Population Denominators from the Census with healthbR

Overview

The Censo Demografico (Demographic Census) is the main source of population denominators in Brazil, essential for calculating mortality rates, disease incidence, and other epidemiological indicators.

The healthbR package provides direct access to Census population data via the IBGE SIDRA API, covering:

Function	Description	Years
censo_populacao()	Population by sex, age, race, situation	1970-2022
censo_estimativa()	Intercensitary population estimates	2001-2021
censo_sidra_data()	Any Census SIDRA table	All available

Getting started

library(healthbR)
library(dplyr)

Check available years

censo_years()
#> [1] "1970" "1980" "1991" "2000" "2010" "2022"

Survey information

censo_info(2022)

Population by state

The most common use case: getting population by state as a denominator for health indicators.

# total population by state, Census 2022
pop_state <- censo_populacao(year = 2022, territorial_level = "state")
pop_state

Population by sex

# population by sex, Brazil level
pop_sex <- censo_populacao(
  year = 2022,
  variables = "sex",
  territorial_level = "brazil"
)
pop_sex

Age pyramids

# population by age and sex
pop_age_sex <- censo_populacao(
  year = 2022,
  variables = "age_sex",
  territorial_level = "brazil"
)
pop_age_sex

Population by race/color

# population by race, 2022
pop_race <- censo_populacao(
  year = 2022,
  variables = "race",
  territorial_level = "state"
)
pop_race

Intercensitary estimates

For years between censuses, IBGE publishes annual population estimates that serve as denominators:

# population estimates 2015-2021
estimates <- censo_estimativa(
  year = 2015:2021,
  territorial_level = "state"
)
estimates

Example: calculating a mortality rate

A typical epidemiological workflow combines mortality data (SIM) with Census denominators:

# step 1: get population denominator
pop_2010 <- censo_populacao(
  year = 2010,
  variables = "total",
  territorial_level = "state"
)

# step 2: suppose you have mortality data (from SIM or other source)
# deaths_by_state <- sim_data(year = 2010) |> count(state)

# step 3: calculate crude mortality rate
# mortality_rate <- deaths_by_state |>
#   left_join(pop_2010, by = "state") |>
#   mutate(rate_per_100k = (n / population) * 100000)

Exploring Census tables

The Census module includes a catalog of SIDRA tables organized by theme:

# list all available tables
censo_sidra_tables()

# filter by theme
censo_sidra_tables(theme = "disability")
censo_sidra_tables(theme = "indigenous")

# search by keyword
censo_sidra_search("quilombola")
censo_sidra_search("saneamento")

Custom SIDRA queries

For full flexibility, use censo_sidra_data() to query any Census table:

# population by race from table 9605
pop_race_raw <- censo_sidra_data(
  table = 9605,
  territorial_level = "state",
  year = 2022,
  variable = 93,
  classifications = list("86" = "allxt")
)
pop_race_raw

Historical comparisons

# compare population across census years
pop_2010 <- censo_populacao(year = 2010, territorial_level = "brazil")
pop_2022 <- censo_populacao(year = 2022, territorial_level = "brazil")

# or use estimates for intercensitary years
pop_series <- censo_estimativa(
  year = 2001:2021,
  territorial_level = "brazil"
)
pop_series