It is important to note that the EQUIST tool works in a step-wise fashion. This means that as data is selected in a module, it will have an impact on the data and analysis in subsequent modules. To be able to properly use EQUIST, users will need to be familiar with the terms and concepts used in the module(s). A glossary of these terms is available in Annex I.
EQUIST must be set up and customized for a country before it can be used (see above). This customization process needs to happen at least once every 3-5 years. The more carefully it is customized, the more precise and valid the results will be. The customization process must be performed by a well-trained and supported team of experts in a given country, and the process should be well documented.
EQUIST comes pre-loaded with globally-accepted data, which the EQUIST technical assistance team regularly updates. EQUIST’s principle data sources are the Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and estimates of mortality rates and causes developed by the Child Health Reference Group (CHERG) and the Inter-agency Group for Child Mortality Estimation (IGME.) Users will often draw heavily upon district health information systems (DHIS).
When using EQUIST, one will perform the following steps.
- Look at situational analysis of a country
- Profile: Conduct brief analysis to assess the general extent, nature and implications of inequities in the country. Examine the key drivers of inequity (the underlying factors that explain inequities (wealth quintile, geography, ethnicity and location) and analyse the scale of inequity (is deprivation mostly concentrated in poorest quintiles, in rural areas, or in some regions?)
- Frontier: Conduct an analysis to identify which factors are most likely to drive inequity, and compare the number of child deaths and malnutrition cases that could be averted by wealth quintile, geography, and location.
- Build a scenario analysis of a country: build multiple scenario(s) within a particular country by choosing a target population, epidemiological priorities, interventions, bottleneck, causes and strategies; and from this a LiST analysis will generate the impact(s) your analyses have on costs and lives saved