This package includes Qualified Health Plan (qhp) data in the Health Insurance Marketplace. So far it contains three years of enrollment data and the initial health plan data.
# install.packages("devtools")
devtools::install_github("jjchern/qhp")
- PLAN SELECTIONS BY ZIP CODE IN THE HEALTH INSURANCE MARKETPLACE, 2014
- PLAN SELECTIONS BY ZIP CODE IN THE HEALTH INSURANCE MARKETPLACE, 2015
- PLAN SELECTIONS BY ZIP CODE AND COUNTY IN THE HEALTH INSURANCE MARKETPLACE: MARCH 2016
- HEALTH PLAN DATASETS
- HEALTH INSURANCE MARKETPLACE: SUMMARY ENROLLMENT REPORT FOR THE INITIAL ANNUAL OPEN ENROLLMENT PERIOD
- ADDENDUM TO THE HEALTH INSURANCE MARKETPLACE SUMMARY ENROLLMENT REPORT FOR THE INITIAL ANNUAL OPEN ENROLLMENT PERIOD
- QHP Selections by Age Groups
- QHP Selections by APTC
- QHP Selections by Household Incomes
- QHP Selections by Race/ethnicity
- QHP Selections by Types of Selections
- QHP Selections by CSR
- QHP Selections by Metal Levels
library(dplyr, warn.conflicts = FALSE)
qhp::enrollment2014
#> Source: local data frame [29,685 x 8]
#>
#> ZipCode StateName PlanSelections zcta state countygeoid
#> <int> <chr> <int> <int> <chr> <int>
#> 1 3031 New Hampshire 332 3031 NH 33011
#> 2 3032 New Hampshire 150 3032 NH 33015
#> 3 3033 New Hampshire 151 3033 NH 33011
#> 4 3034 New Hampshire 122 3034 NH 33015
#> 5 3036 New Hampshire 147 3036 NH 33015
#> 6 3037 New Hampshire 174 3037 NH 33015
#> 7 3038 New Hampshire 945 3038 NH 33015
#> 8 3040 New Hampshire NA 3034 NH 33015
#> 9 3041 New Hampshire NA 3038 NH 33015
#> 10 3042 New Hampshire 195 3042 NH 33015
#> .. ... ... ... ... ... ...
#> Variables not shown: countyname <chr>, copop <int>.
qhp::enrollment2015
#> Source: local data frame [14,619 x 8]
#>
#> ZipCode StateName PlanSelections zcta state countygeoid
#> <int> <chr> <int> <int> <chr> <int>
#> 1 3031 New Hampshire 471 3031 NH 33011
#> 2 3032 New Hampshire 215 3032 NH 33015
#> 3 3033 New Hampshire 228 3033 NH 33011
#> 4 3034 New Hampshire 203 3034 NH 33015
#> 5 3036 New Hampshire 202 3036 NH 33015
#> 6 3037 New Hampshire 226 3037 NH 33015
#> 7 3038 New Hampshire 1237 3038 NH 33015
#> 8 3042 New Hampshire 253 3042 NH 33015
#> 9 3043 New Hampshire 79 3043 NH 33011
#> 10 3044 New Hampshire 172 3044 NH 33015
#> .. ... ... ... ... ... ...
#> Variables not shown: countyname <chr>, copop <int>.
qhp::enrollment2016
#> Source: local data frame [15,198 x 7]
#>
#> ZipCode PlanSelections zcta state countygeoid countyname
#> <int> <int> <int> <chr> <int> <chr>
#> 1 3031 511 3031 NH 33011 Hillsborough County
#> 2 3032 241 3032 NH 33015 Rockingham County
#> 3 3033 231 3033 NH 33011 Hillsborough County
#> 4 3034 203 3034 NH 33015 Rockingham County
#> 5 3036 193 3036 NH 33015 Rockingham County
#> 6 3037 208 3037 NH 33015 Rockingham County
#> 7 3038 1367 3038 NH 33015 Rockingham County
#> 8 3042 321 3042 NH 33015 Rockingham County
#> 9 3043 84 3043 NH 33011 Hillsborough County
#> 10 3044 180 3044 NH 33015 Rockingham County
#> .. ... ... ... ... ... ...
#> Variables not shown: copop <int>.
# 2014
qhp::enrollment2014 %>%
na.omit() %>%
group_by(countygeoid) %>%
summarise(countyname = first(countyname),
enrollment = sum(PlanSelections),
state = first(state),
copop = first(copop)) %>%
arrange(desc(enrollment))
#> Source: local data frame [2,237 x 5]
#>
#> countygeoid countyname enrollment state copop
#> <int> <chr> <int> <chr> <int>
#> 1 12086 Miami-Dade County 256975 FL 2496435
#> 2 12011 Broward County 152745 FL 1748066
#> 3 48201 Harris County 139424 TX 4092459
#> 4 17031 Cook County 100984 IL 5194675
#> 5 48113 Dallas County 85768 TX 2368139
#> 6 4013 Maricopa County 77729 AZ 3817117
#> 7 12099 Palm Beach County 75851 FL 1320134
#> 8 12095 Orange County 66862 FL 1145956
#> 9 48029 Bexar County 61539 TX 1714773
#> 10 42101 Philadelphia County 60724 PA 1526006
#> .. ... ... ... ... ...
# 2015
qhp::enrollment2015 %>%
na.omit() %>%
group_by(countygeoid) %>%
summarise(countyname = first(countyname),
enrollment = sum(PlanSelections),
state = first(state),
copop = first(copop)) %>%
arrange(desc(enrollment))
#> Source: local data frame [2,425 x 5]
#>
#> countygeoid countyname enrollment state copop
#> <int> <chr> <int> <chr> <int>
#> 1 12086 Miami-Dade County 392071 FL 2496435
#> 2 48201 Harris County 228358 TX 4092459
#> 3 12011 Broward County 221918 FL 1748066
#> 4 17031 Cook County 155294 IL 5194675
#> 5 12099 Palm Beach County 136989 FL 1320134
#> 6 48113 Dallas County 130814 TX 2368139
#> 7 4013 Maricopa County 129625 AZ 3817117
#> 8 12095 Orange County 111125 FL 1145956
#> 9 48029 Bexar County 93833 TX 1714773
#> 10 48439 Tarrant County 91381 TX 1809034
#> .. ... ... ... ... ...
# 2014
qhp::enrollment2014 %>%
na.omit() %>%
group_by(countygeoid) %>%
summarise(countyname = first(countyname),
enrollment = sum(PlanSelections),
state = first(state),
copop = first(copop)) %>%
mutate(enroll_per100000 = enrollment / copop * 10000) %>%
filter(copop >= 100000) %>%
arrange(desc(enroll_per100000))
#> Source: local data frame [425 x 6]
#>
#> countygeoid countyname enrollment state copop enroll_per100000
#> <int> <chr> <int> <chr> <int> <dbl>
#> 1 12086 Miami-Dade County 256975 FL 2496435 1029.3679
#> 2 12011 Broward County 152745 FL 1748066 873.7942
#> 3 12097 Osceola County 18655 FL 268685 694.3075
#> 4 16019 Bonneville County 6427 ID 104234 616.5934
#> 5 12095 Orange County 66862 FL 1145956 583.4604
#> 6 12099 Palm Beach County 75851 FL 1320134 574.5705
#> 7 37021 Buncombe County 13567 NC 238318 569.2814
#> 8 28049 Hinds County 13648 MS 245285 556.4140
#> 9 13089 DeKalb County 37971 GA 691893 548.7987
#> 10 13135 Gwinnett County 42625 GA 805321 529.2920
#> .. ... ... ... ... ... ...
# 2015
qhp::enrollment2015 %>%
na.omit() %>%
group_by(countygeoid) %>%
summarise(countyname = first(countyname),
enrollment = sum(PlanSelections),
state = first(state),
copop = first(copop)) %>%
mutate(enroll_per100000 = enrollment / copop * 10000) %>%
filter(copop >= 100000) %>%
arrange(desc(enroll_per100000))
#> Source: local data frame [432 x 6]
#>
#> countygeoid countyname enrollment state copop enroll_per100000
#> <int> <chr> <int> <chr> <int> <dbl>
#> 1 12086 Miami-Dade County 392071 FL 2496435 1570.5236
#> 2 12011 Broward County 221918 FL 1748066 1269.5058
#> 3 12097 Osceola County 31642 FL 268685 1177.6616
#> 4 12099 Palm Beach County 136989 FL 1320134 1037.6901
#> 5 12095 Orange County 111125 FL 1145956 969.7144
#> 6 37021 Buncombe County 20936 NC 238318 878.4901
#> 7 13135 Gwinnett County 69010 GA 805321 856.9254
#> 8 13089 DeKalb County 56580 GA 691893 817.7565
#> 9 28049 Hinds County 19949 MS 245285 813.2988
#> 10 12111 St. Lucie County 22078 FL 277789 794.7759
#> .. ... ... ... ... ... ...
# 2014
qhp::enrollment2014 %>%
na.omit() %>%
group_by(state) %>%
summarise(statename = first(StateName),
enrollment = sum(PlanSelections)) %>%
arrange(enrollment %>% desc) %>%
print(n = 36)
#> Source: local data frame [36 x 3]
#>
#> state statename enrollment
#> <chr> <chr> <int>
#> 1 FL Florida 979721
#> 2 TX Texas 719657
#> 3 NC North Carolina 352841
#> 4 GA Georgia 311072
#> 5 PA Pennsylvania 302288
#> 6 MI Michigan 265480
#> 7 VA Virginia 208011
#> 8 IL Illinois 204256
#> 9 NJ New Jersey 159331
#> 10 TN Tennessee 146091
#> 11 OH Ohio 143967
#> 12 MO Missouri 141734
#> 13 WI Wisconsin 132678
#> 14 IN Indiana 124649
#> 15 AZ Arizona 117842
#> 16 SC South Carolina 115358
#> 17 LA Louisiana 96688
#> 18 AL Alabama 92273
#> 19 UT Utah 82010
#> 20 ID Idaho 73452
#> 21 OK Oklahoma 62815
#> 22 MS Mississippi 56546
#> 23 KS Kansas 49616
#> 24 ME Maine 39600
#> 25 NH New Hampshire 38441
#> 26 AR Arkansas 37155
#> 27 NE Nebraska 35780
#> 28 MT Montana 32704
#> 29 NM New Mexico 28981
#> 30 IA Iowa 20527
#> 31 DE Delaware 13745
#> 32 WV West Virginia 13191
#> 33 AK Alaska 11898
#> 34 WY Wyoming 10226
#> 35 SD South Dakota 9448
#> 36 ND North Dakota 6650
# 2015
qhp::enrollment2015 %>%
na.omit() %>%
group_by(state) %>%
summarise(statename = first(StateName),
enrollment = sum(PlanSelections)) %>%
arrange(enrollment %>% desc) %>%
print(n = 37)
#> Source: local data frame [37 x 3]
#>
#> state statename enrollment
#> <chr> <chr> <int>
#> 1 FL Florida 1591227
#> 2 TX Texas 1192085
#> 3 NC North Carolina 556319
#> 4 GA Georgia 537008
#> 5 PA Pennsylvania 458106
#> 6 VA Virginia 378788
#> 7 MI Michigan 335952
#> 8 IL Illinois 335517
#> 9 NJ New Jersey 252536
#> 10 MO Missouri 243366
#> 11 TN Tennessee 227831
#> 12 OH Ohio 224641
#> 13 IN Indiana 213183
#> 14 SC South Carolina 208214
#> 15 WI Wisconsin 203669
#> 16 AZ Arizona 202896
#> 17 LA Louisiana 182353
#> 18 AL Alabama 167752
#> 19 UT Utah 137353
#> 20 OK Oklahoma 119188
#> 21 OR Oregon 108288
#> 22 MS Mississippi 100495
#> 23 KS Kansas 88551
#> 24 NV Nevada 72528
#> 25 ME Maine 71496
#> 26 NE Nebraska 67265
#> 27 AR Arkansas 59045
#> 28 NH New Hampshire 51330
#> 29 MT Montana 49881
#> 30 NM New Mexico 48470
#> 31 IA Iowa 34001
#> 32 WV West Virginia 25553
#> 33 DE Delaware 24885
#> 34 AK Alaska 19869
#> 35 WY Wyoming 19108
#> 36 SD South Dakota 16612
#> 37 ND North Dakota 13270
qhp::qhp2014
#> Source: local data frame [81,015 x 130]
#>
#> state county metal_level
#> <chr> <chr> <chr>
#> 1 AK ALEUTIANS EAST Bronze
#> 2 AK ALEUTIANS EAST Silver
#> 3 AK ALEUTIANS EAST Gold
#> 4 AK ALEUTIANS EAST Gold
#> 5 AK ALEUTIANS EAST Silver
#> 6 AK ALEUTIANS EAST Silver
#> 7 AK ALEUTIANS EAST Bronze
#> 8 AK ALEUTIANS EAST Bronze
#> 9 AK ALEUTIANS EAST Bronze
#> 10 AK ALEUTIANS EAST Silver
#> .. ... ... ...
#> Variables not shown: issuer_name <chr>, plan_id_standard_component <chr>,
#> plan_marketing_name <chr>, plan_type <chr>, rating_area <chr>,
#> child_only_offering <chr>, source <chr>,
#> customer_service_phone_number_local <chr>,
#> customer_service_phone_number_toll_free <chr>,
#> customer_service_phone_number_tty <chr>, network_url <chr>,
#> plan_brochure_url <chr>, summary_of_benefits_url <chr>,
#> drug_formulary_url <chr>, adult_dental <dbl>, child_dental <chr>,
#> premium_scenarios <dbl>, premium_child <dbl>,
#> premium_adult_individual_age_21 <dbl>, premium_adult_individual_age_27
#> <dbl>, premium_adult_individual_age_30 <dbl>,
#> premium_adult_individual_age_40 <dbl>, premium_adult_individual_age_50
#> <dbl>, premium_adult_individual_age_60 <dbl>, premium_couple_21 <dbl>,
#> premium_couple_30 <dbl>, premium_couple_40 <dbl>, premium_couple_50
#> <dbl>, premium_couple_60 <dbl>, couple_1_child_age_21 <dbl>,
#> couple_1_child_age_30 <dbl>, couple_1_child_age_40 <dbl>,
#> couple_1_child_age_50 <dbl>, couple_2_children_age_21 <dbl>,
#> couple_2_children_age_30 <dbl>, couple_2_children_age_40 <dbl>,
#> couple_2_children_age_50 <dbl>, couple_3_or_more_children_age_21 <dbl>,
#> couple_3_or_more_children_age_30 <dbl>, couple_3_or_more_children_age_40
#> <dbl>, couple_3_or_more_children_age_50 <dbl>, individual_1_child_age_21
#> <dbl>, individual_1_child_age_30 <dbl>, individual_1_child_age_40 <dbl>,
#> individual_1_child_age_50 <dbl>, individual_2_children_age_21 <dbl>,
#> individual_2_children_age_30 <dbl>, individual_2_children_age_40 <dbl>,
#> individual_2_children_age_50 <dbl>, individual_3_or_more_children_age_21
#> <dbl>, individual_3_or_more_children_age_30 <dbl>,
#> individual_3_or_more_children_age_40 <dbl>,
#> individual_3_or_more_children_age_50 <dbl>, standard_plan_cost_sharing
#> <dbl>, medical_deductible_individual_standard <chr>,
#> drug_deductible_individual_standard <chr>,
#> medical_deductible_family_standard <chr>,
#> drug_deductible_family_standard <chr>,
#> medical_maximum_out_of_pocket_individual_standard <chr>,
#> drug_maximum_out_of_pocket_individual_standard <chr>,
#> medical_maximum_out_of_pocket_family_standard <chr>,
#> drug_maximum_out_of_pocket_family_standard <chr>,
#> primary_care_physician_standard <chr>, specialist_standard <chr>,
#> emergency_room_standard <chr>, inpatient_facility_standard <chr>,
#> inpatient_physician_standard <chr>, generic_drugs_standard <chr>,
#> preferred_brand_drugs_standard <chr>, non_preferred_brand_drugs_standard
#> <chr>, specialty_drugs_standard <chr>,
#> 73_percent_actuarial_value_silver_plan_cost_sharing <dbl>,
#> medical_deductible_individual_73_percent <chr>,
#> drug_deductible_individual_73_percent <chr>,
#> medical_deductible_family_73_percent <chr>,
#> drug_deductible_family_73_percent <chr>,
#> medical_maximum_out_of_pocket_individual_73_percent <chr>,
#> drug_maximum_out_of_pocket_individual_73_percent <chr>,
#> medical_maximum_out_of_pocket_family_73_percent <chr>,
#> drug_maximum_out_of_pocket_family_73_percent <chr>,
#> primary_care_physician_73_percent <chr>, specialist_73_percent <chr>,
#> emergency_room_73_percent <chr>, inpatient_facility_73_percent <chr>,
#> inpatient_physician_73_percent <chr>, generic_drugs_73_percent <chr>,
#> preferred_brand_drugs_73_percent <chr>,
#> non_preferred_brand_drugs_73_percent <chr>, specialty_drugs_73_percent
#> <chr>, 87_percent_actuarial_value_silver_plan_cost_sharing <dbl>,
#> medical_deductible_individual_87_percent <chr>,
#> drug_deductible_individual_87_percent <chr>,
#> medical_deductible_family_87_percent <chr>,
#> drug_deductible_family_87_percent <chr>,
#> medical_maximum_out_of_pocket_individual_87_percent <chr>,
#> drug_maximum_out_of_pocket_individual_87_percent <chr>,
#> medical_maximum_out_of_pocket_family_87_percent <chr>,
#> drug_maximum_out_of_pocket_family_87_percent <chr>,
#> primary_care_physician_87_percent <chr>, specialist_87_percent <chr>,
#> and 27 more <...>.
-
26 CFR 1.36B-3 - Computing the premium assistance credit amount.
-
45 CFR 155.300 - Definitions and general standards for eligibility determinations.:
- Applicable Children's Health Insurance Program (CHIP) MAGI-based income standard
- Applicable Medicaid modified adjusted gross income (MAGI)-based income standard
- Federal poverty level or FPL
- Insurance affordability program
- MAGI-based income
-
26 U.S. Code § 36B - Refundable credit for coverage under a qualified health plan
- Applicable percentage
-
42 U.S. Code Subchapter IV - AFFORDABLE COVERAGE CHOICES FOR ALL AMERICANS
-
42 U.S. Code Part A - Establishment of Qualified Health Plans