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Health Gaps by Socio-economic Position of Occupations in England, Wales, English Regions and Local Authorities, 2011 This product is designated as National Statistics

Released: 08 November 2013 Download PDF

Key points

  • Health gaps, which are here represented as the differences in health experienced between all groups, based on occupational class are large and widespread throughout England and Wales.
  • There is a North-South divide in ‘Not Good’ health rates; rates were generally higher in the North for all socio-economic classes grouped by their occupation.
  • Men and women in the least advantaged ‘routine’ occupations have the highest rates of ‘Not Good’ health in every English region and Wales (while the most advantaged ‘higher managerial and professional’ occupations have the lowest rates), although some cross over is observed between local authorities.
  • The regional health gap between classes is mostly larger for women, except for London where it is comparable to men, and the South East where it is larger for men.

  • Islington had the largest health gap in rates of ‘Not Good’ health between occupational classes for both men and women; a difference of 33.3 and 31.4 percentage points respectively.

  • It is estimated that an additional 1.6 million men and 1.8 million women would be assessing their health as ‘Good’ if they had the same self-assessed health rates as those in the most advantaged occupations such as lawyers and medical doctors.

  • There is far more variation in the rates of ‘Not Good’ health in the most socio-economically disadvantaged classes across regions and local authorities; the size of the health gap within areas is mostly driven by the rates in these classes.

  • The local authorities with the largest health gaps are generally found in large population centres, such as Inner London.

 

Introduction

This short story investigates the differences in age standardised rates of ‘Not Good’ health between groups of people based on their occupation and employment contracts (socio-economic class).These differences can be described as the health gap or inequality and can be compared between classes in the same geographical location. It is also valid to compare the rates of the same socio-economic class between areas and between men and women to show within class inequality and gender inequality.

Tackling health inequalities is an important policy issue as highlighted in the Marmot review. Census data provides an opportunity to measure health inequalities in ways that have benefits compared to using conventional survey data. The census has the advantage of high population coverage (the number and types of people surveyed), which increases the accuracy and precision of the rate estimates (greater certainty that the estimates reflect the true values that have been estimated).

Background

How does the census measure health?

The 2011 Census asked two questions related to health. The first question asked about an individual’s general health. An individual responding to Question 13 (Figure 1) can be categorised as having ‘Good’ health if they assessed their health as either Very good or Good, or as having ‘Not Good’ health if they assessed it as Fair, Bad or Very bad.

Figure 1: The general health question included in the 2011 Census

The second health question was asked to measure activity limitations (disability). A similar analysis to that reported here measuring socio-economic gaps in disability prevalence will be released in December 2013.

How does the census assess socio-economic position?

People1 aged 16 years and above who reported a current or (if not currently working) previous occupation and an employment status (whether a manager, a supervisor, an employee or self-employed) were placed into a socio-economic class on the basis of these personal details. The name for this grouping of occupations is the National Statistics Socio-economic Classification (NS-SEC). Those who had never worked or had been unemployed for six months or more or had no occupational information included in their census form were excluded from this analysis.

Unlike the health questions asked in the 2011 Census, the questions used to derive NS-SEC are numerous and the derivation procedure is complex. The 2011 Census questions used in this derivation of NS-SEC have been included in the background notes section of this article. More information about NS-SEC can be found on the ONS website.

Table 1: Indicative examples of professions in each reduced NS-SEC Class [1]

NS-SEC Class Examples of Jobs 
1. Higher managerial and professional  Lawyers, Architects, Medical doctors, Chief executives, Economists,
2. Lower managerial and professional 
Social workers, Nurses, Journalists, Retail managers, Teachers
3. Intermediate  Armed forces up to sergeant, Paramedics, Nursery nurses, Police up to sergeant, Bank staff
4. Small employers and own account workers Farmers, Shopkeepers, Taxi drivers, Driving instructors, Window cleaners  
5. Lower supervisory and technical 
Mechanics, Chefs, Train drivers, Plumbers, Electicians
6. Semi routine  Traffic wardens, Receptionists, Shelf stackers, Care workers, Telephone salespersons
7.Routine  Bar staff, Cleaners, Labourers, Bus drivers, Lorry drivers

Table source: Office for National Statistics

Table notes:

  1. The reduced NS-SEC class to which an individual belongs is not solely based on occupation but also other factors such as whether they are employers and how many people they employ. For example, a window cleaner that is self-employed or is an employer would be in NS-SEC class 4 while a window cleaner who is an employee would be in NS-SEC class 7.

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Health outcomes have been shown to vary markedly between people depending on their socio-economic position based on occupation. Socio-economic position is a good indicator of the general living conditions, access to goods and services, career development prospects, educational attainment, salary range, disposable income, wealth, assets and social standing: Such factors are important drivers of well-being and health.

Analysis of the 2001 Census found that there were significant health gaps between the least and most advantaged in terms of ‘Good’ health, life expectancy and healthy life expectancy (FOH 2006, ONS 2011, White and Edgar 2010). The statistics produced in this short story uses more up-to-date data from the 2011 Census to provide a picture of health inequality between occupations assigned to different socio-economic positions within and across geographical locations. Such information can be used as evidence to justify health policies aimed at tackling inequality in health. 

Why use age standardisation?

In this analysis the age standardised rates of ‘Not Good’ health of those aged 16 and over and classifiable into the reduced NS-SEC scale have been calculated.

Calculating the crude rates of health for a population is relatively straightforward. The problem is that using crude health rates can lead to unreliable comparisons of populations with different age structures. This is because there is a strong relationship between health and age, with health worsening with increasing age. Therefore, it would be expected that a population with an older age structure would have a higher crude rate of ‘Not Good’ health than a population with a younger age structure, all else being equal, which could bias any comparisons. This age bias can mask other important differences between populations such as socio-economic class, gender or geographical location.

The method of age standardisation applies an adjustment through weighting which enables different populations to be compared on an equal footing in terms of age structure. Age standardisation gives a health rate for the whole population of interest, by removing age as a factor and thus allowing meaningful comparisons between genders, socio-economic classes or geographical locations. The European Standard Population (ESP) 2013 has been used in this short story. Please see the background notes section for more detail.

Notes for Background

  1. This analysis includes those aged 16 and over and classifiable into the reduced NS-SEC based on current or former occupation and employment status

National overview of gender distribution into NS-SEC classes

The distribution of men and women in each socio-economic class was not equal. More men and women worked in 2011 (or had worked) in lower managerial and professional occupations (Class 2) than in any other socio-economic class at 22.1 per cent and 25.7 per cent respectively. The smallest class for men was the intermediate occupations (Class 3) at 7.7 per cent and lower supervisory and technical occupations (Class 5) was the smallest for women at 4.8 per cent.

In the most socio-economically advantaged higher managerial and professional occupations (Class 1), there was a larger proportion of men (15.6 per cent) than women (7.7 per cent). In the most disadvantaged routine occupations (Class 7) the proportions of men and women were more comparable at 15.0 per cent for men and 11.9 per cent for women.

It is interesting to note the larger proportions of men in small employers and own account workers (Class 4) and lower supervisory and technical occupations (Class 5). Far greater proportions of women were working or had worked in the intermediate occupations (Class 3).

Figure 2: Proportion of men and women in NS-SEC classes

Figure 2: Proportion of men and women in NS-SEC classes
Source: Census - Office for National Statistics

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Regional and national comparisons of health rates

The rates of ‘Not Good’ health presented and discussed in this analysis are the age standardised rates. Please see background notes for more detail.

For men and women at the England and Wales level there is a general pattern of increasing rates of ‘Not Good ‘health in line with increasing disadvantage associated with the socio-economic position of the occupation (i.e. Class 7 had worse health than Class 1).

This general pattern is also observed in each English region and Wales. However, although the overall pattern between classes is similar in each region, there is considerable variation between the health rates within each class between regions. This suggests it is not only where you are in the socio-economic scale which influences your health but also your geographical location. For example, the rate of ‘Not Good’ health for women in routine occupations in Wales was 38.5 per cent; 8.5 percentage points higher than for women in routine occupations in the South East at 30.0 per cent.

The regions that generally had the lowest rates of ’Not Good’ health for men and women in all NS-SEC classes were the East of England and South East respectively, whereas the North East and Wales generally had the highest rates for men and women. To illustrate the effect of region of residence on the social differences in health rates, men working in the East of England and in Class 7 had lower rates of ‘Not Good’ health than those in Class 5 in the North East at 27.0 per cent and 27.9 per cent respectively (Figure 3). For women, a similar pattern was observed with workers in Class 7 in the South East having better health rates than workers in Class 5 in Wales at 30.0 per cent and 30.9 per cent respectively (Figure 4).

A comparison of regions shows there was more variability in self-assessed health within the less advantaged NS-SEC classes, particularly in Class 7, but less variability in the more advantaged classes, particularly Class 1. For example when considering the regions for men there is only a 2.4 percentage point difference in ‘Not Good’ health rates in Class 1 between the East of England and the North East, while for Class 7 the difference was greater at 7.2 percentage points. This means the health of those in Class 1 seems to be less affected by the region they live in, whereas the health of those in Class 7 were more affected. A similar pattern was also observed for women.

Figure 3: Age standardised rates of ‘Not Good’ health of men by NS-SEC class for England and Wales and selected regions [1]

Figure 3: Age standardised rates of ‘Not Good’ health of men by NS-SEC class for England and Wales and selected regions [1]
Source: Census - Office for National Statistics

Notes:

  1. The East and North East were selected as they were the regions that generally had the lowest and highest rates of ‘Not Good’ health for all NS-SEC classes respectively.

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Figure 4: Age standardised rates of ‘Not Good’ health of women by NS-SEC class for England and Wales and selected regions [1]

Figure 4: Age standardised rates of ‘Not Good’ health of women by NS-SEC class for England and Wales and selected regions [1]
Source: Census - Office for National Statistics

Notes:

  1. The South East and Wales were selected as they were the regions that generally had the lowest and highest rates of ‘Not Good’ health for all NS-SEC classes respectively.

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To summarise the variation between regions and Wales, figures 5 and 6 display the ‘Not Good’ health rates for Class 1 and Class 7 and the range between the two classes, for men and women respectively. Comparing the health rates of Class 1 and Class 7 gives a simple measure of the health gap by using the difference (i.e. the range) between the least and most disadvantaged classes.

Figure 5: Age standardised rates of ‘Not Good’ health for men in NS-SEC classes 1 and 7 and the range between these two classes, regions and Wales [1]

Figure 5: Age standardised rates of ‘Not Good’ health for men in NS-SEC classes 1 and 7 and the range between these two classes, regions and Wales [1]
Source: Census - Office for National Statistics

Notes:

  1. The regions have been sorted in ascending order of ‘Not Good’ health rate for Class 7.
  2. Please note the secondary axis for the range.

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Figure 6: Age standardised rates of ‘Not Good’ health for women in NS-SEC classes 1 and 7 and the range between these two classes, regions and Wales [1]

Figure 6: Age standardised rates of ‘Not Good’ health for women in NS-SEC classes 1 and 7 and the range between these two classes, regions and Wales [1]
Source: Census - Office for National Statistics

Notes:

  1. The regions have been sorted in ascending order of ‘Not Good’ health rate for Class 7.
  2. Please note the secondary axis for the range.

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Generally, the range is greater in those regions where rates of ‘Not Good’ health are highest. For both genders the regions with low rates of ‘Not Good’ health and low ranges are found in the South; specifically, the East, South East and South West. The regions with the highest rates of ‘Not Good’ health and highest ranges, thus having a greater health gap, are the North West, and the North East, plus Wales. As the rates of ‘Not Good’ health are more variable between regions among Class 7, it is the less favourable health of the men and women in Class 7 in the North of England and in Wales that is contributing most to the large health gaps found there.

If the South East, South West and East of England in this context are defined as the South, and the North East, North West, and Yorkshire and the Humber are defined as the North, then a clear North-South divide exists, both in terms of the difference in class specific age standardised health rates and the size of the gaps between Class 1 and Class 7. London is not included in the South due to its differing characteristics which include its transient population, access to services and limited rural population.


 

 

Assessing the health gap or inequality within regions

In the context of current health legislation in England, a duty is placed on health organisations to have regard to reducing health inequalities when formulating policies and making decisions about funding health services. For the purposes of the government policy, a large health gap is undesirable.

The range in health rates can be viewed as a simplified version of the health gap. In order to measure the health gap more reliably, a statistic named the Slope Index of Inequality (SII) is used. The SII measures the health gap by accounting for the inequality existing between the other classes (classes 2 to 6) and also accounting for the varying distribution and numbers working in the differing socio-economic classes between regions and local authorities.

In this analysis the SII represents the absolute difference in health rates between the least and most disadvantaged socio-economic classes, taking account of the health rates in all the classes. Smaller SII values represent narrower health gaps, while larger values represent larger health gaps and greater inequality. The SII is reported here as the percentage of people with ‘Not Good’ health. For example, an SII of 10 per cent means that the difference between the rates of ‘Not Good’ health for the most advantaged men and women in Class 1 and the least advantaged in Class 7 is 10 per cent. Therefore if the most advantaged in Class 1 have a ‘Not Good’ health rate of 15 per cent then the least advantaged in Class 7 would have a ‘Not Good’ health rate of 25 per cent.

Using the SII, the health gap is larger for women in every region except the South East, where the health gap is larger for men, and in London where the health gap is identical1 for both genders (see table 2).

For men the largest health gap was in the North East at 21.6 per cent followed by the North West at 21.5 per cent and Wales at 21.0 per cent. The smallest health gap for men was in the East of England at 16.7 per cent followed by the South West at 17.2 per cent and the South East at 17.7 per cent.

For women the largest health gap was in Wales at 23.4 per cent followed by the North East at 23.1 per cent and the North West at 22.4 per cent. The smallest health gap was in the East of England at 17.2 per cent followed by the South West and South East both at 17.4 per cent.

Between regions the health rates of those in the least advantaged socio-economic classes vary more than the health rates of the most advantaged classes. In addition, regions with higher rates of ‘Not Good’ health across all NS-SEC classes also have the largest health gaps. The main driver for a small health gap in a region was the comparatively low rates of ‘Not Good’ health in classes 5, 6 and 7. The regions with a large inequality had comparatively high rates in these most disadvantaged socio-economic classes.

Table 2: The health gap by region, country and sex, using the Slope Index of Inequality (SII)

England and Wales

% 'Not Good' health
Region/Country  Health gap  men (SII) Heath gap  women (SII) Gender difference  in health gap  Rank  men Health gap  Rank  women  Health Gap 
South East 17.7 17.4 -0.3 3 3
London 20.9 20.9 0.0 7 5
South West 17.2 17.4 0.2 2 2
East 16.7 17.2 0.5 1 1
North West 21.5 22.4 0.9 9 8
East Midlands 18.5 19.7 1.2 4 4
West Midlands 20.0 21.4 1.4 6 7
Yorkshire and The Humber 19.9 21.4 1.5 5 6
North East 21.6 23.1 1.5 10 9
Wales 21.0 23.4 2.4 8 10

Table source: Office for National Statistics

Table notes:

  1. The gender difference in health gap in this table is the health gap for men minus the health gap for women.
  2. The table has been sorted in ascending order by the size of the gender difference in health gap.
  3. 'Not Good’ general health was derived form those responding ‘Fair’ ‘Bad’ or ‘Very Bad’ to the general health question in the 2011 Census.
  4. The age standardised rates used to calculate the SII have been calculated using the 2013 European Standard Population (ESP).
  5. Regions have been ranked on the health gap to more than 1 decimal place.

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The fact that the size of the health gap varies between regions suggests that socio-economic position alone does not fully account for inequality in health. Regional factors such as varying job certainty, housing quality, access to services and differing concentrations of hazardous occupations (within the occupational groupings) can contribute to variations in the size of the health gaps between regions.

It is possible to demonstrate the health benefits of removing inequality between the most advantaged class and the other classes. If all working people (or those that have worked) in England and Wales were to have the same rate of ‘Not Good’ health as someone of comparable age and sex in the higher managerial and professional grouping (Class 1) then 1.8 million more women and 1.6 million more men would have assessed their health as ‘Good’ rather than ‘Not Good’.

 

Notes for Assessing the health gap or inequality within regions

  1. Identical to 1 decimal place.

Health inequality by local authority

As mentioned previously, in the context of health policy, reducing inequality is desirable. Tables 3 and 4 show the 10 local authorities with the smallest health gaps between socio-economic classes and the 10 local authorities with the largest gaps for men and women respectively. Again, the statistic used to measure the health gap is the Slope Index of Inequality (SII), where a small SII value indicates a small health gap or inequality and a large SII value indicates greater inequality.

Unsurprisingly, the 10 local authorities that have the smallest health gaps are generally found in those regions with small health gaps. South Holland, in the East Midlands, was the most equal for men with an SII of 9.8 per cent, while Rochford, in the East of England, was the most equal for women with an SII of 11.1 per cent. South Holland, Blaby and Broadland all feature in the top 10 most equal local authorities for both men and women.

A common feature of the 10 local authorities with the smallest health gap is the absence of large population centres. By examining the health rates of the most disadvantaged Class 7 in these authorities, they all had rates of ‘Not Good’ health much lower than the England and Wales average for routine workers, for both men and women. This demonstrates that the relatively better health of those in the more disadvantaged classes leads the inequality to be smaller.

When considering the 10 local authorities with the largest health gaps for men and women, seven were in London for men and five for women. Islington had the highest health gap for both men and women with an SII of 33.3 per cent and 31.4 per cent respectively. Tower Hamlets, Cardiff, Camden and Newcastle upon Tyne also featured in the 10 with the largest inequality for both men and women.

The local authorities with large health gaps have the common feature of being large urban areas with more pronounced differences in the levels of deprivation their resident populations are exposed to. When examining self-assessed health for those in the more disadvantaged classes in these local authorities, it showed they all had higher rates of ‘Not Good’ health than the England and Wales average for their specific class.

The percentage point difference in the health gap between the local authorities with the highest and lowest gaps was 23.5 per cent for men and 20.3 per cent for women. These differing inequalities mean the health gap for men in Islington is three times greater than that of men in South Holland.

Table 3: 10 local authorities with the smallest and largest health gaps for men

England and Wales

% 'Not Good' Health
Males     
Smallest 10 Local Authority Name  Region  Health Gap  (SII) LA Rank
    South Holland East Midlands 9.8 1
    Blaby East Midlands 10.4 2
    Harlow East 11.3 3
    Fenland East 11.4 4
    Broadland East 11.7 5
    Rushmoor South East 11.7 6
    Purbeck South West  12.3 7
    Boston East Midlands 12.4 8
    Forest Heath East 12.5 9
    Eden North West  12.6 10
Largest  10 Local Authority Name  Region  Health Gap  (SII) LA Rank
    Wandsworth London  25.0 337
    Newcastle upon Tyne North East 25.2 338
    Cambridge East 25.4 339
    Cardiff Wales 26.4 340
    Hammersmith and Fulham London  27.9 341
    Kensington and Chelsea London  28.6 342
    City of London, Westminster London  30.7 343
    Camden London  31.3 344
    Tower Hamlets London  31.9 345
    Islington London  33.3 346

Table source: Office for National Statistics

Table notes:

  1. Local Authorities have been ranked on the health gap to more than 1 decimal place.
  2. Data for City of London has been merged with Westminster, data for Isles of Scilly has been merged with Cornwall due to small population counts. Therefore, in this analysis, there are 346 local authorities in England and Wales.
  3. 'Not Good’ general health was derived form those responding ‘Fair’ ‘Bad’ or ‘Very Bad’ to the general health question in the 2011 Census.
  4. The age standardised rates used to calculate the SII have been calculated using the 2013 European Standard Population (ESP).

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Table 4: 10 local authorities with the smallest and largest health gaps for women

England and Wales

% 'Not Good' Health
Females   
Smallest 10 Local Authority Name  Region  Health Gap  (SII) LA Rank
    Rochford East 11.1 1
    Castle Point East 11.7 2
    East Dorset South West  11.8 3
    Fareham South East 12.5 4
    Broadland East 12.6 5
    South Holland East Midlands 12.6 6
    Hart South East 12.7 7
    Blaby East Midlands 13.0 8
    Wokingham South East 13.0 9
    Horsham South East 13.0 10
Largest 10 Local Authority Name  Region  Health Gap  (SII) LA Rank
    Blackburn with Darwen North West  25.2 337
    Neath Port Talbot Wales 25.4 338
    Rochdale North West  25.5 339
    Camden London 25.9 340
    Hackney London 26.0 341
    Cardiff Wales 26.3 342
    Haringey London 26.9 343
    Tower Hamlets London 28.1 344
    Newcastle upon Tyne North East  28.2 345
    Islington London 31.4 346

Table source: Office for National Statistics

Table notes:

  1. Local Authorities have been ranked on the health gap to more than 1 decimal place.
  2. Data for City of London has been merged with Westminster, data for Isles of Scilly has been merged with Cornwall due to small population counts. Therefore, in this analysis, there are 346 local authorities in England and Wales.
  3. 'Not Good’ general health was derived form those responding ‘Fair’ ‘Bad’ or ‘Very Bad’ to the general health question in the 2011 Census.
  4. The age standardised rates used to calculate the SII have been calculated using the 2013 European Standard Population (ESP).

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In order to reduce the health gaps it is important to understand what is driving the difference between local authorities. Analysing all local authorities there is a strong correlation between the self-assessed ‘Not Good’ health rate of those in Class 7 and the size of the health gap (correlation coefficient1 0.75 for men and 0.86 for women).The correlation between the ‘Not Good’ health rates of those in Class 1 and the health gap is either non existent or very weak (correlation coefficient 0.06 for men, 0.32 for women). Figure 7 shows that there is an increase in the correlation with decreasing socio-economic advantage for men and women. This suggests the health gap observed in all local authorities is determined to a greater extent by the health rates of those in the more disadvantaged socio-economic occupational classes; specifically the Lower supervisory and technical, Semi-routine and Routine classes.

The differing distributions of particular occupations or working practices that are hazardous to health, between regions could explain why the rates of ‘Not good’ health vary more among the most disadvantaged socio-economic classes. For example, night time working may be more prevalent for routine workers in large urban centres in the North than in more rural settings in the South.

In addition, the geographic variations in the rates of ‘Not Good’ health among the most disadvantaged socio-economic classes may also be affected to some extent by the deprivation they experience in their area of residence (White and Edgar 2010).In the context of health policy, these results suggest greater reductions in health inequalities could be made nationally by improving the health of the semi-routine and routine classes in those authorities with the widest health gaps.

Figure 7: Correlation of the health gap and ‘Not Good’ health rate by NS-SEC class and sex

Figure 7: Correlation of the health gap and ‘Not Good’ health rate by NS-SEC class and sex
Source: Census - Office for National Statistics

Notes:

  1. The correlation is between the age standardised ‘Not Good’ health rate of the specific NS-SEC class and the size of the health gap measured by the SII across all local authorities.
  2. The correlation coefficient indicates to what extent two variables are linearly correlated and can take any value between -1 and +1. The correlations in this analysis are all positive and therefore could range from 0 to 1. There is a strong correlation if the correlation coefficient is 0.75 or larger and below 0.3 there is no linear correlation.

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Notes for Health inequality by local authority

  1. The correlation coefficient indicates to what extent two variables are linearly correlated and can take any value between -1 and +1. The correlations in this analysis are all positive and therefore could range from 0 to 1. There is a strong correlation if the correlation coefficient is 0.75 or larger and below 0.3 there is no linear correlation.

Background notes

  1. The Slope Index of Inequality (SII) assesses the absolute health gap or inequality between the least and most advantaged NS-SEC classes taking into account the health rates of all NS-SEC classes. The SII is the slope of a population weighted linear regression of the age standardised rate against half the proportion of the population in the class plus the cumulative proportion of preceding classes in the ranking. The regression line accounts for the varying sizes of the proportions in each NS-SEC class in different geographies giving a standard measure of inequality that can be directly compared. The SII has the same units as the age standardised rates of per cent in ‘Not Good’ health.

  2. Questions 26-38 in the 2011 Census were used to derive each individuals NS-SEC  and can be seen in box 1.




     

  3. The rates of ‘Not Good’ health reported in this short story have been age standardised to the European Standard Population 2013. These age standardised estimates are calculated to allow comparison of populations with differing age structures. Age standardisation is a process where the age specific rates of ‘Not Good’ health for a particular area or class are applied to a hypothetical European standard population (ESP) for the corresponding age group. The hypothetical number of people in the ESP with ‘Not Good’ health in each age group is totalled and then divided by the total ESP for all ages studied, to give age standardised rates. There were 10 age groups in the census tables:16-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65+.The age specific groups in the census tables used in this analysis did not overlap perfectly with the ESP. The ESP has an age group of 15-19 whereas this analysis only focussed on those aged 16 and over. Therefore, in order to create a population total for the age group 16-19, a fifth of the total population in age group 15-19 was deducted. This was then added to the age group 20-24 to give an ESP for the age group 16-24 used in this analysis. Similarly for age group 65+ all age groups in the ESP above 65 were combined to give an ESP weight for those aged 65+.

  4. The European Standard Population 2013 was published by Eurostat, the statistical institute of the European Commission, on 11 July 2013. The publication of the ESP 2013 provides an up-to-date standard population which reflects the average age structure of European countries from 2010-2030; this is important because of population ageing since the original ESP in 1976. ONS held a public consultation on the implementation of the ESP 2013 in UK official statistics which closed on 3 October 2013. Plans for future use of the ESP 2013 in UK official statistics will be published in the near future.

  5. A spreadsheet (64 Kb Excel sheet) detailing the calculation of age standardised rates to the European Standard Population can be found on the ONS website.

  6. The data in this article is all usual residents aged 16 and over in households.

  7. Census day was 27 March 2011.


  8. Regional 2011 Census data for all persons is available via the Nomis website using data table DC6303EWr (General health by NS-SEC by age by sex).

  9. Interactive data visualisations developed by ONS are also available to aid interpretation of the results.

  10. Future releases from the 2011 Census will include cross tabulations by other census characteristics, and tabulations at other geographies. Further information on future releases is available online in the 2011 Census Prospectus (457.4 Kb Pdf) .

  11. ONS has ensured that the data collected meet users' needs via an extensive 2011 Census outputs consultation process in order to ensure that the 2011 Census outputs will be of increased use in the planning of housing, education, health and transport services in future years.

  12. Figures in this publication may not sum due to rounding.

  13. The England and Wales census questionnaires asked the same questions with one exception; an additional question on Welsh language was included on the Wales questionnaire.

  14. ONS is responsible for carrying out the census in England and Wales. Simultaneous but separate censuses took place in Scotland and Northern Ireland. These were run by the National Records of Scotland (NRS) and the Northern Ireland Statistics and Research Agency (NISRA) respectively.

  15. ONS is responsible for the publication of UK statistics (compiling comparable statistics from the UK statistical agencies above) and these are available on the ONS website. These will be compiled as each of the three statistical agencies involved publish the relevant data. The Northern Ireland census prospectus and the Scotland census prospectus are available online.

  16. A person's place of usual residence is in most cases the address at which they stay the majority of the time. For many people this will be their permanent or family home. If a member of the services did not have a permanent or family address at which they are usually resident, they were recorded as usually resident at their base address.

  17. All key terms used in this publication, such as usual resident are explained in the 2011 Census user guide.

  18. All census population estimates were extensively quality assured, using other national and local sources of information for comparison and review by a series of quality assurance panels. An extensive range of quality assurance, evaluation and methodology papers were published alongside the first release in July 2012, including a Quality and Methodology Information (QMI) document. (157.6 Kb Pdf)

  19. The 2011 Census achieved its overall target response rate of 94 per cent of the usually resident population of England and Wales, and over 80 per cent in all local and unitary authorities. The population estimate for England and Wales of 56.1 million is estimated with 95 per cent confidence to be accurate to within +/- 85,000 (0.15 per cent).

  20. Details of the policy governing the release of new data are available by visiting www.statisticsauthority.gov.uk/assessment/code-of-practice/index.html or from the Media Relations Office email: media.relations@ons.gsi.gov.uk

Get all the tables for this publication in the data section of this publication .
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