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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.