knitr::opts_chunk$set(echo = TRUE, results = TRUE, warning = FALSE, message = FALSE, comment = "", collapse = FALSE, class.source = "bg-success", class.output="bg-warning" )
Measuring and monitoring inequalities in health is important for informing policies and programs that aim to tackle health inequities. Broadly defined, inequalities in health are measurable differences in health across population subgroups defined by dimensions of inequality (demographic, socioeconomic or geographic characteristics). Summary measures of inequality summarise the amount of inequality across subgroups in a single number -- which facilitates the comparison of inequalities over time and across different settings and indicators.
Summary measures of health inequality use either disaggregated data or individual-level data as inputs.
Disaggregated data refers to health indicator data recorded for different subgroups of a population that are defined according to dimensions of inequality, such as demographic, socioeconomic or geographic characteristics (such as wealth quintiles or subnational regions). Each record in the dataset contains data for a population subgroup.
Individual-level data refers to data where each record in the dataset contains data pertaining to an individual.
Simple summary measures (difference and ratio) compare two population subgroups. They can be calculated for all dimensions of inequality (with two subgroups or more). Complex measures are calculated for inequality dimensions with more than two population subgroups and consider the situation in all subgroups. They can only be calculated for dimensions with more than two subgroups.
Selecting appropriate measures for analysing and reporting inequality involves considering several methodological issues. There are considerations relating to the characteristics of the underlying data, which determines the types of measures that can be calculated and how they are calculated:
There are also considerations relating to the properties of the different measures and the desired purpose of the analysis:
The paper Summary measures of health inequality: a review of existing measures and their application provides further information about these considerations.
The following summary measures of health inequality are included in the healthequal
library:
d
)r
)aci
)rci
)sii
)rii
)bgv
)bgsd
)covar
)mdmu
and mdmw
)mdbu
and mdbw
)mdru
and mdrw
)idisu
and idisw
)ti
)mld
)parisk
)paf
)healthequal
libraryFirst, load the healthequal
library in your R session. This requires the dplyr
package.
library(healthequal) require(dplyr)
healthequal
packageThe healthequal
package comes with sample data for users to be able to test the package functions. The OrderedSample
and NonorderedSample
data contain data disaggregated by economic status and subnational region, respectively, for a single indicator.
data(OrderedSample) head(OrderedSample)
data(NonorderedSample) head(NonorderedSample)
The OrderedSampleMultipleind
and OrderedSampleMultipleind
data contain disaggregated data by economic status and subnational region, respectively, for two indicators.
data(OrderedSampleMultipleind) head(OrderedSampleMultipleind)
data(NonorderedSampleMultipleind) head(NonorderedSampleMultipleind)
For information about the datasets, type the following commands, which will display the corresponding dataset help file:
?healthequal::OrderedSample ?healthequal::NonorderedSample ?healthequal::OrderedSampleMultipleind ?healthequal::NonorderedSampleMultipleind ?healthequal::IndividualSample
The Absolute Concentration Index (ACI) is a summary measure of health inequality that can be used with ordered dimensions. For information about the ACI function type the following command, which will display the corresponding help file:
?aci
The OrderedSample
dataset can be used to calculate ACI. Two arguments are required: est
(the subgroup estimate, recorded as estimate
in the same dataset), and subgroup_order
(the order of subgroups in an increasing sequence). Other arguments, such as pop
(the number of people within each subgroup, recorded as population
in the sample dataset) or weight
(the sampling weight for survey data), are optional. Lastly, the force
argument can be used to estimate ACI when some estimates are missing.
with(OrderedSample, aci(est = estimate, subgroup_order = subgroup_order, pop = population ) )
The Slope Index of Inequality (SII) is a summary measure of health inequality that can be used with ordered dimensions. The slope index of inequality (SII) is an absolute measure of inequality that represents the difference in estimated indicator values between the most-advantaged and most-disadvantaged, while taking into consideration the situation in all other subgroups/individuals -- using an appropriate regression model.
For information about the SII function type the following command, which will display the corresponding help file:
?sii
SII can be calculated using disaggregated or individual data. In this example, the IndividualSample
dataset, a survey weighted dataset, is used. For this type of data, five arguments are required: est
(the individual estimate, recorded as sba
in the same dataset), subgroup_order
(the order of subgroups in an increasing sequence), weight
(the sampling weight), psu
(the primary sampling unit) and strata
(the variable identifying the strata).
with(IndividualSample, aci(est = sba, subgroup_order = subgroup_order, weight = weight, psu = psu, strata = strata ) )
Between-group variance (BGV) is a summary measure of health inequality that it can be used to measure inequality across non-ordered dimensions of inequality. It is calculated as the weighted average of squared differences between subgroup estimates and the weighted mean. Type ?bgv
to view the corresponding help file.
The NonorderedSample
dataset can be used to calculate BGV, which requires two arguments: pop
(the number of people within each subgroup, recorded as population
in the sample dataset), and est
(the subgroup estimate, recorded as estimate
in the same dataset). The argument se
(the standard error of the subgroup estimate) is required only to compute the corresponding 95% confidence intervals.
with(NonorderedSample, bgv(pop = population, est = estimate, se = se ) )
The previous examples showed the calculation of a single measure of inequality for a single indicator-dimension combination. These next examples use the dataset NonorderedSampleMultipleind
, which contains disaggregated data by subnational region for two indicators:
The data can be inspected as follows:
head(NonorderedSampleMultipleind) unique(NonorderedSampleMultipleind$indicator)
The Coefficient of Variation (COV) is a summary measure of health inequality that it can be used to measure inequality across non-ordered dimensions of inequality. COV is a relative measure of inequality that considers all population subgroups. Type ?covar
to view the corresponding help file. The NonorderedSampleMultipleind
dataset can be used to calculate COV for two different dimensions.
library(dplyr) measures <- NonorderedSampleMultipleind %>% dplyr::group_by(indicator) %>% dplyr::summarize(covar(pop = population, est = estimate, scaleval = indicator_scale ) ) measures
The NonorderedSampleMultipleind
dataset can also be used to calculate two or more different summary measures (in the example below COV and BGV) for multiple dimensions.
multiplemeasures <- NonorderedSampleMultipleind %>% dplyr::group_by(indicator, dimension) %>% dplyr::summarize( covar = covar(pop = population, est = estimate, scaleval = indicator_scale), bgv = bgv(pop = population, est = estimate, se = se ) ) multiplemeasures # It is possible to extract the measures separetly multiplemeasures$covar multiplemeasures$bgv
Ahn J, Harper S, Yu M, Feuer EJ, Liu B, Luta G. Variance Estimation and Confidence Intervals for 11 Commonly Used Health Disparity Measures. JCO Clin Cancer Inform. 2018 Dec;2:1--19. https://doi.org/10.1200/CCI.18.00031
Erreygers G. Correcting the Concentration Index. Journal of Health Economics. 2009;28:504–515. https://doi.org/10.1016/j.jhealeco.2008.02.003
Harper S, Lynch J, Meersman SC, Breen N, Davis WW, Reichman ME. An overview of methods for monitoring social disparities in cancer with an example using trends in lung cancer incidence by area-socioeconomic position and race-ethnicity, 1992-2004. Am J Epidemiol. 2008;167(8):889-899. https://doi.org/10.1093/aje/kwn016
Keppel K, Pamuk E, Lynch J, Carter-Pokras O, Kim I, Mays V, Pearcy J, Schoenbach V, Weissman JS. Methodological issues in measuring health disparities. Vital Health Stat. Ser. 2. 2005;141:1--16. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681823/
O'Donnell O, van Doorslaer E, Wagstaff A, Lindelow M. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation. World Bank Institute; Washington, D.C: 2008. https://hdl.handle.net/10986/6896
Schlotheuber A, Hosseinpoor AR. Summary Measures of Health Inequality: A Review of Existing Measures and Their Application. Int J Environ Res Public Health. 2022 Mar 20;19(6):3697. https://doi.org/10.3390/ijerph19063697
Wagstaff A. The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health Economics. 2005;14:429–432. https://doi.org/10.1002/hec.953
World Health Organization. Handbook on health inequality monitoring: with a special focus on low- and middle-income countries. Geneva: World Health Organization; 2013. https://www.who.int/publications/i/item/9789241548632
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