Employment and earnings trajectories before and after sickness absence due to major depressive disorder: a nationwide case-control study

Seasonal patterns of sickness absence due to diagnosed mental disorders: a nationwide 12-year register linkage study

AIMS

Although seasonality has been documented for mental disorders, it is unknown whether similar patterns can be observed in employee sickness absence from work due to a wide range of mental disorders with different severity level, and to what extent the rate of change in light exposure plays a role. To address these limitations, we used daily based sickness absence records to examine seasonal patterns in employee sickness absence due to mental disorders.

METHODS

We used nationwide diagnosis-specific psychiatric sickness absence claims data from 2006 to 2017 for adult individuals aged 16-67 (n = 636,543 sickness absence episodes) in Finland, a high-latitude country with a profound variation in daylength. The smoothed time-series of the ratio of observed and expected (O/E) daily counts of episodes were estimated, adjusted for variation in all-cause sickness absence rates during the year.

RESULTS

Unipolar depressive disorders peaked in October-November and dipped in July, with similar associations in all forms of depression. Also, anxiety and non-organic sleep disorders peaked in October-November. Anxiety disorders dipped in January-February and in July-August, while non-organic sleep disorders dipped in April-August. Manic episodes reached a peak from March to July and dipped in September-November and in January-February. Seasonality was not dependent on the severity of the depressive disorder.

CONCLUSIONS

These results suggest a seasonal variation in sickness absence due to common mental disorders and bipolar disorder, with high peaks in depressive, anxiety and sleep disorders towards the end of the year and a peak in manic episodes starting in spring. Rapid changes in light exposure may contribute to sickness absence due to bipolar disorder. The findings can help clinicians and workplaces prepare for seasonal variations in healthcare needs.

Sickness absence around contact with outpatient mental health care services - differences between migrants and non-migrants: a Norwegian register study

BACKGROUND

Mental disorders are a leading cause of sickness absence. Some groups of migrants are at higher risk of both mental disorder and sickness absence. Yet, research on sickness absence in relation to mental disorders among migrants is limited. This study investigates differences in sickness absence in the twelve-month period around contact with outpatient mental health services between non-migrants and various migrant groups with different length of stays. It also considers whether these differences are similar for men and women.

METHODS

Using linked Norwegian register data, we followed 146,785 individuals, aged 18-66 years, who had attended outpatient mental health services and who had, or had recently had, a stable workforce attachment. The number of days of sickness absence was calculated for the 12-month period surrounding contact with outpatient mental health services. We applied logistic regression and zero-truncated negative binomial regression to assess differences in any sickness absence and number of days of absence between non-migrants and migrants, including refugees and non-refugees. We included interaction terms between migrant category and sex.

RESULTS

Refugee men and other migrant men from countries outside the European Economic Area (EEA) had a higher probability of any sickness absence in the period surrounding contact with outpatient mental health services than their non-migrant counterparts. Women from EEA countries with stays of less than 15 years had a lower probability than non-migrant women. Additionally, refugees, both men and women, with 6-14 years in Norway had more days of absence while EEA migrants had fewer days than their non-migrant counterparts.

CONCLUSIONS

Refugee men and other non-EEA migrant men appear to have higher sickness absence than non-migrant men around the time of contact with services. This finding does not apply to women. Several probable reasons for this are discussed, though further research is required to understand why. Targeted strategies to reduce sickness absence and support the return to work for refugees and other non-EEA migrant men are needed. Barriers to timely help-seeking should also be addressed.

Socioeconomic position at the age of 30 and the later risk of a mental disorder: a nationwide population-based register study

BACKGROUND

A study was undertaken to examine the association between multiple indicators of socioeconomic position (SEP) at the age of 30 and the subsequent risk of the most common mental disorders.

METHODS

All persons born in Finland between 1966 and 1986 who were alive and living in Finland at the end of the year when they turned 30 were included. Educational attainment, employment status and personal total income were used as the alternative measures of SEP. Cox proportional hazards models were used to examine the association of SEP at the age of 30 with later risk of mental disorders. Additional analyses were conducted using a sibling design to account for otherwise unobserved shared family characteristics. Competing risks models were used to estimate absolute risks.

RESULTS

The study population included 1 268 768 persons, 26% of whom were later diagnosed with a mental disorder. Lower SEP at age 30 was consistently associated with a higher risk of being later diagnosed with a mental disorder, even after accounting for shared family characteristics and prior history of a mental disorder. Diagnosis-specific analyses showed that the associations were considerably stronger when substance misuse or schizophrenia spectrum disorders were used as an outcome. Absolute risk analyses showed that, by the age of 52 years, 58% of persons who had low educational attainment at the age of 30 were later diagnosed with a mental disorder.

CONCLUSIONS

Poor SEP at the age of 30 is associated with an increased risk of being later diagnosed with a mental disorder.

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.

Major Depressive Episodes and Employment Earnings Trajectories over the Following Decade among Working-aged Canadian Men and Women

OBJECTIVE

Quantify the association between experiencing a major depressive episode (MDE) and employment earnings over the following decade among working-aged Canadian men and women.

METHODS

Ten national Canadian Community Health Survey (CCHS 2003-2014) cycles were linked to administrative tax records (1997-2016) for individuals 18-54 years old with information on past year MDE defined by the Composite International Diagnostic Interview Short Form (n=85,155) measured at baseline. Subsequent earnings were measured annually two to ten years post-MDE. MDE cases were matched with adult controls without MDE using 1:1 greedy nearest-neighbour without replacement propensity score matching for women and men.

RESULTS

Using random-effects longitudinal modelling, among women (n=6,974) in the matched cohort (average initial earnings ~$52,119/annum, 2016 Canadian real dollars), experiencing an MDE was associated with average earnings of $4,473 less in the year the MDE was reported (95%CI=-$3,215,-$5,731) and an additional $363 less/year over the following decade (95%CI=-$132,-$594). Among men (n=3,620, average initial earnings ~$76,110/annum), an MDE was associated with an initial reduction in earnings of $5,023 (95%CI=-$2,453,-$7,593) followed by an earnings decline starting with $730/year, increasing to ~$1,810/year at ten years post-MDE.

LIMITATIONS

Residual confounding by measures excluded from the propensity score. Only one MDE measurement limited exploring earnings trajectories of those with multiple episodes.

CONCLUSIONS

Consistently lower earnings were seen for ten years after experiencing one MDE. The magnitude of earnings difference immediately after the MDE was similar for men and women. Findings reveal the critical period surrounding an MDE where effective clinical treatment and labor policy may help buffer longstanding earnings loss.