rci {healthequal}

Relative concentration index (RCI)

Description

The relative concentration index (RCI) is a relative measure of inequality that shows the gradient across population subgroups. It indicates the extent to which an indicator is concentrated among disadvantaged or advantaged subgroups, on a relative scale. RCI can be calculated using disaggregated data and individual-level data. Subgroups in disaggregated data are weighted according to their population share, while individuals are weighted by sample weight in the case of data from surveys.

Usage

rci(
  est,
  subgroup_order,
  scaleval = NULL,
  pop = NULL,
  weight = NULL,
  psu = NULL,
  strata = NULL,
  fpc = NULL,
  method = NULL,
  lmin = NULL,
  lmax = NULL,
  conf.level = 0.95,
  force = FALSE,
  ...
)

Arguments

est

The subgroup estimate. Estimates must be available for all subgroups.

subgroup_order

The order of subgroups in an increasing sequence.

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.

pop

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

weight

Individual sampling weight (required if data come from a survey)

psu

Primary sampling unit (required if data come from a survey)

strata

Strata (required if data come from a survey)

fpc

Finite population correction

method

Normalisation method for bounded indicators. Options available Wagstaff (wagstaff) or Erreygers (erreygers) correction.

lmin

Theoretical minimum for bounded indicators.

lmax

Theoretical maximum for bounded indicators.

conf.level

confidence level of the interval.

force

TRUE/FALSE statement to force calculation with missing indicator estimate values.

...

Further arguments passed to or from other methods.

Details

RCI is calculated by dividing the absolute concentration index (ACI) by the setting average. RCI may be more easily interpreted when multiplied by 100. The calculation of ACI is based on a ranking of the whole population from the most-disadvantaged subgroup (at rank 0) to the most-advantaged subgroup (at rank 1), which is inferred from the ranking and size of the subgroups. For more information on this inequality measure see Schlotheuber, A., & Hosseinpoor, A. R. (2022) below.

Interpretation: RCI is bounded between -1 and +1 (or between -100 and +100, when multiplied by 100). The larger the absolute value of RCI, the higher the level of inequality. For favourable indicators, positive values indicate a concentration of the indicator among the advantaged, while negative values indicate a concentration of the indicator among the disadvantaged. For adverse indicators, it is the reverse: positive values indicate a concentration of the indicator among the disadvantaged, while negative values indicate a concentration of the indicator among the advantaged. RCI is zero if there is no inequality.

Type of summary measure: Complex; relative; weighted

Applicability: Ordered; more than two subgroups

Warning: The confidence intervals are approximate and might be biased.

Value

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

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(IndividualSample)
head(IndividualSample)
with(IndividualSample,
     rci(est = sba,
         subgroup_order = subgroup_order,
         weight = weight,
         psu = psu,
         strata = strata
         )
     )