The impact of socioeconomic status on the association between biomedical and psychosocial well-being and all-cause mortality in older Spanish adults

Being actively engaged in life in old age: determinants, temporal trends, and prognostic value

PURPOSE

Recently, the concept of successful ageing has shifted from healthy ageing to active ageing, the latter emphasising even more the subjective perspective. Active agency is a marker for better functioning. However, the concept of active ageing lacks a clear definition so far. The specific aims of the study were to identify the determinants of being actively engaged in life (BAEL), to explore the changes in BAEL over 3 decades, and to explore the prognostic value of BAEL.

METHODS

This is a repeated cross-sectional cohort study of older (≥ 75 years) community-dwelling people in Helsinki in 1989 (N = 552), 1999 (N = 2396), 2009 (N = 1492), and 2019 (N = 1614). The data were gathered by a postal questionnaire at each time point. Being actively engaged in life was defined by two questions "Do you feel needed?" and "Do you have plans for the future?", which was further converted into BAEL score.

RESULTS

An increasing temporal trend in BAEL score was observed through the study years. Male sex, good physical functioning and subjective health, and meaningful social contacts were determinants for higher BAEL score. Active agency measured by BAEL score predicted lower 15-year mortality.

CONCLUSIONS

Older home-dwelling, urban Finnish people have become more actively engaged in recent years. The underlying causes are diverse but improved socioeconomic status observed over the study years was one of them. Social contacts and not feeling lonely were found to be determinants for being actively engaged. Two simple questions describing active engagement in life may help to predict mortality among older people.

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project

The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-022-00171-1.

Socioeconomic status and ethnic variation associated with type 2 diabetes mellitus in patients with uncontrolled hypertension in Singapore

INTRODUCTION

The burden of type 2 diabetes mellitus (T2DM) and related vascular complications is particularly high in Asians and ethnic minorities living in the West. However, the association of T2DM with socioeconomic status (SES) and ethnicity has not been widely studied in populations living in Asia. Therefore, we investigated these associations among the multiethnic population with uncontrolled hypertension in Singapore.

RESEARCH DESIGN AND METHODS

In a cross-sectional study using baseline data of a 2-year randomized trial in Singapore, we obtained demographic, SES, lifestyle and clinical factors from 915 patients aged ≥40 years with uncontrolled hypertension. T2DM was defined as having either: (i) self-reported 'physician-diagnosed diabetes confirmed through medical records' or taking antidiabetes medications, (ii) fasting blood glucose levels ≥7.0 mmol/dL or (iii) hemoglobin A1c ≥6.5%. The SES proxies included education, employment status, housing ownership and housing type, and the ethnicities were Chinese, Malays and Indians. Logistic regression analyses were used to evaluate the association of T2DM with SES and ethnicity.

RESULTS

Higher proportion of T2DM was observed in Malays (40.0%) and Indians (56.0%) than Chinese (26.8%) (p<0.001), and in patients with lower SES (ranging from 25.7% to 66.2% using different proxies) than those with higher SES (19.4% to 32.0%). In a multivariate model comprising age, gender, ethnicity and SES, Malay ethnicity (OR 1.59; 95% CI 1.04 to 2.44, p=0.031) or Indian ethnicity (OR 3.65; 95% CI 2.25 to 5.91, p<0.001) versus Chinese and housing type (residing in one to three rooms (OR 2.00; 95% CI 1.16 to 3.43, p=0.012) or four to five rooms public housing (OR 1.86; 95% CI 1.13 to 3.04, p=0.013) vs private housing) were associated with higher T2DM odds. The associations of Indians and one to three rooms public housing with T2DM met the significance after accounting for multiple testing (p≤0.0125).

CONCLUSION

Our study suggests that housing type and ethnic variation are independently associated with higher T2DM risk in patients with uncontrolled hypertension in Singapore. Further studies are needed to validate our results.

TRIAL REGISTRATION NUMBER

NCT02972619.

The WHO active ageing pillars and its association with survival: Findings from a population-based study in Spain

BACKGROUND

The World Health Organization's active ageing model is based on the optimisation of four key "pillars": health, lifelong learning, participation and security. It provides older people with a policy framework to develop their potential for well-being, which in turn, may facilitate longevity. We sought to assess the effect of active ageing on longer life expectancy by: i) operationalising the WHO active ageing framework, ii) testing the validity of the factors obtained by analysing the relationships between the pillars, and iii) exploring the impact of active ageing on survival through the health pillar.

METHODS

Based on data from a sample of 801 community-dwelling older adults, we operationalised the active ageing model by taking each pillar as an individual construct using principal component analysis. The interrelationship between components and their association with survival was analysed using multiple regression models.

RESULTS

A three-factor structure was obtained for each pillar, except for lifelong learning with a single component. After adjustment for age, gender and marital status, survival was only significantly associated with the physical component of health (HR = 0.66; 95% CI = 0.47-0.93; p = 0.018). In turn, this component was loaded with representative variables of comorbidity and functionality, cognitive status and lifestyles, and correlated with components of lifelong learning, social activities and institutional support.

CONCLUSION

According to how the variables clustered into the components and how the components intertwined, results suggest that the variables loading on the biomedical component of the health pillar (e.g. cognitive function, health conditions or pain), may play a part on survival chances.

Frailty as the Future Core Business of Public Health: Report of the Activities of the A3 Action Group of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA)

Background: The prevalence of frailty at population-level is expected to increase in Europe, changing the focus of Public Health. Here, we report on the activities of the A3 Action Group, focusing on managing frailty and supporting healthy ageing at community level. Methods: A three-phased search strategy was used to select papers published between January 2016 and May 2018. In the third phase, the first manuscript draft was sent to all A3-Action Group members who were invited to suggest additional contributions to be included in the narrative review process. Results: A total of 56 papers were included in this report. The A3 Action Group developed three multidimensional tools predicting short–medium term adverse outcomes. Multiple factors were highlighted by the group as useful for healthcare planning: malnutrition, polypharmacy, impairment of physical function and social isolation were targeted to mitigate frailty and its consequences. Studies focused on the management of frailty highlighted that tailored interventions can improve physical performance and reduce adverse outcomes. Conclusions: This review shows the importance of taking a multifaceted approach when addressing frailty at community level. From a Public Health perspective, it is vital to identify factors that contribute to successful health and social care interventions and to the health systems sustainability.