Abstract
Background/Objectives
One possible effect of mental disorders among general workers is their impact on cardiovascular health. This study aimed to investigate the relationship between mental disorders—specifically depression, anxiety, depression-anxiety, and post-traumatic stress disorder (PTSD)—and heart disease in workers.
Methods
This systematic review and meta-analysis followed PRISMA guidelines. A comprehensive search was conducted across five electronic bibliographic databases. Two independent researchers screened and selected studies by examining titles and abstracts. Data was independently extracted by two reviewers from the chosen studies. Moreover, a meta-analysis was performed focusing on the odds ratio values.
Results
Thirty-four studies were included in the analysis. Of these, 29 focused on myocardial infarction, 2 on heart failure, and 2 on ischemic heart disease. Among the reviewed studies, 30 out of 34 demonstrated a significant association between depression, anxiety, and post-traumatic stress disorder with heart diseases. Based on the results of meta-analysis, pooled values of odds ratio related to association between depression, anxiety, and PTSD with heart disease were 1.53 (95% CI: 1.26–1.80), 1.87 (95% CI: 0.13–3.61), and 1.70 (95% CI: 1.34–2.05), respectively.
Conclusions
The findings suggest that depression, anxiety, depression-anxiety, and PTSD may contribute to the development of heart disease in workers. However, the high heterogeneity warrants cautious interpretation. Consequently, it is imperative that preventive measures, such as workplace mental health programs, stress management interventions, occupational therapy programs, and policies targeting occupational risk factors, are implemented.
Keywords
Introduction
There are various harmful agents in the workplaces that threaten the human health (Khoshakhlagh, Askari Majdabadi et al., 2023; Khoshakhlagh et al., 2024). Mental health disorders, also known as mental illnesses, are conditions that influence the cognitive performance, emotions, moods, and actions in humans (Dehkordi et al., 2025). Those can happen for a short time or a long time. Three common types of these disorders in workplaces include depression, anxiety, and post-traumatic stress disorder (PTSD) (Knudsen et al., 2013).
Work-related factors can contribute to mental disorders among the working population (Yazdanirad et al., 2021). There are risk factors in workplaces, such as job hazards, work environment, level of job security, health perception, organizational justice, atypical working hours, workplace conflict, role stress, and the availability of adequate social support, which can cause mental disorders in the workers (Hill et al., 2022). So mental disorders have now surpassed musculoskeletal conditions as the primary reason for sick leave and prolonged inability to work in the majority of developed nations (Hill et al., 2022).
The results of previous research revealed that these mental disorders in addition to psychological effects can directly lead to physical diseases, such as stomach pain, back pain, headaches, or other unexplained aches and pains, immune deficiency disorders, and cardiovascular diseases among workers (Giorgi et al., 2020). Moreover, the mentioned mental disorders can lead to decreased productivity, absenteeism, job burnout, and job turnover at the workplace (Giorgi et al., 2020). Cardiovascular consequences, particularly myocardial infarction and heart failure, are one of the important effects due to mental disorders among the workers (Giorgi et al., 2020).
The findings of several studies suggest some probable mechanisms of the associations between mental disorders and heart diseases (Shao et al., 2020). Some studies have investigated the relationship between mental disorders and heart diseases. For example, the results of a meta-analysis showed that individuals with depression had a 30% higher risk of coronary heart disease (CHD) compared to those without depression (Gan et al., 2014). The results of a meta-analysis indicated that depression is one of the main causes of mortality and hospitalization for cardiac patients (Moradi et al., 2022). The findings of a meta-analysis showed that depression is associated with significant negative impact on development of cardiovascular disease (Krittanawong et al., 2023). The results of another meta-analysis revealed that anxiety is associated with a 41% higher risk of developing chronic heart disease (Emdin et al., 2016). The results of a meta-analysis showed that anxiety can be associated with prognosis of patients with myocardial infarction (Wen et al., 2021). Based on the results of a meta-analysis study, post-traumatic stress disorder was associated with a 27% increased risk of chronic heart disease (Edmondson et al., 2013). The results of a meta-analysis showed that there is a significant association between PTSD and an increased risk of several cardiovascular outcomes (Padhi et al., 2024). However, despite these findings, the evidence remains fragmented, and previous reviews have generally focused on single mental disorders rather than providing a comprehensive comparison across multiple conditions. Moreover, these studies were performed on the general population. Since these relationships are important and may differ in the working population, it is necessary to investigate these associations specifically among workers. To the best of our knowledge, no review or meta-analysis has been conducted on this topic to date, the present systematic review and meta-analysis aim to examine the relationship between mental disorders (depression, anxiety, and post-traumatic stress disorder) and heart diseases among workers.
Methods
Details of the PECO Framework
Eligibility Criteria
A variety of study types featuring peer-reviewed, and full-text articles published in English were included in this systematic review. The inclusion criteria encompassed studies involving workers aged 18 years and older diagnosed with depression, anxiety, depression-anxiety, and PTSD and heart diseases. In these studies, mental disorders (depression, anxiety, depression-anxiety, and PTSD) were treated as exposures, while various heart diseases and their outcomes were considered as the outcomes of interest. Consequently, studies that did not examine these specific exposures and outcomes were excluded from the review. All study types were eligible except for review articles, meta-analyses, editorial letters, case reports, and conference papers, which were excluded. Additionally, studies that used invalid diagnostic tools for mental disorders were also excluded from the review.
Search Strategies
A comprehensive search was conducted across five electronic bibliographic databases: Scopus, PubMed, Medline, Web of Science, and Embase, with a full search from inception to November 05, 2024. Two independent reviewers conducted the search and screening process. Three groups of keywords were selected for search in the databases as keywords of mental disorders AND keywords of heart diseases AND keywords of occupational setting. The keywords of the first group were Depress* OR Anxiety OR “Post-Traumatic*” OR “Post Traumatic*” OR Posttraumatic* OR “Acute Post-Traumatic*” OR “Acute Post Traumatic*” OR “Chronic Post-Traumatic*” OR “Chronic Post Traumatic*” OR “Delayed Onset Post-Traumatic*” OR “Delayed Onset Post Traumatic*” OR “Moral Injur ”*OR “PTSD”. The keywords in the second group were “Myocardial Infarct*” OR “Cardiac Failure*” OR “Heart Decompensation” OR “Congestive Heart Failure” OR “Heart Failure” OR “Myocardial Failure” OR “Heart Attack”. The keywords in the third group also were Occupation* OR job OR work* OR employee* OR industr* OR staff. An example of search strategy is available in Table 1 in supplemental material.
Study Selection
All articles retrieved from the databases were transferred into Endnote for evaluation, where any duplicates were identified and eliminated. Two independent reviewers (M.W. and X.Z.) performed the search and screening. They screened the studies by examining the titles and abstracts to determine their relevance. Therefore, duplicates were first removed by the EndNote software and then by reviewing the titles and abstracts of the studies by the reviewers. Also, articles with unrelated titles and abstracts were discarded. The full texts of the remaining articles were thoroughly examined by the researchers to verify the criteria. Relevant studies were subsequently included in the review. In instances of disagreement between the two primary researchers, a third researcher (Y.W.) was consulted to resolve the issue.
Data Extraction
The Details of the Articles Incorporated Into This Systematic Review
Q1: high quality, Q2: medium quality, Q3: low quality.
Appraisal of Study Quality and Risk of Bias
To assess the quality of the studies, appraisal instruments from the Joanna Briggs Institute (JBI) designated for case-control, cross-sectional, and cohort research were utilized (Santos et al., 2018). The JBI checklist serves as an effective instrument to examine the quality across different studies. Upon filling out these checklists, the number of “Yes” responses was converted into a percentage. Studies were then categorized as high (≥75%), moderate (50%–74%), or low (<50%) quality (Aromataris & Munn, 2020). Two independent reviewers (M.W. and X.Z.) performed this appraisal. In instances of disagreement between the two primary researchers, a third researcher (Y.W.) was consulted to resolve the issue.
Data Synthesis
The concordance among evaluators was assessed using Cohen’s kappa coefficient (Pérez et al., 2020). The resulting kappa values for the various stages were recorded at 0.91 and 0.93, respectively. The descriptive/narrative synthesis of results are conducted, especially for studies not included in the meta-analysis. For this purpose, the synthesis summarized characteristics and main findings of the studies, the consistencies and divergences were identified, and possible reasons for their heterogeneity were mentioned. Beyond the descriptive results, a meta-analysis was performed focusing on the values of odds ratio with 95% confidence interval (Chang & Hoaglin, 2017). In this review, random-effect models were applied to integrate data. The heterogeneity of the data was assessed by the Cochrane Q-test (Higgins & Thompson, 2002). Furthermore, I2 was defined as the proportion of total variation explained by heterogeneity (Higgins et al., 2003). Publication bias was also evaluated by Egger’s test. Also, funnel plot was used to investigate publication bias. For this purpose, plot asymmetry was evaluated by the Egger test (Higgins et al., 2003). For subgroup analyses, countries were categorized into low and middle-income (LMIC) and high-income (HIC) groups, following the World Bank’s classifications (Yu et al., 2022). Studies were categorized into four geographic regions: Europe, East/Southeast Asia, the Middle East, and the Americas. The studies were temporally divided based on whether they occurred before or after the year 2000. All data processing was carried out using STATA version 14.2.
Results
Study Selection and Characteristics
In this review, we collected 8,582 articles from multiple databases. From this source, 700 duplicated papers were removed. Following this, two researchers scrutinized the titles and abstracts of the 7,882 remaining articles. After this preliminary assessment, 7,840 articles were eliminated either because they failed to meet the inclusion criteria or because they met the criteria for exclusion. Consequently, the full texts of 42 articles were thoroughly evaluated, and 34 of these were ultimately chosen that be entered into the study. It must be mentioned that studies that were conducted in occupational settings and the target population was employees were considered occupational research, and studies that were conducted in public settings and the target population was the general population were considered non-occupational research. The process is depicted in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram (Figure 1). The flow diagram of PRISMA (Preferred reporting items for systematic reviews and meta-analyses)
Table 2 outlines the details of the articles incorporated into this systematic review. Among the 34 qualified studies, 10 studies were conducted using a case-control design, 19 studies using a cohort design, 4 studies using a cross-sectional design, and 1 study using a longitudinal design. 1 study has been carried out in Japan, 8 studies in Sweden, 3 studies in France, 12 studies in the United States, 1 study in Italy, 1 study in Scotland, 1 study in Norway, 2 studies in the United Kingdom, 1 study in Finland, 1 study in Russia, 1 study in China, and 2 studies in various countries. Among these studies, 6 papers were performed on females, 5 papers on males, and 23 papers on males and females.
These studies were performed on blue-collar workers, white-collar workers, power company workers, clericals, non-manual/ manual workers, skilled workers, unskilled and semiskilled workers, self-employed, teachers, World Trade Center disaster workforces, homemakers, secretaries, technical workers, non-industrial civil servants, healthcare professionals, farmers, debris cleaner, trucks/ bus drivers, construction equipment/ marine/ industrial engines workers, managers, manual workers, workers in food processing company, postman, local government employees, oil and gas workers, etc.
The main risk factors of job stress mentioned in the studies included depression (29 studies), anxiety (15 studies), and PTSD (3 studies). In terms of the disease type, 29 studies were performed on myocardial infarction, 2 studies on heart failure, and 2 studies on ischemic heart disease.
Quality Assessment
The quality evaluation of the studies incorporated in the review was conducted using the JBI critical appraisal checklist. From the overall selection, 29 papers were identified as high quality, three as moderate quality, and two as low quality. Table 2 in supplemental material represents the details related to the quality assessment of the reviewed studies.
Main Findings
Among the reviewed studies, 30 out of 34 studies showed that there is a significant relationship between depression, anxiety, and PTSD with heart diseases. Only 4 studies found a non-significant association between depression and anxiety with heart diseases.
Depression
Among seventeen studies performed on this mental disorder, fifteen studies showed that there is an association between depression and heart diseases. Among these, six studies investigated the relationship between depression and the overall risk of heart diseases. The results of four papers revealed that depression can increase this risk (hazard ratio: 1.30 to 1.55) (Almas et al., 2019; Gafarov et al., 2013; Hamieh et al., 2019; Orth-Gomér & Leineweber, 2005) and results of two studies did not show this association (odds ratio: 1.03) (Løvlien et al., 2008; Wasserman et al., 2000). Moreover, eleven studies specially examined the relationship between depression and myocardial infarction and ischemic heart disease. Their findings indicated that this mental disorder can affect risk of myocardial infarction and ischemic heart disease (incidence ratio: 1.42, hazard ratio: 1.30, and odds ratio: 1.55 to 5.40) (Alcántara et al., 2015; Azevedo Da Silva et al., 2015; Gonzales et al., 2017; Hausvater et al., 2023; Joshi et al., 2007; Kjellström et al., 2017; Liu et al., 2022; Nyström et al., 2022; Penttinen & VALONEN, 1996; Rosengren et al., 2004; Tao et al., 2011).
Anxiety
Two studies conducted on this mental disorder observed a relationship between anxiety and heart diseases. Therefore, this mental disorder can increase the risk of myocardial infarction (relative risk: 7.8) (Eaker et al., 1992; Uehata, 1991).
Depression and Anxiety
Among twelve studies investigating both depression and anxiety, ten demonstrated an association between these mental disorders and heart diseases. Of these, five studies specifically examined the relationship between this disorder and the overall risk of heart diseases. The results from four these papers revealed that depression and anxiety can increase this risk (odds ratio: 1.5) (Albert et al., 2017; Almas et al., 2015; Low et al., 1994; Orth-Gomér et al., 2018). Only one study did not indicate this association (hazard ratio: 0.94) (Nicholson et al., 2005). Moreover, seven studies specifically examined the relationship between depression and myocardial infarction, heart failure, and ischemic heart disease. Their findings revealed that these mental disorders can influence the risk of myocardial infarction, heart failure, and ischemic heart disease (hazard ratio: 3.16, odds ratio: 2.05, and relative risk: 1.07) (Allonier et al., 2004; Haines et al., 2001; Karaslavova, 2011; Lopes & Kamau-Mitchell, 2024; Rollman et al., 2012; Welin et al., 2000). The results of one study did not show this relationship (odds ratio: 1.02) (Vella et al., 2023).
PTSD
Among three studies performed on PTSD, all revealed a relationship between this mental disorder and heart diseases. Of these, two studies examined the association between PTSD and the overall risk of heart diseases, with both reporting that PTSD increases this risk (hazard ratio: 1.62 to 1.68) (Jordan et al., 2011; Seligowski et al., 2022). Another study investigated the relationship between PTSD and myocardial infarction, finding that PTSD significantly elevates the risk of myocardial infarction (hazard ratio: 2.22) (Remch et al., 2018).
Results of Meta-Analysis
Although single-study odds ratios, hazard ratios, and incidence ratios were reported, these values were used to calculate pooled estimates to better understand these associations. Given the high heterogeneity of the findings, the meta-analysis of pooled odds ratios was performed using a random-effects model. Figure 2 shows the meta-analysis results for the odds ratios of depression, depression and anxiety, anxiety, and PTSD. The results indicated that depression [1.53 (95% CI: 1.26–1.80); p < .001; I2 = 87.1], depression and anxiety [1.45 (95% CI: 0.98–1.92); p = .003; I2 = 75.2], anxiety [1.87 (95% CI: 0.13–3.61); p < .001; I2 = 95.2], and PTSD [1.70 (95% CI: 1.34–2.05); p < .001; I2 = 00.0] were associated with an increased risk of developing heart diseases. Additionally, the results revealed that mental disorders overall significantly increased the risk of heart disease development [1.52 (95% CI: 1.33–1.71); p < .001; I2 = 87.1]. However, the meta-analysis demonstrated high heterogeneity. Publication bias was detected, as indicated by Egger’s test (p < .05) (Figure 1 in supplemental material). Nevertheless, sensitivity analysis showed no significant difference in the overall effect estimates after excluding studies with a higher risk of bias. The meta-analysis results pertain to the values of the odds ratio. 1: depression, 2: depression and anxiety, 3: anxiety, and 4: PTSD
Results of Subgroup Analysis for General Mental Health
LMICs: low and middle-income countries; PTSD: post-traumatic stress disorder; Q1: high quality; Q2: moderate quality; Q3: low quality; OR: odds ratio; CI: confidence interval; I2: I square statistic; Q statistic: chi-square statistic; df: degree of freedom; P: significance level.
Discussion
The results of this review indicate that a substantial number of studies support a potential link between mental disorders (depression, anxiety, depression-anxiety, and PTSD) and the incidence of heart diseases in the workers.
There are multiple potential pathways through which mental disorders can influence the development of heart diseases. To identify these pathways, the mechanisms discussed in the reviewed papers were analyzed and synthesized. These pathways can be categorized into two groups: biological effects and behavioral patterns induced by mental disorders that contribute to heart diseases.
Biological Mechanisms
The biological mechanisms include autonomic nervous system dysfunction, inflammation, increased platelet reactivity, and medicine consumption (De Hert et al., 2018). In the mechanism of autonomic nervous system dysfunction, mental disorders can increase the activity of the sympathetic systems and alter body physiological parameters such as heart rate, blood pressure, and vasodilation because of elevated levels of catecholamines (Knezevic et al., 2023). The changes in these physiological parameters are known as the risk factors for cardiovascular diseases (Knezevic et al., 2023). In the mechanism of inflammation, some mental disorders increase the levels of biomarkers related to inflammation, including interleukin (IL)-1 and IL-6 and C-reactive protein (Nielsen et al., 2021). These biomarkers can develop atherosclerosis through endothelial dysfunction due to a decrease in vasodilatory and anti-inflammatory agents and an increase in vasoconstrictive and pro-inflammatory agents (Nielsen et al., 2021). In addition to the biological mechanisms mentioned, thrombus formation resulting from platelet activation and aggregation is another crucial factor that plays a primary role in the development of chronic heart disease (Nielsen et al., 2021). Previous studies have shown that depression can be associated with this mechanism through making changes in the serotonin (Nielsen et al., 2021). Moreover, some medicines used by individuals with depression, anxiety, and PTSD, such as antipsychotics and antidepressants, can affect the development of heart diseases (Nielsen et al., 2021). These medications can make changes in the physiological and biological process, such as activation of sympathetic system and incidence of metabolic syndrome and in this way, those cause cardiovascular consequences (Nielsen et al., 2021).
Behavioral Mechanisms
In addition to biological mechanisms, previous studies have revealed that mental disorders can be associated with a range of the alternations in behavioral and lifestyle factors, such as smoking, alcohol consumption, physical inactivity, high-fat diet, and lack of regular use of medications and preformation care measures (Celano et al., 2018). These behaviors increase the probability of diabetes, high cholesterol, hypertension, obesity, dyslipidemia, and other risk factors (Alshehri, 2010). These situations can also be known as metabolic syndrome, and previous studies have demonstrated the relationships between this syndrome and heart disease (Celano et al., 2018).
These mechanisms can be triggered by both non-occupational and occupational factors. Kim identified sex, age, education level, occupational type, job satisfaction, shift work, and occupational exposure as risk factors for depression, anxiety, and general fatigue (Kim, 2023). Gan et al. observed that certain occupational characteristics influence the prevalence of anxiety and depression. Their research identified direct interaction with the public, exposure to diseases or infections, engagement in physically demanding tasks, and managing difficult or aggressive individuals as risk factors for these mental health conditions. Conversely, job roles involving prolonged periods of sitting, frequent communication, decision-making responsibilities, creative or analytical tasks, and increased accountability are associated with a reduced risk of anxiety and depression (Gan et al., 2023). Moreover, workplace accidents and diseases can lead to PTSD among workers, which may subsequently cause heart disease. Clarner et al. found that work-related PTSD can result from traumatic events (Clarner et al., 2015). Salleh et al. identified several predictors of workplace PTSD, including demographic factors (age, educational level, marital status), job factors (years of service, rank, number of traumatic events, job stress, organizational stress and burnout, occupational effort, internal locus of control, resource availability, debriefing attendance), social support, and posttraumatic growth (Salleh et al., 2020). Occupational factors can not only cause mental disorders but also exacerbate symptoms in affected workers. However, it is important to note that jobs with desirable characteristics can help treat and improve the condition of individuals with mental illnesses. Wagman et al. concluded that higher occupational balance can decrease the risk of anxiety and depression in the workplaces (Wagman et al., 2021). Additionally, many occupational risk factors for heart disease exist, such as air pollution and noise, which may have a synergistic effect on heart disease when combined with psychosocial factors (Dzhambov & Dimitrova, 2016; Khoshakhlagh, Al Sulaie et al., 2023; Khoshakhlagh & Ghasemi, 2017; Torén et al., 2007). Future studies should examine the effects of these factors and in combination on heart disease. The presence of these positive and negative factors in the workplace results in a different incidence of mental disorders and subsequent heart disease among workers compared to the general population.
Meta-Analysis Findings
The results of the meta-analysis showed that depression [1.53 (95% CI: 1.26–1.80)], anxiety [1.87 (95% CI: 0.13–3.61)], and PTSD [1.70 (95% CI: 1.34–2.05)] are associated with an increased risk of heart disease. Based on the results, the probability of developing heart disease was higher in individuals with anxiety and PTSD. Additionally, the findings revealed that mental disorders overall can increase the occurrence of heart diseases [OR: 1.52 (95% CI: 1.33–1.71)]. Other meta-analyses conducted in the general population have shown that depression can increase the hazard risk of myocardial infarction by 1.36 and the relative risk of chronic heart disease by 1.30 (Wu & Kling, 2016). Similarly, a meta-analysis in the general population found that anxiety is associated with a 1.26-fold increase in the hazard risk of chronic heart disease (Roest et al., 2010). Regarding PTSD, a study in the general population demonstrated that this disorder can elevate the risk of chronic heart disease by 1.27 (Edmondson et al., 2013). Comparing the findings of the present meta-analysis with those of other studies indicates that the risk of heart disease related to mental disorders is higher in occupational populations than general population. This difference may be due to the presence of additional workplace risk factors alongside general risk factors that contribute to increased risk.
However, the meta-analysis revealed substantial heterogeneity (I2 ≈ 90.7%). This variability may be attributed to differences in the intensity and duration of mental disorders, characteristics of the studied populations, patient profiles, measurement instruments for mental disorders and heart diseases, other occupational and non-occupational factors influencing the occurrence of heart diseases, and variations in implementation contexts. Regarding the intensity and duration of mental disorders, more severe conditions persisting for longer periods appear to have a greater impact on heart disease, although this has not been explicitly reported in the studies. The studied populations also varied in terms of age, gender, country, and race. Additionally, patient characteristics, such as history of mental disorders and cardiovascular disease, differed across studies. Furthermore, the instruments used to assess mental disorders and heart diseases were inconsistent among the reviewed papers, potentially contributing to this variability. Implementation contexts—including sample selection methods, inclusion and exclusion criteria, and study periods—also varied between studies. Moreover, other occupational and non-occupational factors, such as job stress, psychological stress, and diet, can influence the occurrence of heart diseases, and these background factors likely differ across studies. Therefore, although the meta-analysis indicates that mental disorders may increase the risk of heart diseases, the high heterogeneity necessitates cautious interpretation. It is recommended that future studies employ more standardized protocols and provide comprehensive details to enable subgroup analyses.
Subgroup Analysis
The results of the subgroup analysis in the present study also indicated that the odds ratios for heart diseases related to mental disorders were higher in low- and middle-income countries compared to high-income countries, and in East/Southeast Asia/Oceania compared to other regions. These results should be interpreted with caution because a limited number of studies have been conducted in some subgroups, such as low- and middle-income countries and the East/Southeast Asia/Oceania region. Additionally, no studies have been performed in certain regions, such as the Middle East, making it difficult to compare findings. Therefore, it is recommended that these differences be investigated in future research. One possible explanation is that job-related psychological pressures are higher in low- and middle-income countries compared to high-income countries. Factors contributing to psychological stress at work include job hazards, work environment, job security, health perception, organizational justice, atypical working hours, workplace conflict, role stress, and the availability of adequate social support, all of which can lead to mental disorders among workers (Harvey et al., 2017; Henderson et al., 2011, 2012). Consequently, it is essential that future studies accurately identify the key occupational risk factors for these mental disorders. It is suggested that such investigations employ specific study designs, such as prospective cohort studies, standardized assessments of mental disorders, or multi-country comparative studies. Moreover, subgroup analysis indicated that the pooled odds ratio was higher in studies conducted in or before the year 2000 compared to those conducted after 2000. This difference may be due to the increased implementation of preventive and therapeutic measures aimed at improving workplace conditions in recent years (Pomaki et al., 2012). Moreover, the results of subgroup analysis showed that the pooled odds ratio in the studies with high quality was higher than that in the studies with low and moderate quality. That indicates that the increase in study quality was associated with increased effects of mental disorders on heart diseases.
Limitations
One limitation is that the impact of different mental disorders on heart diseases has not been fully investigated in a comprehensive model. Furthermore, the inclusion of non-occupational risk factors as confounders, such as social economic status, smoking, antidepressant or psychotropic medication use, and comorbidities, adds complexity to analyzing this relationship, requiring a precise definition of their roles in the next studies. Moreover, low number of studies have been conducted in some subgroups, such as low- and middle-income countries and the East/Southeast Asia/Oceania and in some regions such as Middle East, no study has not performed which makes it difficult to compare the findings. Furthermore, there were design limitations, including high heterogeneity across included studies, potential publication bias, reliance on self-reported mental health measures, and the language restriction to English.
Conclusions
The findings of this research suggest that mental disorders (depression, anxiety, depression-anxiety, and PTSD) can contribute to the development of heart diseases in workers. Consequently, it is imperative to implement preventive measures of heart disease such as workplace mental health programs, screening, stress management interventions, occupational therapy programs, and policies targeting occupational risk factors. Furthermore, it is recommended that the effectiveness of these occupational therapy interventions be assessed in future studies. Additionally, high-quality prospective and longitudinal studies using validated tools, conducted in diverse income settings with control for confounders and occupational subgroup analyses, are warranted.
Supplemental Material
Supplemental Material - The Relationship Between Mental Disorders and Heart Diseases Among General Workers: A Systematic Review and Meta-Analysis
Supplemental Material for The Relationship Between Mental Disorders and Heart Diseases Among General Workers: A Systematic Review and Meta-Analysis by Mingxing Wang, Yun Wu, Xiaoyu Zhu in Hong Kong Journal of Occupational Therapy
Footnotes
Ethical Considerations
All methods were performed in accordance with relevant guidelines and regulations.
Author Contributions
Mingxing Wang: Methodology, Formal analysis, Investigation, Writing – original draft, Visualization. Yun Wu: Writing – original draft, Writing – review & editing, Visualization. Xiaoyu Zhu: Conceptualization, Writing – review & editing, Supervision, Project administration.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declaration of AI and AI-Assisted Technologies in the Writing Process
The authors didn’t use AI and AI-assisted technologies in the writing process.
Supplemental Material
Supplemental material for this article is available online.
References
Supplementary Material
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