Paul Fryers Measuring Health Inequalities

A brief history In the beginning (1992) there were targets The Lord (Ken Clarke) said “Thou shalt reduce your mortality rates” But the people (public health people mostly) said “But the easiest way to reduce mortality rates is to concentrate on the richest, and inequalities will increase, and society as a whole will be diminished” And the Lord’s boss (run with it) said “There is no such thing as society, so get on with it” And it came to pass that mortality rates reduced, inequalities increased and there was much wailing and gnashing of teeth So (in 2000) the next Lord (Alan Milburn) said “Thou shalt reduce inequalities in the most deprived wards at a faster rate than the rest” and thus the first inequalities target was born And it came to pass that mortality rates reduced, inequalities increased and the wailing and gnashing of teeth continued (mostly Danny Dorling)

Gaps vs gradients Inequalities targets were set in terms of a gap between (usually) the most deprived areas and the rest But this represents a false dichotomy – there are not ‘haves’ and ‘have-nots’, but a gradient of inequalities or, in reality, gaps or gradients in many dimensions: ‘deprivation’ (itself multi- dimensional), ethnicity, age, sex, ethnicity, etc. Gaps require essentially arbitrary cut-offs, including some populations and excluding others when the differences between them are small, and cannot accurately be determined They don’t necessarily target the right people, being based on areas and suffering from the ecological fallacy They are statistically insensitive, based on a small subset of the population and, depending on the definition, can be affected by regression to the mean Over time, the status of populations changes, presenting anomalies in monitoring and continuing prioritisation

Gradient measures of inequality Gini coefficient Concentration index Slope index of inequality –Relative index of inequality Others: –Linear regression model (mortality vs IMD) –Population-weighted linear regression model

Gini coefficient Gini coefficient measures the extent to which something is inequitably distributed across a population If deaths were distributed totally evenly across the population, the cumulative wealth line (blue) would follow the red diagonal exactly The further below the diagonal the cumulative cumulative wealth line gets, the less equitable the distribution of mortality Gini coefficient here is 7.1% BUT, this takes no account of the ages of deaths All cause <75 mortality, , East Midlands LAs

Gini coefficient – standardised deaths Here, the deaths have been adjusted to compensate for age: i.e. for each local authority, the numbers of deaths are those that would have occurred in a standard population experiencing the same age- specific death rates as that LA Gini coefficient is now 8.8% Using non-standardised deaths was hiding a lot of the inequality because the more deprived (city) LAs tend to have younger populations (hence fewer deaths) All cause <75 mortality, , East Midlands LAs

Concentration index Concentration index is similar to the Gini coefficient, but orders the areas according to an external variable (rather than mortality itself) and measures the extent to which mortality is inequalitable in relation to that factor In this case the areas are ranked using IMD scores The Gini coefficient (8.8%) and concentra- tion index (8.4%) are similar because the rankings (by IMD and mortality) are so similar This example uses standardised deaths Most affluentMost deprived All cause <75 mortality, , East Midlands LAs

Slope index of inequality The slope index of inequality (SII) measures the gradient of the relationship between deprivation and a health outcome, calculating the difference in health outcome between the most deprived and the most affluent Here the SII is 157 (95% confidence interval 133–181), i.e. there are between 133 and 181 more deaths per 100,000 population per year in the most deprived LA in the East Midlands than in the most affluent Most affluentMost deprived All cause <75 mortality, , East Midlands LAs

Slope index of inequality The SII has some advantages: It is intuitive: the value has an obvious meaning, as it is expressed in terms of the original health outcome – in the examples used here, it is deaths per 100,000 population Similarly a graph showing the index is intuitive: steeper gradients represent greater inequality As the SII is the straightforward gradient of a linear regression line, it is easy to calculate confidence intervals It takes account of the relative population sizes of the local authorities: the spacing along the x-axis is proportional to population However, it does not take account of relative differences in IMD, as it is based only on the IMD ranks of the local authorities, so if one local authority was far more deprived than all the others, this would be ignored by the calculation of the index

Relative index of inequality The RII is the SII divided by the average value (in our case Life Expectancy) It has the advantage of levelling the scale of the indicator, although it is not clear that this is needed here, or desirable It has the disadvantage that it returns an abstract statistic (ie no longer a number of years difference in life expectancy between the extremes

Summary of indices There are several options Using a deprived quintile of the population ignores most of the distribution of mortality The Gini coefficient looks at absolute inequality in deaths, not related to any specific determinant, e.g. deprivation The slope index of inequalities and concentration index show inequalities in mortality in relation to deprivation (or similar) The SII can be affected by outliers if the individual points are too variable, but is more intuitive than the Gini or concentration index We can calculate confidence intervals for all except the Gini It is possible to monitor changes, and but year-on-year changes in individual areas are small and hence very unlikely to be statistically significant – this is better than observing wildly fluctuating single- year gaps, however.

Monitoring changes in SII The SII is presented with confidence intervals: these can be used crudely to test the significance of changes. Between and , no LA has a significant reduction or increase in its SII However, amongst UTLAs, 104 have seen a rise in their SII, 39 have seen a fall, and 7 have remained unchanged (1dp) This difference between the number rising and the number falling is extremely significant Hence we can conclude that inequalities have widened significantly, in general, but we cannot say that about any particular LA.

Change in SIIs, to

Public Health Outcomes Framework PHOF includes high-level indicators based on inequalities in life expectancy and healthy life expectancy Life expectancy: –National SII based on deprivation deciles of LSOAs within England –For each LA, SII based on deprivation deciles of LSOAs within the LA –Number of UTLAs for which the local SII has decreased –For each LA, gap in years between LA LE and England LE Healthy life expectancy: –National SII based on deprivation deciles of LSOAs within England

Public Health Outcomes Framework Details still under discussion: –Defining ‘deprivation’: IMD or something else, eg Income Domain of IMD –For trends, whether to define fixed deciles (based on latest IMD/whatever) or moving deciles (based on IMD/whatever at different points in time) Time scales: –Dependent on publication of rebased LSOA mid-year estimates of population, currently expected to be August/September. If they aren’t delayed, the baseline figures ( ) will be in the November PHOF release. If they are significantly delayed, it will be February