimed to estimate the prevalence and clustering of CVD risk factors and to investigate the association between relevant characteristics and the clustering of CVD risk factors among medical staff in Northeast China. Methods: A cross-sectional survey of 3720 medical staff from 93 public hospitals in Jilin Province was used in this study. Categorical variables were presented as percentages and were compared using the *χ 2* test. Multiple logistic regression analysis was used to evaluate the association between relevant characteristics and the clustering of CVD risk factors. Results: The prevalence of hypertension, diabetes, dyslipidemia, being overweight, smoking, and drinking were 10.54%, 3.79%, 17.15%, 39.84%, 9.87%, and 21.75%, respectively. Working in a general hospital, male, and age group 18–44 years were more likely to have 1, 2, and ≥3 CVD risk factors, compared with their counterparts. In particular, compared with being a doctor, being a nurse or medical technician was less likely to have 1, 2, and ≥3 CVD risk factors only in general hospitals. Conclusions: The findings suggest that medical staff of general hospitals, males, and older individuals have a high chance associated with CVD risk factor clustering and that more effective interventions should be undertaken to reduce the prevalence and clustering of CVD risk factors, especially among older male doctors who work in general hospitals.

**Keywords:** cardiovascular diseases; medical staff; risk factors; clustering; prevalence

#### **1. Introduction**

Cardiovascular disease (CVD) has become the primary cause of death in China and around the world [1,2], accounting for an estimated 17.9 million deaths globally in 2019, and more than three-quarters of these deaths occur in low- and middle-income countries [3]. Moreover, the prevalence of CVD is increasing in China; it killed nearly 4 million people in 2016 [4]. The increasing burden of CVD has become a major public health problem.

At present, most of the research involves the general population [5–7], while research on medical staff is almost entirely absent. Medical staff are essential to protect the health of the general population. Especially during the outbreak of the COVID-19 epidemic, medical staff were fighting on the front line against the epidemic and saving the lives of patients, but they were neglecting their own health. Studies have pointed out that during the COVID-19 epidemic, at least 62 medical workers in China participating in the anti-epidemic effort died on duty, including 23 cases (37.1%) due to an early lack of protection who died from COVID-19, 23 cases (37.1%) due to CVD, 6 cases (9.7%) of possible CVD, and 10 cases (16.1%) due to other reasons [8]. The number of deaths caused by CVD even exceeds the number caused by infection due to insufficient early protection.

Hypertension, diabetes, dyslipidemia, being overweight, smoking, and drinking are the main risk factors for CVD [9–12]. A considerable number of studies have pointed

**Citation:** Yu, J.; Jia, H.; Zheng, Z.; Cao, P.; Yu, X. Prevalence and Clustering of Cardiovascular Risk Factors among Medical Staff in Northeast China. *Healthcare* **2021**, *9*, 1227. https://doi.org/10.3390/ healthcare9091227

Academic Editors: Alberto Modenese and Fabriziomaria Gobba

Received: 28 July 2021 Accepted: 16 September 2021 Published: 17 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

out that the occurrence and development of CVD can be reduced through appropriate management and control of these six risk factors [13–15]. In addition, clustering multiple risk factors in the same person significantly increases the risk of CVD compared with having only a single risk factor [6,15,16].

Due to the characteristics of medical jobs, such as shift work, inflexible working hours, extended working hours, and heavy workloads, medical staff face extreme stress, which not only impairs their health, but also reduces their productivity and prevents them from performing their work effectively in the workplace [17–20]. The purpose of this study was to investigate the exposure and clustering of CVD risk factors (hypertension, diabetes, dyslipidemia, being overweight, smoking, drinking) among medical staff in Northeast China, and to analyze the individual characteristics (e.g., gender, age, marriage, education, and occupation) affecting their clustering, to provide a scientific basis for the formulation of CVD prevention strategies and measures.

#### **2. Materials and Methods**

#### *2.1. Study Population*

A cross-sectional survey of medical staff was implemented in Jilin Province from 21 December 2020 to 10 January 2021. In this study, a public general hospital and a public traditional Chinese medical hospital were selected from each county, and 25% of the urban public hospitals were selected from each city in Jilin Province. In general, a total of 93 public hospitals were selected as research objects by a stratified sampling method, including 50 general hospitals and 43 traditional Chinese medical (TCM) hospitals. Through convenience sampling, 20 doctors, 10 nurses, and 10 medical technicians were selected from each hospital. The study participants were selected as medical staff between the ages of 18 and 60. The subjects were substituted if they did not wish to participate in the study. Finally, a total of 3720 medical staff from 93 public hospitals in Jilin Province took part in the study.

#### *2.2. Ethics Statement*

The Ethics Committee of the School of Public Health, Jilin University, reviewed and approved the study protocol (NO. 2019-12-03). Each participating medical worker signed an informed consent form prior to data collection.

#### *2.3. Data Collection and Measurement*

All data were collected through standard questionnaires to ensure consistency and accuracy. The questionnaire included basic demographic information (e.g., sex, age, marriage, education, and occupation), health-related behaviors (e.g., smoking and drinking), as well as physical measurements (e.g., height, weight, and hypertension) and laboratory tests (e.g., diabetes and dyslipidemia). Physical measurements and laboratory tests were based on the medical staff's physical examination data in the last 2 months. In addition, to ensure the quality and integrity of the questionnaire, the survey supervisor conducted a second review of the submitted questionnaires on the same day to determine the validity of each answer.

#### *2.4. Assessment Criteria*

The six major CVD risk factors were clearly defined as follows: hypertension was defined as having been treated with antihypertensive medication within the past 2 weeks, and/or an average systolic blood pressure (SBP) ≥ 140 mmHg and/or an average diastolic blood pressure (DBP) ≥ 90 mmHg [21]. Diabetes was defined as having been treated with antidiabetes medication (insulin or oral hypoglycemic agents) and/or fasting blood glucose (FPG) ≥ 7.0 mmol/L [22]. Dyslipidemia was defined as having been treated with antilipemic medication or having at least one of the following: low-density lipoprotein cholesterol (LDL-C) ≥ 4.14 mmol/L, high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/L, triglycerides (TG) ≥ 2.26 mmol/L, and total cholesterol (TC) ≥ 6.22 mmol/L [23]. Overweight was

defined as a body mass index (BMI) ≥ 24.0 kg/m2 [24]. Smoking was defined as having smoked at least one cigarette daily continuously over the past 30 days or at least 18 packs in total each year [25]. Drinking was defined as an average alcohol consumption of at least one (women) or two (men) standard drinks per day over the last 30 days, and the total amount of alcohol intake was calculated as the number of standard drinks (10 g of pure ethanol per drink) [26].

#### *2.5. Clustering of CVD Risk Factors*

The clustering of CVD risk factors was assessed based on the presence of six major risk factors: hypertension, dyslipidemia, diabetes, being overweight, smoking, and drinking. If one medical staff had 0, 1, 2, ≥3 major risk factors (RFs), then RFs = 0, RFs = 1, RFs = 2, RFs ≥ 3, respectively.

#### *2.6. Statistical Analyses*

Data were analyzed using IBM SPSS 25.0 software (IBM Corporation, New York, NY, USA). Categorical variables were presented as percentages and were compared using the *χ 2* test. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by multiple logistic regression, and 95% confidence intervals (CIs) that did not include one revealed that they were statistically significant. Statistical significance was set at *p*-value < 0.05.

#### **3. Results**

As shown in Table 1, among a total of 3720 medical staff, 2000 (53.76%) medical staff worked at general hospitals, and 1720 (46.24%) medical staff worked at TCM hospitals. More than two-thirds (62.69%) of the medical staff were women, and 74.22% of the medical staff were in the 18–44 age group. Nearly four-fifths (79.44%) of the medical staff were married, 62.31% of the medical staff had an undergraduate education level, and half of the medical staff were doctors. In brief, by hospital category, there were significant differences between age and education (*p* < 0.05) but no difference for gender, marriage, and occupation (*p* > 0.05).

Table 2 shows that the prevalence of hypertension, diabetes, dyslipidemia, being overweight, smoking, and drinking was 10.54%, 3.79%, 17.15%, 39.84%, 9.87%, and 21.75%, respectively. The prevalence of hypertension, diabetes, and dyslipidemia was higher in general hospitals than in TCM hospitals (*p* ≤ 0.05). In addition, the prevalence of the six risk factors differed significantly by gender, being higher in men than in women (*p* < 0.001), especially for being overweight, smoking, and drinking. Furthermore, except for smoking and drinking, the prevalence of hypertension, diabetes, dyslipidemia, and being overweight differed significantly by age and marriage (*p* < 0.001). Their prevalence was higher in the 45–60 age group than in the 18–44 age group, and their prevalence was the lowest in the unmarried group compared with the other marriage groups. Except for dyslipidemia and drinking, the prevalence of the other factors showed decreasing trends with education level (*p* < 0.05). However, the prevalence of the six risk factors was the highest in the doctor group (*p* < 0.001).

Table 3 shows that the prevalence of RFs = 0, RFs = 1, RFs = 2, and RFs ≥ 3 was 45.89%, 29.68%, 13.92%, and 10.51%, respectively. Overall, the number of CVD risk factors differed significantly by hospital category, gender, age, marriage, education, and occupation (*p* < 0.001). Working in a general hospital, male, 45–60 age group, postsecondary education, and being a doctor had a higher prevalence of RFs = 1, RFs = 2, and RFs ≥ 3. However, unmarried individuals had the lowest prevalence of RFs = 1, RFs = 2, and RFs ≥ 3 compared with married individuals.

**Table 1.** Descriptive characteristics of medical staff by hospital category.

**Table 2.** The prevalence of CVD risk factors by relevant characteristics.

**Table 3.** The prevalence with different numbers of CVD risk factors.

\* "Other" included divorced and widowed.

The results of the multiple logistic regression analysis are shown in Table 4, in terms of the adjusted OR (95% CIs) of 1, 2, ≥3 CVD risk factors when having 0 CVD risk factors was set as the reference category. Staff working in a general hospital, men, and the 45–60 age group were more likely to have 1, 2, and ≥3 CVD risk factors than staff working in a TCM hospital, women, and the 18–44 age group (*p* < 0.05). In addition, married and other staff were also more likely to have 1, 2, and ≥3 CVD risk factors than unmarried staff (*p* < 0.05). Moreover, as the number of CVD risk factors increased, the adjusted OR (95% CIs) also increased. In contrast, the adjusted ORs (95% CIs) of 1 and 2 CVD risk factors with an undergraduate education were 0.80 (0.66, 0.97) and 0.74 (0.56, 0.98) compared with those with a postsecondary education, respectively (*p* < 0.05). Compared with being a doctor, the adjusted OR (95% CIs) of ≥3 CVD risk factors for nurses was 0.50 (0.30, 0.84), and the adjusted ORs (95% CIs) of 2 and ≥3 CVD risk factors for medical technicians were 0.72 (0.54, 0.97) and 0.70 (0.49, 0.98), respectively (*p* < 0.05).

Table 5 shows the multiple logistic analysis of the CVD risk factor clustering by hospital category. The 0 CVD risk factors were set as the reference category. The results show that men, 45–60 years old, and married were more likely to have 1, 2, and ≥3 CVD risk factors than women, 18–44 years old, and unmarried (*p* < 0.05). In addition, as the number of CVD risk factors increased, the adjusted OR (95% CIs) also increased. It should be noted that for TCM hospitals, the adjusted ORs (95% CIs) of 1, 2, and ≥3 CVD risk factors were not significant for education or occupation (*p* > 0.05). In contrast, for general hospitals, the adjusted ORs (95% CIs) of RFs = 1 and RFs = 2 for those with an undergraduate education were 0.65 (0.48, 0.89) and 0.51 (0.34, 0.77) compared with those with a postsecondary education, respectively (*p* < 0.05). Moreover, compared with being a doctor, being a nurse or medical technician was less likely to have 1, 2, and ≥3 CVD risk factors only in general hospitals (*p* < 0.05).

**Table 4.** The multiple logistic analysis of the CVD risk factor clustering.

\* "Other" included divorced and widowed. A multiple logistic regression model was used to estimate OR with 95% CIs, and all other factors were adjusted when OR with 95% CIs of each variable were estimated.

**Table 5.** The multiple logistic analysis of the CVD risk factor clustering by hospital category.

\* "Other" included divorced and widowed. A multiple logistic regression model was used to estimate OR with 95% CIs, and all other factors were adjusted when OR with 95% CIs of each variable were estimated.

#### **4. Discussion**

With the development of China's economy and changes in people's lifestyles, the prevalence of CVD and its related risk factors in China has been increasing year by year [4,7,27]. However, people's understanding of the disease is still insufficient, resulting in a continuous increase in the prevalence and mortality of CVD in China [28,29]. This is the first study to assess the prevalence and clustering of major CVD risk factors in a medical worker population in Northeast China.

This cross-sectional study was based on medical staff, and this study found that being overweight and alcohol consumption were the top two risk factors for CVD among medical staff. In addition, the prevalence of being overweight was higher than the average rate in the general adult population [5,6]. This finding may be due to Jilin Province being located in the central part of Northeast China, which has a temperate continental monsoon climate and an annual average temperature of 4.8°C. This climate leads people to eat a lot of meat and not engage in outdoor sports, especially in the cold winter [15]. Furthermore, according to the Global Burden of Disease study, the number of deaths attributable to alcohol consumption in China rose from 368,000 in 1990 to 70,300 in 2017 [30], and other studies have also pointed to the heavy economic burden of alcohol-related deaths in China [31–33]. However, the prevalence of dyslipidemia, hypertension, diabetes, and smoking were significantly lower than those found in other studies [34–38], which may be related to the medical occupation. Compared with the general population, medical staff know more about the prevention and control of related diseases and the harm of smoking on the body.

This study also found that the prevalence of hypertension, diabetes, and dyslipidemia was higher among the staff of general hospitals than TCM hospitals (*p* ≤ 0.05). At the same time, compared with TCM hospitals, general hospitals had a higher prevalence of risk factors 1, 2, and ≥3, which may be due to general hospital medical staff having more work stress and a higher workload because the number of patients treated in general hospitals is much higher than that in TCM hospitals. In addition, the prevalence of the risk factors differed significantly by gender, being more predominant among men (*p* < 0.001), especially for being overweight, smoking, and drinking. In addition, compared with women, men had a higher prevalence of RFs = 1, RFs = 2, and RFs ≥ 3, similar to the findings of other previous studies [27,39,40]. This result may be because men assume more responsibilities in society and tend to have more social parties, drink more alcohol, and smoke more cigarettes than women. In contrast, women tend to be more aware of their weight, especially during young and middle ages, which may translate into a favorable cardiovascular risk profile. Furthermore, except for smoking and drinking, the prevalence of hypertension, diabetes, dyslipidemia, and being overweight showed differences by age and marriage status (*p* < 0.001). The 45–60 age group had a higher prevalence of RFs = 1, RFs = 2, and RFs ≥ 3 than the 18–44 age group, which is similar to the findings of previous studies [5]. With increasing age, physical function declines, leading to a higher prevalence of hypertension, diabetes, dyslipidemia, and being overweight than the younger population, while smoking and drinking alcohol are personal habits that are not affected by age. In addition, unmarried individuals had the lowest prevalence of RFs = 1, RFs = 2, and RFs ≥ 3 than married individuals, possibly because unmarried people in general are younger and under less pressure. Except for drinking, the prevalence of risk factors was higher among those with postsecondary education (*p* < 0.05). In addition, compared with the other groups, those with postsecondary education had the highest prevalence of RFs = 1, RFs = 2, and RFs ≥ 3, which may be related to a higher education level and a better awareness of disease prevention and control [15]. Moreover, the prevalence of the six risk factors was the highest in the doctor group (*p* < 0.001). Compared with other groups, doctors had a higher prevalence of 1, 2, and ≥3 risk factors. Other studies have also pointed out that doctors have the most work stress and the highest workloads [41–43].

In addition, this study found that individuals working in a general hospital, men, and the age group 18–44 were more likely to have 1, 2, and ≥3 CVD risk factors, compared with their counterparts. Furthermore, the adjusted ORs were lower than those in other studies [5,6], possibly because the study subjects were medical staff. Medical staff generally perform better regarding disease control and prevention than the general population. Finally, the clustering of CVD risk factors in different hospital categories was studied separately, which was similar to the overall study results. However, compared with being

a doctor, nurses or medical technicians were less likely to have 1, 2, and ≥3 CVD risk factors only in general hospitals. Another study has also pointed out that doctors in general hospitals not only treat more patients with more complex conditions but also have greater work pressure and workloads than those in TCM hospitals [44]. Thus, doctors in general hospitals are more likely to have clustered CVD risk factors than nurses and medical technicians.

This study has the following limitations. First, the smoking and drinking status of the medical staff is based on self-reporting, which may have a certain reporting bias. Second, this study was a cross-sectional study, and it was not possible to determine the causal relationship between relevant characteristics and the clustering of CVD risk factors. Third, some other confounding factors that might have impacts on the clustering of CVD risk factors, such as socioeconomic factors, lifestyle (eating, physical activity), and work conditions (shift work, work hours), were not under consideration, which might be the limitation of our study.

#### **5. Conclusions**

This cross-sectional study provides information on the regional prevalence and clustering of CVD risk factors among medical staff in Northeast China and fills an information gap. The findings suggest that individuals working in general hospitals, men, and older individuals have a high chance associated with CVD risk factor clustering and that more effective interventions should be implemented to reduce the prevalence and clustering of CVD risk factors, especially among older male doctors working in general hospitals.

**Author Contributions:** J.Y. and X.Y. had the original idea for the study and, with all co-authors carried out the design. X.Y. provided valuable insight regarding the methodological approach and organization of the manuscript. H.J. was responsible for data cleaning and Z.Z. carried out the analyses. P.C. contributed to data collection. J.Y. drafted the manuscript, which was revised by all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study is funding by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No.18YJAZH118).

**Institutional Review Board Statement:** The Ethics Committee of the School of Public Health, Jilin University, reviewed and approved the study protocol (NO. 2019-12-03).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Because of relevant regulations, the data cannot be shared.

**Acknowledgments:** We express our gratitude to the participants and colleagues who were involved in the study. We are also appreciative for the support of our funders.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*Article* **Recovery of Raw Materials from Ceramic Waste Materials for the Refractory Industry**

**Severin Seifert 1,\*, Sebastian Dittrich 1 and Jürgen Bach 2**

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** Products of the refractory industry are key for the production of heavy industry goods such as steel and iron, cement, aluminum and glass. Corresponding industries are dependent on thermal processes to manufacture their products, which in turn would not be possible if there were no refractory materials, such as refractory bricks or refractory mixes. For the production of refractory materials, primary raw materials or semi-finished products such as corundum, bauxite or zircon are used. Industrial recycling of refractory raw materials would reduce dependencies, conserve resources and reduce global CO2 emissions. Today, only a small quantity of the refractory materials used can be recycled because raw materials (regenerates) obtained from end-of-life materials are of insufficient quality. In this study, regenerates from different refractory waste products could be obtained using the innovative processing method of electrodynamic fragmentation. It was shown that these regenerates have a high chemical purity and are therefore of high quality. It could be confirmed that the use of these regenerates in refractory materials does not affect the characteristic properties, such as refractoriness and mechanical strength. Thus, electrodynamic fragmentation is a process, which is able to provide high-quality raw materials for the refractory industry from used materials.

**Keywords:** recycling; refractory; regenerate; electrodynamic fragmentation; innovative process


*1.5. Social Media News*

Finally, with the rise of mobile technology, accessing news and information on social media has become commonplace and frequent [48]. In 2019, 53% of U.S. adults received news from social media, up from 47% in 2018 [48]. While social media share traditional media's ability to provide news to users [49], social media have unique characteristics that are markedly different from traditional forms of media. First, while traditional media are defined as either textual media (e.g., newspapers) or audiovisual media (e.g., television news), social media provide a combination of modality (i.e., both textual and audiovisual mode). Social media users can share dramatic multimedia clips about apparent health risks using video sharing sites such as YouTube [21], many of which are unverified. Second, social media are highly personalized platforms, connecting users with similar interests, often with personal or professional relationships [50]. Social media can reflect a social endorsement from 'people like me' via established social contacts (e.g., Facebook) or through like-minded individuals (e.g., Twitter). This aspect of social media allows for the rapid spread of misinformation [51] because users rely on social endorsement [52] rather than verified information. According to a report from the Pew Research Center, those who get most of their news from social media reported seeing at least some misinformation about the COVID-19 outbreak [53]. These same news consumers said media have exaggerated the threat posed by COVID-19.

All of these features of social media may have caused the discourse on social media concerning COVID-19 to be emotionally arousing and stressful. Prior research shows higher levels of emotional distress among social media news users than other media users. One study showed that individuals who consumed news solely from news feeds, or news feeds plus online news websites, had higher rates of neuroticism (feeling anxious or depressed/worried) compared to participants consuming news exclusively offline [54]. Another study compared post-traumatic stress one month after Hurricane Sandy among those who learned about the disaster through traditional media (television, newspapers, and radio) versus those who learned about it through social media (Facebook, YouTube, and Twitter; [21]). The researchers found that posttraumatic stress was higher in those using social media relative to those using only traditional media. This could be because social media exert direct and personal impact, owing to the type of content being shared, compared to traditional media that provide more 'objective' information.

The modality of social media (i.e., combination of audiovisual and textual information), its endorsement functions (i.e., likes, shares), and the lack of gatekeeping of information sources circulated on social media may strengthen emotional responses in those who rely on this as a source for news. Accordingly, we predict the following hypotheses:

**Hypothesis 6.** *Accounting for information seeking about COVID-19, consuming news via social media will be related to increased emotional distress.*

**Hypothesis 7.** *The association between COVID-19 information seeking and emotional distress will be moderated by social media news use, with the association between information seeking and emotional distress stronger for individuals with higher social media news use.*

### **2. Methods**

#### *2.1. Data*

Responding to widespread "community transmission" within the U.S. (the virus being transmitted by individuals with no travel history) in mid-March 2020, a survey was rapidly assembled and collected by a cross-disciplinary team of researchers at a large Midwestern university. Data were collected from 26 March to 1 April 2020 using a Qualtrics panel, a representative sample of U.S. residents based on a pre-recruited pool of panelists (*n* = 2251). This sample also contained a probability sub-sample of residents of the Midwestern state in which the sponsoring university is located. Participants had a mean age of 46.6 (*SD* = 17.0), 58.2% were female, and 68.9% were white. In terms of education, 22.4% had some high

school education or a high school diploma, 21.4% had some college education but no degree, 35.8% had an associate's or bachelor's degree, and 20.4% had an advanced degree.


*5.2. Results*

These final scores can aid us in finding out which VIVALDI, CLASSIC and temperature cues are the ones that express the bright, dark, warm, and cold dimensions of colors the best. For finding that out, it is necessary to select the sound and temperature cues that scored the highest for each dimension. Table 15 presents the cues that score the highest for each dimension and method and the name of that cue.

**Table 15.** Highest scores attained by each method for each color dimension. The name of the cue which attained that highest score can be seen inside the parenthesis.

> Several interesting points can be observed from these results:

Taking into consideration these observations, it was possible to reach several conclusions. First of all, all the cues that expressed most accurately the different dimensions of color were clearly defined. Additionally, it is clear that, for expressing color dimensions, VIVALDI sounds were better than CLASSIC sounds since, except for one dimension, the scores reached by VIVALDI sound cues were higher. In addition, it was better to use temperatures as a way of expressing color hues and sounds to express color dimensions, since, as observed above, the warmest and the coldest temperature conveyed the best two dimensions each, but it was convenient for the user that each dimension was represented by one and only one cue. Therefore, temperature was not a good modality for representing color dimensions.

It can be concluded that the best multi-sensory algorithm was one where the temperature cues expressed color hue and VIVALDI sounds expressed the other color dimensions. In other words, the best multi-sensory method was the temperature–sound–color coding method presented in Table 4, which is shown now in its final complete state in Table 16. The algorithm of Table 16 is the temperature–sound–color designed through the tests and results that were observed.

**Table 16.** Final temperature–sound–color coding.

### *5.3. Final Temperature–Sound Coding Multimodal Test and Results*

For assessing the final temperature–sound multimodal coding that was designed (Table 16, a final multimodal test with the final system was performed. The number of participants was 12. They were college students who had normal eyesight and an average age of 22 years. The test sessions included an explanation of the test and the method, a short training time so the user could familiarize himself/herself with the different temperatures and sounds, and a final test in which the user had to guess which color the multimodal system was representing through its sound and temperature. Test duration lasted around 25 min per person. The testing procedure was the following:

The accuracy during the test can be seen in Table 17, and a list with all the wrong and correct answers, divided into hue (represented through temperature) and dimension (represented through sound) during the test can be seen in Table 18. Additionally, in Tables 19 and 20, confusion matrices for both the hue and the dimension of the color (each one presented to the user as a temperature cue and as a sound, respectively) are presented. The total accuracy was 67.5%. Considering only the hue of the colors, the accuracy went up a little bit to 71.6%. On the other hand, the accuracy of guessing the dimension of the color through sound reached up to 92.5%. While 67.5% might not seem too high, it is important to consider the limited training time the users were given with. With a longer training time, the accuracy would likely increase. In addition, previous work [5] seems to suggest that the capacity for visually impaired people to discern temperatures might be higher than the one reflected here (with sighted users). On the other hand, the confusion matrices show clearly that the main recognition problem is caused by the colors yellow and orange, whose temperature cues (30 ◦C and 34 ◦C) are not easily discernible. Therefore, the temperatures cues for orange and yellow, and that particular temperature range of (30 ◦C, 34 ◦C) are things that will have to be modified and improved in the future in order to improve the system. Overall, the results are promising and the system seems to have the potential to be developed and improved on future iterations.

**Table 17.** Accuracy when discerning a color correctly (both hue and dimension) and same value when only taking hue or dimension into account.

**Table 18.** Final multimodal system test results for 12 users. "H" means the answer related to hue, "D" the one related to the dimension. As a result, if for the case of Dark Red a user answered Dark Blue, the "H" answer would be wrong while the "D" answer would be correct. "Tot\_D" gives the total number of correct answers per user per dimension. "Tot" gives the total correct answers per user considering both hue and dimension together.

**Table 19.** Confusion matrix for the six color hues (presented through temperature cues). The main recognition problem is caused by the colors yellow and orange, whose temperature cues (30 ◦C and 34 ◦C) are not easily discernible.

**Table 20.** Confusion matrix for the four dimensions of the colors (presented through sounds).


**Abstract**

Malignant melanoma is an aggressive disease. The anorectal region is the most common site of primary gastrointestinal malignant melanoma. Due to its low incidence, the diagnosis is often delayed. The most characteristic clinical feature of this tumor is its brown-black appearance due to the melanin pigment. However, the pigmentation may be absent in up to 20% cases. Timely diagnosis and treatment are crucial for achieving good long-term outcomes. Surgical excision remains the treatment of choice for localized disease. However, the extent of surgery has been a matter of debate. Anorectal melanoma is a highly malignant disease, and more than 50% cases have metastasis at the time of diagnosis. Targeted therapies especially immune check point inhibitors have brought about a paradigm shift in the management of cutaneous melanoma. They are being increasingly used for mucosal melanomas, and their role in anorectal melanoma is being investigated in various clinical trials.

**Keywords:** malignant melanoma, anus, rectum, mucosal, check point inhibitors