Select whether the intervention population is defined by disease (ICD) code (e.g. C50, breast cancer) or risk factor (e.g. smokers). Multiple ICD codes and risk factors can be included.

A list of ICD-10 code descriptions can be found here


Select within range of 1-100


Once complete, proceed to the 'CEA inputs' tab


Cost-effectiveness analysis results

It is strongly recommended that users define their own recipient population sizes. The estimates are automatically derived from the disease/risk factor population data and do not relate to specific interventions.


Once complete, proceed to the 'Distributional inputs' tab

The Index of Multiple Deprivation

Socioeconomic differences are analysed using the Index of Multiple Deprivation (IMD), which computes an index score for over 30,000 neighbourhoods in England based on information on education, training, employment, housing, health, crime and income in the area.

IMD1 contains people living in the most deprived 20% of neighbourhoods in England. IMD5 contains those living in the least deprived 20%.


Eligible population



Uptake

The uptake rate (a value between 0 and 1) defines the proportion of the eligible population utilising the intervention in each IMD quintile group.

Base case uptake
Alternative uptake scenario

Health effects

The per person incremental QALY effect of an intervention can vary by IMD quintile group. Ticking the box below allows you to modify a set of multipliers for each IMD quintile group that are applied to the average incremental QALY effect defined in 'CEA inputs' tab (a value of 1 yields the average effect).


Health opportunity costs

The cost impact of the intervention(s) fall on those across the health service and not just the intervention population. By default, an empirical estimate of the distribution of opportunity costs is used. A custom distribution can be defined by ticking the box below.


Results can now be viewed on the 'Equity impact analysis' and 'Equity trade-off analysis' pages in the top menu

Summary of socioeconomically varied parameters

Note: IMD1 is the most deprived group and IMD5 is the least deprived.


Downloadble report
A summary report can be downloaded by clicking the button in the 'Input summary' tab. The report contains key figures and tables produced in the 'Equity impact analysis' and 'Equity trade-off analysis' pages.

Download summary report

Socioeconomic distribution of intervention recipients

Base case uptake
Alternative uptake

Distribution of intervention health effects (population totals)





Inequality in net health benefits – do worse-off people gain more (-ve), less (+ve) or the same?

The SII value below represents the modelled difference in net QALY benefit between the most and least deprived IMD group at population level. The measure differs from the observed gap by incorporating information on the net QALY benefits of IMD2-IMD4 using a simple linear regression model.


Net health equity benefit – is health inequality reduced (+ve), increased (-ve), or unchanged?

The SII value below represents how the intervention has changed the modelled difference in expected individual lifetime health between the most and least deprived IMD group. It uses a similar linear model to the one described above, but is reported at individual level on a much smaller scale than the population level slope index for net benefits.

Interpreting the inequality measures

The nature of the health inequality impact sometimes depends on the nature of the health inequality concern. For example, providing equal net health benefits to all groups will keep absolute differences in health constant but might reduce relative differences. We therefore present an absolute measure of inequality (the slope index) as well as two relative measures (the relative and concentration indices). We do not present relative measures for inequality in net health benefit, however, since net health benefit can take negative and zero values and relative inequality is ill-defined and badly behaved in such cases – one cannot compare proportions of zero.


Slope index of inequality (SII): The modelled gap between the most and least deprived individuals calculated from a simple linear regression line. This is similar to the observed gap between most and least deprived groups, but also takes into account outcomes for the middle groups. A negative SII indicates the most deprived have worse health outcomes than the least deprived. A reduction in the SII indicates absolute health inequality has been reduced.

Relative index of inequality (RII): The proportional gap between the most and least deprived individuals, derived from the SII. An RII of -1 means the estimated health outcomes for the most deprived are 100% greater than those for the least deprived. A reduction in the RII indicates relative health inequality has been reduced.

Concentration index: Another index of relative inequality, similar to the Gini index of income inequality and based on the correlation between deprivation rank and relative 'shares' of population total health. Equals -1 or 1 when only the most or least deprived individual experiences any health at all, and 0 if health is equally distributed. A reduction in a positive concentration index (or an increase for a negative index) indicates health inequality has been reduced.




Note: Altering the inequality aversion parameter relates to the inequality impact and does not affect the population health impact.



Note: Altering the inequality aversion parameter relates to the inequality impact and does not affect the incremental cost-effectiveness ratio.




About this application

This application was developed by James Love-Koh and Richard Cookson, Centre for Health Economics, University of York, with advisory input from Susan Griffin, Rita Faria and Fan Yang. The NICE Project Leads were Lesley Owen and Monica Desai.


Acknowledgements

For their helpful and detailed feedback on the draft calculator we would like to thank Deborah O’Callaghan, James Lomas, Mike Paulden and the many NICE officials, advisers and committee members that we consulted during development.

Key references

Below is a list of publications that detail some of the concepts and methods that have been used to build this calculator.


Overview of economic evaluation and equity concepts

Cookson, R., Griffin, S., Norheim, O.F., Culyer, A.J., Chalkidou, K., 2020. Distributional Cost-Effectiveness Analysis Comes of Age. Value in Health, 24(1), 118-120. [Link]

Cookson, R., Griffin, S., Norheim O.F., Culyer, A.J. (Eds), 2021. Distributional cost-effectiveness analysis: quantifying health equity impacts and trade-offs. Oxford University Press. [Link]

Distributional cost-effectiveness using aggregate data

Griffin, S., Love-Koh, J., Pennington, B., Owen, L., 2019. Evaluation of Intervention Impact on Health Inequality for Resource Allocation. Medical Decision Making, 39(3), 172–181. [Link]

Love-Koh, J., Cookson, R., Gutacker, N., Patton, T., Griffin, S., 2019. Aggregate Distributional Cost-Effectiveness Analysis of Health Technologies. Value in Health, 22(5), 518–526. [Link]

Distribution of health opportunity costs of NHS expenditure

Love-Koh, J., Cookson, R., Claxton, K., Griffin, S., 2020. Estimating Social Variation in the Health Effects of Changes in Health Care Expenditure. Medical Decision Making, 40(2), 170–182. [Link]

Baseline levels of health inequality

Love-Koh, J., Asaria, M., Cookson, R., Griffin, S., 2015. The Social Distribution of Health: Estimating Quality-Adjusted Life Expectancy in England. Value in Health, 18(5), 655–662. [Link]

The inequality staircase effects of health interventions

Tugwell, P., de Savigny, D., Hawker, G., Robinson, V., 2006. Applying clinical epidemiological methods to health equity: the equity effectiveness loop. BMJ 332, 358–61. [Link]