The next section of the dashboard is the equity analysis. This module is intended to identify which factors most contribute to addressing inequity and reducing the disparity between the different groups analysed through the Population modules.
The generated charts do not show absolute numbers of deaths but rather amenable deaths. Amenable deaths refer to deaths in disadvantaged populations caused by the fact that these populations have lower coverage of high impact interventions than the least disadvantaged populations in the same country. Another way of understanding this is excess deaths among the poor that, in principle, could be averted by bringing the coverage of interventions among the poor to a certain predefined level. This analysis is a crucial component of EQUIST, which explicitly encourages countries to focus on reducing health disparities prior to, or at least concurrently with, efforts to move towards universal health coverage.
The equity frontier that is generated indicates how many lives could have been saved or malnutrition cases averted if the country of selection were to equalize coverage values for the least disadvantaged within the most disadvantaged population. In the example above, there are approximately 1,000 deaths that could have been averted if the coverage gaps for the most disadvantaged population was equivalent to that of the richest for the country in context. The way that the excess deaths by quintile is calculated is: [under-five deaths in the poorest 4 wealth quintiles based on existing coverage of high impact interventions]-[under-five deaths in the poorest 4 wealth quintiles if coverage of high impact interventions is raised to the same levels as the wealthiest quintile in the same country]. The equity frontier serves as a benchmark to what seems feasible in that particular context.
As an example, the amenable deaths for U5MR (i.e. equity frontier) in the Tanzania in 2010 by residence (rural vs. urban) would appear as shown:
This graph shows that approximately 20k deaths could have been averted (excess deaths) if the coverage values for the rural (most disadvantaged) population was equivalent to that of the urban (least disadvantaged) population. Therefore, burden of deaths being heavier in rural area, strategies would be defined to specifically address the situation in rural area if we want the plan or programme to be effective and generate results at large scale.
In the UNICEF’s recent Narrowing the Gap II study, a group of over 50 countries was included ; one of the key areas of analysis was the measurement of changes in supply, demand and quality bottlenecks for these 50+ countries over a period of around 5 years. This allowed to identify the best results found among these countries in reducing each of bottlenecks analysed. This has thus provided us with a benchmark of “good practices” that serve us as realistic benchmarks of what is feasibly within a medium-term scenario.
The operational frontier is an analysis that compares the country you are looking at to the best performing countries over a period of around 5 years, based upon. The operational frontier refers to the maximum expected improvement in bottleneck reductions, subsequent expected improvements in effective coverage and expected deaths averted. This gives us another benchmark of what is operationally feasible in optimal circumstances.
Based on the settings for 2010, for Tanzania by provinces , the model allows you to visualize operational frontiers and equity frontiers in a stacked bar format disaggregated by group, disease, intervention and bottleneck. The operational frontier graph will appear as follows: