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()