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()