# Health Data from PNAD Continua with healthbR

## Overview

The PNAD Continua (Pesquisa Nacional por Amostra de Domicilios Continua) is IBGE’s continuous household sample survey. While its core questionnaire focuses on labor and income, it includes supplementary health-related modules that are applied in specific quarters.

The `healthbR` package provides access to 4 health-related supplementary modules:

Module | Description | Years
--- | --- | ---
`deficiencia` | Persons with disabilities | 2019, 2022, 2024
`habitacao` | Housing characteristics (sanitation, water) | 2012–2024
`moradores` | General resident characteristics | 2012–2024
`aps` | Primary health care access | 2022 (Q2)

## Getting started

    library(healthbR)
    library(dplyr)

### Module information

    pnadc_info()

### Available modules and years

    pnadc_modules()
    #> # A tibble: 4 x 4
    #>   module       name_pt                            name_en                years
    #>   <chr>        <chr>                              <chr>                  <list>
    #> 1 deficiencia  Pessoas com deficiencia             Persons with disab... <int>
    #> 2 habitacao    Caracteristicas dos domicilios      Housing character...  <int>
    #> 3 moradores    Caracteristicas gerais dos morad... General character...  <int>
    #> 4 aps          Atencao primaria a saude            Primary health care   <int>

    # Years for a specific module
    pnadc_years("deficiencia")
    #> [1] 2019 2022 2024

## Downloading data

### Basic download

    # Disability module, 2022
    df <- pnadc_data(module = "deficiencia", year = 2022)

### Select variables

    df <- pnadc_data(
      module = "deficiencia",
      year = 2022,
      vars = c("UF", "V2007", "V2009", "V2010", "G001", "G003", "G006")
    )

### Multiple years

    # Housing conditions across all available years
    df <- pnadc_data(module = "habitacao")

## Exploring variables

    # List variable names
    pnadc_variables(module = "deficiencia", year = 2022)

    # Full dictionary with positions and widths
    pnadc_dictionaries(module = "deficiencia", year = 2022)

## Survey design

PNAD Continua uses a complex sample design. Survey design variables (`UPA`, `Estrato`, `V1028`) are always included in the output. Use `as_survey = TRUE` to create a survey design object:

    # Requires srvyr package
    svy <- pnadc_data(
      module = "deficiencia",
      year = 2022,
      as_survey = TRUE
    )

    # Use srvyr verbs for proper variance estimation
    library(srvyr)
    svy |>
      group_by(UF) |>
      survey_mean(G001 == "1", na.rm = TRUE)  # disability prevalence by state

## Example: Disability prevalence

    df <- pnadc_data(module = "deficiencia", year = 2022)

    # G001: "Tem dificuldade permanente de enxergar" (vision difficulty)
    # 1 = Sim, nao consegue de modo algum (cannot at all)
    # 2 = Sim, muita dificuldade (great difficulty)
    # 3 = Sim, alguma dificuldade (some difficulty)
    # 4 = Nao, nenhuma dificuldade (no difficulty)
    vision <- df |>
      filter(G001 %in% c("1", "2", "3", "4")) |>
      count(G001) |>
      mutate(pct = n / sum(n) * 100)

## Example: Housing sanitation over time

    df <- pnadc_data(module = "habitacao", year = c(2016, 2019, 2022))

    # Analyze water supply and sanitation trends
    # Variables vary by year -- check pnadc_variables() for each edition

## Example: Primary health care access

    # APS module only available for 2022 Q2
    df <- pnadc_data(module = "aps", year = 2022)

## Cache and performance

    # Check cache
    pnadc_cache_status()

    # Clear cache
    pnadc_clear_cache()

    # Lazy evaluation
    lazy_df <- pnadc_data(
      module = "deficiencia",
      year = 2022,
      lazy = TRUE,
      backend = "arrow"
    )

## Further reading

- PNAD Continua on IBGE (`www.ibge.gov.br/estatisticas/sociais/trabalho/17270-pnad-continua`)
- PNAD Continua microdata documentation (`ftp.ibge.gov.br`)