Predictive Modeling of Respiratory Hospitalizations in Brazil Using Integrated Public Health and Environmental Data

This dataset contains information on hospital admissions due to respiratory diseases and atmospheric monitoring data for the city of Rio de Janeiro, Brazil. The dataset integrates two main sources: Hospital admissions (INTERNACOES_DOENCA_RESP_RJ.csv): Derived from the Brazilian Unified Health System (SUS) through DATASUS, filtered by ICD-10 codes starting with "J" (respiratory diseases). It provides daily aggregated records of hospitalizations between 2012 and 2024. Air quality and meteorological data (QUALIAR_RIO_DE_JANEIRO_TRATADO.csv): Based on the DataRio/SMAC monitoring network, including daily aggregated measurements of pollutants (PM₂.₅, PM₁₀, NO₂, NOx, SO₂, CO, O₃) and meteorological variables (temperature, humidity, rainfall, etc.) across 8 monitoring stations in Rio de Janeiro, covering the same period. Merged dataset (INTERNACOES_x_QUALIAR.csv): A unified table linking hospital admissions with atmospheric and meteorological indicators on a daily basis. Purpose The dataset was prepared to support research in public health, epidemiology, environmental sciences, and machine learning, particularly for predictive modeling of hospital admissions due to respiratory diseases based on air quality and climate variables. Coverage Geographical scope: Rio de Janeiro, Brazil Temporal coverage: 2012–2024 Granularity: Daily aggregation File descriptions INTERNACOES_DOENCA_RESP_RJ.csv – Daily hospital admissions for respiratory diseases (DATASUS). QUALIAR_RIO_DE_JANEIRO_TRATADO.csv – Cleaned and aggregated air quality and meteorological data. INTERNACOES_x_QUALIAR.csv – Integrated dataset combining admissions and environmental variables.