30 research outputs found
Self-perceived weather sensitivity and joint pain in older people with osteoarthritis in six European countries: Results from the European Project on OSteoArthritis (EPOSA)
People with osteoarthritis (OA) frequently report that their joint pain is influenced by weather conditions. This study aimed to examine whether there are differences in perceived joint pain between older people with OA who reported to be weather-sensitive versus those who did not in six European countries with different climates and to identify characteristics of older persons with OA that are most predictive of perceived weather sensitivity.
Methods
Baseline data from the European Project on OSteoArthritis (EPOSA) were used. ACR classification criteria were used to determine OA. Participants with OA were asked about their perception of weather as influencing their pain. Using a two-week follow-up pain calendar, average self-reported joint pain was assessed (range: 0 (no pain)-10 (greatest pain intensity)). Linear regression analyses, logistic regression analyses and an independent t-test were used. Analyses were adjusted for several confounders.
Results
The majority of participants with OA (67.2%) perceived the weather as affecting their pain. Weather-sensitive participants reported more pain than non-weather-sensitive participants (M = 4.1, SD = 2.4 versus M = 3.1, SD = 2.4; p < 0.001). After adjusting for several confounding factors, the association between self-perceived weather sensitivity and joint pain remained present (B = 0.37, p = 0.03). Logistic regression analyses revealed that women and more anxious people were more likely to report weather sensitivity. Older people with OA from Southern Europe were more likely to indicate themselves as weather-sensitive persons than those from Northern Europe.
Conclusions
Weather (in)stability may have a greater impact on joint structures and pain perception in people from Southern Europe. The results emphasize the importance of considering weather sensitivity in daily life of older people with OA and may help to identify weather-sensitive older people with OA.The Indicators for Monitoring COPD and Asthma - Activity and Function in
the Elderly in Ulm study (IMCA - ActiFE) is supported by the European Union
(No.: 2005121) and the Ministry of Science, Baden-Württemberg. The Italian
cohort study is part of the National Research Council Project on Aging (PNR).
The Longitudinal Aging Study Amsterdam (LASA) is financially supported by
the Dutch Ministry of Health, Welfare and Sports. The Peñagrande study was
partially supported by the National Fund for Health Research (Fondo de
Investigaciones en Salud) of Spain (project numbers FIS PI 05/1898; FIS RETICEF
RD06/0013/1013 and FIS PS09/02143). The Swedish Twin Registry is supported
in part by the Swedish Ministry of Higher Education. The Hertfordshire Cohort
Study was supported by the Medical Research Council, UK
European Project on Osteoarthritis (EPOSA): methodological challenges in harmonization of existing data from five European population-based cohorts on aging
BackgroundThe European Project on OSteoArthritis (EPOSA), here presented for the first time, is a collaborative study involving five European cohort studies on aging. This project focuses on the personal and societal burden and its determinants of osteoarthritis (OA). The aim of the current report is to describe the purpose of the project, the post harmonization of the cross-national data and methodological challenges related to the harmonization process MethodsThe study includes data from cohort studies in five European countries (Germany, Italy, the Netherlands, Spain and the United Kingdom) on older community-dwelling persons aged ? 59 years. The study design and main characteristics of the five cohort studies are described. Post harmonization algorithms are developed by finding a "common denominator" to merge the datasets and weights are calculated to adjust for differences in age and sex distribution across the datasets. ResultsA harmonized database was developed, consisting of merged data from all participating countries. In total, 10107 persons are included in the harmonized dataset with a mean age of 72.8 years (SD 6.1). The female/male ratio is 53.3/46.7%. Some variables were difficult to harmonize due to differences in wording and categories, differences in classifications and absence of data in some countries. The post harmonization algorithms are described in detail in harmonization guidelines attached to this paper. ConclusionsThere was little evidence of agreement on the use of several core data collection instruments, in particular on the measurement of OA. The heterogeneity of OA definitions hampers comparing prevalence rates of OA, but other research questions can be investigated using high quality harmonized data. By publishing the harmonization guidelines, insight is given into (the interpretation of) all post harmonized data of the EPOSA study. <br/
Utilization of acute and long-term care in the last year of life: comparison with survivors in a population-based study
Background. It is well-known that the use of care services is most intensive in the last phase of life. However, so far only a few determinants of end-of-life care utilization are known. The aims of this study were to describe the utilization of acute and long-term care among older adults in their last year of life as compared to those not in their last year of life, and to examine which of a broad range of determinants can account for observed differences in care utilization. Methods. Data were used from the Longitudinal Aging Study Amsterdam (LASA). In a random, age and sex stratified population-based cohort of 3107 persons aged 55 ? 85 years at baseline and representative of the Netherlands, follow-up cycles took place at 3, 6 and 9 years. Those who died within one year directly after a cycle were defined as the "end-of-life group" (n = 262), and those who survived at least three years after a cycle were defined as the "survivors". Utilization of acute and long-term care services, including professional and informal care, were recorded at each cycle, as well as a broad range of health-related and psychosocial variables. Results. The end-of-life group used more care than the survivors. In the younger-old this difference was most pronounced for acute care, and in the older-old, for long-term care. Use of both acute and long-term home care in the last year of life was fully accounted for by health problems. Use of institutional care at the end of life was partly accounted for by health problems, but was not fully explained by the determinants included. Conclusion. This study shows that severity of health problems are decisive in the explanation of the increase in use of care services towards the end-of-life. This information is essential for an appropriate allocation of professional health care to the benefit of older persons themselves and their informal caregivers. © 2009 Pot et al; licensee BioMed Central Ltd
Explanations of socioeconomic differences in changes in physical function in older adults: results from the Longitudinal Aging Study Amsterdam
BACKGROUND: This study examines the association between socioeconomic status and changes in physical function in younger- (aged 55–70 years) and older-old (aged 70–85 years) adults and seeks to determine the relative contribution of diseases, behavioral, and psychosocial factors in explaining this association. METHODS: Data were from 2,366 men and women, aged 55–85 years, participating in the Longitudinal Aging Study Amsterdam (LASA). Two indicators of socioeconomic status were used: education and income. Physical function was measured by self-reported physical ability over nine years of follow-up. RESULTS: In older adults, low socioeconomic status was related to a poorer level of physical function during nine years of follow-up. In subjects who were between 55 and 70 years old, there was an additional significant socioeconomic-differential decline in physical function, while socioeconomic differentials did not further widen in subjects 70 years and older. Behavioral factors, mainly BMI and physical activity, largely explained the socioeconomic differences in physical function in the youngest age group, while psychosocial factors reduced socioeconomic status differences most in the oldest age group. CONCLUSION: The findings indicate age-specificity of both the pattern of socioeconomic status differences in function in older persons and the mechanisms underlying these associations
Decision-making capacity and communication about care of older people during their last three months of life
Association of Cardiometabolic Multimorbidity With Mortality.
IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity
Association of Cardiometabolic Multimorbidity With Mortality.
IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity
A life-course perspective on physical functioning in women
Objective To validate Kalache & Kickbusch's model: namely, that functional capacity peaks in early adulthood, then declines at a rate dependent on fitness level until a “disability threshold” is reached. Methods Data came from the Australian Longitudinal Study on Women's Health, which followed three cohorts from 1996 to 2011: a young, a mid-aged and an older cohort (born in 1973–78, 1946–51 and 1921–26, respectively). The Short Form (36) Health Survey was used to measure physical functioning (score 1–100). The disability threshold was the mean physical functioning score in older women requiring assistance with daily activities (62.8). The relationship between age and physical functioning was modelled using spline regression for the entire sample, and by baseline physical functioning quintile and physical activity level. Findings Physical decline quickened with age: 0.05 annual units (95% confidence interval, CI: −0.13 to 0.22) at ages 18–23 years (i.e. no decline); –2.43 (95% CI: −2.64 to −2.23) at ages 82–90 years. Decline was faster in quintiles with lower baseline physical functioning in the younger and mid-age cohorts and in quintiles with higher baseline physical functioning in the older cohort. The disability threshold was reached at a mean age of 79 years, but the range was 45–88 years, depending on baseline physical functioning and physical activity. Conclusion Age and physical decline are not linearly related, as traditionally believed; decline accelerates with age. However, baseline physical functioning, but not physical activity, influences the rate of decline
Utilization of acute and long-term care in the last year of life: comparison with survivors in a population-based study
Abstract Background It is well-known that the use of care services is most intensive in the last phase of life. However, so far only a few determinants of end-of-life care utilization are known. The aims of this study were to describe the utilization of acute and long-term care among older adults in their last year of life as compared to those not in their last year of life, and to examine which of a broad range of determinants can account for observed differences in care utilization. Methods Data were used from the Longitudinal Aging Study Amsterdam (LASA). In a random, age and sex stratified population-based cohort of 3107 persons aged 55 – 85 years at baseline and representative of the Netherlands, follow-up cycles took place at 3, 6 and 9 years. Those who died within one year directly after a cycle were defined as the "end-of-life group" (n = 262), and those who survived at least three years after a cycle were defined as the "survivors". Utilization of acute and long-term care services, including professional and informal care, were recorded at each cycle, as well as a broad range of health-related and psychosocial variables. Results The end-of-life group used more care than the survivors. In the younger-old this difference was most pronounced for acute care, and in the older-old, for long-term care. Use of both acute and long-term home care in the last year of life was fully accounted for by health problems. Use of institutional care at the end of life was partly accounted for by health problems, but was not fully explained by the determinants included. Conclusion This study shows that severity of health problems are decisive in the explanation of the increase in use of care services towards the end-of-life. This information is essential for an appropriate allocation of professional health care to the benefit of older persons themselves and their informal caregivers.</p