Chronic Disease Risk Factors from VIGITEL with healthbR

Overview

VIGITEL (Vigilancia de Fatores de Risco e Protecao para Doencas Cronicas por Inquerito Telefonico) is an annual telephone survey conducted by the Brazilian Ministry of Health since 2006. It monitors risk and protective factors for chronic non-communicable diseases among adults (18+) in all 26 state capitals and the Federal District.

Topic Examples
Tobacco Smoking prevalence, cessation
Alcohol Consumption patterns, binge drinking
Diet Fruit/vegetable intake, ultra-processed foods
Physical activity Leisure, commuting, sedentary behavior
Chronic diseases Diabetes, hypertension, obesity self-report
Preventive exams Mammography, Pap smear, colonoscopy

Each annual edition interviews approximately 54,000 adults via landline telephone, with post-stratification weighting (pesorake) to match the adult population of each city.

Getting started

library(healthbR)
library(dplyr)

Check available years

vigitel_years()
#> [1] 2006 2007 2008 ... 2023 2024

Survey information

vigitel_info()

Downloading data

All years at once

VIGITEL is distributed as a single consolidated file covering 2006–2024. By default, all years are downloaded:

df <- vigitel_data()

Specific years

df <- vigitel_data(year = 2020:2024)

Select variables

df <- vigitel_data(year = 2024, vars = c("cidade", "sexo", "idade", "pesorake",
                                          "q6", "q7", "q9"))

Data format

Two formats are available: Stata (.dta, default) and CSV. The Stata format preserves variable labels:

df_dta <- vigitel_data(format = "dta")  # default, with labels
df_csv <- vigitel_data(format = "csv")  # alternative

Exploring variables

Data dictionary

vigitel_dictionary()

Search variables

vigitel_variables()

Example: Smoking prevalence over time

# Download smoking-related variables
df <- vigitel_data(
  year = 2006:2024,
  vars = c("ano", "cidade", "sexo", "pesorake", "q6")
)

# q6: "Atualmente, o(a) sr(a) fuma?" (1 = sim, 2 = nao)
smoking <- df |>
  filter(q6 %in% c("1", "2")) |>
  group_by(ano) |>
  summarise(
    smokers = sum(pesorake[q6 == "1"], na.rm = TRUE),
    total = sum(pesorake, na.rm = TRUE),
    prevalence = smokers / total * 100
  )

Example: Obesity by capital city

df <- vigitel_data(
  year = 2024,
  vars = c("cidade", "sexo", "pesorake", "q8", "q9")
)

# q8 = weight (kg), q9 = height (cm)
# BMI >= 30 = obesity
obesity <- df |>
  filter(!is.na(q8), !is.na(q9), q9 > 0) |>
  mutate(
    bmi = as.numeric(q8) / (as.numeric(q9) / 100)^2,
    obese = bmi >= 30
  ) |>
  group_by(cidade) |>
  summarise(
    prevalence = weighted.mean(obese, as.numeric(pesorake), na.rm = TRUE) * 100
  ) |>
  arrange(desc(prevalence))

Cache and performance

Data is automatically cached in partitioned parquet format (when arrow is installed). Subsequent calls load instantly from cache:

# First call downloads (~30 seconds)
df <- vigitel_data(year = 2024)

# Second call loads from cache (instant)
df <- vigitel_data(year = 2024)

# Check cache status
vigitel_cache_status()

# Clear cache if needed
vigitel_clear_cache()

Lazy evaluation

For large analyses, use lazy evaluation to query without loading all data into memory:

lazy_df <- vigitel_data(lazy = TRUE, backend = "arrow")

Further reading