Jenni Blomgren

Objectives: The rise in mental disorders in Organisation for Economic Co-operation and Development (OECD) countries warrants close consideration, as mental disorders cause long-term sickness absences (LTSA) and increase the risk of disability pensioning (DP). This study compared how different mental disorders as a basis for LTSA pose risks for a subsequent DP. Design: Population-based prospective cohort study. Setting and participants: 18–59-year-old Finns starting a new psychiatric LTSA in 2020. LTSA was measured through sickness allowance accumulated over 12 months. A 48-month follow-up started from the onset of LTSA. Six mental disorder types were identified. Outcome measures: The outcome variable was the time in months to a first paid DP spell. Using Cox proportional hazards regression stratified by sex, the DP risks of different mental disorders were examined, adjusting for comorbidity, length of LTSA and sociodemographic variables. The risks are presented as absolute risk differences and both unadjusted and covariate-adjusted HRs. Results: Compared with depression-related LTSA, psychoses (HRs 2.93 (95% CI 2.55 to 3.36) for women/2.99 (95% CI 2.67 to 3.36) for men) and other mood disorders (HRs 1.64 (95% CI 1.40 to 1.92)/1.49 (95% CI 1.25 to 1.78)) were associated with higher covariate-adjusted HRs, while anxiety disorders (HRs 0.65 (95% CI 0.59 to 0.72)/0.76 (95% CI 0.67 to 0.87)) and psychiatric sleep disorders (HRs 0.47 (95% CI 0.38 to 0.59)/0.58 (95% CI 0.44 to 0.78)) showed lower HRs. Higher age, lower education and socioeconomic status, comorbid mental disorders, chronic diseases and longer LTSA increased the risk. Conclusions: Different mental disorders as basis for LTSA pose differing risks for permanent work disability. Among mental disorders, depression poses a relatively average risk for DP. Although anxiety and sleep disorders commonly cause LTSA, they are not associated with high DP risks. Psychotic and bipolar disorders are especially detrimental to work ability in today’s work life.

Long-term sickness absences (LTSA) are often linked to mental disorders. Up-to-date register-based evidence on the prevalence and trends of LTSA based on mental disorders is lacking, as is understanding of socioeconomic differences. This study examines the trends in prevalence of LTSA based on mental disorders in various socioeconomic groups both employed and outside employment. Finnish 18–67 years old non-retired residents were included in yearly study populations for 2010–2023. LTSA prevalence was examined through receipt of compensated sickness allowance. Yearly age-adjusted prevalences of LTSA were calculated for six socioeconomic groups using direct standardization. Adjusting for various covariates, relative risks (RR) between socioeconomic groups in LTSA prevalences were then analysed for 2010 and 2023 using Modified Poisson regression models. All analyses were run separately for both sexes, and for all mental disorders combined, for mood disorders, anxiety disorders, and other mental disorders combined. The prevalence of LTSA based on mental disorders increased from 2010 to 2023 among all socioeconomic groups and for both sexes, but especially among lower non-manual female employees due to anxiety disorders, and among students and unemployed persons due to mood disorders. Compared to upper non-manual employees, the higher covariate-adjusted relative risks for LTSA based on mental disorders in lower non-manual employees increased slightly during the study period (women: RRs 1,13 to 1,25; men: RRs 1,20 to 1,30). While age-group-standardized trends showed an approximately similar risk for manual workers compared to upper non-manual employees, covariate-adjusted modelling revealed a slightly lower relative risk, most apparent for anxiety disorders (RR in 2023 0,79). Entrepreneurs had a consistently lower relative risk compared to other groups. The prevalence of long-term sickness absences due to mood and anxiety disorders have increased among all socioeconomic groups, but register data also reveals group differences in prevalences and trends. Factors related to covid-19 pandemic, intensity of job demands, and buffering resources may explain these differing trends. Employees, but also students and unemployed persons need support to curb the increase in LTSA due to mental disorders.

Objectives To examine the prevalence and days of long-term sickness absence (LTSA) by occupational class and by most important diagnostic groups in Finland during 2011–2021. Design Population-based cross-sectional study. Setting and participants National comprehensive register data were linked for all employed persons and entrepreneurs in Finland aged 25–64 for years 2011–2021 (yearly number of individuals in the study population around 2 million persons). Main outcome measures LTSA was measured by sickness allowance that covers over 10-day long absences. Yearly age-standardised LTSA prevalences and average number of LTSA days were calculated for women and men in four occupational classes, separately for all-cause LTSA and LTSA due to mental disorders, musculoskeletal diseases and injuries. Modified Poisson regression and negative binomial regression models were run to assess relative differences between occupational classes, adjusted for age, marital status, education and region of residence. Results All-cause LTSA slightly decreased between years 2011 and 2021, but the trends varied by occupational class and diagnostic group. LTSA due to mental disorders increased in all occupational classes after 2016 among both sexes, while LTSA due to musculoskeletal diseases and injuries continued to decrease in all occupational classes. The increase in LTSA due to mental disorders was largest among lower non-manual employees, especially among women, whereby all-cause LTSA prevalence among female lower non-manual employees reached the level of female manual workers. Men showed broadly similar trends, but manual workers still had the highest all-cause LTSA prevalence at the end of the study period. The main results were similar adjusted for covariates. Conclusions The magnitude and order of the occupational-class differences in LTSA changed between 2011 and 2021, along with increasing LTSA due to mental disorders, especially among employees, and decreasing LTSA due to somatic diagnoses, especially among manual workers. Occupational-class differences should be taken into account when aiming to prevent LTSA and especially further increases in LTSA due to mental disorders.

Background: Sickness absence (SA) due to mental disorders has strongly increased in Finland during recent years while sickness absence due to somatic diagnoses has continued to decrease. Research is lacking on recent trends in SA by occupational class, especially during years of increasing mental disorders. The aim of the study is to analyze these trends during 2011-2021. Methods: National register data from the Social Insurance Institution of Finland (Kela), Statistics Finland and Finnish Centre for Pensions were linked for all employed Finns aged 25-64 for years 2011-2021 (yearly N about 2 million). SA was measured by sickness allowance, compensated by Kela after 10 days of absence. Age-standardized yearly prevalences of SA due to different diagnoses were calculated for women and men in four occupational classes over 2011-2021. Logistic regression was used to assess differences between occupational classes adjusted for age, marital status, education and region of residence. Results: SA due to mental disorders has greatly increased in all occupational classes in Finland after year 2016 among both women and men, while SA due to musculoskeletal and other somatic diagnoses has continued to decrease. Increase in SA due to mental disorders has been largest among lower non-manual employees, especially among women, whereby also overall SA prevalence among female lower non-manual employees has reached the level of manual workers. Men show broadly similar trends but manual workers still have the highest overall SA prevalence. Relative adjusted differences between occupational classes decreased slightly between 2011 and 2021. Conclusions: Clear occupational class differences persisted in diagnosis-specific SA throughout 2011-2021. However, the magnitude and order of the class differences changed along with increasing SA due to mental disorders. Occupational class differences should be taken into account when aiming to prevent further increases in SA due to mental disorders. Key messages: • Sickness absence (SA) due to mental disorders has increased in all occupational classes in Finland after year 2016, while SA due to somatic diagnoses has continued to decrease. • Increase in SA due to mental disorders has changed the overall occupational class differences in SA in Finland largely because the increase has been strongest among lower non-manual employees.

Aims: Large differences exist in the risk of disability retirement between Finnish municipalities. This study examined whether individual-level and municipality-level characteristics explain these differences and which municipality-level characteristics are particularly important for the risk of disability retirement. Methods: Individual-level register data were supplemented with 10 municipality-level characteristics from various databases. A 20% sample of the Finnish population ( N =626,391) was followed for transition to disability retirement from 2016 to 2019 using multilevel Weibull models. Results: Of the total variation in the risk of disability retirement, 4.3% was attributed to the municipal-level and decreased to 1.8% when individual-level characteristics (gender, age, education level and entitlement to special reimbursement for medical expenses, reflecting morbidity) were controlled for. Further adjustment for municipality-level characteristics fully erased the differences between municipalities. The proportion of municipality-level variation was larger for disability retirement due to somatic illnesses than mental disorders. Of the municipality-level characteristics, socioeconomic structure, unemployment rate, poverty, net migration between municipalities, dependency ratio, the amount of tax revenue per capita and morbidity were associated with the risk of disability retirement. Conclusions: The municipality-level variation in the risk of disability retirement is largely explained by the individual characteristics of the inhabitants. However, various characteristics of the municipalities show associations with the risk of disability retirement. Recognizing such factors is essential for shaping policies that mitigate disability retirement risk.

Objectives This study followed the labour market pathways of unemployed persons who started a sickness absence (SA) spell. We aimed to unravel subgroups based on altering labour market states and to identify covariates of these subgroups. Design Register-based longitudinal study, with nine labour market states and 36-month units. Setting and participants All Finnish persons aged 18–59 years with an SA in 2016 who were unemployed at the start of the SA spell (N=12 639). Outcome measures Sequence analysis was used to study transitions between nine labour market states based on monthly register data on permanent and temporary (full and partial) disability pensions (DP), rehabilitation, all-cause SA, unemployment and employment. Individuals were grouped into clusters based on cluster analysis and intersequence distances. Multinomial regression analysis was used to examine covariates of cluster memberships. Results Six clusters with the following pathway identities were found: (1) recurring unemployment (44%); (2) employment after a short SA (18%); (3) rehabilitation, recurring SA and unemployment (12%); (4) unknown sources of income (11%); (5) permanent DP after a prolonged SA (9%) and (6) temporary DP after a prolonged SA (7%). Compared with the reference cluster 2, all other clusters were associated with less employment days and having a chronic illness before the SA spell, SA based on a mental disorder and a rejected DP application during the follow-up. In addition, the clusters had some unique covariates. Conclusions Unemployed persons starting an SA are a heterogeneous group, with different labour market pathways. For many, the combination of unemployment and work disability means low chances for employment or regained work ability during the following years. Unemployed persons with poorer health, long history outside employment, older age, low educational level, a rejected DP application and a mental disorder could benefit from targeted support.

A thorough understanding of the use of services in the population is important in order to comprehend the varying service needs of different groups. This explorative study aimed to find distinct user profiles in a working-age population based on individuals’ annual use of healthcare, social and employment services and to explore socio-demographic and morbidity-related predictors of the user groups. Administrative register data on the use of various services and individual-level covariates from year 2018 were linked for all residents aged 18–64 of the municipality of Oulu, Finland (N = 119,740). K-means cluster analysis was used to group the study subjects into clusters, based on their frequency of using 22 distinct healthcare, social and employment services during 2018. Multinomial logistic regression models were utilized to assess the associations of cluster assignment with socio-demographic and health-related covariates (sex, age, marital status, education, occupational class, income, days in employment, chronic disease and receipt of different social benefits). Five distinct clusters were identified in terms of service use, labelled low to moderate users of healthcare (82.0%), regular employment services users with moderate use of healthcare (9.6%), supported employment services users with moderate use of healthcare with an emphasis on preventive care (2.9%), frequent users of healthcare, social and employment services (2.9%), and rehabilitation, disability services and specialized healthcare users (2.6%). Each cluster not only showed different patterns of service use but were also differently associated with demographic, socio-economic and morbidity-related covariates, creating distinct service user types. Knowledge on the different user profiles and their determinants may help predict future need and use of services in a population, plan timely, coordinated and integrated services, and design early interventions and prevention measures. This is important in order to save costs and improve the effectiveness of services for groups with different care needs.

Objectives The use of part-time sickness absence (pSA) enables return to part-time work from full sickness absence. However, subsequent labour market outcomes of pSA users depend on various individual and work-related characteristics. We investigated labour market paths of private and public sector employees after having a pSA spell. Moreover, we examined individual and work-related factors associated with following them. Design Longitudinal register-based cohort study. Setting Finnish employed population. Participants 9896 receivers of partial sickness allowance aged 45–56 in the years 2010–2014. Outcome We constructed labour market trajectories based on the proportion of time spent in various labour market statuses measured over 3 years after the end of the pSA spell using multiresponse trajectory analysis. We then examined how different individual and work-related factors were associated with assignment to the different trajectory groups using logistic regression analyses. Results The majority of the pSA users followed paths where work participation was consistently elevated (Sustained Work group, 40.4%), or only slightly reduced (Slightly Reduced Work group, 31.6%). Moreover, more than 1/10th of the users followed a path where receiving partial work disability benefits became predominant (Partial Work Disability group, 12.5%). The rest followed paths where other non-employment (Other Non-Employed group, 7.8%) or full work disability (Full Work Disability group, 7.7%) became the prevailing status. Lower educational level and income predicted assignment to all other groups than the Sustained Work group. Additional predictors were identified, yet these differed between the trajectory groups. Conclusions The majority of the pSA users maintained a connection to working life, yet weaker working life paths were also identified. The paths were determined by various individual and work-related factors that can help health professionals and employers to better target support measures particularly towards individuals whose connection to working life is at risk to weaken after the use of pSA.

Background: Understanding the population's overall use of healthcare, social and employment services is important for comprehending service needs and improving the coordination of services. The aim of this exploratory study was to find separate groups in a working-age population based on individuals’ use of various healthcare, social and employment services during one year and to investigate sociodemographic and health-related predictors of user groups. Methods: Administrative register data on the use of 22 distinct healthcare, social and employment services and on socio-demographic and health-related covariates from year 2018 were linked for all working-age residents of the municipality of Oulu, Finland (N = 119,740). K-means cluster analysis was used to group the study subjects into clusters, based on their yearly frequency of using each service. Multinomial logistic regression was used to assess the associations of sociodemographic and health-related covariates with cluster assignment. Results: Five distinct clusters were identified, labelled ‘low to moderate users of healthcare’ (82%), ‘regular employment services users with moderate use of healthcare’ (10%), ‘supported employment services users’ (3%), ‘frequent users of healthcare, social and employment services’ (3%) and ‘disability and specialized services users’ (3%). Each cluster showed different patterns of service use and was also uniquely associated with sociodemographic and health-related covariates, reflecting distinct service user types. Conclusions: The study identified five distinct groups of healthcare, social and employment services users in a working-age population. Knowledge of different user profiles and their determinants can help predict future need and use of services, coordinate and integrate services, and plan early interventions and prevention measures. This is important in order to save costs and improve the overall quality and effectiveness of services for groups with different care needs. Key messages: • In terms of their use of healthcare, social and employment services, the working-age population can be classified into distinct groups, uniquely associated with various determinants. • Understanding the population’s use of different services can help predict future needs and improve coordination, integration and effectiveness of services in groups with different needs.

Aims: The aim was to examine the use of outpatient healthcare services in different sectors of healthcare before and after the onset of unemployment and to study whether job loss affected the use of these services. Methods: Longitudinal individual-level register-based data was utilized on all individuals living in the City of Oulu, Finland, who became unemployed in 2017 (N = 1,999), their propensity matched controls (N = 1,999), and unmatched controls (N = 58,459) in a quasi-experimental design. Use of outpatient healthcare services was examined in one-month periods from 12 months before to 12 months after the onset of unemployment. Several socio-demographic factors, along with sickness and employment histories, were used for matching. Difference-in-differences analysis was used to measure the differences in the use of outpatient healthcare services between the unemployed and their matched controls. Results: The use of health services decreased significantly after the onset of unemployment. This was due to a decrease in the use of occupational health services. No change related to job loss was observed in the use of public or private healthcare services. The number of healthcare visits increased again after the unemployment ended. Difference-in-differences analyses showed that compared to propensity score matched controls, becoming unemployed reduced the use of health services. Conclusions: When access to occupational healthcare services ceases, other health services do not appear to fill the gap among those who become unemployed. Adequate healthcare services should be guaranteed to all population groups equally based on need, irrespective of the labour market status.