Overview of inequality measurement

Introduction to measurement of inequality

Health inequalities are defined as avoidable differences in health outcomes between groups or populations, such as differences in how long we live, or the age at which we get preventable diseases or health conditions.

Inequalities exist across a range of dimensions, including personal characteristics, lifestyle factors, social networks, living and working conditions, and socio-economic and environmental conditions.

A variety of methods are used to measure health inequalities. Choosing the most appropriate measure is not straightforward and there are advantages and disadvantages to each method. The choice depends on the particular dimension of inequality being considered, data availability, and the specific question being addressed. For example, if inequality between two specific population subgroups is being examined, then a simple range measure may be the most appropriate method to use, whereas if a summary measure of inequality is required which takes into account all subgroups of the population, then a more complex measure of inequality is more likely to be appropriate.

Absolute and relative inequality

Inequality can be expressed in both absolute terms and relative terms. Absolute inequality shows the magnitude of difference between subgroups of the population. It is most simply calculated by subtracting the value for one group from that of another. Relative inequality shows the proportional difference between subgroups. It is most simply calculated by dividing the value for one group by that of another.

For example, if the mortality rate in Group A is 300 deaths per 100,000 population, and 200 deaths per 100,000 population in Group B then the absolute inequality between the groups is 100 deaths per 100,000 population and the relative inequality is 1.5, ie the mortality rate is 1.5 times higher in Group A than Group B.

Both absolute and relative inequality are useful measures and there are advantages and disadvantages to each approach.

One of the advantages of the relative measure is that it is scale neutral, meaning inequality can be compared for outcomes measured on different scales. However, information about the overall importance or burden of the condition/indicator is lost in the relative measure. For example, a difference of between 1 and 4 deaths per 100,000 population is the same as the difference between 100 and 400 deaths per 100,000 population in relative terms.

The burden of the condition/indicator is clearer in the absolute measure. In the example above, inequality would be 3 deaths per 100,000 population and 300 deaths per 100,000 population in absolute terms.

It is generally recommended that both absolute and relative measures are used together, in order to give a more complete picture of inequality for a given indicator.

Interactive example: Calculating absolute and relative inequality in the mortality rate between group A and group B

Enter the mortality rate per 100,000 population for groups A and B:
Note: for simplicity, the value of group B cannot exceed the value of group A in this example

viewof a = Inputs.number([0, 1000], {step: 1, value: 300, width: 100, label: "Group A:"})

viewof b = Inputs.number([0, a], {step: 1, value: 200, width: 100, label: "Group B:"})

input_data = [
  {Group: 'Group A', Mortality_rate: a},
  {Group: 'Group B', Mortality_rate: b}
]

Plot.plot({
  ariaLabel:"Bar chart",
  ariaDescription:"A bar chart showing mortality rates per 100000 population for Group A and Group B",
  marginTop: 40,  
  marginBottom: 60,
  y: {label: "Mortality rate (per 100,000 population)"},
  x: {padding: 0.4,
      label: ""},
  style: {fontSize: 16},
  marks: [
    Plot.barY(input_data, {
      x: a => a.Group, 
      y: "Mortality_rate", 
      fill: "Group",
      title: a => `${a.Group}: ${a.Mortality_rate}%`
                          })
  ]
})

Figure 1: Mortality rate per 100,000 population for Groups A and B

abs_val = a - b

rel_val = ((a / b).toFixed(2))

md`The **absolute inequality** is the difference between two values: ${a}–${b} = **${abs_val} deaths per 100,000 population**.`

md`The **relative inequality** is the ratio of two values: ${a}/${b} = **${rel_val}**.`

Measuring changes in inequality over time

Changes in both absolute and relative inequality can be monitored over time. However, in some cases, this can lead to differing conclusions about the direction of the change in inequality.

For example, if the mortality rate is 300 deaths per 100,000 population in Group A, and 200 deaths per 100,000 population in Group B in time period 1, then the absolute inequality between the groups is 100 deaths per 100,000 population and the relative inequality is 1.5.

If the mortality rate in Group A reduces to 240 deaths per 100,000 population and in Group B it reduces to 150 deaths per 100,000 population in time period 2, then the absolute inequality between them has decreased to 90 deaths per 100,000 population. However, the relative inequality between them has increased to 1.6.

This is because absolute inequality is sensitive to the trajectory of the indicator overall. For example, if the value of an indicator halved across all groups within a population, then the absolute inequality would also halve (whilst relative inequality would remain the same). Absolute inequality would double if there is a doubling in the underlying rate of the indicator over time.

This effect is observed in several indicators included in OHID’s Health Inequalities Dashboard, including premature mortality rates from cardiovascular disease and cancer, percentage of 5-year-olds with visually obvious dental decay, and school readiness. All of these indicators have seen a decline in the underlying rates of the indicator over time, a decrease in absolute inequality, and an increase in relative inequality.

Interactive example: Calculating the change in absolute and relative inequality in mortality rates per 100,000 population between group A and group B

Enter the mortality rate per 100,000 population for group A for time periods 1 and 2:

viewof a1 = Inputs.number([0, 1000], {step: 1, value: 300, width: 100, label: "Time period 1:"})

viewof a2 = Inputs.number([0, 1000], {step: 1, value: 240, width: 100, label: "Time period 2:"})

Enter the mortality rate per 100,000 population for group B for time periods 1 and 2:
Note: for simplicity, values for group B cannot exceed those of group A for the same time period in this example

viewof b1 = Inputs.number([0, a1], {step: 1, value: 200, width: 100, label: "Time period 1:"})

viewof b2 = Inputs.number([0, a2], {step: 1, value: 150, width: 100, label: "Time period 2:"})

input_data2 = [
  {Timeperiod: 'Time period 1', Group: 'Group A', Mortality_rate: a1},
  {Timeperiod: 'Time period 2', Group: 'Group A', Mortality_rate: a2},
  {Timeperiod: 'Time period 1', Group: 'Group B', Mortality_rate: b1},
  {Timeperiod: 'Time period 2', Group: 'Group B', Mortality_rate: b2}
]

Plot.plot({
  ariaLabel:"A line chart",
  ariaDescription:"A line chart showing the change in mortality rate per 100,000 population between time period 1 and time period 2 for Group A and Group B",
  marginTop: 40,  
  marginBottom: 60,
  x: {label: ""},
  y: {label: "Mortality rate (per 100,000 population)"},
  color: {legend: true,
    className: 'large-font'},
  style: {fontSize: 16},
  marks: [
    Plot.ruleY([0]),
    Plot.line(input_data2, {x: "Timeperiod", 
                            y: "Mortality_rate", 
                            stroke: "Group"}),
    Plot.dot(input_data2, {x: a => a.Timeperiod, 
                           y: "Mortality_rate", 
                           stroke: "Group", 
                           fill: "Group",
                           title: a => `${a.Timeperiod}, ${a.Group} : ${a.Mortality_rate}%`})
  ]
})

Figure 2: Change in mortality rate per 100,000 population for Groups A and B

abs_val_1 = a1 - b1
abs_val_2 = a2 - b2
abs_diff = abs_val_2 - abs_val_1
abs_diff_words = {if(abs_diff <0){return "decreased";
                  } else if (abs_diff >0) {return "increased";
                  } else {return "not changed";
                  }
                  }

rel_val_1 = ((a1 / b1).toFixed(2))
rel_val_2 = ((a2 / b2).toFixed(2))
rel_diff = rel_val_2 - rel_val_1
rel_diff_words = {if(rel_diff <0){return "decreased";
                  } else if (rel_diff >0) {return "increased";
                  } else {return "not changed";
                  }
                  }

Time period 1

md`Absolute inequality = ${a1}–${b1} = **${abs_val_1} deaths per 100,000 population**.`

md`Relative inequality = ${a1}/${b1} = **${rel_val_1}**.`

Time period 2

md`Absolute inequality = ${a2}–${b2} = **${abs_val_2} deaths per 100,000 population**.`

md`Relative inequality = ${a2}/${b2} = **${rel_val_2}**.`

Change between the two time periods

md`**Absolute inequality** between Group A and Group B has **${abs_diff_words}** from ${abs_val_1} per 100,000 population in time period 1 to ${abs_val_2} per 100,000 population in time period 2.`

md`**Relative inequality** between Group A and Group B has **${rel_diff_words}** from ${rel_val_1} in time period 1 to ${rel_val_2} in time period 2.`

Commonly used measures of inequality within OHID

This section introduces a number of commonly used measures of inequality and the advantages and disadvantages of each. Further detail on the calculation of these measures can be found on the relevant pages via links in the menu on the left.

Range, absolute gap and relative gap

As set out in the Absolute and relative inequality section, two groups can be compared simply in absolute or relative terms. When comparing two groups, or one group with a national average, the statistics can be referred to as (absolute or relative) gaps. When the groups being compared are the extremes of a set of groups, the statistics can be referred to as the (absolute or relative) range.

Advantages

Easy to calculate and explain

Disadvantages

Don’t take into account any intermediate groups so can be skewed by outliers.

Mean absolute difference

The mean difference is a more complex measure which uses all subgroups of a population to calculate the summary measure.

The measure shows the average of the absolute differences (this means differences irrespective of direction: all differences treated as positive numbers, not negatives) between each of subgroups and another subgroup which has been selected as the reference group.

This measure is commonly used where inequality needs to be summarised for dimensions of inequality where the subgroups within it cannot be logically ordered. This may include indicators by ethnic group, sexual orientation or employment status, for example.

Advantages

Takes into account all subgroups within the dimension of inequality of interest.

Disadvantages

Does not take into account the size of the subgroups (all subgroups carry equal weight, so individuals in larger groups are under-represented, compared with individuals in smaller groups).
Can be affected if there are large fluctuations in any of the subgroups.
Can be difficult to interpret change without seeing the data for the underlying subgroups.
Does not have an associated relative measure.

Slope index of inequality and relative index of inequality

The slope index of inequality (SII) and relative index of inequality (RII) provide a summary measure of inequality in health outcomes by deprivation.

The SII is a measure of how much an indicator varies with deprivation. It takes account of variation across the whole range of deprivation within a population and summarises this in a single number representing the range in indicator values across the social gradient from most to least deprived.

The RII is related to the SII. The SII measures the absolute difference between values for the most and least deprived, while the RII measures the relative difference and is presented as a ratio of the indicator values for the least deprived to the most deprived (or vice versa).

Advantages

Takes into account the whole population rather than specific subgroups
Easy to interpret

Disadvantages

Complex to calculate
Only describes inequality where there is a gradient across the deprivation groups (although this is usually the case).

Other measures of inequality

The measures of inequality set out above are those commonly used within OHID, but many other measures of inequality exist which are appropriate to use in certain circumstances. These include the standard deviation, relative mean absolute difference, coefficient of variation, index of dispersion, concentration index and Gini coefficient.

Page last updated: December 2025