---
title: "Hospital Admissions from SIH with healthbR"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Hospital Admissions from SIH with healthbR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)
```

## Overview

The **SIH (Sistema de Informacoes Hospitalares)** records all hospital admissions financed by the Brazilian public health system (SUS) through the *Autorizacao de Internacao Hospitalar* (AIH). It is managed by the Ministry of Health through DATASUS.

| Feature | Details |
|---------|---------|
| Coverage | Per state (UF), all 27 states |
| Years | 2008--2024 |
| Granularity | Monthly (one file per UF/month) |
| Unit | One row per hospital admission (AIH) |
| Format | .dbc files from DATASUS FTP |

## Getting started

```{r setup}
library(healthbR)
library(dplyr)
```

### Check available years

```{r}
sih_years()
sih_years(status = "all")
```

### Module information

```{r}
sih_info()
```

## Downloading data

### Basic download

```{r}
# All months of 2022 for Acre
admissions <- sih_data(year = 2022, uf = "AC")
```

### Specific months

```{r}
# First semester only
admissions <- sih_data(year = 2022, uf = "SP", month = 1:6)

# Single month
admissions <- sih_data(year = 2022, uf = "SP", month = 3)
```

### Filter by diagnosis

Use CID-10 code prefixes to filter the principal diagnosis:

```{r}
# Acute myocardial infarction (I21)
mi <- sih_data(year = 2022, uf = "SP", diagnosis = "I21")

# All respiratory diseases (Chapter X)
respiratory <- sih_data(year = 2022, uf = "SP", diagnosis = "J")

# Dengue (A90-A91)
dengue_hosp <- sih_data(year = 2022, uf = "SP", diagnosis = "A9")
```

### Select variables

```{r}
admissions <- sih_data(
  year = 2022,
  uf = "SP",
  month = 1,
  vars = c("DIAG_PRINC", "DT_INTER", "DT_SAIDA", "SEXO",
           "MORTE", "MUNIC_RES", "VAL_TOT")
)
```

## Key variables

| Variable | Description |
|----------|-------------|
| DIAG_PRINC | Principal diagnosis (CID-10) |
| DT_INTER | Admission date |
| DT_SAIDA | Discharge date |
| SEXO | Sex (0=Unknown, 1=Male, 3=Female) |
| NASC | Date of birth |
| MORTE | Hospital death (0=No, 1=Yes) |
| MUNIC_RES | Municipality of residence (IBGE code) |
| MUNIC_MOV | Municipality of hospitalization |
| VAL_TOT | Total AIH value (R$) |
| DIAS_PERM | Length of stay (days) |
| PROC_REA | Procedure performed (SIGTAP code) |
| UTI_MES_TO | ICU days |

Note: Sex codes in SIH differ from SIM/SINASC (0=Unknown, 1=Male, **3**=Female).

### Data dictionary

```{r}
sih_dictionary()
sih_dictionary("SEXO")
sih_dictionary("MORTE")
```

### Explore variables

```{r}
sih_variables()
sih_variables(search = "diag")
sih_variables(search = "valor")
```

## Example: Hospital mortality by diagnosis chapter

```{r}
admissions <- sih_data(year = 2022, uf = "SP", month = 1:6)

mortality <- admissions |>
  mutate(chapter = substr(DIAG_PRINC, 1, 1)) |>
  group_by(chapter) |>
  summarise(
    total = n(),
    deaths = sum(MORTE == "1", na.rm = TRUE),
    mortality_rate = deaths / total * 100
  ) |>
  arrange(desc(mortality_rate))
```

## Example: Hospitalization costs

```{r}
admissions <- sih_data(year = 2022, uf = "SP", month = 1)

costs <- admissions |>
  mutate(
    chapter = substr(DIAG_PRINC, 1, 1),
    cost = as.numeric(VAL_TOT)
  ) |>
  group_by(chapter) |>
  summarise(
    admissions = n(),
    total_cost = sum(cost, na.rm = TRUE),
    mean_cost = mean(cost, na.rm = TRUE)
  ) |>
  arrange(desc(total_cost))
```

## Example: Seasonal patterns

```{r}
# respiratory admissions across all months
resp <- sih_data(year = 2022, uf = "SP", diagnosis = "J")

seasonal <- resp |>
  count(month, name = "admissions") |>
  arrange(month)
```

## Smart type parsing

```{r}
# parsed types (default)
admissions <- sih_data(year = 2022, uf = "AC", month = 1)
class(admissions$DT_INTER)  # Date
class(admissions$VAL_TOT)   # double

# all character
admissions_raw <- sih_data(year = 2022, uf = "AC", month = 1, parse = FALSE)
```

## Cache and lazy evaluation

```{r}
sih_cache_status()
sih_clear_cache()

# lazy query
lazy <- sih_data(year = 2022, uf = "SP", lazy = TRUE)
lazy |>
  filter(MORTE == "1") |>
  select(DIAG_PRINC, DT_INTER, SEXO, MUNIC_RES) |>
  collect()
```

## Further reading

- SIH on DATASUS (`datasus.saude.gov.br`)
- SIGTAP procedure table (`wiki.saude.gov.br/sigtap`)
- [SIA vignette](sia-outpatient.html) for outpatient data