Mika Kivimäki’s research while affiliated with University of Helsinki and other places

This study investigated the associations between personality traits of the Five Factor Model and cardiovascular mortality, with a specific focus on whether pre-existing cardiovascular conditions modified these associations. We used data from 43,027 participants across five cohort studies: Health and Retirement Study (HRS); Wisconsin Longitudinal Study (WLS); National Social Life, Health, and Aging Project (NSHAP); Midlife in the United States (MIDUS); Household, Income, and Labour Dynamics in Australia (HILDA) with a mean age 55.9 years and 6493 individuals with pre-existing cardiovascular disease. We conducted meta-analyses examining conscientiousness, emotional stability, agreeableness, openness to experience, and extraversion in relation to mortality due to coronary heart disease and stroke. During a mean follow-up of 12.1 years, 1620 participants died from coronary heart disease and 454 from stroke. Lower conscientiousness was associated with higher mortality risk from both coronary heart disease (hazard ratio per 1SD = 0.82, 95%CI = 0.75–0.90) and stroke (HR = 0.84, CI = 0.72–0.99). Lower emotional stability predicted increased coronary heart disease mortality (HR = 0.91, CI = 0.85–0.97). The association between conscientiousness and cardiovascular mortality did not differ between individuals with or without baseline cardiovascular conditions. In addition, adjustments for health behaviors and other covariates only slightly attenuated this association. Other personality traits were not associated with cardiovascular disease mortality. Our findings highlight the role of low conscientiousness, and to a lesser extent low emotional stability, in the development and progression of fatal cardiovascular disease through pathways that may extend beyond established health behaviors.

Employee turnover is a challenge for public sector employers. In this study, we used machine learning to develop and validate models to predict actualized turnover of Finnish public sector workers. The development cohort data (N=52 291) included 158 variables from 2018. We defined overall turnover (regardless of reason) and net turnover (excluding workers in retirement age) through eligibility to a follow-up survey in 2020. The validation cohort included 9030 hospital workers who responded to survey in 2017, with turnover assessed in 2019. Area under the curve (AUC) value was 0.75 (95% CI: 0.74-0.76) for overall turnover and 0.75 (95% CI 0.73-0.76) for net turnover. The validation yielded similar AUC values. Key predictors of turnover were younger age, shorter job tenure, and turnover intentions totaling over 70% of the net gain. Work-related exposures, of which low threat of lay-off and satisfaction with challenges at work were most important, had considerably lower predictive power (about 1% each). These results may offer insights for public sector employers in their efforts to reduce employee turnover.

Aims Exposure to work-related sexual harassment may increase the risk for certain adverse behavioural and emotional outcomes but less is known about its association with somatic diseases such as cardiovascular disease (CVD) and type 2 diabetes. This study investigated the prospective association of work-related sexual harassment and risk of cardiometabolic diseases. Methods and results This cohort study included 88 904 Swedish men and women in paid work who responded to questions on workplace sexual harassment in the Swedish Work Environment Survey (1995–2015) and were free from cardiometabolic diseases at baseline. Cardiometabolic diseases (CVD and type 2 diabetes) were identified from the National Patient Register and Causes of Death Register through linkage. Cox proportional hazard regression was used, adjusting for socio-demographic, work-related psychosocial, and physical exposure at baseline. Overall, 4.8% of the participants (n = 4300) reported exposure to workplace sexual harassment during the previous 12 months. After adjustment for sex, birth country, family situation, education, income, and work-related factors, workplace sexual harassment was associated with increased incidence of CVD [hazard ratio (HR) 1.25, 95% confidence interval 1.03–1.51] and type 2 diabetes (1.45, 1.21–1.73). The HR for CVD (1.57, 1.15–2.15) and type 2 diabetes (1.85, 1.39–2.46) was increased for sexual harassment from superior or fellow workers, and sexual harassment from others was associated with type 2 diabetes (1.39, 1.13–1.70). The HR for both CVD (1.31, 0.95–1.81) and type 2 diabetes (1.72, 1.30–2.28) was increased for frequent exposure. Conclusion The results of this study support the hypothesis that workplace sexual harassment is prospectively associated with cardiometabolic diseases. Future research is warranted to understand causality and mechanisms behind these associations.

Background Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic.

Objectives To investigate if the Jenkins Sleep Scale (JSS) demonstrates sex-related differential item functioning (DIF). Design Cross-sectional study. Setting Survey data from the Finnish Public Sector study (2015–2017). Participants 77 967 employees in the Finnish public sector, with a mean age of 51.9 (SD 13.1) years and 82% women. Outcome measures Item response theory estimates: difficulty and discrimination parameters of the JSS and differences in these parameters between men and women. Results The mean JSS total score was 6.4 (4.8) points. For all four items of the JSS, the difficulty parameter demonstrated a slight shift towards underestimation of the severity of sleep difficulties. The discrimination ability of all four items was moderate to high. For the JSS composite score, overall discrimination ability was moderate (0.98, 95% CI 0.97 to 0.99). Mild uniform DIF (p<0.001) was seen: two items showed better discrimination ability among men and two others among women. Conclusions The JSS showed overall good psychometric properties among this healthy population of employees in the Finnish public sector. The JSS was able to discriminate people with different severities of sleep disturbances. However, when using the JSS, the respondents might slightly underestimate the severity of these disturbances. While the JSS may produce slightly different results when answered by men and women, these sex-related differences are probably negligible when applied to clinical situations.

Objectives To develop a risk prediction algorithm for identifying work units with increased risk of violence in the workplace. Design Prospective cohort study. Setting Public sector employees in Finland. Participants 18 540 nurses, social and youth workers, and teachers from 4276 work units who completed a survey on work characteristics, including prevalence and frequency of workplace violence/threat of violence at baseline in 2018–2019 and at follow-up in 2020–2021. Those who reported daily or weekly exposure to violence or threat of violence daily at baseline were excluded. Exposures Mean scores of responses to 87 survey items at baseline were calculated for each work unit, and those scores were then assigned to each employee within that work unit. The scores measured sociodemographic characteristics and work characteristics of the work unit. Primary outcome measure Increase in workplace violence between baseline and follow-up (0=no increase, 1=increase). Results A total of 7% (323/4487) of the registered nurses, 15% (457/3109) of the practical nurses, 5% of the social and youth workers (162/3442) and 5% of the teachers (360/7502) reported more frequent violence/threat of violence at follow-up than at baseline. The area under the curve values estimating the prediction accuracy of the prediction models were 0.72 for social and youth workers, 0.67 for nurses, and 0.63 for teachers. The risk prediction model for registered nurses included five work unit characteristics associated with more frequent violence at follow-up. The model for practical nurses included six characteristics, the model for social and youth workers seven characteristics and the model for teachers included four characteristics statistically significantly associated with higher likelihood of increased violence. Conclusions The generated risk prediction models identified employees working in work units with high likelihood of future workplace violence with reasonable accuracy. These survey-based algorithms can be used to target interventions to prevent workplace violence.

Background: Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations. Objective: We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study. Methods: We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression. Findings: Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score). Clinical implications: Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.