Available data sources • datasusr

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()