National Health Survey (PNS) with healthbR

Overview

The PNS (Pesquisa Nacional de Saude) is Brazil’s most comprehensive household health survey, conducted by IBGE in partnership with the Ministry of Health. It provides nationally representative data on health conditions, lifestyle, access to health services, and preventive care.

Two editions are available: 2013 and 2019, each with approximately 100,000+ respondents.

The healthbR package provides two complementary access paths:

Access path Function Description
Microdata pns_data() Individual-level records via IBGE FTP
SIDRA tables pns_sidra_data() Pre-tabulated indicators via IBGE API

Getting started

library(healthbR)
library(dplyr)

Check available years

pns_years()
#> [1] "2013" "2019"

Survey information

pns_info(2019)

Thematic modules

PNS organizes questions into thematic modules (A through Z). Use pns_modules() to see what’s available:

pns_modules(year = 2019)
#> # A tibble: 20 x 3
#>    code  name_pt                          name_en
#>    <chr> <chr>                            <chr>
#>  1 A     Informacoes do domicilio         Household information
#>  2 C     Caracteristicas dos moradores    Resident characteristics
#>  3 ...

Microdata access

Download microdata

# All modules for 2019
df <- pns_data(year = 2019)

# Select specific variables
df <- pns_data(year = 2019, vars = c("C006", "C008", "C009", "Q002", "Q00201"))

Explore variables

# List all variables
pns_variables(year = 2019)

# Filter by module
pns_variables(year = 2019, module = "Q")

# Data dictionary
pns_dictionary(year = 2019)

SIDRA tabulated data

For pre-calculated indicators with confidence intervals, use the SIDRA API path. This is ideal for quick analyses without downloading full microdata.

Discover available tables

PNS has 69 SIDRA tables organized by 14 health themes:

# Browse all tables
pns_sidra_tables()

# Filter by theme
pns_sidra_tables(theme = "Chronic diseases")

# Search by keyword
pns_sidra_search("diabetes")
pns_sidra_search("tabagismo")

Query a SIDRA table

# Table 7666: Self-reported diabetes prevalence
diabetes <- pns_sidra_data(
  table = 7666,
  territorial_level = "state",
  year = 2019
)

Geographic levels

# National level
pns_sidra_data(table = 7666, territorial_level = "brazil")

# By state
pns_sidra_data(table = 7666, territorial_level = "state")

# By capital city
pns_sidra_data(table = 7666, territorial_level = "capital")

# Specific state (e.g., Sao Paulo = 35)
pns_sidra_data(table = 7666, territorial_level = "state", geo_code = "35")

Example: Chronic disease prevalence by state

Using SIDRA for quick tabulated results:

# Self-reported hypertension by state
hypertension <- pns_sidra_data(
  table = 7659,
  territorial_level = "state",
  year = 2019
)

Example: Health service access from microdata

df <- pns_data(
  year = 2019,
  vars = c("C006", "C008", "C009", "J001", "J007", "J009", "V0024", "UPA_PNS")
)

# J001: Had a medical visit in the last 12 months?
# C006: Sex, C008: Age, C009: Race
access <- df |>
  filter(J001 %in% c("1", "2")) |>
  group_by(C006) |>
  summarise(
    visited = sum(J001 == "1"),
    total = n(),
    pct = visited / total * 100
  )

Cache and performance

# Check cache
pns_cache_status()

# Clear cache
pns_clear_cache()

# Lazy evaluation for large datasets
lazy_df <- pns_data(year = 2019, lazy = TRUE, backend = "arrow")

Further reading