Calculate Slope Index of Inequality (SII)

Fits a weighted regression of health on ridit scores to estimate the absolute health difference between the most and least deprived groups. The SII is the regression coefficient on the ridit score, interpretable as the total health gap across the full socioeconomic range.

Usage

calc_sii(data, health_var, group_var, weight_var)

Arguments

data: A data frame with health and group columns.
health_var: Name of the health variable column (character).
group_var: Name of the socioeconomic group column (ordered integer, 1 = most deprived).
weight_var: Name of the population share column (sums to 1).

Value

A named list with elements:

sii: Slope Index of Inequality (numeric)
rii: Relative Index of Inequality (numeric)
se_sii: Standard error of SII
p_value: p-value for SII
model: The underlying lm object

References

Mackenbach JP, Kunst AE (1997) Measuring the magnitude of socioeconomic inequalities in health: an overview of available measures illustrated with two examples from Europe. Social Science and Medicine 44(6): 757-771. doi:10.1016/S0277-9536(96)00073-1

Examples

df <- tibble::tibble(
  group      = 1:5,
  mean_hale  = c(60, 63, 66, 69, 72),
  pop_share  = rep(0.2, 5)
)
calc_sii(df, "mean_hale", "group", "pop_share")
#> Warning: essentially perfect fit: summary may be unreliable
#> $sii
#> [1] 15
#> 
#> $rii
#> [1] 0.2272727
#> 
#> $se_sii
#> [1] 1.868784e-15
#> 
#> $p_value
#> [1] 4.264559e-48
#> 
#> $model
#> 
#> Call:
#> stats::lm(formula = h ~ ridit, weights = w)
#> 
#> Coefficients:
#> (Intercept)        ridit  
#>        58.5         15.0  
#> 
#>