Occupational Factors and Labor Market Outcomes Among Individuals with Sickness Absence due to Common Mental Disorders: A Population-Wide Cohort Study

Occupational Factors and Labor Market Outcomes Among Individuals with Sickness Absence due to Common Mental Disorders: A Population-Wide Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Occupational Factors and Labor Market Outcomes Among Individuals with Sickness Absence due to Common Mental Disorders: A Population-Wide Cohort Study Gerda Stutaite, Ellenor Mittendorfer-Rutz, Magnus Helgesson, Kristin Farrants, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7583607/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Journal of Occupational Rehabilitation → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose To investigate how job demands, control, strain, and occupational sector and branch affect labor market outcomes following sickness absence (SA) due to common mental disorders (CMDs). Methods This nationwide register-based cohort study included all residents in Sweden aged 25–55 who began a new > 30-day SA spell due to a CMD (ICD-10: F32-33, F40-43) in 2011–2013 (n = 79,673). Occupational sector and branch were identified through registers, and job demands, control, and strain were assessed using the Swedish Job Exposure Matrix. We used multinomial logistic regression to estimate associations between occupational factors and different unemployment and SA/disability pension (DP) durations during a three-year follow-up. Results Public sector workers were less likely to have > 180 unemployment days (OR = 0.3, 95% CI: 0.31–0.35). Working in education and public administration and in health and social services was associated with a lower likelihood of > 180 unemployment days, but a higher likelihood of > 365 SA/DP days. Low-control, passive (low control/low demands), and high-strain (low control/high demand) jobs were associated with an increased likelihood of both > 180 unemployment days and > 365 SA/DP days. For > 180 unemployment days, the ORs were 1.7 (95% CI: 1.62–1.82) for low-control, 1.8 (95% CI: 1.70–1.98) for passive, and 1.4 (95% CI: 1.23–1.54) for high-strain jobs. For > 365 SA/DP days, the ORs were 1.3 (95% CI: 1.22–1.34), 1.3 (95% CI: 1.22–1.41), and 1.3 (95% CI: 1.15–1.39), respectively. Conclusion Particularly among individuals with SA due to CMDs, job demands, control, and strain are associated with future labor market exclusion and may be important targets for intervention. Depression Anxiety Disorders Stress Disorders Sick Leave Disability Pension Job Strain Figures Figure 1 Introduction Diagnoses of common mental disorders (CMDs), including depression, anxiety, and stress-related disorders, have increased significantly in recent decades in Sweden and other OECD countries, constituting a major public health challenge [ 1 , 2 ]. It is estimated that around 20% of the working-age population has a clinically diagnosed mental disorder, the majority of which are CMDs [ 1 ]. Due to their negative effect on work ability [ 3 ], CMDs are also the leading cause of sickness absence episodes in Sweden and other OECD countries [ 1 , 4 ]. For instance, in Sweden, CMDs accounted for approximately 90% of all psychiatric sickness absence spells exceeding 14 days that began in 2018 and 2019 [ 5 ]. Most sickness absence spells due to CMDs last around 40 days; however, individuals with certain stress-related disorders may have sickness absence lasting for up to half a year and longer [ 6 ]. Consequently, CMDs not only cause suffering to the affected individuals but also pose a substantial economic burden at the societal level [ 1 ]. While a sickness absence spell can provide temporary financial support, enabling individuals with CMDs to seek treatment and recover, it can also, particularly when prolonged, contribute to long-term labor market exclusion [ 7 – 9 ] and worsening health [ 10 ]. Employment, on the other hand, promotes long-term financial stability, social connectedness, a sense of identity, and better overall health [ 11 ]. Notably, studies have shown that many individuals with CMDs can remain in employment, despite their illness [ 12 , 13 ]. Therefore, it is important to understand the factors that influence not only the risk of sickness absence among individuals with CMDs but also affect their labor market outcomes following a sickness absence spell due to CMDs. Occupational factors, particularly those related to the psychosocial work environment, are often modifiable and can be targeted in interventions to improve the health of individuals with CMDs, facilitating their return to work, and consequently reducing societal costs. In occupational stress research, the psychosocial work environment is often evaluated using the job strain model, developed by R. Karasek and T. Theorell [ 14 , 15 ]. The model focuses on the interaction between job demands and job control, defined as job strain, and suggests that high job demands (e.g., high workload and time pressures) combined with low job control (e.g., limited decision authority) may lead to mental distress. Several studies have found that high job strain (high job demands and low job control) is associated with increased rates of sickness absence among individuals with CMDs [ 16 – 20 ]. In contrast, some studies have shown that jobs characterized by low demands and low control are associated with higher rates of sickness absence in the general population [ 21 ], while a recent study suggested that the relationship between job strain and sickness absence may vary depending on the specific characteristics of an occupational group [ 22 ]. Knowledge about how job strain influences future labor market outcomes following a sickness absence episode due to CMDs remains especially scarce. A 2018 scoping review reported insufficient evidence—defined as fewer than three qualifying studies—regarding the impact of job demands, job control, and job strain on labor market outcomes, such as recurrent sickness absence and return to work among individuals on sickness absence due to CMDs [ 16 ]. Nevertheless, some studies have shown that higher psychosocial hazards and increased psychological and emotional demands at work are associated with delayed return to work in this population [ 23 , 24 ]. However, these studies are relatively small and primarily examine time to return to work, rather than a broader range of labor market outcomes following a sickness absence spell due to a CMD. Thus, the impact of job demands, control, and strain on future labor market outcomes in this particular group is not yet fully understood. Regarding the role of structural occupational factors, such as sector and branch, research has shown that public sector workers have a higher risk of CMDs than private sector workers, with the risk being especially high among individuals working in health and social services [ 25 ]. Similarly, public sector workers were also found to be at a higher risk of sickness absence and disability pension due to CMDs than private sector workers [ 26 – 28 ]. However, little is known about how working in the public versus private sector and across different occupational branches affects labor market outcomes following a period of sickness absence due to CMDs. Therefore, this study aims to investigate how differences in job demands, control, and strain, and occupational sector and branch affect labor market outcomes among individuals with a sickness absence spell due to a CMD. To gain a more comprehensive understanding of how these individuals might be excluded from the labor market, we draw on the social insurance perspective, which conceptualizes labor market marginalization as periods of both unemployment and work disability, the latter primarily driven by illness or injury and measured through sickness absence and disability pension [ 12 ]. This study is the first population-based study of its scale using high-quality register data that includes detailed sociodemographic, socioeconomic, and health-related factors to assess the impact of occupational factors on a range of labor market outcomes following a sickness absence spell due to CMDs. The findings of this study could contribute valuable knowledge needed to inform future policy initiatives aimed at facilitating sustainable labor market inclusion for these individuals. Methods Study data, design, and population To achieve our aim, we conducted a register-based prospective cohort study, including all Swedish residents, aged 25-55, who were in gainful employment and started a new sickness absence (SA) spell due to a CMD lasting longer than 30 days between 2011 and 2013, and who had lived in Sweden for at least two years before the start of the study. CMD diagnoses were defined according to the International Classification of Diseases (ICD-10) codes: F32-F33 for depressive disorders, F40-41 for anxiety disorders, F42 for obsessive-compulsive disorder (OCD), and F43 for stress-induced disorders. Gainful employment status was defined by Statistics Sweden [29]. The earliest spell was designated the index spell for individuals with multiple SA spells matching the inclusion criteria. Due to the low likelihood of return to work and high risk of permanent disability, we excluded individuals with a severe psychiatric or neurological disorder diagnosed in specialized outpatient or inpatient care (ICD-10) in the two years before the start of the study, or anytime during the study's follow-up (Online Resource). These conditions were psychosis spectrum disorders (F20-29), bipolar disorder (F30-F31), early-onset neurodegenerative disorders (F00-F03 or G30-G32), anorexia nervosa (F50.0), and intellectual disabilities (F70-F79). We further excluded individuals with SA or disability pension (DP) due to any condition one year before the start of the index SA spell and individuals with missing information on their occupation, occupational sector, or branch (n total excluded = 37,967). Data sources We used the following national Swedish registers and databases, which are linked together by a unique identification number assigned to each individual: The “Micro-Data for Analyses of Social Insurance (MiDAS)” [30] for information about diagnoses, dates, and grades (part-time versus full-time) of SA and DP benefits. The “Longitudinal Integration Database for Health Insurance and Labor Market Studies (LISA)” [29] for information about sociodemographic, socioeconomic, and occupational factors, including occupation, occupational sector, and branch. The National Patient Register [31] for information about primary and secondary diagnoses from specialized outpatient care visits and inpatient care stays to identify mental and somatic comorbidities. The Causes of Death Register [32] for information about dates of death. The Prescribed Drug Register [33] for information about dispensed psychotropic medications. Social security in Sweden Swedish residents with qualifying income from work or benefits related to parental leave or unemployment can receive SA compensation if their work capacity is impaired due to illness or injury [4,34]. For employed individuals, during the initial 14 days of sickness, the employer pays the SA benefits, reaching up to 80% of lost qualifying earnings [4,34]. The Swedish Social Insurance Agency disburses subsequent benefits [4,34]. For the unemployed, the Swedish Social Insurance Agency pays benefits from the second SA day [4,34]. Consequently, as information for this study is collected through the Swedish Social Insurance Agency, only SA spells lasting longer than 14 days were included. Additionally, Swedish residents aged 19 to 66 with an illness or disability that impairs their ability to work for an extended period may qualify for DP benefits [4,34]. Individuals aged 19-29 may receive temporary activity compensation either due to decreased work ability or because they could not complete compulsory education in time, while those aged 30-66 may receive permanent compensation, reaching up to 64% of lost income [4,34]. Individuals can receive either full (100%) or partial (75%, 50%, or 25%) SA and DP benefits [4,34]. Unemployed Swedish residents above age 20, registered as job seekers with the Swedish Public Employment Service and capable of working, can receive basic unemployment benefits, while some may also qualify for additional income-based benefits [35]. Exposures The exposure variables in this study were job demands, job control, and job strain, as well as occupational sector and branch. We assessed job demands, control, and strain using the Swedish Job Exposure Matrix (SweJEM) for the years 1997-2013 [36,37]. It consists of occupational-level scores for psychosocial job demands and control derived from the Swedish Work Environment and Health surveys [36,37]. In these surveys, job demands were measured using questions focused on workload, stress, and concentration required at work, while job control was assessed using questions focused on decision authority and work task repetitiveness [36,37]. Individual self-reported values were averaged and assigned to all persons in a specific occupation, based on the Swedish version of the International Standard Classification of Occupations (ISCO-88), separately for men and women [36,37]. We categorized job demands and job control into low, medium, and high based on their tertile distributions [38]. Additionally, we derived job strain based on the interaction between job demands and job control. It included 1) Low-strain (low demands/high control), 2) Passive (low demands/low control), 3) Active (high demands/high control), 4) High-strain (high demands/low control), and 5) Intermediate-strain jobs, which included the remaining job demands and control combinations: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control [14]. We then assigned the SweJEM values for job demands, control, and strain to individuals in the study population based on their occupational code at the end of the year preceding the start of their index SA spell due to a CMD. The occupational sector was divided into 1) Private and 2) Public, and the occupational branch was grouped into 1) Production, industrial, and resource-based branch (agriculture, forestry, fishing, manufacturing, extraction, energy supply, environmental services, and construction), 2) Trade, transport, and storage, 3) Information, financial, and business services (information, communication, finance, insurance, and real estate activities and business services), 4) Hospitality and personal services (hotel and restaurant operations and cultural and personal services), 5) Education and public administration (public administration and defense and training and education), and 6) Health and social services. We assessed the occupational sector and branch at the end of the year preceding the start of the index SA spell due to a CMD. Outcomes and follow-up The two outcomes of interest in this study were the sum of unemployment days and SA/DP days during the follow-up period. SA days used to define the study population, and all following SA and DP days refer to net days, which means, for example, that two half-days are counted as one net day. We categorized unemployment days into 1) No (zero) unemployment days, 2) 0 180 unemployment days, to assess both medium-term (up to 180 days) and long-term (more than 180 days) unemployment. Net SA/DP days were categorized into 1) 30 < SA/DP days ≤ 90 Days, 2) 90 < SA/DP days ≤ 180, 3) 180 365 SA/DP days, based on the National Board of Health and Welfare’s recommendations for SA durations for different CMDs, which consider specific diagnoses, disorder severity, and work ability [39]. We assessed unemployment days per calendar year for three consecutive years, beginning the year after the start of the index SA spell (Online Resource). SA/DP days, on the other hand, were assessed for three consecutive years, beginning on the first day of the index SA spell. Individuals who emigrated or died during the follow-up period were excluded. Covariates Covariates assessed on December 31 of the year before the start of the index SA spell were age, education level, type of living area, and family composition (Table 1). SA/DP history was assessed in the two-year period prior to the exclusion window, which began one year before the index SA. Psychotropic medication dispensations were assessed six months before and three months after the first day of the index SA spell. Additionally, based on ICD-10 codes from inpatient and specialized outpatient care, we identified mental and somatic comorbidities in the two years before the start of the index SA, detailed in Table 1. Mental comorbidities included CMDs other than the main diagnosis of the index SA spell and burn-out (F32-F33, F40-F41, F42, F43, and Z730), substance use disorders (F10-F19), other affective disorders (F34, F38, and F39), personality disorders (F60-F69), attention-deficit hyperactivity disorder (ADHD) (F90.0), and other mental disorders, other behavioral emotional disorders (F50-F59, excl. F50.0, F90.1-F98), other psychological developmental disorders and autism-spectrum disorder (F80–F89, excl. F84.3–F84.9), and other mental comorbidities (F04-F09, F44-F48, and F99). Somatic comorbidities included cancer (C00-D48), endocrine, nutritional, and metabolic disorders (E00-E90), neurological disorders (G00-G99, excluding G30-G32), circulatory system disorders (I00-I99), respiratory disorders (J00-J99), musculoskeletal disorders (M00-M99), and other chronic somatic disorders. Finally, we assessed the duration, grade (part-time versus full-time), and diagnosis of the index SA spell. Statistical analyses First, we computed descriptive characteristics for the entire study population (Tables 1, Online Resource). Then, we conducted separate multinomial logistic regression analyses to examine the associations between occupational factors and varying numbers of unemployment and SA/DP days during the three-year follow-up. The reference group for the unemployment analysis was no (zero) unemployment days, and for the SA/DP analysis, it was 30 < days ≤ 90. Crude and adjusted analyses were conducted for each outcome, with Model 1 adjusted for sex and age, Model 2 additionally adjusted for education, country of birth, and type of living area, and Model 3 further adjusted for index SA diagnosis, SA/DP history, and mental and somatic comorbidities. Odds ratios (ORs) with 95 % confidence intervals (CIs) were calculated for each association. Individuals who emigrated or died during the follow-up period were excluded (unemployment: n = 773 (0.97%) and SA/DP: n = 706 (0.89%) for the SA/DP analysis). We computed descriptive statistics to compare baseline characteristics between the individuals included in the unemployment analysis and the excluded ones (Online Resource). Additionally, we excluded individuals with low job strain (low demands/high control) from both job strain analyses (n = 183 (0.23%) for the unemployment analysis and n = 184 (0.23%) for the SA/DP analysis) due to low observation counts. To assess whether this exclusion altered the results, we conducted a sensitivity analysis that included those with low job strain (Online Resource). We performed data management using SAS 9.4 [40] and conducted data analysis using R [41]. Results Descriptive characteristics The final study population consisted of 79,673 individuals, with a mean age of 40.3 years (standard deviation (SD) = 8.2) (Table 1). Most were females (71.4%) and born in Sweden (86.8%). About half had completed high school (45.9%), and 45.7% had a university education. 43.4% of individuals lived with a partner and children, while 32.7% lived without either. Most resided in towns or suburban areas (42.0%), while 39.6% lived in cities. Regarding occupational factors, most individuals worked in the private sector (57.7%), and the most common occupational branches were health and social services (25.4%) and education and public administration (22.5%). Medium-demand (47.0%), medium-control (38.4%), and intermediate-strain (72.2%) jobs were the most common, while 15.5% worked in active (high demands/high control) jobs. Moreover, 92.5% of individuals had no unemployment days in the year before the start of the index SA spell. During follow-up, most (76.9%) remained without any unemployment days, and among those with at least one, the average was 184.1 days (SD = 180.1). Regarding health-related factors, 15.0% individuals had SA/DP days due to any condition in the two years before the start of the index SA spell, while only 0.5% had DP days in the same timeframe. The most common comorbidity in the two years before the study began was another CMD, including burn-out (ICD-10: Z730) (4.7%). The index SA spell was predominantly full-time (90.2%), with stress-related disorders (ICD-10: F43) as the most common diagnosis (50.1%). During follow-up, individuals had an average of 245.2 SA/DP days (SD = 254.6). Unemployment We found that, adjusted for age and sex, working in passive (low demands/low control) and high-strain (high demands/low control) jobs was associated with higher odds of having both up to 180 and >180 days of unemployment compared to working in intermediate-strain jobs (Table 2, Figure 1). For >180 unemployment days, the OR was 1.8 (95% CI: 1.70-1.98) for passive, and 1.4 (95% CI: 1.23-1.54) for high-strain jobs. Results with further adjustments for socioeconomic and health-related factors are presented in Table 2. When job demands and job control were assessed separately, low job control was associated with higher odds of having >180 unemployment days across all models compared to medium job control (OR = 1.7, 95% CI: 1.62-1.82). In contrast, high job demands were inversely associated with both unemployment durations (OR = 0.7, 95% CI: 0.64-0.72 for >180 unemployment days). SA/DP Similar to the unemployment analysis, working in passive and high-strain jobs was associated with an increased likelihood of having >365 days of SA/DP (Table 3, Figure 1). In the model adjusted for sex and age, the OR was 1.3 (95% CI: 1.22-1.41) for passive and 1.3 (95% CI: 1.15-1.39) for high-strain jobs. Active (high demands/high control) jobs, in contrast, showed an inverse association with the same outcome category (OR = 0.8, 95% CI: 0.72-0.81 for >365 days of SA/DP). Additionally, high job control, when assessed separately from job demands, was associated with lower odds of having >365 SA/DP days compared to medium job control (OR = 0.8, 95% CI: 0.73-0.81). Occupational sector and branch Adjusted for age and sex, individuals working in the public sector were less likely to have both up to 180 unemployment days and >180 unemployment days (OR = 0.3, 95% CI: 0.31-0.35 for >180 unemployment days) compared with those working in the private sector (Table 2). This pattern was not observed for SA/DP days (Table 3). Additionally, working in education and public administration and in health and social services was associated with lower odds of having unemployment days. In the model adjusted for sociodemographic and health-related factors, for >180 unemployment days, the OR was 0.5 (95% CI: 0.47-0.57) for education and public administration, and 0.5 (95% CI: 0.48-0.58) for health and social services. In contrast, individuals in these branches had higher odds of having >365 SA/DP days during the follow-up (OR = 1.2, 95% CI: 1.13-1.31 for education and public administration; OR = 1.2, 95% CI: 1.14-1.32 for health and social services). Discussion Main findings This study examined how differences in job demands, job control, job strain, occupational branch, and occupational sector affected labor market outcomes, namely unemployment and SA/DP, among individuals on an SA spell due to a CMD. Our findings showed that in this population, high-strain (high demands/low control) and passive (low demands/low control) jobs were associated with both medium (up to 180 days) and long-term (> 180 days) unemployment and long-term (> 365 days) SA/DP. Working in active (high demands/high control) jobs, on the other hand, was associated with a lower likelihood of having > 365 days of SA/DP. When job demands and job control were assessed separately, we found that individuals in low control jobs were more likely to have both medium and long-term unemployment and long-term SA/DP, while those in jobs with high demands were less likely to experience both medium and long-term unemployment. Additionally, public sector workers were less likely to experience medium and long-term unemployment, but more likely to experience long-term SA/DP. Similarly, working in education and public administration, and in health and social services, was associated with a lower likelihood of medium and long-term unemployment, yet a higher likelihood of long-term SA/DP. Comparison with literature Direct comparisons with studies specifically analyzing labor market outcomes among individuals with SA due to CMDs are limited, as few such studies exist. However, we found that individuals in high-strain (high demands/low control) jobs were more likely to have long-term SA/DP following the initial SA spell, which aligns with previous findings suggesting that high psychosocial hazards—a concept comparable to high job strain, defined as high workload and emotional demands combined with low decision authority—are associated with longer SA durations among individuals with CMDs [ 23 ]. Our results are also consistent with previous studies showing that high-strain jobs are associated with an increased risk of SA and DP among individuals with CMDs [ 16 – 20 , 42 ]. Additionally, we found that working in passive (low demands/low control) jobs was associated with a higher likelihood of having long-term SA/DP following SA due to CMDs, while working in active (high demands/high control) jobs was associated with a lower likelihood. These findings are consistent with some previous research showing lower SA risk among individuals with CMDs in active jobs [ 13 ]. According to Karasek’s job strain model, active jobs combine a challenging workload with the opportunity to exercise decision-making and autonomy, which have been argued to contribute to professional growth, increase job satisfaction, and reduce depression [ 14 , 15 ]. Conversely, passive jobs have been associated with increased rates of depression [ 43 ]. However, the relationship between active and passive jobs and the risk of CMDs, SA, and SA due to CMDs remains not fully understood, with several studies reporting inconclusive results, particularly when comparing men and women [ 38 , 44 ]. Specifically among women, some findings suggest that working in passive jobs is associated with an increased risk of SA/DP [ 38 ], while others show that working in active jobs may contribute to increased rates of long-term SA [ 44 ]. Moreover, several studies assessed job demands and job control independently of each other and found associations between high job demands and delayed return to work among individuals on SA due to CMDs [ 24 , 45 ]. In our study, working in high-demand jobs was not associated with any of the examined SA/DP durations following the initial SA spell due to CMDs. However, we found that individuals in low-control jobs were more likely to experience long-term SA/DP compared to those in medium-control jobs. This suggests that low job control might be a stronger predictor of potentially detrimental labor market outcomes following SA due to CMDs than high job demands, when assessed separately. While the populations examined are not directly comparable, our findings are consistent with some of the previous studies suggesting a protective effect of increased job control against SA among individuals with CMDs [ 46 ] and against delayed return to work in studies involving individuals already on SA due to CMDs [ 45 , 47 ]. Thus, future research should continue to examine both dimensions of job strain—job demands and job control—to identify aspects that may promote return to work and reduce SA rates in this population. It is also worthwhile noting that in this study, associations between job demands, control, and strain and SA/DP were only observed in the group with > 365 days of SA/DP during the three follow-up years. This finding highlights the importance of distinguishing between short-term and long-term SA/DP outcomes, as the latter could indicate the path to more permanent labor market marginalization and appear to be influenced by different factors than short-term outcomes, such as, for instance, health status, disorder severity, and work ability [ 48 ]. Additionally, while some individuals with > 365 SA/DP days may experience continuous long-term absence, others may have recurrent shorter SA spells, alternating between return to work and starting a new SA spell. These transitions could, at least partially, reflect a detrimental workplace environment. Regarding unemployment, a Swedish twin study found that higher levels of job control were associated with a reduced risk of subsequent unemployment in the general population [ 49 ]. However, to the best of our knowledge, our study is the first to investigate how job demands, control, and strain affect subsequent unemployment outcomes specifically among individuals on SA due to CMDs. As with SA/DP outcomes, we found that high-strain (high demands/low control), passive (low demands/low control), and low-control jobs were associated with both medium and long-term unemployment in this population. Low control appears to be the common factor contributing to post-SA unemployment in this population, as was also suggested for the SA/DP outcomes. This may reflect a lack of flexibility over one’s working hours and pace, and limited opportunities to accommodate the needs of individuals on SA due to CMDs, hindering their return-to-work process and contributing to labor market marginalization [ 13 ]. It is also plausible that jobs characterized by low control are inherently more precarious, leading to reduced physical and mental health and increased unemployment [ 50 ]. Finally, regarding the associations between the structural occupational factors, namely occupational sector and branch, we found that individuals in the public sector were less likely to experience both medium and long-term unemployment but more likely to have long-term SA/DP following an SA spell due to a CMD. While not directly aligned given the differences in study populations, our results are consistent with some previous findings showing that workers in the public sector are more likely to have a CMD [ 25 ] and are also more likely to experience SA due to CMDs compared to those in the private sector [ 25 , 27 ]. In Sweden, many companies, particularly within health and social care, are private companies contracted to deliver publicly financed health and social care. Therefore, we decided to also investigate how working in different branches might affect labor market outcomes after SA due to CMDs, as opposed to only examining the occupational sector. We found that working in the education and public administration branch, as well as in the health and social care branch, was associated with a lower likelihood of having both medium and long-term unemployment, yet a higher likelihood of experiencing medium and long-term SA/DP. The findings regarding the SA/DP outcomes are in line with several previous studies [ 27 , 28 ]. A lower likelihood of unemployment in the public sector, the education and public administration branch, and the health and social care branch is likely due to greater job security, more stable employment conditions, and a higher demand for skilled professionals. On the other hand, the higher likelihood of long-term SA/DP for the education and public administration branch and the health and social care branch can potentially reflect higher workplace mental strain [ 27 ]. Strengths and limitations First, this large-scale, population-based study included 79,673 working-age individuals and used high-quality national register data, resulting in a representative sample that enhances the generalizability of the findings to the broader population with SA benefits due to CMDs. Second, since assessing unemployment alone tends to undermine labor market marginalization in this population, including health-related marginalization using SA/DP measures contributes to a more comprehensive understanding of how individuals with SA due to CMDs can be excluded from the labor market. Third, we assessed the psychosocial work environment at the occupational level using the SweJEM, which minimizes the subjectivity of the self-reported individual measures, as the individual scores were averaged out for all individuals within a specific occupational group. Lastly, we categorized job demands and job control into low, medium, or high, which allowed us to better understand how more extreme conditions contribute to unemployment and SA/DP following an SA spell due to a CMD. If we had dichotomized job demands and job control scores into low and high, we could have diluted the effects, since most individuals in this study and several previous studies fell in the middle of the score scales [ 38 ]. Our study also has several limitations. First, as the data from the Swedish Social Insurance Agency only covers SA spells exceeding 14 days, only those spells could be included. Second, because data from primary care settings were unavailable, our analyses did not include mental and somatic comorbidities diagnosed in these settings. Third, the occupational-level assessment of job demands, control, and strain meant that individual variation could not be captured. As a result, all individuals within the same occupation had identical values for job demands, control, and strain, even though their specific work environments may have differed. Finally, we were unable to assess potential changes in individuals’ occupations during the follow-up. However, such occupational-level changes are likely uncommon. Conclusion Job demands, job control, and job strain, and structural occupational factors, such as occupational sector and branch, play a role in shaping labor market outcomes following a sickness absence spell due to CMDs. Particularly, high-strain jobs, characterized by high demands and low control, and passive jobs, characterized by both low demands and low control, were found to be associated with medium and long-term unemployment and long-term sickness absence and disability pension, suggesting a potential path to labor market marginalization. Future studies are necessary to deepen our understanding of these associations and inform effective interventions aimed at promoting labor market inclusion for individuals with sickness absence due to CMDs. Declarations Ethical Approval This study was conducted in accordance with the principles of the 1964 Declaration of Helsinki. The approval was obtained from the Regional Ethical Review Board, Karolinska Institutet, Stockholm, Sweden (Dnr: 2007/762-31 and Dnr: 2024-08708-02), who also waived the need for informed consent. Funding This study was financially supported by the Research Council for Health, Working Life and Welfare (Dnr:2022-00564). We utilized data from the REWHARD consortium supported by the Swedish Research Council (grant no. 2021-00154). Competing Interests The authors have no competing interests to declare. Author Contributions All authors made substantial contributions to this work and reviewed the manuscript. G.S. contributed to the conceptualization of the study, wrote the original manuscript, prepared figures and tables, and edited and reviewed the text. E.M.-R. and K.G. contributed to the conceptualization of the study, reviewed and edited the manuscript, and provided resources and supervision. M.H. and K.F. contributed to the conceptualization of the study and reviewed and edited the manuscript. Data Availability The data used in this study cannot be made publicly available due to privacy regulations. According to the General Data Protection Regulation, the Swedish law SFS 2018:218, the Swedish Data Protection Act, the Swedish Ethical Review Act, and the Public Access to Information and Secrecy Act, these types of sensitive data can only be made available for specific purposes, including research, that meets the criteria for access to this type of sensitive and confidential data as determined by a legal review. Readers may contact Professor Ellenor Mittendorfer-Rutz ( [email protected] ) regarding the data. Ethical Approval and Consent to Participate The ethical approval was obtained from the Regional Ethical Review Board, Karolinska Institutet, Stockholm, Sweden (Dnr: 2007/762-31 and Dnr: 2024-08708-02), who also waived the need for informed consent. Acknowledgements The authors would like to thank Daniel Falkstedt, principal researcher and associate professor at the Institute of Environmental Medicine, Karolinska Institutet, and member of the Swedish Job Exposure Matrix (SweJEM) steering committee, for his collaboration and support with the SweJEM scores in this study. We also thank the rest of the SweJEM steering committee for their contributions. References OECD. Sick on the job? Myths and realities about mental health and work. Mental Health and Work, OECD Publishing. 2012; https://doi.org/10.1787/9789264124523-en OECD. Health at a glance 2023. Health at a glance. OECD Publishing. 2023. https://doi.org/10.1787/7a7afb35-en . Riihimäki K, Vuorilehto M, Isometsä E. A 5-year prospective study of predictors for functional and work disability among primary care patients with depressive disorders. Eur Psychiatry. 2015;30(1):51–7. https://doi.org/10.1016/j.eurpsy.2014.02.005 . Swedish Social Insurance Agency. Social insurance in Figs. 2024. Swedish Social Insurance Agency; 2024. Swedish Social Insurance Agency. Social insurance report 2020: sickness absence due to psychiatric diagnoses. Swedish Social Insurance Agency; 2020. Swedish Social Insurance Agency. Mental ill-health in today’s working life. Swedish Social Insurance Agency; 2023. Alexanderson K, Kivimäki M, Ferrie JE, Westerlund H, Vahtera J, Singh-Manoux A, et al. Diagnosis-specific sick leave as a long-term predictor of disability pension: a 13-year follow-up of the GAZEL cohort study. J Epidemiol Commun Health. 2012;66(2):155–9. https://doi.org/10.1136/jech.2010.126789 . Helgesson M, Johansson B, Nordqvist T, Lundberg I, Vingård E. Sickness absence at a young age and later sickness absence, disability pension, death, unemployment and income in native Swedes and immigrants. Eur J Public Health. 2015;25(4):688–92. https://doi.org/10.1093/eurpub/cku250 . Hultin H, Lindholm C, Malfert M, Möller J. Short-term sick leave and future risk of sickness absence and unemployment - the impact of health status. BMC Public Health. 2012;12(1). https://doi.org/10.1186/1471-2458-12-861 . Staland Nyman C, Andersson L, Spak F, Hensing G. Exploring consequences of sickness absence – a longitudinal study on changes in self-rated physical health. Work. 2009;34(3):315–24. https://doi.org/10.3233/WOR-2009-0929 . Fleuren BB, de Grip A, Jansen NW, Kant I, Zijlstra FR. Critical reflections on the currently leading definition of sustainable employability. Scand J Work Environ Health. 2016;42(6):557–60. https://doi.org/10.5271/sjweh.3585 . Helgesson M, Tinghög P, Wang M, Rahman S, Saboonchi F, Mittendorfer-Rutz E. Trajectories of work disability and unemployment among young adults with common mental disorders. BMC Public Health. 2018;18(1). https://doi.org/10.1186/s12889-018-6141-y . Helgesson M, Gustafsson K, Leineweber C. Suffering of common mental disorders but still at work: a longitudinal study during periods of differences in regulations for having sick leave. J Occup Rehabil. 2025. https://doi.org/10.1007/s10926-025-10269-4 . Karasek RA. Job demands, job decision latitude, and mental strain: Implications for job redesign. Adm Sci Q. 1979;24(2):285–308. https://doi.org/10.2307/2392498 . Karasek R, Theorell T. Healthy work: stress, productivity, and the reconstruction of working life. New York (N.Y): Basic Books; 1990. de Vries H, Fishta A, Weikert B, Rodriguez Sanchez A, Wegewitz U. Determinants of sickness absence and return to work among employees with common mental disorders: a scoping review. J Occup Rehabil. 2018;28(3):393–417. https://doi.org/10.1007/s10926-017-9730-1 . Duchaine CS, Aubé K, Gilbert-Ouimet M, Vézina M, Ndjaboué R, Massamba V, et al. Psychosocial stressors at work and the risk of sickness absence due to a diagnosed mental disorder. JAMA Psychiatry. 2020;77(8):842. https://doi.org/10.1001/jamapsychiatry.2020.0322 . Helgesson M, Gustafsson K, Leineweber C. Psychosocial and organisational work factors as predictors of sickness absence among professionally active adults with common mental disorders. BMC Psychiatry. 2023;23(1). https://doi.org/10.1186/s12888-023-05020-3 . Mather L, Bergström G, Blom V, Svedberg P. High job demands, job strain, and iso-strain are risk factors for sick leave due to mental disorders. J Occup Environ Med. 2015;57(8):858–65. https://doi.org/10.1097/JOM.0000000000000504 . Virtanen M, Vahtera J, Pentti J, Honkonen T, Elovainio M, Kivimäki M. Job strain and psychologic distress. Am J Prev Med. 2007;33(3):182–7. https://doi.org/10.1016/j.amepre.2007.05.003 . Farrants K, Norberg J, Framke E, Rugulies R, Alexanderson K. Job demands and job control and future labor market situation. J Occup Environ Med. 2020;62(6):403–11. https://doi.org/10.1097/JOM.0000000000001859 . Ropponen A, Wang M, Farrants K, Narusyte J, Svedberg P. Psychosocial working conditions and subsequent sickness absence—effects of pain and common mental disorders in a population-based Swedish twin sample. J Occup Environ Med. 2022;64(6):451–7. https://doi.org/10.1097/JOM.0000000000002501 . Flach PA, Groothoff JW, Krol B, Bultmann U. Factors associated with first return to work and sick leave durations in workers with common mental disorders. Eur J Public Health. 2012;22(3):440–5. https://doi.org/10.1093/eurpub/ckr102 . Holmlund L, Bültmann U, Bergström G, Warnqvist A, Björk Brämberg E. Are psychosocial work factors and work-home interference associated with time to first full return-to-work after sick leave due to common mental disorders? Int Arch Occup Environ Health. 2023;96. https://doi.org/10.1007/s00420-023-01970-z . Björkenstam E, Helgesson M, Gustafsson K, Virtanen M, Hanson LLM, Mittendorfer-Rutz E. Occupational class and employment sector differences in common mental disorders: a longitudinal Swedish cohort study. Eur J Pub Health. 2021;31(4):809–15. https://doi.org/10.1093/eurpub/ckab091 . Amin R, Mittendorfer-Rutz E, Björkenstam E, Virtanen M, Helgesson M, Gustafsson N, et al. Time period effects in work disability due to common mental disorders among young employees in Sweden—a register-based cohort study across occupational classes and employment sectors. Eur J Pub Health. 2023;33(2):272–8. https://doi.org/10.1093/eurpub/ckad026 . Björkenstam E, Helgesson M, Gustafsson K, Virtanen M, Hanson LLM, Mittendorfer-Rutz E. Sickness absence due to common mental disorders in young employees in Sweden: are there differences in occupational class and employment sector? Soc Psychiatry Psychiatr Epidemiol. 2022;57(5):1097–106. https://doi.org/10.1007/s00127-021-02152-3 . Rantonen O, Alexanderson K, Pentti J, Kjeldgård L, Hämäläinen J, Mittendorfer-Rutz E, et al. Trends in work disability with mental diagnoses among social workers in Finland and Sweden in 2005–2012. Epidemiol Psychiatric Sci. 2017;26(6):644–54. https://doi.org/10.1017/S2045796016000597 . Ludvigsson JF, Svedberg P, Olén O, Bruze G, Neovius M. The longitudinal integrated database for health insurance and labour market studies (LISA) and its use in medical research. Eur J Epidemiol. 2019;34(4):423–37. https://doi.org/10.1007/s10654-019-00511-8 . Österlund N. MiDAS - Sickness Benefit and Rehabilitation Allowance. Swedish Social Insurance Agency; 2011. Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim JL, Reuterwall C, et al. External review and validation of the Swedish national inpatient register. BMC Public Health. 2011;11(1). https://doi.org/10.1186/1471-2458-11-450 . Brooke HL, Talbäck M, Hörnblad J, Johansson LA, Ludvigsson JF, Druid H, et al. The Swedish cause of death register. Eur J Epidemiol. 2017;32(9):765–73. https://doi.org/10.1007/s10654-017-0316-1 . Wettermark B, Hammar N, Fored CM, Leimanis A, Otterblad Olausson P, Bergman U, et al. The new Swedish Prescribed Drug Register—opportunities for pharmacoepidemiological research and experience from the first six months. Pharmacoepidemiol Drug Saf. 2007;16(7):726–35. https://doi.org/10.1002/pds.1294 . Swedish Social Insurance Agency. Privatperson [Internet]. Forsakringskassan.se. 2021. Available from: https://www.forsakringskassan.se Swedish Public Employment Service. Arbetsförmedlingen. [Internet]. arbetsformedlingen.se. 2025. Available from: https://arbetsformedlingen.se Fredlund P, Hallqvist J, Diderichsen F. Psychosocial job exposure matrix. An updated version of a classification system for work-related psychosocial exposure. Swedish National Institute for Working Life; 2000. http://hdl.handle.net/2077/4243 . Jarroch R, Falkstedt D, Nevriana A, Pan KY, Kauhanen J, Almroth M. The role of job strain in the relationship between depression and long-term sickness absence: a register-based cohort study. Soc Psychiatry Psychiatr Epidemiol. 2024;59(11):2031–9. https://doi.org/10.1007/s00127-024-02700-7 . Norberg J, Alexanderson K, Framke E, Rugulies R, Farrants K. Job demands and control and sickness absence, disability pension and unemployment among 2,194,692 individuals in Sweden. Scand J Public Health. 2019;48(2):125–33. https://doi.org/10.1177/1403494819846367 . National Board of Health and Welfare. Diagnoses [Internet]. Support in insurance medicine. 2025. Available from: https://forsakringsmedicin.socialstyrelsen.se/beslutsstod-for-diagnoser/diagnoser/ SAS Institute Inc. SAS 9.4 (TS1M7) software. 2023. R Core Team. R: A language and environment for statistical computing. Version 2023.12.1 + 402. 2023. Available from: https://www.r-project.org Laine S, Gimeno D, Virtanen M, Oksanen T, Vahtera J, Elovainio M, et al. Job strain as a predictor of disability pension: the Finnish Public Sector Study. J Epidemiol Community Health. 2009;63(1):24–30. https://doi.org/10.1136/jech.2007.071407 . Almroth M, Hemmingsson T, Sörberg Wallin A, Kjellberg K, Burström B, Falkstedt D. Psychosocial working conditions and the risk of diagnosed depression: a Swedish register-based study. Psychol Med. 2022;52(15):1–9. https://doi.org/10.1017/S003329172100060X . Lidwall U, Marklund S. What is healthy work for women and men? – A case-control study of gender- and sector-specific effects of psycho-social working conditions on long-term sickness absence. Work. 2006;27(2):153–63. https://doi.org/10.3233/WOR-2006-00558 . Netterstrøm B, Eller NH, Borritz M. Prognostic factors of returning to work after sick leave due to work-related common mental disorders: a one- and three-year follow-up study. Biomed Res Int. 2015;2015:1–7. https://doi.org/10.1155/2015/596572 . Knutsen RH, Nielsen MB, Lunde LK, Skare Ø, Johannessen HA. Impact of psychosocial work factors on risk of medically certified sick leave due to common mental disorders: a nationwide prospective cohort study of Norwegian home care workers. BMC Public Health. 2024;24(1). https://doi.org/10.1186/s12889-024-18299-y . Gragnano A, Negrini A, Miglioretti M, Corbière M. Common psychosocial factors predicting return to work after common mental disorders, cardiovascular diseases, and cancers: a review of reviews supporting a cross-disease approach. J Occup Rehabil. 2018;28(2):215–31. https://doi.org/10.1007/s10926-017-9714-1 . Rose U, Kersten N, Pattloch D, Conway PM, Burr H. Associations between depressive symptoms and 5-year subsequent work nonparticipation due to long-term sickness absence, unemployment and early retirement in a cohort of 2,413 employees in Germany. BMC Public Health. 2023;23(1). https://doi.org/10.1186/s12889-023-17090-9 . Wang M, Svedberg P, Narusyte J, Farrants K, Ropponen A. Effects of age on psychosocial working conditions and future labour market marginalisation: a cohort study of 56,867 Swedish twins. Int Arch Occup Environ Health. 2022;95(1):199–211. https://doi.org/10.1007/s00420-021-01704-z . Pyöriä P, Ojala S. Precarious work and intrinsic job quality: evidence from Finland, 1984–2013. Economic Labour Relations Rev. 2016;27(3):349–67. https://doi.org/10.1177/1035304616659190 . Tables Table 1. Population Baseline Characteristics (n = 79,673). Characteristic n % Sex: Female 56,906 71.4 Male 22,767 28.6 Age: 25-29 9,481 11.9 30-34 12,511 15.7 35-39 40-44 45-49 50-55 15,384 15,319 13,936 13,042 19.3 19.2 17.5 16.4 Country of birth: Sweden 69,125 86.8 Other Nordic 1,774 2.2 Other European 4,101 5.1 Rest of the World 4,673 5.9 Education level: Elementary ( 12 years) 36,397 45.7 Family composition: Living without a partner and without children 26,011 32.7 Living without a partner and with children 9,086 11.4 Living with a partner and without children 9,982 12.5 Living with a partner and with children 34,594 43.4 Type of living area: City 31,541 39.6 Town or suburban area 33,491 42.0 Rural area 14,641 18.4 Occupational sector: Private 45,983 57.7 Public 33,690 42.3 Occupational branch: Production, industrial, and resource-based: 10,965 13.8 Agriculture, forestry, and fishing 373 0.5 Manufacturing and extraction 7,147 9.0 Energy supply and environmental activities 594 0.7 Construction 2,851 3.6 Trade, transport, and storage: 12,438 15.6 Trade 9,106 11.4 Transport and storage 3,332 4.2 Information, financial, and business services: 14,459 18.1 Information and communication 3,070 3.9 Finance and insurance activities 1,620 2.0 Real estate activities 830 1.0 Business services 8,939 11.2 Hospitality and personal services: 3,639 4.6 Hotel and restaurant operations 1,827 2.3 Cultural and personal services 1,812 2.3 Education and public administration: 17,952 22.5 Public administration and defense 5,590 7.0 Training and education 12,362 15.5 Health and social services 20,220 25.4 Job demands (SweJEM): Low 12,059 15.2 Medium 37,478 47.0 High 30,136 37.8 Job control (SweJEM): Low 26,656 33.5 Medium 30,621 38.4 High 22,396 28.1 Job strain (SweJEM): Low-strain jobs (low demands/high control) 186 0.2 Active jobs (high demands/high control) 12,302 15.5 Intermediate strain 57,541 72.2 Passive jobs (low demands/low control) 6,371 8.0 High-strain jobs (high demands/low control) 3,273 4.1 Any unemployment days: Yes 6,000 7.5 No 73,673 92.5 SA history: 0 days (no SA history) 67,750 85.0 0 days < SA history ≤ 30 days 4,878 6.1 30 days < SA history ≤ 90 days 4,183 5.3 90 days < SA history ≤ 180 days 1,781 2.2 180 days < SA history 1,081 1.4 DP history: Yes 369 0.5 No 79,304 99.5 Index SA diagnosis: Depressive disorders (F32-F33) 29,837 37.4 Anxiety disorders and OCD (F40-F41 and F42) 9,937 12.5 Stress-induced disorders (F43) 39,899 50.1 Index SA grade: Full-time (100%) 71,870 90.2 Part-time (75%) 1,082 1.4 Part-time (50%) 5,763 7.2 Part-time (25%) 958 1.2 Net index SA days: 30 days < SA days ≤ 90 days 40,806 51.2 90 days < SA days ≤ 180 days 17,601 22.1 180 days < SA days 21,266 26.7 Mental comorbidities: Other CMDs and burn-out (F32-F33, F40-F41, F42, F33, and Z730) 3,723 4.7 Substance use disorders (F10-F19) 1,257 1.6 Other affective disorders (F34, F38, and F39) 180 0.2 Personality disorders (F60-F69) 342 0.4 Attention-deficit hyperactivity disorder (ADHD) (F90.0) 278 0.4 Other behavioral emotional disorders (F50-F59, excl. F50.0, F90.1-F98) 819 1.0 Other psychological developmental disorders and autism-spectrum disorder (F80–F89, excl. F84.3–F84.9) 69 0.1 Other mental comorbidities (F04-F09, F44-F48, and F99) 289 0.4 Somatic comorbidities: Cancer (C00-D48) 4,075 5.1 Endocrine, nutritional, and metabolic disorders (E00-E90) 3,234 4.1 Neurological disorders (G00-G99, excl. G30-G32) 2,884 3.6 Circulatory system disorders (I00-I99) 3,191 4.0 Respiratory disorders (J00-J99) 3,700 4.6 Musculoskeletal disorders (M00-M99) 7,514 9.4 Other chronic somatic disorders 23,330 29.3 Medications: Antidepressants (ATC: N06A): No dispensations 61,560 77.3 One dispensation 9,920 12.4 Two or more dispensations 8,193 10.3 Other psychotropic medications: No dispensations 63,189 79.3 One or more dispensations 16,484 20.7 a. Abbreviations (in alphabetical order): ADHD = Attention Deficit Hyperactivity Disorder, ATC = Anatomical Therapeutic Chemical, CMDs = Common Mental Disorders, DP = Disability Pension, OCD = Obsessive Compulsive Disorder, SA = Sickness Absence, SweJEM = The Swedish Job Exposure Matrix. b. Measurement timepoints: Age, education level, family composition, type of living area, occupation, and occupational sector and branch were assessed on December 31 of the year before the index SA began. UE days were measured for the entire year preceding the start of the index SA spell. SA/DP history was assessed in the two-year period prior to the exclusion window, which began one year before the index SA. Mental and somatic comorbidities were assessed within two years of the start of the index SA spell, and medication use was assessed six months before and three months after the index SA spell started. c. Missing information was classified as the lowest education level – “elementary education (<10 years)” (n = 186 (0.23%)). d. Intermediate job strain: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control. e. All diagnostic codes refer to the 10th version of the International Classification of Diseases (ICD-10) codes. f. Other chronic somatic disorders: infectious and parasitic diseases (ICD-10: A00-B99), diseases of the blood and blood-forming organs (ICD-10: D50-D89), diseases of the eye and ear (ICD-10: H00-H95), diseases of the digestive system (ICD-10: K00-K93), diseases of the skin and subcutaneous tissue (ICD-10: L00-L99), and diseases of the genitourinary system (ICD-10: N00-N99). g. Other psychotropic medications: anxiolytics (ATC: N05B), hypnotics and sedatives (ATC: N05C), psychostimulants, ADHD agents, and nootropics (ATC: N06B), psycholeptics and psychoanaleptics in combination (ATC: N06C). Table 2. Multinomial Logistic Regression Results for Unemployment (UE) Days (n = 78,900). Model 1 was adjusted for sex and age, Model 2 was additionally adjusted for education, country of birth, and type of living area, and Model 3 was further adjusted for index SA diagnosis, SA/DP history, and mental and somatic comorbidities. Reference category: 0 UE days during the follow-up period. Occupational Characteristic Outcome Category Crude OR (95% CI) Model 1 OR (95% CI) Model 2 OR (95% CI) Model 3 OR (95% CI) Job strain: Intermediate 0 180 UE Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) Active jobs (high demands/high control) 0 180 UE Days 0.89 (0.83-0.96) 0.84 (0.78-0.90) 0.98 (0.91-1.06) 1.01 (0.93-1.09) Passive jobs (low demands/low control) 0 180 UE Days 2.28 (2.11-2.45) 1.83 (1.70-1.98) 1.50 (1.39-1.62) 1.48 (1.36-1.60) High-strain jobs (high demands/low control) 0 180 UE Days 1.67 (1.50-1.87) 1.38 (1.23-1.54) 1.22 (1.09-1.37) 1.21 (1.08-1.36) Job demands: Low 0 180 UE Days 1.55 (1.46-1.66) 1.25 (1.17-1.34) 1.15 (1.07-1.23) 1.13 (1.05-1.21) Medium 0 180 UE Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) High 0 180 UE Days 0.69 (0.66-0.73) 0.68 (0.64-0.72) 0.80 (0.76-0.85) 0.81 (0.77-0.86) Job control: Low 0 180 UE Days 1.79 (1.69-1.89) 1.72 (1.62-1.82) 1.42 (1.34-1.51) 1.40 (1.32-1.49) Medium 0 180 UE Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) High 0 180 UE Days 1.06 (0.99-1.13) 0.98 (0.92-1.05) 1.08 (1.01-1.16) 1.10 (1.03-1.18) Job sector: Private 0 180 UE Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) Public 0 180 UE Days 0.30 (0.28-0.31) 0.33 (0.31-0.35) 0.36 (0.34-0.39) 0.36 (0.34-0.38) Occupational branch: Production, industrial, and resource-based 0 180 UE Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) Trade, transport, and storage 0 180 UE Days 1.17 (1.08-1.27) 1.25 (1.15-1.36) 1.19 (1.09-1.29) 1.19 (1.10-1.29) Information, financial, and business services 0 180 UE Days 1.08 (0.99-1.17) 1.19 (1.10-1.28) 1.25 (1.15-1.35) 1.25 (1.15-1.35) Hospitality and personal services 0 180 UE Days 1.33 (1.19-1.49) 1.49 (1.33-1.67) 1.37 (1.22-1.54) 1.36 (1.21-1.52) Education and public administration 0 180 UE Days 0.37 (0.34-0.40) 0.44 (0.40-0.48) 0.52 (0.47-0.57) 0.52 (0.47-0.57) Health and social services 0 180 UE Days 0.43 (0.40-0.47) 0.53 (0.48-0.57) 0.54 (0.49-0.59) 0.53 (0.48-0.58) a. Individuals with low job strain (low demands/low control) were excluded due to low observation counts (n = 183 (0.23%)). b. Intermediate job strain: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control. c. Production, industrial, and resource-based branch: agriculture, forestry, fishing, manufacturing, extraction, energy supply, environmental services, and construction. d. Information and business services: information, communication, finance, insurance, and real estate activities and business services. Table 3. Multinomial Logistic Regression Results for Sickness Absence and Disability Pension (SA/DP) Days (n = 78,967). Model 1 was adjusted for sex and age, Model 2 was additionally adjusted for education, country of birth, and type of living area, and Model 3 was further adjusted for index SA diagnosis, SA/DP history, and mental and somatic comorbidities. Reference category: 30 < SA/DP Days ≤ 90 during the follow-up period. Occupational Characteristic Outcome Category Crude OR (95% CI) Model 1 OR (95% CI) Model 2 OR (95% CI) Model 3 OR (95% CI) Job strain: Intermediate 90 < SA/DP Days ≤ 180 1 (ref) 1 (ref) 1 (ref) 1 (ref) 180 365 SA/DP Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) Active jobs (high demands/high control) 90 < SA/DP Days ≤ 180 0.96 (0.91-1.01) 0.98 (0.93-1.03) 0.98 (0.93-1.04) 1.00 (0.95-1.06) 180 365 SA/DP Days 0.75 (0.71-0.80) 0.76 (0.72-0.81) 0.85 (0.80-0.90) 0.90 (0.85-0.96) Passive jobs (low demands/low control) 90 < SA/DP Days ≤ 180 0.92 (0.86-0.98) 0.98 (0.91-1.05) 0.98 (0.91-1.05) 0.96 (0.89-1.04) 180 365 SA/DP Days 1.16 (1.08-1.24) 1.31 (1.22-1.41) 1.14 (1.06-1.23) 1.09 (1.01-1.17) High-strain jobs (high demands/low control) 90 < SA/DP Days ≤ 180 0.99 (0.90-1.09) 1.05 (0.95-1.16) 1.05 (0.95-1.16) 1.04 (0.94-1.15) 180 365 SA/DP Days 1.14 (1.03-1.25) 1.26 (1.15-1.39) 1.20 (1.08-1.32) 1.15 (1.04-1.27) Job demands: Low 90 < SA/DP Days ≤ 180 0.97 (0.91-1.02) 1.03 (0.97-1.09) 1.02 (0.97-1.08) 1.01 (0.95-1.07) 180 365 SA/DP Days 1.10 (1.04-1.17) 1.24 (1.17-1.31) 1.15 (1.08-1.21) 1.10 (1.03-1.16) Medium 90 < SA/DP Days ≤ 180 1 (ref) 1 (ref) 1 (ref) 1 (ref) 180 365 SA/DP Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) High 90 < SA/DP Days ≤ 180 1.01 (0.97-1.05) 1.01 (0.97-1.06) 1.02 (0.98-1.07) 1.03 (0.99-1.08) 180 365 SA/DP Days 0.85 (0.82-0.89) 0.85 (0.81-0.89) 0.95 (0.91-1.00) 0.99 (0.94-1.03) Job control: Low 90 < SA/DP Days ≤ 180 0.98 (0.94-1.03) 0.99 (0.95-1.04) 0.99 (0.94-1.04) 0.97 (0.93-1.02) 180 365 SA/DP Days 1.23 (1.18-1.29) 1.28 (1.22-1.34) 1.15 (1.10-1.21) 1.10 (1.05-1.16) Medium 90 < SA/DP Days ≤ 180 1 (ref) 1 (ref) 1 (ref) 1 (ref) 180 365 SA/DP Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) High 90 < SA/DP Days ≤ 180 0.95 (0.90-0.99) 0.96 (0.92-1.01) 0.97 (0.92-1.01) 0.99 (0.94-1.03) 180 365 SA/DP Days 0.75 (0.72-0.79) 0.77 (0.73-0.81) 0.81 (0.77-0.85) 0.86 (0.82-0.91) Job sector: Private 90 < SA/DP Days ≤ 180 1 (ref) 1 (ref) 1 (ref) 1 (ref) 180 365 SA/DP Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) Public 90 < SA/DP Days ≤ 180 1.09 (1.05-1.13) 1.05 (1.00-1.09) 1.05 (1.01-1.09) 1.04 (1.00-1.09) 180 365 SA/DP Days 1.12 (1.08-1.17) 1.02 (0.98-1.06) 1.11 (1.07-1.16) 1.09 (1.05-1.14) Occupational branch: Production, industrial, and resource-based 90 < SA/DP Days ≤ 180 1 (ref) 1 (ref) 1 (ref) 1 (ref) 180 365 SA/DP Days 1 (ref) 1 (ref) 1 (ref) 1 (ref) Trade, transport, and storage 90 < SA/DP Days ≤ 180 1.02 (0.95-1.09) 0.99 (0.92-1.06) 0.99 (0.93-1.06) 0.99 (0.93-1.06) 180 365 SA/DP Days 1.19 (1.10-1.27) 1.16 (1.08-1.25) 1.16 (1.07-1.24) 1.16 (1.08-1.25) Information, financial, and business services 90 < SA/DP Days ≤ 180 0.97 (0.91-1.03) 0.93 (0.88-1.00) 0.94 (0.88-1.01) 0.95 (0.89-1.01) 180 365 SA/DP Days 1.07 (1.00-1.15) 1.03 (0.96-1.10) 1.13 (1.05-1.21) 1.13 (1.05-1.21) Hospitality and personal services 90 < SA/DP Days ≤ 180 0.98 (0.88-1.08) 0.93 (0.84-1.03) 0.94 (0.85-1.04) 0.94 (0.85-1.04) 180 365 SA/DP Days 1.45 (1.31-1.60) 1.39 (1.26-1.55) 1.40 (1.26-1.55) 1.36 (1.23-1.52) Education and public administration 90 < SA/DP Days ≤ 180 1.10 (1.03-1.17) 1.02 (0.96-1.09) 1.04 (0.97-1.11) 1.04 (0.97-1.11) 180 365 SA/DP Days 1.16 (1.08-1.24) 1.04 (0.97-1.11) 1.22 (1.14-1.32) 1.22 (1.13-1.31) Health and social services 90 < SA/DP Days ≤ 180 1.08 (1.02-1.15) 1.00 (0.94-1.07) 1.01 (0.95-1.08) 0.99 (0.93-1.06) 180 365 SA/DP Days 1.36 (1.28-1.46) 1.21 (1.13-1.30) 1.29 (1.20-1.38) 1.23 (1.14-1.32) a. Individuals with low job strain (low demands/low control) were excluded due to low observation counts (n = 184 (0.24%)). b. Intermediate job strain: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control. c. Production, industrial, and resource-based branch: agriculture, forestry, fishing, manufacturing, extraction, energy supply, environmental services, and construction. d. Information, financial, and business services: information, communication, finance, insurance, and real estate activities and business services. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationOccupationalFactorsandCMDSA.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Journal of Occupational Rehabilitation → Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers invited by journal 12 Sep, 2025 Editor assigned by journal 10 Sep, 2025 Submission checks completed at journal 10 Sep, 2025 First submitted to journal 10 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7583607","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515725728,"identity":"d0efe77c-f17f-4de7-bec1-96801c42af8a","order_by":0,"name":"Gerda Stutaite","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYJACCRjjAAODjQEbsVokGIBKDxxgSCNRC9CawwYElcu79z688YOhrs7gfvPDwx8qzhvzMbA/fIBPi+GZ48aWPQyHJQyOsRkcOHDmthkbA48xXqsMZ6SxSfAwHABqYTA4cLDttg1QC5sEXi3zn7FJ/mGoA2ph/3Dg4L9zQC3sz3/g9YsEG5s0DwMzUAsP0JaGA0CHMZjh08FgwJPGbC1jcFhy5rGcggNnjiUbszHzGON1mHz7Mcabbyrq+PkOH9/8oaLGznB+e/vDD3htOQAmkYWY8ToLaEsDAQWjYBSMglEwChgARsdGKYBgEY0AAAAASUVORK5CYII=","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Gerda","middleName":"","lastName":"Stutaite","suffix":""},{"id":515725731,"identity":"b913b78f-7786-44b5-bbb3-40e2bc62b5f0","order_by":1,"name":"Ellenor Mittendorfer-Rutz","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Ellenor","middleName":"","lastName":"Mittendorfer-Rutz","suffix":""},{"id":515725733,"identity":"899b58c6-7ce6-4e63-a79e-8fff10d7b75e","order_by":2,"name":"Magnus Helgesson","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Magnus","middleName":"","lastName":"Helgesson","suffix":""},{"id":515725734,"identity":"cf53339e-6747-4eae-b9c8-fd8aa19c60a9","order_by":3,"name":"Kristin Farrants","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Kristin","middleName":"","lastName":"Farrants","suffix":""},{"id":515725735,"identity":"fdf3337a-ff8b-4b8a-afd8-dff5a7ccf3b1","order_by":4,"name":"Katalin Gémes","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Katalin","middleName":"","lastName":"Gémes","suffix":""}],"badges":[],"createdAt":"2025-09-10 13:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7583607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7583607/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10926-025-10348-6","type":"published","date":"2025-11-27T15:58:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91696791,"identity":"4ea259f4-756d-4f86-b683-e8aa1d87eb64","added_by":"auto","created_at":"2025-09-19 09:40:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds ratio (OR) Plots of the Multinomial Logistic Regression Models (n = 78,900 for Unemployment and n = 78,967 for Sickness Absence and Disability Pension (SA/DP)).\u003c/strong\u003eModel 1 was adjusted for sex and age, and Model 3 was additionally adjusted for education, country of birth, type of living area, index SA diagnosis, SA/DP history, and mental and somatic comorbidities. Reference categories: 0 unemployment days for the unemployment analysis, and 30 \u0026lt; SA/DP Days ≤ 90 for the SA/DP analysis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7583607/v1/800a0f52dfc4621946956280.png"},{"id":97178672,"identity":"5ac34c47-3348-4f3b-8d62-8ae4a181b598","added_by":"auto","created_at":"2025-12-01 16:12:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2069836,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7583607/v1/7717b4ef-f4e5-4b66-a21a-19a2463e71c7.pdf"},{"id":91697066,"identity":"5e97a871-92a8-4cbf-98fd-ef5047151082","added_by":"auto","created_at":"2025-09-19 09:48:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":317091,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationOccupationalFactorsandCMDSA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7583607/v1/4ae4b5fae0d88770bcf66d63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occupational Factors and Labor Market Outcomes Among Individuals with Sickness Absence due to Common Mental Disorders: A Population-Wide Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiagnoses of common mental disorders (CMDs), including depression, anxiety, and stress-related disorders, have increased significantly in recent decades in Sweden and other OECD countries, constituting a major public health challenge [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is estimated that around 20% of the working-age population has a clinically diagnosed mental disorder, the majority of which are CMDs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to their negative effect on work ability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], CMDs are also the leading cause of sickness absence episodes in Sweden and other OECD countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For instance, in Sweden, CMDs accounted for approximately 90% of all psychiatric sickness absence spells exceeding 14 days that began in 2018 and 2019 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Most sickness absence spells due to CMDs last around 40 days; however, individuals with certain stress-related disorders may have sickness absence lasting for up to half a year and longer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, CMDs not only cause suffering to the affected individuals but also pose a substantial economic burden at the societal level [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile a sickness absence spell can provide temporary financial support, enabling individuals with CMDs to seek treatment and recover, it can also, particularly when prolonged, contribute to long-term labor market exclusion [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and worsening health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Employment, on the other hand, promotes long-term financial stability, social connectedness, a sense of identity, and better overall health [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, studies have shown that many individuals with CMDs can remain in employment, despite their illness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, it is important to understand the factors that influence not only the risk of sickness absence among individuals with CMDs but also affect their labor market outcomes following a sickness absence spell due to CMDs. Occupational factors, particularly those related to the psychosocial work environment, are often modifiable and can be targeted in interventions to improve the health of individuals with CMDs, facilitating their return to work, and consequently reducing societal costs.\u003c/p\u003e\u003cp\u003eIn occupational stress research, the psychosocial work environment is often evaluated using the job strain model, developed by R. Karasek and T. Theorell [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The model focuses on the interaction between job demands and job control, defined as job strain, and suggests that high job demands (e.g., high workload and time pressures) combined with low job control (e.g., limited decision authority) may lead to mental distress. Several studies have found that high job strain (high job demands and low job control) is associated with increased rates of sickness absence among individuals with CMDs [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast, some studies have shown that jobs characterized by low demands and low control are associated with higher rates of sickness absence in the general population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while a recent study suggested that the relationship between job strain and sickness absence may vary depending on the specific characteristics of an occupational group [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKnowledge about how job strain influences future labor market outcomes following a sickness absence episode due to CMDs remains especially scarce. A 2018 scoping review reported insufficient evidence\u0026mdash;defined as fewer than three qualifying studies\u0026mdash;regarding the impact of job demands, job control, and job strain on labor market outcomes, such as recurrent sickness absence and return to work among individuals on sickness absence due to CMDs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nevertheless, some studies have shown that higher psychosocial hazards and increased psychological and emotional demands at work are associated with delayed return to work in this population [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, these studies are relatively small and primarily examine time to return to work, rather than a broader range of labor market outcomes following a sickness absence spell due to a CMD. Thus, the impact of job demands, control, and strain on future labor market outcomes in this particular group is not yet fully understood.\u003c/p\u003e\u003cp\u003eRegarding the role of structural occupational factors, such as sector and branch, research has shown that public sector workers have a higher risk of CMDs than private sector workers, with the risk being especially high among individuals working in health and social services [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, public sector workers were also found to be at a higher risk of sickness absence and disability pension due to CMDs than private sector workers [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, little is known about how working in the public versus private sector and across different occupational branches affects labor market outcomes following a period of sickness absence due to CMDs.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to investigate how differences in job demands, control, and strain, and occupational sector and branch affect labor market outcomes among individuals with a sickness absence spell due to a CMD. To gain a more comprehensive understanding of how these individuals might be excluded from the labor market, we draw on the social insurance perspective, which conceptualizes labor market marginalization as periods of both unemployment and work disability, the latter primarily driven by illness or injury and measured through sickness absence and disability pension [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study is the first population-based study of its scale using high-quality register data that includes detailed sociodemographic, socioeconomic, and health-related factors to assess the impact of occupational factors on a range of labor market outcomes following a sickness absence spell due to CMDs. The findings of this study could contribute valuable knowledge needed to inform future policy initiatives aimed at facilitating sustainable labor market inclusion for these individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy data, design, and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo achieve our aim, we conducted a register-based prospective cohort study, including all Swedish residents, aged 25-55, who were in gainful employment and started a new sickness absence (SA) spell due to a CMD lasting longer than 30 days between 2011 and 2013, and who had lived in Sweden for at least two years before the start of the study. CMD diagnoses were defined according to the International Classification of Diseases (ICD-10) codes: F32-F33 for depressive disorders, F40-41 for anxiety disorders, F42 for obsessive-compulsive disorder (OCD), and F43 for stress-induced disorders. Gainful employment status was defined by Statistics Sweden [29]. The earliest spell was designated the index spell for individuals with multiple SA spells matching the inclusion criteria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to the low likelihood of return to work and high risk of permanent disability, we excluded individuals with a severe psychiatric or neurological disorder diagnosed in specialized outpatient or inpatient care (ICD-10) in the two years before the start of the study, or anytime during the study\u0026apos;s follow-up (Online Resource). These conditions were psychosis spectrum disorders (F20-29), bipolar disorder (F30-F31), early-onset neurodegenerative disorders (F00-F03 or G30-G32), anorexia nervosa (F50.0), and intellectual disabilities (F70-F79). We further excluded individuals with SA or disability pension (DP) due to any condition one year before the start of the index SA spell and individuals with missing information on their occupation, occupational sector, or branch (n total excluded = 37,967).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the following national Swedish registers and databases, which are linked together by a unique identification number assigned to each individual:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe \u0026ldquo;Micro-Data for Analyses of Social Insurance (MiDAS)\u0026rdquo; [30] for information about diagnoses, dates, and grades (part-time versus full-time) of SA and DP benefits.\u003c/li\u003e\n \u003cli\u003eThe \u0026ldquo;Longitudinal Integration Database for Health Insurance and Labor Market Studies (LISA)\u0026rdquo; [29] for information about sociodemographic, socioeconomic, and occupational factors, including occupation, occupational sector, and branch.\u003c/li\u003e\n \u003cli\u003eThe National Patient Register [31] for information about primary and secondary diagnoses from specialized outpatient care visits and inpatient care stays to identify mental and somatic comorbidities.\u003c/li\u003e\n \u003cli\u003eThe Causes of Death Register [32] for information about dates of death.\u003c/li\u003e\n \u003cli\u003eThe Prescribed Drug Register [33] for information about dispensed psychotropic medications.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSocial security in Sweden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSwedish residents with qualifying income from work or benefits related to parental leave or unemployment can receive SA compensation if their work capacity is impaired due to illness or injury [4,34]. For employed individuals, during the initial 14 days of sickness, the employer pays the SA benefits, reaching up to 80% of lost qualifying earnings [4,34]. The Swedish Social Insurance Agency disburses subsequent benefits [4,34]. For the unemployed, the Swedish Social Insurance Agency pays benefits from the second SA day [4,34]. Consequently, as information for this study is collected through the Swedish Social Insurance Agency, only SA spells lasting longer than 14 days were included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, Swedish residents aged 19 to 66 with an illness or disability that impairs their ability to work for an extended period may qualify for\u0026nbsp;DP benefits [4,34]. Individuals aged 19-29 may receive temporary activity compensation either due to decreased work ability or because they could not complete compulsory education in time, while those aged 30-66 may receive permanent compensation, reaching up to 64% of lost income [4,34].\u003c/p\u003e\n\u003cp\u003eIndividuals can receive either full (100%) or partial (75%, 50%, or 25%) SA and DP benefits [4,34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnemployed Swedish residents above age 20, registered as job seekers with the Swedish Public Employment Service and capable of working, can receive basic unemployment benefits, while some may also qualify for additional income-based benefits [35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exposure variables in this study were job demands, job control, and job strain, as well as occupational sector and branch.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe assessed job demands, control, and strain using the Swedish Job Exposure Matrix (SweJEM) for the years 1997-2013 [36,37]. It consists of occupational-level scores for psychosocial job demands and control derived from the Swedish Work Environment and Health surveys [36,37]. In these surveys, job demands were measured using questions focused on workload, stress, and concentration required at work, while job control was assessed using questions focused on decision authority and work task repetitiveness [36,37]. Individual self-reported values were averaged and assigned to all persons in a specific occupation, based on the Swedish version of the International Standard Classification of Occupations (ISCO-88), separately for men and women [36,37].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe categorized job demands and job control into low, medium, and high based on their tertile distributions [38]. Additionally, we derived job strain based on the interaction between job demands and job control. It included 1) Low-strain (low demands/high control), 2) Passive (low demands/low control), 3) Active (high demands/high control), 4) High-strain (high demands/low control), and 5) Intermediate-strain jobs, which included the remaining job demands and control combinations: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control [14]. We then assigned the SweJEM values for job demands, control, and strain to individuals in the study population based on their occupational code at the end of the year preceding the start of their index SA spell due to a CMD.\u003c/p\u003e\n\u003cp\u003eThe occupational sector was divided into 1) Private and 2) Public, and the occupational branch was grouped into 1) Production, industrial, and resource-based branch (agriculture, forestry, fishing, manufacturing, extraction, energy supply, environmental services, and construction), 2) Trade, transport, and storage, 3) Information, financial, and business services (information, communication, finance, insurance, and real estate activities and business services), 4) Hospitality and personal services (hotel and restaurant operations and cultural and personal services), 5) Education and public administration (public administration and defense and training and education), and 6) Health and social services. We assessed the occupational sector and branch at the end of the year preceding the start of the index SA spell due to a CMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes and follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two outcomes of interest in this study were the sum of unemployment days and SA/DP days during the follow-up period. SA days used to define the study population, and all following SA and DP days refer to net days, which means, for example, that two half-days are counted as one net day. We categorized unemployment days into 1) No (zero) unemployment days, 2) 0 \u0026lt; unemployment days \u0026le; 180, and 3) \u0026gt; 180 unemployment days, to assess both medium-term (up to 180 days) and long-term (more than 180 days) unemployment. Net SA/DP days were categorized into 1) 30 \u0026lt; SA/DP days \u0026le; 90 Days, 2) 90 \u0026lt; SA/DP days \u0026le; 180, 3) 180 \u0026lt; SA/DP days \u0026le; 365, and 4) \u0026gt; 365 SA/DP days, based on the National Board of Health and Welfare\u0026rsquo;s recommendations for SA durations for different CMDs, which consider specific diagnoses, disorder severity, and work ability [39].\u003c/p\u003e\n\u003cp\u003eWe assessed unemployment days per calendar year for three consecutive years, beginning the year after the start of the index SA spell (Online Resource). SA/DP days, on the other hand, were assessed for three consecutive years, beginning on the first day of the index SA spell. Individuals who emigrated or died during the follow-up period were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates assessed on December 31 of the year before the start of the index SA spell were age, education level, type of living area, and family composition (Table 1). SA/DP history was assessed in the two-year period prior to the exclusion window, which began one year before the index SA. Psychotropic medication dispensations were assessed six months before and three months after the first day of the index SA spell.\u003c/p\u003e\n\u003cp\u003eAdditionally, based on ICD-10 codes from inpatient and specialized outpatient care, we identified mental and somatic comorbidities in the two years before the start of the index SA, detailed in Table 1. Mental comorbidities included CMDs other than the main diagnosis of the index SA spell and burn-out (F32-F33, F40-F41, F42, F43, and Z730), substance use disorders (F10-F19), other affective disorders (F34, F38, and F39), personality disorders (F60-F69), attention-deficit hyperactivity disorder (ADHD) (F90.0), and other mental disorders, other behavioral emotional disorders (F50-F59, excl. F50.0, F90.1-F98), other psychological developmental disorders and autism-spectrum disorder (F80\u0026ndash;F89, excl. F84.3\u0026ndash;F84.9), and other mental comorbidities (F04-F09, F44-F48, and F99). Somatic comorbidities included cancer (C00-D48), endocrine, nutritional, and metabolic disorders (E00-E90), neurological disorders (G00-G99, excluding G30-G32), circulatory system disorders (I00-I99), respiratory disorders (J00-J99), musculoskeletal disorders (M00-M99), and other chronic somatic disorders.\u003c/p\u003e\n\u003cp\u003eFinally, we assessed the duration, grade (part-time versus full-time), and diagnosis of the index SA spell.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we computed descriptive characteristics for the entire study population (Tables 1, Online Resource). Then, we conducted separate multinomial logistic regression analyses to examine the associations between occupational factors and varying numbers of unemployment and SA/DP days during the three-year follow-up. The reference group for the unemployment analysis was no (zero) unemployment days, and for the SA/DP analysis, it was 30 \u0026lt; days \u0026le; 90. Crude and adjusted analyses were conducted for each outcome, with Model 1 adjusted for sex and age, Model 2 additionally adjusted for education, country of birth, and type of living area, and Model 3 further adjusted for index SA diagnosis, SA/DP history, and mental and somatic comorbidities. Odds ratios (ORs) with 95 % confidence intervals (CIs) were calculated for each association.\u003c/p\u003e\n\u003cp\u003eIndividuals who emigrated or died during the follow-up period were excluded (unemployment: n = 773 (0.97%) and SA/DP: n = 706 (0.89%) for the SA/DP analysis). We computed descriptive statistics to compare baseline characteristics between the individuals included in the unemployment analysis and the excluded ones (Online Resource). Additionally, we excluded individuals with low job strain (low demands/high control) from both job strain analyses (n = 183 (0.23%) for the unemployment analysis and n = 184 (0.23%) for the SA/DP analysis) due to low observation counts. To assess whether this exclusion altered the results, we conducted a sensitivity analysis that included those with low job strain (Online Resource).\u003c/p\u003e\n\u003cp\u003eWe performed data management using SAS 9.4 [40] and conducted data analysis using R [41].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final study population consisted of 79,673 individuals, with a mean age of 40.3 years (standard deviation (SD) = 8.2) (Table 1). Most were females (71.4%) and born in Sweden (86.8%). About half had completed high school (45.9%), and 45.7% had a university education. 43.4% of individuals lived with a partner and children, while 32.7% lived without either. Most resided in towns or suburban areas (42.0%), while 39.6% lived in cities.\u003c/p\u003e\n\u003cp\u003eRegarding occupational factors, most individuals worked in the private sector (57.7%), and the most common occupational branches were health and social services (25.4%) and education and public administration (22.5%). Medium-demand (47.0%), medium-control (38.4%), and intermediate-strain (72.2%) jobs were the most common, while 15.5% worked in active (high demands/high control) jobs. Moreover, 92.5% of individuals had no unemployment days in the year before the start of the index SA spell. During follow-up, most (76.9%) remained without any unemployment days, and among those with at least one, the average was 184.1 days (SD = 180.1).\u003c/p\u003e\n\u003cp\u003eRegarding health-related factors, 15.0% individuals had SA/DP days due to any condition in the two years before the start of the index SA spell, while only 0.5% had DP days in the same timeframe. The most common comorbidity in the two years before the study began was another CMD, including burn-out (ICD-10: Z730) (4.7%). The index SA spell was predominantly full-time (90.2%), with stress-related disorders (ICD-10: F43) as the most common diagnosis (50.1%). During follow-up, individuals had an average of 245.2 SA/DP days (SD = 254.6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnemployment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found that, adjusted for age and sex, working in passive (low demands/low control) and high-strain (high demands/low control) jobs was associated with higher odds of having both up to 180 and \u0026gt;180 days of unemployment compared to working in intermediate-strain jobs (Table 2, Figure 1). For \u0026gt;180 unemployment days, the OR was 1.8 (95% CI: 1.70-1.98) for passive, and 1.4 (95% CI: 1.23-1.54) for high-strain jobs. Results with further adjustments for socioeconomic and health-related factors are presented in Table 2. When job demands and job control were assessed separately, low job control was associated with higher odds of having \u0026gt;180 unemployment days across all models compared to medium job control (OR = 1.7, 95% CI: 1.62-1.82). In contrast, high job demands were inversely associated with both unemployment durations (OR = 0.7, 95% CI: 0.64-0.72 for \u0026gt;180 unemployment days).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSA/DP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the unemployment analysis, working in passive and high-strain jobs was associated with an increased likelihood of having \u0026gt;365 days of SA/DP (Table 3, Figure 1). In the model adjusted for sex and age, the OR was 1.3 (95% CI: 1.22-1.41) for passive and 1.3 (95% CI: 1.15-1.39) for high-strain jobs. Active (high demands/high control) jobs, in contrast, showed an inverse association with the same outcome category (OR = 0.8, 95% CI: 0.72-0.81 for \u0026gt;365 days of SA/DP). Additionally, high job control, when assessed separately from job demands, was associated with lower odds of having \u0026gt;365 SA/DP days compared to medium job control (OR = 0.8, 95% CI: 0.73-0.81).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOccupational sector and branch\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted for age and sex, individuals working in the public sector were less likely to have both up to 180 unemployment days and \u0026gt;180 unemployment days (OR = 0.3, 95% CI: 0.31-0.35 for \u0026gt;180 unemployment days) compared with those working in the private sector (Table 2). This pattern was not observed for SA/DP days (Table 3). Additionally, working in education and public administration and in health and social services was associated with lower odds of having unemployment days. In the model adjusted for sociodemographic and health-related factors, for \u0026gt;180 unemployment days, the OR was 0.5 (95% CI: 0.47-0.57) for education and public administration, and 0.5 (95% CI: 0.48-0.58) for health and social services. In contrast, individuals in these branches had higher odds of having \u0026gt;365 SA/DP days during the follow-up (OR = 1.2, 95% CI: 1.13-1.31 for education and public administration; OR = 1.2, 95% CI: 1.14-1.32 for health and social services).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMain findings\u003c/h2\u003e\u003cp\u003eThis study examined how differences in job demands, job control, job strain, occupational branch, and occupational sector affected labor market outcomes, namely unemployment and SA/DP, among individuals on an SA spell due to a CMD. Our findings showed that in this population, high-strain (high demands/low control) and passive (low demands/low control) jobs were associated with both medium (up to 180 days) and long-term (\u0026gt;\u0026thinsp;180 days) unemployment and long-term (\u0026gt;\u0026thinsp;365 days) SA/DP. Working in active (high demands/high control) jobs, on the other hand, was associated with a lower likelihood of having\u0026thinsp;\u0026gt;\u0026thinsp;365 days of SA/DP. When job demands and job control were assessed separately, we found that individuals in low control jobs were more likely to have both medium and long-term unemployment and long-term SA/DP, while those in jobs with high demands were less likely to experience both medium and long-term unemployment.\u003c/p\u003e\u003cp\u003eAdditionally, public sector workers were less likely to experience medium and long-term unemployment, but more likely to experience long-term SA/DP. Similarly, working in education and public administration, and in health and social services, was associated with a lower likelihood of medium and long-term unemployment, yet a higher likelihood of long-term SA/DP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eComparison with literature\u003c/h2\u003e\u003cp\u003eDirect comparisons with studies specifically analyzing labor market outcomes among individuals with SA due to CMDs are limited, as few such studies exist. However, we found that individuals in high-strain (high demands/low control) jobs were more likely to have long-term SA/DP following the initial SA spell, which aligns with previous findings suggesting that high psychosocial hazards\u0026mdash;a concept comparable to high job strain, defined as high workload and emotional demands combined with low decision authority\u0026mdash;are associated with longer SA durations among individuals with CMDs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our results are also consistent with previous studies showing that high-strain jobs are associated with an increased risk of SA and DP among individuals with CMDs [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, we found that working in passive (low demands/low control) jobs was associated with a higher likelihood of having long-term SA/DP following SA due to CMDs, while working in active (high demands/high control) jobs was associated with a lower likelihood. These findings are consistent with some previous research showing lower SA risk among individuals with CMDs in active jobs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to Karasek\u0026rsquo;s job strain model, active jobs combine a challenging workload with the opportunity to exercise decision-making and autonomy, which have been argued to contribute to professional growth, increase job satisfaction, and reduce depression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Conversely, passive jobs have been associated with increased rates of depression [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the relationship between active and passive jobs and the risk of CMDs, SA, and SA due to CMDs remains not fully understood, with several studies reporting inconclusive results, particularly when comparing men and women [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Specifically among women, some findings suggest that working in passive jobs is associated with an increased risk of SA/DP [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], while others show that working in active jobs may contribute to increased rates of long-term SA [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, several studies assessed job demands and job control independently of each other and found associations between high job demands and delayed return to work among individuals on SA due to CMDs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In our study, working in high-demand jobs was not associated with any of the examined SA/DP durations following the initial SA spell due to CMDs. However, we found that individuals in low-control jobs were more likely to experience long-term SA/DP compared to those in medium-control jobs. This suggests that low job control might be a stronger predictor of potentially detrimental labor market outcomes following SA due to CMDs than high job demands, when assessed separately. While the populations examined are not directly comparable, our findings are consistent with some of the previous studies suggesting a protective effect of increased job control against SA among individuals with CMDs [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and against delayed return to work in studies involving individuals already on SA due to CMDs [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Thus, future research should continue to examine both dimensions of job strain\u0026mdash;job demands and job control\u0026mdash;to identify aspects that may promote return to work and reduce SA rates in this population.\u003c/p\u003e\u003cp\u003eIt is also worthwhile noting that in this study, associations between job demands, control, and strain and SA/DP were only observed in the group with \u0026gt;\u0026thinsp;365 days of SA/DP during the three follow-up years. This finding highlights the importance of distinguishing between short-term and long-term SA/DP outcomes, as the latter could indicate the path to more permanent labor market marginalization and appear to be influenced by different factors than short-term outcomes, such as, for instance, health status, disorder severity, and work ability [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, while some individuals with \u0026gt;\u0026thinsp;365 SA/DP days may experience continuous long-term absence, others may have recurrent shorter SA spells, alternating between return to work and starting a new SA spell. These transitions could, at least partially, reflect a detrimental workplace environment.\u003c/p\u003e\u003cp\u003eRegarding unemployment, a Swedish twin study found that higher levels of job control were associated with a reduced risk of subsequent unemployment in the general population [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, to the best of our knowledge, our study is the first to investigate how job demands, control, and strain affect subsequent unemployment outcomes specifically among individuals on SA due to CMDs. As with SA/DP outcomes, we found that high-strain (high demands/low control), passive (low demands/low control), and low-control jobs were associated with both medium and long-term unemployment in this population. Low control appears to be the common factor contributing to post-SA unemployment in this population, as was also suggested for the SA/DP outcomes. This may reflect a lack of flexibility over one\u0026rsquo;s working hours and pace, and limited opportunities to accommodate the needs of individuals on SA due to CMDs, hindering their return-to-work process and contributing to labor market marginalization [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is also plausible that jobs characterized by low control are inherently more precarious, leading to reduced physical and mental health and increased unemployment [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, regarding the associations between the structural occupational factors, namely occupational sector and branch, we found that individuals in the public sector were less likely to experience both medium and long-term unemployment but more likely to have long-term SA/DP following an SA spell due to a CMD. While not directly aligned given the differences in study populations, our results are consistent with some previous findings showing that workers in the public sector are more likely to have a CMD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and are also more likely to experience SA due to CMDs compared to those in the private sector [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Sweden, many companies, particularly within health and social care, are private companies contracted to deliver publicly financed health and social care. Therefore, we decided to also investigate how working in different branches might affect labor market outcomes after SA due to CMDs, as opposed to only examining the occupational sector. We found that working in the education and public administration branch, as well as in the health and social care branch, was associated with a lower likelihood of having both medium and long-term unemployment, yet a higher likelihood of experiencing medium and long-term SA/DP. The findings regarding the SA/DP outcomes are in line with several previous studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A lower likelihood of unemployment in the public sector, the education and public administration branch, and the health and social care branch is likely due to greater job security, more stable employment conditions, and a higher demand for skilled professionals. On the other hand, the higher likelihood of long-term SA/DP for the education and public administration branch and the health and social care branch can potentially reflect higher workplace mental strain [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eFirst, this large-scale, population-based study included 79,673 working-age individuals and used high-quality national register data, resulting in a representative sample that enhances the generalizability of the findings to the broader population with SA benefits due to CMDs. Second, since assessing unemployment alone tends to undermine labor market marginalization in this population, including health-related marginalization using SA/DP measures contributes to a more comprehensive understanding of how individuals with SA due to CMDs can be excluded from the labor market. Third, we assessed the psychosocial work environment at the occupational level using the SweJEM, which minimizes the subjectivity of the self-reported individual measures, as the individual scores were averaged out for all individuals within a specific occupational group. Lastly, we categorized job demands and job control into low, medium, or high, which allowed us to better understand how more extreme conditions contribute to unemployment and SA/DP following an SA spell due to a CMD. If we had dichotomized job demands and job control scores into low and high, we could have diluted the effects, since most individuals in this study and several previous studies fell in the middle of the score scales [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study also has several limitations. First, as the data from the Swedish Social Insurance Agency only covers SA spells exceeding 14 days, only those spells could be included. Second, because data from primary care settings were unavailable, our analyses did not include mental and somatic comorbidities diagnosed in these settings. Third, the occupational-level assessment of job demands, control, and strain meant that individual variation could not be captured. As a result, all individuals within the same occupation had identical values for job demands, control, and strain, even though their specific work environments may have differed. Finally, we were unable to assess potential changes in individuals\u0026rsquo; occupations during the follow-up. However, such occupational-level changes are likely uncommon.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eJob demands, job control, and job strain, and structural occupational factors, such as occupational sector and branch, play a role in shaping labor market outcomes following a sickness absence spell due to CMDs. Particularly, high-strain jobs, characterized by high demands and low control, and passive jobs, characterized by both low demands and low control, were found to be associated with medium and long-term unemployment and long-term sickness absence and disability pension, suggesting a potential path to labor market marginalization. Future studies are necessary to deepen our understanding of these associations and inform effective interventions aimed at promoting labor market inclusion for individuals with sickness absence due to CMDs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the 1964 Declaration of Helsinki. The approval was obtained from the Regional Ethical Review Board, Karolinska Institutet, Stockholm, Sweden (Dnr: 2007/762-31 and Dnr: 2024-08708-02), who also waived the need for informed consent.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by the Research Council for Health, Working Life and Welfare (Dnr:2022-00564). We utilized data from the REWHARD consortium supported by the Swedish Research Council (grant no. 2021-00154).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made substantial contributions to this work and reviewed the manuscript. G.S. contributed to the conceptualization of the study, wrote the original manuscript, prepared figures and tables, and edited and reviewed the text. E.M.-R. and K.G. contributed to the conceptualization of the study, reviewed and edited the manuscript, and provided resources and supervision. M.H. and K.F. contributed to the conceptualization of the study and reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study cannot be made publicly available due to privacy regulations. According to the General Data Protection Regulation, the Swedish law SFS 2018:218, the Swedish Data Protection Act, the Swedish Ethical Review Act, and the Public Access to Information and Secrecy Act, these types of sensitive data can only be made available for specific purposes, including research, that meets the criteria for access to this type of sensitive and confidential data as determined by a legal review. Readers may contact Professor Ellenor Mittendorfer-Rutz ([email protected]) regarding the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical approval was obtained from the Regional Ethical Review Board, Karolinska Institutet, Stockholm, Sweden (Dnr: 2007/762-31 and Dnr: 2024-08708-02), who also waived the need for informed consent.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Daniel Falkstedt, principal researcher and associate professor at the Institute of Environmental Medicine, Karolinska Institutet, and member of the Swedish Job Exposure Matrix (SweJEM) steering committee, for his collaboration and support with the SweJEM scores in this study. We also thank the rest of the SweJEM steering committee for their contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOECD. Sick on the job? Myths and realities about mental health and work. Mental Health and Work, OECD Publishing. 2012; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/9789264124523-en\u003c/span\u003e\u003cspan address=\"10.1787/9789264124523-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOECD. Health at a glance 2023. Health at a glance. OECD Publishing. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/7a7afb35-en\u003c/span\u003e\u003cspan address=\"10.1787/7a7afb35-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiihim\u0026auml;ki K, Vuorilehto M, Isomets\u0026auml; E. A 5-year prospective study of predictors for functional and work disability among primary care patients with depressive disorders. Eur Psychiatry. 2015;30(1):51\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eurpsy.2014.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.eurpsy.2014.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwedish Social Insurance Agency. Social insurance in Figs. 2024. Swedish Social Insurance Agency; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwedish Social Insurance Agency. Social insurance report 2020: sickness absence due to psychiatric diagnoses. Swedish Social Insurance Agency; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwedish Social Insurance Agency. Mental ill-health in today\u0026rsquo;s working life. Swedish Social Insurance Agency; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexanderson K, Kivim\u0026auml;ki M, Ferrie JE, Westerlund H, Vahtera J, Singh-Manoux A, et al. Diagnosis-specific sick leave as a long-term predictor of disability pension: a 13-year follow-up of the GAZEL cohort study. J Epidemiol Commun Health. 2012;66(2):155\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech.2010.126789\u003c/span\u003e\u003cspan address=\"10.1136/jech.2010.126789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelgesson M, Johansson B, Nordqvist T, Lundberg I, Ving\u0026aring;rd E. Sickness absence at a young age and later sickness absence, disability pension, death, unemployment and income in native Swedes and immigrants. Eur J Public Health. 2015;25(4):688\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurpub/cku250\u003c/span\u003e\u003cspan address=\"10.1093/eurpub/cku250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHultin H, Lindholm C, Malfert M, M\u0026ouml;ller J. Short-term sick leave and future risk of sickness absence and unemployment - the impact of health status. BMC Public Health. 2012;12(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-12-861\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-12-861\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStaland Nyman C, Andersson L, Spak F, Hensing G. Exploring consequences of sickness absence \u0026ndash; a longitudinal study on changes in self-rated physical health. Work. 2009;34(3):315\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/WOR-2009-0929\u003c/span\u003e\u003cspan address=\"10.3233/WOR-2009-0929\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFleuren BB, de Grip A, Jansen NW, Kant I, Zijlstra FR. Critical reflections on the currently leading definition of sustainable employability. Scand J Work Environ Health. 2016;42(6):557\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5271/sjweh.3585\u003c/span\u003e\u003cspan address=\"10.5271/sjweh.3585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelgesson M, Tingh\u0026ouml;g P, Wang M, Rahman S, Saboonchi F, Mittendorfer-Rutz E. Trajectories of work disability and unemployment among young adults with common mental disorders. BMC Public Health. 2018;18(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-018-6141-y\u003c/span\u003e\u003cspan address=\"10.1186/s12889-018-6141-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelgesson M, Gustafsson K, Leineweber C. Suffering of common mental disorders but still at work: a longitudinal study during periods of differences in regulations for having sick leave. J Occup Rehabil. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10926-025-10269-4\u003c/span\u003e\u003cspan address=\"10.1007/s10926-025-10269-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarasek RA. Job demands, job decision latitude, and mental strain: Implications for job redesign. Adm Sci Q. 1979;24(2):285\u0026ndash;308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2392498\u003c/span\u003e\u003cspan address=\"10.2307/2392498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarasek R, Theorell T. Healthy work: stress, productivity, and the reconstruction of working life. New York (N.Y): Basic Books; 1990.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Vries H, Fishta A, Weikert B, Rodriguez Sanchez A, Wegewitz U. Determinants of sickness absence and return to work among employees with common mental disorders: a scoping review. J Occup Rehabil. 2018;28(3):393\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10926-017-9730-1\u003c/span\u003e\u003cspan address=\"10.1007/s10926-017-9730-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuchaine CS, Aub\u0026eacute; K, Gilbert-Ouimet M, V\u0026eacute;zina M, Ndjabou\u0026eacute; R, Massamba V, et al. Psychosocial stressors at work and the risk of sickness absence due to a diagnosed mental disorder. JAMA Psychiatry. 2020;77(8):842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamapsychiatry.2020.0322\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2020.0322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelgesson M, Gustafsson K, Leineweber C. Psychosocial and organisational work factors as predictors of sickness absence among professionally active adults with common mental disorders. BMC Psychiatry. 2023;23(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12888-023-05020-3\u003c/span\u003e\u003cspan address=\"10.1186/s12888-023-05020-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMather L, Bergstr\u0026ouml;m G, Blom V, Svedberg P. High job demands, job strain, and iso-strain are risk factors for sick leave due to mental disorders. J Occup Environ Med. 2015;57(8):858\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/JOM.0000000000000504\u003c/span\u003e\u003cspan address=\"10.1097/JOM.0000000000000504\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVirtanen M, Vahtera J, Pentti J, Honkonen T, Elovainio M, Kivim\u0026auml;ki M. Job strain and psychologic distress. Am J Prev Med. 2007;33(3):182\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amepre.2007.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.amepre.2007.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarrants K, Norberg J, Framke E, Rugulies R, Alexanderson K. Job demands and job control and future labor market situation. J Occup Environ Med. 2020;62(6):403\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/JOM.0000000000001859\u003c/span\u003e\u003cspan address=\"10.1097/JOM.0000000000001859\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRopponen A, Wang M, Farrants K, Narusyte J, Svedberg P. Psychosocial working conditions and subsequent sickness absence\u0026mdash;effects of pain and common mental disorders in a population-based Swedish twin sample. J Occup Environ Med. 2022;64(6):451\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/JOM.0000000000002501\u003c/span\u003e\u003cspan address=\"10.1097/JOM.0000000000002501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlach PA, Groothoff JW, Krol B, Bultmann U. Factors associated with first return to work and sick leave durations in workers with common mental disorders. Eur J Public Health. 2012;22(3):440\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurpub/ckr102\u003c/span\u003e\u003cspan address=\"10.1093/eurpub/ckr102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolmlund L, B\u0026uuml;ltmann U, Bergstr\u0026ouml;m G, Warnqvist A, Bj\u0026ouml;rk Br\u0026auml;mberg E. Are psychosocial work factors and work-home interference associated with time to first full return-to-work after sick leave due to common mental disorders? Int Arch Occup Environ Health. 2023;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00420-023-01970-z\u003c/span\u003e\u003cspan address=\"10.1007/s00420-023-01970-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBj\u0026ouml;rkenstam E, Helgesson M, Gustafsson K, Virtanen M, Hanson LLM, Mittendorfer-Rutz E. Occupational class and employment sector differences in common mental disorders: a longitudinal Swedish cohort study. Eur J Pub Health. 2021;31(4):809\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurpub/ckab091\u003c/span\u003e\u003cspan address=\"10.1093/eurpub/ckab091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmin R, Mittendorfer-Rutz E, Bj\u0026ouml;rkenstam E, Virtanen M, Helgesson M, Gustafsson N, et al. Time period effects in work disability due to common mental disorders among young employees in Sweden\u0026mdash;a register-based cohort study across occupational classes and employment sectors. Eur J Pub Health. 2023;33(2):272\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurpub/ckad026\u003c/span\u003e\u003cspan address=\"10.1093/eurpub/ckad026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBj\u0026ouml;rkenstam E, Helgesson M, Gustafsson K, Virtanen M, Hanson LLM, Mittendorfer-Rutz E. Sickness absence due to common mental disorders in young employees in Sweden: are there differences in occupational class and employment sector? Soc Psychiatry Psychiatr Epidemiol. 2022;57(5):1097\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00127-021-02152-3\u003c/span\u003e\u003cspan address=\"10.1007/s00127-021-02152-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRantonen O, Alexanderson K, Pentti J, Kjeldg\u0026aring;rd L, H\u0026auml;m\u0026auml;l\u0026auml;inen J, Mittendorfer-Rutz E, et al. Trends in work disability with mental diagnoses among social workers in Finland and Sweden in 2005\u0026ndash;2012. Epidemiol Psychiatric Sci. 2017;26(6):644\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S2045796016000597\u003c/span\u003e\u003cspan address=\"10.1017/S2045796016000597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLudvigsson JF, Svedberg P, Ol\u0026eacute;n O, Bruze G, Neovius M. The longitudinal integrated database for health insurance and labour market studies (LISA) and its use in medical research. Eur J Epidemiol. 2019;34(4):423\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-019-00511-8\u003c/span\u003e\u003cspan address=\"10.1007/s10654-019-00511-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026Ouml;sterlund N. MiDAS - Sickness Benefit and Rehabilitation Allowance. Swedish Social Insurance Agency; 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLudvigsson JF, Andersson E, Ekbom A, Feychting M, Kim JL, Reuterwall C, et al. External review and validation of the Swedish national inpatient register. BMC Public Health. 2011;11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-11-450\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-11-450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrooke HL, Talb\u0026auml;ck M, H\u0026ouml;rnblad J, Johansson LA, Ludvigsson JF, Druid H, et al. The Swedish cause of death register. Eur J Epidemiol. 2017;32(9):765\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-017-0316-1\u003c/span\u003e\u003cspan address=\"10.1007/s10654-017-0316-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWettermark B, Hammar N, Fored CM, Leimanis A, Otterblad Olausson P, Bergman U, et al. The new Swedish Prescribed Drug Register\u0026mdash;opportunities for pharmacoepidemiological research and experience from the first six months. Pharmacoepidemiol Drug Saf. 2007;16(7):726\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pds.1294\u003c/span\u003e\u003cspan address=\"10.1002/pds.1294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwedish Social Insurance Agency. Privatperson [Internet]. Forsakringskassan.se. 2021. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.forsakringskassan.se\u003c/span\u003e\u003cspan address=\"https://www.forsakringskassan.se\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwedish Public Employment Service. Arbetsf\u0026ouml;rmedlingen. [Internet]. arbetsformedlingen.se. 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arbetsformedlingen.se\u003c/span\u003e\u003cspan address=\"https://arbetsformedlingen.se\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFredlund P, Hallqvist J, Diderichsen F. Psychosocial job exposure matrix. An updated version of a classification system for work-related psychosocial exposure. Swedish National Institute for Working Life; 2000. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/2077/4243\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/2077/4243\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJarroch R, Falkstedt D, Nevriana A, Pan KY, Kauhanen J, Almroth M. The role of job strain in the relationship between depression and long-term sickness absence: a register-based cohort study. Soc Psychiatry Psychiatr Epidemiol. 2024;59(11):2031\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00127-024-02700-7\u003c/span\u003e\u003cspan address=\"10.1007/s00127-024-02700-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorberg J, Alexanderson K, Framke E, Rugulies R, Farrants K. Job demands and control and sickness absence, disability pension and unemployment among 2,194,692 individuals in Sweden. Scand J Public Health. 2019;48(2):125\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1403494819846367\u003c/span\u003e\u003cspan address=\"10.1177/1403494819846367\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Board of Health and Welfare. Diagnoses [Internet]. Support in insurance medicine. 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://forsakringsmedicin.socialstyrelsen.se/beslutsstod-for-diagnoser/diagnoser/\u003c/span\u003e\u003cspan address=\"https://forsakringsmedicin.socialstyrelsen.se/beslutsstod-for-diagnoser/diagnoser/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSAS Institute Inc. SAS 9.4 (TS1M7) software. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing. Version 2023.12.1\u0026thinsp;+\u0026thinsp;402. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaine S, Gimeno D, Virtanen M, Oksanen T, Vahtera J, Elovainio M, et al. Job strain as a predictor of disability pension: the Finnish Public Sector Study. J Epidemiol Community Health. 2009;63(1):24\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech.2007.071407\u003c/span\u003e\u003cspan address=\"10.1136/jech.2007.071407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmroth M, Hemmingsson T, S\u0026ouml;rberg Wallin A, Kjellberg K, Burstr\u0026ouml;m B, Falkstedt D. Psychosocial working conditions and the risk of diagnosed depression: a Swedish register-based study. Psychol Med. 2022;52(15):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S003329172100060X\u003c/span\u003e\u003cspan address=\"10.1017/S003329172100060X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLidwall U, Marklund S. What is healthy work for women and men? \u0026ndash; A case-control study of gender- and sector-specific effects of psycho-social working conditions on long-term sickness absence. Work. 2006;27(2):153\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/WOR-2006-00558\u003c/span\u003e\u003cspan address=\"10.3233/WOR-2006-00558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNetterstr\u0026oslash;m B, Eller NH, Borritz M. Prognostic factors of returning to work after sick leave due to work-related common mental disorders: a one- and three-year follow-up study. Biomed Res Int. 2015;2015:1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2015/596572\u003c/span\u003e\u003cspan address=\"10.1155/2015/596572\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnutsen RH, Nielsen MB, Lunde LK, Skare \u0026Oslash;, Johannessen HA. Impact of psychosocial work factors on risk of medically certified sick leave due to common mental disorders: a nationwide prospective cohort study of Norwegian home care workers. BMC Public Health. 2024;24(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-18299-y\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-18299-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGragnano A, Negrini A, Miglioretti M, Corbi\u0026egrave;re M. Common psychosocial factors predicting return to work after common mental disorders, cardiovascular diseases, and cancers: a review of reviews supporting a cross-disease approach. J Occup Rehabil. 2018;28(2):215\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10926-017-9714-1\u003c/span\u003e\u003cspan address=\"10.1007/s10926-017-9714-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRose U, Kersten N, Pattloch D, Conway PM, Burr H. Associations between depressive symptoms and 5-year subsequent work nonparticipation due to long-term sickness absence, unemployment and early retirement in a cohort of 2,413 employees in Germany. BMC Public Health. 2023;23(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-023-17090-9\u003c/span\u003e\u003cspan address=\"10.1186/s12889-023-17090-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang M, Svedberg P, Narusyte J, Farrants K, Ropponen A. Effects of age on psychosocial working conditions and future labour market marginalisation: a cohort study of 56,867 Swedish twins. Int Arch Occup Environ Health. 2022;95(1):199\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00420-021-01704-z\u003c/span\u003e\u003cspan address=\"10.1007/s00420-021-01704-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePy\u0026ouml;ri\u0026auml; P, Ojala S. Precarious work and intrinsic job quality: evidence from Finland, 1984\u0026ndash;2013. Economic Labour Relations Rev. 2016;27(3):349\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1035304616659190\u003c/span\u003e\u003cspan address=\"10.1177/1035304616659190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Population Baseline Characteristics (n = 79,673).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e56,906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e71.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e22,767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e9,481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e12,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;35-39\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;40-44\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;45-49\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;50-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e15,384\u003c/p\u003e\n \u003cp\u003e15,319\u003c/p\u003e\n \u003cp\u003e13,936\u003c/p\u003e\n \u003cp\u003e13,042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of birth:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Sweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e69,125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e86.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other Nordic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1,774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4,101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Rest of the World\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Elementary (\u0026lt; 10 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e6,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;High school (10-12 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e36,586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e45.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;University or college (\u0026gt; 12 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e36,397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily composition:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Living without a partner and without children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e26,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e32.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Living without a partner and with children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e9,086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Living with a partner and without children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e9,982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Living with a partner and with children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e34,594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of living area:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e31,541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Town or suburban area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e33,491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Rural area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e14,641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational sector:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Private\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e45,983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e33,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational branch:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Production, industrial, and resource-based:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e10,965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Agriculture, forestry, and fishing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e373\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Manufacturing and extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e7,147\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e9.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Energy supply and environmental activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e594\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Construction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e2,851\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e3.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Trade, transport, and storage:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e12,438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Trade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e9,106\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e11.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Transport and storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e3,332\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e4.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Information, financial, and business services:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e14,459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Information and communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e3,070\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e3.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Finance and insurance activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e1,620\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e2.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Real estate activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e830\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e1.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Business services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e8,939\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e11.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hospitality and personal services:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e3,639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hotel and restaurant operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e1,827\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e2.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cultural and personal services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e1,812\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e2.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Education and public administration:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e17,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Public administration and defense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e5,590\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e7.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Training and education \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e12,362\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u003cem\u003e15.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Health and social services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e20,220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob demands (SweJEM):\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e12,059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e37,478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e30,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob control (SweJEM):\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e26,656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e30,621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e22,396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob strain (SweJEM):\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low-strain jobs (low demands/high control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Active jobs (high demands/high control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e12,302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Intermediate strain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e57,541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e72.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Passive jobs (low demands/low control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e6,371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; High-strain jobs (high demands/low control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e3,273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny unemployment days:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e6,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e73,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSA history:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0 days (no SA history)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e67,750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0 days \u0026lt; SA history \u0026le; 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4,878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 30 days \u0026lt; SA history \u0026le; 90 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4,183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 90 days \u0026lt; SA history \u0026le; 180 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1,781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 180 days \u0026lt; SA history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1,081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDP history:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e79,304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e99.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex SA diagnosis:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Depressive disorders (F32-F33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e29,837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Anxiety disorders and OCD (F40-F41 and F42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e9,937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Stress-induced disorders (F43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e39,899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex SA grade:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Full-time (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e71,870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e90.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Part-time (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1,082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Part-time (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e5,763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Part-time (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNet index SA days:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 30 days \u0026lt; SA days \u0026le; 90 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e40,806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 90 days \u0026lt; SA days \u0026le; 180 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e17,601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 180 days \u0026lt; SA days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e21,266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental comorbidities:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other CMDs and burn-out (F32-F33, F40-F41, F42, F33, and Z730)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e3,723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Substance use disorders (F10-F19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1,257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other affective disorders (F34, F38, and F39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Personality disorders (F60-F69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Attention-deficit hyperactivity disorder (ADHD) (F90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other behavioral emotional disorders (F50-F59, excl. F50.0, F90.1-F98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other psychological developmental disorders and autism-spectrum disorder (F80\u0026ndash;F89, excl. F84.3\u0026ndash;F84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other mental comorbidities (F04-F09, F44-F48, and F99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSomatic comorbidities:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cancer (C00-D48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4,075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Endocrine, nutritional, and metabolic disorders (E00-E90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e3,234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Neurological disorders (G00-G99, excl. G30-G32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e2,884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Circulatory system disorders (I00-I99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e3,191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Respiratory disorders (J00-J99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e3,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Musculoskeletal disorders (M00-M99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e7,514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other chronic somatic disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e23,330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003eAntidepressants (ATC: N06A):\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No dispensations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e61,560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e77.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; One dispensation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e9,920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Two or more dispensations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e8,193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003eOther psychotropic medications:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003eNo dispensations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e63,189\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e79.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73.7201%;\"\u003e\n \u003cp\u003eOne or more dispensations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e16,484\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1399%;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea. Abbreviations (in alphabetical order): ADHD = Attention Deficit Hyperactivity Disorder, ATC = Anatomical Therapeutic Chemical, CMDs = Common Mental Disorders, DP = Disability Pension, OCD = Obsessive Compulsive Disorder, SA = Sickness Absence, SweJEM = The Swedish Job Exposure Matrix.\u003c/p\u003e\n\u003cp\u003eb. Measurement timepoints: Age, education level, family composition, type of living area, occupation, and occupational sector and branch were assessed on December 31 of the year before the index SA began. UE days were measured for the entire year preceding the start of the index SA spell. SA/DP history was assessed in the two-year period prior to the exclusion window, which began one year before the index SA. Mental and somatic comorbidities were assessed within two years of the start of the index SA spell, and medication use was assessed six months before and three months after the index SA spell started.\u003c/p\u003e\n\u003cp\u003ec. Missing information was classified as the lowest education level \u0026ndash; \u0026ldquo;elementary education (\u0026lt;10 years)\u0026rdquo; (n = 186 (0.23%)).\u003c/p\u003e\n\u003cp\u003ed. Intermediate job strain: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control.\u003c/p\u003e\n\u003cp\u003ee. All diagnostic codes refer to the 10th version of the International Classification of Diseases (ICD-10) codes.\u003c/p\u003e\n\u003cp\u003ef.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eOther chronic somatic disorders: infectious and parasitic diseases (ICD-10: A00-B99), diseases of the blood and blood-forming organs (ICD-10: D50-D89), diseases of the eye and ear (ICD-10: H00-H95), diseases of the digestive system (ICD-10: K00-K93), diseases of the skin and subcutaneous tissue (ICD-10: L00-L99), and diseases of the genitourinary system (ICD-10: N00-N99).\u003c/p\u003e\n\u003cp\u003eg.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eOther psychotropic medications: anxiolytics (ATC: N05B), hypnotics and sedatives (ATC: N05C), psychostimulants, ADHD agents, and nootropics (ATC: N06B), psycholeptics and psychoanaleptics in combination (ATC: N06C).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Multinomial Logistic Regression Results for Unemployment (UE) Days (n = 78,900).\u003c/strong\u003e Model 1 was adjusted for sex and age, Model 2 was additionally adjusted for education, country of birth, and type of living area, and Model 3 was further adjusted for index SA diagnosis, SA/DP history, and mental and somatic comorbidities. Reference category: 0 UE days during the follow-up period.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational Characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude OR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob strain:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Intermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Active jobs (high demands/high control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.79 (0.74-0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.79 (0.74-0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.88 (0.83-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.91 (0.85-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.89 (0.83-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.84 (0.78-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.98 (0.91-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.01 (0.93-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Passive jobs (low demands/low control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.44 (1.34-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.31 (1.22-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.14 (1.06-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.11 (1.03-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e2.28 (2.11-2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.83 (1.70-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.50 (1.39-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.48 (1.36-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High-strain jobs (high demands/low control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.48 (1.34-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.37 (1.25-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.28 (1.17-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.27 (1.15-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.67 (1.50-1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.38 (1.23-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.22 (1.09-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.21 (1.08-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob demands:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.17 (1.10-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.07 (1.01-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.01 (0.95-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.98 (0.92-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.55 (1.46-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.25 (1.17-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.15 (1.07-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.13 (1.05-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.72 (0.69-0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.73 (0.70-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.83 (0.79-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.84 (0.80-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.69 (0.66-0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.68 (0.64-0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.80 (0.76-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.81 (0.77-0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob control:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.41 (1.34-1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.36 (1.30-1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.20 (1.14-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.18 (1.12-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.79 (1.69-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.72 (1.62-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.42 (1.34-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.40 (1.32-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.85 (0.81-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.83 (0.79-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.89 (0.84-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.91 (0.86-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.06 (0.99-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.98 (0.92-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.08 (1.01-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.10 (1.03-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob sector:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Private\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.44 (0.42-0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.45 (0.43-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.47 (0.45-0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.47 (0.45-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.30 (0.28-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.33 (0.31-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.36 (0.34-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.36 (0.34-0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational branch:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Production, industrial, and resource-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Trade, transport, and storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.28 (1.19-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.27 (1.19-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.26 (1.17-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.26 (1.17-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.17 (1.08-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.25 (1.15-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.19 (1.09-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.19 (1.10-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Information, financial, and business services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.10 (1.03-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.11 (1.03-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.18 (1.10-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.18 (1.10-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.08 (0.99-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.19 (1.10-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.25 (1.15-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.25 (1.15-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hospitality and personal services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.47 (1.33-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.46 (1.32-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.44 (1.30-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.42 (1.28-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.33 (1.19-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.49 (1.33-1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.37 (1.22-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e1.36 (1.21-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Education and public administration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.59 (0.55-0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.62 (0.57-0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.71 (0.65-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.70 (0.65-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.37 (0.34-0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.44 (0.40-0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.52 (0.47-0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.52 (0.47-0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Health and social services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e0 \u0026lt; UE Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.66 (0.61-0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.69 (0.64-0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.71 (0.66-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.69 (0.64-0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026gt; 180 UE Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.43 (0.40-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.53 (0.48-0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.54 (0.49-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e0.53 (0.48-0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6709%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.738%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea. Individuals with low job strain (low demands/low control) were excluded due to low observation counts (n = 183 (0.23%)).\u003c/p\u003e\n\u003cp\u003eb. Intermediate job strain: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control.\u003c/p\u003e\n\u003cp\u003ec. Production, industrial, and resource-based branch: agriculture, forestry, fishing, manufacturing, extraction, energy supply, environmental services, and construction.\u003c/p\u003e\n\u003cp\u003ed. Information and business services: information, communication, finance, insurance, and real estate activities and business services.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multinomial Logistic Regression Results for Sickness Absence and Disability Pension (SA/DP) Days (n = 78,967).\u003c/strong\u003e Model 1 was adjusted for sex and age, Model 2 was additionally adjusted for education, country of birth, and type of living area, and Model 3 was further adjusted for index SA diagnosis, SA/DP history, and mental and somatic comorbidities. Reference category: 30 \u0026lt; SA/DP Days \u0026le; 90 during the follow-up period.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational Characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude OR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob strain:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Intermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Active jobs (high demands/high control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.96 (0.91-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.98 (0.93-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.98 (0.93-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.00 (0.95-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.95 (0.90-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.98 (0.93-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.01 (0.95-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.04 (0.98-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.75 (0.71-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.76 (0.72-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.85 (0.80-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.90 (0.85-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Passive jobs (low demands/low control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.92 (0.86-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.98 (0.91-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.98 (0.91-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.96 (0.89-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.88 (0.81-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.00 (0.93-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.98 (0.90-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.95 (0.88-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.16 (1.08-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.31 (1.22-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.14 (1.06-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.09 (1.01-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High-strain jobs (high demands/low control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.99 (0.90-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.05 (0.95-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.05 (0.95-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.04 (0.94-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.94 (0.85-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.05 (0.95-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.04 (0.94-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.02 (0.92-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.14 (1.03-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.26 (1.15-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.20 (1.08-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.15 (1.04-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob demands:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.97 (0.91-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.03 (0.97-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.02 (0.97-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.01 (0.95-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.93 (0.87-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.05 (0.99-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.03 (0.97-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.01 (0.95-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.10 (1.04-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.24 (1.17-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.15 (1.08-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.10 (1.03-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.01 (0.97-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.01 (0.97-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.02 (0.98-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.03 (0.99-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.01 (0.96-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.01 (0.97-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.04 (0.99-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.06 (1.01-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.85 (0.82-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.85 (0.81-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.95 (0.91-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.99 (0.94-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob control:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.98 (0.94-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.99 (0.95-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.99 (0.94-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.97 (0.93-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.98 (0.93-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.00 (0.96-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.98 (0.93-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.95 (0.91-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.23 (1.18-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.28 (1.22-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.15 (1.10-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.10 (1.05-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.95 (0.90-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.96 (0.92-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.97 (0.92-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.99 (0.94-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.91 (0.87-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.94 (0.90-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.96 (0.92-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.99 (0.94-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.75 (0.72-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.77 (0.73-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.81 (0.77-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.86 (0.82-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob sector:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Private\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.09 (1.05-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.05 (1.00-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.05 (1.01-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.04 (1.00-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.22 (1.18-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.11 (1.07-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.13 (1.09-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.12 (1.07-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.12 (1.08-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.02 (0.98-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.11 (1.07-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.09 (1.05-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational branch:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Production, industrial, and resource-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Trade, transport, and storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.02 (0.95-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.99 (0.92-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.99 (0.93-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.99 (0.93-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.07 (0.99-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.02 (0.95-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.03 (0.95-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.03 (0.95-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.19 (1.10-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.16 (1.08-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.16 (1.07-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.16 (1.08-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Information, financial, and business services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.97 (0.91-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.93 (0.88-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.94 (0.88-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.95 (0.89-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.06 (0.98-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.99 (0.92-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.02 (0.95-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.03 (0.96-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.07 (1.00-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.03 (0.96-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.13 (1.05-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.13 (1.05-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hospitality and personal services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.98 (0.88-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.93 (0.84-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e0.94 (0.85-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.94 (0.85-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.06 (0.95-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e0.98 (0.88-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.00 (0.89-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.99 (0.89-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.45 (1.31-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.39 (1.26-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.40 (1.26-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.36 (1.23-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Education and public administration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.10 (1.03-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.02 (0.96-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.04 (0.97-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.04 (0.97-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.24 (1.16-1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.08 (1.01-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.13 (1.05-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.13 (1.05-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.16 (1.08-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.04 (0.97-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.22 (1.14-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.22 (1.13-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Health and social services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e90 \u0026lt; SA/DP Days \u0026le; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.08 (1.02-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.00 (0.94-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.01 (0.95-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e0.99 (0.93-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e180 \u0026lt; SA/DP Days \u0026le; 365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.29 (1.21-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.11 (1.04-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.14 (1.06-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.10 (1.03-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.622%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1818%;\"\u003e\n \u003cp\u003e\u0026gt; 365 SA/DP Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.36 (1.28-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4402%;\"\u003e\n \u003cp\u003e1.21 (1.13-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5997%;\"\u003e\n \u003cp\u003e1.29 (1.20-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7161%;\"\u003e\n \u003cp\u003e1.23 (1.14-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea. Individuals with low job strain (low demands/low control) were excluded due to low observation counts (n = 184 (0.24%)).\u003c/p\u003e\n\u003cp\u003eb. Intermediate job strain: low demands/medium control, medium demands/low control, medium demands/medium control, medium demands/high control, high demands/medium control.\u003c/p\u003e\n\u003cp\u003ec. Production, industrial, and resource-based branch: agriculture, forestry, fishing, manufacturing, extraction, energy supply, environmental services, and construction.\u003c/p\u003e\n\u003cp\u003ed. Information, financial, and business services: information, communication, finance, insurance, and real estate activities and business services.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joor","sideBox":"Learn more about [Journal of Occupational Rehabilitation](https://www.springer.com/journal/10926)","snPcode":"10926","submissionUrl":"https://submission.nature.com/new-submission/10926/3","title":"Journal of Occupational Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Depression, Anxiety Disorders, Stress Disorders, Sick Leave, Disability Pension, Job Strain","lastPublishedDoi":"10.21203/rs.3.rs-7583607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7583607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo investigate how job demands, control, strain, and occupational sector and branch affect labor market outcomes following sickness absence (SA) due to common mental disorders (CMDs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis nationwide register-based cohort study included all residents in Sweden aged 25\u0026ndash;55 who began a new\u0026thinsp;\u0026gt;\u0026thinsp;30-day SA spell due to a CMD (ICD-10: F32-33, F40-43) in 2011\u0026ndash;2013 (n\u0026thinsp;=\u0026thinsp;79,673). Occupational sector and branch were identified through registers, and job demands, control, and strain were assessed using the Swedish Job Exposure Matrix. We used multinomial logistic regression to estimate associations between occupational factors and different unemployment and SA/disability pension (DP) durations during a three-year follow-up.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePublic sector workers were less likely to have \u0026gt;\u0026thinsp;180 unemployment days (OR\u0026thinsp;=\u0026thinsp;0.3, 95% CI: 0.31\u0026ndash;0.35). Working in education and public administration and in health and social services was associated with a lower likelihood of \u0026gt;\u0026thinsp;180 unemployment days, but a higher likelihood of \u0026gt;\u0026thinsp;365 SA/DP days. Low-control, passive (low control/low demands), and high-strain (low control/high demand) jobs were associated with an increased likelihood of both \u0026gt;\u0026thinsp;180 unemployment days and \u0026gt;\u0026thinsp;365 SA/DP days. For \u0026gt;\u0026thinsp;180 unemployment days, the ORs were 1.7 (95% CI: 1.62\u0026ndash;1.82) for low-control, 1.8 (95% CI: 1.70\u0026ndash;1.98) for passive, and 1.4 (95% CI: 1.23\u0026ndash;1.54) for high-strain jobs. For \u0026gt;\u0026thinsp;365 SA/DP days, the ORs were 1.3 (95% CI: 1.22\u0026ndash;1.34), 1.3 (95% CI: 1.22\u0026ndash;1.41), and 1.3 (95% CI: 1.15\u0026ndash;1.39), respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eParticularly among individuals with SA due to CMDs, job demands, control, and strain are associated with future labor market exclusion and may be important targets for intervention.\u003c/p\u003e","manuscriptTitle":"Occupational Factors and Labor Market Outcomes Among Individuals with Sickness Absence due to Common Mental Disorders: A Population-Wide Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 09:40:31","doi":"10.21203/rs.3.rs-7583607/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-08T15:00:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T14:24:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-16T06:42:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105294007725358324081290198756706421350","date":"2025-09-13T13:44:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13762111202817655337165155926019648561","date":"2025-09-13T12:29:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T12:57:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T14:24:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T14:24:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Occupational Rehabilitation","date":"2025-09-10T13:51:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joor","sideBox":"Learn more about [Journal of Occupational Rehabilitation](https://www.springer.com/journal/10926)","snPcode":"10926","submissionUrl":"https://submission.nature.com/new-submission/10926/3","title":"Journal of Occupational Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"21406c7c-1d40-4da3-a72b-e5ce8e26edae","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:04:37+00:00","versionOfRecord":{"articleIdentity":"rs-7583607","link":"https://doi.org/10.1007/s10926-025-10348-6","journal":{"identity":"journal-of-occupational-rehabilitation","isVorOnly":false,"title":"Journal of Occupational Rehabilitation"},"publishedOn":"2025-11-27 15:58:28","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-09-19 09:40:31","video":"","vorDoi":"10.1007/s10926-025-10348-6","vorDoiUrl":"https://doi.org/10.1007/s10926-025-10348-6","workflowStages":[]},"version":"v1","identity":"rs-7583607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7583607","identity":"rs-7583607","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}