mdmu {healthequal}

Mean difference from mean (unweighted) (MDMU)

Description

The Mean Difference from Mean (MDM) is an absolute measure of inequality that shows the mean difference between each subgroup and the setting average. For the unweighted version (MDMU), all subgroups are weighted equally.

Usage

mdmu(
  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 all subgroups.

se

The standard error of the subgroup estimate. If this is missing, 95% confidence intervals of MDMU 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

seed

The random number generator (RNG) state for the 95% confidence interval simulation

...

Further arguments passed to or from other methods.

Details

The unweighted version (MDMU) is calculated as the sum of the absolute differences between the subgroup estimates and the setting average, divided by the number of subgroups. 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 MDMU is calculated for each of the simulated samples. The 95% confidence intervals are based on the 2.5th and 97.5th percentiles of the MDMU results.

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

Type of summary measure: Complex; absolute; non-weighted

Applicability: Non-ordered; more than two subgroups

Value

The estimated MDMU 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(NonorderedSample)
head(NonorderedSample)
with(NonorderedSample,
     mdmu(pop = population,
          est = estimate,
          se = se,
          scaleval = indicator_scale,
          setting_average
         )
     )