This dataset contains sample data for computing ordered summary measures of health inequality. It contains data from a household survey for the proportion of births attended by skilled health personnel disaggregated by economic status, measured by wealth quintiles.
Format
OrderedSample
A data frame with 5 rows and 11 columns.
- indicator
indicator name
- dimension
dimension of inequality
- subgroup
population subgroup within a given dimension of inequality
- subgroup_order
the order of subgroups in an increasing sequence
- estimate
subgroup estimate
- se
standard error of the subgroup estimate
- population
number of people within each subgroup
- setting_average
indicator average for the setting
- favourable_indicator
favourable (1) or non-favourable (0) indicator
- ordered_dimension
ordered (1) or non-ordered (0) dimension
- indicator_scale
scale of the indicator
Source
WHO Health Inequality Data Repositoryhttps://www.who.int/data/inequality-monitor/data
Details
The proportion of births attended by skilled health personnel is calculated as the number of births attended by skilled health personnel divided by the total number of live births to women aged 15-49 years occurring in the period prior to the survey.
Skilled health personnel include doctors, nurses, midwives and other medically trained personnel, as defined according to each country. This is in line with the definition used by the Countdown to 2030 Collaboration, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and Reproductive Health Surveys (RHS).
Economic status is determined using a wealth index, which is based on owning selected assets and having access to certain services. The wealth index is divided into five equal subgroups (quintiles) that each account for 20% of the population. Economic status is an ordered dimension (meaning that the subgroups have an inherent ordering).
This dataset can be used to calculate ordered summary measures of health inequality, including: absolute concentration index (ACI), relative concentration index (RCI), slope index of inequality (SII) and relative index of inequality (RII). It can also be used to calculate the impact measures population attributable risk (PAR) and population attributable fraction (PAF).
Examples
head(OrderedSample)
#> indicator
#> 1 Births attended by skilled health personnel (%)
#> 2 Births attended by skilled health personnel (%)
#> 3 Births attended by skilled health personnel (%)
#> 4 Births attended by skilled health personnel (%)
#> 5 Births attended by skilled health personnel (%)
#> dimension subgroup subgroup_order
#> 1 Economic status (wealth quintile) Quintile 1 (poorest) 1
#> 2 Economic status (wealth quintile) Quintile 2 2
#> 3 Economic status (wealth quintile) Quintile 3 3
#> 4 Economic status (wealth quintile) Quintile 4 4
#> 5 Economic status (wealth quintile) Quintile 5 (richest) 5
#> estimate se population setting_average favourable_indicator
#> 1 75.60530 1.5996131 2072.436 91.59669 1
#> 2 91.01997 1.1351504 2112.204 91.59669 1
#> 3 96.03959 0.6461946 1983.059 91.59669 1
#> 4 97.04223 0.5676206 2052.124 91.59669 1
#> 5 99.22405 0.2237683 1884.510 91.59669 1
#> ordered_dimension indicator_scale
#> 1 1 100
#> 2 1 100
#> 3 1 100
#> 4 1 100
#> 5 1 100
summary(OrderedSample)
#> indicator dimension subgroup subgroup_order
#> Length:5 Length:5 Length:5 Min. :1
#> Class :character Class :character Class :character 1st Qu.:2
#> Mode :character Mode :character Mode :character Median :3
#> Mean :3
#> 3rd Qu.:4
#> Max. :5
#> estimate se population setting_average
#> Min. :75.61 Min. :0.2238 Min. :1885 Min. :91.6
#> 1st Qu.:91.02 1st Qu.:0.5676 1st Qu.:1983 1st Qu.:91.6
#> Median :96.04 Median :0.6462 Median :2052 Median :91.6
#> Mean :91.79 Mean :0.8345 Mean :2021 Mean :91.6
#> 3rd Qu.:97.04 3rd Qu.:1.1352 3rd Qu.:2072 3rd Qu.:91.6
#> Max. :99.22 Max. :1.5996 Max. :2112 Max. :91.6
#> favourable_indicator ordered_dimension indicator_scale
#> Min. :1 Min. :1 Min. :100
#> 1st Qu.:1 1st Qu.:1 1st Qu.:100
#> Median :1 Median :1 Median :100
#> Mean :1 Mean :1 Mean :100
#> 3rd Qu.:1 3rd Qu.:1 3rd Qu.:100
#> Max. :1 Max. :1 Max. :100