DATASUS

DATASUS - SIM (System of Mortality Information)

Each original SIM data file contains rows corresponding to a declaration of death (DO), and columns with several characteristics of the person, the place of death, and the cause of death. The data comes from the main SIM-DO (Declarations of Death) dataset, which goes by the option "datasus_sim_do". There are also 4 subsets of SIM-DO, namely SIM-DOFET (Fetal), SIM-DOMAT (Maternal), SIM-DOINF (Children), and SIM-DOEXT (External Causes), with corresponding dataset options "datasus_sim_dofet", "datasus_sim_domat", "datasus_sim_doinf", "datasus_sim_doext". Note that only SIM-DO provides separate files for each state, so all other dataset options always contain data from the whole country.

Below is an example of downloading the raw data, and also using the raw_data = FALSE option to obtain treated data. When this option is selected, we create several variables for deaths from each cause, which are encoded by their CID-10 codes. The function then returns, by default, the aggregated data of mortality sources at the municipality level. In this process, all the individual information such as age, sex, race, and schooling are lost, so we also offer the option of keep_all = TRUE, which creates all the indicator variables for cause of death, adds variable labels, and does not aggregate, thereby keeping all individual-level variables.

Examples:

library(datazoom.amazonia)

# download raw data for the year 2010 in the state of AM.
data <- load_datasus(
  dataset = "datasus_sim_do",
  time_period = 2010,
  states = "AM",
  raw_data = TRUE
)

# download treated data with the number of deaths by cause in AM and PA.
data <- load_datasus(
  dataset = "datasus_sim_do",
  time_period = 2010,
  states = c("AM", "PA"),
  raw_data = FALSE
)

# download treated data with the number of deaths by cause in AM and PA
# keeping all individual variables.
data <- load_datasus(
  dataset = "datasus_sim_do",
  time_period = 2010,
  states = c("AM", "PA"),
  raw_data = FALSE,
  keep_all = TRUE
)

DATASUS - CNES (National Register of Health Establishments)

Provides information on health establishments, avaliable hospital beds, and active physicians. The data is split into 13 datasets: LT (Beds), ST (Establishments), DC (Complimentary data), EQ (Equipment), SR (Specialized services), HB (License), PF (Practitioner), EP (Teams), RC (Contractual Rules), IN (Incentives), EE (Teaching establishments), EF (Philanthropic establishments), and GM (Management and goals).

Raw data is avaliable for all of them using the dataset option datasus_cnes_lt, datasus_cnes_st, and so on, and treated data is only avaliable for CNES - LT. When raw_data = FALSE is chosen, we return data on the number of total hospital beds and the ones avaliable through SUS, which can be aggregated by municipality (with option keep_all = FALSE) or keeping all original variables (keep_all = TRUE).

Examples:

library(datazoom.amazonia)

# download treated data with the number of avaliable beds in AM and PA.
data <- load_datasus(
  dataset = "datasus_cnes_lt",
  time_period = 2010,
  states = c("AM", "PA"),
  raw_data = FALSE
)

DATASUS - SIH (System of Hospital Information)

Contains data on hospitalizations. Treated data only gains variable labels, with no extra manipulation. Beware that this is a much heavier dataset.

Examples:

library(datazoom.amazonia)

# download raw data
data <- load_datasus(
  dataset = "datasus_sih",
  time_period = 2010,
  states = "AM",
  raw_data = TRUE
)

# download data in a single tibble, with variable labels
data <- load_datasus(
  dataset = "datasus_sih",
  time_period = 2010,
  states = "AM",
  raw_data = FALSE
)