README.md
In jjchern/qhp: Qualified Health Plan Data in the Health Insurance Marketplace
About
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.
Installation
# install.packages("devtools")
devtools::install_github("jjchern/qhp")
Links
General
Sources
- 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
Inital Enrollment Data
- 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
Second Enrollment Data
- 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
Usage
Zip Code Level Enrollment Data
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>.
Top 10 Counties, Sorted According to Total Enrollment
# 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
#> .. ... ... ... ... ...
Top 10 Counties, Sorted According to Enrollment per 10,000 Residents
# 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
#> .. ... ... ... ... ... ...
Enrolloment per 10,000 Residents by counties
Total enrollment by states
# 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
Health Plan Data
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 <...>.
Reference
-
-
-
-
-
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
- Health Insurance Market Reforms
jjchern/qhp documentation built on May 19, 2019, 11:39 a.m.
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About
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.
Installation
# install.packages("devtools")
devtools::install_github("jjchern/qhp")
Links
General
Sources
- 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
Inital Enrollment Data
- 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
Second Enrollment Data
- 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
Usage
Zip Code Level Enrollment Data
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>.
Top 10 Counties, Sorted According to Total Enrollment
# 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
#> .. ... ... ... ... ...
Top 10 Counties, Sorted According to Enrollment per 10,000 Residents
# 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
#> .. ... ... ... ... ... ...
Enrolloment per 10,000 Residents by counties
Total enrollment by states
# 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
Health Plan Data
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 <...>.
Reference
-
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
- Health Insurance Market Reforms
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