This bulletin presents trends in estimates of mortality rates for men and women of working age over the decade 2001 to 2010, using the seven class reduced National Statistics Socio-economic Classification (NS-SEC), calculated based on an individual's occupation and employment status.
It is well-known that there are inequalities in mortality in England and Wales according to socio-economic position; a number of studies, including the Black Report (1980) and the Acheson Report (1998), have observed a social gradient in health (social gradient in health refers to the fact that inequalities in population health status are related to inequalities in social status).
A 2009 report from the Department of Health described health inequalities as "persistent, stubborn and difficult to change" (DH, 2009). Following this, a strategic review of health inequalities (The Marmot Review, 2010) in 2010 concluded that there continues to be a social gradient in health and that action should be taken to reduce it. In 2012, the Department of Health report Improving Outcomes and Supporting Transparency further stressed the importance of reducing "differences in life expectancy and healthy life expectancy between communities" and "preventing premature mortality" (DH, 2012).
Following a review of Government Social Classification in 1994, the National Statistics Socio-economic Classification (NS-SEC) was introduced in 2001. This measure has subsequently been used in official statistics, replacing the Registrar General’s Social Classification (RGSC). NS-SEC is based on occupation, employment status. Compared with RGSC, NS-SEC was believed to have an improved conceptual and theoretical basis (Rose and Pevalin, 2003). The conceptual basis for the NS-SEC is the structure of employment relations operating in modern, developed economies. Occupations are differentiated in terms of reward mechanisms, promotion prospects, notice periods and job security. While not designed as a hierarchy, there are differences in social advantage across the classes. The most advantaged NS-SEC groups (higher managerial and professional occupations), typically exhibit personalised reward structures, have good opportunities for advancement, relatively high levels of autonomy within the job, and are relatively secure. These attributes tend to be reversed for the most disadvantaged group (routine occupations). NS-SEC is described in greater detail in the Additional Informa tion section.
This analysis is an extension of the work presented in two earlier articles: Trends in social inequalities in male mortality, 2001–08: Intercensal estimates for England and Wales (Langford and Johnson, 2010) and Trends in social inequalities in female mortality, 2001–08: Intercensal estimates for England and Wales (Johnson and Al-Hamad, 2011). The method for producing these annual intercensal estimates is described in Intercensal denominators – feasibility of using the Labour Force Survey to estimate mortality rates by NS-SEC (Johnson and Langford, 2010).
In this analysis, the Labour Force Survey (LFS) was used to provide population denominators by age and NS-SEC for men aged 25–64 and women aged 25–59 for each year between 2001 and 2010. Numbers of deaths by NS-SEC for the corresponding period were obtained from death registrations (numerators). The populations were derived from the weighted LFS datasets (denominators). Methods were developed to extract the appropriate LFS data, adjust it to compensate for several technical issues, and align the totals to Office for National Statistics (ONS) mid-year population estimates. Testing was carried out to ensure statistical reliability and sufficient coherence between the resulting mortality rates. To examine the size of socio-economic health inequalities, two new (absolute and relative) indicators were developed. Please note that mortality rates obtained using the LFS have to be compared with each other rather than the census-based rates. For further information see the article Intercensal denominators – feasibility of using the Labour Force Survey to estimate mortality rates by NS-SEC (Johnson and Langford, 2010a).
Figure 1 shows that there was a mostly steady decline in male mortality rates for each class over the period 2001–2010, see Table 1.
Rates are directly standardised to the European standard population. Numerators and denominators have been adjusted as described in the bulletin.
At around five deaths per 100,000 person years, the 'Higher professional and managerial' class experienced the smallest average annual decline, compared with an average annual decline of around six deaths for the 'Intermediate' and 'Lower supervisory' classes, seven for the 'Self employed and own account worker' and 'Semi Routine' classes, eight for the 'Lower managerial and professional' class, and 12 for the 'Routine' class. The decline was statistically significant for each class, as illustrated by Table 2.
|NS-SEC||Estimated annual decrease||Standard error|
|Higher managerial and professional||-5.3||0.4*|
|Lower managerial and professional||-7.5||0.6*|
|Self-employed and own-account||-7.1||1.4*|
|Lower supervisory and technical||-6.4||0.7*|
* indicates statistically significant at the 95 per cent level
Figure 2 shows how the mortality rates by NS-SEC for women are extremely volatile compared with those for men over the period 2001–2010. However, the 'Higher managerial and professional', 'Lower managerial and professional', 'Self-employed and own account worker' and 'Routine' classes show a pronounced downward trend over the period, see Table 3.
The greatest annual decrease in female mortality rates occurred in the 'Routine' class, with an average annual fall of almost five deaths per 100,000 person years. This was followed by the 'Higher managerial and professional' and the 'Self-employed and own account worker' classes with an annual average decline of three deaths per 100,000 person years and the 'Lower managerial and professional class' (around two deaths). The 'Intermediate', 'Lower supervisory' and 'Semi-routine' classes did not see statistically significant annual changes, as shown by Table 4.
|NS-SEC||Estimated annual decrease||Standard error|
|Higher managerial and professional||-3.0||0.4*|
|Lower managerial and professional||-2.3||0.3*|
|Self-employed and own-account||-2.9||1.0*|
|Lower supervisory and technical||-1.5||0.8|
Figure 3 illustrates the absolute differences between the mortality of the least and most advantaged classes ('Routine' and 'Higher professional and managerial' respectively) using two methods. The first, the absolute difference of mortality rates between 'Higher professional and managerial' and 'Routine' is simply the difference between the mortality rates of the most advantaged class ('Higher professional and managerial') and the least advantaged class ('Routine'). The second method, the slope index of inequality (SII) uses all of the available data, i.e. the mortality rates of the other classes, to model the difference between mortality rates of those with the hypothetically highest and lowest socio-economic position. Both indicators are measures of absolute inequality and are calculated in terms of differences in the numbers of deaths per head of population among the NS-SEC classes. They have been indexed to 2001=100 to enable a visual comparison of the two methods. More information about this can be found in the section 'Absolute and Relative Measures'. Both measures illustrated in Figure 3 agree that, in terms of absolute numbers of deaths per 100,000 person years, inequalities decreased over the period, see Table 5. The downward trend of the slope index of inequality is statistically significant, with the largest annual reductions occurring in 2004 and 2007.
|Absolute difference of mortality rates between 'Higher managerial and professional' and 'Routine'||Slope index of inequality|
Although at first glance these estimates for women (Figure 4) suggest an increase in inequality, also see Table 6, there is enough volatility in the data to undermine confidence in the existence of a trend. The year on year change in the range of deaths for six of the years is greater than the range over the whole period, as shown in Table 3.
|Absolute difference of mortality rates between 'Higher managerial and professional' and 'Routine'||Slope index of inequality|
Figures 5 and 6 present two measures of relative inequality for men and women respectively, which have been indexed to 2001=100 for ease of interpretation.
The first indicator, the ratio of mortality rates between 'Higher managerial and professional' and 'Routine', is based on the least and most advantaged of the seven NS-SEC classes ('Routine' and 'Higher managerial and professional' respectively). The second indicator, the relative index of inequality, uses all of the available data to model the difference between the hypothetically lowest and highest person on the socio-economic scale. Both figures indicate an increase in relative inequalities. As the size of the groups at the two extremes compared decreases, so the relative difference can be expected to increase, as shown by Tables 7 and 8.
|Ratio of mortality rates between the 'Higher managerial and professional' and the 'Routine' classes, based on 2001=100||Relative index of inequality|
|Ratio of mortality rates between 'Higher managerial and professional' and 'Routine', based on 2001=100||Relative index of inequality|
The decline in overall mortality rates alongside an increase in relative inequalities between the classes may be in part due to changes in health-related behaviours (such as giving up smoking, eating a healthier diet and increasing exercise) taking place at a faster rate among the more advantaged socio-economic classes (Mackenbach et al., 2003). In the report Tackling health inequalities: 10 years on, the Department of Health describe this situation as an 'inverse care law', whereby the more advantaged groups, who are easier to reach and have a greater understanding of 'how to use the system' benefit more from health interventions than the less advantaged groups. The report goes on to say that this problem is not restricted to interventions in healthcare: "All policies in these areas, whether on education and employment, transport or the environment, have the potential unintentionally to widen the health gap" (DH, 2009).
Another explanation as to why relative inequalities are increasing when overall mortality rates are decreasing is that this is an arithmetic difference. For a policy to reduce inequalities it would need to impact on a greater number of people in the 'Routine' class compared with the 'Higher managerial and professional' class simply because there are more people in the 'Routine' class.
The comparison between the sexes is complicated by the omission of women in the age group 60-64 (excluded because of the difference in retirement ages between men and women) because there is naturally a higher rate of mortality for this age group than between ages 25 and 59. However, the results are considerably different nonetheless.
Across the classes, the average annual decline in mortality rates for men was considerably greater than for women. For example, men in the ‘Routine’ class saw an average annual decline of 12 deaths per 100,000 person years compared with almost five for women in this class. For some classes of women, there was no significant average annual decline in mortality rates. In terms of inequalities, absolute measures were greater for men, and declined over time, whereas there was no discernable trend for women. Relative measures for both sexes showed a small but significant increase in inequalities.
There are important issues to be considered when studying female mortality rates by NS-SEC. Female occupations are underreported at death registration. Therefore, for this analysis, if a woman’s occupation was missing and a spouse’s occupation was available, this was used as a proxy. The interactions between the two are complex and have not been fully explored. Women may be more influenced by the socio-economic position of their partners than vice versa. Literature on health inequalities suggests that patterns in women’s health are more strongly affected than men’s health by non-occupational factors. Also, many of the diseases with very steep social gradients, such as lower respiratory diseases and circulatory diseases are more prevalent in men than women whereas breast cancer mortality has a negligible social gradient (Mackenbach et all, 1999, Koskinen and Marelin, 1994, White et al, 2008, Langford et al, 2009). Moreover, it can be argued that women’s historically greater life expectancy means that there is greater potential for mortality improvement in men generally. These issues are discussed in the article Trends in socio-economic inequalities in female mortality: Intercensal estimates, 2001-08.
There has been a steady decrease in mortality rates for most classes, for both men and women, over the period 2001 to 2010. The least advantaged (Routine) class saw a greater average annual decline in mortality rates for both men and women than the most advantaged (Higher managerial and professional) class.
While absolute measures show that, in terms of numbers of deaths per 100,000 person years, inequalities for men decreased over the period (for women, there was no discernable trend), relative measures show that inequalities between the most advantaged class (Higher managerial and professional) and the least advantaged (Routine) increased for both sexes over the period.
An ONS article looking at inequalities in disability-free life expectancy by area of deprivation (Smith et al, 2010) reported similar findings. The study reported that while life expectancy and disability-free life expectancy increased over the period studied (2001–04 and 2005–08), the gap between the most and least deprived quintiles (20 per cent) also increased.
Further information about social inequalities in mortality can be found in the Quality and Methodological Information report (QMI). QMIs are overview reports which pull together key qualitative information on the various dimensions of the statistics as well as providing a summary of the methods used to compile the output. Information about key users of these statistics is also provided.
The results presented are dependent on the LFS as a source of the population denominators and so any changes in LFS methodology over the time period could have affected the results. Further information about this and other issues relating to the LFS is available in the article Trends in Social Inequalities in male mortality, 2001–08. Intercensal estimates for England and Wales (Langford and Johnson, 2010b).
A number of assumptions were made during the production of the population estimates, principally the health selection adjustment, which was used to correct for the known bias in the assignment of populations to NS-SEC classes. There is a case for not making these health selection adjustments, however, health selection effects are well known and to ignore them would invite a known bias. These adjustments are described in detail in the article Intercensal Denominators - Fesaibility of Using the Labour Force Survey to Estimate Mortality Rates by NS-SEC (Johnson and Langford, 2010a).
There are additional, more important, limitations to the analysis of the data for women, owing mainly to the very sparse recording of women’s occupations at death after normal retirement age. Therefore analysis for women was restricted to women aged 25–59. Further information about these limitations can be found in the article Trends in Socio-Economic Inequalities in Female Mortality, 2001-08. Intercensal Estimates for England and Wales (Johnson and Al-Hamad, 2011).
The NS-SEC category is derived from an individual’s occupation and employment status and the size of their organisation. Since size of organisation is not collected on the death register, a version of NS-SEC is used which is derived from occupation and employment status alone. This is known as ‘reduced NS-SEC’ and differs in terms of its typical distribution among NS-SEC classes by less than 3 per cent. Reduced NS-SEC was used for the analysis in this Bulletin. The table below (Table 9) lists the 7 classes and offers examples of occupations included in these classes:
|National Statistics Socio-economic Classification: analytic classes|
|1 Higher Managerial and Professional||Senior officials in national and local government, directors and chief executives of major organisations, civil engineers, medical practitioners, IT strategy and planning professionals, legal professionals, architects|
|2 Lower Managerial and Professional||Teachers in primary and secondary schools, quantity surveyors, public service administrative professionals, social workers, nurses, IT technicians|
|3 Intermediate||Non-commissioned Officers (NCOs) and other ranks in the Armed Forces, graphic designers, medical and dental technicians, local government clerical officers, counter clerks|
|4 Small Employers and Own Account Workers||Hairdressing and beauty salon proprietors, shopkeepers, dispensing opticians in private practice, farmers, self-employed taxi-drivers|
|5 Lower Supervisory and Technical||Bakers and flour confectioners, screen-printers, plumbers, electricians and motor mechanics employed by others, gardeners, rail transport operatives|
|6 Semi-routine||Pest-control officers, clothing cutters, traffic wardens, scaffolders, assemblers of vehicles, farm workers, veterinary nurses and assistants, shelf fillers|
|7 Routine||Hairdressing employees, floral arrangers, sewing machinists, van, bus and coach drivers, labourers, hotel porters, bar staff, cleaners and domestics, road sweepers, car park attendants|
Further information about NS-SEC is available in The National Statistics Socio-Economic Classification Online Edition.
To illustrate the difference between Absolute and Relative measures, consider the following hypothetical example where the mortality rate for the ‘Routine’ class is 500 deaths per 100,000 person years, and the mortality rate for the 'Higher managerial and professional' class is 100 deaths per 100,000 person years. Imagine that the rates change to 450 deaths and 75 deaths respectively. In absolute terms of deaths per 100,000 person years, the gap between the most and least advantaged classes is 400 in the first instance (500–100) and 375 in the second instance (450–75). This implies that the inequality, in terms of the absolute number of deaths involved, has reduced.
In the same hypothetical example, however, the deaths in the ‘Routine’ class are five times as high (500/100) as those in the more advantaged class. In the second instance the relative inequalities imply that mortality rates of the disadvantaged are now six times as high (450/75). So inequality in relative terms has become larger.
In this example the reduction in mortality rates for the more advantaged class, the ‘Higher managerial and professional’ class has been small in terms of the number of deaths (25 deaths) compared to the reduction achieved in the ‘Routine’ class (50 deaths). But because the more advantaged class starts at a much lower level the percentage improvement is large (25 per cent) compared to the percentage improvement in the ‘Routine’ class (10 per cent). Thus because the more advantaged class is at a lower level it is much harder to achieve similar percentage reductions in the more disadvantaged class, and hence maintain the relative gap.
For more information on how the measures are calculated, please see the article: Trends in Social Inequalities in Male Mortality, 2001-08. Intercensal Estimates for England and Wales (Langford and Johnson, 2010b).
The authors would like to thank the Labour Force Survey branch of ONS for making available the relevant data and for guidance on its use. The authors would also like to thank Brian Johnson for his help and support.
The Acheson Report (1998) Independent Inquiry into Inequalities in Health, TSO: London
The Black Report (1992) in Townsend P and Davidson N (eds) Inequalities in Health. The Black Report and the Health Divide. Penguin Books, London.
Mackenbach J P, Bos V, Andersen O, Cardano M, Costa G, Harding S, Reid A, Hemstrom O and Kunst E (2003). Widening socio-economic inequalities in mortality in six Western European countries, International Journal of Epidemiology, Vol 32, Issue 5, pp 830-837 [Accessed 22 February 2012]
Department of Health, Tackling Health Inequalities: 10 years on. A review of developments in tackling health inequalities in England over the last 10 years, May 2009 [Accessed 22 February 2012]
Department of Health Improving outcomes and supporting transparency, Part 1: A public health outcomes framework for England, 2013-2016, January 2012 [Accessed 4 March 2012]
The Marmot Review (2010) Fair society healthy lives (The Marmot Review), The Marmot Review, London [Accessed 22 February 2012]
Johnson B and Langford A (2010a) Intercensal denominators – feasibility of using the Labour Force Survey to estimate mortality rates by NS-SEC, Health Statistics Quarterly 45 pp 3–27 [Accessed 22 February 2012]
Langford A and Johnson B (2010b) Trends in social inequalities in male mortality, 2001–08. Intercensal estimates for England and Wales, Health Statistics Quarterly 47 pp 5–32 [Accessed 22 February 2012]
Johnson B and Al-Hamad A (2011) Trends in social inequalities in female mortality, 2001–08. Intercensal estimates for England and Wales Health Statistics Quarterly 52 [Accessed 22 February 2012]
Smith, M P, Olatunde, O and White, C (2010) Inequalities in disability-free life expectancy by area deprivation: England, 2001–04 and 2005–08 Health Statistics Quarterly 48 [Accessed 9 March 2012]
Office for National Statistics (2007) The National Statistics Socio-economic Classification on-line edition [Accessed 22 February 2012]
This bulletin presents age-standardised (also known as ‘directly standardised’) rates, standardised to the European Standardised Population. These make allowances for differences in the age structure of the population over time and between the sexes.
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