Artigos

INTRODUCTION
Police work encompasses countless duties in order to ensure public order and safety. International and national studies have shown that most Police officers would not change their profession, due to the fulfillment and satisfaction it provides them, through the activities they perform.¹,² However, it can also be source of distress and illnesses, given the social and corporate undervalue police officers face,¹ and the several professional risks, such as continuous exposure to violence, vulnerability to crime and societal pressures.³
Current evidence reveals the impact of work on Police officers' physical health, in both well developed and underdeveloped countries. The main alterations include musculoskeletal injuries, gastrointestinal, cardiovascular and sleep disorders, among other conditions.4,5,6 These conditions are caused by the nature of the profession itself, performed in shifts, interfering in sleep quality. It also requires running, jumping and using firearm, thus affecting the musculoskeletal system.6,7,8
In addition, this profession has inherent risks related to high psychological demands and precarious working conditions, such as poor management, low income and lack of planning1. These factors can negatively affect the workers' psychological health, and lead to work-related stress, Burnout Syndrome and Minor Psychiatric Disorders (MPDs).
High levels of occupational stress increases the risk of absenteeism, MPDs and Burnout Syndrome, which affects professionals, employers, as well as public safety.9 Burnout Syndrome is characterized by emotional exhaustion, depersonalization and low personal fulfillment.10 This syndrome directly affects emotional balance, reflecting on Police officers’ performance, and enabling the development of other psychological diseases.11
About 20% of police officers face MPDs, especially those that worked 10 to 20 years in the profession. They often experience symptoms of sadness, anxiety, fatigue, decreased attention span, somatization, forgetfulness, irritation and insomnia.12,13 Police officers' quality of life is potentially linked to those physical and psychological alterations.
Quality of life is a multifaceted concept defined as an individual's positive or negative perception of different aspects of their life. The combination of these different aspects, and their effect on each other characterizes quality of life. The individual associations between depression, physical disorders, high stress levels, and quality of life has been described.14
Current research identified that negative experiences related to a specific area of life can negatively affect an individual’s perception of other life aspects. Therefore, exposure to work experiences and environments that predispose psychological and physical health decline can negatively affect a professional’s quality of life as a whole. 15,16
Although specific psychological and physical diseases have been studied in this population - such as musculoskeletal injuries and MPDs - there is a knowledge gap on Police officers’ physical and psychological health decline, and the impact on their quality of life.17,18 In addition, a scarcity of publications implementing the Correlation Network Analysis methodology is evident, mostly limited to well developed countries.
Therefore, given the presented data, this research was able to assess the relationship of physical and psychological alterations with quality of life in Civil Police officers from Porto Alegre-RS.


MATERIALS AND METHODS
A cross-sectional design research was executed, led by the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) tool. The research incorporated the Civil Police of Porto Alegre, the capital city of Rio Grande do Sul (RS) - Brazil, encompassing 12 departments.
The study population consisted of 1,540 Civil Police officers from Porto Alegre. Sample calculation was performed by means of the G* Power software, Version 3.1.2 (2009), considering a 95% level of statistical power, for a 5% significance level. Calculation parameters were based on other studies with similar variables and outcomes,19,20,21 defining a minimum sample of 237 Civil Police officers. Systematic random sampling was used for the Police departments, and sampling by clusters for the Police stations.
Participants were active workers, male or female, aged between 18 and 65 years old, allocated to the Civil Police of Porto Alegre-RS. Police officers distanced from their function, on leave during the data collection period, or with less than a year of work in the corporation were excluded.
Data collection took place between September 2017 and July 2018, using a self-reported responses’ questionnaire containing sociodemographic, lifestyle and occupational questions, such as health diet, physical activity, smoking, alcoholic beverage consumption. Nordic Musculoskeletal Questionnaire (NMQ), Effort-Reward Imbalance (ERI), Self-Reporting Questionnaire (SRQ-20), Maslach Burnout Inventory (MBI) and World Health Organization Quality of Life (WHOQOL-bref) instruments were also used.
The NMQ was used to assess the musculoskeletal injuries. This version investigates such symptoms based on three dichotomous questions (yes or no), and related to the anatomical areas (neck, shoulders, elbows, wrist or hand, upper back, lower back, thighs, legs, knees and ankles): 1- “In the last year, have you felt any pain or discomfort in [...] ?”; 2- “Has this problem ever hindered doing anything in or outside the house in the last year?” And 3- “Have you felt this pain in the last seven days?”.22 A summed raw score of the whole scale and subscales was used for further analysis.
The Police officers' psychological health alterations were assessed with the use of questionnaires aimed at identifying symptoms of Occupational Stress, MPDs and Burnout Syndrome.
Occupational stress was assessed by means of the ERI model,23 which is grounded on the reciprocity between these two constructs in professional life.23,24,25,26 The instrument consists of 23 items divided into three dimensions: Effort (six items); Reward (11 items that represent all three dimensions of financial reward and related to career, esteem and job safety, in the respective subscales), and Commitment Excess (six items that represent the personal element or the intrinsic model). In the Effort and Reward dimensions, the answers vary between agreement and disagreement, with scores from 1 to 5. In the Commitment Excess dimension, the answers vary between I strongly disagree and I strongly agree, with scores between 1 and 4.24
To assess MPDs, the SRQ-20 was used,13 The instrument has 20 dichotomous questions, referring to symptoms and problems within the 30 days prior to answering the instrument. The assessment encompasses four subcategories: anxious and depressive mood (4, 6, 9 and 10); somatic symptoms (1, 2, 3, 5, 7 and 19); decreased energy (8, 11, 12, 18, 13 and 20) and depressive thoughts (14, 15, 16 and 17). Each alternative has a score from zero (0) to one (1), where a value of one (1) indicates that the symptoms were present in the last month, and zero (0) means absence of symptoms. The cutoff point to characterize presence of Minor Psychiatric Disorders corresponded to values equal to or greater than 7 points.27
Burnout Syndrome was evaluated by means of the MBI, comprised by 22 questions in a Likert scale, with options varying from 0 to 5 points. Among them, nine questions assess emotional wear out (questions 1, 2, 3, 6, 8, 13, 14, 16 and 20), five assess depersonalization (questions 5, 10, 11, 15 and 22), and eight assess professional fulfillment with an inverse score (questions 4, 7, 9, 12, 17, 18, 19, and 21).28 A summed raw score of the whole scale and subscales was used for further analysis.
In order to assess quality of life, the WHOQOL-bref instrument29 was used, with 26 questions evaluating four domains: Physical (pain, energy, sleep, mobility, activities of daily living, dependence on medication/treatments, capacity to work); Psychological (positive/negative feelings, thoughts, self-esteem, body image, spirituality); Social Relationships (personal relationships, social support, and sexual activity); Environment (physical safety, home environment, financial resources, health and social care, opportunities to acquire information and skills, recreation/leisure, physical environment and transportation). A summed raw score of the whole scale and subscales was used for further analysis.
The analysis of the quantitative data was executed with the Statistical Package for the Social Sciences (SPSS) software, version 18.0. Categorical variables were described using absolute and relative frequencies, and the continuous variables were described by means of central tendency and dispersion measures. Shapiro-Wilk normality test was performed and asymmetry and kurtosis values were obtained. Bivariate Spearman correlations were performed, suitable for the analyses that include ordinal and continuous variables. Mann-Whitney was used to assess the difference between two groups and the quality of life domains (Physical, Psychological, Environment, Social Relations and Overall domains). For more than two groups, Kruskal-Wallis and Dunn was used. Subsequently, it was verified which independent variables (sociodemographic, health and work-related variables) presented significant relationships (p<0.05) with at least one of the dependent variables (assessed with the previously described questionnaires). In addition, aiming at better data presentation, Principal Component Analysis was used to create a composite, general score from the WHOQOL-bref domains. The single score created is called “Overall Quality of Life (OQL)”.
Bivariate analyses were used to select the variables that entered in the network analysis. Those results were not shown in the article due to the high number of comparisons that can be obtained with the implemented type of analysis. Statistical effects with a two-tailed “p-value” below 0.05, or with a 95% confidence interval, were considered significant. Only variables that presented p= <0.05 were selected for the network analysis. All selected variables are shown in the descriptive and network analysis.
To investigate the interactions of the variables selected through bivariate analysis and assess their influence on Police officers' quality of life, a network analysis was carried out. Network analysis allows for quick visualization and comprehension of the associations between multiple variables, by forming a statistical graph. This type of graphic structures are commonly used in cross-sectional research, especially non-directional and weighted networks, making it possible to easily assess correlations or associations between variables. Univariate analyses can only capture the isolated action of each variable as for the outcome under study, which may not be enough to explain the study object. For that reason, network analysis makes it possible to visually explore relationships that occur simultaneously between multiple variables.30,31
This method encompassed two stages. The first one consisted of estimating the matrix of partial regularized correlations. The data obtained was fed into a partial correlation algorithm, which computes the relationship between two variables (X and Y), while eliminating the effect of another variable (Z), assessing the underlying correlation between them. The algorithm provided a partial correlation matrix, that is produced by aligning the analyzed variables in rows and columns, and displaying the coefficient of partial correlation (rp) between in the convergence point.30,31
In the second stage, the matrix of partial correlations is represented in a two-dimensional plan on a graphic object. The vertices represent the variables investigated and the edges represent the partial relationship between them, with possible variation in thickness (magnitude) and color (blue for positive, red for negative). The distribution of the nodes also indicates the magnitude of the relationship, and is influenced by a variable’s centrality measurement. 30,31
When analyzing variables, their influence within a network can differ. Centrality measurements allow for detection of the most relevant variables in a network structure. When estimating the partial correlation matrix of variables, it is also possible to estimate variables’ centrality, and the chosen measure was Expected Influence. The Expected Influence is calculated by summing the first and second derivate edges from adjacent variables. Stated another way, the sum of direct and indirect edges produces a measure of how each variable can “activate” or influence their adjacent linked variables. As exemplified by other researchers, traditional types of centrality measures, such as Centrality Strength, may not accurately predict the influence between variables, especially in networks that describe both positive and negative correlations between variables.32
In this research, network analysis was carried out twice. Firstly, for the partial correlations between the ‘Quality of Life’ Domains, and the ‘Musculoskeletal Injuries’, ‘MPD’, ‘Occupational Stress’ and ‘Burnout Syndrome’ variables. Secondly, for the partial correlations of sociodemographic and work-related variables that presented significant statistical effects (p= <0,05) with the ‘Musculoskeletal Injuries’, ‘MPDs’, ‘Occupational Stress’, ‘Burnout Syndrome’ and ‘Overall Quality of Life’.
According to the precepts set forth in Resolution 466/12 of the National Health Council (Conselho Nacional de Saúde, CNS, the Research Ethics Committee of the Federal University of Rio Grande do Sul approved this study, under CAAE: 65391717.1.0000.5347. Participation took place by signing the Free and Informed Consent Form, preserving anonymity and secrecy of the information.

RESULTS
The final sample was composed of 237 participants. The highest percentage of the 237 Civil Police workers was male (51.9%), with a mean age of 41.4 years old (±8.58). The results referring to the work-related variables showed that the working time median was 7 (3-16) years. A significant part of police officers worked as clerks (46.8%), with a moderate work pace (59.1%) and previous training for the function (62%). When assessing the level of satisfaction with the monthly remuneration, the median was 3(2-4); with the workplace, 4(3-4.5); with job recognition, 3(2-4); with interpersonal relationships, 4(4-5), and with motivation to work, 4(3-5).
As for life habits, following a healthy diet (73.8%), exercising regularly (67.9%), smoking (8%), and drinking alcoholic beverages weekly (54.4%) were reported. The median of daily sleeping hours was 7 (6-8). Workers declared sufficient resting (59.5%) and leisure (54.4%) time.
A significant percentage of Police officers endured psychological (35.9%) and physical (10.1%) violence at work, and half reported feeling exposed to violence (50.6%) on a daily basis. In addition, physical (41.35) and psychological (44.3%) health alterations were also reported.
Psychological assistance was considered important by the majority of workers (89.9%), and some sought psychological treatment due to work-related problems (21.1%). Policemen reported observing coworkers developing illnesses (80.2%) and becoming absent from work (31.6%). Almost half of workers reported musculoskeletal symptoms (48.1%), especially in the neck (21.9%), shoulders (20.3%) and upper back (20.7%).
Most Police officers experienced occupational stress (67.8%), and to a lesser extent, MPDs (26.2%). Workers presented emotional wear out (26.2%), depersonalization (27.4%) and low professional fulfillment (26.6%), characterizing Burnout Syndrome (3.8%). The data is shown in Table 1.

Table 1 - Distribution of the sociodemographic, work-related, physical health, psychological health and variables of the Civil Police officers from Porto Alegre (RS), Brazil. (n=237).

The correlation network between quality of life and the physical and psychological health variables is presented in Figure 1.

Figure 1. Correlation network between quality of life domains, physical and psychological health variables.

The highest correlation value was between the “Burnout Syndrome” and “Occupational Stress” variables (rp=0.39). The “MPD”s variable proved to be strongly correlated with “Burnout Syndrome” (rp= 0.28) and “Musculoskeletal Injuries” (rp= 0.27); the latter is also related to quality of life’s “Physical Domain” (rp= 0.15).
A cluster is observed with the different Quality of Life Domain variables, as each variable Domain presents strong correlations between their counterparts. In this group of variables, the “Psychological Domain” variable is centralized, as the four other quality of life variables relate to it.
The “Psychological Domain” also presents a stronger correlation with the “Overall Quality of Life” variable (rp= 0.28) than the other Domains, defining it as the most influential domain on the police officers’ quality of life. Conversely, the “Physical Domain” presents the weakest correlation to “Overall Quality of Life”. The “Physical Domain” also presents a negative correlation with “MPD”s (rp= -0.22).
“Musculoskeletal Injuries” (rp= -0.15) and “MPD”s (rp= -0.11) had strong negative relation to the “Overall Quality of Life” of policemen. In addition, the “Environment Domain” presented a negative correlation with “Occupational Stress” (rp= -0.19).
Expected influence measurements of the network variables are shown on Figure 2.

Figure 2. Expected Influence measurements for the correlation network of quality of life domains, physical and psychological health variables.

As previously described, variables are able to stimulate (positive values) or inhibit (negative values) other variables in the network. Expected influence signals the variables’ impact on each other when activated, be it positive or negative. To illustrate, Burnout Syndrome, when activated, exerts a positive influence on the variables close to them, such as Occupational Stress.
The other variables that presented significant statistical relation with the ‘Musculoskeletal Injuries’, ‘MPDs’, ‘Occupational Stress’, ‘Burnout Syndrome’ and/or ‘Quality of Life’ variables were selected for further network analysis. The correlation network presented in Figure 3 demonstrates the correlations of sociodemographic, occupational, and life habits variables, with the physical health, psychological health, and quality of life assessment.

Figure 3. General correlation network

By analyzing the general correlation network, the relationship between variables can be understood. For better understanding of the network, the measurements were not shown in the figure, as it would prevent from clearly viewing graphic depiction of those values. The most relevant partial correlation values are described in the following descriptive paragraphs.
The research subjects’ ‘Quality of Life’ presents a strong negative correlation to the ‘MPDs’ variable (rp= -0.31). In turn, the ‘MPDs’ presented a strong correlation to the ‘Burnout Syndrome’ (rp= 0.21). The ‘Burnout Syndrome’ variable was also strongly correlated to an individual’s ‘Occupational Stress’(rp= 0.21), which in turn, negatively correlates to their sense of “Job Recognition” (rp= -0,21).
This population’s ‘MPDs’ are also positively correlated to the ‘Musculoskeletal Injuries’ (rp= 0.22) and ‘Physical Health Alterations’ (rp= 0.17). Their ‘Psychological Health Alterations’ are strongly correlated to their need for “Psychological Treatment” (rp= 0.30), as well as ‘Physical Health Alterations’ (rp= 0.30).
Seeking “Psychological Treatment” is positively correlated to noticing the “Illness of Coworkers” (rp= 0.22) and ‘Psychological Health Alterations’ (rp= 0.30). These last two variables also correlate to each other (rp= 0,16), characterizing a “cluster” - a group of three or more variables that influence each other, boosting their correlation.
Similarly, on the bottom-left side of the General Correlation Network, another cluster of correlations is observed. This cluster demonstrates that good “Interpersonal Relationships” between coworkers has a positive impact on their sense of “Job Recognition” (rp= 0.20) and their “Work Motivation” (rp= 0.26). These variables correlate to each other (rp= 0.24), and also positively influence the officer’s “Work Environment” (rp= 0.26 and rp= 0.28, respectively).
Officer’s perception of their “Work Environment” is also positively correlated to sufficient “Leisure Time” (rp= 0.25). Subsequently, the “Leisure Time” variable correlates to the “Healthy Diet” variable (rp= 0.21), which finally correlates to the regular “Exercising” variable (rp= 0.33).
Workers in “Police Chief” position have a strong correlation to “Salary Satisfaction” (rp= 0.30), and demonstrated negative correlation to experiencing “Workplace Physical Violence” (rp= -0.16) and “Workplace Psychological Violence” (rp= -0.19). Lastly, regular “Consumption of Alcoholic Beverages” is strongly correlated to “Smoking” (rp= 0.36).
Figure 4 explicits the expected influence measurements from the variables shown on the general correlation network.

Figure 4. Expected influence measurements for the general correlation network

The variables “Physical Health Alterations” and “Psychological Health Alterations” represent the highest positive values. When activated, they are more likely to stimulate “Musculoskeletal Injuries” and “MPDs” variables, in addition to presenting feedback to each other.

DISCUSSION
Quality of life was influenced primordially by alterations in the psychological domain. Hence, negative alterations on other domains had less influence on quality of life than psychological health decline.
By analyzing the variables that could further explain this effect, MPDs were found to be the most impactful variable on police officers’ quality of life. MPDs have been previously reported as an important stressor of quality of life, but not through a global analysis of variables and their possible correlation to quality of life.14 Thus, by using varying questionnaires and assessing a large number of variables, it was possible to analyze the correlations between these variables, as well as their impact on the workers’ quality of life.
A brazilian clinical trial identified a high prevalence of depression and anxiety symptoms (MPDs), as well as diminished quality of life. The correlation between these variables, however, were not identified.33 Contrastingly, the present research identified a strong correlation between quality of life decline and MPDs. Moreover, high levels of occupational stress, alterations in physical health and musculoskeletal injuries intensified police officers’ MPDs.
The relationship between stress and Burnout Syndrome is commonly described in literature,9 and was reinforced in this study. Police officers’ negative workplace experiences increase their levels of stress, and make them more vulnerable to developing Burnout Syndrome. This correlation is reciprocal, meaning police officers’ with Burnout Syndrome also experience increased levels of stress.
The development of musculoskeletal injuries, physical health alterations, and MPDs also lead to greater chances of developing Burnout. Similarly, Burnout Syndrome worsens MPDs. A study shows that Burnout Syndrome caused lower sleep quality among north-american police officers, leading to insomnia symptoms.34 Conversely, the current research identified only a weak negative correlation between MPDs and police officers’ sleep time.
Occupational stress was shown to be heavily influenced by a professional's sense of job recognition, as well as other work variables. In this context, a positive work environment and good relationships with coworkers can reduce levels of stress. Contrastingly, Police officers who suffered bullying by their peers presented higher levels of dissatisfaction with their workplace, as well as higher psychological stress rates and more suicide attempts.35
Brazilian researchers found that problems in the interprofessional relations (favoritism, lack of recognition) are an important source of occupational stress for policemen, which exerts negative impacts on work engagement and job satisfaction.36 In the current research, it was possible to verify that those relationships can also exert a positive impact on work motivation and the sense of job recognition, positively impacting on quality of life.
A study found that higher social support levels can lower post-traumatic stress symptoms in police officers, also positively influencing other psychological symptoms, such as anxiety and depression.37 Although the current research wasn't able to identify this direct correlation, an indirect influence between Interpersonal Relationships and MPDs can occur, through the sequential activation of other correlated variables. Officers’ positive work relationships improve their sense of recognition and motivation, which reduces stress and the risk of Burnout Syndrome, leading to less MPDs and possibly better quality of life.
Even though psychological health alterations are prevalent among police officers, only a handful actively seek psychological treatment. The few that do, are more likely to develop positive views of psychological care, and become able to identify the development of psychological illnesses in coworkers. Similarly, a study showed that only 17% of the professionals with negative mental health symptoms sought mental health services. Officers reported interest in seeking psychological support, as long as confidentiality was guaranteed, as they feared being exposed before their peers.38 A stigma around seeking psychological support is prevalent in this profession, which perpetuates a cycle of psychologically ill police officers that never seek help.
Studies with Police officers generally have a sociodemographic profile characterized mostly by men, going up to 84.7% of male workers.36 However, in this research, the sample of workers was comprised by a similar number of men and women, evidencing a different profile in the civil police of the south of the country.
Police chief officers are more satisfied with their salary than other workers. In addition, they are also less likely to be exposed to physical and psychological violence, due to the management nature of the job position. Evidence shows that lower exposure to violence is often related to lower occupational stress for police officers.39 Additionally, workers’ salary satisfaction, as well as their exposure to mental health stressors influences their perception and satisfaction with work environment.40 Thus, it is expected that chief officers don’t commonly experience high levels of stress, at least not from the same causes as their coworkers, given their high reward job rank.
Police officers that reported having satisfactory amounts of leisure time, also followed healthy diets, and tended to regularly practice physical activities. Literature shows that exercising and following a nutritious diet exerts positive influences on body composition and physical health, acting as a protective factor against obesity in Police officers.41 Additionally, regular exercise is linked to reducing negative mental health symptoms and improving the mood of these workers.41 The officers that have sufficient leisure time and developed healthy habits, also tend to experience higher work motivation and positive views on their work environment. This can be attributed to the protective effect that healthy habits have on psychological health.
Currently, it is known that regular alcohol consumption increases an individual’s tobacco smoking. Correspondingly, smoking also induces a higher alcohol consumption, characterizing a vicious cycle.43 This cycle can also be identified in the studied police officers, since this research exposes a strong positive correlation between regular alcohol consumption and smoking.
Study limitations include the cross-sectional design, as it does not allow establishing a definitive cause-effect relationship. There was also a healthy worker bias, as the officers on leave for health treatments were not included in the sample analyzed in this research.
It is possible to demonstrate that police officers’ quality of life was most intensely influenced by alterations in the psychological domain, especially by MPDs. It is also possible to identify the role played by other variables in this relationship between psychological health and quality of life. Studies like this are necessary for the social diagnosis of work-related illness risk run by vulnerable population groups, such as Police officers. Additionally, it can support the development of strategies to improve the quality of life of these professionals, promoting healthier workers and, consequently, ensuring the nation's public safety.

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