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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

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They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.