: Objective.  We aimed to disentangle the effect of chronic multimorbidity and disability on 3‐year functional decline and survival in the elderly.Design.  Prospective cohort study with a mean of follow‐up of 2.8 years.Setting.  Swedish elderly persons from the Kungsholmen Project (1987–2000).Subjects.  A total of 1099 subjects, 77–100 years old, living in the community and institutions.Main outcome measurements.  Medical diagnoses (based on clinical examination, drug use, medical records and blood tests), and functional assessment (according to Katz Index) at baseline were investigated in relation to functional decline and death occurring during follow‐up.Results.  At baseline, 12.1% of participants had disability, and 52.3% were affected by multimorbidity. During follow‐up, 363 persons died and 85 worsened in functioning. The number of chronic conditions incrementally increased the risk of functional decline [hazard ratio (HR) increased from 1.5 in subjects with one disease to 6.2 in persons with 4+ diseases]. However, this was not the case for mortality, as the HR of death was the same for people with one disease as well as 4+ diseases (HR = 2.3). Baseline disability had the highest impact on survival, independently of number of diseases [HR = 8.1; 95% confidence interval (CI) = 4.8–13.7 in subjects with one disease and HR = 7.7; 95% CI = 4.7–12.6 in those with 2+ diseases].Conclusions.  In the elderly subjects, chronic disability rather than multimorbidity emerged as the strongest negative prognostic factor for functionality and survival.

Objectives: To determine the effect of chronic disorders and their co‐occurrence on survival and functioning in community‐dwelling older adults. Design: Population‐based cohort study. Setting: Kungsholmen, Stockholm, Sweden. Participants: Individuals aged 78 and older examined by physicians four times over 11 years (N = 1,099). Measurements: Chronic diseases (grouped according to 10 organ systems according to the International Classification of Diseases, Tenth Revision, code) and multimorbidity (≥2 coexisting chronic diseases) were evaluated in terms of mortality, population attributable risk of death, median years of life lost, and median survival time with and without disability (need of assistance in ≥1 activities of daily living). Results: Approximately one in four deaths were attributable to cardiovascular and one in six to neuropsychiatric diseases. Malignancy was the condition with the shortest survival time (2.5 years). Malignancies and cardiovascular disorders each accounted for approximately 5 years of life lost. In contrast, neurosensorial and neuropsychiatric conditions had the longest median survival time (>6 years), and affected people were disabled for more than half of this time. The most‐prevalent and ‐burdensome condition was multimorbidity, affecting 70.4% of the population, accounting for 69.3% of total deaths, and causing 7.5 years of life lost. Finally, people with multimorbidity lived 81% of their remaining years of life with disability (median 5.2 years). Conclusion: Survival in older adults differs in length and quality depending on specific conditions. The greatest negative effect at the individual (shorter life, greater dependence) and societal (number of attributable deaths, years spent with disability) level was from multimorbidity, which has made multimorbidity a clinical and public health priority.

The aim of this study was to examine the cross-sectional and longitudinal associations of different multimorbidity patterns with physical frailty in older adults. we used data from the Swedish National study on Aging and Care in Kungsholmen to generate a physical frailty measure, and clusters of participants with similar multimorbidity patterns were identified through fuzzy c-means cluster analyses. The cross-sectional association (n = 2,534) between multimorbidity clusters and physical frailty was measured through logistic regression analyses. Six- (n = 2,122) and 12-year (n = 2,140) longitudinal associations were determined through multinomial logistic regression analyses. six multimorbidity patterns were identified at baseline: psychiatric diseases; cardiovascular diseases, anaemia and dementia; sensory impairments and cancer; metabolic and sleep disorders; musculoskeletal, respiratory and gastrointestinal diseases; and an unspecific pattern lacking any overrepresented diseases. Cross-sectionally, each pattern was associated with physical frailty compared with the unspecific pattern. Over 6 years, the psychiatric diseases (relative risk ratio [RRR]: 3.04; 95% confidence intervals [CI]: 1.59–5.79); cardiovascular diseases, anaemia and dementia (RRR 2.25; 95% CI: 1.13–4.49) and metabolic and sleep disorders (RRR 1.99; 95% CI: 1.25–3.16) patterns were associated with incident physical frailty. The cardiovascular diseases, anaemia and dementia (RRR: 4.81; 95% CI: 1.59–14.60); psychiatric diseases (RRR 2.62; 95% CI: 1.45–4.72) and sensory impairments and cancer (RRR 1.87; 95% CI: 1.05–3.35) patterns were more associated with physical frailty, compared with the unspecific pattern, over 12 years. we found that older adults with multimorbidity characterised by cardiovascular and neuropsychiatric disease patterns are most susceptible to developing physical frailty.

Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence. The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and ozone (O3) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines. The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO2, PM2.5, BC and O3 (warm season) were 24.2 µg/m3 (19.5, 29.7), 15.4 µg/m3 (12.8, 17.3), 1.6 10-5m-1 (1.3, 1.8), and 86.6 µg/m3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m3). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m3. We did not observe associations between NO2, BC or O3 and lung cancer incidence. Long-term ambient PM2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines.

Objective To identify modifiable factors associated with longevity among adults aged 75 and older. Design Population based cohort study. Setting Kungsholmen, Stockholm, Sweden. Participants 1810 adults aged 75 or more participating in the Kungsholmen Project, with follow-up for 18 years. Main outcome measure Median age at death. Vital status from 1987 to 2005. Results During follow-up 1661 (91.8%) participants died. Half of the participants lived longer than 90 years. Half of the current smokers died 1.0 year (95% confidence interval 0.0 to 1.9 years) earlier than non-smokers. Of the leisure activities, physical activity was most strongly associated with survival; the median age at death of participants who regularly swam, walked, or did gymnastics was 2.0 years (0.7 to 3.3 years) greater than those who did not. The median survival of people with a low risk profile (healthy lifestyle behaviours, participation in at least one leisure activity, and a rich or moderate social network) was 5.4 years longer than those with a high risk profile (unhealthy lifestyle behaviours, no participation in leisure activities, and a limited or poor social network). Even among the oldest old (85 years or older) and people with chronic conditions, the median age at death was four years higher for those with a low risk profile compared with those with a high risk profile. Conclusion Even after age 75 lifestyle behaviours such as not smoking and physical activity are associated with longer survival. A low risk profile can add five years to women’s lives and six years to men’s. These associations, although attenuated, were also present among the oldest old (≥85 years) and in people with chronic conditions.