# International Applications of DCEA

## Canada (CADTH workflow)

CADTH uses income quintile stratification. Use `country = "canada"` to access the preloaded Statistics Canada HALE data.

    canada_baseline <- get_baseline_health("canada", "income_quintile")
    canada_baseline
    #> # A tibble: 5 × 12
    #>   income_quintile group quintile_label      group_label mean_hale mean_hale_male
    #>             <int> <int> <chr>               <chr>           <dbl>          <dbl>
    #> 1               1     1 Q1 (lowest income)  Q1 (lowest…      62.4           60.8
    #> 2               2     2 Q2                  Q2               65.1           63.6
    #> 3               3     3 Q3                  Q3               67.3           65.9
    #> 4               4     4 Q4                  Q4               69.4           68.1
    #> 5               5     5 Q5 (highest income) Q5 (highes…      71.8           70.5
    #> # ℹ 6 more variables: mean_hale_female <dbl>, se_hale <dbl>, pop_share <dbl>,
    #> #   cumulative_rank <dbl>, year <int>, source <chr>

    result_ca <- run_aggregate_dcea(
      icer                       = 50000,   # CAD/QALY
      inc_qaly                   = 0.40,
      inc_cost                   = 20000,
      population_size            = 8000,
      baseline_health            = canada_baseline,
      wtp                        = 50000,
      opportunity_cost_threshold = 30000
    )
    summary(result_ca)
    #> == Aggregate DCEA Result ==
    #>   ICER:             £50,000 / QALY
    #>   Incremental QALY: 0.4000
    #>   Incremental cost: £20,000
    #>   Population size:  8,000
    #>   Net Health Benefit: -2133.33 QALYs
    #>   SII change:         -0.0000
    #>   Decision:           Trade-off: equity gain, efficiency loss
    #>
    #> -- Per-group results --
    #> # A tibble: 5 × 4
    #>   group_label         baseline_hale post_hale   nhb
    #>   <chr>                       <dbl>     <dbl> <dbl>
    #> 1 Q1 (lowest income)           62.4      62.3 -427.
    #> 2 Q2                           65.1      65.0 -427.
    #> 3 Q3                           67.3      67.2 -427.
    #> 4 Q4                           69.4      69.3 -427.
    #> 5 Q5 (highest income)          71.8      71.7 -427.
    #>
    #> -- Inequality impact --
    #> # A tibble: 4 × 5
    #>   index           pre     post    change pct_change
    #>   <chr>         <dbl>    <dbl>     <dbl>      <dbl>
    #> 1 sii        11.5     11.5     -1.07e-14  -9.23e-14
    #> 2 rii         0.172    0.172    1.37e- 4   7.94e- 2
    #> 3 gini        0.0275   0.0275   2.18e- 5   7.94e- 2
    #> 4 atkinson_1  0.00119  0.00119  1.89e- 6   1.59e- 1

## WHO regional analysis

For global health or multi-country analyses, use WHO regional HALE data.

    who_baseline <- get_baseline_health("who_regions")
    who_baseline
    #> # A tibble: 6 × 10
    #>   who_region region_label          group group_label mean_hale se_hale pop_share
    #>   <chr>      <chr>                 <int> <chr>           <dbl>   <dbl>     <dbl>
    #> 1 AFR        African Region            1 African Re…      53.8     1.2     0.163
    #> 2 AMR        Region of the Americ…     2 Region of …      66.1     0.8     0.13
    #> 3 SEAR       South-East Asia Regi…     3 South-East…      60.3     0.9     0.271
    #> 4 EUR        European Region           4 European R…      68.9     0.6     0.147
    #> 5 EMR        Eastern Mediterranea…     5 Eastern Me…      59.4     1       0.088
    #> 6 WPR        Western Pacific Regi…     6 Western Pa…      68.3     0.7     0.201
    #> # ℹ 3 more variables: cumulative_rank <dbl>, year <int>, source <chr>

    result_who <- run_aggregate_dcea(
      icer                       = 1000,
      inc_qaly                   = 0.35,
      inc_cost                   = 350,
      population_size            = 500000,
      baseline_health            = who_baseline,
      wtp                        = 1000,
      opportunity_cost_threshold = 600
    )
    plot_equity_impact_plane(result_who)

## Custom country workflow

For countries without preloaded data, supply your own baseline:

    custom_baseline <- tibble::tibble(
      group           = 1:4,
      group_label     = c("Poorest quartile", "Q2", "Q3", "Richest quartile"),
      mean_hale       = c(55.0, 60.0, 65.0, 70.0),
      se_hale         = c(0.8, 0.7, 0.6, 0.5),
      pop_share       = rep(0.25, 4),
      cumulative_rank = c(0.125, 0.375, 0.625, 0.875),
      year            = 2022L,
      source          = "Custom country data"
    )

    result_custom <- run_aggregate_dcea(
      icer                       = 5000,
      inc_qaly                   = 0.3,
      inc_cost                   = 1500,
      population_size            = 100000,
      baseline_health            = custom_baseline,
      wtp                        = 5000,
      opportunity_cost_threshold = 3000
    )
    #> Warning in summary.lm(model): essentially perfect fit: summary may be
    #> unreliable
    #> Warning in summary.lm(model): essentially perfect fit: summary may be
    #> unreliable
    #> Warning in summary.lm(model): essentially perfect fit: summary may be
    #> unreliable
    #> Warning in summary.lm(model): essentially perfect fit: summary may be
    #> unreliable
    #> Warning in summary.lm(model): essentially perfect fit: summary may be
    #> unreliable
    #> Warning in summary.lm(model): essentially perfect fit: summary may be
    #> unreliable
    summary(result_custom)
    #> == Aggregate DCEA Result ==
    #>   ICER:             £5,000 / QALY
    #>   Incremental QALY: 0.3000
    #>   Incremental cost: £1,500
    #>   Population size:  100,000
    #>   Net Health Benefit: -20000.00 QALYs
    #>   SII change:         0.0000
    #>   Decision:           Lose-Lose (efficiency loss + equity loss)
    #>
    #> -- Per-group results --
    #> # A tibble: 4 × 4
    #>   group_label      baseline_hale post_hale   nhb
    #>   <chr>                    <dbl>     <dbl> <dbl>
    #> 1 Poorest quartile            55      55.0 -5000
    #> 2 Q2                          60      60.0 -5000
    #> 3 Q3                          65      65.0 -5000
    #> 4 Richest quartile            70      70.0 -5000
    #>
    #> -- Inequality impact --
    #> # A tibble: 4 × 5
    #>   index           pre     post   change pct_change
    #>   <chr>         <dbl>    <dbl>    <dbl>      <dbl>
    #> 1 sii        20       20.0     1.42e-14   7.11e-14
    #> 2 rii         0.32     0.320   2.56e- 4   8.01e- 2
    #> 3 gini        0.0500   0.0500  4.00e- 5   8.01e- 2
    #> 4 atkinson_1  0.00402  0.00402 6.47e- 6   1.61e- 1