Download Medicare Disparities Data — dcf_download

Download data from the Centers for Medicare & Medicaid Services (CMS) Mapping Medicare Disparities by Population (MMD) tool.

Usage

dcf_download_cmsmmd(
  measure,
  population = NULL,
  year = NULL,
  geography = NULL,
  adjust = NULL,
  condition = NULL,
  sex = c(1:2, "."),
  age = c(0:4, "."),
  race = c(1:6, "."),
  dual_elig = ".",
  medicare_elig = ".",
  refresh_codebook = FALSE,
  codebook_only = FALSE,
  row_limit = 9999999,
  out_file = NULL,
  state = NULL,
  parquet = FALSE,
  verbose = TRUE
)

dcf_standardize_cmsmmd(raw_data = NULL)

Arguments

measure: Name or letter code of the measure to download.
population: The population code; either f (Medicare Fee For Service) or m (Medicare Advantage).
year: Year(s) to download (e.g., 2015:2020). If not specified, all available years will be included.
geography: Geography code(s) to include, between n (national), s (state), and c (county). If not specified, all available geographies will be included.
adjust, condition, sex, age, race, dual_elig, medicare_elig: One or more codes indicating the variable levels to include (see dcf_standardize_cmsmmd). If ".", values will be across all levels, whereas if NULL, all available levels will be included (aggregated and disaggregated). See the Making Requests section.
refresh_codebook: Logical; if TRUE, will re-download the codebook even if it exists in the temporary location (which is cleared each R session).
codebook_only: Logical; if TRUE, will return the codebook without downloading data.
out_file: Path to the CSV or Parquet file to write data to.
state: The codebook state (MD5 hash) recorded during a previous download; if provided, will only download if the new state does not match.
parquet: Logical; if TRUE, will convert the downloaded CSV file to Parquet.
verbose: Logical; if FALSE, will not display status messages.
raw_data: The raw data as downloaded with dcf_download_cmsmmd to be standardized.
limit: Maximum number of rows to return in each request. The API limit appears to be 100,000.

Value

dcf_download_cmsmmd: A list:

codebook: The codebook.
codebook_state: MD5 hash of the codebook.
data: The downloaded data.

dcf_standardize_cmsmmd: If raw_data is NULL, a list with an entry for each API parameter, containing named vectors with level codes as names mapping to level values (as they appear in the tool's menus). Otherwise, a version of raw_data with coded values converted to labels.

Making Requests

The API operates over several large files, partitioned by measure, year, adjust, and dual and medicaid eligibility. These are identified with the codebook (dcf_download_cmsmmd(codebook_only = TRUE)).

The files are larger than the API's limit, so requests for each file have to be broken up by the other variables within them (sex, age, race, and condition).

For best performance, make requests as big as possible while staying under 100,000 rows each (e.g., by setting sex, age, or race to NULL).

Examples

# find the codes associated with menu values
variable_codes <- dcf_standardize_cmsmmd()
variable_codes[c(
  "sex", "age", "race",
  "adjust", "dual_elig", "medicare_elig"
)]
#> $sex
#>        1        2 
#>   "Male" "Female" 
#> 
#> $age
#>       0       1       2       3       4 
#>   "<65" "65-74" "75-84"   "85+"   "65+" 
#> 
#> $race
#>                               1                               2 
#>                         "White"                         "Black" 
#>                               3                               4 
#>                         "Other"        "Asian/Pacific Islander" 
#>                               5                               6 
#>                      "Hispanic" "American Indian/Alaska Native" 
#> 
#> $adjust
#>                             1                             2 
#>           "Unsmoothed actual" "Unsmoothed age standardized" 
#>                             3                             4 
#>             "Smoothed actual"   "Smoothed age standardized" 
#> 
#> $dual_elig
#>               0               1 
#> "Medicare only"     "Dual only" 
#> 
#> $medicare_elig
#>                                             0 
#>              "Old Age / Survivor's Insurance" 
#>                                             1 
#>               "Disability Insurance Benefits" 
#>                                             2 
#>                     "End-Stage Renal Disease" 
#>                                             3 
#> "Both Disability and End-Stage Renal Disease" 
#> 

# look at the codebook which defines source files
codebook <- dcf_download_cmsmmd(codebook_only = TRUE)
#> ℹ retrieving codebook
#> ✔ retrieving codebook [101ms]
#> 
codebook
#> # A tibble: 1,537 × 13
#>    File    File.Name Number.of.observations population measure description  year
#>    <chr>   <chr>                      <dbl> <chr>      <chr>   <chr>       <dbl>
#>  1 ""      Mdcr_pmt…                7497844 f          b       medicare r…     2
#>  2 "dual … Dschrg_F…                9424772 f          d       discharge …     2
#>  3 "dual … Dschrg_F…                8122052 f          d       discharge …     2
#>  4 "dual … Dschrg_F…                6318880 f          d       discharge …     2
#>  5 ""      Ipdays_F…                7497844 f          i       inpatient …     2
#>  6 ""      Mortalit…                3944721 f          m       mortality       2
#>  7 "dual … Admsn_Fi…                9425012 f          n       admission …     2
#>  8 "dual … Admsn_Fi…                8122188 f          n       admission …     2
#>  9 "dual … Admsn_Fi…                6319016 f          n       admission …     2
#> 10 "year … Principa…               11686440 f          p       principal …     2
#> # ℹ 1,527 more rows
#> # ℹ 6 more variables: elig <chr>, race_code <chr>, sex_code <chr>,
#> #   adjust <chr>, dual <chr>, url <chr>

if (FALSE) { # \dontrun{
  # download data
  downloaded <- dcf_download_cmsmmd(
    "preventive care",
    population = "f",
    race = ".",
    sex = ".",
    age = NULL,
    condition = c(83, 85, 86, 88, 89, 95, 101, 102, 104, 105:107),
    adjust = 1
  )

  # convert codes to levels
  data_standard <- dcf_standardize_cmsmmd(downloaded$data)
} # }