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Additional file 1: Table A1. Weight used in the calculation of weighted kappa. Table A2. Distribution of self-rated health responses according to the order of questions stratified by groups. Table A3. Distribution of self-rated health responses according to the order of questions after adjusting for baseline weights. Table A4. Cross-tabulation of self-rated health measured by scale 1 and scale 2 after adjusting for baseline weights. Table A5. Cross-tabulation of self-rated health measured at two occasions after adjusting for baseline weights. Table A6. Cross-tabulation of self-rated health responses at two occasions among group 1 and group 2 respondents (n=11429). Table A7. Cross-tabulation of three-point scale 1 and scale 2 responses among group 1 respondents (n=7898). Table A8. Cross-tabulation of three-point scale 1 and scale 2 responses among group 2 respondents (n=3531). Table A9. Cross-tabulation of three-point scale 1 and scale 2 responses among group 1 and group 2 respondents (n=11429). Table A10. Cross tabulation of three-point scale 2 responses among group 3 respondents (n=4533). Table A11. Distribution of self-rated health responses among group 1 respondents (n=7898). Table A12. Distribution of self-rated health responses among group 2 respondents (n=3531). Table A13. Reliability of self-rated health after adjusting for baseline weights. Table A14. Test-retest reliability of self-rated health according to sample characteristics after adjusting for baseline weights. Table A15. Hazard rate ratio of death (HR), mortality rates per 1,000 person-years (MR), relative index of inequality (RII) and slope index of inequality (SII) among Chinese middle-aged and older adults (n=11429) (Results adjusted for baseline weights). Table A16. Self-rated health and age-adjusted hazard rate ratio of death (HR) stratified by education.

Additional file 1: Table S1. Comparison of ELSA sample in waves 3 and 7. Table S2. Comparison of wave 7 of analytical sample (those participating in waves 3, 6, and 7) and those who dropped-out. Table S3. Bivariate regression analyses between two measures of loneliness and socio-demographic characteristics. Table S4. Bivariate regression analyses between two measures of loneliness and geographical characteristics. Table S5. Loneliness by geographical characteristics in waves 3 and 7. Table S6a, b. Cross-tabulation of reported loneliness measures (R-UCLA and ‘Often feel lonely living in this area’) by wave (N (%). Table S7. Cross-tabulation of urban/rural characteristic and IMD (in %), wave 6.

Background\ud models projecting future disease burden have focussed on one or two diseases. Little is known on how risk factors of younger cohorts will play out in the future burden of multi-morbidity (two or more concurrent long-term conditions).\ud \ud Design\ud a dynamic microsimulation model, the Population Ageing and Care Simulation (PACSim) model, simulates the characteristics (sociodemographic factors, health behaviours, chronic diseases and geriatric conditions) of individuals over the period 2014–2040.\ud \ud Population\ud about 303,589 individuals aged 35 years and over (a 1% random sample of the 2014 England population) created from Understanding Society, the English Longitudinal Study of Ageing, and the Cognitive Function and Ageing Study II.\ud \ud Main outcome measures\ud the prevalence of, numbers with, and years lived with, chronic diseases, geriatric conditions and multi-morbidity.\ud \ud Results\ud between 2015 and 2035, multi-morbidity prevalence is estimated to increase, the proportion with 4+ diseases almost doubling (2015:9.8%; 2035:17.0%) and two-thirds of those with 4+ diseases will have mental ill-health (dementia, depression, cognitive impairment no dementia). Multi-morbidity prevalence in incoming cohorts aged 65–74 years will rise (2015:45.7%; 2035:52.8%). Life expectancy gains (men 3.6 years, women: 2.9 years) will be spent mostly with 4+ diseases (men: 2.4 years, 65.9%; women: 2.5 years, 85.2%), resulting from increased prevalence of rather than longer survival with multi-morbidity.\ud \ud Conclusions\ud our findings indicate that over the next 20 years there will be an expansion of morbidity, particularly complex multi-morbidity (4+ diseases). We advocate for a new focus on prevention of, and appropriate and efficient service provision for those with, complex multi-morbidity.

Sample description bivariates. Description and bivariate association from linear regression between each theory-driven independent variable considered for entry and cognition at age 50. Description and bivariate association from linear regression analyses.

Background: Some studies have indicated that social engagement is associated with better cognitive outcomes. This study aimed to investigate associations between life-course social engagement (civic participation) and cognitive status at age 50, adjusting for social networks and support, behavioural, health, social and socio-economic characteristics. Methods: The vehicle for the study was the National Child Development Study (1958 Birth Cohort Study), which is a general population sample in England, Scotland and Wales (9119: 4497 men and 4622 women) participating in nationally representative, prospective birth cohort surveys. The primary outcome variable was cognitive status at age 50, measured by memory test (immediate and delayed word recall test) and executive functioning test (word fluency and letter cancelation tests). The influence of hypothesised predictor variables was analysed using linear multiple regression analysis. Results: Cognitive ability at age 11 (β = 0.19;95% CI = 0.17 to 0.21), participation in civic activities at ages 33 (0.12; 0.02 to 0.22) and 50 (0.13; 0.07 to 0.20), frequent engagement in physical activity (sport) (β from 0.15 to 0.18), achieving higher level qualifications (β from 0.23 to 1.08), and female gender (β = 0.49;95% CI = 0.38 to 0.60) were positively, significantly and independently associated with cognitive status at age 50. Having low socio-economic status at ages 11 (β from -0.22 to -0.27) and 42 (β from -0.28 to -0.38), and manifesting worse mental well-being at age 42 (β = -0.18; 95% CI = -0.33 to -0.02) were inversely associated with cognitive status at age 50. The proportion of explained variance in the multiple regression model (18%), while modest, is impressive given the multi-faceted causal nature of cognitive status. Conclusions: The results indicate that modest associations between adult social engagement and cognitive function at age 50 persist after adjusting for covariates which included health, socio-economic status and gender, supporting theories of neuroplasticity. In addition to the continuing emphasis on physical activity, the encouragement of civic participation, at least as early as mid-life, should be a targeted policy to potentially promote and protect cognitive function in later mid-life. This study formed a work-package within the wider MODEM protocol (modelling outcome and cost impacts of interventions for dementia), funded by the Economic and Social Research Council (ESRC award no. ES/L001896/1), and led by Professor Martin Knapp at LSE: gtr.rcuk.ac.uk/project/2ADEAE65- E884-4B74-9283-31AD7B235014 . The funders of the study had no role in the study design, data collection, analysis, interpretation of data, writing up or publication.

Variable description. Question wording of variables included in the final model. (DOCX 23Â kb)

Response by wave. National Child Development Study (NCDS) longitudinal response by follow-up sweep number. Response rates to NCDS survey sweeps. (DOCX 19Â kb)

Change tables cognition. Distribution of categorised cognitive scores. Cognitive score changes between ages 11 and 50. (DOCX 18Â kb)

BackgroundPet ownership is thought to make a positive contribution to health, health behaviours and the general well-being of older people. More specifically pet ownership is often proposed as a solution to the problem of loneliness in later life and specific ‘pet based’ interventions have been developed to combat loneliness. However the evidence to support this relationship is slim and it is assumed that pet ownership is a protection against loneliness rather than a response to loneliness. The aim of this paper is to examine the association between pet ownership and loneliness by exploring if pet ownership is a response to, or protection against, loneliness using Waves 0–5 from the English Longitudinal Study of Ageing (ELSA).MethodsUsing data from 5,210 men and women in the English Longitudinal Study of Ageing, cross-sectional and longitudinal regression analysis was used to assess the bi-directional relationship between loneliness and pet ownership among adults aged 50+.ResultsIn 2001 (wave 0) 41% of participants were pet owners compared with 30% in 2010 (Wave 5). The association between pet ownership and loneliness is stronger in women than men, and in both directions (i.e. pet ownership predicting loneliness and loneliness predicting pet ownership) and of the similar magnitude (OR 1.2-1.4). Age, social relationships, demographic factors and health behaviour variables have only a minimal influence upon the association between loneliness and pet ownership. The results of our longitudinal analysis showed that women who reported being lonely always in Waves 0 to 5 were more likely to have a pet in Wave 5.ConclusionReported loneliness is dependent on socio-demographic characteristics such as gender, household income, household living arrangements and health status. Taking those factors into account, owning a pet significantly influences later reporting of loneliness in women in our longitudinal analysis. In the reverse direction, reported loneliness influences pet ownership in later waves. In both directions, the relatively strong gender interaction suggests the association is limited to women with effects for men minimal or non-existent.