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 unweighted version (IDISU), all subgroups are weighted equally.
Usage
idisu(
pop = NULL,
est,
se = NULL,
scaleval,
setting_average = NULL,
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 IDISU 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.
- setting_average
The reported setting average. Setting average must be unique for each setting, year, indicator combination. If population is not specified for all subgroups, the setting average is used.
- 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 IDISU value, corresponding estimated standard error,
and confidence interval as a data.frame.
Details
IDISU is calculated as the average of absolute differences between the subgroup estimates and the setting average, divided by the number of subgroups and the setting average, and multiplied by 100. 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 IDISU is calculated for each of the simulated samples. The 95% confidence intervals are based on the 2.5th and 97.5th percentiles of the IDISU results.
Interpretation: IDISU has only positive values, with larger values indicating higher levels of inequality. IDISU is zero if there is no inequality.
Type of summary measure: Complex; relative; non-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,
idisu(pop = population,
est = estimate,
se = se,
scaleval = indicator_scale
)
)
#> measure estimate lowerci upperci
#> 1 idisu 7.222503 6.561635 8.455798