This dataset contains sample data for computing ordered summary measures of health inequality. It contains data from a household survey for two indicators, the proportion of births attended by skilled health personnel and under-five mortality rate, disaggregated by economic status.
Format
OrderedSampleMultipleind
A data frame with 10 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).
The under-five mortality rate is the probability (expressed as a rate per 1000 live births) of a child born in a specific year or period dying before reaching the age of five years. It is calculated as the number of deaths at age 0-5 years divided by the number of surviving children at the beginning of the specified age range during the 10 years prior to the survey.
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(OrderedSampleMultipleind)
#> 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 (%)
#> 6 Under-five mortality rate (deaths per 1000 live births)
#> 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
#> 6 Economic status (wealth quintile) Quintile 1 (poorest) 1
#> 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
#> 6 52.46997 3.1404064 7119.732 340.38391 0
#> ordered_dimension indicator_scale
#> 1 1 100
#> 2 1 100
#> 3 1 100
#> 4 1 100
#> 5 1 100
#> 6 1 1000
summary(OrderedSampleMultipleind)
#> indicator dimension subgroup subgroup_order
#> Length:10 Length:10 Length:10 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. :23.90 Min. :0.2238 Min. :1885 Min. : 91.6
#> 1st Qu.:31.67 1st Qu.:0.7684 1st Qu.:2057 1st Qu.: 91.6
#> Median :64.04 Median :1.9412 Median :4428 Median :216.0
#> Mean :62.84 Mean :1.7732 Mean :4472 Mean :216.0
#> 3rd Qu.:94.78 3rd Qu.:2.6824 3rd Qu.:6893 3rd Qu.:340.4
#> Max. :99.22 Max. :3.1404 Max. :7120 Max. :340.4
#> favourable_indicator ordered_dimension indicator_scale
#> Min. :0.0 Min. :1 Min. : 100
#> 1st Qu.:0.0 1st Qu.:1 1st Qu.: 100
#> Median :0.5 Median :1 Median : 550
#> Mean :0.5 Mean :1 Mean : 550
#> 3rd Qu.:1.0 3rd Qu.:1 3rd Qu.:1000
#> Max. :1.0 Max. :1 Max. :1000