Live Birth Data from SINASC with healthbR

Overview

The SINASC (Sistema de Informacoes sobre Nascidos Vivos) is Brazil’s national live birth information system, managed by the Ministry of Health through DATASUS. It records individual birth certificates (Declaracao de Nascido Vivo) with maternal, delivery, and newborn characteristics.

Feature Details
Coverage Per state (UF), all 27 states
Years 1996–2024
Unit One row per live birth certificate
Format .dbc files from DATASUS FTP

Getting started

library(healthbR)
library(dplyr)

Check available years

sinasc_years()

# include preliminary data
sinasc_years(status = "all")

Module information

sinasc_info()

Downloading data

Basic download

births <- sinasc_data(year = 2022, uf = "AC")

Multiple states and years

births <- sinasc_data(year = 2020:2022, uf = c("SP", "RJ"))

Filter by congenital anomaly

Use CID-10 code prefixes to filter births with congenital anomalies:

# Down syndrome (Q90)
down <- sinasc_data(year = 2022, uf = "SP", anomaly = "Q90")

# All congenital anomalies (Chapter XVII)
anomalies <- sinasc_data(year = 2022, uf = "SP", anomaly = "Q")

Select variables

births <- sinasc_data(
  year = 2022,
  uf = "SP",
  vars = c("DTNASC", "SEXO", "PESO", "IDADEMAE", "GESTACAO",
           "PARTO", "CONSULTAS", "CODMUNRES")
)

Key variables

Variable Description
DTNASC Birth date
SEXO Sex (1=Male, 2=Female, 0=Unknown)
PESO Birth weight (grams)
IDADEMAE Mother’s age (years)
GESTACAO Gestational age (weeks, categorized)
PARTO Delivery type (1=Vaginal, 2=Cesarean)
CONSULTAS Prenatal consultations (categorized)
CODANOMAL Congenital anomaly (CID-10 code)
CODMUNRES Municipality of mother’s residence (IBGE 6 digits)
ESCMAE Mother’s education level
RACACOR Newborn’s race/color
APGAR1, APGAR5 Apgar score at 1 and 5 minutes

Data dictionary

sinasc_dictionary()
sinasc_dictionary("PARTO")
sinasc_dictionary("GESTACAO")

Explore variables

sinasc_variables()
sinasc_variables(search = "mae")
sinasc_variables(search = "peso")

Example: Low birth weight by state

births <- sinasc_data(year = 2022, uf = c("SP", "RJ", "MG", "BA", "RS"))

lbw <- births |>
  filter(!is.na(PESO), PESO != "0") |>
  mutate(
    weight = as.numeric(PESO),
    low_weight = weight < 2500
  ) |>
  group_by(uf_source) |>
  summarise(
    total = n(),
    low_weight_n = sum(low_weight),
    low_weight_pct = low_weight_n / total * 100
  )

Example: Cesarean rates over time

births <- sinasc_data(year = 2018:2022, uf = "SP",
                      vars = c("PARTO", "CODMUNRES"))

cesarean <- births |>
  filter(PARTO %in% c("1", "2")) |>
  group_by(year) |>
  summarise(
    vaginal = sum(PARTO == "1"),
    cesarean = sum(PARTO == "2"),
    cesarean_rate = cesarean / (vaginal + cesarean) * 100
  )

Example: Teen pregnancy

births <- sinasc_data(year = 2022, uf = "SP")

teen <- births |>
  filter(!is.na(IDADEMAE)) |>
  mutate(
    mother_age = as.integer(IDADEMAE),
    teen_mother = mother_age < 20
  ) |>
  summarise(
    total = n(),
    teen_n = sum(teen_mother, na.rm = TRUE),
    teen_pct = teen_n / total * 100
  )

Smart type parsing

# parsed types (default)
births <- sinasc_data(year = 2022, uf = "AC")
class(births$DTNASC)  # Date
class(births$PESO)    # integer

# all character
births_raw <- sinasc_data(year = 2022, uf = "AC", parse = FALSE)

Cache and lazy evaluation

sinasc_cache_status()
sinasc_clear_cache()

# lazy query
lazy <- sinasc_data(year = 2022, uf = "SP", lazy = TRUE)
lazy |>
  filter(PARTO == "2") |>
  collect()

Further reading