Health Facility Registry with CNES • healthbR

Overview

The CNES (Cadastro Nacional de Estabelecimentos de Saude) is Brazil’s national health facility registry, maintained by the Ministry of Health through DATASUS. It contains monthly snapshots of all registered health establishments, professionals, beds, equipment, and more.

The healthbR package provides access to CNES data from the DATASUS FTP:

Feature	Details
File types	13 (establishments, beds, professionals, etc.)
Frequency	Monthly (one file per type/UF/month)
Years	2005–2024 (final + preliminary)
Coverage	Per state (27 UFs)
Format	.dbc files, decompressed internally

File types

CNES data is organized into 13 file types:

Code	Name	Description
ST	Estabelecimentos	Health facility registry (default)
LT	Leitos	Hospital beds
PF	Profissional	Health professionals
DC	Dados Complementares	Supplementary facility data
EQ	Equipamentos	Health equipment
SR	Servico Especializado	Specialized services
HB	Habilitacao	Facility accreditations
EP	Equipes	Health teams
RC	Regra Contratual	Contractual rules
IN	Incentivos	Financial incentives
EE	Estab. de Ensino	Teaching facilities
EF	Estab. Filantropico	Philanthropic facilities
GM	Gestao e Metas	Management and targets

Getting started

library(healthbR)
library(dplyr)

Check available years

cnes_years()
#> [1] 2005 2006 ... 2023

cnes_years(status = "all")
#> [1] 2005 2006 ... 2023 2024

Module information

cnes_info()

Downloading data

Basic download (establishments)

# all establishments in Acre, January 2023
ac_jan <- cnes_data(year = 2023, month = 1, uf = "AC")
ac_jan

Hospital beds

leitos <- cnes_data(year = 2023, month = 1, uf = "AC", type = "LT")
leitos

Health professionals

prof <- cnes_data(year = 2023, month = 1, uf = "AC", type = "PF")
prof

Month parameter

The month parameter controls which monthly snapshots to download:

# single month
jan <- cnes_data(year = 2023, month = 1, uf = "AC")

# first semester
sem1 <- cnes_data(year = 2023, month = 1:6, uf = "AC")

# specific months
q1_q3 <- cnes_data(year = 2023, month = c(3, 6, 9), uf = "AC")

# all 12 months (default when month = NULL)
full_year <- cnes_data(year = 2023, uf = "AC")

Selecting variables

# only key variables (faster)
cnes_data(
  year = 2023, month = 1, uf = "AC",
  vars = c("CNES", "CODUFMUN", "TP_UNID", "VINC_SUS")
)

Key variables (ST type)

Variable	Description
CNES	Facility CNES code
CODUFMUN	Municipality (UF + IBGE 6 digits)
TP_UNID	Facility type (22 categories)
VINC_SUS	SUS-linked (0=No, 1=Yes)
TP_GESTAO	Management type (M=Municipal, E=State, D=Dual)
ESFERA_A	Administrative sphere (1=Federal, 2=State, 3=Municipal, 4=Private)
TURNO_AT	Operating hours
NIV_HIER	Hierarchy level
ATV_AMBUL	Outpatient care (0/1)
ATV_HOSP	Hospital care (0/1)
ATV_URG	Emergency care (0/1)
COMPETEN	Reference period (YYYYMM)

Using the dictionary

# all coded variables
cnes_dictionary()

# facility types (22 categories)
cnes_dictionary("TP_UNID")

# administrative sphere
cnes_dictionary("ESFERA_A")

Joining dictionary labels

# get facility type labels
tp_unid_labels <- cnes_dictionary("TP_UNID") |>
  select(code, label)

# join to data
ac_facilities <- cnes_data(year = 2023, month = 1, uf = "AC") |>
  left_join(tp_unid_labels, by = c("TP_UNID" = "code")) |>
  rename(facility_type = label)

ac_facilities |>
  count(facility_type, sort = TRUE)

Example: SUS-linked facilities by type

ac <- cnes_data(year = 2023, month = 1, uf = "AC")

sus_by_type <- ac |>
  filter(VINC_SUS == "1") |>
  count(TP_UNID, sort = TRUE)

# add labels
tp_labels <- cnes_dictionary("TP_UNID") |>
  select(code, label)

sus_by_type |>
  left_join(tp_labels, by = c("TP_UNID" = "code"))

Example: hospital beds per capita

Combine CNES bed data with Census population:

# step 1: count beds by UF (December snapshot)
beds <- cnes_data(year = 2023, month = 12, type = "LT") |>
  group_by(uf_source) |>
  summarize(total_beds = n(), .groups = "drop")

# step 2: population from Census 2022
pop <- censo_populacao(year = 2022, territorial_level = "state")

# step 3: calculate beds per 1,000 inhabitants
# beds_rate <- beds |>
#   left_join(pop, by = ...) |>
#   mutate(beds_per_1000 = (total_beds / population) * 1000) |>
#   arrange(desc(beds_per_1000))

Example: tracking facility counts over time

# quarterly snapshots for Sao Paulo
sp_quarterly <- cnes_data(
  year = 2020:2023,
  month = c(3, 6, 9, 12),
  uf = "SP"
)

facility_trend <- sp_quarterly |>
  group_by(year, month) |>
  summarize(
    total = n(),
    sus_linked = sum(VINC_SUS == "1", na.rm = TRUE),
    .groups = "drop"
  ) |>
  arrange(year, month)

facility_trend

Download tips

CNES data is monthly and per-state, so full downloads can involve many files:

1 UF, 1 month, 1 type = 1 file
1 UF, 12 months = 12 files
27 UFs, 12 months = 324 files per type

Use uf and month to limit downloads. Start with a single UF and month to explore the data before scaling up.

Smart type parsing

# parsed types (default)
ac <- cnes_data(year = 2023, month = 1, uf = "AC")
class(ac$COMPETEN)  # Date

# raw character columns
ac_raw <- cnes_data(year = 2023, month = 1, uf = "AC", parse = FALSE)

Cache management

Downloaded data is cached locally for faster future access:

# check cache status
cnes_cache_status()

# clear cache if needed
cnes_clear_cache()

If the arrow package is installed, data is cached in Parquet format. You can also use lazy evaluation:

# lazy query (requires arrow)
cnes_lazy <- cnes_data(year = 2023, uf = "AC", lazy = TRUE)
cnes_lazy |>
  filter(VINC_SUS == "1", month == 1L) |>
  select(CNES, CODUFMUN, TP_UNID) |>
  collect()

Additional resources

CNES official page (cnes.datasus.gov.br)
CNES open data (dados.gov.br)
Census vignette for population denominators