DATASUS Modules: SIM, SINASC, SIH, SIA, SINAN, CNES, SI-PNI, and SISAB

Overview

The healthbR package provides access to eight DATASUS information systems, covering mortality, live births, hospital admissions, outpatient production, notifiable diseases, the health facility registry, vaccination data, and primary care coverage:

Module	Function	Source document	Granularity	Years
SIM	`sim_data()`	Declaracao de Obito (DO)	Annual/UF	1996–2024
SINASC	`sinasc_data()`	Declaracao de Nascido Vivo (DN)	Annual/UF	1996–2024
SIH	`sih_data()`	AIH (Autorizacao de Internacao Hospitalar)	Monthly/UF	2008–2024
SIA	`sia_data()`	BPA / APAC	Monthly/type/UF	2008–2024
SINAN	`sinan_data()`	Ficha de Notificacao	Annual/National	2007–2024
CNES	`cnes_data()`	Cadastro de Estabelecimentos	Monthly/type/UF	2005–2024
SI-PNI	`sipni_data()`	PNI (doses, cobertura, microdados)	Annual/UF	1994–2025
SISAB	`sisab_data()`	Cobertura da Atencao Primaria	Monthly	2007–present

All seven modules share the same infrastructure:

DBC decompression: .dbc files (compressed DBF) are decompressed internally using vendored C code – no external dependencies required.
FTP download: files are fetched from ftp.datasus.gov.br with automatic retry and exponential backoff.
Cache: downloaded data is cached locally in Parquet (if arrow is installed) or .rds format.
Consistent API: every module exposes *_years(), *_info(), *_variables(), *_dictionary(), *_data(), *_cache_status(), and *_clear_cache().

Getting started

library(healthbR)
library(dplyr)

Common helper functions

Each module provides the same set of helper functions. Here is a quick tour using SIM as an example:

# available years
sim_years()
#> [1] 1996 1997 1998 ... 2023

# module information (data source, key variables, usage tips)
sim_info()

# list all variables with descriptions
sim_variables()

# search for a specific variable
sim_variables(search = "causa")

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

The same pattern works for sinasc_*(), sih_*(), sia_*(), sinan_*(), cnes_*(), and sipni_*().

SIM – Mortality

The SIM (Sistema de Informacoes sobre Mortalidade) contains individual death records based on the Declaracao de Obito (DO).

Basic download

# all deaths in Acre, 2022
obitos_ac <- sim_data(year = 2022, uf = "AC")
obitos_ac

Filter by cause of death

The cause parameter filters by underlying cause of death (CAUSABAS) using CID-10 prefix matching:

# deaths from acute myocardial infarction (I21)
obitos_iam <- sim_data(year = 2022, uf = "AC", cause = "I21")

# all cardiovascular deaths (chapter I)
obitos_cardio <- sim_data(year = 2022, uf = "AC", cause = "I")

Key variables

Variable	Description
`CAUSABAS`	Underlying cause of death (CID-10)
`DTOBITO`	Date of death
`SEXO`	Sex (M = Male, F = Female, I = Unknown)
`IDADE`	Age (encoded: 1st digit = unit, digits 2-3 = value)
`CODMUNRES`	Municipality of residence (IBGE code)

Example: deaths by cause chapter

obitos_ac <- sim_data(year = 2022, uf = "AC")

obitos_ac |>
  mutate(chapter = substr(CAUSABAS, 1, 1)) |>
  count(chapter, sort = TRUE)

SINASC – Live births

The SINASC (Sistema de Informacoes sobre Nascidos Vivos) contains individual live birth records from the Declaracao de Nascido Vivo (DN).

Basic download

nasc_ac <- sinasc_data(year = 2022, uf = "AC")
nasc_ac

Filter by congenital anomaly

The anomaly parameter filters by the CODANOMAL variable using CID-10 prefix matching:

# births with any congenital anomaly (chapter Q)
anomalias <- sinasc_data(year = 2022, uf = "AC", anomaly = "Q")

Key variables

Variable	Description
`DTNASC`	Date of birth
`SEXO`	Sex (1 = Male, 2 = Female, 0 = Unknown)
`PESO`	Birth weight (grams)
`IDADEMAE`	Mother’s age
`CODMUNRES`	Municipality of residence (IBGE code)
`CODANOMAL`	Congenital anomaly code (CID-10)

Example: birth weight distribution

nasc_ac <- sinasc_data(year = 2022, uf = "AC")

nasc_ac |>
  mutate(peso_num = as.numeric(PESO)) |>
  filter(!is.na(peso_num), peso_num > 0) |>
  mutate(weight_group = case_when(
    peso_num < 1500 ~ "Very low (<1500g)",
    peso_num < 2500 ~ "Low (1500-2499g)",
    peso_num < 4000 ~ "Normal (2500-3999g)",
    TRUE            ~ "High (>=4000g)"
  )) |>
  count(weight_group)

SIH – Hospital admissions

The SIH (Sistema de Informacoes Hospitalares) contains individual hospital admission records from the AIH (Autorizacao de Internacao Hospitalar). Unlike SIM and SINASC, data is organized monthly.

Basic download

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

The `month` parameter

SIH data is monthly – one file per UF per month. Use month to control which months to download:

# single month
sih_data(year = 2022, month = 6, uf = "AC")

# first semester
sih_data(year = 2022, month = 1:6, uf = "AC")

# all 12 months (default when month = NULL -- downloads 12 files per UF)
sih_data(year = 2022, uf = "AC")

Filter by diagnosis

The diagnosis parameter filters by the principal diagnosis (DIAG_PRINC) using CID-10 prefix matching:

# respiratory admissions (chapter J)
resp <- sih_data(year = 2022, month = 1, uf = "AC", diagnosis = "J")

# pneumonia specifically (J12-J18)
pneum <- sih_data(year = 2022, month = 1, uf = "AC",
                  diagnosis = c("J12", "J13", "J14", "J15", "J16", "J17", "J18"))

Key variables

Variable	Description
`DIAG_PRINC`	Principal diagnosis (CID-10)
`DT_INTER`	Admission date
`SEXO`	Sex (1 = Male, 3 = Female, 0 = Unknown)
`MORTE`	In-hospital death (1 = Yes, 0 = No)
`VAL_TOT`	Total value (R$)
`DIAS_PERM`	Length of stay (days)

Example: admissions by diagnosis chapter

intern <- sih_data(year = 2022, month = 1, uf = "AC")

intern |>
  mutate(chapter = substr(DIAG_PRINC, 1, 1)) |>
  count(chapter, sort = TRUE)

SIA – Outpatient production

The SIA (Sistema de Informacoes Ambulatoriais) contains outpatient production records. Like SIH, data is monthly, but SIA also has 13 file types covering different categories of outpatient care.

File types

Code	Name	Description
PA	Producao Ambulatorial	BPA consolidated (default)
BI	Boletim Individualizado	BPA individualized
AD	APAC Laudos Diversos	High-complexity authorizations
AM	APAC Medicamentos	High-cost medications
AN	APAC Nefrologia	Nephrology procedures
AQ	APAC Quimioterapia	Oncology chemotherapy
AR	APAC Radioterapia	Oncology radiotherapy
AB	APAC Cirurgia Bariatrica	Bariatric surgery
ACF	APAC Confeccao de Fistula	Arteriovenous fistula
ATD	APAC Tratamento Dialitico	Dialysis
AMP	APAC Acompanhamento Multiprofissional	Multiprofessional follow-up
SAD	RAAS Atencao Domiciliar	Home care services
PS	RAAS Psicossocial	CAPS and psychosocial services

Basic download

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

# different file type: high-cost medications
med <- sia_data(year = 2022, month = 1, uf = "AC", type = "AM")

Filter by procedure and diagnosis

# filter by SIGTAP procedure code (prefix match on PA_PROC_ID)
consult <- sia_data(year = 2022, month = 1, uf = "AC", procedure = "0301")

# filter by CID-10 diagnosis (prefix match on PA_CIDPRI)
resp <- sia_data(year = 2022, month = 1, uf = "AC", diagnosis = "J")

Key variables (PA type)

Variable	Description
`PA_PROC_ID`	Procedure code (SIGTAP)
`PA_CIDPRI`	Principal diagnosis (CID-10)
`PA_SEXO`	Sex (1 = Male, 2 = Female)
`PA_IDADE`	Patient age
`PA_VALAPR`	Approved value (R$)
`PA_QTDAPR`	Approved quantity

Example: production by procedure group

ambul <- sia_data(year = 2022, month = 1, uf = "AC")

ambul |>
  mutate(proc_group = substr(PA_PROC_ID, 1, 2)) |>
  count(proc_group, sort = TRUE)

SINAN – Notifiable diseases

The SINAN (Sistema de Informacao de Agravos de Notificacao) contains individual notification records for 31 compulsorily notifiable diseases. Unlike other DATASUS modules, SINAN files are national (one file per disease per year, covering all of Brazil).

Available diseases

SINAN covers 31 diseases. Use sinan_diseases() to see all available codes:

sinan_diseases()
#> # A tibble: 31 x 3
#>    code  name                      description
#>    <chr> <chr>                     <chr>
#>  1 DENG  Dengue                    Dengue
#>  2 CHIK  Chikungunya               Febre de Chikungunya
#>  3 ZIKA  Zika                      Zika virus
#>  4 TUBE  Tuberculose               Tuberculose
#>  ...

# search for a specific disease
sinan_diseases(search = "sifilis")

Basic download

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

# tuberculosis notifications, 2020-2022
tb <- sinan_data(year = 2020:2022, disease = "TUBE")

# select specific variables
sinan_data(year = 2022, disease = "DENG",
           vars = c("DT_NOTIFIC", "CS_SEXO", "NU_IDADE_N",
                    "ID_MUNICIP", "CLASSI_FIN"))

Filtering by state

Since files are national, filter by UF after download:

dengue <- sinan_data(year = 2022)

# filter by state of notification
dengue_sp <- dengue |>
  filter(SG_UF_NOT == "35")  # Sao Paulo (IBGE code)

# or by municipality
dengue_rio <- dengue |>
  filter(substr(ID_MUNICIP, 1, 2) == "33")  # Rio de Janeiro state

Key variables

Variable	Description
`DT_NOTIFIC`	Notification date
`ID_AGRAVO`	Disease code (CID-10)
`CS_SEXO`	Sex (M = Male, F = Female, I = Unknown)
`NU_IDADE_N`	Age (encoded: 1st digit = unit, digits 2-4 = value)
`ID_MUNICIP`	Municipality of notification (IBGE code)
`CLASSI_FIN`	Final classification (1 = Confirmed, 2 = Discarded)
`EVOLUCAO`	Outcome (1 = Cure, 2 = Death from disease)

Example: confirmed dengue by month

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

dengue |>
  filter(CLASSI_FIN %in% c("1", "5")) |>  # confirmed cases
  mutate(month = substr(DT_NOTIFIC, 4, 5)) |>
  count(month, sort = TRUE)

CNES – Health facility registry

The CNES (Cadastro Nacional de Estabelecimentos de Saude) is the national registry of all health facilities in Brazil. Like SIH and SIA, data is organized monthly (one file per type/UF/month), and there are 13 file types covering different aspects of the registry.

File types

Code	Name	Description
ST	Estabelecimentos	Facility registry (default)
LT	Leitos	Hospital beds
PF	Profissional	Health professionals
DC	Dados Complementares	Complementary facility data
EQ	Equipamentos	Health equipment
SR	Servico Especializado	Specialized services
HB	Habilitacao	Facility certifications
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

Basic download

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

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

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

Key variables (ST type)

Variable	Description
`CNES`	Facility CNES code
`CODUFMUN`	Municipality (UF + IBGE 6-digit code)
`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 (1-4)

Example: facility types in a state

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

estab |>
  count(TP_UNID, sort = TRUE) |>
  left_join(
    cnes_dictionary("TP_UNID") |> select(code, label),
    by = c("TP_UNID" = "code")
  )

SI-PNI – Vaccination data

The SI-PNI (Sistema de Informacao do Programa Nacional de Imunizacoes) provides vaccination data from two sources:

FTP (1994–2019): Aggregated data with dose counts and coverage rates per municipality/vaccine/age group. Plain .DBF files (not DBC-compressed).
OpenDataSUS API (2020–2025): Individual-level microdata with one row per vaccination dose (~47 fields per record).

sipni_data() transparently routes to the correct source based on the requested year.

File types

Code	Name	Description
DPNI	Doses Aplicadas	Doses applied per municipality, age group, vaccine, and dose type (FTP, default)
CPNI	Cobertura Vacinal	Vaccination coverage per municipality and vaccine (FTP)
API	Microdados	Individual-level microdata via OpenDataSUS (2020+, automatic)

Basic download

# FTP: doses applied in Acre, 2019 (default type = "DPNI")
doses_ac <- sipni_data(year = 2019, uf = "AC")
doses_ac

# FTP: vaccination coverage
cob_ac <- sipni_data(year = 2019, type = "CPNI", uf = "AC")

# API: individual-level microdata, Acre, January 2024
micro_ac <- sipni_data(year = 2024, uf = "AC", month = 1)
micro_ac

Key variables (DPNI)

Variable	Description
`IMUNO`	Vaccine code (immunobiological)
`QT_DOSE`	Number of doses applied
`DOSE`	Dose type (1st, 2nd, booster, etc.)
`FX_ETARIA`	Age group (coded)
`MUNIC`	Municipality (IBGE 6-digit code)
`ANOMES`	Year and month (YYYYMM)

Key variables (CPNI)

Variable	Description
`IMUNO`	Vaccine code
`QT_DOSE`	Number of doses applied
`POP`	Target population
`COBERT`	Vaccination coverage (%)
`MUNIC`	Municipality (IBGE 6-digit code)

Example: doses by vaccine

doses <- sipni_data(year = 2019, uf = "AC")

doses |>
  group_by(IMUNO) |>
  summarize(total_doses = sum(as.numeric(QT_DOSE), na.rm = TRUE)) |>
  arrange(desc(total_doses)) |>
  left_join(
    sipni_dictionary("IMUNO") |> select(code, label),
    by = c("IMUNO" = "code")
  )

Cross-module analyses

A key strength of healthbR is the ability to combine data from different DATASUS modules and Census denominators in a single workflow. Below are three practical examples.

Mortality rate (SIM + Census)

Calculate the crude cardiovascular mortality rate per 100,000 population:

# step 1: count cardiovascular deaths in Sao Paulo, 2022
obitos_cardio <- sim_data(year = 2022, uf = "SP", cause = "I")
n_obitos <- nrow(obitos_cardio)

# step 2: get population denominator from Census 2022
pop_sp <- censo_populacao(year = 2022, territorial_level = "state") |>
  filter(grepl("Paulo", territorial_unit))

# step 3: calculate rate
taxa_mortalidade <- n_obitos / pop_sp$population * 100000
taxa_mortalidade

Live births to deaths ratio (SINASC + SIM)

Compare the number of live births and deaths in a state:

# births and deaths in Acre, 2022
nascimentos <- sinasc_data(year = 2022, uf = "AC")
obitos <- sim_data(year = 2022, uf = "AC")

razao <- nrow(nascimentos) / nrow(obitos)
razao
#> ratio > 1 means more births than deaths (population growth)

Hospital vs. outpatient care (SIH + SIA)

Compare volumes and costs of respiratory care (CID-10 chapter J) between hospital and outpatient settings:

# hospital admissions for respiratory diseases, January 2022
intern_resp <- sih_data(year = 2022, month = 1, uf = "AC", diagnosis = "J")

# outpatient production for respiratory diseases, January 2022
ambul_resp <- sia_data(year = 2022, month = 1, uf = "AC", diagnosis = "J")

# compare volumes
n_internacoes <- nrow(intern_resp)
n_ambulatorial <- nrow(ambul_resp)

# compare costs
custo_intern <- sum(as.numeric(intern_resp$VAL_TOT), na.rm = TRUE)
custo_ambul <- sum(as.numeric(ambul_resp$PA_VALAPR), na.rm = TRUE)

tibble::tibble(
  setting = c("Hospital (SIH)", "Outpatient (SIA)"),
  records = c(n_internacoes, n_ambulatorial),
  total_cost_brl = c(custo_intern, custo_ambul)
)

Cache and performance

Automatic caching

All DATASUS modules cache downloaded data automatically. When the arrow package is installed, data is saved in Parquet format (fast and compact); otherwise, .rds is used as fallback.

# install arrow for optimized caching (recommended)
install.packages("arrow")

Cache management

Each module provides *_cache_status() and *_clear_cache():

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

# clear cache for a specific module
sim_clear_cache()

Tips for managing downloads

Use uf to download only the states you need instead of all 27 (SIM, SINASC, SIH, SIA, CNES).
Use month (SIH, SIA, CNES) to limit monthly downloads. Downloading a full year for all states requires 324 files per module (27 UFs x 12 months).
Use vars to keep only the variables you need, reducing memory usage.
SIM and SINASC are annual (one file per UF per year), so a full-year download is 27 files.
SINAN files are national (one file per disease per year), so downloads are fast but files can be large.
SIH, SIA, and CNES are monthly, so a full-year download is 324 files per type. SIA and CNES each have 13 file types – always filter by type, uf, and month.
SI-PNI FTP is annual with plain .DBF files (one per type/UF/year, 1994–2019). API data (2020+) is per-UF/year; use month to limit months.

DATASUS Modules: SIM, SINASC, SIH, SIA, SINAN, CNES, SI-PNI, and SISAB

Overview

Getting started

Common helper functions

SIM – Mortality

Basic download

Filter by cause of death

Key variables

Example: deaths by cause chapter

SINASC – Live births

Basic download

Filter by congenital anomaly

Key variables

Example: birth weight distribution

SIH – Hospital admissions

Basic download

The month parameter

Filter by diagnosis

Key variables

Example: admissions by diagnosis chapter

SIA – Outpatient production

File types

Basic download

Filter by procedure and diagnosis

Key variables (PA type)

Example: production by procedure group

SINAN – Notifiable diseases

Available diseases

Basic download

Filtering by state

Key variables

Example: confirmed dengue by month

CNES – Health facility registry

File types

Basic download

Key variables (ST type)

Example: facility types in a state

SI-PNI – Vaccination data

File types

Basic download

Key variables (DPNI)

Key variables (CPNI)

Example: doses by vaccine

Cross-module analyses

Mortality rate (SIM + Census)

Live births to deaths ratio (SINASC + SIM)

Hospital vs. outpatient care (SIH + SIA)

Cache and performance

Automatic caching

Cache management

Tips for managing downloads

Additional resources

The `month` parameter