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Outpatient Production Data from SIA with healthbR

Overview

The SIA (Sistema de Informacoes Ambulatoriais) records all outpatient procedures performed in the Brazilian public health system (SUS), including consultations, exams, and high-complexity procedures. It is managed by the Ministry of Health through DATASUS.

Feature Details
Coverage Per state (UF), all 27 states
Years 2008–2024
Granularity Monthly (one file per type/UF/month)
Unit One row per outpatient procedure record
Format .dbc files from DATASUS FTP

Getting started

library(healthbR)
library(dplyr)

Check available years

sia_years()
sia_years(status = "all")

Module information

sia_info()

File types

SIA organizes data into 13 file types. The default is PA (outpatient production):

Type Description
PA Outpatient production (BPA, default)
BI Individualized BPA
AD APAC - Dialysis
AM APAC - Chemotherapy/Radiotherapy
AN APAC - Nephrology
AQ APAC - Other procedures
AR APAC - Orthopedic Surgery
AB APAC - Bariatric Surgery
ACF APAC - Cleft Lip/Palate
ATD APAC - TFD (Treatment Away from Home)
AMP APAC - Specialized Medicines
SAD RAAS - Home Care
PS RAAS - Psychosocial Care

Downloading data

Basic download (PA type)

outpatient <- sia_data(year = 2022, uf = "AC")

Specific type

# chemotherapy/radiotherapy APAC records
chemo <- sia_data(year = 2022, uf = "SP", type = "AM")

Specific months

outpatient <- sia_data(year = 2022, uf = "SP", month = 1:3)

Filter by procedure

Use SIGTAP procedure code prefixes:

# Medical consultations (group 03.01)
consults <- sia_data(year = 2022, uf = "SP", month = 1, procedure = "0301")

# Imaging exams (group 02.04)
imaging <- sia_data(year = 2022, uf = "SP", month = 1, procedure = "0204")

Filter by diagnosis

# Diabetes-related outpatient care (E10-E14)
diabetes <- sia_data(year = 2022, uf = "SP", month = 1, diagnosis = "E1")

Select variables

outpatient <- sia_data(
  year = 2022,
  uf = "SP",
  month = 1,
  vars = c("PA_PROC_ID", "PA_CIDPRI", "PA_SEXO", "PA_IDADE",
           "PA_MUNPCN", "PA_VALAPR")
)

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_MUNPCN Municipality of patient’s residence
PA_VALAPR Approved value (R$)
PA_QTDAPR Approved quantity
PA_CODUNI Health facility (CNES code)
PA_GESTAO Management level
PA_CONDIC Processing condition

Data dictionary

sia_dictionary()
sia_dictionary("PA_SEXO")

Explore variables

sia_variables()
sia_variables(search = "valor")

# variables for a specific type
sia_variables(type = "AM")

Example: Top procedures by volume

outpatient <- sia_data(year = 2022, uf = "SP", month = 1)

top_procedures <- outpatient |>
  count(PA_PROC_ID, sort = TRUE) |>
  head(20)

Example: Outpatient spending by diagnosis

outpatient <- sia_data(year = 2022, uf = "SP", month = 1)

spending <- outpatient |>
  filter(!is.na(PA_CIDPRI), PA_CIDPRI != "") |>
  mutate(
    chapter = substr(PA_CIDPRI, 1, 1),
    value = as.numeric(PA_VALAPR)
  ) |>
  group_by(chapter) |>
  summarise(
    records = n(),
    total_value = sum(value, na.rm = TRUE)
  ) |>
  arrange(desc(total_value))

Example: Chemotherapy APAC records

chemo <- sia_data(year = 2022, uf = "SP", type = "AM", month = 1:6)

chemo |>
  count(month, name = "records") |>
  arrange(month)

Smart type parsing

# parsed types (default)
outpatient <- sia_data(year = 2022, uf = "AC", month = 1)
class(outpatient$PA_VALAPR)  # double

# all character
outpatient_raw <- sia_data(year = 2022, uf = "AC", month = 1, parse = FALSE)

Cache and lazy evaluation

sia_cache_status()
sia_clear_cache()

# lazy query
lazy <- sia_data(year = 2022, uf = "SP", lazy = TRUE)
lazy |>
  filter(PA_CIDPRI >= "E10", PA_CIDPRI <= "E14") |>
  collect()

Further reading

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.