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.

Multimorbidity and functional impairment-bidirectional interplay, synergistic effects and common pathways.

This review discusses the interplay between multimorbidity (i.e. co-occurrence of more than one chronic health condition in an individual) and functional impairment (i.e. limitations in mobility, strength or cognition that may eventually hamper a person's ability to perform everyday tasks). On the one hand, diseases belonging to common patterns of multimorbidity may interact, curtailing compensatory mechanisms and resulting in physical and cognitive decline. On the other hand, physical and cognitive impairment impact the severity and burden of multimorbidity, contributing to the establishment of a vicious circle. The circle may be further exacerbated by people's reduced ability to cope with treatment and care burden and physicians' fragmented view of health problems, which cause suboptimal use of health services and reduced quality of life and survival. Thus, the synergistic effects of medical diagnoses and functional status in adults, particularly older adults, emerge as central to assessing their health and care needs. Furthermore, common pathways seem to underlie multimorbidity, functional impairment and their interplay. For example, older age, obesity, involuntary weight loss and sedentarism can accelerate damage accumulation in organs and physiological systems by fostering inflammatory status. Inappropriate use or overuse of specific medications and drug-drug and drug-disease interactions also contribute to the bidirectional association between multimorbidity and functional impairment. Additionally, psychosocial factors such as low socioeconomic status and the direct or indirect effects of negative life events, weak social networks and an external locus of control may underlie the complex interactions between multimorbidity, functional decline and negative outcomes. Identifying modifiable risk factors and pathways common to multimorbidity and functional impairment could aid in the design of interventions to delay, prevent or alleviate age-related health deterioration; this review provides an overview of knowledge gaps and future directions.

Rapidly developing multimorbidity and disability in older adults: does social background matter?

BACKGROUND: Multimorbidity is among the most disabling geriatric conditions. In this study, we explored whether a rapid development of multimorbidity potentiates its impact on the functional independence of older adults, and whether different sociodemographic factors play a role beyond the rate of chronic disease accumulation. METHODS: A random sample of persons aged ≥60 years (n = 2387) from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) was followed over 6 years. The speed of multimorbidity development was estimated as the rate of chronic disease accumulation (linear mixed models) and further dichotomized into the upper versus the three lower rate quartiles. Binomial negative mixed models were used to analyse the association between speed of multimorbidity development and disability (impaired basic and instrumental activities of daily living), expressed as the incidence rate ratio (IRR). The effect of sociodemographic factors, including sex, education, occupation and social network, was investigated. RESULTS: The risk of new activity impairment was higher among participants who developed multimorbidity faster (IRR 2.4, 95% CI 1.9-3.1) compared with those who accumulated diseases more slowly overtime, even after considering the baseline number of chronic conditions. Only female sex (IRR for women vs. men 1.6, 95% CI 1.2-2.0) and social network (IRR for poor vs. rich social network 1.7, 95% CI 1.3-2.2) showed an effect on disability beyond the rate of chronic disease accumulation. CONCLUSIONS: Rapidly developing multimorbidity is a negative prognostic factor for disability. However, sociodemographic factors such as sex and social network may determine older adults' reserves of functional ability, helping them to live independently despite the rapid accumulation of chronic conditions.

Global health care use by patients with type-2 diabetes: Does the type of comorbidity matter?

AIM: To identify patterns of health care use among diabetic patients with multimorbidity across primary, specialised, hospital and emergency care, depending on their type of chronic comorbidity. METHODS: Longitudinal study of a population-based retrospective cohort conformed by adult patients with type-2 diabetes assigned to any of the primary care centres in Aragon during 2010 and 2011 (n=65,716). Negative binomial regressions were run to model the effect of the type of comorbidity on the number of visits to each level of care. Comorbidities were classified as concordant, discordant or mental based on expert consensus and depending on whether they shared the same overall pathophysiologic risk profile and disease management plan designed for type-2 diabetes. RESULTS: Mental comorbidity was independently associated with total and unplanned admissions (incidence rate ratio [IRR]:1.25; 95% confidence interval [CI]:1.12-1.39, IRR:1.21; 95% CI:1.06-1.39), average length of stay (IRR:1.47; 95% CI:1.25-1.73), and total and priority emergency room visits (IRR:1.26; 95% CI:1.17-1.35, IRR:1.30; 95% CI:1.18-1.42). Patients with discordant comorbidities showed the strongest associations with the number of visits to specialists (IRR:1.38; 95% CI:1.33-1.43) and to different specialties (IRR:1.36; 95% CI:1.32-1.39). Differences regarding GP visits were lower but still significant for patients with discordant comorbidity (IRR:1.08; 95% CI:1.06-1.11), but especially for those with mental comorbidity (IRR:1.17; 95% CI:1.14-1.21). CONCLUSION: In patients with type-2 diabetes, the coexistence of mental comorbidity significantly increases the use of unplanned hospital services, and discordant comorbidities have an important effect on specialised care use. Differences with respect to primary care use are not as prominent.

Does higher quality of primary healthcare reduce hospital admissions for diabetes complications? A national observational study.

AIM: To determine if hospital admission rates for diabetes complications (acute complications, chronic complications, no complications and hypoglycaemia) were associated with primary care diabetes management. METHODS: We performed an observational study in the population in England during the period 2004-2009 (54 741 278 people registered with 8140 general practices). We used multivariable negative binomial regression to model the associations between indirectly standardized hospital admission rates for complications and primary healthcare quality, supply and access indicators, diabetes prevalence and population factors. RESULTS: In multivariate regression models, increasing deprivation (incidence rate ratio: 1.0154; P < 0.001, 95% CI 1.0141-1.0166) and diabetes prevalence (incidence rate ratio: 1.0956; P < 0.001, 95% CI 1.0677-1.1241) were risk factors for admission, while most healthcare covariates, i.e. a larger practice population (incidence rate ratio 0.9999, P = 0.013, 95% CI 0.9999-0.9999), better patient-perceived urgent and non-urgent access to primary care (incidence rate ratio: 0.9989, P = 0.023; 95% CI 0.9979-0.9998 and incidence rate ratio: 0.9988; P = 0.003, 95% CI 0.9980-0.9996, respectively) and better HbA1c target achievement (incidence rate ratio: 0.9971; P < 0.001, 95% CI 0.9958-0.9984), were protective. Diabetes admissions decreased significantly during the period 2004-2009. CONCLUSIONS: After controlling for population factors, better scheduled primary care access and glycaemic control were associated with lower hospital admission rates across most complications. There is little rationale to restrict primary care-sensitive condition definitions to acute complications. They should be revised to improve the usefulness of hospital admission data as an outcome measure, and to facilitate international comparisons. The risk of emergency hospital admission should be monitored routinely.