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SARS-COV-2; COVID-19

Introduction

Mental illness is the second most common cause of workrelated problems after musculoskeletal disorders, accounting for more than one-third of work-related illnesses. Regardless of mental illness, the psychopathological condition of the workplace adversely affects performance, absenteeism, and days of sick leave. One such underestimated and unaddressed area is the mental health problem developed by healthcare professionals during a pandemic. In late 2019, an emerging infection called Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV- 2) affected healthcare professionals (HCW). Caused a pandemic that gave In unprecedented challenges and tremendous psychological consequences [3,4,5]. Healthcare professionals have played a leading role in the fight against COVID-19. Healthcare workers are at the forefront of the fight against COVID-19, providing healthcare services in the most devastated areas. Healthcare workers' mental health faces serious problems during Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). As is well known, COVID-19 is more infectious than SARS and MERS and can put healthcare professionals at the forefront of mental health problems. Similarly, health care professionals faced tremendous psychological stress during the COVID-19 pandemic, with a high prevalence of depression, anxiety, insomnia, and distress. In addition, healthcare professionals at the forefront of the fight against COVID-19 exhibit more serious mental health symptoms than other healthcare professionals. Apart from the impact of mental health problems on individuals, mental health problems of health professionals can be associated with poor patient care and increased malpractice. Reliable estimates of the prevalence of mental health problems in health care workers during the COVID-19 pandemic are critical to their prevention, detection and treatment [6,7].

Materials and Method

Sample data was collected retroactively from July to September 2021 using the Web platform Infoclin, the software used by Polyclinic Sant'Orsola-Malpighi to manage clinical health data. Only mental health visits were included. Social demographic information was collected for gender, age, occupation, and the sector in which the individual worked.

It also reports information on the number of company medical visits, the year of first visit, diagnosis of mental illness by DSM-5, psychotherapy or drug therapy, and proof of suitability and limitation in the workplace by company doctors. It was done, Depending on their health status, all subjects received fitness notes: workable, incapable of working, and meeting restrictions. Restrictions were prescribed after orthopaedics or mental illness [8,9]. Regarding work restrictions due to orthopaedic problems, Sant'Orsola-Malpighi Polyclinic's occupational medicine department has changed from type A to type D based on the "Professional Dictionary", depending on the degree of physical tension. It depends on the classification. Workers can tolerate. In fact, Type A means that workers can do very heavy work.

Data Analysis

The sample size has not been tentatively calculated as this study follows the case series design. Descriptive and exploratory data analysis has been performed. Continuous data is shown as mean (± standard deviation, SD) and categorical data is shown as frequency and percentage. A general linear model (ANOVA) was run to determine statistical differences between participants with different DSM-5 psychiatric diagnoses (fixed factors) by age (dependent variable) [10,11].

Quantitative data analysis was performed using State version 14.0 (State Corp, College Station, Texas, USA). Descriptive statistics were used to examine the characteristics of "healthcare professionals" and their impact on mental health. The association of results (burnout syndrome, anxiety, depression, PTSD) with variables was assessed using the chi-square test, Fisher's exact test, independent sample t-test, or the Mann-Whitney U test. Binary logistic regression followed by multiple logistic regressions was used to calculate the odds ratio. If the variable's p-value is & then the variable was included in the multivariable model. 0.05 For univariate analysis. For qualitative data, the answers to open questions were analysed using content analysis. Two trained researchers used inductive analysis to individually code the responses, using the emergence of topics and subtopics from the text. In the event of a discrepancy, researchers compared the analysis and reached consensus, extracting the statements that best represent each identified theme and sub-theme [12,13].

A review of all six articles showed that the current study focuses on assessing multiple aspects of health care workers' mental health disorders with COVID-19. Some socio-demographic variables such as gender, occupation, age, place of work, work sector, and psychological variables such as inadequate social support and self-efficacy are stress, anxiety, and depression in health care workers. It was associated with increased symptoms and insomnia. There is increasing evidence that COVID-19 may be an independent risk factor for health care workers' stress.

Elements of Monogenic Auto Inflammatory Disorders Treatment

Controlling symptoms, enhancing quality of life, and avoiding long-term consequences are the major three goals of therapy for people with monogenic AIDs. No steroidal anti-inflammatory medications (NSAIDs), high-dose corticosteroids, colchicine, or immunomodulators have been the sole stay of symptomatic treatment for years. These treatments, with the exception of colchicine in FMF, frequently fall short of providing adequate control of symptoms and inflammation indexes, especially serum amyloid-A (SAA), which must be kept within a normal range because the result of its cleavage accumulates gradually in various tissues and results in systemic amyloidosis. Generally speaking, any treatment should be modified to keep the concentration of SAA within the range [14].

Discussion

The purpose of this study is to explain the clinical picture of psychiatry according to current nosography by medical staff at the Sant'Orsola-Malpighi polyclinic in Bologna, which required one or more visits to the Faculty of Industrial Medicine from 2016 to 2019. It was to do. Clinical procedures for prescribing fitness notes and work restrictions according to a psychiatric diagnosis. The restrictions of orthopaedics are the same. First, a descriptive analysis showed demand from nearly three-quarters of women and the prevalence of nurses unevenly distributed in different departments of the hospital. This is consistent with the literature showing that adult women report a higher prevalence of mood disorders and psychosis between health care workers and the general population than men. Consistent with the description in, this study showed a significant association between gender and psychiatric diagnosis. Indeed, gender can be seen as a factor that can influence the onset or exacerbation of disorders such as depression in individuals at risk. Consistent with current findings, the literature notes the lack of standardized guidelines for assessing psychological fitness and restriction assignments for a particular job, and the fact that effective assessment tools are not used. Pointed out. The clinometric approach can fill this gap. In fact, it emphasizes the importance of including both observers and self-assessment tools in the clinical assessment process. This makes it possible to distinguish between groups of patients who have the same clinical diagnosis for the same disability. Current research has some limitations to be discussed.

The first is represented by the cross-sectional design and preliminary properties of the results retroactively obtained through reference to electronic medical records (i.e., the Infoclin platform). Second, the expedient sampling method was used, limiting the generalization of research results. Finally, the third limitation of this study is unicentricity, as it only includes staff from hospitals in northern Italy. Future studies will need to consider larger samples from different hospitals in different regions of Italy and in different countries in Europe.

Conclusion

The results of this study show that categorical approaches to identify nuances of mental illness severity are inconsistent with human complexity and therefore with aptitude notes and workplace restriction assignments during the assessment process of occupational health screening. Showed inconsistency. This procedure cannot take into account specific clinical factors that may be associated with more specific conformance notes and task limit assignments. Usefulness of a clinical approach that includes not only categorical tools but also dimensional tools for assessing the course of mental illness in order to standardize the approach in the occupational health environment and put the psychiatrist's clinical judgment into practice. Needs further research to test the severity and comorbidity of the patient's clinical picture of the disease. These factors can be discriminatory in both patient prognosis and treatment, and therefore may be more specific and reliable when considering various aptitude notes and occupational restrictions.

1. Glozier N (2002) . Occup Environ Med 59: 714–720.

,

2. Stansfeld S, Feeney A, Head J, Canner R, North F, et al. (1995) . Soc Sci Med 40: 189-197.

,

3. Martin JK, Blum TC, Beach SR, Roman PM (1996) . Soc Psychiatry Psychiatr Epidemiol 31: 3-9.

,

4. Dewa CS, Lin E (2000)  Soc Sci Med 51: 41-50.

,

5. Duchaine CS, Aubé K, Gilbert-Ouimet M, Vézina M, Ndjaboué R, et al. (2020) . JAMA Psychiatry 77: 842-851.

,

6. Hennekam S, Richard S, Grima F (2020) . Empl Relat 42: 626-645.

,

7. Rapisarda F, Vallarino M, Brousseau-Paradis C, Benedictis LD, Corbière M, et al. (2022) . Int J Environ Res Public Health 19: 3806.

,

8. Balducci C, Avanzi L Fraccaroli F (2014) . Med Lav 105: 100-108.

,

9. Balducci C, Vignoli M, Rosa G, Consiglio C (2020) . Med Lav 111: 388–398.

,

10. Gorman T, Dropkin J, Kamen J, Nimbalkar S, Zuckerman N, et al. (2013) . New Solut 23: 1-167.

,

11. Lambert VA . Nurs Health Sci 3: 161-172.

,

12. Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH (2002. JAMA 288:1987–1993.

,

13. McVicar A (2003) . J Adv Nurs 44: 633–642.

,

14. Vahey DC, Aiken LH, Sloane DM, Clarke SP, Vargas D (2004)  Med Care 42: 57-66. 

,