Available data sources

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This vignette provides a reference of every DATASUS data source registered in datasusr, with ready-to-run examples for each one.

Source catalog overview

library(datasusr)

datasus_sources()

The access column indicates which function to use with each source:

fetch — use datasus_fetch(), or the step-by-step workflow with datasus_list_files(), datasus_download(), and read_datasus_dbc(). These sources contain .dbc files on the DATASUS FTP.
territory — use datasus_get_territory(). These are CSV reference tables (municipalities, health regions, etc.).
ftp_only — use datasus_ftp_ls() to browse. These are software downloads (TabWin, TabNet), not data files.

Hospital information (SIH)

The Hospital Information System (SIHSUS) publishes monthly files by state.

# Reduced Hospital Admission Records
df <- datasus_fetch(
  source = "SIHSUS", file_type = "RD",
  year = 2024, month = 1, uf = "PE",
  select = c("uf_zi", "ano_cmpt", "munic_res", "val_tot")
)

# Rejected admissions
df <- datasus_fetch(
  source = "SIHSUS", file_type = "RJ",
  year = 2024, month = 1, uf = "PE"
)

# Professional services
df <- datasus_fetch(
  source = "SIHSUS", file_type = "SP",
  year = 2024, month = 1, uf = "PE"
)

Outpatient information (SIA)

The Outpatient Information System (SIASUS) also publishes monthly files by state.

# Outpatient production
df <- datasus_fetch(
  source = "SIASUS", file_type = "PA",
  year = 2024, month = 1, uf = "PE"
)

# Medication authorisations (APAC)
df <- datasus_fetch(
  source = "SIASUS", file_type = "AM",
  year = 2024, month = 1, uf = "PE"
)

Mortality (SIM)

The Mortality Information System (SIM) publishes yearly files. Death records (DO) are scoped by state; specialised subsets (DOFET, DOEXT, DOINF, DOMAT) cover all of Brazil.

# Death records by state (4-digit year in file name)
df <- datasus_fetch(
  source = "SIM", file_type = "DO",
  year = 2022, uf = "PE"
)

# Foetal deaths
df <- datasus_fetch(
  source = "SIM", file_type = "DOFET",
  year = 2022
)

# Deaths from external causes
df <- datasus_fetch(
  source = "SIM", file_type = "DOEXT",
  year = 2022
)

# Infant deaths
df <- datasus_fetch(
  source = "SIM", file_type = "DOINF",
  year = 2022
)

# Maternal deaths
df <- datasus_fetch(
  source = "SIM", file_type = "DOMAT",
  year = 2022
)

Live births (SINASC)

The Live Birth Information System publishes yearly files by state.

df <- datasus_fetch(
  source = "SINASC", file_type = "DN",
  year = 2022, uf = "PE"
)

Health facilities (CNES)

The National Registry of Health Facilities publishes monthly files by state across many subtypes.

# Facilities
df <- datasus_fetch(
  source = "CNES", file_type = "ST",
  year = 2024, month = 1, uf = "PE"
)

# Hospital beds
df <- datasus_fetch(
  source = "CNES", file_type = "LT",
  year = 2024, month = 1, uf = "PE"
)

# Professionals
df <- datasus_fetch(
  source = "CNES", file_type = "PF",
  year = 2024, month = 1, uf = "PE"
)

# Equipment
df <- datasus_fetch(
  source = "CNES", file_type = "EQ",
  year = 2024, month = 1, uf = "PE"
)

# Specialised services
df <- datasus_fetch(
  source = "CNES", file_type = "SR",
  year = 2024, month = 1, uf = "PE"
)

See datasus_file_types(source = "CNES") for the full list of CNES subtypes (LT, ST, DC, EQ, SR, HB, PF, EP, RC, IN, EE, EF, GM).

Hospital and outpatient reporting (CIHA / CIH)

CIHA replaced CIH in 2011. Both publish monthly files by state.

# CIHA (2011 onwards)
df <- datasus_fetch(
  source = "CIHA", file_type = "CIHA",
  year = 2024, month = 1, uf = "PE"
)

# CIH (historical, 2008-2011)
df <- datasus_fetch(
  source = "CIH", file_type = "CR",
  year = 2010, month = 1, uf = "PE"
)

Notifiable diseases (SINAN)

SINAN publishes yearly files with national scope (no UF filter needed).

# Dengue
df <- datasus_fetch(
  source = "SINAN", file_type = "DENG",
  year = 2023
)

# Chikungunya
df <- datasus_fetch(
  source = "SINAN", file_type = "CHIK",
  year = 2023
)

# Zika
df <- datasus_fetch(
  source = "SINAN", file_type = "ZIKA",
  year = 2023
)

# Malaria
df <- datasus_fetch(
  source = "SINAN", file_type = "MALA",
  year = 2023
)

Preliminary SINAN data is available through the SINAN_P source.

Other disease surveillance

# e-SUS Notifica (chronic Chagas disease)
df <- datasus_fetch(
  source = "ESUSNOTIFICA", file_type = "DCCR",
  year = 2023
)

# Suspected congenital Zika syndrome (RESP)
df <- datasus_fetch(
  source = "RESP", file_type = "RESP",
  year = 2022, uf = "PE"
)

Oncology panel

df <- datasus_fetch(
  source = "PO", file_type = "PO",
  year = 2022
)

Schistosomiasis control (PCE)

df <- datasus_fetch(
  source = "PCE", file_type = "PCE",
  year = 2022, uf = "PE"
)

Discontinued and replaced systems

SISCOLO and SISMAMA were replaced by SISCAN and are no longer available on the DATASUS FTP. SISPRENATAL data may still be available for historical periods.

# Prenatal monitoring (historical)
df <- datasus_fetch(
  source = "SISPRENATAL", file_type = "PN",
  year = 2014, month = 1, uf = "PE"
)

Territorial reference tables

Territorial data (municipality names, health regions, geographic divisions) is published as CSV files organised by year. Use datasus_get_territory():

# Municipality table (defaults to current year)
municipalities <- datasus_get_territory("tb_municip")
municipalities

# Specific year
municipalities_2023 <- datasus_get_territory("tb_municip", year = 2023)

# Browse available years and tables
datasus_ftp_ls("ftp://ftp.datasus.gov.br/territorio/tabelas/")

Documentation and data dictionaries

Each information system has documentation files on the DATASUS FTP. Use datasus_docs_url() to find them:

# All known documentation paths
datasus_docs_url()

# List documentation files for a specific system
datasus_ftp_ls(datasus_docs_url("CNES")$docs_url[[1]])

Connectivity check

The following code tests path resolution for every source and file type in the catalog:

library(dplyr)

sources_dbc <- datasus_sources() |>
  filter(access == "fetch")

results <- purrr::map(seq_len(nrow(sources_dbc)), \(i) {
  src <- sources_dbc$source[[i]]
  fts <- datasus_file_types(source = src)

  purrr::map(seq_len(nrow(fts)), \(j) {
    ft <- fts$file_type[[j]]
    ok <- tryCatch({
      datasus_build_path(source = src, file_type = ft, year = 2023, month = 1)
      TRUE
    }, error = function(e) FALSE)
    tibble::tibble(source = src, file_type = ft, has_path = ok)
  }) |> purrr::list_rbind()
}) |> purrr::list_rbind()

results |> print(n = Inf)

Cleaning up

The examples above download files to the local cache. To remove all cached files after testing:

datasus_cache_info()
datasus_cache_clear()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.