The impact of depressive symptoms on exit from paid employment in Europe:a longitudinal study with 4 years follow-up

Background: Mental health problems are a risk factor for loss of paid employment. This study investigates (i) the relation between depressive symptoms and different involuntary pathways of labour force exit and (ii) explores gender and geographical differences in this relation.Methods: The study population consisted of 5263 individuals in paid employment aged between 50 years and the country-specific retirement age from 11 European countries participating in the longitudinal Survey of Health, Ageing and Retirement in Europe (SHARE). Self-reported depressive symptoms at baseline were assessed using the EURO-D. Employment status was derived from interviews after 2 and 4 years. Cox proportional hazards regression analyses were used to investigate the association between depressive symptoms and labour force exit via disability benefit and unemployment. Population attributable fractions (PAFs) were calculated to estimate the contribution of depressive symptoms to these pathways of labour force exit.Results: Both men and women with a EURO-D score ≥4 had a &gt;2-fold increased risk of a disability benefit (HR: 2.46, 95%CI 1.68-3.60) after adjustment for demographics and work-related characteristics. Among men depressive symptoms elevated the risk of becoming unemployed at follow-up (HR 1.55; 95%CI: 0.94-2.57). The PAF was 0.18 for disability benefit and 0.04 for unemployment, and varied across European regions.Conclusions: Individuals with depressive symptoms are at increased risk of losing paid employment, which in turn may aggravate their symptoms. Targeting depressive symptoms with public health and occupational policies should be considered to reduce the burden of mental diseases in Europe.</p

Longitudinal associations of effort-reward imbalance and overcommitment with burnout symptoms among Italian university students

Background: Burnout symptoms are highly prevalent among university students. The effort-reward imbalance (ERI) model is predictive for workers’ mental health. This study aims to investigate the associations of ERI and overcommitment with burnout symptoms among students. Methods: An Italian version of the Oldenburg Burnout Inventory-Student (OLBI-S) and the Effort-Reward Imbalance-Student Questionnaire (ERI-SQ) were administered to assess burnout symptoms (range: 16–64), effort (range: 2–8), reward (range: 5–20) and overcommitment (range: 5–20) among 545 students twice with six months of follow-up. ERI (range: 0.25–4) was estimated multiplying the effort/reward ratio by a correction factor to account the difference in items investigating effort and reward. A between-within linear regression model was used to investigate whether ERI and overcommitment were associated with burnout symptoms (between individuals) and whether individual changes in ERI and overcommitment during the follow-up were associated with changes in burnout symptoms (within individuals). Results: Higher levels of ERI (β: 10.13, 95 % CI: 9.21–11.05) and overcommitment (β: 1.09, 95 % CI: 0.95–1.23) were associated with higher levels of burnout symptoms. An increase in ERI (β: 4.93, 95 % CI: 3.02–6.84) and overcommitment (β: 0.92, 95 % CI: 0.59–1.26) within individuals was associated with an increase in burnout symptoms. Discussion: This study supports the validity of the ERI model in the university setting. ERI and overcommitment may be determinants of burnout symptoms among university students. Interventions at individual and environmental level may aim to decrease ERI and overcommitment to tackle the burden of burnout among students. Future research may investigate the drivers of students’ ERI and overcommitment among students.</p

Impact of mental disorders during education on work participation:a register-based longitudinal study on young adults with 10 years follow-up

BACKGROUND: Mental disorders are a leading cause of disability and a major threat to work participation in young adults. This register-based longitudinal study aims to investigate the influence of mental disorders on entering and exiting paid employment among young graduates and to explore differences across socioeconomic groups.METHODS: Register information on sociodemographics (age, sex, migration background) and employment status of 2 346 393 young adults who graduated from secondary vocational (n=1 004 395) and higher vocational education or university (n=1 341 998) in the period 2010-2019 was provided by Statistics Netherlands. This information was enriched with register information on the prescription of nervous system medication for mental disorders in the year before graduation as a proxy for having a mental disorder. Cox proportional hazards regression models were used to estimate the influence of mental disorders on (A) entering paid employment among all graduates and (B) exiting from paid employment among graduates who had entered paid employment.RESULTS: Individuals with mental disorders were less likely to enter (HR 0.69-0.70) and more likely to exit paid employment (HR 1.41-1.42). Individuals using antipsychotics were the least likely to enter (HR 0.44) and the most likely to exit paid employment (HR 1.82-1.91), followed by those using hypnotics and sedatives. The association between mental disorders and work participation was found across socioeconomic subgroups (ie, educational level, sex and migration background). DISCUSSION: Young adults with mental disorders are less likely to enter and maintain paid employment. These results ask for prevention of mental disorders and for a more inclusive labour market.</p

Associations of university student life challenges with mental health and self-rated health:A longitudinal study with 6 months follow-up

BACKGROUND: Mental health problems are highly prevalent among university students. Stress due to student life challenges may be a risk factor for poorer health. This study investigates to what extent student life challenges and changes therein are associated with mental health and self-rated health. METHODS: In a longitudinal study with 568 Italian university students mental health was assessed using the Mental Health Inventory-5 (MHI-5) and self-rated health with a single item from the Short Form 36 Health Survey (SF36) (score ranges: 0-100) at baseline and at six months follow-up. Student life challenges were investigated using six subscales (score ranges: 1-4) of the Higher Education Stress Inventory (HESI). A between-within linear regression model was used to investigate whether a higher exposure to life challenges was associated with poorer health (between individuals) and whether changes in student life challenges were associated with changes in health (within individuals). RESULTS: Higher exposure to student life challenges was associated with poorer mental health (b ranging from -5.3 to -10.3) and self-rated health (b ranging from -3.1 to -9.6). An increase in student life challenges within individuals was associated with poorer mental health and self-rated health, in particular for high workload (b up to -5.9), faculty shortcomings (b up to -5.7), and unsupportive climate (b up to -5.6). DISCUSSION: Exposure to student life challenges and changes therein are associated with university students' health. Our findings suggest that student life challenges may be a target for interventions to improve mental health and self-rated health among university students

Stress among medical students: factor structure of the University Stress Scale among Italian students

OBJECTIVES: The main purpose of the current study was to investigate the psychometric properties of the Italian version of the University Stress Scale (USS) among Italian medical students. DESIGN, SETTING AND PARTICIPANTS: A cross-sectional observational study based on data from an online cross-sectional survey from 11 to 23 December 2018. A total of 1858 Italian medical students participated in the study. OUTCOME MEASURES: We measured perceived stress among medical students using the USS, the Effort-Reward Imbalance Student Questionnaire (ERI-SQ) and the Kessler-10 (K10). RESULTS: Results showed that a bifactor-Exploratory Structural Equation Modeling solution provided excellent levels of fit to the data. Our results suggest that the modified version of 19 items of the Italian version of the USS does not have a simple unidimensional structure. Overall, an inspection of ancillary indices (omega indices, ECV and percentage of uncontaminated correlations) revealed that these were too low to suggest the use of the USS as a composite measure of university stress. We tested an alternative unidimensional short form (eight items; USS-S) that assessed all the five sources of stress. This version provided a good fit to the data. Evidence of convergent validity of the USS-S was observed by analysing the correlations between the USS and ERI-SQ (ranging from -0.34 to 0.37, all p<0.01). Finally, based on the clinical cut-off recommended on the K10, results from receiver operating characteristic showed that considering the clinical cut-off of the USS is 7.5 and that 59.70% of medical students reported stress levels in the clinical range. CONCLUSION: Finally, our results showed a lack of support for using the USS to measure a general university stress factor, as the general USS factor accounted for little variance in our sample. In this sense, stress scores among Italian students can be better assessed by the use of the USS-S

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021:a systematic analysis for the Global Burden of Disease Study 2021

Background: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. </p

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021:a systematic analysis for the Global Burden of Disease Study 2021

Background: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. </p