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Area of Science:

  • Public Health
  • Data Science
  • Environmental Health

Background:

  • The Donabedian model is a framework for evaluating healthcare quality.
  • Planetary health considers the health of human civilizations and the state of the natural systems on which they depend.
  • Big data offers new opportunities for large-scale health program evaluation.

Purpose of the Study:

  • To integrate planetary health and big data concepts into the Donabedian model.
  • To evaluate the Brazilian dengue control program in São Paulo state.

Main Methods:

  • Utilized data science methods to analyze dengue-related data from 2010-2019.
  • Integrated data from DATASUS, IBGE, WorldClim, and MapBiomas into a Data Warehouse.
  • Applied K-means clustering, statistical analysis, and spatial visualization.

Main Results:

  • Identified four distinct groups of municipalities based on climate variables.
  • Worst-performing municipalities in dengue control had better economic conditions but fewer healthcare resources per capita.
  • Higher urbanization rates and human activity correlated with poorer dengue control outcomes.

Conclusions:

  • The methodology identified significant deficiencies in São Paulo's dengue control program implementation.
  • Large-scale evaluation using integrated databases and data science is feasible.
  • Findings can inform public administration for targeted actions and investments.