Background

Socio-economic inequalities in mortality are observed at the country level in both North America and Europe. The purpose of this work is to investigate the contribution of specific risk factors to social inequalities in cause-specific mortality using a large multi-country cohort of Europeans.

Methods

A total of 3,456,689 person/years follow-up of the European Prospective Investigation into Cancer and Nutrition (EPIC) was analysed. Educational level of subjects coming from 9 European countries was recorded as proxy for socio-economic status (SES). Cox proportional hazard model's with a step-wise inclusion of explanatory variables were used to explore the association between SES and mortality; a Relative Index of Inequality (RII) was calculated as measure of relative inequality.

Results

Total mortality among men with the highest education level is reduced by 43% compared to men with the lowest (HR 0.57, 95% C.I. 0.52–0.61); among women by 29% (HR 0.71, 95% C.I. 0.64–0.78). The risk reduction was attenuated by 7% in men and 3% in women by the introduction of smoking and to a lesser extent (2% in men and 3% in women) by introducing body mass index and additional explanatory variables (alcohol consumption, leisure physical activity, fruit and vegetable intake) (3% in men and 5% in women). Social inequalities were highly statistically significant for all causes of death examined in men. In women, social inequalities were less strong, but statistically significant for all causes of death except for cancer-related mortality and injuries.

Discussion

In this European study, substantial social inequalities in mortality among European men and women which cannot be fully explained away by accounting for known common risk factors for chronic diseases are reported.

Population

The European Prospective Investigation into Cancer and Nutrition (EPIC) is a multicenter prospective cohort recruiting more than 520,000 persons in years 1992–1998, mostly aged 40 to 65 years. In the present analysis, participants recruited in Norway, Sweden (Malmö, Umea), Denmark (Copenhagen, Aarhus), The Netherlands (Utrecht, Bilthoven), Great Britain (Cambridge), Germany (Potsdam, Heidelberg), France, Italy (Florence, Varese, Ragusa, Turin, and Naples), Spain (Asturias, Granada, Murcia, Navarra, and San Sebastian), and Greece were included. In most centres, subjects were recruited from the general population in a given geographic area; some Spanish and Italian centres included blood donors; the Utrecht cohort was based on women participating in a mammography screening program; the cohorts in Norway, Utrecht, and Naples include women only. Although some sub-cohorts were not selected directly from the general population, this does not affect internal comparisons which remain valid. At the time of enrolment, all subjects completed a dietary and lifestyle questionnaire including questions on smoking status and intensity, alcohol consumption, leisure physical activity, and fruit and vegetable consumption[10]. Information on fruit, vegetables, and alcohol consumption was obtained via a Food Frequency Questionnaire enquiring about diet and alcoholic beverage consumption in the previous 12 months; this information was calibrated on a 24 h dietary recall interview generating detailed information in food and beverages consumed during the day before the interview resulting in an estimated consumption of fruit, vegetables, and alcohol in g/day10,11. Information on physical activity was derived by a combination of an index of self-reported physical activity at work (sedentary, standing, manual, heavy manual) and the sum of time spent cycling or performing any other sport (none, ≤3.5 hrs/wk, >3.5 and ≤7 hrs/wk, >7 hrs/wk)[12]. Anthropometric characteristics were also measured at enrolment.

Individuals with missing information on educational level (N = 5,697; 1.5%), smoking status (N = 4,741; 1.2%), and alcohol consumption (N = 4,674; 1.2%) were excluded from the analysis which was based on 371,295 participants. In total, 9,892 men (7.4% of men) and 7,791 women (3.3% of women) died in 3,456,689 person/years follow-up.

Information about the highest attained educational level was collected using country-specific questionnaires and classified as primary education or less, technical or professional education, secondary education, and college or university. These correspond to the UNESCO Standard Classification of Education-Attainment (ISCED-A) class 0–1 (less than primary, and primary), 2 (lower secondary), 3 (upper secondary), and 4–8 (post-secondary non-tertiary, short-cycle tertiary, Bachelor or equivalent, Master or equivalent, and Doctoral or equivalent)[13]. The analysis is based on education as a proxy for socio-economic status (SES). The study has been approved by the IARC and Imperial College Ethics Committees and by all the local Ethics Committees (The Committee of Bioethics and Deontology of the Hellenic Health Foundation, Athens, Greece; Norwich District Ethics Committee, Cambridge, UK; The National Committee on Health Research Ethic, Denmark; Comité de Protection des Personnes, France; Ethics Committee of the Heidelberg University Medical School, Germany; Comitato Etico Indipendente, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Comitato Etico Locale Azienda Sanitaria di Firenze, Florence, Italy; Ethics Committee of Lundst University, Malmo, Sweden; The Medical Ethical Committee (METC =  Medisch Ethische Toetsingscommissie) of the University Medical Center Utrecht (UMCU), Utrecht, the Netherlands; The Regional Committee for Medical and Health Research Ethics, North Norway; Scotland A Research Ethics Committee, Oxford, UK; Ethikkommission der Landesärztekammer Brandenburg Cottbus, Germany; CEIC Comité de Ética de Investigación Clínica, Spain; Human Genetics Foundation Torino: Ethics Committee, Turin, Italy; Umea Regional Ethical Review Board, Sweden); all participants gave their written informed consent to take part in the study.

End points

The follow-up period considered for this analysis ended between December 2002 and December 2006, the mean follow-up period being 9.3 years (SD 2.3). The outcome variable was death from any cause. Causes of death have been coded according to International Classification of Disease- 10^th revision (ICD-10); grouping of specific causes of death aimed at maximising consistency with previous works[3],[14]: cancer-related deaths were defined as those deaths whose underlying cause of death was coded ICD-10 C00 to D48 (including breast cancer death – C50, and cancer of trachea, bronchus and lung – C33–C34 and C39); cardiovascular-related deaths as those coded I00–I99 (including ischemic heart disease (IHD) – I20–I25; and cerebrovascular diseases – I60–I69); injuries as those coded V01-Y98[3],[14].

Statistical analyses

Analyses were conducted separately by gender with Cox regression models stratified by centre and age in one-year age category at baseline, using attained age as main time variable. Because comparisons of socioeconomic gradients based on categorical variables may be biased if the proportionate allocation of subjects across socioeconomic strata differs, a relative index of inequality (RIIs) was also computed, separately for each stratum of sex and centre, as a relative measure of education using the lowest educational level as the referent category (adapted from[7]). This ranked variable was computed as follows: if the lowest educational group is 20% of the population, the ranked variable is assigned a value of 0.20/2 = 0.10. If the next higher educational group is 30% of the population, it is assigned a value of 0.20+0.30/2 = 0.35, etc. We used a Cox regression model with mortality as the outcome variable and the ranked variable as the explanatory variable. The RII corresponds to the estimate obtained for the ranked variable and quantifies the assumed linear effect of the relative level of education on mortality. Thus, the RII expresses inequality within the whole socioeconomic continuum and can be interpreted as the ratio of mortality between the most educated (0^th percentile) and the least educated (100^th percentile). Because the RII takes into account the size and relative position of each educational group, it is appropriate for comparing populations with different educational distributions. (for more details, see[15]).

The cumulative mortality by sex and educational level in 5-year age bands was plotted for exploring the sex and age-effect of education on mortality within the cohort (each point was calculated by comparing people recruited within the same age-group in the study across educational levels).

Covariates such as gender (where appropriate), smoking status at recruitment (never smoker, former ≥10 years, former <10 years, former unknown, current <15 cigarettes/day, 15–24 cigarettes/day, ≥25 cigarettes/day), Body Mass Index (BMI) in 2.5 kg/m² categories (<20.0 to ≥37.6), alcohol consumption in deciles of distribution of g/day, leisure physical activity (divided in sex- and centre-specific tertiles of physical activity calculated on the basis of metabolic equivalent – MET – for recreational and household physical activity, plus a category for undetermined) and fruit and vegetable consumption (g/day, as continuous variable) were included into the models in a stepwise manner to estimate the extent of variation in mortality explained by these variables.

The following models were considered (stratified by centre and age): Model 1, crude (including sex if appropriate); Model 2, additionally including smoking status; Model 3, additionally including BMI; Model 4, additionally including alcohol consumption, leisure physical activity, and fruit and vegetable intake. In models investigating lung cancer mortality a more complex set of intermediate variables was used for smoking including smoking status at recruitment, age at the start and duration of smoking (in years) as continuous variables; a linear and a quadratic term for current quantity smoked (number of cigarettes per day); and two interaction terms between duration and quantity and between age at start and duration[7]. Finally, sensitivity analyses restricted to geographical regions (Northern Europe including Norway, Sweden, and Denmark; Central Europe including Netherlands, UK, and Germany; and Southern Europe, including Span, Italy, and Greece), to never smokers (Model 4a and Table S3), and replacing BMI with waist circumference in the subsample for which this information was available were conducted for all endpoints. All statistical tests were two-sided; p-values less than 0.05 were considered statistically significant; the analysis was conducted with STATA statistical software.

Table 1. Demographic characteristics of the sample, and smoking status, alcohol consumption, physical activity, and BMI at recruitment in men.

Table 2. Demographic characteristics of the sample, and smoking status, alcohol consumption, physical activity, and BMI at recruitment in women.

Table 3. Cox regression-derived Hazard Ratios (HR) for total mortality across educational levels and across Relative Inequality Index (RII) in crude (Model 1) and adjusted (Model 2 to 4) models.

Table 4. Relative Index of Inequality (RII) for specific causes of death in men and women.

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Supplementary Materials