Medicare Monthly Enrollment API
Source:vignettes/articles/mcr-month-enroll.Rmd
mcr-month-enroll.RmdCurrent information on the number of Medicare beneficiaries with hospital/medical and prescription drug coverage, available for several geographical areas.
The Medicare Monthly Enrollment data provides current monthly information on the number of Medicare beneficiaries with hospital/medical coverage and prescription drug coverage, available for several geographic areas including national, state/territory, and county.
The hospital/medical coverage data can be broken down further by health care delivery (Original Medicare versus Medicare Advantage and Other Health Plans) and the prescription drug coverage data can be examined by those enrolled in stand-alone Prescription Drug Plans and those enrolled in Medicare Advantage Prescription Drug plans.
The dataset includes enrollee counts on a rolling 12 month basis and also provides information on yearly trends.
Search Examples
Set the level argument to “National” and the
period argument to “Year” to return year-by-year national
data:
beneficiary_enrollment(level = "National",
period = "Year") |>
dplyr::select(!c(period:fips))
#> # A tibble: 10 × 20
#> year bene_total bene_orig bene_ma_oth bene_aged_total bene_aged_esrd
#> <int> <int> <int> <int> <int> <int>
#> 1 2013 52425659 37613096 14812563 43761388 234590
#> 2 2014 54013038 37790373 16222665 45216665 245705
#> 3 2015 55496222 38025274 17470948 46630952 258350
#> 4 2016 56981183 38610384 18370800 48143217 269019
#> 5 2017 58457244 38667830 19789414 49678033 278642
#> 6 2018 59989883 38665082 21324800 51303898 290243
#> 7 2019 61514510 38577012 22937498 52991455 301798
#> 8 2020 62840267 37776345 25063922 54531919 305080
#> 9 2021 63892626 36356380 27536246 55851321 305227
#> 10 2022 65027175 35197077 29830099 57300339 304390
#> # ℹ 14 more variables: bene_aged_no_esrd <int>, bene_dsb_total <int>,
#> # bene_dsb_esrd <int>, bene_dsb_no_esrd <int>, bene_ab_total <int>,
#> # bene_ab_orig <int>, bene_ab_ma_oth <int>, bene_rx_total <int>,
#> # bene_rx_pdp <int>, bene_rx_mapd <int>, bene_rx_elig <int>,
#> # bene_rx_full <int>, bene_rx_part <int>, bene_rx_none <int>Or set just the level argument to include month-by-month
data as well:
beneficiary_enrollment(level = "National") |>
dplyr::select(!c(level:fips)) |>
dplyr::filter(period %in% month.name)
#> # A tibble: 121 × 21
#> year period bene_total bene_orig bene_ma_oth bene_aged_total bene_aged_esrd
#> <int> <chr> <int> <int> <int> <int> <int>
#> 1 2013 January 51727774 37204960 14522814 43355761 257243
#> 2 2013 Februa… 51810690 37247253 14563437 43388016 252347
#> 3 2013 March 51946603 37336191 14610412 43467837 248322
#> 4 2013 April 52052091 37436020 14616071 43523878 244081
#> 5 2013 May 52166970 37475723 14691247 43592624 240089
#> 6 2013 June 52301688 37547041 14754647 43662467 236203
#> 7 2013 July 52466118 37618358 14847760 43772726 232469
#> 8 2013 August 52626340 37713161 14913179 43882570 228612
#> 9 2013 Septem… 52782073 37803887 14978186 43982185 224793
#> 10 2013 October 52940709 37908729 15031980 44091229 220966
#> # ℹ 111 more rows
#> # ℹ 14 more variables: bene_aged_no_esrd <int>, bene_dsb_total <int>,
#> # bene_dsb_esrd <int>, bene_dsb_no_esrd <int>, bene_ab_total <int>,
#> # bene_ab_orig <int>, bene_ab_ma_oth <int>, bene_rx_total <int>,
#> # bene_rx_pdp <int>, bene_rx_mapd <int>, bene_rx_elig <int>,
#> # bene_rx_full <int>, bene_rx_part <int>, bene_rx_none <int>Setting the level to “State” will return data for each
state:
beneficiary_enrollment(period = "Year",
level = "State") |>
dplyr::select(!c(period:state, county:fips))
#> # A tibble: 570 × 21
#> year state_name bene_total bene_orig bene_ma_oth bene_aged_total
#> <int> <chr> <int> <int> <int> <int>
#> 1 2013 Alabama 921477 711448 210029 698720
#> 2 2013 Alaska 76224 75676 549 63712
#> 3 2013 Arizona 1050128 658948 391180 898705
#> 4 2013 Arkansas 572713 467399 105315 438855
#> 5 2013 California 5300177 3318396 1981781 4582391
#> 6 2013 Colorado 721035 468492 252542 620821
#> 7 2013 Connecticut 608512 467291 141221 526964
#> 8 2013 Delaware 167686 155803 11883 141355
#> 9 2013 District of Columbia 83964 74946 9018 69193
#> 10 2013 Florida 3753454 2402926 1350528 3239566
#> # ℹ 560 more rows
#> # ℹ 15 more variables: bene_aged_esrd <int>, bene_aged_no_esrd <int>,
#> # bene_dsb_total <int>, bene_dsb_esrd <int>, bene_dsb_no_esrd <int>,
#> # bene_ab_total <int>, bene_ab_orig <int>, bene_ab_ma_oth <int>,
#> # bene_rx_total <int>, bene_rx_pdp <int>, bene_rx_mapd <int>,
#> # bene_rx_elig <int>, bene_rx_full <int>, bene_rx_part <int>,
#> # bene_rx_none <int>Select a particular state:
beneficiary_enrollment(period = "Year",
level = "State",
state = "GA")
#> # A tibble: 10 × 26
#> year period level state state_name county fips bene_total bene_orig
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <int> <int>
#> 1 2013 Year State GA Georgia Total 13 1412936 1039767
#> 2 2014 Year State GA Georgia Total 13 1470380 1046071
#> 3 2015 Year State GA Georgia Total 13 1523200 1044530
#> 4 2016 Year State GA Georgia Total 13 1573277 1049087
#> 5 2017 Year State GA Georgia Total 13 1624056 1051459
#> 6 2018 Year State GA Georgia Total 13 1676019 1046723
#> 7 2019 Year State GA Georgia Total 13 1727412 1040090
#> 8 2020 Year State GA Georgia Total 13 1773148 1012084
#> 9 2021 Year State GA Georgia Total 13 1808944 953980
#> 10 2022 Year State GA Georgia Total 13 1848723 916979
#> # ℹ 17 more variables: bene_ma_oth <int>, bene_aged_total <int>,
#> # bene_aged_esrd <int>, bene_aged_no_esrd <int>, bene_dsb_total <int>,
#> # bene_dsb_esrd <int>, bene_dsb_no_esrd <int>, bene_ab_total <int>,
#> # bene_ab_orig <int>, bene_ab_ma_oth <int>, bene_rx_total <int>,
#> # bene_rx_pdp <int>, bene_rx_mapd <int>, bene_rx_elig <int>,
#> # bene_rx_full <int>, bene_rx_part <int>, bene_rx_none <int>Each county in that state:
beneficiary_enrollment(period = "Year",
level = "County",
state = "GA")
#> # A tibble: 1,590 × 26
#> year period level state state_name county fips bene_total bene_orig
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <int> <int>
#> 1 2013 Year County GA Georgia Appling 13001 3389 2686
#> 2 2013 Year County GA Georgia Atkinson 13003 1248 991
#> 3 2013 Year County GA Georgia Bacon 13005 1977 1552
#> 4 2013 Year County GA Georgia Baker 13007 608 478
#> 5 2013 Year County GA Georgia Baldwin 13009 7722 4660
#> 6 2013 Year County GA Georgia Banks 13011 3239 2504
#> 7 2013 Year County GA Georgia Barrow 13013 9738 7093
#> 8 2013 Year County GA Georgia Bartow 13015 15446 11826
#> 9 2013 Year County GA Georgia Ben Hill 13017 3463 2628
#> 10 2013 Year County GA Georgia Berrien 13019 3442 2616
#> # ℹ 1,580 more rows
#> # ℹ 17 more variables: bene_ma_oth <int>, bene_aged_total <int>,
#> # bene_aged_esrd <int>, bene_aged_no_esrd <int>, bene_dsb_total <int>,
#> # bene_dsb_esrd <int>, bene_dsb_no_esrd <int>, bene_ab_total <int>,
#> # bene_ab_orig <int>, bene_ab_ma_oth <int>, bene_rx_total <int>,
#> # bene_rx_pdp <int>, bene_rx_mapd <int>, bene_rx_elig <int>,
#> # bene_rx_full <int>, bene_rx_part <int>, bene_rx_none <int>Or a particular county:
beneficiary_enrollment(period = "Year",
level = "County",
state = "GA",
county = "Lowndes")
#> # A tibble: 10 × 26
#> year period level state state_name county fips bene_total bene_orig
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <int> <int>
#> 1 2013 Year County GA Georgia Lowndes 13185 14991 12192
#> 2 2014 Year County GA Georgia Lowndes 13185 15604 12343
#> 3 2015 Year County GA Georgia Lowndes 13185 16137 12549
#> 4 2016 Year County GA Georgia Lowndes 13185 16644 12879
#> 5 2017 Year County GA Georgia Lowndes 13185 17172 13007
#> 6 2018 Year County GA Georgia Lowndes 13185 17806 13137
#> 7 2019 Year County GA Georgia Lowndes 13185 18173 12964
#> 8 2020 Year County GA Georgia Lowndes 13185 18736 12660
#> 9 2021 Year County GA Georgia Lowndes 13185 19135 12134
#> 10 2022 Year County GA Georgia Lowndes 13185 19770 11714
#> # ℹ 17 more variables: bene_ma_oth <int>, bene_aged_total <int>,
#> # bene_aged_esrd <int>, bene_aged_no_esrd <int>, bene_dsb_total <int>,
#> # bene_dsb_esrd <int>, bene_dsb_no_esrd <int>, bene_ab_total <int>,
#> # bene_ab_orig <int>, bene_ab_ma_oth <int>, bene_rx_total <int>,
#> # bene_rx_pdp <int>, bene_rx_mapd <int>, bene_rx_elig <int>,
#> # bene_rx_full <int>, bene_rx_part <int>, bene_rx_none <int>Visualizations
Code for table
# National by Year
tot_nation <- beneficiary_enrollment(level = "National",
period = "Year") |>
dplyr::select(year,
level,
total = bene_total,
orig = bene_orig,
ma = bene_ma_oth,
aged = bene_aged_total,
disabled = bene_dsb_total,
partsAB = bene_ab_total,
partD = bene_rx_total)
# State by Year
tot_state <- beneficiary_enrollment(level = "State",
period = "Year",
state = "GA") |>
dplyr::select(year,
state_name,
total = bene_total,
orig = bene_orig,
ma = bene_ma_oth,
aged = bene_aged_total,
disabled = bene_dsb_total,
partsAB = bene_ab_total,
partD = bene_rx_total) |>
dplyr::rename(level = state_name)
# County by Year
tot_county <- beneficiary_enrollment(level = "County",
period = "Year",
state = "GA",
county = "Lowndes") |>
dplyr::select(year,
county,
total = bene_total,
orig = bene_orig,
ma = bene_ma_oth,
aged = bene_aged_total,
disabled = bene_dsb_total,
partsAB = bene_ab_total,
partD = bene_rx_total) |>
dplyr::rename(level = county)
# Bind together, calculate stats
final <- dplyr::bind_rows(
tot_nation,
tot_state,
tot_county) |>
dplyr::group_by(level) |>
provider:::change_abs(total, year) |>
provider:::change_pct(total, total_chg, year) |>
dplyr::ungroup() |>
dplyr::mutate(orig_pct = orig / total,
ma_pct = ma / total,
aged_pct = aged / total,
disabled_pct = disabled / total,
ab_pct = partsAB / total,
d_pct = partD / total) |>
dplyr::select(year,
level,
total_pct_chg = total_pct,
orig_pct,
ma_pct,
aged_pct,
disabled_pct,
ab_pct,
d_pct)
## gt Table
gt_pct <- final |>
gt::gt() |>
gt::tab_style(style = gt::cell_text(
font = c(gt::google_font(name = "Fira Code"), gt::default_fonts())),
locations = gt::cells_body(columns = dplyr::contains("pct"))) |>
gtExtras::gt_theme_538() |>
gt::fmt_percent(columns = dplyr::contains("pct"), decimals = 1) |>
gt::fmt_percent(columns = c(total_pct_chg), decimals = 1, force_sign = TRUE) |>
gt::sub_missing(columns = dplyr::everything(), missing_text = "--") |>
gt::cols_label(total_pct_chg = "% Change",
orig_pct = "Original",
ma_pct = "MA",
aged_pct = "Aged",
disabled_pct = "Disabled",
ab_pct = "Pts A & B",
d_pct = "Part D") |>
gtExtras::gt_add_divider(
columns = c("level",
"total_pct_chg",
"ma_pct",
"disabled_pct"),
style = "solid",
color = "black",
weight = gt::px(3),
include_labels = FALSE) |>
gt::tab_header(
title = gt::md("Medicare Beneficiaries: **2013 - 2022**"),
subtitle = gt::md("National, State, & County Percentage Comparisons for **Valdosta, Georgia**")) |>
gt::tab_source_note(source_note = "Source: data.cms.gov")
#|> gt::opt_table_font(font = gt::google_font(name = "Karla"))| Medicare Beneficiaries: 2013 - 2022 | ||||||||
| National, State, & County Percentage Comparisons for Valdosta, Georgia | ||||||||
| year | level | % Change | Original | MA | Aged | Disabled | Pts A & B | Part D |
|---|---|---|---|---|---|---|---|---|
| 2013 | National | – | 71.7% | 28.3% | 83.5% | 16.5% | 90.8% | 68.1% |
| 2014 | National | +3.0% | 70.0% | 30.0% | 83.7% | 16.3% | 90.9% | 69.8% |
| 2015 | National | +2.7% | 68.5% | 31.5% | 84.0% | 16.0% | 90.8% | 71.2% |
| 2016 | National | +2.7% | 67.8% | 32.2% | 84.5% | 15.5% | 90.8% | 72.3% |
| 2017 | National | +2.6% | 66.1% | 33.9% | 85.0% | 15.0% | 90.7% | 73.1% |
| 2018 | National | +2.6% | 64.5% | 35.5% | 85.5% | 14.5% | 90.6% | 73.8% |
| 2019 | National | +2.5% | 62.7% | 37.3% | 86.1% | 13.9% | 90.5% | 74.5% |
| 2020 | National | +2.2% | 60.1% | 39.9% | 86.8% | 13.2% | 90.7% | 75.5% |
| 2021 | National | +1.7% | 56.9% | 43.1% | 87.4% | 12.6% | 90.8% | 76.4% |
| 2022 | National | +1.8% | 54.1% | 45.9% | 88.1% | 11.9% | 90.9% | 77.4% |
| 2013 | Georgia | – | 73.6% | 26.4% | 80.7% | 19.3% | 91.9% | 67.7% |
| 2014 | Georgia | +4.1% | 71.1% | 28.9% | 81.0% | 19.0% | 92.0% | 69.3% |
| 2015 | Georgia | +3.6% | 68.6% | 31.4% | 81.4% | 18.6% | 92.0% | 70.8% |
| 2016 | Georgia | +3.3% | 66.7% | 33.3% | 82.0% | 18.0% | 92.0% | 71.8% |
| 2017 | Georgia | +3.2% | 64.7% | 35.3% | 82.5% | 17.5% | 91.8% | 72.4% |
| 2018 | Georgia | +3.2% | 62.5% | 37.5% | 83.1% | 16.9% | 91.7% | 73.0% |
| 2019 | Georgia | +3.1% | 60.2% | 39.8% | 83.9% | 16.1% | 91.6% | 73.7% |
| 2020 | Georgia | +2.6% | 57.1% | 42.9% | 84.6% | 15.4% | 91.6% | 74.6% |
| 2021 | Georgia | +2.0% | 52.7% | 47.3% | 85.3% | 14.7% | 91.8% | 76.1% |
| 2022 | Georgia | +2.2% | 49.6% | 50.4% | 86.1% | 13.9% | 91.8% | 77.0% |
| 2013 | Lowndes | – | 81.3% | 18.7% | 78.9% | 21.1% | 93.8% | 64.9% |
| 2014 | Lowndes | +4.1% | 79.1% | 20.9% | 79.1% | 20.9% | 94.1% | 66.0% |
| 2015 | Lowndes | +3.4% | 77.8% | 22.2% | 79.2% | 20.8% | 94.1% | 66.8% |
| 2016 | Lowndes | +3.1% | 77.4% | 22.6% | 79.5% | 20.5% | 93.9% | 67.0% |
| 2017 | Lowndes | +3.2% | 75.7% | 24.3% | 79.8% | 20.2% | 93.7% | 67.6% |
| 2018 | Lowndes | +3.7% | 73.8% | 26.2% | 79.9% | 20.1% | 93.4% | 67.8% |
| 2019 | Lowndes | +2.1% | 71.3% | 28.7% | 80.7% | 19.3% | 93.1% | 68.3% |
| 2020 | Lowndes | +3.1% | 67.6% | 32.4% | 81.2% | 18.8% | 93.0% | 69.0% |
| 2021 | Lowndes | +2.1% | 63.4% | 36.6% | 81.7% | 18.3% | 93.0% | 69.8% |
| 2022 | Lowndes | +3.3% | 59.3% | 40.7% | 82.4% | 17.6% | 92.7% | 71.0% |
| Source: data.cms.gov | ||||||||
Code for table
national <- beneficiary_enrollment(level = "National")
yr_nat_list <- national |>
dplyr::filter(period == "Year") |>
dplyr::select(year, bene_orig, bene_ma_oth) |>
dplyr::mutate(pct_orig = bene_orig / (bene_orig + bene_ma_oth),
pct_ma = bene_ma_oth / (bene_orig + bene_ma_oth)) |>
tidyr::pivot_longer(cols = c(pct_orig, pct_ma),
names_to = "measure",
values_to = "pct") |>
dplyr::group_by(year) |>
dplyr::summarise(list_data = list(pct), .groups = "drop")
yr_nat_base <- national |>
dplyr::filter(period == "Year") |>
dplyr::select(year, bene_total) |>
provider:::change_abs(bene_total, year) |>
provider:::change_pct(bene_total, bene_total_chg, year)
nat <- dplyr::left_join(yr_nat_base, yr_nat_list, by = dplyr::join_by(year))
gt_nat <- nat |>
gt::gt() |>
gt::tab_style(
style = gt::cell_text(font = c(gt::google_font(name = "Fira Code"),
gt::default_fonts())),
locations = gt::cells_body(columns = c(bene_total,
bene_total_chg))) |>
gtExtras::gt_theme_538() |>
gt::fmt_number(bene_total,
suffixing = TRUE,
decimals = 2) |>
gt::fmt_number(bene_total_chg,
suffixing = TRUE,
force_sign = TRUE,
decimals = 1) |>
gt::fmt_percent(columns = bene_total_pct,
decimals = 1,
force_sign = TRUE) |>
gt::sub_missing(columns = dplyr::everything(),
missing_text = "--") |>
gt::cols_merge_n_pct(col_n = bene_total_chg,
col_pct = bene_total_pct) |>
gt::cols_label(bene_total = "Total",
bene_total_chg = gt::md("Change (%)")) |>
gtExtras::gt_add_divider(columns = c("year",
"bene_total_chg"),
style = "solid",
color = "black",
weight = gt::px(2),
include_labels = FALSE) |>
gtExtras::gt_plt_bar_stack(column = list_data,
width = 80,
labels = c("Original ",
" Advantage"),
palette = c("#ff4343", "black"),
fmt_fn = scales::label_percent(accuracy = 0.1,
suffix = " %")) |>
gt::tab_header(title = gt::md("Medicare Beneficiary Enrollment: **2013 - 2022**"),
subtitle = gt::md("National Totals, Year-Over-Year")) |>
gt::tab_source_note(source_note = "Source: data.cms.gov")
#|> gt::opt_table_font(font = gt::google_font(name = "Karla"))| Medicare Beneficiary Enrollment: 2013 - 2022 | |||
| National Totals, Year-Over-Year | |||
| year | Total | Change (%) | Original || Advantage |
|---|---|---|---|
| 2013 | 52.43M | – | |
| 2014 | 54.01M | +1.6M (+3.0%) | |
| 2015 | 55.50M | +1.5M (+2.7%) | |
| 2016 | 56.98M | +1.5M (+2.7%) | |
| 2017 | 58.46M | +1.5M (+2.6%) | |
| 2018 | 59.99M | +1.5M (+2.6%) | |
| 2019 | 61.51M | +1.5M (+2.5%) | |
| 2020 | 62.84M | +1.3M (+2.2%) | |
| 2021 | 63.89M | +1.1M (+1.7%) | |
| 2022 | 65.03M | +1.1M (+1.8%) | |
| Source: data.cms.gov | |||
Code for table
gt_comp <- beneficiary_enrollment(level = "National",
period = "Year") |>
dplyr::select(!c(period:fips)) |>
tidyr::pivot_longer(cols = dplyr::starts_with("bene_"),
names_to = "stat",
values_to = "value") |>
tidyr::pivot_wider(names_from = year,
values_from = value) |>
gt::gt(rowname_col = "stat") |>
gt::tab_style(
style = gt::cell_text(font = c(gt::google_font(name = "Fira Code"),
gt::default_fonts())),
locations = gt::cells_body(columns = c(2:11))) |>
gtExtras::gt_theme_538() |>
gt::fmt_number(columns = c(2:11), suffixing = TRUE, decimals = 1) |>
gt::tab_row_group(label = gt::md("**Disabled Beneficiaries**"),
rows = c("bene_dsb_total",
"bene_dsb_no_esrd",
"bene_dsb_esrd")) |>
gt::tab_row_group(label = gt::md("**Aged Beneficiaries**"),
rows = c("bene_aged_total",
"bene_aged_no_esrd",
"bene_aged_esrd")) |>
gt::tab_row_group(label = gt::md("**Medicare Part D**"),
rows = c("bene_rx_total",
"bene_rx_pdp",
"bene_rx_mapd",
"bene_rx_elig",
"bene_rx_full",
"bene_rx_part",
"bene_rx_none")) |>
gt::tab_row_group(label = gt::md("**Medicare Parts A & B**"),
rows = c("bene_ab_total",
"bene_ab_orig",
"bene_ab_ma_oth")) |>
gt::tab_row_group(label = gt::md("**Original Medicare** / **Medicare Advantage**"),
rows = c("bene_orig",
"bene_ma_oth")) |>
gt::tab_row_group(label = gt::md("**Total Beneficiaries**"),
rows = c("bene_total")) |>
gt::tab_header(title = gt::md(
"Medicare Beneficiary Enrollment: **2013 - 2022**"),
subtitle = gt::md("National Totals, Month-Over-Month")) |>
gt::tab_source_note(source_note = "Source: data.cms.gov")| Medicare Beneficiary Enrollment: 2013 - 2022 | ||||||||||
| National Totals, Month-Over-Month | ||||||||||
| 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Total Beneficiaries | ||||||||||
| bene_total | 52.4M | 54.0M | 55.5M | 57.0M | 58.5M | 60.0M | 61.5M | 62.8M | 63.9M | 65.0M |
| Original Medicare / Medicare Advantage | ||||||||||
| bene_orig | 37.6M | 37.8M | 38.0M | 38.6M | 38.7M | 38.7M | 38.6M | 37.8M | 36.4M | 35.2M |
| bene_ma_oth | 14.8M | 16.2M | 17.5M | 18.4M | 19.8M | 21.3M | 22.9M | 25.1M | 27.5M | 29.8M |
| Medicare Parts A & B | ||||||||||
| bene_ab_total | 47.6M | 49.1M | 50.4M | 51.7M | 53.0M | 54.3M | 55.7M | 57.0M | 58.0M | 59.1M |
| bene_ab_orig | 32.8M | 32.9M | 33.0M | 33.4M | 33.2M | 33.1M | 32.8M | 31.9M | 30.5M | 29.3M |
| bene_ab_ma_oth | 14.8M | 16.2M | 17.4M | 18.4M | 19.8M | 21.3M | 22.9M | 25.0M | 27.5M | 29.8M |
| Medicare Part D | ||||||||||
| bene_rx_total | 35.7M | 37.7M | 39.5M | 41.2M | 42.7M | 44.2M | 45.8M | 47.4M | 48.8M | 50.3M |
| bene_rx_pdp | 22.7M | 23.4M | 24.1M | 24.8M | 25.2M | 25.6M | 25.6M | 25.2M | 24.2M | 23.4M |
| bene_rx_mapd | 13.0M | 14.3M | 15.4M | 16.4M | 17.5M | 18.7M | 20.2M | 22.2M | 24.7M | 26.9M |
| bene_rx_elig | 10.0M | 10.3M | 10.6M | 10.9M | 10.9M | 11.4M | 11.6M | 11.7M | 11.7M | 12.2M |
| bene_rx_full | 1.0M | 1.1M | 1.1M | 1.1M | 1.0M | 1.1M | 1.1M | 1.1M | 1.1M | 1.1M |
| bene_rx_part | 409.2K | 401.5K | 406.3K | 414.0K | 383.8K | 406.4K | 410.2K | 371.2K | 323.2K | 285.2K |
| bene_rx_none | 24.2M | 26.0M | 27.4M | 28.8M | 30.5M | 31.4M | 32.8M | 34.3M | 35.7M | 36.7M |
| Aged Beneficiaries | ||||||||||
| bene_aged_total | 43.8M | 45.2M | 46.6M | 48.1M | 49.7M | 51.3M | 53.0M | 54.5M | 55.9M | 57.3M |
| bene_aged_esrd | 234.6K | 245.7K | 258.4K | 269.0K | 278.6K | 290.2K | 301.8K | 305.1K | 305.2K | 304.4K |
| bene_aged_no_esrd | 43.5M | 45.0M | 46.4M | 47.9M | 49.4M | 51.0M | 52.7M | 54.2M | 55.5M | 57.0M |
| Disabled Beneficiaries | ||||||||||
| bene_dsb_total | 8.7M | 8.8M | 8.9M | 8.8M | 8.8M | 8.7M | 8.5M | 8.3M | 8.0M | 7.7M |
| bene_dsb_esrd | 243.2K | 249.1K | 254.5K | 256.3K | 258.4K | 261.2K | 262.2K | 255.9K | 248.9K | 238.8K |
| bene_dsb_no_esrd | 8.4M | 8.5M | 8.6M | 8.6M | 8.5M | 8.4M | 8.3M | 8.1M | 7.8M | 7.5M |
| Source: data.cms.gov | ||||||||||
Data Dictionary
| Variable | Description |
|---|---|
|
Year
Indicates the calendar year of Medicare enrollment
|
|
Month
Indicates the month of Medicare enrollment
|
|
Beneficiary State
Area of beneficiary residence
|
|
Beneficiary County
County of beneficiary residence
|
|
Total Beneficiaries
Count of all Medicare beneficiaries
|
|
Original Medicare Beneficiaries
Count of all Original Medicare beneficiaries
|
|
Medicare Advantage and Other Health Plan Beneficiaries
Count of all Medicare Advantage and Other Health Plan beneficiaries
|
|
Total Aged Beneficiaries
Count of Medicare aged beneficiaries
|
|
Aged ESRD Beneficiaries
Count of Medicare aged beneficiaries with End Stage Renal Disease
|
|
Aged Beneficiaries Without ESRD
Count of Medicare aged beneficiaries without End Stage Renal Disease
|
|
Total Disabled Beneficiaries
Count of Medicare disabled beneficiaries
|
|
Disabled Beneficiaries with ESRD and ESRD Only Beneficiaries
Count of Medicare disabled beneficiaries with End Stage Renal Disease and beneficiaries with End Stage Renal Disease only
|
|
Disabled Beneficiaries Without ESRD
Count of Medicare disabled beneficiaries without End Stage Renal Disease
|
|
Total Medicare Part A and Part B Beneficiaries
Count of Medicare beneficiaries enrolled in Hospital Insurance (or Part A) and Supplementary Medical Insurance (or Part B)
|
|
Original Medicare Part A and Part B Beneficiaries
Count of Original Medicare beneficiaries enrolled in Hospital Insurance (or Part A) and Supplementary Medical Insurance (or Part B)
|
|
Medicare Advantage and Other Health Plan Part A and Part B Beneficiaries
Count of Medicare Advantage and Other Health Plan beneficiaries enrolled in Hospital Insurance (or Part A) and Supplementary Medical Insurance (or Part B)
|
|
Total Medicare Part D beneficiaries
Count of all Medicare Prescription Drug (or Part D) beneficiaries
|
|
Total Medicare Prescription Drug Plan beneficiaries
Count of Medicare Prescription Drug (or Part D) beneficiaries enrolled in a Prescription Drug Plan
|
|
Total Medicare Advantage Prescription Drug Plan beneficiaries
Count of Medicare Prescription Drug (or Part D) beneficiaries enrolled in a Medicare Advantage Prescription Drug plan
|