Describing socio-economic variation in life expectancy according to an individual's education, occupation and wage in England and Wales: An analysis of the ONS Longitudinal Study

Abstract

People who live in more deprived areas have poorer health outcomes, and this inequality is a major driver of health and social policy. Many interventions targeting these disparities implicitly assume that poorer health is predominantly associated with area-level factors, and that these inequalities are the same for men and women. However, health differentials due to individual socio-economic status (SES) of men and women are less well documented. We used census data linked to the ONS Longitudinal Study to derive individual-level SES in terms of occupation, education and estimated wage, and examined differences in adult mortality and life expectancy. We modelled age-, sex- and SES-specific mortality using Poisson regression, and summarised mortality differences using life expectancy at age 20. We compared the results to those calculated using area-level deprivation metrics. Wide inequalities in life expectancy between SES groups were observed, although differences across SES groups were smaller for women than for men. The widest inequalities were found across men's education (7.2-year (95% CI: 3.0-10.1) difference in life expectancy between groups) and wage (7.0-year (95% CI: 3.5-9.8) difference), and women's education (5.4-year (95% CI: 2.2-8.1) difference). Men with no qualifications had the lowest life expectancy of all groups. In terms of the number of years' difference in life expectancy, the inequalities measured here with individual-level data were of a similar magnitude to inequalities identified previously using area-level deprivation metrics. These data show that health inequalities are as strongly related to individual SES as to area-level deprivation, highlighting the complementary usefulness of these different metrics. Indeed, poor outcomes are likely to be a product of both community and individual influences. Current policy which bases health spending decisions on evidence of inequalities between geographical areas may overlook individual-level SES inequalities for those living in affluent areas, as well as missing important sex differences.

Keywords: Census data; Educational status; Income; Life expectancy; Mortality; Occupational groups; Socio-economic status.

Fig. 1

Consort diagram describing the dataset linkage and variables used in the analysis, as well as the flow of LS members through the data processing steps: overall numbers, analysis cohort filtering, and missing data exclusions. Data source: ONS LS.

Fig. 2

Mortality rates for 1 person-year (log scale) for ages 20-100 separated by socio-economic group and sex. In each case, the dashed black line shows the publicly-available estimates for England and Wales, 2011 (Office for National Statistics, 2019a, Office for National Statistics, 2019b). Occupation groups for men (A) and women (B); education groups for men (C) and women (D); and wage quintiles for men (E) and women (F). Shaded grey area shows ages 86–100, which were estimated by out-of-sample prediction using model coefficients (see text for details). Data source: ONS LS.

Fig. 3

Life expectancy from age 20 calculated for ages 20-100 separated by socio-economic group and sex. In each case, the dashed black line shows the publicly-available estimates for England and Wales, 2011 (Office for National Statistics, 2019a, Office for National Statistics, 2019b). Occupation groups for men (A) and women (B); education groups for men (C) and women (D); and wage quintiles for men (E) and women (F). Shaded grey area shows ages 86–100, which were estimated by out-of-sample prediction using model coefficients (see text for details). Data source: ONS LS.

Fig. 4

Life expectancy from age 20 (±95% CI) estimated by socio-economic group for (A) men and (B) women. Dotted line indicates life expectancy from age 20 for each sex calculated from the same data but for all socio-economic groups combined. Data source: ONS LS.