# Introduction to Distributional Cost-Effectiveness Analysis

## Why standard CEA ignores equity

Standard cost-effectiveness analysis (CEA) answers one question: does this intervention generate more health per pound spent than the alternatives? It aggregates health across all patients as if a QALY gained by the most deprived person equals a QALY gained by the least deprived.

This approach is silent on who benefits — and therefore on how interventions affect health inequalities between socioeconomic groups.

## The DCEA framework

Distributional Cost-Effectiveness Analysis (DCEA), developed by Cookson, Griffin, Norheim and Culyer (2020), extends standard CEA by:

1. Distributing aggregate health gains across socioeconomic groups.
2. Measuring the impact on health inequality (SII, Atkinson index, etc.).
3. Evaluating the social welfare gain using inequality-aversion weights.
4. Visualising the equity-efficiency trade-off on an impact plane.

## When to use aggregate vs full-form DCEA

Method | When to use | Data required
--- | --- | ---
Aggregate DCEA | Standard TA; disease-level HES data available | ICER, incremental QALY/cost, disease ICD
Full-form DCEA | Subgroup trial data available; HST or exceptional case | Per-group QALY/cost estimates

NICE (2025) recommends aggregate DCEA as the default supplementary analysis for technology appraisals where equity is relevant.

## Quick start

    result <- run_aggregate_dcea(
      icer            = 28000,
      inc_qaly        = 0.45,
      inc_cost        = 12600,
      population_size = 12000,
      wtp             = 20000,
      opportunity_cost_threshold = 13000
    )

    summary(result)
    #> == Aggregate DCEA Result ==
    #>   ICER:             £28,000 / QALY
    #>   Incremental QALY: 0.4500
    #>   Incremental cost: £12,600
    #>   Population size:  12,000
    #>   Net Health Benefit: -6230.77 QALYs
    #>   SII change:         0.0000
    #>   Decision:           Lose-Lose (efficiency loss + equity loss)
    #>
    #> -- Per-group results --
    #> # A tibble: 5 × 4
    #>   group_label         baseline_hale post_hale    nhb
    #>   <chr>                       <dbl>     <dbl>  <dbl>
    #> 1 Q1 (most deprived)           52.1      52.0 -1246.
    #> 2 Q2                           56.3      56.2 -1246.
    #> 3 Q3                           59.8      59.7 -1246.
    #> 4 Q4                           63.2      63.1 -1246.
    #> 5 Q5 (least deprived)          66.8      66.7 -1246.
    #>
    #> -- Inequality impact --
    #> # A tibble: 4 × 5
    #>   index           pre     post   change pct_change
    #>   <chr>         <dbl>    <dbl>    <dbl>      <dbl>
    #> 1 sii        18.1     18.1     1.07e-14   5.87e-14
    #> 2 rii         0.304    0.305   5.31e- 4   1.74e- 1
    #> 3 gini        0.0487   0.0488  8.49e- 5   1.74e- 1
    #> 4 atkinson_1  0.00374  0.00376 1.32e- 5   3.52e- 1

    plot_equity_impact_plane(result)

## Key references

- Cookson R, Griffin S, Norheim OF, Culyer AJ (2020). Distributional Cost-Effectiveness Analysis. Oxford University Press (ISBN:9780198838197).

- NICE (2025). Technology Evaluation Methods: Health Inequalities (PMG36).

- Love-Koh J et al. (2019). Value in Health 22(5): 518-526.