Causes, mortality rates and risk factors of death in community-dwelling Europeans aged 50 years and over: Results from the Survey of Health, Ageing and Retirement in Europe 2013-2015

Objective: To determine mortality rates and to rank the causes and predictors of mortality using a wide range of sociodemographic and clinical variables. Materials and Methods: It is a prospective population-based cohort study of adults living in the community, 2013-15 (N = 48,691, age ≥50; deceased = 1,944). Clinical and sociodemographic data were obtained from the Survey of Health, Ageing and Retirement in Europe (SHARE): Age, Gender, Marital Status, Years of Schooling, Income, Loneliness, Cognition, Self-Rated Health, Diseases, Activities of daily living (ADL), Frailty and Mobility. Mortality rates were calculated. A Cox proportional hazards model were used to determine risk-adjusted mortality ratios with confidence intervals (99% CI). Results: The crude mortality rate was 18.39 (1000 person-years at risk), (99% CI, 18.37-18.42). The factors most associated with an increased mortality risk were older age, lower self-rated health, lower cognition, male gender, ADL deficits, higher comorbidity, frailty and loneliness. The diseases with a higher mortality risk were: cancer (Hazard ratio, HR = 2.67), dementia (HR = 2.19), depressive symptoms (HR = 2.10), fractures (hip, femur) (HR = 1.57), stroke (HR = 1.55), chronic lung disease (HR = 1.52), diabetes (HR = 1.36) and heart attack (HR = 1.21). Conclusions: The main mortality risk factors, associated independently in the eight diseases were: older age, poor self-rated health, ADL deficits, male gender, lower cognition, comorbidity and the presence of depressive symptoms. The need to evaluate and treat the depressive symptoms that accompanies diseases with higher risk of mortality is stressed

Association of depression phenotypes and antidepressant treatment with mortality due to cancer and other causes: a community-based cohort study

ObjectiveThis study aimed to assess the association of somatic depressive symptoms (SDS), cognitive/emotional depressive symptoms (C-EDS), and antidepressant treatment on mortality due to cancer and other causes in a community cohort.MethodsA community-based sample recruited in 1995, 2000, and 2005 aged between 35 and 75 years was examined in two waves and followed for a median of 6.7 years. SDS and C-EDS phenotypes were assessed using the Patient Health Questionnaire-9. Medication used by participants was collected. Deaths and their causes were registered during follow-up. Cox proportional hazard models stratified by sex were performed to determine the association between depressive phenotypes and mortality.ResultsThe cohort consisted of 5,646 individuals (53.9% women) with a mean age of 64 years (SD = 11.89). During the follow-up, 392 deaths were recorded, of which 27.8% were due to cancer. C-EDS phenotype was associated with an increased risk of cancer mortality in both men (HR = 2.23; 95% CI = 1.11-4.44) and women (HR = 3.69; 95% CI = 1.69-8.09), and SDS was significantly associated with non-cancer mortality in men (HR = 2.16; 95 CI % = 1.46-3.18). Selective serotonin reuptake inhibitors (SSRIs) were significantly associated with both cancer (HR = 2.78; 95% CI = 1.10-6.98) and non-cancer mortality (HR = 2.94; 95% CI = 1.76-4.90) only in the male population.ConclusionC-EDS phenotype was related to an increased risk of cancer mortality at 6 years. In addition, the use of SSRIs in the male population was associated with cancer and all-cause mortality

What seems to matter in public policy and the health of informal caregivers? A cross-sectional study in 12 European countries

In Europe, informal caregiving is frequent and is expected to grow. Caregiving has an impact on caregivers' health, but its effect may vary according to the policies of support that are available to caregivers. The aim of this study was to assess the association between the policies of support to caregivers available in 12 European countries and the health of caregivers, considering separately the policies based on financial help and those based on training and other non- financial services. We used data from 13,507 caregivers from 12 European countries from the fifth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) to build a path model. Poor health among caregivers was associated with living in a family-based care country (β = 0.50; 95% CI = 0.42-0.59), and with an increased extent of caregiving (β = 0.18; 95% CI = 0.15-0.22). Non-financial support measures seem to have a larger protective impact (β = -0.33; 95% CI = -0.38 - -0.28) on the health of caregivers than do financial support measures (β = 0.03; 95% CI = 0.01-0.04), regardless of the gender of the caregiver. According to our results, the currently available policies of support associated with better health among caregivers are those that: 1) provide them with some free time, 2) help them to deal emotionally with caregiving, and 3) give them skills to both improve the care situation and to deal with it better