Mortality Data from SIM with healthbR

Overview

The SIM (Sistema de Informacoes sobre Mortalidade) is Brazil’s national mortality information system, managed by the Ministry of Health through DATASUS. It records individual death certificates (Declaracao de Obito) with cause of death coded by ICD-10.

Feature Details
Coverage Per state (UF), all 27 states
Years 1996–2024 (CID-10 era)
Unit One row per death certificate
Format .dbc files from DATASUS FTP

Getting started

library(healthbR)
library(dplyr)

Check available years

sim_years()

# include preliminary data
sim_years(status = "all")

Module information

sim_info()

Downloading data

Basic download (one state, one year)

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

Multiple states and years

deaths_se <- sim_data(year = 2020:2022, uf = c("SP", "RJ", "MG"))

All states (default)

# downloads all 27 states -- may take several minutes
deaths_all <- sim_data(year = 2022)

Filter by cause of death

Use CID-10 code prefixes to filter by cause:

# Acute myocardial infarction (I21)
mi <- sim_data(year = 2022, uf = "SP", cause = "I21")

# All cardiovascular diseases (Chapter IX)
cardio <- sim_data(year = 2022, uf = "SP", cause = "I")

# All neoplasms (Chapter II)
cancer <- sim_data(year = 2022, uf = "SP", cause = "C")

Select variables

deaths <- sim_data(
  year = 2022,
  uf = "SP",
  vars = c("CAUSABAS", "DTOBITO", "SEXO", "IDADE", "RACACOR", "CODMUNRES")
)

Age decoding

The IDADE variable uses a 3-digit encoding where the first digit indicates the unit and the remaining two indicate the value:

First digit Unit Example
0 Minutes 005 = 5 minutes
1 Hours 112 = 12 hours
2 Days 215 = 15 days
3 Months 306 = 6 months
4 Years 445 = 45 years
5 100+ years 502 = 102 years

By default, decode_age = TRUE adds an age_years column:

deaths <- sim_data(year = 2022, uf = "AC")
deaths$age_years  # numeric age in years

# disable decoding
deaths_raw <- sim_data(year = 2022, uf = "AC", decode_age = FALSE)

Key variables

Variable Description
CAUSABAS Underlying cause of death (CID-10)
DTOBITO Date of death
SEXO Sex (1=Male, 2=Female, 0=Unknown)
IDADE Age (3-digit encoded)
RACACOR Race/color (1=White, 2=Black, 3=Yellow, 4=Brown, 5=Indigenous)
CODMUNRES Municipality of residence (IBGE 6 digits)
LINHAA-D Cause of death lines A-D from the certificate
ESCMAE Mother’s education level
ESTCIV Marital status

Data dictionary

sim_dictionary()
sim_dictionary("SEXO")
sim_dictionary("RACACOR")

Explore variables

sim_variables()
sim_variables(search = "causa")

Example: Mortality by cause chapter

deaths <- sim_data(year = 2022, uf = "SP")

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

Example: Age-specific mortality rate

Combine SIM data with Census population denominators:

# deaths by age group
deaths <- sim_data(year = 2022, uf = "SP") |>
  filter(!is.na(age_years)) |>
  mutate(age_group = cut(age_years,
    breaks = c(0, 1, 5, 15, 30, 45, 60, 80, Inf),
    right = FALSE
  )) |>
  count(age_group, name = "deaths")

# population from Census 2022
pop <- censo_populacao(year = 2022, territorial_level = "state", geo_code = "35")

# join and calculate rates per 100,000

Smart type parsing

# parsed types (default)
deaths <- sim_data(year = 2022, uf = "AC")
class(deaths$DTOBITO)  # Date

# all character (backward-compatible)
deaths_raw <- sim_data(year = 2022, uf = "AC", parse = FALSE)

Cache and lazy evaluation

sim_cache_status()
sim_clear_cache()

# lazy query (requires arrow)
lazy <- sim_data(year = 2022, uf = "SP", lazy = TRUE)
lazy |>
  filter(CAUSABAS >= "I20", CAUSABAS < "I26") |>
  collect()

Further reading