Retirement age and type as predictors of frailty: a retrospective cohort study of older businessmen

Association Between Retirement Age and Incidence of Depressive Disorders: A 19-Year Population-Based Study

OBJECTIVE

To investigate the occurrence of depressive disorders spanning the transition to retirement, and explore the relationship between retirement age and depressive disorders.

METHODS

We utilized a national population-based health insurance database encompassing 2 million Taiwanese individuals from 2000 to 2019. The study focused on individuals aged 50 years and older who were employed at the baseline, and 84,224 individuals had records of retirement during the follow-up period. Depressive disorders were identified using codes from the International Classification of Diseases. To assess the trend in the incidence of depressive disorders 7-year period before and after retirement, an interrupted time series analysis was performed. Cox-proportional hazard models were employed to investigate the association between retirement age and the occurrence of depressive disorders following retirement.

RESULTS

The incidence of depression peaks at the time of retirement and shows a significant decrease after retirement. Incidence of depressive disorders was 6.4 and 7.6 per 1000 person-years among individuals who retired between the ages of 60-64 and 65-69. Comparing the two groups, those who retired between 65 and 69 exhibits a higher risk of developing depressive disorders (hazard ratio = 1.10, 95% confidence interval = 1.02-1.18). This association is particularly pronounced among women and individuals residing in areas with low urbanization levels.

CONCLUSION

Retirement marks a crucial life milestone accompanied by a peak in depressive disorders. It is important to address the higher risk of depression associated with late retirement among socially disadvantaged groups.

Midlife cardiovascular health factors as predictors of retirement age, work-loss years, and years spent in retirement among older businessmen

Cardiovascular disease (CVD) is one of the leading causes of premature retirement. However, the relationship between CVD risk factors and workforce participation is not well known. We studied the relationship between midlife CVD risk, age at retirement, work-loss years, and survival in retirement. Middle-aged Finnish men (initial n = 3490, mean age = 47.8 years) were assessed for CVD risk factors and general health in the 1970s. They worked as business executives and provided information on their retirement status in the year 2000. Survival was followed up to the 9th decade of life with a follow-up of up to 44 years. Work-loss years were calculated as death or retirement occurring at age ≤ 65 years. Smoking, body mass index, and alcohol use were used as covariates, excluding models of CVD risk, which were adjusted for alcohol use only. Higher risk of 10-year fatal CVD was associated with 0.32 more years (relative risk < 1 vs. 1, covariate-adjusted β = 0.32, 95% CI = 0.13, 0.53) of work-loss. Higher risk of 5-year incident (covariate-adjusted time-constant HR = 1.32, 95% CI = 1.19, 1.47) and 10-year fatal (covariate-adjusted time-dependent HR = 1.55, 95% CI = 1.30, 1.85) CVD in midlife were associated with fewer years spent in retirement. Poorer self-rated health and physical fitness and higher levels of triglycerides were associated with increased hazard of earlier retirement, more work-loss years, and fewer years spent in retirement. Poorer health and greater midlife CVD risk may be associated with earlier exit from the workforce and fewer years spent in retirement. Management of CVD risk in midlife may support people to work longer.

Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study

OBJECTIVES

Numerous studies have been performed on frailty, but rarely do studies explore the integrated impact of socio-demographic, behavioural and social support factors on frailty. This study aims to establish a comprehensive frailty risk prediction model including multiple risk factors.

METHODS

The 2018 wave of the Chinese Longevity and Health Longitudinal Survey was used. Univariate and multivariate logistic regressions were performed to identify the relationship between frailty and multiple risk factors and establish the frailty risk prediction model. A nomogram was utilized to illustrate the prediction model. The area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test and calibration curve were used to appraise the prediction model.

RESULTS

Variables from socio-demographic, social support and behavioural dimensions were included in the final frailty risk prediction model. Risk factors include older age, working as professionals and technicians before 60 years old, poor economic condition and poor oral hygiene. Protective factors include eating rice as a staple food, regular exercise, having a spouse as the first person to share thoughts with, doing physical examination once a year and not needing a caregiver when ill. The AUC (0.881), Hosmer-Lemeshow test (p = 0.618), and calibration curve showed that the risk prediction model was valid.

CONCLUSION

Risk factors from socio-demographic, behavioural and social support dimensions had a comprehensive effect on frailty, further supporting that a comprehensive and individualized intervention is necessary to prevent frailty.