Estimating the lifetime risk of dementia using nationwide individually linked cause-of-death and health register data.

BACKGROUND: Previous estimates of the lifetime risk of dementia are restricted to older age groups and may suffer from selection bias. In this study, we estimated the lifetime risk of dementia starting at birth using nationwide integral linked health register data. METHODS: We studied all deaths in The Netherlands in 2017 (n = 147 866). Dementia was assessed using the cause-of-death registration, individually linked with registers covering long-term care, specialized mental care, dispensed medicines, hospital discharges and claims, and primary care. The proportion of deaths with dementia was calculated for the total population and according to age at death and sex. RESULTS: According to all data sources combined, 24.0% of the population dies in the presence of dementia. This proportion is higher for females (29.4%) than for males (18.3%). Using multiple causes of death only, the proportion with dementia is 17.9%. Sequential addition of long-term care and hospital discharge data increased the estimate by 4.0 and 1.5%-points, respectively. Further addition of dispensed medicines, hospital claims and specialized mental care data added another 0.6%-points. Among persons who die at age ≤65-70 years, the proportion with dementia is ≤6.2%. After age 70, the proportion rises sharply, with a peak of 43.9% for females and 33.1% for males at age 90-95 years. CONCLUSIONS: Around one-fourth of the Dutch population is diagnosed with dementia at some point in life and dies in the presence of dementia. It is a major challenge to arrange optimal care for this group.

Systemic autoimmune disease as a cause of death: mortality burden and comorbidities.

OBJECTIVES: Systemic autoimmune diseases (SAIDs) have chronic trajectories and share characteristics of self-directed inflammation, as well as aspects of clinical expression. Nonetheless, burden-of-disease studies rarely investigate them as a distinct category. This study aims to assess the mortality rate of SAIDs as a group and to evaluate co-occurring causes of death. METHODS: We used death certificate data in the Netherlands, 2013-2017 (N = 711 247), and constructed a SAIDs list at the fourth-position ICD-10 level. The mortality rate of SAIDs as underlying cause of death (CoD), non-underlying CoD, and any-mention CoD was calculated. We estimated age-sex-standardized observed/expected (O/E) ratios to assess comorbidities in deaths with SAID relative to the general deceased population. RESULTS: We observed 3335 deaths with SAID on their death certificate (0.47% of all deaths). The mortality rate of SAID was 14.6 per million population as underlying CoD, 28.0 as non-underlying CoD, and 39.7 as any-mention CoD. The mortality rate was higher for females and increased exponentially with age. SAID-related deaths were positively associated with all comorbidities except for solid neoplasms and mental conditions. Particularly strong was the association with diseases of the musculoskeletal system (O/E = 3.38; 95% CI: 2.98, 3.82), other diseases of the genitourinary system (O/E = 2.73; 95% CI: 2.18, 3.38), influenza (O/E = 2.71; 95% CI: 1.74, 4.03), blood diseases (O/E = 2.02; 95% CI: 1.70, 2.39), skin and subcutaneous tissue diseases (O/E = 1.95; 95% CI: 1.54, 2.45), and infectious diseases (O/E = 1.85; 95% CI: 1.70, 2.01). CONCLUSION: Systemic autoimmune diseases constitute a rare group of causes of death, but contribute to mortality through multiple comorbidities. Classification systems could be adapted to better encompass these diseases as a category.

Prevalence of diabetes mellitus at the end of life: An investigation using individually linked cause-of-death and medical register data.

AIMS: Although diabetes mellitus at the end of life is associated with complex care, its end-of-life prevalence is uncertain. Our aim is to estimate diabetes prevalence in the end-of-life population, to evaluate which medical register has the largest added value to cause-of-death data in detecting diabetes cases, and to assess the extent to which reporting of diabetes as a cause of death is associated with disease severity. METHODS: Our study population consisted of deaths in the Netherlands (2015-2016) included in Nivel Primary Care Database (Nivel-PCD; N = 18,162). The proportion of deaths with diabetes (Type 1 or 2) within the last two years of life was calculated using individually linked cause-of-death, general practice, medication, and hospital discharge data. Severity status of diabetes was defined with dispensed medicines. RESULTS: According to all data sources combined, 28.7% of the study population had diabetes at the end of life. The estimated end-of-life prevalence of diabetes was 7.7% using multiple cause-of-death data only. Addition of general practice data increased this estimate the most (19.7%-points). Of the cases added by primary care data, 76.3% had a severe or intermediate status. CONCLUSIONS: More than one fourth of the Dutch end-of-life population has diabetes. Cause-of-death data are insufficient to monitor this prevalence, even of severe cases of diabetes, but could be enriched particularly with general practice data.

No outcome disparities in patients with diffuse large B-cell lymphoma and a low socioeconomic status.

INTRODUCTION: In patients with diffuse large B-cell lymphoma (DLBCL) socioeconomic status (SES) is associated with outcome in several population-based studies. The aim of this study was to further investigate the existence of disparities in treatment and survival. METHODS: A population-based cohort study was performed including 343 consecutive patients with DLBCL, diagnosed between 2005 and 2012, in the North-west of the Netherlands. SES was based on the socioeconomic position within the Netherlands by use of postal code and categorized as low, intermediate or high. With multivariable logistic regression and Cox proportional hazard models the association between SES and respectively treatment and overall survival (OS) was evaluated. RESULTS: Two-third of patients was positioned in low SES. Irrespective of SES an equal proportion of patients received standard immunochemotherapy. SES was not a significant risk indicator for OS (intermediate versus low SES: hazard ratio (HR) 1.31 (95%CI 0.78-2.18); high versus low SES: HR 0.83 (95%CI 0.48-1.46)). The mortality risk remained significantly increased with higher age, advanced performance status, elevated LDH and presence of comorbidity. CONCLUSION: Within the setting of free access to health care, in this cohort of patients with DLBCL no disparities in treatment and survival were seen in those with lower SES.

Do social relations buffer the effect of neighborhood deprivation on health-related quality of life? Results from the LifeLines Cohort Study.

We investigated whether social relations buffer the effect of neighborhood deprivation on mental and physical health-related quality of life. Baseline data from the LifeLines Cohort Study (N=68,111) and a neighborhood deprivation index were used to perform mixed effect linear regression analyses. Results showed that fewer personal contacts (b, 95%CI: -0.88(-1.08;-0.67)) and lower social need fulfillment (-4.52(-4.67;-4.36)) are associated with lower mental health-related quality of life. Higher neighborhood deprivation was also associated with lower mental health related quality of life (-0.18(-0.24;-0.11)), but only for those with few personal contacts or low social need fulfillment. Our results suggest that social relations buffer the effect of neighborhood deprivation on mental health-related quality of life.

Associations between Childhood Parental Mental Health Difficulties and Depressive Symptoms in Late Adulthood: The Influence of Life-Course Socioeconomic, Health and Lifestyle Factors.

BACKGROUND: Depression among older adults (i.e., the 50+) is a major health concern. The objective of this study is to investigate whether growing up with a parent suffering from mental health problems is associated with depressive symptoms in late-adulthood and how this association is influenced by life-course socio-economic, health and lifestyle factors in childhood and late adulthood. METHODS: We used life-history data from the SHARE survey, consisting of 21,127 participants living in 13 European countries. Symptoms of depression were assessed using the EURO-D scale. Parental mental health was assessed by asking respondents to report whether any of their parents had mental health problems during the respondents' childhood. Logistic regression models were used to assess the association between parental mental health status and depression. Variables on childhood and late-life socio-economic, health and lifestyle factors were sequentially added to the model to assess the extent to which this association is influenced by life-course circumstances. RESULTS: Individuals who were exposed during childhood to a parent with mental health problems suffered from depressive symptoms more often in late adulthood than those who were not (OR 1.76, 95% CI: 1.43-2.17). Adjustment for life-course socio-economic, health and lifestyle factors in childhood and late adulthood diminished this association to an OR of 1.54 (95% CI: 1.24-1.90) and OR of 1.45 (95% CI: 1.16-1.82), respectively. CONCLUSION: Our results indicate a substantial association between parental mental health problems in childhood and depression in late adulthood and that this association is partly explained by childhood as well as late adulthood socio-economic, health and lifestyle factors.

The LifeLines Cohort Study: a resource providing new opportunities for environmental epidemiology.

BACKGROUND: Lifelines is a prospective population-based cohort study investigating the biological, behavioral and environmental determinants of healthy ageing among 167,729 participants from the North East region of the Netherlands. The collection and geocoding of (history of) home and work addresses allows linkage of individual-level health data to detailed exposure data. We describe the reasons for choosing particular assessments of environmental exposures in LifeLines and consider the implications for future investigations. METHODS: Exposure to ambient air pollution and road traffic noise was estimated using harmonized models. Data on noise annoyance, perceived exposure to electromagnetic fields, perceived living environment, and neighborhood characteristics were collected with questionnaires. A comprehensive medical assessment and questionnaires were completed in order to assess determinants of health and well-being. Blood and urine samples were collected from all participants and genome wide association data are available for a subsample of 15,638 participants. RESULTS: Mean age was 45 years (standard deviation (SD) 13 years), and 59 % were female. Median levels of NO2 and PM10 were 15.7 (interquartile range (IQR) 4.9) µg/m(3) and 24.0 (IQR 0.6) µg/m(3) respectively. Median levels of daytime road traffic noise were 54.0 (IQR 4.2) dB(A). CONCLUSIONS: The combination of harmonized environmental exposures and extensive assessment of health outcomes in LifeLines offers great opportunities for environmental epidemiology. LifeLines aims to be a resource for the international scientific community.

Neighborhood income and major depressive disorder in a large Dutch population: results from the LifeLines Cohort study.

BACKGROUND: Previous studies are inconclusive on whether poor socioeconomic conditions in the neighborhood are associated with major depressive disorder. Furthermore, conceptual models that relate neighborhood conditions to depressive disorder have not been evaluated using empirical data. In this study, we investigated whether neighborhood income is associated with major depressive episodes. We evaluated three conceptual models. Conceptual model 1: The association between neighborhood income and major depressive episodes is explained by diseases, lifestyle factors, stress and social participation. Conceptual model 2: A low individual income relative to the mean income in the neighborhood is associated with major depressive episodes. Conceptual model 3: A high income of the neighborhood buffers the effect of a low individual income on major depressive disorder. METHODS: We used adult baseline data from the LifeLines Cohort Study (N = 71,058) linked with data on the participants' neighborhoods from Statistics Netherlands. The current presence of a major depressive episode was assessed using the MINI neuropsychiatric interview. The association between neighborhood income and major depressive episodes was assessed using a mixed effect logistic regression model adjusted for age, sex, marital status, education and individual (equalized) income. This regression model was sequentially adjusted for lifestyle factors, chronic diseases, stress, and social participation to evaluate conceptual model 1. To evaluate conceptual models 2 and 3, an interaction term for neighborhood income*individual income was included. RESULTS: Multivariate regression analysis showed that a low neighborhood income is associated with major depressive episodes (OR (95 % CI): 0.82 (0.73;0.93)). Adjustment for diseases, lifestyle factors, stress, and social participation attenuated this association (ORs (95 % CI): 0.90 (0.79;1.01)). Low individual income was also associated with major depressive episodes (OR (95 % CI): 0.72 (0.68;0.76)). The interaction of individual income*neighborhood income on major depressive episodes was not significant (p = 0.173). CONCLUSIONS: Living in a low-income neighborhood is associated with major depressive episodes. Our results suggest that this association is partly explained by chronic diseases, lifestyle factors, stress and poor social participation, and thereby partly confirm conceptual model 1. Our results do not support conceptual model 2 and 3.

The role of life-course socioeconomic and lifestyle factors in the intergenerational transmission of the metabolic syndrome: results from the LifeLines Cohort Study.

BACKGROUND: The risk of metabolic syndrome is associated between parents and offspring, but studies are inconsistent on differences by sex of parents and offspring. Our aim is to investigate to what extent metabolic syndrome present in fathers and mothers is associated with risk of metabolic syndrome in sons and daughters. Furthermore, we investigate to what extent these associations are explained by socioeconomic factors and health behaviours. METHODS: We used data from the LifeLines Cohort Study (N = 7239). Metabolic syndrome was defined according to the NCEP-ATPIII criteria. Logistic regression analyses were performed to investigate associations of metabolic syndrome present in parents with the risk of metabolic syndrome in offspring. Analyses were sequentially adjusted for: age and sex; childhood factors (socioeconomic position and parental smoking); and adult factors (education, income, smoking, physical activity, alcohol intake, and dietary factors). RESULTS: Multivariate regression analysis adjusted for age and sex showed associations of the metabolic syndrome between father-son: odds ratio (OR) [95% confidence interval (CI)] 2.41 (1.93-3.00), father-daughter: OR (95% CI) 1.80 (1.39-2.33)), mother-son: OR (95% CI) 1.82 (1.44-2.29) and mother-daughter: OR (95% CI) 1.97 (1.52-2.55). Furthermore, each individual factor underlying the metabolic syndrome in parents was associated with metabolic syndrome in offspring, but not for all parent-offspring combinations. None of the parent-offspring associations was attenuated when adjusting for socioeconomic factors and health behaviours. CONCLUSIONS: High risk of metabolic syndrome is transmitted from fathers and mothers to sons and daughters. Our results suggest that this transmission is irrespective of the socioeconomic position and health behaviours of the offspring.

(Un)Healthy in the City: Respiratory, Cardiometabolic and Mental Health Associated with Urbanity.

BACKGROUND: Research has shown that health differences exist between urban and rural areas. Most studies conducted, however, have focused on single health outcomes and have not assessed to what extent the association of urbanity with health is explained by population composition or socioeconomic status of the area. Our aim is to investigate associations of urbanity with four different health outcomes (i.e. lung function, metabolic syndrome, depression and anxiety) and to assess whether these associations are independent of residents' characteristics and area socioeconomic status. METHODS: Our study population consisted of 74,733 individuals (42% males, mean age 43.8) who were part of the baseline sample of the LifeLines Cohort Study. Health outcomes were objectively measured with spirometry, a physical examination, laboratory blood analyses, and a psychiatric interview. Using multilevel linear and logistic regression models, associations of urbanity with lung function, and prevalence of metabolic syndrome, major depressive disorder and generalized anxiety disorder were assessed. All models were sequentially adjusted for age, sex, highest education, household equivalent income, smoking, physical activity, and mean neighborhood income. RESULTS: As compared with individuals living in rural areas, those in semi-urban or urban areas had a poorer lung function (ß -1.62, 95% CI -2.07;-1.16), and higher prevalence of major depressive disorder (OR 1.65, 95% CI 1.35;2.00), and generalized anxiety disorder (OR 1.58, 95% CI 1.35;1.84). Prevalence of metabolic syndrome, however, was lower in urban areas (OR 0.51, 95% CI 0.44;0.59). These associations were only partly explained by differences in residents' demographic, socioeconomic and lifestyle characteristics and socioeconomic status of the areas. CONCLUSIONS: Our results suggest a differential health impact of urbanity according to type of disease. Living in an urban environment appears to be beneficial for cardiometabolic health but to have a detrimental impact on respiratory function and mental health. Future research should investigate which underlying mechanisms explain the differential health impact of urbanity.

Representativeness of the LifeLines Cohort Study.

BACKGROUND: LifeLines is a large prospective population-based three generation cohort study in the north of the Netherlands. Different recruitment strategies were adopted: recruitment of an index population via general practitioners, subsequent inclusion of their family members, and online self-registration. Our aim was to investigate the representativeness of the adult study population at baseline and to evaluate differences in the study population according to recruitment strategy. METHODS: Demographic characteristics of the LifeLines study population, recruited between 2006-2013, were compared with the total adult population in the north of the Netherlands as registered in the Dutch population register. Socioeconomic characteristics, lifestyle, chronic diseases, and general health were further compared with participants of the Permanent Survey of Living Conditions within the region (2005-2011, N = 6,093). Differences according to recruitment strategy were assessed. RESULTS: Compared with the population of the north of the Netherlands, LifeLines participants were more often female, middle aged, married, living in a semi-urban place and Dutch native. Adjusted for differences in demographic composition, in LifeLines a smaller proportion had a low educational attainment (5% versus 14%) or had ever smoked (54% versus 66%). Differences in the prevalence of various chronic diseases and low general health scores were mostly smaller than 3%. The age profiles of the three recruitment groups differed due to age related inclusion criteria of the recruitment groups. Other differences according to recruitment strategy were small. CONCLUSIONS: Our results suggest that, adjusted for differences in demographic composition, the LifeLines adult study population is broadly representative for the adult population of the north of the Netherlands. The recruitment strategy had a minor effect on the level of representativeness. These findings indicate that the risk of selection bias is low and that risk estimates in LifeLines can be generalized to the general population.

Educational disparities in the burden of disability: contributions of disease prevalence and disabling impact.

OBJECTIVES: We assessed the contributions of the prevalence and disabling impact of specific diseases to educational disparities in the prevalence of disability. METHODS: We examined a large representative survey of the Dutch population, the Dutch Permanent Survey of Living Conditions (2001-2007; n = 24 883; ages 40-97 years). We attributed the prevalence of disability to chronic diseases by using their empirical associations and assuming independent competing causes of disability. We estimated contributions of prevalence and the disabling impact of diseases to disparities in disability using counterfactuals. RESULTS: We found that the prevalence of disability in individuals with only an elementary education was 19 to 20 percentage points higher than that in individuals with tertiary education. Sixty-five percent of this difference could be attributed to specific chronic diseases, but more so to their disabling impact (49%-51%) than to their prevalence (20%-29%). Back pain, neck or arm conditions, and peripheral vascular disease contributed most to the disparity in men, and arthritis, back pain, and chronic nonspecific lung disease contributed most to the disparity in women. CONCLUSIONS: Educational disparities in the burden of disability were primarily caused by high disabling impacts of chronic diseases among low educated groups. Tackling disparities might require more effective treatment or rehabilitation of disability in lower socioeconomic groups.

Nationwide individual record linkage study showed poor agreement of causes of death and hospital diagnoses at individual level but reasonable agreement at population level.

OBJECTIVES: To investigate to what extent underlying and multiple causes of death represent end-of-life morbidity in individuals and at population level. STUDY DESIGN AND SETTING: Cause of death and national hospital data were individually linked for all deaths at the age of 50-84 years, in 2005 in the Netherlands (n = 86,987). The individual agreement of diseases registered as a diagnosis of discharge in the last 2 years of life and underlying and multiple causes of death recorded was assessed. Cause-of-death ranking was compared with ranking of hospital diagnoses. RESULTS: The percentage of persons with a hospital diagnosis registered as the underlying cause of death was <30% for most diseases, except for cerebrovascular disease, chronic obstructive pulmonary disease and bronchiectasis, acute myocardial infarction (40-60%), and cancers (70-90%). Low Cohen's kappa values confirmed poor individual agreement between hospital diagnoses and underlying and secondary causes of death recorded. At population level, however, frequency rankings of underlying and multiple causes of death agreed reasonably well with frequency ranking of hospital diagnoses (Spearman ρ of 0.58-0.60 and 0.61-0.63). CONCLUSION: Underlying and multiple causes of death poorly represent diseases present at the end of life in individuals but show reasonably well which diseases are most common at population level.

Screening for type 2 diabetes in a high-risk population: effects of a negative screening test after 4 years follow-up.

BACKGROUND: A negative diabetes screening test may unintentionally provide reassurance, resulting in reduced incentive to follow a healthy lifestyle. PURPOSE: The purpose of this study is to assess negative test result effects on lifestyle and risk perception at 4 years follow-up. METHODS: Risk perception and changes in smoking, physical activity, body mass index (BMI), and waist circumference were compared between 706 high-risk participants with a negative test result and 706 high-risk participants not offered screening (controls) in a randomized controlled trial of diabetes screening. RESULTS: Negative-screened individuals experienced a small but significant increase in BMI and waist circumference, but there was no significant difference with controls. The negative-screened group had significantly higher perception of risk of developing diabetes (p = 0.009) than controls, but no differences were observed in perceived personal control, worry, and optimistic bias. CONCLUSION: Screening negative for diabetes did not lead to overt long-term changes in lifestyle, despite a high perception of risk of developing diabetes. (ISRCTN75983009.).

Screening for type 2 diabetes in a high-risk population: study design and feasibility of a population-based randomized controlled trial.

BACKGROUND: We describe the design and present the results of the first year of a population-based study of screening for type 2 diabetes in individuals at high risk of developing the disease. High risk is defined as having abdominal obesity. METHODS: Between 2006 and 2007, 79,142 inhabitants of two Dutch municipalities aged 40-74 years were approached to participate in screening. Eligible participants had a self-reported waist circumference of ≥ 80 cm for women and ≥ 94 cm for men, and no known pre-existing diabetes. Of the respondents (n = 20,578; response rate 26%), 16,135 were abdominally obese. In total, 10,609 individuals gave written informed consent for participation and were randomized into either the screening (n = 5305) or the control arm (n = 5304). Participants in the screening arm were invited to have their fasting plasma glucose (FPG) measured and were referred to their general practitioner (GP) if it was ≥ 6.1 mmol/L. In addition, blood lipids were determined in the screening arm, because abdominal obesity is often associated with cardiovascular risk factors. Participants in both arms received written healthy lifestyle information. Between-group differences were analyzed with Chi-square tests and logistic regression (categorical variables) and unpaired t-tests (continuous variables). RESULTS: The screening attendance rate was 84.1%. Attending screening was associated with age at randomization (OR = 1.03, 95% CI 1.02-1.04), being married (OR = 1.57, 95% CI 1.33-1.83) and not-smoking currently (OR = 0.52, 95% CI 0.44-0.62). Of the individuals screened, 5.6% had hyperglycemia, and a further 11.6% had an estimated absolute cardiovascular disease risk of 5% or higher, according to the Systematic Coronary Risk Evaluation risk model. These participants were referred to their GP. CONCLUSIONS: Self-reported home-assessed waist circumference could feasibly detect persons at high risk of hyperglycemia or cardiovascular disease. Continuation of the large-scale RCT is warranted to test the hypothesis that targeted population-based screening for type 2 diabetes leads to a significant reduction in cardiovascular morbidity and mortality. TRIAL REGISTRATION: ISRCTN75983009.

Mortality risk associated with disability: a population-based record linkage study.

OBJECTIVES: We assessed the association between mortality and disability and quantified the effect of disability-associated risk factors. METHODS: We linked data from cross-sectional health surveys in the Netherlands to the population registry to create a large data set comprising baseline covariates and an indicator of death. We used Cox regression models to estimate the hazard ratio of disability on mortality. RESULTS: Among men, the unadjusted hazard ratio for activities of daily living, mobility, or mild disability defined by the Organization for Economic Co-operation and Development at age 55 years was 7.85 (95% confidence interval [CI] = 4.36, 14.13), 5.21 (95% CI = 3.19, 8.51), and 1.87 (95% CI = 1.58, 2.22), respectively. People with disability in activities of daily living and mobility had a 10-year shorter life expectancy than nondisabled people had, of which 6 years could be explained by differences in lifestyle, sociodemographics, and major chronic diseases. CONCLUSIONS: Disabled people face a higher mortality risk than nondisabled people do. Although the difference can be explained by diseases and other risk factors for those with mild disability, we cannot rule out that more severe disabilities have an independent effect on mortality.

Contribution of chronic disease to the burden of disability.

BACKGROUND: Population ageing is expected to lead to strong increases in the number of persons with one or more disabilities, which may result in substantial declines in the quality of life. To reduce the burden of disability and to prevent concomitant declines in the quality of life, one of the first steps is to establish which diseases contribute most to the burden. Therefore, this paper aims to determine the contribution of specific diseases to the prevalence of disability and to years lived with disability, and to assess whether large contributions are due to a high disease prevalence or a high disabling impact. METHODOLOGY/PRINCIPAL FINDINGS: Data from the Dutch POLS-survey (Permanent Onderzoek Leefsituatie, 2001-2007) were analyzed. Using additive regression and accounting for co-morbidity, the disabling impact of selected chronic diseases was calculated, and the prevalence and years lived with ADL and mobility disabilities were partitioned into contributions of specific disease. Musculoskeletal and cardiovascular disease contributed most to the burden of disability, but chronic non-specific lung disease (males) and diabetes (females) also contributed much. Within the musculoskeletal and cardiovascular disease groups, back pain, peripheral vascular disease and stroke contributed particularly by their high disabling impact. Arthritis and heart disease were less disabling but contributed substantially because of their high prevalence. The disabling impact of diseases was particularly high among persons older than 80. CONCLUSIONS/SIGNIFICANCE: To reduce the burden of disability, the extent diseases such as back pain, peripheral vascular disease and stroke lead to disability should be reduced, particularly among the oldest old. But also moderately disabling diseases with a high prevalence, such as arthritis and heart disease, should be targeted.

Obesity, smoking, alcohol consumption and years lived with disability: a Sullivan life table approach.

BACKGROUND: To avoid strong declines in the quality of life due to population ageing, and to ensure sustainability of the health care system, reductions in the burden of disability among elderly populations are urgently needed. Life style interventions may help to reduce the years lived with one or more disabilities, but it is not fully understood which life style factor has the largest potential for such reductions. Therefore, the primary aim of this paper is to compare the effect of BMI, smoking and alcohol consumption on life expectancy with disability, using the Sullivan life table method. A secondary aim is to assess potential improvement of the Sullivan method by using information on the association of disability with time to death. METHODS: Data from the Dutch Permanent Survey of the Living Situation (POLS) 1997-1999 with mortality follow-up until 2006 (n = 6,446) were used. Using estimated relative mortality risks by risk factor exposure, separate life tables were constructed for groups defined in terms of BMI, smoking status and alcohol consumption. Logistic regression models were fitted to predict the prevalence of ADL and mobility disabilities in relationship to age and risk factor exposure. Using the Sullivan method, predicted age-specific prevalence rates were included in the life table to calculate years lived with disability at age 55. In further analysis we assessed whether adding information on time to death in both the regression models and the life table estimates would lead to substantive changes in the results. RESULTS: Life expectancy at age 55 differed by 1.4 years among groups defined in terms of BMI, 4.0 years by smoking status, and 3.0 years by alcohol consumption. Years lived with disability differed by 2.8 years according to BMI, 0.2 years by smoking and 1.6 by alcohol consumption. Obese persons could expect to live more years with disability (5.9 years) than smokers (3.8 years) and drinkers (3.1 years). Employing information on time to death led to lower estimates of years lived with disability, and to smaller differences in these years according to BMI (2.1 years), alcohol (1.2 years), and smoking (0.1 years). CONCLUSIONS: Compared with smoking and drinking alcohol, obesity is most strongly associated with an increased risk of spending many years of life with disability. Although employing information on the relation of disability with time to death improves the precision of Sullivan life table estimates, the relative importance of risk factors remained unchanged.

Future disability projections could be improved by connecting to the theory of a dynamic equilibrium.

OBJECTIVE: Projections of future trends in the burden of disability could be guided by models linking disability to life expectancy, such as the dynamic equilibrium theory. This article tests the key assumption of this theory that severe disability is associated with proximity to death, whereas mild disability is not. STUDY DESIGN AND SETTING: Using data from the GLOBE study (Gezondheid en Levensomstandigheden Bevolking Eindhoven en omstreken), the association of three levels of self-reported disabilities in activities of daily living with age and proximity to death was studied using logistic regression models. Regression estimates were used to estimate the number of life years with disability for life spans of 75 and 85 years. RESULTS: Odds ratios of 0.976 (not significant) for mild disability, 1.137 for moderate disability, and 1.231 for severe disability showed a stronger effect of proximity to death for more severe levels of disability. A 10-year increase of life span was estimated to result in a substantial expansion of mild disability (4.6 years) compared with a small expansion of moderate (0.7 years) and severe (0.9 years) disability. CONCLUSION: These findings support the theory of a dynamic equilibrium. Projections of the future burden of disability could be substantially improved by connecting to this theory and incorporating information on proximity to death.

Disability occurrence and proximity to death.

PURPOSE: This paper aims to assess whether disability occurrence is related more strongly to proximity to death than to age. METHOD: Self reported disability and vital status were available from six annual waves and a subsequent 12-year mortality follow-up of the Dutch GLOBE longitudinal study. Logit and Poisson regression methods were used to study associations of disability occurrence with age and with proximity to death. RESULTS: For disability in activities of daily living (ADL), regression models with proximity to death had better goodness of fit than models with age. With approaching death, the odds for ADL disability prevalence and incidence rates increased 20.0% and 18.9% per year, whereas severity increased 4.1% per year. For the ages younger than 60, 60-69 and older than 70 years, the odds for ADL disability prevalence increased 6.4%, 16.0% and 23.0% per year. Among subjects with asthma/chronic obstructive pulmonary disease, heart disease and diabetes increases were 25.1%, 19.5% and 22.7% per year. Functional impairments were more strongly related to age. CONCLUSIONS: The strong association of (ADL) disability occurrence with proximity to death implies that a substantial part of the disability burden may shift to older ages with further increases in life expectancy.