(untitled) (Research, Society and Development, 2026)
Reproducible access to DATASUS microdata is critical for health research and management, yet it often faces operational barriers, particularly when dealing with large datasets. This study aimed to demonstrate the feasibility of a reproducible Python/PySUS pipeline to extract and analyze SIH/SUS data (RD files), using hospitalizations for urinary tract infection (UTI) as a use case. UTI was defined as ICD-10 N39.0 recorded as the primary diagnosis (DIAG_PRINC = N390) in Minas Gerais (MG) and Acre (AC), Brazil, in 2024. The pipeline was executed in Google Colab, performing month-by-month downloads (by competence), DataFrame conversion, and Parquet caching to improve robustness and enable reruns. Descriptive analyses included monthly time series and sex distribution. A total of 29,720 UTI hospitalizations were identified in 2024 (MG: 28,860; AC: 860), with a peak in January (MG: 2,690; AC: 95). Female hospitalizations predominated in both states (AC: 75.9%; MG: 65.2%). The findings indicate that PySUS enables automated and reproducible access to DATASUS, efficiently producing tables and figures and supporting secondary-data studies across settings with different data volumes.