This bulletin presents estimates of directly age-standardised all-cause mortality rates for males and females of working age (25 to 64 for men and 25 to 59 for women) in English regions and Wales for three-year aggregated periods from 2001–03 to 2008–10, using the seven-class reduced National Statistics Socio-economic Classification (NS-SEC). Changes in absolute inequality, calculated using the Slope Index of Inequality (SII), are also reported.
The results in this bulletin are presented for three-year aggregated periods to ensure that the figures are sufficiently robust, as recommended in an earlier feasibility study ( Johnson and Langford, 2010 (648.4 Kb Pdf) ). They show that there is a lot of variation in mortality rates across different socio-economic classes and regions, and that inequality between the most and least advantaged people reduced in some regions but increased in others, suggesting that geographical variations in health persist.
Since 2001, the National Statistics Socio-economic Classification (NS-SEC) has been used in official statistics to classify individuals by socio-economic position. The NS-SEC class is derived from an individual’s occupation and employment status and the size of the organisation in which they work. 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%. Reduced NS-SEC was used for the analysis in this bulletin. Table 1 below lists the seven analytic classes and provides examples of occupations included in these classes:
|Analytic class||Examples of occupations|
|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|
The conceptual basis for NS-SEC is the structure of employment relations operating in modern developed economies (Rose and Pevalin, 2003). Occupations are differentiated in terms of reward mechanisms, promotion prospects, notice periods and job security. Those occupations exhibiting a high level of such characteristics are said to be operating on a ‘service contract’. Those with the least of these attributes are said to be operating under a ‘labour contract’. While not designed as a hierarchy, there are differences in social advantage across the classes. The most advantaged NS-SEC classes (managerial and professional occupations), typically exhibit personalised reward structures, have good opportunities for advancement, relatively high levels of autonomy within the job, and have relatively secure employment contracts. These attributes are reversed for the most disadvantaged class (Routine occupations).
Further information about NS-SEC is available in The National Statistics Socio-Economic Classification Online Edition.
Numbers of deaths by sex, age, NS-SEC and region for deaths registered in the 2001–03 to 2008–10 aggregated calendar year periods (numerators) were extracted from the ONS Death Registrations database. Adjustments were made to correct for a known bias in the assignment of deaths to NS-SEC classes, whereby some were incorrectly classified to class 3 (Intermediate) rather than class 2 (Lower Managerial and Professional) in this data source. Further adjustments were made to female data, to use the ‘combined’ approach to classification, whereby the ‘most advantaged’ NS-SEC of the woman and her husband (if she has one) is used, which better represents the socio-economic position of the household in which she resides. Further detail on these adjustments can be found in the Quality and Methodological Information report (QMI).
The Labour Force Survey (LFS) was used to provide weighted population denominators by NS-SEC for men aged 25–64 and women aged 25–59 for combined three-year periods from 2001–03 to 2008–10. This was done using the latest ONS mid-year population estimates Previously developed and tested methods (648.4 Kb Pdf) were applied to adjust the data to compensate for technical issues and align the totals to ONS mid-year population estimates(Johnson and Langford, 2010).
For each three-year period, age-standardised mortality rates for each of the seven analytical NS-SEC classes were calculated, using the European Standard Population. Confidence intervals (95%) for each mortality rate were also calculated, taking into account the variance of the death counts and the sampling variance of the LFS population estimates.
Within this bulletin, ‘statistically significant’ differences were assessed using 95% confidence intervals. They are a measure of the statistical precision of an estimate and show the range of uncertainty around the estimated figure. Calculations based on small numbers of events are often subject to random fluctuations. As a general rule, if the confidence interval around one figure overlaps with the interval around another, there is uncertainty about whether the difference between the two point estimates is statistically meaningful, because of random fluctuation in the rate of death.
To examine inequalities, two types of indicators were considered; absolute and relative. Absolute indicators measure the difference between the least and most advantaged in terms of the number of deaths per 100,000 population. Relative indicators measure inequality as a ratio of the mortality rate of the least to the most advantaged.
To illustrate the difference between the two types of measures, consider the following hypothetical example where the mortality rate for the 'Routine' class (NS- SEC class 7) is 500 deaths per 100,000 population, and the mortality rate for the 'Higher Managerial and Professional' class (NS-SEC class 1) is 100 deaths per 100,000 population. Imagine that the rates change to 450 deaths and 75 deaths respectively. In absolute terms of deaths per 100,000 population, 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%) compared to the percentage improvement in the 'Routine' class (10%). Thus because the more advantaged class is at a lower level it is harder to achieve similar percentage reductions in the mortality rate of the more disadvantaged class, and hence maintain the relative gap.
Four measures of inequality are presented in this analysis; absolute inequality, Slope Index of Inequality (SII), relative inequality, and Relative Index of Inequality (RII).
The first indicator, absolute inequality, is simply the difference in mortality rates between the most and least advantaged NS-SEC classes; the ‘Higher Managerial and Professional’ class and the ‘Routine’ class respectively. Some have criticised these types of indicators for being solely based on the extremes of the socio-economic scale and recommend that indicators use all of the available data (Low and Low, 2004).
The second indicator, the Slope Index of Inequality (SII), uses all of the available data to model the difference between mortality rates of those with the hypothetically lowest and highest socio-economic position. The SII is calculated using population-weighted linear regression and in this context it represents the hypothetical absolute difference between the extremes of the NS-SEC scale. Both indicators are measures of absolute inequality and are calibrated in terms of differences in the numbers of deaths per head of population among the NS-SEC classes.
Two indicators of relative inequality were also calculated. The first, relative inequality, is a ratio of the mortality rate of the least to the most advantaged class. The second, the Relative Index of Inequality (RII) is a similar measure to the SII. This indicator takes into account the intervening classes, in addition to the most and least advantaged classes. Both measures are relative measures of inequality and compare the position of the more disadvantaged groups in terms of the more advantaged groups.
The SII and RII indices were calculated following the method described in Sergeant and Firth (2006) and previously used by Kunst and Mackenbach (1994), among others.
The socio-economic groups were ordered from lowest to highest socio-economic status. The fraction of the population in class i or lower was calculated ( ci ), thus ci represents the cumulative proportion in class i or lower. Each group was then assigned a median social rank
x = (ci + ci-1)/2
The mortality rate for each class, y, was then regressed against the median social rank, using group population totals as weights, yielding a straight line estimate, y = a+bx
Where (a) is the hypothetically most advantaged proportion of the population, the slope index of inequality (b) represents the difference in mortality rates between the hypothetically highest and lowest on the socio-economic scale. (As calculated, b is negative, but has been reported as positive for ease of presentation.) The RII was then calculated as a/(a+b) and thus represents the ratio of mortality rates of the least advantaged to the most advantaged.
Results for all of the inequality indicators are available in the accompanying reference table (323 Kb Excel sheet) .
More information on the methods used to produce these statistics and the indicators calculated can be found in previous articles: Johnson and Langford, 2010a; Johnson and Langford, 2010b; Johnson and Al-Hamad, 2011. 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 quality dimensions of the statistics as well as providing a summary of the methods used to compile the output.
The results presented in this analysis are dependent on the Labour Force Survey (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 (346.9 Kb Pdf) (Langford and Johnson, 2010). The feasibility study which explored the use of LFS population denominators for calculating mortality rates by NS-SEC concluded that the LFS can be used to produce regular population denominators for the estimation of mortality rates, to assess health inequalities by NS-SEC at the regional level over three-year time periods. It is recommended that, in order for the LFS-based estimates to be effective over time, they would have to be related to each other rather than to the census-based ones. Below this level of geography, estimates would be insufficiently precise.
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 ( Johnson and Langford, 2010 (648.4 Kb Pdf) ).There is a case for making these health selection adjustments because health selection effects can bias the estimation of social inequalities in mortality (Goldblatt and Whitehead, 2000).
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 analyses for women were 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).
Figure 1 shows a steady decline in mortality rates for men in each socio-economic class in England and Wales over the 2001–03 to 2008–10 period. Compared with 2001–03, rates in 2008–10 were statistically significantly lower in all classes.
Figure 2 shows that there were only small changes in female mortality rates across socio-economic classes over the decade. The rates of death were statistically significantly lower for most classes in 2008–10 compared with 2001–03, other than the 'Intermediate' and 'Semi-routine' classes, where rates remained constant.
Figures 3 and 4 show four measures of inequality for men and women respectively; absolute, Slope Index of Inequality (SII), relative, and Relative Index of Inequality (RII).
For men the absolute difference in mortality rates between the least and most advantaged classes declined by 54.8 deaths per 100,000 between 2001–03 and 2008–10 which means that inequality reduced by 14.5% over the period, but the ratio increased from 3.2 times higher in 2001–03 to 3.4 times higher in 2008–10, representing a 6.3% increase in the relative ratio over the period 2001–03 to 2008–10.
When all socio-economic classes were included in the models to calculate the SII and RII, the pattern of inequality in men was consistent with the previous measures, decreasing in terms of absolute differences (SII) and increasing in terms of relative differences (RII). However, the absolute inequality was narrower using the SII at 39.3 deaths per 100,000, while the relative inequality using the RII was greater suggesting a 24% increase over the 2001–03 to 2008–10 period. These measures reflect inequality across the whole population as opposed to inequality simply between the most and least advantaged classes.
For women the absolute difference in mortality rates between the least and most advantaged classes declined by 17.1 deaths per 100,000 between 2001–03 and 2008–10 which means that the inequality has decreased by 8.3% over the period, but the ratio increased from 2.9 times higher in 2001–03 to 3.3 times higher in 2008–10, representing a 13.8% increase in the relative ratio over the period 2001–03 to 2008–10.
When all socio-economic classes were included in the models both the SII and RII increased over the period. However, the absolute inequality increased less using the SII at 10.9 deaths per 100,000, while the relative inequality using the RII was greater, suggesting a 34.3% increase over the period 2001–03 to 2008–10. This increase in absolute inequality reflects the increases in mortality rates in the intervening classes.
Comparing the results in Figures 3 and 4 with the mortality rates in Figures 1 and 2,the reduction in mortality rates for the more advantaged class, the ‘Higher Managerial and Professional’ class, has been small compared to the reduction achieved in the ‘Routine’ class. This means that simply using the absolute difference measure would imply that the gap between the most and least advantaged classes has narrowed. However, in relative terms because mortality rates for the more advantaged classes started at a lower level, the percentage improvement was greater than the percentage improvement in the least advantaged classes which started at a higher level causing the relative inequality to increase.
Figures 3 and 4 illustrate that using measures which only compare the two extremes of the socio-economic scale risks misrepresenting the overall trend in the scale of inequality in mortality rates. It also emphasises that in an age of predominantly falling mortality, absolute inequality can fall at the same time as relative inequality increases.
The remaining analysis in this Bulletin focuses on the SII indicator to show inequalities in mortality at each time period and how inequality has changed over time. It has been suggested that in terms of public health planning, where the objective is to maximise the population impact of a health policy investment, it might be preferable to use absolute differences rather than relative differences. A policy which achieves a large relative reduction in inequality, might only deliver a small difference in the overall number of lives saved (Mustard and Etches, 2003; Bartley, 2004).
Figures for all four inequality indicators are available in the accompanying reference table (323 Kb Excel sheet) .
Tables 2 and 3 show the regional mortality rates for males of working age (25–64) in each of the seven analytical NS-SEC classes for the 2001–03 and 2008–10 periods.
|Region/Country||Class 1||Class 2||Class 3||Class 4||Class 5||Class 6||Class 7|
|Yorkshire and the Humber||175||262||259||333||373||486||595|
|Region/Country||Class 1||Class 2||Class 3||Class 4||Class 5||Class 6||Class 7|
|Yorkshire and the Humber||142||219||239||293||308||398||506|
Class 1 - Higher Managerial and Professional; Class 2 - Lower Managerial and Professional; Class 3 - Intermediate; Class 4 - Small employers and own account workers; Class 5 - Lower supervisory and technical; Class 6 - Semi-routine; Class 7 - Routine
Within each region, mortality rates were mainly highest in the ‘Routine’ class and lowest in the ‘Higher Managerial and Professional’ class, following the England and Wales trend. In the intervening classes, there was not a consistent order, with mortality rates for some classes intersecting with others over the period.
There is greater variation in mortality rates across the regions within the least advantaged classes compared with the most advantaged classes. In 2001–03 the mortality rate in the ‘Higher Managerial and Professional’ Class ranged from 158 to 188 per 100,000 compared with 450 to 692 per 100,000 in the ‘Routine’ class. Although these ranges reduced by 2008–10, the wider variation in the least advantaged classes across the regions persisted.
Males living in the North West region had the highest mortality rates in most classes for the majority of the period from 2001–03 to 2008–10. Other regions with high mortality rates throughout the period were the North East region and Wales.
In the ‘Intermediate’ class, males living in London had the highest mortality rates in 2001–03. The rate then decreased so that only three other regions had lower mortality rates than London for this class in 2008–10. Males in the ‘Self-employed and Own Account Workers’ class in London also had high mortality rates in 2001–03 . This may be due to a random spike in death counts in London during those periods.
The South East and East regions had the lowest mortality rates in the majority of NS-SEC classes for males for the majority of the period from 2001–03 to 2008–10.
|Country/Region||Class 1||Class 2||Class 3||Class 4||Class 5||Class 6||Class 7|
|England and Wales||-5.4||-7.8||-7.5||-7.3||-6.6||-8.0||-13.5|
|North East||-7.4 *||-7.7 *||-12.2 *||-7.9 *||-14.4 *||-3.9 *||-22.1 *|
|North West||-6.2 *||-7.9 *||-5.3 *||-7.1 *||-12.5 *||-7.3 *||-13.4 *|
|Yorkshire and the Humber||-3.0||-7.0 *||-4.6||-5.8 *||-8.9 *||-14 *||-13.7 *|
|East Midlands||-4.6 *||-9.2 *||-3.3 *||-4.7 *||-4.9 *||-9.3 *||-9.3 *|
|West Midlands||-7.6 *||-8.8 *||-2.4||-8.8 *||-4.4 *||-8.6 *||-12.1 *|
|East||-6.5 *||-5.3 *||0.3||-5.3 *||-4.4 *||-4.0||-10.6 *|
|London||-2.7 *||-9.4 *||-21.5 *||-16.5 *||-7.9 *||-13.0 *||-18.3 *|
|South East||-6.1 *||-10.2 *||-6 *||-4.9 *||-3.7 *||-2.9||-6.7 *|
|South West||-4.1 *||-4.8 *||-8.2 *||-6.1 *||-3.8||-9.4 *||-16.5 *|
|Wales||-6.4 *||-8.2 *||-8.3 *||-5.4 *||-3.7 *||-2.5||-11.5 *|
Table 4 presents the estimated period change in mortality rates for men of working age in each NS-SEC class and region. This is estimated from a simple linear regression against time for the period 2001–03 to 2008–10 for each region. This shows that mortality rates decreased in almost all classes and areas. Men in the ‘Routine’ class in the North East region experienced the greatest estimated period decrease in mortality rates, where deaths reduced by 22.1 per 100,000 population on average in each period. In other regions, men in the ‘Routine’ class also had the highest or second highest period decreases across all classes. This suggests that in terms of the difference between the least and the most advantaged classes, there should be a reduction in inequality in most regions. This is confirmed by the SII results in figures 5 and 6.
The greatest difference in estimated period decreases between the most and least advantaged classes was observed in London, with improvement for men in the ‘Routine’ class being seven times higher than the improvement for the ‘Higher Managerial and Professional’ class. There was also a large period decrease for males in the ‘Intermediate’ class in London, where deaths reduced by 21.5 per 100,000 population on average in each period.
Only men in the ‘Intermediate’ class in the East region experienced an estimated period increase of 0.3 per 100,000 population in each period, but this was very small, and not statistically significant.
Figures 5 and 6 show the trend in the Slope Index of Inequality (SII) for males of working age (25–64) in each region and Wales over the period 2001–03 to 2008–10.
In the un-indexed chart, the SII was highest in the North East and the North West regions, which were the regions with the highest mortality rates. However, compared with other regions, inequality in the North East reduced quite considerably.
Although the un-indexed chart shows that the East Midlands had one of the lowest SIIs throughout the period, the indexed chart shows that there was a sharp increase in inequality in this region during the first three periods, before it started to decrease and level off.
In five of the periods, the SII for men in London showed the greatest decreases. Most other regions also showed decreases, however the SII for men in Wales has been increasing since 2003–05. Although men in the South East had the lowest SII for most of the period, the SII in this region has been increasing since 2002–04.
Tables 5 and 6 show the regional mortality rates for women of working age (25–59) in each of the seven analytical NS-SEC classes over the 2001–03 to 2008–10 period.
|Region/Country||Class 1||Class 2||Class 3||Class 4||Class 5||Class 6||Class 7|
|Yorkshire and the Humber||103||138||144||207||173||222||305|
|Region/Country||Class 1||Class 2||Class 3||Class 4||Class 5||Class 6||Class 7|
|Yorkshire and the Humber||81||123||149||190||193||247||304|
The trends in women’s mortality rates across regions and socio-economic classes were less regular than those for men.
As for males, within each region, mortality rates tended to be highest in the ‘Routine’ class and lowest in the ‘Higher Managerial and Professional’ class, following the England and Wales trend. In the intervening classes, there was not a consistent order, with mortality rates for some classes intersecting with others over the period.
As for men, there was greater variation in mortality rates across the regions within the least advantaged classes compared with the most advantaged classes. In 2001–03 the mortality rate in the ‘Higher Managerial and Professional’ class ranged from 96 to 117 per 100,000 compared with 269 to 367 per 100,000 in the ‘Routine’ class. This variation was maintained in 2008–10 for the ‘Higher Managerial and Professional’ class, and the gap increased by over 50% for the ‘Routine’ class. This is due to low mortality rates for this class in London in 2008–10.
Females living in the North West region had the highest mortality rates in almost all classes for the majority of the decade from 2001–03 to 2008–10. Conversely, females in the South East had the lowest mortality rates.
|Country/Region||Class 1||Class 2||Class 3||Class 4||Class 5||Class 6||Class 7|
|England and Wales||-3.1 *||-2.3 *||-0.3||-2.7 *||-1.3 *||-0.2||-6.4 *|
|North East||-3.8 *||-3.3 *||-3.7 *||-6.0 *||-1.1||2.0||-3.6 *|
|North West||-1.4||-1.4||0.7||-5.2 *||-4.6 *||1.7||-6.4 *|
|Yorkshire and the Humber||-3.3 *||-2.2 *||0.2||-0.3||4.6 *||3.9 *||1.3|
|East Midlands||-3.2 *||-5.0 *||1.8 *||-2.6||-1.3 *||2.0||-2.4|
|West Midlands||-4.0 *||-2.2 *||3.1 *||-1.8||-3.5 *||2.2 *||-3.7 *|
|Eastern||-4.2 *||-1.0||-1.6||-4.1||1.6||-4.1 *||-11.4 *|
|London||-2.3 *||-2.8 *||-2.0 *||-5.2||-0.6||-8.5 *||-12.0 *|
|South East||-3.1 *||-2.1 *||1.7||-1.5||-2.1 *||1.9||-11.4 *|
|South West||-2.5 *||-1.7 *||-1.2 *||-0.4||-0.6||-3.4 *||-4.7 *|
|Wales||-2.7 *||-2.6 *||-1.3||-0.9||-4.1 *||5.8 *||-7.4 *|
Negative (-) figures show the estimated decrease in mortality rates, positive (+) figures show the estimated increase.
Table 7 presents the estimated period change in mortality rates for women of working age (25–59) in each NS-SEC class and region. This is estimated from a simple linear regression against time for the period 2001–03 to 2008–10 for each region. This shows that the changes are much smaller for women than they were for men, and there were also notable increases across the different classes in several regions.
The mortality rates among women assigned to the ‘Routine’ class resident in the East, London and South East regions showed the greatest estimated period decreases. These large decreases are comparable with those for men.
In all except three regions, mortality rates for women in the ‘Semi-routine’ class had an estimated period increase ranging from 1.7 per 100,000 population in the North West to 5.8 per 100,000 in Wales. However the increases in the North East, North West, East Midlands and South East regions were not statistically significant.
Figures 7 and 8 show the trend in the Slope Index of Inequality (SII) for women of working age (25–59) in each region over the period 2001–03 to 2008–10.
The un-indexed chart shows that the SII was lowest in the South East for the first five periods and the indexed chart shows that the SII in this region had the greatest decrease in SII for those five periods.
There was a decrease in the SII in London from 2003–05 onwards and the indexed chart shows that compared with other regions, the SII in London decreased sharply over the last three periods and reached its lowest level in 2008–10.
Although the North West region had the highest SII for the majority of the period (un-indexed), the SII in Yorkshire and the Humber increased most sharply throughout the period.
The comparison between the sexes is limited by the omission of women in the age group 60–64 (excluded because of the difference in the traditional retirement ages between men and women and the restricted coverage of NS-SEC coding for women in this age-group). There is a naturally higher rate of mortality in this age group than between ages 25 and 59, which will affect the magnitude of period change gender comparisons, as there is greater scope for improvement among those aged 60-64 where mortality is higher than at younger ages. However, notwithstanding this limitation, the results for males and females are considerably different nonetheless.
Across the socio-economic classes and regions, the average period decline in mortality rates for men was considerably greater than for women. For example, men in the ‘Routine’ class in England and Wales experienced an average decline of 13.5 deaths per 100,000 population in each period compared with 6.4 deaths for women. Among women, for some classes no significant average declines in mortality rates were detected, and for the ‘Semi-routine’ class an increase occurred for most regions. In terms of inequalities, absolute measures were greater for men, and declined over time for most regions, whereas there was no discernable trend for women.
There are important issues to be considered when studying female mortality rates by NS-SEC. Female occupations are under-reported 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 (advantaged) 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 (Johnson and Al-Hamad, 2011).
It is important to measure inequalities in mortality experienced by different populations for policy, monitoring and research purposes. Knowing the nature and size of health inequalities, and how they change over time is necessary to inform those involved in developing and monitoring strategies and interventions designed to reduce health inequalities, such as policy makers in central and local governments, and health professionals.
It is expected that users of the data in this release will include the Department of Health and Welsh Government, Public Health England and NHS England for policy development and monitoring, local authorities and clinical commissioning groups for more local based interventions, and academics and researchers for background context and hypothesis testing to further build the evidence base on the determinant s and distribution of health inequality.
Government commissioned reports such as the Black Report (1980) and the Acheson Report (1998) identified a lack of improvement in the health experience of people working in disadvantaged occupations, and proposed a number of initiatives and strategies designed to tackle the poorer life chances of the socially disadvantaged. More recently, the Marmot Review (2010) provided a strategic review of health inequalities, which affirmed the persistence of a social gradient in health.
The Public Health Outcomes Framework Healthy lives, healthy people: Improving outcomes and supporting transparency (originally published in January 2012), sets out a vision for public health, desired outcomes and indicators to aid understanding of how well public health is being improved and protected. The framework concentrates on two high-level outcomes to be achieved across the public health system - (1) increased healthy life expectancy at birth, and (2) a reduction in differences in life expectancy and healthy life expectancy at birth between communities. These outcomes cover both mortality and morbidity, and the second outcome is focused on reducing health inequalities and covers inequalities both within areas and between areas.
Our Healthy Future outlines the Welsh Government’s ambitions for public health. In particular, it focuses on six action areas in place up to 2020, including ‘Reduced inequities in health’ which aims to reduce the gap between the health of the poorest people in Wales and those who are better off. This means tackling the unfair and avoidable differences in health by improving people’s social and economic prospects and by helping them to avoid action which can damage their health.
This is underpinned by the Fairer Health Outcomes for All: Reducing Inequities in Health Strategic Action Plan. It highlights the issue that there is an increased risk of premature mortality and ill health at each step between the least and most advantaged people in society. To address this, the plan states that in an environment of scarce resources, it is important not to focus solely on the groups nearest to the best, where it is often easiest to make a difference, and therefore increase the gap between those who are most disadvantaged and the rest.
This bulletin will contribute to the evidence base, informing users on inequalities in mortality for men and women of working age across socio-economic groups and it shows how the gap between the least and most advantaged has changed over the last decade.
Overall for England and Wales, the absolute difference in mortality rates between the least and most advantaged classes declined, but the relative ratio increased, for both sexes. The regional trends are generally similar to those at national level, but due to smaller numbers they fluctuate more Compared with 2001–03, mortality rates in all socio-economic classes in all regions and Wales were lower in 2008–10 for men, except for the ‘Intermediate’ class in the East region. Comparing the same two periods for women, mortality rates in only two regions, London and the South East, decreased in all classes.
For both sexes in the majority of the classes, the North West region had the highest mortality rates and the South East and East regions the lowest mortality rates for the majority of the period from 2001–03 to 2008–10.
While the SII for men seems to be generally decreasing across the English regions and Wales, the SII for women shows a slight upward trend. Men in the East Midlands however experienced a sharp increase in inequality during the first three periods before it started to decrease and level off. For women in London there was a notable decrease in the SII from 2003–05 onwards and it reached the lowest level in 2008–10.
The results of this analysis show that a pronounced social inequality in mortality persisted over the intercensal (between censuses) period both within and between English regions and Wales. While there is clear evidence that mortality rates improved for most classes over the 2001–03 to 2008–10 period, the SII suggests that the social inequality in absolute mortality for men has reduced, but that there has been little reduction among women, while relative mortality continues to rise for both sexes
The Acheson Report (1998) Independent Inquiry into Inequalities in Health, TSO:London
Al-Hamad (2012) ‘Intercensal Mortality Rates by NS-SEC, 2001–2010’ Health Inequalities team, Office for National Statistics.
Bartley (2004) ‘Health Inequality: An Introduction to Theories, Concepts and Methods’, International Journal of Epidemiology Volume 34, Issue 2 pp 500–502
The Black Report (1992) in Townsend P and Davidson N (eds) Inequalities in Health. The Black Report and The Health Divide. Penguin Books, London
Department of Health (2013) Public Health Outcomes Framework Healthy lives, healthy people: Improving outcomes and supporting transparency
Goldblatt P and Whitehead M (2000) ‘Inequalities in health - development and change’, Population Trends 100 pp 14–19
Johnson B and Langford A (2010)
’Intercensal denominators-feasibility of using the Labour Force Survey to estimate mortality rates by NS-SEC’ (648.4 Kb Pdf)
, Health Statistics Quarterly 45 pp 3–27
Johnson B and Al-Hamad A (2011) ‘Trends in socio-economic inequalities in female mortality, 2001–08. Intercensal estimates for England and Wales’ Health Statistics Quarterly 52 pp 3–32
Kunst A and Mackenbach J (1994) ‘Measuring Socio-economic inequalities in health’. World Health Organisation, Copenhagen
Langford A and Johnson B (2010) ‘ Trends in social inequalities in male mortality, 2001–08. Intercensal estimates for England and Wales’ (346.9 Kb Pdf) , Health Statistics Quarterly 47 pp 5–32
Low A, Low A (2004) ‘Measuring the gap: quantifying and comparing local health inequalities’ , Journal of Public Health vol. 26 no. 4
The Marmot Review (2001) Fair Society, Healthy Lives. Strategic Review of Health Inequalities in England post-2010.
Mustard C A and Etches J (2003) ‘Gender differences in socioeconomic inequality in mortality’, Journal of Epidemiology and Community Health 57 pp 974–980.
Office for National Statistics (2007) The National Statistics Socio-economic Classification online edition.
Office for National Statistics (2012) Quality and Methodological Information report 'Trends in social inequalities in males’ and females’ mortality. Intercensal estimates for England and Wales'
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Sergeant J and Firth D (2006) ‘Relative index of inequality: definition, estimation and inference’ Biostatistics 7:2 pp 213–224
The Office for National Statistics (ONS) and its predecessors have provided evidence of variations in mortality between different social groups since the 19th century. Since the Registrar General first measured inequalities using death registrations and census data, it has been conventional to report these differences decennially (every ten years) using the data available from the most recent census of population. The census provides the only detailed count of the population by age, sex, occupation and employment status. Occupation and employment status information is used to assign individuals to a standardised classification of socio-economic position. Until 2000, the Registrar General’s Social Class classification was widely used. In 2001, the National Statistics Socio-economic Classification (NS-SEC) was introduced and mortality rates for the seven analytical NS-SEC classes are reported in this bulletin. More information on NS-SEC can be found in the Methods section.
In 2010, ONS investigated the feasibility of reporting mortality rates by socio-economic position for intercensal periods, using population estimates derived from the Labour Force Survey (LFS) ( Johnson and Langford, 2010 (648.4 Kb Pdf) ). In the analysis, significant differences were detected for most NS-SEC classes between mortality rate estimates based on the 2001 Census, and those based on the contemporary LFS dataset. However the results for different years, based on LFS denominators, suggested that a series of mortality rates using LFS-based denominators appeared to have internal consistency. It was therefore concluded that the LFS can be used to produce regular population denominators for the estimation of mortality rates to assess social inequalities in health using NS-SEC for men at the level of England and Wales, both on an annual basis and over three-year aggregated time periods. It was also recommended that, in order for the LFS-based estimates to be effective over time, they would have to be related to each other rather than to the census-based ones. Further, the study recommended that it would be possible to produce mortality rates by NS-SEC at regional level, but on a three-year period basis only. This analysis was later extended to produce rates for women (Johnson and Al-Hamad, 2011).
Previously, intercensal mortality rates by NS-SEC based on LFS population denominators have only been produced at national-level for England and Wales from 2001 to 2010 ( Langford and Johnson (346.9 Kb Pdf) , 2010; Johnson and Al-Hamad, 2011; Al-Hamad, 2012) . To assess geographical variations, this Bulletin presents rates for English regions and Wales, for three-year periods from 2001–03 to 2008–10, thereby covering the entire intercensal period.
Special extracts and tabulations for mortality data by NS-SEC or Occupational Coding for England and Wales are available to order for a charge (subject to legal frameworks, disclosure control, resources and agreement of costs, where appropriate). Enquiries should be made to: Health Inequalities Team, Health and Life Events Division, Office for National Statistics, Government Buildings, Cardiff Road, Newport, Gwent, NP10 8XG or Tel: 01633 455865 or E-mail: firstname.lastname@example.org
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