Data from a national and representative study of the English population, i.e., the English Longitudinal Study of Aging (ELSA) [8], were used in this work. ELSA is large-scale, panel study of 12,099 participants, aged ≥50 years, living in England, and is one of the longitudinal studies included in the ATHLOS project (an EU/HORIZON2020 funded project that aims to identify health trajectories and determinants of aging). Study’s participants were re-examined during the study’s course (2002–2012) in six-periodic examinations (waves), every 2 years, i.e., in 2004 (wave 2), 2006 (wave 3), 2008 (wave 4), 2010 (wave 5) and 2012 (wave 6). The methods of the ELSA study mentioned in the present work have also previously been described in detail [8].
All participants have given written informed consent. Ethical approval for all the ELSA waves was granted from the National Research Ethics Service (MREC/01/2/91). Details of the ELSA study design, sample and data collection are available at the ELSA’s project website [https://www.elsa-project.ac.uk/].
From the initial sample of 12,099 participants at baseline evaluation, 175 subjects (1.45%) were excluded from the analysis since they were unable to be interviewed due to poor health. Moreover, 18 subjects (0.15%) were also excluded since their information was missing for at least the 25% of the health questions and measured tests at baseline examination and, 1000 participants were excluded because they reported “foreign/other” highest educational qualification and, thus, could not be classified in one of the educational levels generated for the purposes of the present work. The working sample was comprised of 10,906 participants (4967 men (64 ± 10 years) and 5939 women (64 ± 12 years)) who participated in the ELSA baseline examination. No differences as regards, age and sex distributions were observed between the initial and the working sample.
Socio-demographic and lifestyle measurements
Socio-demographic measurements retrieved from ELSA database and used in this work were: sex (men/women), age (in years), formal household wealth and educational qualification. Specifically, according to the ELSA study’s protocol, wealth status refer to household wealth including financial, physical, and housing wealth, but not pension wealth. Wealth was calculated as net of debt and included the value of any home and other property (fewer mortgages); financial assets covering all types of savings available in England; the value of any business assets and physical wealth, such as artwork and jewellery. For the purposes of the present work, participants were classified into 3 classes of household wealth: Low (1st–2nd quintile, n = 3980, 39%), Moderate (3rd quintile, n = 2002, 20%) and High (4th–5th quintile, n = 4082, 41%). For the educational status, participants were classified into three groups based on the highest qualification achieved, i.e.,: Low status, indicating that the individual left education without any formal qualifications (i.e., 0–12 years of education – compulsory education) or after only completing National Vocational Qualifications (NVQs) at level 1 (n = 5488, 50%); Moderate status, which included participants who had completed high school (i.e., 12–14 years of education) or equivalent qualifications (O-level, A-level, or National Vocational Qualifications [NVQs] at levels 2–3) (n = 2710, 25%); and High status, including those participants with college or university degrees or NVQ at Level 4 or 5 (i.e., 15+ years of education) (n = 2688, 25%). Education and wealth status were assessed at baseline, as well as at each of the six waves.
Participants were also asked how often they took part in vigorous-intensity (e.g., running/jogging, swimming, cycling, aerobics/gym workout, tennis, and digging with a spade), moderate-intensity (gardening, cleaning the car, walking at moderate pace, dancing) and low-intensity (laundry and home repairs) physical activities, using prompt cards with different activities to help them interpret different activity intensities. Response options were: more than once a week, once a week, one to three times a month, and hardly ever/never. At each time point, physical activity was further categorized into 3 categories: Inactive; only light activity at least once a week (but no moderate or vigorous) (n = 920, 8%); Moderate activity at least once a week (but no vigorous) (n = 1155, 11%), and Vigorous activity at least once a week (n = 8829, 81%). These thresholds were chosen based on previous work in ELSA demonstrating robust dose-response associations with mortality [9]. According to their smoking habits, participants were classified as never smokers (n = 3890, 36%), former smokers (i.e., those who had quitted smoking before their enrollment in the study, n = 5017, 46%) and current smokers (i.e, those who were still smoking during their enrollment in the study, n = 1998, 18%). Moreover, the frequency of any alcohol consumed in the past 12 months was recorded and responses varied from 1 “almost every day” to 8 “not at all in the past 12 months” and were classified into 3 groups: “Never” (n = 1278, 12%), “Twice a week or less” (n = 6537, 60%) and “More than twice a week” (n = 3087, 28%).
A metric of health status across all ELSA waves
To answer the research question of the present work, i.e., whether the effect of education and wealth status on all-cause mortality is mediated by aging factors, a health metric, as a proxy of healthy aging, has been used. In a previous publication of the ATHLOS project a health metric that incorporated factors associated with aging process, has already been introduced and validated [4]. Briefly, a set of 45 items were identified, comprising: 39 self-reported health questions related to impairments in body functions, limitations in Activities of Daily Living (ADL), and limitations in Instrumental Activities of Daily Living (IADL), and the other six variables were a set of tests covering cognitive functioning and walking speed [4]. This health metric has been calculated for each one of the six ELSA waves. The theoretical range of the health metric was from 0 to 100; higher values in the health metric score are indicative of better health status. The Health metric score descriptives (i.e., mean ± sd and median (range)) of the ELSA project participants across the six ELSA waves are presented in Additional file 1: Appendix 1.
10-year follow-up (2002–2012)
All-cause deaths were recorded. The time of death was available only by year, thus, binary time-specific event indicators were created for each study period. However, for n = 358 participants although the status of death was available, time of death was unknown, thus, information was retrieved looking whether respondents took part in the following surveys; if they were interviewed in later waves, they were assumed to be alive at least until the year of the last survey they responded, otherwise they were considered lost to follow-up. Thus, the participants were characterized as: (1) survivors or censored: if they did not experience a fatal event and were followed-up for all time-periods; (2) dead: if they had a fatal event during the study period; or (3) lost to follow-up: drop-outs. Mortality status was updated at February 2012. using the National Health Service Central Register, based on the informed consent provided by the participants at baseline.
Statistical analysis
The unadjusted associations were assessed by applying t-test, ANOVA and Pearson r correlation where appropriate. In order to assess the over time age/gender-adjusted effect of educational level and household wealth on the health metric calculated at each one of the 6 ELSA waves, a mixed-effects, multilevel regression was conducted using data from all of waves (specified as levels and included in the model as random effects). Then, smoking habits, physical activity and alcohol consumption, were introduced into the model, to assess their potential mediating effect on the education-health and wealth-health associations. Sobel’s test was applied to test the significance of the mediation hypothesis [10]. Furthermore, Cox proportional hazard models assessed the associations of health metric, household wealth and educational qualification on the 10-year mortality rate. The proportionality assumption was assessed graphically and the estimated mortality hazard ratios (HRs) per 10 units increase in the health metric, adjusted for age, sex, education and household wealth for each of the ELSA waves were presented in Fig. 1. Moreover, the corresponding 10-year survival curves were constructed (i.e., according to the educational and household wealth level recorded at baseline) and compared by applying the log-rank test (Fig. 2). All reported p-values were based on two-sided tests. STATA 14 (Stata Corp LLC, Texas, USA) software was used for the statistical calculations.
