Data

Indicator values are drawn from the Public Health Outcomes Framework. Data for these use a variety of data sources, and these are specified for each indicator in the ‘Definitions’ tab of the tool.

Inequality summary measures, for the most part, have been calculated by PHE for the Health Inequalities Dashboard, using data from the Public Health Outcomes Framework, and have not been published elsewhere.

Time periods

The ‘Overview’ tab presents data for a baseline period and a reporting period. For each indicator, the baseline period is the period closest to the inception of PHE in 2013. For some indicators, particularly where there has been a change in indicator definition, data for 2013 is not available, so the earliest possible time period is used.

The reporting period shows data for the latest time period.

Where available, the ‘Data’ tab shows longer term trend data for the indicator.

Inequality dimensions

The tool provides breakdowns of data across a range of determinants referred to as dimensions of inequality eg personal characteristics, including age, sex and ethnic group, in addition to socio-economic deprivation.

For each dimension, a summary measure of inequality has been calculated using one of the measures described in the section below. Where possible, the same summary measure has been used for the same dimension of inequality across all indicators.

Absolute and relative gap

The absolute and relative gap measures show inequality between two groups. The absolute range is calculated by subtracting the lower value from the higher value, and the relative range is calculated by dividing the higher value by the lower value.

The gap can be between a specific population group and the national average, or between two independent population groups. Where this measure has been used, the groups being compared are stated on the ‘Definitions’ tab of the tool.

Figure 1: Example of the absolute and relative gap, based on the school readiness indicator

Slope index of inequality (SII) and relative index of inequality (RII)

The slope index of inequality (SII) and relative index of inequality (RII) are used to measure inequality by deprivation. These measures are used for indicators where data is available at lower super output area (LSOA) level to be grouped into deprivation deciles.

The slope index of inequality is a measure of the social gradient in an indicator, i.e. how much an indicator varies with deprivation. It takes account of health inequalities across the whole range of deprivation within an area and summarises this in a single number. This represents the range in indicator values across the social gradient from most to least deprived.

The chart in Figure 2 based on life expectancy at birth shows how the SII is calculated. The population has been divided by level of deprivation, based on the Index of Multiple Deprivation. This has been done by ranking Lower Super Output Areas (LSOAs) from most to least deprived. These have then been divided into 10 groups, or deprivation deciles, with approximately equal numbers of LSOAs in each. Decile 1 contains people living in the most deprived areas and Decile 10 contains people in the least deprived areas. Life expectancy at birth has been calculated for each of these deciles, illustrated by the blue dots in Figure 2.

Figure 2: Life expectancy by deprivation decile and the slope index of inequality

The life expectancy figures have also been plotted to take account of their population size. While the deprivation deciles have roughly one-tenth of the population in each, they are not precisely equal because they are aggregated up from LSOAs. The horizontal x-axis along the bottom of the chart in Figure 1 represents the whole population of an area. Each blue dot in Figure 1 represents the life expectancy for each deprivation decile. If Decile 1 includes exactly 10% of the population, the first blue dot is positioned at 5%, the mid-point of the range of population covered by that decile. If the second decile includes 11% of the population, this would cover the range from 10% to 21%, so the midpoint is 15.5%, and that is where the point would be located on the x-axis.

The red line on the chart is a linear regression line of best fit for the data, calculated by the least squares method. The SII is simply the gradient of that line, or the difference between the top of the line (at 100% on the horizontal axis) and the bottom (0% on the horizontal axis). In the example in Figure 1, the regression line goes from 78.0 to 85.9 years. This gives an SII of 7.9 years (with a 95% confidence interval of 6.7 to 9.1 years). The range in life expectancy across the social gradient from most to least deprived in this area is therefore 7.9 years. An SII of zero indicates there is no inequality. When looking at health outcomes such as life expectancy and mortality, a positive SII indicates a higher concentration of the indicator among the most deprived populations whereas a negative value would indicate a higher concentration of the indicator among the least deprived populations.

For more technical guidance on how to interpret SII measures see the PHOF technical guidance

The relative index of inequality is a summary measure of inequality related to the SII. While the SII measures the absolute difference between the most and least deprived, the RII measures the relative difference and is presented as a ratio of the least deprived to the most deprived for an indicator. For example, the RII for cancer premature mortality in 2016-18 was 2.2. This means that the cancer mortality rate is 2.2 times higher in the most deprived compared to the least deprived areas. A relative measure of 1 would indicator that there was no inequality by deprivation.

When calculating the SII and RII it is assumed that the there is a linear relationship between the indicator decile values and deprivation.

Log slope index of inequality (log SII) and log relative index of inequality (log RII)

The log slope index of inequality and log relative index of inequality are used to measure inequality by deprivation for indicators where there is not a linear relationship between the indicator decile values and deprivation.

This method has been used for three indicators in the dashboard:

• Premature cardiovascular disease mortality
• Infant mortality
• Alcohol related hospital admissions

In this method, decile values for the indicator are logged before calculation of the SII and RII. This results in a regression line which better fits the data for each of the three indicators. The SII and RII based on the log scale are more difficult to interpret, so the figures in the dashboard are presented in the original units of indicator (converting back to original units by taking the anti-log of extreme values of the SII line and recalculating SII and RII.

The example below presents the data for one time period for premature cardiovascular disease mortality in England which was identified as being non-linear. The left hand chart shows the indicator values for each decile and the SII line using the standard PHE SII method. The centre chart shows logged indicator values for each decile and the SII line. The right hand chart shows the indicator values for each decile and the transformed SII line. The SII and RII values below this chart are those that are/would be used in the dashboard.

Figure 3: CVD premature mortality (DSR per 100,000), 2014-16

Mean Difference

Where the dimension of inequality being considered contains a number of population groups which cannot be logically ordered, such as indicators by ethnic group, a summary measure called the mean difference has been presented.

The measure shows the average of the absolute differences between each of the groups and a reference group. The difference between each group and the reference is treated as a positive number regardless of whether it is higher or lower than the reference group. For each indicator, the largest group is selected as the reference group, and this is group is stated on the ‘Definitions’ tab.

No relative measure is presented for these indicators.

Figure 4: Example of the mean difference, based on the smoking prevalence indicator

Odds Ratio

For smoking prevalence, the odds ratio has been presented, representing the likelihood of those working in routine and manual occupations (exposure) being current smokers (outcome) compared with those working in professional or intermediate occupations (exposure).

An odds ratio of 1 represents no difference in the outcome in different exposure groups. If the confidence intervals overlap, we are able to say with 95% confidence that there is no significant difference in the outcome between groups.

An odds ratio higher than 1 signifies that one population is more likely to have the outcome than their counterparts, for example if the odds ratio is 2, they have twice the odds of the outcome. On the other hand, an odds ratio between 0 and 1 signifies that they are less likely to have the outcome, for example if the odds ratio is 0.5 they have half the odds of the outcome.