healthbR

Overview

healthbR provides easy access to Brazilian public health data directly from R: the DATASUS microdata systems (SIM mortality, SINASC live births, SIH hospital admissions, SIA outpatient production, SINAN notifiable diseases, CNES facilities, SI-PNI vaccination), the IBGE and Ministry of Health surveys (VIGITEL, PNS, PNAD Continua, POF, Censo) and the regulatory agencies (ANS, ANVISA). The package downloads, caches, and processes data from official sources, returning clean, analysis-ready tibbles following tidyverse conventions.

If you already use microdatasus (or PySUS in Python) for DATASUS, see healthbR vs microdatasus – a measured comparison of the two packages on the systems both read.

Surveys (IBGE / Ministry of Health)

DATASUS (Ministry of Health FTP)

DATASUS modules download .dbc files (compressed DBF) and decompress them internally using vendored C code – no external dependencies required. SIH and SI-PNI read by default from the healthbr-data Parquet mirror (byte-identical to the Ministry’s files, with per-file provenance: source URL, MD5, timestamp) and fall back to the FTP.

Primary Care & Regulatory Agencies

healthbR and microdatasus

Two R packages read DATASUS microdata; they overlap on six systems and differ in what they do with the bytes. Measured on 2026-09-05 (healthbR 0.3.1 dev, microdatasus 3.0.0) and detailed in the vignette healthbR vs microdatasus:

Same rows, same records, same death counts on the three downloads compared. Each package fits a different workflow; the vignette says which.

Installation

From CRAN (0.2.0):

install.packages("healthbR")

The development version, with the SIH mirror and the newest year coverage, from GitHub:

# install.packages("pak")
pak::pak("SidneyBissoli/healthbR")

Quick start

library(healthbR)

# see all available data sources
list_sources()

DATASUS modules

All DATASUS modules follow a consistent API: *_years(), *_info(), *_variables(), *_dictionary(), *_data(), *_cache_status(), *_clear_cache().

# mortality data -- deaths in Acre, 2022
obitos <- sim_data(year = 2022, uf = "AC")

# filter by cause of death (CID-10 prefix)
obitos_cardio <- sim_data(year = 2022, uf = "AC", cause = "I")

# live births in Acre, 2022
nascimentos <- sinasc_data(year = 2022, uf = "AC")

# hospital admissions in Acre, January 2022
internacoes <- sih_data(year = 2022, month = 1, uf = "AC")

# filter by diagnosis (CID-10 prefix)
intern_resp <- sih_data(year = 2022, month = 1, uf = "AC", diagnosis = "J")

# outpatient production in Acre, January 2022
ambulatorial <- sia_data(year = 2022, month = 1, uf = "AC")

# different file type (e.g., high-cost medications)
medicamentos <- sia_data(year = 2022, month = 1, uf = "AC", type = "AM")

Additional DATASUS modules

# disease notifications -- dengue, 2022
dengue <- sinan_data(year = 2022, disease = "DENG")

# health facilities in Acre, January 2023
cnes <- cnes_data(year = 2023, month = 1, uf = "AC")

# vaccination data -- Acre (R2 mirror by default, DATASUS fallback)
vacinas <- sipni_data(year = 2019, uf = "AC")
micro <- sipni_data(year = 2024, uf = "AC", month = 1)

Survey modules

# VIGITEL telephone survey
vigitel <- vigitel_data(year = 2024)

# PNS national health survey
pns <- pns_data(year = 2019)

# PNAD Continua
pnadc <- pnadc_data(year = 2023, quarter = 1)

# POF household budget survey
pof <- pof_data(year = 2018, register = "morador")

# Census population
pop <- censo_populacao(year = 2022, territorial_level = "state")

Primary care & regulatory agencies

# SISAB -- primary care coverage by state, January 2024
sisab <- sisab_data(year = 2024, month = 1)

# ANS -- health plan beneficiaries in Acre, December 2023
ans <- ans_data(year = 2023, month = 12, uf = "AC")

# ANVISA -- registered medicines
med <- anvisa_data(type = "medicines")

Explore variables and dictionaries

# list variables for any module
sim_variables()
sia_variables(search = "sexo")
sinan_diseases(search = "dengue")

# data dictionary with category labels
sim_dictionary("SEXO")
sia_dictionary("PA_RACACOR")

Parallel downloads

When downloading multiple files (e.g., several years, months, or states), you can speed up downloads by enabling parallel processing. Install furrr and future, then set a parallel plan before calling any *_data() function:

install.packages(c("furrr", "future"))

library(future)
plan(multisession, workers = 4)

# downloads 4 states in parallel
df <- sih_data(year = 2022, month = 1:6, uf = c("SP", "RJ", "MG", "BA"))

# reset to sequential when done
plan(sequential)

All 12 download-based modules support parallel downloads: SIM, SINASC, SIH, SIA, SINAN, CNES, SI-PNI, SISAB, ANS, ANVISA, PNS, and PNADC.

Caching

All modules cache downloaded data automatically. Install arrow for optimized Parquet caching:

install.packages("arrow")

Each module provides cache management functions:

# check what is cached
sim_cache_status()
sih_cache_status()
sia_cache_status()

# clear cache for a module
sim_clear_cache()

Data sources

All data is downloaded from official Brazilian government repositories (DATASUS, IBGE, ANS, ANVISA):

• VIGITEL: Ministry of Health
• PNS / PNAD Continua / POF / Censo: IBGE
• SIM / SINASC / SIA / SINAN / CNES: DATASUS FTP
• SIH / SI-PNI: healthbr-data mirror on Cloudflare R2 (byte-identical to the Ministry’s files, with provenance metadata), falling back to DATASUS FTP / OpenDataSUS
• SISAB: Ministry of Health REST API
• ANS: ANS Open Data Portal
• ANVISA: ANVISA Open Data Portal

Citation

If you use healthbR in your research, please cite it:

citation("healthbR")

Contributing

Please see CONTRIBUTING.md for guidelines on how to contribute to this project.

Code of Conduct

Please note that the healthbR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

MIT © Sidney da Silva Pereira Bissoli