In jjchern/qhp: Qualified Health Plan Data in the Health Insurance Marketplace

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-",
  warning = FALSE,
  message = FALSE,
  cache = TRUE
)

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

• ASPE AFFORDABLE CARE ACT RESEARCH

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
qhp::enrollment2015
qhp::enrollment2016

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))

# 2015
qhp::enrollment2015 %>% 
  na.omit() %>% 
  group_by(countygeoid) %>% 
  summarise(countyname = first(countyname),
            enrollment = sum(PlanSelections),
            state = first(state),
            copop = first(copop)) %>% 
  arrange(desc(enrollment))

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))

# 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))

Enrolloment per 10,000 Residents by counties

# 2014
qhp::enrollment2014 %>% 
  na.omit() %>% 
  group_by(countygeoid) %>% 
  summarise(countyname = first(countyname),
            enrollment = sum(PlanSelections),
            state = first(state),
            copop = first(copop)) %>% 
  mutate(enroll_per10000 = enrollment / copop * 10000) %>% 
  arrange(desc(enroll_per10000)) %>% 
  mutate(countygeoid = sprintf("%05.0f", countygeoid)) %>% 
  arrange(countygeoid) %>% 
  mutate(year = 2014) -> en_county_2014

# 2015
qhp::enrollment2015 %>% 
  na.omit() %>% 
  group_by(countygeoid) %>% 
  summarise(countyname = first(countyname),
            enrollment = sum(PlanSelections),
            state = first(state),
            copop = first(copop)) %>% 
  mutate(enroll_per10000 = enrollment / copop * 10000) %>% 
  arrange(desc(enroll_per10000)) %>% 
  mutate(countygeoid = sprintf("%05.0f", countygeoid)) %>% 
  arrange(countygeoid) %>% 
  mutate(year = 2015) -> en_county_2015

# 2016
qhp::enrollment2016 %>% 
  na.omit() %>% 
  group_by(countygeoid) %>% 
  summarise(countyname = first(countyname),
            enrollment = sum(PlanSelections),
            state = first(state),
            copop = first(copop)) %>% 
  mutate(enroll_per10000 = enrollment / copop * 10000) %>% 
  arrange(desc(enroll_per10000)) %>% 
  mutate(countygeoid = sprintf("%05.0f", countygeoid)) %>% 
  arrange(countygeoid) %>% 
  mutate(year = 2016) -> en_county_2016

# devtools::install_github("jjchern/usmapdata")
usmapdata::county %>% 
  left_join(en_county_2014, by = c("id" = "countygeoid")) %>% 
  mutate(region = id) -> en_county_2014_map

usmapdata::county %>% 
  left_join(en_county_2015, by = c("id" = "countygeoid")) %>% 
  mutate(region = id) -> en_county_2015_map

usmapdata::county %>% 
  left_join(en_county_2016, by = c("id" = "countygeoid")) %>% 
  mutate(region = id) -> en_county_2016_map

en_county_map = bind_rows(
  en_county_2014_map, 
  en_county_2015_map,
  en_county_2016_map
) %>% 
  filter(enroll_per10000 < 3000)

usmapdata::state %>% 
  mutate(region = id) -> state_map

library(ggplot2)

ggplot() +
  geom_map(data = en_county_2014_map, map = en_county_2014_map,
           aes(x = long, y = lat, map_id = region, fill = enroll_per10000),
           colour = alpha("white", 0.1), size=0.2) +
  geom_map(data = state_map, map = state_map,
           aes(x = long, y = lat, map_id = region),
           colour = "white", fill = "NA") +
  coord_map("albers", lat0 = 30, lat1 = 40) +
  scale_fill_gradientn(colors = viridis::viridis(en_county_map$enroll_per10000, option = "B"),
                       limits=c(min(en_county_map$enroll_per10000, na.rm = TRUE),
                                max(en_county_map$enroll_per10000, na.rm = TRUE))) +
  ggtitle("Enrollment per 10,000 Population in 2014") +
  ggthemes::theme_map() +
  theme(legend.position = c(.85, .3),
        legend.title=element_blank())

ggplot() +
  geom_map(data = en_county_2015_map, map = en_county_2015_map,
           aes(x = long, y = lat, map_id = region, fill = enroll_per10000),
           colour = alpha("white", 0.1), size=0.2) +
  geom_map(data = state_map, map = state_map,
           aes(x = long, y = lat, map_id = region),
           colour = "white", fill = "NA") +
  coord_map("albers", lat0 = 30, lat1 = 40) +
  scale_fill_gradientn(colors = viridis::viridis(en_county_map$enroll_per10000, option = "B"),
                       limits=c(min(en_county_map$enroll_per10000, na.rm = TRUE),
                                max(en_county_map$enroll_per10000, na.rm = TRUE))) +
  ggtitle("Enrollment per 10,000 Population in 2015") +
  ggthemes::theme_map() +
  theme(legend.position = c(.85, .3),
        legend.title=element_blank())

ggplot() +
  geom_map(data = en_county_2016_map, map = en_county_2016_map,
           aes(x = long, y = lat, map_id = region, fill = enroll_per10000),
           colour = alpha("white", 0.1), size=0.2) +
  geom_map(data = state_map, map = state_map,
           aes(x = long, y = lat, map_id = region),
           colour = "white", fill = "NA") +
  coord_map("albers", lat0 = 30, lat1 = 40) +
  scale_fill_gradientn(colors = viridis::viridis(en_county_map$enroll_per10000, option = "B"),
                       limits=c(min(en_county_map$enroll_per10000, na.rm = TRUE),
                                max(en_county_map$enroll_per10000, na.rm = TRUE))) +
  ggtitle("Enrollment per 10,000 Population in 2016") +
  ggthemes::theme_map() +
  theme(legend.position = c(.85, .3),
        legend.title=element_blank())

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)

# 2015
qhp::enrollment2015 %>% 
  na.omit() %>% 
  group_by(state) %>% 
  summarise(statename = first(StateName),
            enrollment = sum(PlanSelections)) %>% 
  arrange(enrollment %>% desc) %>% 
  print(n = 37)

Health Plan Data

qhp::qhp2014

Reference

• Summary of the Affordable Care Act from KFF

• 42 CFR Part 435, Subpart C - Options for Coverage as Categorically Needy: § 435.218 — Individuals with MAGI-based income above 133 percent FPL.

• 45 CFR 155.305 - Eligibility standards.

• 45 CFR 155.320 - Verification process related to eligibility for insurance affordability programs. - Verification process for advance payments of the premium tax credit and cost-sharing reductions.

• 26 CFR 1.36B-3 - Computing the premium assistance credit amount.

• 26 CFR 1.36B-1 - Premium tax credit definitions.

• 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

  • PART A - Premium Tax Credits and Cost-Sharing Reductions (§ 18071)
  • PART B - Eligibility Determinations (§§ 18081 to 18084)

• 42 U.S. Code Part A - Establishment of Qualified Health Plans

  • 42 U.S. Code § 18021 - Qualified health plan defined
  • 42 U.S. Code § 18022 - Essential health benefits requirements

• Health Insurance Market Reforms

  • Annual Limits
  • Coverage for Young Adults
  • Grandfathered Plans
  • Market Rating Reforms
    • Geographic Rating Areas
    • Other State Specific Rating Variations
  • Medical Loss Ratio
  • Minimum Essential Coverage
  • Patient's Bill of Rights
  • Prevention
  • Review of Insurance Rates
    • Rate Review Grants
      • Overview of the health insurance premium grant program
  • Student Health Plans
  • Self-Funded, Non-Federal Governmental Plans
  • Compliance

jjchern/qhp documentation built on May 19, 2019, 11:39 a.m.