Brazil is creating an integrated open platform to monitor health outcomes, evaluate the effects of interventions, and forecast scenarios for SDGs

One of the winners of the call for proposals for the implementation of IS4H projects promoted by the Pan American Health Organization is a project from Brazil that is creating the Integrated Platform for Evaluation and Forecasting (IPEF). This platform will strengthen and systematize the use of the Datasus of the Brazilian Ministry of Health, the Matrix of Social Information of the Ministry of Social Development, and all socioeconomic and demographic data from the Brazilian Institute of Geography and Statistics(IBGE). All these data are publicly available and routinely updated, but they have never been systematically interlinked for advanced retrospective and forecast analyses.

Man studies some datasets

The IPEF will be innovative because it will create unique algorithms to integrate all the operations above in an automated way and to evaluate at the same time an unprecedented large number of exposures and outcomes.

The IPEF—which is being developed at the Instituto de Saúde Coletiva (ISC) of the Federal University of Bahia—will merge them at different aggregation levels (available in all data sources): municipal, state, and country level, and will create a set of integrated multivariate longitudinal databases. The IPEF will be structured in three intercommunicating modules, each focused on progressive steps of the analyses, from descriptive (Module 1) to evaluative (Module 2), and forecasting (Module 3):

Module 1: it will be able to integrate data for all country from different sources, adjust health data for poor-quality and under-notification, create and standardize rates at different government levels (country, state, municipality), for different causes, for different age-groups, and analyze their trends with special attention to health inequalities.

Module 2: it will be able to evaluate statistical associations between changes in socioeconomic determinants of health, coverage or density of public interventions and changes in trends of health outcomes using multivariate regression models for longitudinal data since the year 2000 and for the all country.

Module 3: based on Module 1 data and Module 2 measures of association, it will be able to forecast scenarios and measure how changes in socioeconomic determinants of health, or policy options of coverage and density of public interventions will affect morbidity and mortality, with a special focus to the attainment of SDGs.

In order to test the platform a pilot project will be developed, evaluating the effects of the Brazilian Primary Care Program (Estrategia Saude da Familia) on all ages and groups of causes of hospitalization and mortality in Brazil in 2000-2017, and forecasting scenarios of the effects of reductions or expansions of its coverage on all these outcomes up to 2030.

The authors of the project have an extensive experience in all analyses and codes involved in the three modules, having published more than 20 papers in high impact journals using the same techniques. The IPEF will be innovative because it will create unique algorithms to integrate all the operations above in an automated way and to evaluate at the same time an unprecedented large number of exposures and outcomes, moreover with its user-friendly interface will allow an easy visualization of the results and an interactive scenario testing for health managers and policy makers. IPEF could also be used, with slight modifications, in countries with similar data availability.

The IPEF itself represents the concretization of the main goal of IS4H: open-access Brazilian data from different sources will be integrated in a unique set of longitudinal databases, from which descriptive and analitic information will be obtained, including on the effects of changes of social determinants of health and coverage of public intereventions, and based on this information health managers and policymakers will be able – with the support of researchers - to forecast scenarios on the effects of different policy options on potentially all kind of health outcome and health inequalities.

The results of all this process will be a more evidence-based policy making process, based on integration and advanced analyses of pre-existing data. The pilot project will allow to evaluate how primary care coverage has contributed to the decrease of each hospitalization and mortality cause and if and how an increased coverage will help to reach the SDGs goals, while a decrease would compromise them.