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Background: Accumulation of problems in physical, psychological, cognitive, or social functioning is characteristic for frail individuals. Using a four-domain approach of frailty, this study explored how sociodemographic and lifestyle factors, life events and health are associated with frailty.Methods: The study sample included 4019 men and women (aged 40-81 years) examined during the fifth round (2008-2012) of the Doetinchem Cohort Study. Four domains of frailty were considered: physical (≥4 of 8 criteria: unintentional weight loss, exhaustion, strength, perceived health, walking, balance, hearing and vision impairments), psychological (2 criteria: depressive symptoms, mental health), cognitive (<10th percentile on global cognitive functioning), and social frailty (≥2 of 3 criteria: loneliness, social support, social participation). Logistic regression was used to study the cross-sectional association of sociodemographic factors, lifestyle, life events and chronic diseases with frailty domains.Results: About 17% of the population was frail on one or more domains. Overlap between the frailty domains was limited since 82% of the frail population was frail on one domain only. Low educated respondents were at higher risk of being psychologically and socially frail. Having multiple diseases was associated with a higher risk of being physically and psychologically frail. Being physically active was consistently associated with a lower risk of frailty on each of the four domains. Short or long sleep duration was associated with a higher risk of being physically, psychologically, and socially frail.Conclusions: Sociodemographic factors, lifestyle and multimorbidity contributed differently to the four frailty domains. It is important to consider multiple frailty domains since this helps to identify different groups of frail people, and as such to provide tailored care and support. Lifestyle factors including physical activity, smoking and sleep duration were associated with multiple domains of frailty. [ABSTRACT FROM AUTHOR]

Background Multimorbidity is common among ageing populations and it affects the demand for health services. The objective of this study was to examine the relationship between multimorbidity (i.e. the number of diseases and specific combinations of diseases) and the use of general practice services in the Dutch population of 55 years and older. Methods Data on diagnosed chronic diseases, contacts (including face-to-face consultations, phone contacts, and home visits), drug prescription rates, and referral rates to specialised care were derived from the Netherlands Information Network of General Practice (LINH), limited to patients whose data were available from 2006 to 2008 (N = 32,583). Multimorbidity was defined as having two or more out of 28 chronic diseases. Multilevel analyses adjusted for age, gender, and clustering of patients in general practices were used to assess the association between multimorbidity and service utilization in 2008. Results Patients diagnosed with multiple chronic diseases had on average 18.3 contacts (95%CI 16.8 19.9) per year. This was significantly higher than patients with one chronic disease (11.7 contacts (10.8 12.6)) or without any (6.1 contacts (5.6 6.6)). A higher number of chronic diseases was associated with more contacts, more prescriptions, and more referrals to specialized care. However, the number of contacts per disease decreased with an increasing number of diseases, patients with a single disease had between 9 to 17 contacts a year and patients with five or more diseases had 5 or 6 contacts per disease per year. Contact rates for specific combinations of diseases were lower than what would be expected on the basis of contact rates of the separate diseases. Conclusion Multimorbidity is associated with increased health care utilization in general practice, yet the increase declines per additional disease. Still, with the expected rise in multimorbidity in the coming decades more extensive health resources are required. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity is increasingly recognized as a major public health challenge of modern societies. However, knowledge about the size of the population suffering from multimorbidity and the type of multimorbidity is scarce. The objective of this study was to present an overview of the prevalence of multimorbidity and comorbidity of chronic diseases in the Dutch population and to explore disease clustering and common comorbidities. Methods: We used 7 years data (2002-2008) of a large Dutch representative network of general practices (212,902 patients). Multimorbidity was defined as having two or more out of 29 chronic diseases. The prevalence of multimorbidity was calculated for the total population and by sex and age group. For 10 prevalent diseases among patients of 55 years and older (N = 52,014) logistic regressions analyses were used to study disease clustering and descriptive analyses to explore common comorbid diseases. Results: Multimorbidity of chronic diseases was found among 13% of the Dutch population and in 37% of those older than 55 years. Among patients over 55 years with a specific chronic disease more than two-thirds also had one or more other chronic diseases. Most disease pairs occurred more frequently than would be expected if diseases had been independent. Comorbidity was not limited to specific combinations of diseases; about 70% of those with a disease had one or more extra chronic diseases recorded which were not included in the top five of most common diseases. Conclusion: Multimorbidity is common at all ages though increasing with age, with over two-thirds of those with chronic diseases and aged 55 years and older being recorded with multimorbidity. Comorbidity encompassed many different combinations of chronic diseases. Given the ageing population, multimorbidity and its consequences should be taken into account in the organization of care in order to avoid fragmented care, in medical research and healthcare policy. [ABSTRACT FROM AUTHOR]

Background: Stress has been shown to be a determinant of weight change and risk for obesity. To date, there is scarce evidence that stressful life events and their severity contribute to changes in body weight. We studied the association between the occurrence, impact of and adaptation to adverse life events and weight change and the role of initial weight status.Methods: Analyses were based on data from a population-based cohort of 2789 adults. Adverse life events, their impact and adaptation were measured retrospectively after baseline and follow-up weight and height measurements.Results: Over six years, participants gained an average of 2.8 kg. There were no differences in weight change between those who had experienced an adverse life event versus those who had not. However, the impact of life events had a significant interaction with initial weight status. Adults with a healthy weight showed an average weight reduction of 0.2 kg (95% CIs: -0.7 - 0.2), and overweight adults showed an average weight gain of 0.4 kg (95% CIs: -0.3 - 1.1) for each point increase in impact after experiencing an adverse life event. Further, a slower adaptation to events was significantly associated with greater weight loss among those who lost weight.Conclusions: We found no proof for an association between life events and weight change in the entire study sample, but we found that adults at a healthy weight responded differently to adverse life events than those who were overweight. [ABSTRACT FROM AUTHOR]

Background: Cross-sectional studies have reported associations between social support and health, but prospective evidence is less conclusive. This study aims to investigate the associations of positive and negative experiences of social support with current and future lifestyle factors, biological risk factors, self-perceived health and mental health over a 10-year period., Methods: Data were from 4,724 Dutch men and women aged 26-65 years who participated in the second (1993-1997) and in the third (1998-2002) or fourth (2003-2007) study round of the Doetinchem Cohort Study. Social support was measured at round two using the Social Experiences Checklist. Health was assessed by several indicators such as smoking, alcohol consumption, physical activity, fruit and vegetable intake, overweight, hypertension, hypercholesterolemia, self-perceived health and mental health. Tertiles of positive and negative experiences of social support were analysed in association with repeated measurements of prevalence and incidence of several health indicators using generalised estimating equations (GEE)., Results: Positive and negative experiences of social support were associated with prevalence and incidence of poor mental health. For the lowest tertile of positive support, odds ratios were 2.74 (95% CI 2.32-3.23) for prevalent poor mental health and 1.86 (95% CI 1.39-2.49) for incident poor mental health. For the highest tertile of negatively experienced support, odds ratios for prevalent and incident poor mental health were 3.28 (95% CI 2.78-3.87) and 1.60 (95% CI 1.21-2.12), respectively. Low levels of positive experiences of social support were also associated with low current intake of fruits and vegetables, but not with future intake. Negative experiences of social support were additionally associated with current smoking, physical inactivity, overweight and poor self-perceived health. Furthermore, high levels of negative experiences of social support were associated with future excessive alcohol consumption (OR 1.42; 95% CI 1.10-1.84), physical inactivity (95% CI 1.28; 1.03-1.58) and poor self-perceived health (OR 1.36; 95% CI 1.01-1.82)., Conclusions: This study showed that social support might have a beneficial effect on lifestyle and health, with negative experiences of social support affecting lifestyle and health differently from positive experiences of social support.