Background
Implementation
Feature | Description |
---|---|
General | |
Software | HEAT was developed using the R statistical software and the R package shiny. Additional R packages used to support HEAT include: ggplot2, dplyr, RColorBrewer, grid and grid Extra. |
License | GNU GPL version 2. |
Availability | HEAT is available as an online application and as a standalone version for use offline. |
Compatibility | The online version can be accessed using any web browser on all desktop or laptop computers and mobile devices (minimum screen size of 7.9” is recommended). |
The standalone version can be accessed on all computers with a Windows or Macintosh operating system. | |
Installation | The online version requires no installation. |
The standalone version is available in a zip folder and needs to be extracted and saved to the computer’s hard drive. The extracted HEAT folder contains portable versions of the R statistical software and the web browser Mozilla Firefox, which are required to run HEAT, but do not require any installation. The standalone version can simply be launched by double-clicking the start file. | |
Built-in database | |
Disaggregated data | HEAT contains the WHO Health Equity Monitor database. Its 2015 update includes estimates and 95 % confidence intervals for more than 30 reproductive, maternal, newborn and child health indicators, disaggregated by five dimensions of inequality (economic status, education, place of residence, subnational region and child’s sex (where applicable)) from Demographic and Health Surveys and Multiple Indicator Cluster Surveys conducted in 94 countries between 1993 and 2013. The database is updated regularly. |
Summary measures of inequality | HEAT calculates 15 summary measures of inequality and their 95 % confidence intervals based on analytic and/or bootstrap methods. |
Export options | |
Tables | Data can be exported as text files with values separated by comma or by tab. Users can choose their preferred field separator. |
Graphs | Data can be visualised in bar graphs, line graphs and scatterplots. Users can adjust the height and width of graphs, specify axes ranges and add titles and axis labels. In addition, users can display 95 % confidence intervals. Graphs can be exported as pdf, jpg or png files. |
Supporting material | |
User manual | The user manual provides detailed information on how to set up and work with HEAT. Each feature of the toolkit is explained in detail and recommendations are made on how best to assess and interpret the data. |
Technical notes | The technical notes provide detailed information about the data displayed in HEAT, including the disaggregated data from the WHO Health Equity Monitor database and the 15 summary measures of inequality that were calculated based on the disaggregated data. |
Indicator compendium | The indicator compendium includes a comprehensive definition of each indicator included in the WHO Health Equity Monitor database. |
Dimension of inequality | |||||||
---|---|---|---|---|---|---|---|
Summary measure | Definition | Formulaa | Economic status | Education | Place of residence | Sex | Subnational region |
Absolute measures | |||||||
Absolute concentration index (ACI) | The ACI is a complex, weighted measure of inequality that indicates the extent to which a health indicator is concentrated among the disadvantaged or advantaged, on an absolute scale. |
\( ACI={\displaystyle \sum_j}{p}_j\left(2{X}_j-1\right){y}_j \)
| ✓ | ✓ | |||
Between-group variance (BGV) | The BGV is a complex, weighted measure of inequality that shows the squared difference between each subgroup and the national level, on average. The BGV is sensitive to large deviations from the national level (by use of squaring). |
\( BGV={\displaystyle \sum_j}{p}_j{\left({y}_j-\mu \right)}^2 \)
| ✓ | ||||
Difference (D) | The D is a simple measure of inequality that shows the absolute inequality between two subgroups. | D = y
high
− y
low
| ✓ | ✓ | ✓ | ✓ | ✓ |
Mean difference from best performing subgroup (MDB) | The MDB is a complex, weighted measure of inequality that shows the difference between each subgroup and the best performing subgroup, on average. |
\( MDB={\displaystyle \sum_j}{p}_j\left|{y}_j-{y}_{ref}\right| \)
| ✓ | ||||
Mean difference from mean (MDM) | The MDM is a complex, weighted measure of inequality that shows the absolute difference between each subgroup and the national level, on average. |
\( MDM={\displaystyle \sum_j}{p}_j\left|{y}_j-\mu \right| \)
| ✓ | ||||
Population attributable risk (PAR) | The PAR is a complex, weighted measure of inequality that shows the potential for improvement in the national level of a health indicator that could be achieved if all subgroups had the same level of health as a reference subgroup. | PAR = y
ref
− μ | ✓ | ✓ | ✓ | ✓ | ✓ |
Slope index of inequality (SII) | The SII is a complex, weighted measure of inequality that represents the absolute difference in predicted values of a health indicator between the most-advantaged and most-disadvantaged (or vice versa for adverse health outcome indicators), while taking into consideration all the other subgroups – using an appropriate regression model. | SII = v1 − v0 for favourable health intervention indicators; SII = v0 − v1 for adverse health outcome indicators | ✓ | ✓ | |||
Relative measures | |||||||
Index of disparity (IDIS) | The IDIS is a complex measure of inequality that shows the proportional difference between each subgroup and the national level, on average. |
\( IDIS=\frac{1}{n}*\frac{{\displaystyle {\sum}_j}\left|{y}_j-\mu \right|}{\mu }*100 \)
| ✓ | ||||
Kunst-Mackenbach index (KMI) | The KMI is a complex, weighted measure of inequality that represents the ratio of predicted values of a health indicator of the most-advantaged to the most-disadvantaged (or vice versa for adverse health outcome indicators), while taking into consideration all the other subgroups – using an appropriate regression model. | KMI = v1/v0 for favourable health intervention indicators; KMI = v0/v1 for adverse health outcome indicators | ✓ | ✓ | |||
Mean log deviation (MLD) | The MLD is a complex measure of inequality that takes into account the population share of each subgroup. The MLD is sensitive to large deviations from the national level (by use of logarithm). |
\( \mathrm{M}\mathrm{L}\mathrm{D}={\displaystyle \sum_j}{p}_j\left(- \ln \left(\frac{y_j}{\mu}\right)\right)*1000 \)
| ✓ | ||||
Population attributable fraction (PAF) | The PAF is a complex, weighted measure of inequality that shows the potential for improvement in the national level of a health indicator, in relative terms, that could be achieved if all subgroups had the same level of health as a reference subgroup. |
\( PAF=\frac{PAR}{\mu }*100 \)
| ✓ | ✓ | ✓ | ✓ | ✓ |
Ratio (R) | The R is a simple measure of inequality that shows the relative inequality between two subgroups. | R = y
high
/y
low
| ✓ | ✓ | ✓ | ✓ | ✓ |
Relative concentration index (RCI) | The RCI is a complex, weighted measure of inequality that indicates the extent to which a health indicator is concentrated among the disadvantaged or the advantaged, on a relative scale. |
\( RCI=\frac{ACI}{\mu }*100 \)
| ✓ | ✓ | |||
Relative index of inequality (RII) | The RII is a complex, weighted measure of inequality that represents the relative difference (proportional to the national level) in predicted values of health indicator between the most-advantaged and most-disadvantaged, while taking into consideration all the other subgroups – using an appropriate regression model. |
\( RII=\frac{SII}{\mu } \)
| ✓ | ✓ | |||
Theil index (TI) | The TI is a complex measure of inequality, that takes into account the population share of each subgroup. The TI is sensitive to large deviations from the national level (by use of logarithm). |
\( TI={\displaystyle \sum_j}{p}_j\frac{y_j}{\mu } \ln \frac{y_j}{\mu }*1000 \)
| ✓ |