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The Index of Disparity (IDIS) is a relative measure of inequality that shows the average difference between each subgroup and the setting average, in relative terms. In the weighted version (IDISW), subgroups are weighted according to their population share.

Usage

idisw(pop, est, se = NULL, scaleval, sim = NULL, seed = 123456, ...)

Arguments

pop

The number of people within each subgroup. Population size must be available for all subgroups.

est

The subgroup estimate. Estimates must be available for at least 85% of subgroups.

se

The standard error of the subgroup estimate. If this is missing, 95% confidence intervals of IDISW cannot be calculated.

scaleval

The scale of the indicator. For example, the scale of an indicator measured as a percentage is 100. The scale of an indicator measured as a rate per 1000 population is 1000.

sim

The number of simulations to estimate 95% confidence intervals. Default is 100.

seed

The random number generator (RNG) state for the 95% confidence interval simulation. Default is 123456.

...

Further arguments passed to or from other methods.

Value

The estimated IDISW value, corresponding estimated standard error, and confidence interval as a data.frame.

Details

IDISW is calculated as the weighted average of absolute differences between the subgroup estimates and the setting average, divided by the setting average, and multiplied by 100. Absolute differences are weighted by each subgroup’s population share. For more information on this inequality measure see Schlotheuber, A., & Hosseinpoor, A. R. (2022) below.

95% confidence intervals are calculated using a methodology of simulated estimates. The dataset is simulated a large number of times (e.g., 100) and IDISW is calculated for each of the simulated samples. The 95% confidence intervals are based on the 2.5th and 97.5th centiles of the IDISW results.

Interpretation: IDISW has only positive values, with larger values indicating higher levels of inequality. IDISW is zero if there is no inequality.

Type of summary measure: Complex; relative; weighted

Applicability: Non-ordered; more than two subgroups

References

Schlotheuber, A., & Hosseinpoor, A. R. (2022). Summary measures of health inequality: A review of existing measures and their application. International Journal of Environmental Research and Public Health, 19 (6), 3697.

Examples

# example code
data(NonorderedSample)
head(NonorderedSample)
#>                                         indicator          dimension
#> 1 Births attended by skilled health personnel (%) Subnational region
#> 2 Births attended by skilled health personnel (%) Subnational region
#> 3 Births attended by skilled health personnel (%) Subnational region
#> 4 Births attended by skilled health personnel (%) Subnational region
#> 5 Births attended by skilled health personnel (%) Subnational region
#> 6 Births attended by skilled health personnel (%) Subnational region
#>          subgroup  estimate        se population setting_average
#> 1            aceh  95.11784 1.5384434  230.20508        91.59669
#> 2            bali 100.00000 0.0000000  149.46272        91.59669
#> 3 bangka balitung  97.41001 1.2676437   55.66533        91.59669
#> 4          banten  80.35694 3.5440531  451.26550        91.59669
#> 5        bengkulu  94.25756 2.7740061   70.17540        91.59669
#> 6    central java  98.56168 0.6476116 1221.94446        91.59669
#>   favourable_indicator ordered_dimension indicator_scale reference_subgroup
#> 1                    1                 0             100                  1
#> 2                    1                 0             100                  0
#> 3                    1                 0             100                  0
#> 4                    1                 0             100                  0
#> 5                    1                 0             100                  0
#> 6                    1                 0             100                  0
with(NonorderedSample,
     idisw(pop = population,
           est = estimate,
           se,
           scaleval = indicator_scale
          )
     )
#>   measure estimate  lowerci  upperci
#> 1   idisw 5.814742 5.296582 7.211044