Between-Group Variance (BGV) is an absolute measure of inequality that considers all population subgroups. Subgroups are weighted according to their population share.
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 at least 85% of subgroups.
- se
The standard error of the subgroup estimate. If this is missing, confidence intervals of BGV cannot be calculated.
- conf.level
Confidence level of the interval. Default is 0.95 (95%).
- ...
Further arguments passed to or from other methods.
Value
The estimated BGV value, corresponding estimated standard error,
and confidence interval as a data.frame.
Details
BGV is calculated as the weighted average of squared differences between the subgroup estimates and the setting average. Squared differences are weighted by each subgroup’s population share. For more information on this inequality measure see Schlotheuber, A., & Hosseinpoor, A. R. (2022) below.
Interpretation: BGV has only positive values, with larger values indicating higher levels of inequality. BGV is zero if there is no inequality. BGV is more sensitive to outlier estimates as it gives more weight to the estimates that are further from the setting average. It is reported as the squared unit of the health indicator.
Type of summary measure: Complex; absolute; weighted
Applicability: Non-ordered; more than two subgroups
Warning: The confidence intervals are approximate and might be biased. See Ahn J. et al. (2018) below for further information on the standard error formula.
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.
Ahn J, Harper S, Yu M, Feuer EJ, Liu B, Luta G. (2018). Variance Estimation and Confidence Intervals for 11 Commonly Used Health Disparity Measures. JCO Clin Cancer Inform. Dec;2:1--19.
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,
bgv(pop = population,
est = estimate,
se = se
)
)
#> measure estimate se lowerci upperci
#> 1 bgv 50.42023 9.560196 31.68259 69.15787