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entitled 'Private Health Insurance: Estimates of Individuals with Pre-
Existing Conditions Range from 36 Million to 122 Million' which was 
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United States Government Accountability Office: 
GAO: 

Report to Congressional Requesters: 

March 2012: 

Private Health Insurance: 

Estimates of Individuals with Pre-Existing Conditions Range from 36 
Million to 122 Million: 

GAO-12-439: 

GAO Highlights: 

Highlights of GAO-12-439, a report to congressional requesters. 

Why GAO Did This Study: 

Individuals who buy coverage directly from a health insurer are often 
denied coverage due to a pre-existing condition during a process 
called medical underwriting, which assesses an applicant’s health 
status and other risk factors. Beginning January 1, 2014, the Patient 
Protection and Affordable Care Act (PPACA) prohibits health insurers 
in the individual market from denying coverage, increasing premiums, 
or restricting benefits because of a pre-existing condition. GAO was 
asked to examine the effect of this provision on adults who are 19-64 
years old. GAO examined (1) the most common medical conditions that 
would cause an insurance company to restrict or deny insurance 
coverage for adults and the average annual costs associated with these 
conditions, (2) estimates of the number of adults with pre-existing 
conditions, and (3) the geographic and demographic profile of adults 
with pre-existing conditions. 

To address these three issues, GAO (1) identified four recent studies 
that narrowly or more broadly identified five lists of conditions 
likely to result in restricted coverage in the individual insurance 
market and (2) used the 2009 Medical Expenditure Panel Survey to 
generate five separate estimates, referred to as estimates 
1 through 5. There is no commonly accepted list of pre-existing 
conditions because each insurer determines the conditions it will use 
for medical underwriting. We also contacted state insurance department 
officials in all 50 states and the District of Columbia to confirm 
information about state insurance protections that currently limit or 
prohibit medical underwriting. 

What GAO Found: 

Hypertension was the most commonly reported medical condition among 
adults that could result in a health insurer denying coverage, 
requiring higher-than-average premiums, or restricting coverage. GAO’s 
analysis found that about 33.2 million adults age 19-64 years old, or 
about 18 percent, reported hypertension in 2009. Individuals with 
hypertension reported average annual expenditures related to treating 
the condition of $650, but maximum reported expenditures were $61,540. 
Mental health disorders and diabetes were the second and third most 
commonly reported conditions among adults. Cancer was the condition 
with the highest average annual treatment expenditures—about $9,000.
Depending on the list of conditions used to define pre-existing 
conditions in each of the five estimates, GAO found that between 36 
million and 122 million adults reported medical conditions that could 
result in a health insurer restricting coverage. This represents 
between 20 and 66 percent of the adult population, with a midpoint 
estimate of about 32 percent. The differences among the estimates can 
be attributed to the number and type of conditions included in the 
different lists of pre-existing conditions. For example, estimate 1, 
which is the lowest estimate, includes adults reporting that they had 
ever been told they had 1 or more of 8 conditions. Estimate 3, the 
midpoint estimate, includes any individual reporting they had one of 
over 60 conditions. Estimate 5, the highest estimate, includes any 
individual reporting a chronic condition in 2009. 

Figure: Estimates of Adults (Age 19 to 64) with Pre-Existing 
Conditions, 2009: 

[Refer to PDF for image: vertical bar graph] 

Estimate 1: 36 million; 
Estimate 2: 59 million; 
Estimate 3: 60 million; 
Estimate 4: 95 million; 
Estimate 5: 122 million. 

Source: GAO analysis of 2009 Medical Expenditure Panel Survey (MEPS). 

Note: The 95 percent confidence intervals for estimates in this figure 
are within +/-1 percent of the estimates themselves. 

[End of figure] 

The estimated number of adults with pre-existing conditions varies by 
state, but most individuals, 88-89 percent depending on the list of 
pre-existing conditions included, live in states that do not report 
having insurance protections similar to those in PPACA. Compared to 
others, adults with pre-existing conditions spend thousands of dollars 
more annually on health care, but pre-existing conditions are common 
across all family income levels. 

The Department of Health and Human Services reviewed a draft of this 
report and had no substantive or technical comments. 

View [hyperlink, http://www.gao.gov/products/GAO-12-439]. For more 
information, contact John E. Dicken at (202) 512-7114 or 
DickenJ@gao.gov. 

[End of section] 

Contents: 

Letter: 

Background: 

Hypertension, Mental Health Disorders, and Diabetes Were the Most 
Commonly Reported Conditions among Adults, with Average Annual 
Expenditures for Cancer Nearly $9,000: 

Between 36 and 122 Million Adults in 2009 Reported Pre-Existing 
Conditions, Depending on the Conditions Included in the Estimates: 

Pre-Existing Conditions Are Common across States and Demographic 
Groups, but Prevalence Is Higher for Certain Populations: 

Agency Comments: 

Appendix I: Scope and Methodology: 

Appendix II: Number of Adults (Age 19-64) with Pre-Existing Conditions 
in States, Low, Midpoint, and High Estimate, 2009: 

Appendix III: State Protections for Individuals with Pre-Existing 
Conditions, as of September 2011: 

Appendix IV: Comments from the Department of Health and Human Services: 

Appendix V: GAO Contact and Staff Acknowledgments: 

Tables: 

Table 1: Information on Studies Used to Estimate the Number of Adults 
with Pre-existing Conditions: 

Table 2: Estimates of Adults (Age 19-64) Reporting Specified 
Conditions and Average Expenditures Associated with Each Condition for 
Those with Any Expenditures, 2009: 

Table 3: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
across States, Low, Midpoint, or High Estimate, 2009: 

Table 4: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
within Race and Ethnicity Groups for Low, Midpoint, and High 
Estimates, 2009: 

Table 5: Distribution of All Adults (Age 19-64) and Those with Pre-
Existing Conditions across Total Family Income Categories, Low, 
Midpoint, and High Estimates, 2009: 

Table 6: Distribution of All Adults (Age 19-64) and Those with Pre-
Existing Conditions across Categories of Family Income as Percent of 
Poverty Level, Low, Midpoint, and High Estimates, 2009: 

Figures: 

Figure 1: Estimates of Adults (Age 19-64) with Pre-Existing Medical 
Conditions, 2009: 

Figure 2: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
by Gender, for Low, Midpoint, and High Estimates, 2009: 

Figure 3: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
within Age Groups for Low, Midpoint, and High Estimates, 2009: 

Figure 4: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
within Insurance Coverage Groups for Low, Midpoint, and High 
Estimates, 2009: 

Figure 5: Average Annual Health Care Expenditures for Adults (Age 19-
64) and among the Low, Midpoint, and High Estimates of Adults with Pre-
Existing Conditions, 2009: 

Abbreviations: 

AHRQ: Agency for Healthcare Research and Quality: 

FPL: federal poverty level: 

HCERA: Health Care and Education Reconciliation Act of 2010: 

HHS: Department of Health and Human Services: 

HIPAA: Health Insurance Portability and Accountability Act of 1998: 

ICD-9-CM: International Classification of Diseases, Ninth Edition, 
Clinical Modification: 

MEPS: Medical Expenditure Panel Survey: 

PCIP: Pre-Existing Condition Insurance Plan: 

PPACA: Patient Protection and Affordable Care Act: 

[End of section] 

United States Government Accountability Office: 
Washington, DC 20548: 

March 27, 2012: 

Congressional Requesters: 

Americans obtain health insurance coverage through a variety of 
private and public sources, but a majority--67 percent of adults as of 
2010--rely on private insurance, most through employer-sponsored group 
coverage. Another 16 percent of adults in 2010 had coverage through 
public programs, such as Medicare and Medicaid, and about 22 percent 
were uninsured.[Footnote 1],[Footnote 2] While group health plans and 
health insurers offering group coverage may not condition eligibility 
based on an individual's health status, such protections generally do 
not exist for individuals attempting to purchase coverage directly 
through the private individual market, which accounted for about 14.6 
million adults aged 18-64 years old in 2010.[Footnote 3] In addition, 
individuals are at risk of losing the protections of employer-
sponsored group coverage if they become unemployed or retire before 
eligibility for Medicare begins at age 65 and they have a break in 
health coverage. With certain exceptions, such individuals and any 
other individual attempting to purchase coverage in the private 
individual market can have coverage denied, offered at a higher-than-
average premium, or offered with a rider that excludes coverage of a 
pre-existing condition. A pre-existing condition is a health condition 
that exists before someone applies for or enrolls in a new health 
insurance policy. Each insurer develops its own list of pre-existing 
conditions that influences to whom and under what terms it offers 
coverage. Pre-existing conditions can be conditions that an individual 
is currently being treated for (such as heart disease) or past health 
problems (such as cancer in remission). 

With the enactment of the Patient Protection and Affordable Care Act 
(PPACA) in March 2010, enrollment in private health insurance could 
expand significantly, particularly for individuals and families that 
do not have access to group coverage through their employer.[Footnote 
4] PPACA provisions that could expand such coverage include income-
based subsidies to make coverage more affordable for certain 
individuals, penalties for individuals not obtaining coverage, and new 
insurance requirements to increase access to coverage, particularly 
for individuals with pre-existing conditions. Specifically, beginning 
January 1, 2014, health insurers in the individual market will be 
prohibited from denying coverage, increasing premiums, or restricting 
benefits because of a pre-existing condition.[Footnote 5] 

To better understand the potential impact of PPACA, you asked us to 
examine the demographics of adults with pre-existing conditions. In 
this report, we examine: (1) the most common medical conditions that 
would cause an insurance company to restrict or deny insurance 
coverage for adults and the average annual costs associated with these 
conditions, (2) estimates of the number of adults with pre-existing 
conditions, and (3) the geographic and demographic profile of adults 
with pre-existing conditions. We define adults as age 19-64 years old 
in this report because a PPACA provision prohibiting insurers from 
denying coverage or restricting benefits because of a pre-existing 
condition for individuals under the age of 19 became effective 
September 23, 2010, and Medicare covers individuals 65 and older. 

To address these three issues, we identified several recent studies 
that listed conditions considered likely to result in restricted 
coverage in the individual insurance market and then used the 2009 
Medical Expenditure Panel Survey (MEPS) to analyze the conditions 
listed in each study in order to develop estimates of (1) the number 
of adults reporting and costs associated with treating specific pre-
existing conditions, (2) size of the total affected adult population 
with pre-existing conditions, and (3) their geographic and demographic 
characteristics. MEPS is a commonly used, nationally representative, 
and publicly available panel survey of Americans; 2009 was the most 
recent year data were available.[Footnote 6] It includes information 
on medical conditions and the costs associated with those medical 
conditions, both self-reported and from a review of medical events. 
[Footnote 7] 

We identified four recent studies that used different lists of pre-
existing conditions to develop five estimates of the number of 
affected individuals. Each of these studies identified a set of 
conditions that would cause an insurance company to deny health 
insurance coverage, offer coverage at a higher-than-average premium, 
or offer coverage with restrictions. The sets of conditions run the 
continuum from narrow (having one of eight conditions that would 
qualify an individual for participation in a high-risk pool) to broad 
(reporting any condition considered chronic).[Footnote 8],[Footnote 9] 
Each insurer separately determines which conditions will result in 
restricted coverage in the individual insurance market, so lists of 
conditions may not be consistent from insurer to insurer. 

As shown in table 1, there were important differences across the four 
studies, including the conditions and time frames considered, the 
populations that the estimates were developed for (e.g., children and 
adults under age 65, or solely the uninsured), the data sources used 
to develop the estimates, and whether the study included only 
individuals treated for a condition during a given year or individuals 
ever diagnosed with a condition. We used the set of conditions 
developed for each study to generate estimates of adults with pre-
existing conditions using a consistent data source (2009 MEPS) and 
population (19-64 year olds not enrolled in Medicare). We assigned 
each condition list an estimate number from 1 to 5 in order to more 
readily identify which set of study conditions each estimate is based 
on. For example, the condition list in the study authored by Harold 
Pollack is referred to as estimate 1. 

Table 1: Information on Studies Used to Estimate the Number of Adults 
with Pre-existing Conditions: 

Estimate 1; 
Study author: Harold Pollack[A]; 
Population studied: Uninsured of any age; 
Estimated number of population studied with pre-existing conditions: 
4.1 million; 
Included conditions respondent reported as occurring during the survey 
year: No; 
Included conditions respondent reported ever being diagnosed with: Yes; 
Data source: National Health and Nutrition Examination Survey[B]; 
Data year: 2005/2006. 

Estimate 2[C]; 
Study author: Department of Health and Human Services[C]; 
Population studied: Age 0-64; 
Estimated number of population studied with pre-existing conditions: 
50 million; 
Included conditions respondent reported as occurring during the survey 
year: Yes; 
Included conditions respondent reported ever being diagnosed with: Yes; 
Data source: Medical Expenditure Panel Survey (MEPS)[D]; 
Data year: 2008. 

Estimate 3; 
Study author: Families USA[E]; 
Population studied: Age 0-64; 
Estimated number of population studied with pre-existing conditions: 
57.2 million; 
Included conditions respondent reported as occurring during the survey 
year: Yes; 
Included conditions respondent reported ever being diagnosed with: No; 
Data source: MEPS; 
Data year: 2007. 

Estimate 4[C]; 
Study author: Department of Health and Human Services[C]; 
Population studied: Age 0-64; 
Estimated number of population studied with pre-existing conditions: 
129 million; 
Included conditions respondent reported as occurring during the survey 
year: Yes; 
Included conditions respondent reported ever being diagnosed with: Yes; 
Data source: MEPS; 
Data year: 2008. 

Estimate 5; 
Study author: GAO[F]; 
Population studied: Uninsured of any age in 34 states with high-risk 
health insurance pools; 
Estimated number of population studied with pre-existing conditions: 
4.0 million; 
Included conditions respondent reported as occurring during the survey 
year: Yes; 
Included conditions respondent reported ever being diagnosed with: No; 
Data source: MEPS; 
Data year: 2006. 

Source: GAO analysis. 

Note: High-risk pools are designed for individuals who cannot obtain 
insurance in the individual market because of a pre-existing 
condition. Therefore, estimates of individuals potentially eligible 
for high-risk pools should be roughly equivalent to those of 
individuals with pre-existing conditions. 

[A] H. Pollack, "High-Risk Pools for the Sick and Uninsured under 
Health Reform: Too Little and Thus Too Late," Journal of General 
Internal Medicine, published online Sept. 2, 2010. 

[B] The National Health and Nutrition Examination Survey is sponsored 
by the Centers for Disease Control and Prevention, an agency within 
the Department of Health and Human Services (HHS). 

[C] Department of Health and Human Services, "At Risk: Pre-Existing 
Conditions Could Affect 1 in 2 Americans: 129 Million People Could Be 
Denied Affordable Coverage without Health Reform" (Washington, D.C.: 
January 2011). This study produced two separate estimates of 
individuals with pre-existing conditions based on two different 
condition lists. 

[D] The Medical Expenditure Panel Survey is sponsored by the Agency 
for Healthcare Research and Quality, an agency within HHS. 

[E] Families USA, "Health Reform: Help for Americans with Pre-Existing 
Conditions" (Washington, D.C.: May 2010). 

[F] GAO, Health Insurance: Enrollment, Benefits, Funding, and Other 
Characteristics of State High-Risk Health Insurance Pools, [hyperlink, 
http://www.gao.gov/products/GAO-09-730R] (Washington, D.C.: July 22, 
2009). 

[End of table] 

To describe the geographic and demographic profile of adults with pre-
existing conditions, we further analyzed data on the low, midpoint, 
and high estimates of adults with pre-existing conditions. To enable 
us to develop estimates of individuals with pre-existing conditions 
living in states reporting insurance protections similar to those that 
will be offered under PPACA in 2014, we updated information on state 
insurance protections previously reported by the Kaiser Family 
Foundation by contacting state insurance department officials in all 
50 states and the District of Columbia. Appendix I contains a full 
description of our scope and methodology. 

To assess the reliability of the MEPS data, we reviewed the relevant 
documentation, compared our estimates to other published results, and 
interviewed an official at the Agency for Healthcare Research and 
Quality (AHRQ), the federal agency responsible for the MEPS. We 
determined the data are sufficiently reliable for the purposes of our 
report. 

We conducted this performance audit from July 2011 to March 2012 in 
accordance with generally accepted government auditing standards. 
Those standards require that we plan and perform the audit to obtain 
sufficient, appropriate evidence to provide a reasonable basis for our 
findings and conclusions based on our audit objectives. We believe 
that the evidence obtained provides a reasonable basis for our 
findings and conclusions based on our audit objectives. 

Background: 

Individuals who buy coverage directly from a health insurer are often 
denied coverage due to a pre-existing condition. We previously 
reported that in the first quarter of 2010, 19 percent of applicants 
in the individual market were denied enrollment and a quarter of 
insurers had denial rates of 40 percent or higher.[Footnote 10] 
Similarly, a study by America's Health Insurance Plans reported that 
in 2008, 15 percent of individual insurance applications for adults 
age 18 through 64 that went through medical underwriting were denied 
coverage.[Footnote 11] Medical underwriting is the process conducted 
by insurers to assess an applicant's health status and other risk 
factors to determine whether and on what terms to offer coverage to 
applicants. 

The Health Insurance Portability and Accountability Act of 1996 
(HIPAA) established consumer protections on access, portability, and 
renewability of health insurance coverage.[Footnote 12] With respect 
to individuals leaving group coverage and applying for coverage in the 
individual market, HIPAA prohibited health insurers from denying 
coverage to or imposing any pre-existing condition exclusion on 
individuals who: 

* have had at least 18 months of prior creditable coverage with no 
break of more than 63 days; 

* have exhausted any available continuation coverage; 

* are uninsured and are not eligible for other group coverage, 
Medicare, or Medicaid; and: 

* did not lose group coverage because of the nonpayment of premiums or 
fraud.[Footnote 13] 

Unless the practice is prohibited by state law, individuals who do not 
meet these HIPAA eligibility criteria can be denied insurance coverage 
in the individual market due to a pre-existing condition. 
Additionally, insurers in the individual market can accept both HIPAA-
eligible and noneligible applicants but offer coverage at a premium 
that is higher than the standard rate based on the presence of a pre-
existing condition. A medical condition reported by an individual is 
considered a pre-existing condition by the insurer if it exists at the 
time the individual applies for coverage or if an individual was 
treated for or diagnosed with the condition in the past. 

States, which have the responsibility for regulating private 
insurance, have in some instances required protections for individuals 
with pre-existing conditions. State protections include the following: 

* Guaranteed issue requirements prohibit the denial of coverage to 
individuals based on pre-existing conditions. In some states, all 
products must be guaranteed issue, while in other states guaranteed 
issue requirements are only applicable to some products or to some 
individuals, for instance individuals with 12 months of continuous 
coverage, or to some standard products, such as a basic health plan. 

* An insurer of last resort law requires one insurer in the state to 
issue products to individuals regardless of pre-existing conditions. 

* Rating restrictions prohibit insurers in the individual market from 
adjusting an individual's health insurance premiums based on an 
individual's health status. 

- Two types of rating restrictions are pure community rating and 
adjusted community rating. Under the former, insurers may not adjust 
premiums due to health status, age, or gender; under the latter, they 
may adjust premiums according to characteristics such as gender or 
age, depending on the state. 

- Rate bands limit the extent to which premiums can vary based on an 
individual's health status, for example, to a certain percentage of 
the average premium. 

* Limitations on the number of years a health insurer can look back at 
an individual's health history when making its determination as to 
whether and at what price to offer coverage. 

* High-risk health insurance pools that provide coverage to 
individuals whose health status, including the presence of pre-
existing conditions, limits their access to coverage in the private 
individual health insurance market.[Footnote 14] High-risk insurance 
pools--typically publicly subsidized, state-run nonprofit 
associations--often contract with a private health insurer to 
administer the pool and offer a range of health plan options. 

Until 2014, PPACA requires that individuals who have pre-existing 
conditions and have been uninsured for 6 months be offered the 
opportunity to enroll in a temporary national high-risk pool program, 
known as the Pre-Existing Condition Insurance Plan (PCIP).[Footnote 
15] PCIP will close at the end of 2013, which is when PPACA will begin 
to require insurers to accept every individual who applies for 
coverage, regardless of factors related to health status. States were 
given the option of running their own PCIP with federal funding, or 
allowing the Department of Health and Human Services (HHS) to 
administer the PCIP in their state. Twenty-seven states elected to 
administer a PCIP for their residents, while 23 states and the 
District of Columbia opted to allow HHS to administer their PCIPs. 
Despite similar goals, PCIP and state high-risk pools are separate 
entities and differ from one another in certain ways.[Footnote 16] 

Beginning in 2014, PPACA prohibits insurers in the private individual 
market from denying coverage, charging higher-than-average premiums, 
or restricting coverage to individuals based on the individual's 
health status. PPACA will, therefore, make guaranteed issue and 
adjusted community rating national requirements. 

Hypertension, Mental Health Disorders, and Diabetes Were the Most 
Commonly Reported Conditions among Adults, with Average Annual 
Expenditures for Cancer Nearly $9,000: 

Hypertension was the most commonly reported medical condition among 
adults during 2009 that could result in a health insurer denying 
coverage, requiring higher-than-average premiums, or restricting 
coverage with an exclusionary rider.[Footnote 17] Our analysis of MEPS 
data found that about 33.2 million adults age 19-64 years old, or 
about 18 percent, reported hypertension in 2009. Individuals with 
hypertension reported average annual expenditures of $650 related to 
treating the condition, though the maximum expenditures reported were 
$61,540.[Footnote 18] (See table 2.) Mental health disorders and 
diabetes were the second and third most commonly reported medical 
conditions by adults age 19-64 years old.[Footnote 19] About 19.0 
million adults, or 10.3 percent, reported mental health disorders, and 
about 11.9 million adults, or about 6.4 percent, reported diabetes. 
Average annual expenditures for these conditions were $1,757 for 
mental health disorders and $1,782 for diabetes. Cancer was the 
condition with the highest average annual treatment expenditures, 
nearly $9,000 per adult. Individuals may have multiple medical 
conditions, which would increase their total expenditures. 

Table 2: Estimates of Adults (Age 19-64) Reporting Specified 
Conditions and Average Expenditures Associated with Each Condition for 
Those with Any Expenditures, 2009: 

Total population: 
Estimate (number): 184,648,891; 
Range based on 95 percent confidence interval: 176,372,785 - 
192,924,998; 
Percentage estimate: 100.0%; 
Percentage range based on 95 percent confidence interval: [Empty]; 
Average annual expenditures related to condition (dollars): [Empty]; 
Maximum expenditures related to condition (dollars): [Empty]. 

Hypertension: 
Estimate (number): 33,207,272; 
Range based on 95 percent confidence interval: 31,145,377 - 35,269,167; 
Percentage estimate: 18.0; 
Percentage range based on 95 percent confidence interval: 17.2 - 18.8; 
Average annual expenditures related to condition (dollars): $650; 
Maximum expenditures related to condition (dollars): $61,540. 

Mental health disorders[A]: 
Estimate (number): 18,995,244; 
Range based on 95 percent confidence interval: 17,516,845 - 20,473,642; 
Percentage estimate: 10.3; 
Percentage range based on 95 percent confidence interval: 9.7 - 10.9; 
Average annual expenditures related to condition (dollars): $1,757; 
Maximum expenditures related to condition (dollars): $98,058. 

Diabetes: 
Estimate (number): 11,899,811; 
Range based on 95 percent confidence interval: 11,007,195 - 12,792,427; 
Percentage estimate: 6.4; 
Percentage range based on 95 percent confidence interval: 6.0 - 6.9; 
Average annual expenditures related to condition (dollars): $1,782; 
Maximum expenditures related to condition (dollars): $66,007. 

Asthma: 
Estimate (number): 10,047,807; 
Range based on 95 percent confidence interval: 9,177,963 - 10,917,652; 
Percentage estimate: 5.4; 
Percentage range based on 95 percent confidence interval: 5.0 - 5.9; 
Average annual expenditures related to condition (dollars): $1,234; 
Maximum expenditures related to condition (dollars): $63,003. 

Arthritis[B]: 
Estimate (number): 9,518,122; 
Range based on 95 percent confidence interval: 8,513,144 - 10,523,099; 
Percentage estimate: 5.2; 
Percentage range based on 95 percent confidence interval: 4.7 - 5.7; 
Average annual expenditures related to condition (dollars): $1,875; 
Maximum expenditures related to condition (dollars): $78,617. 

Chronic obstructive pulmonary disease: 
Estimate (number): 9,091,824; 
Range based on 95 percent confidence interval: 8,305,480 - 9,878,167; 
Percentage estimate: 4.9; 
Percentage range based on 95 percent confidence interval: 4.5 - 5.3; 
Average annual expenditures related to condition (dollars): $1,423[C]; 
Maximum expenditures related to condition (dollars): $172,583. 

Cancer (excluding skin): 
Estimate (number): 4,150,263; 
Range based on 95 percent confidence interval: 3,588,601 - 4,711,924; 
Percentage estimate: 2.3; 
Percentage range based on 95 percent confidence interval: 2.0 - 2.5; 
Average annual expenditures related to condition (dollars): $8,955; 
Maximum expenditures related to condition (dollars): $294,473. 

Rheumatoid arthritis: 
Estimate (number): 3,041,838; 
Range based on 95 percent confidence interval: 2,621,846 - 3,461,831; 
Percentage estimate: 1.7; 
Percentage range based on 95 percent confidence interval: 1.4 - 1.9; 
Average annual expenditures related to condition (dollars): $2,691; 
Maximum expenditures related to condition (dollars): $39,228. 

Heart attack (myocardial infarction): 
Estimate (number): 2,056,226; 
Range based on 95 percent confidence interval: 1,678,883 - 2,433,568; 
Percentage estimate: 1.1; 
Percentage range based on 95 percent confidence interval: 0.9 - 1.3; 
Average annual expenditures related to condition (dollars): $4,013; 
Maximum expenditures related to condition (dollars): $53,666. 

Stroke: 
Estimate (number): 1,291,310; 
Range based on 95 percent confidence interval: 1,008,677 - 1,573,943; 
Percentage estimate: 0.7; 
Percentage range based on 95 percent confidence interval: 0.6 - 0.9; 
Average annual expenditures related to condition (dollars): $6,224[C]; 
Maximum expenditures related to condition (dollars): $109,324. 

Source: GAO analysis of Medical Expenditure Panel Survey (MEPS). 

Notes: Adults are defined as individuals age 19-64. Adults with 
Medicare coverage were excluded. Estimates are of individuals who 
reported that these conditions were the cause of a medical event, the 
reason for a disability day, or bothered a respondent during 2009. 
Estimates were generated only for conditions with a cell size of more 
than 100. Expenditure information is reported only for those with 
expenditures greater than $0 and includes expenditures from all 
sources for hospital inpatient stays, emergency room visits, 
outpatient department visits, office-based medical provider visits, 
and prescribed medicines. Expenditure information does not include 
amounts for dental expenses and other medical expenses, such as 
durable and nondurable supplies, medical equipment, eyeglasses, and 
ambulance services because these items could not be linked to specific 
conditions. We also did not include expenditures for home health care. 

[A] Mental health disorders are defined as mood and psychotic 
disorders, which include conditions such as depression, bipolar 
disorder, and schizophrenia. 

[B] Arthritis includes osteoarthritis, rheumatoid arthritis, and 
infective arthritis. 

[C] The relative standard error is greater than or equal to 30 
percent. Relative standard error is the proportion of the standard 
error divided by the estimate itself. 

[End of table] 

The number of adults reporting specific conditions in any given year 
does not include all adults who may have had that condition in the 
past. For instance, there are many individuals who may have had cancer 
in the past who would not report cancer in a given year. Therefore, 
the number of adults who have ever had cancer is higher than the 
approximately 4.2 million reporting cancer in 2009. About 11 million 
adults, or 6.0 percent, reported ever being told by a health 
professional that they had cancer. Health insurers commonly look at 
both current medical conditions and conditions an individual was 
treated for or diagnosed with in the past when making a decision 
whether and at what price to offer health insurance coverage. 

Between 36 and 122 Million Adults in 2009 Reported Pre-Existing 
Conditions, Depending on the Conditions Included in the Estimates: 

Depending on the list of conditions used to define pre-existing 
conditions in each of five estimates, we found that between 36 million 
and 122 million adults (age 19-64) reported having medical conditions 
in 2009 that could result in a health insurer denying coverage, 
requiring higher-than-average premiums, or restricting coverage with 
an exclusionary rider (see figure 1). This represents from 20 to 66 
percent of the adult population, with a midpoint estimate of 32 
percent. 

Figure 1: Estimates of Adults (Age 19-64) with Pre-Existing Medical 
Conditions, 2009: 

[Refer to PDF for image: vertical bar graph] 

Estimate 1: 36 million; 
Estimate 2: 59 million; 
Estimate 3: 60 million; 
Estimate 4: 95 million; 
Estimate 5: 122 million. 

Source: GAO analysis of 2009 Medical Expenditure Panel Survey (MEPS). 

Notes: Information on the conditions included in each estimate is 
detailed in appendix I. Estimate 1 is based on the methodology in 
Harold Pollack's article "High-Risk Pools for the Sick and Uninsured 
Under Health Reform: Too Little and Thus Too Late," in the Journal of 
General Internal Medicine (published online on September 2, 2010). 
Estimates 2 and 4 are based on the methodologies in the Department of 
Health and Human Services report "At Risk: Pre-Existing Conditions 
Could Affect 1 in 2 Americans: 129 Million People Could Be Denied 
Affordable Coverage without Health Reform" (Washington, D.C.: January 
2011). Estimate 3 is based on the methodology in the Families USA 
report "Health Reform: Help for Americans with Pre-Existing 
Conditions," published in May 2010. Estimate 5 is based on the 
methodology used in the GAO report Health Insurance: Enrollment, 
Benefits, Funding, and Other Characteristics of State High-Risk Health 
Insurance Pools, GAO-09-730R (Washington, D.C.: July 22, 2009). The 95 
percent confidence intervals for estimates in this figure are within 
+/-1 percent of the estimates themselves. 

[End of figure] 

The differences among the estimates can be attributed to the number 
and type of conditions included in the different lists of pre-existing 
conditions. For example, estimate 1, which is the lowest estimate, 
includes adults reporting that they had ever been told they had 1 or 
more of 8 conditions. All 8 conditions were designated as priority 
conditions by AHRQ because of the conditions' prevalence, expense, or 
relevance to policy.[Footnote 20] Estimate 3, the midpoint estimate, 
includes any individual who had one of over 60 conditions commonly 
used to determine eligibility in state high-risk pools. Estimate 5, 
the highest estimate, includes any individual reporting they had 
experienced any condition considered chronic during 2009.[Footnote 21] 
The list of chronic conditions used for this estimate included 417 
separate conditions. See appendix I for information on the list of 
conditions included in each estimate. 

Pre-Existing Conditions Are Common across States and Demographic 
Groups, but Prevalence Is Higher for Certain Populations: 

The number of adults with pre-existing conditions varies by state, but 
most individuals with pre-existing conditions live in states that 
report not having insurance protections similar to PPACA. Certain 
groups had higher rates of pre-existing conditions than others, 
including women, whites, and those with public insurance. Compared to 
others, adults with pre-existing conditions spend thousands of dollars 
more annually on health care, but pre-existing conditions are common 
across all family income levels. 

The Number of Adults with Pre-Existing Conditions Varies by State, but 
Most Adults with Pre-Existing Conditions Live in States That Report 
Not Having Insurance Protections Similar to PPACA: 

We found that the percentage of adults with pre-existing conditions 
varies among states. We analyzed data from the 35 states where MEPS 
generated a reliable estimate.[Footnote 22] Our low estimate (estimate 
1) ranged from 17 percent in New York and New Jersey to 25 percent in 
Ohio. For the midpoint estimate (estimate 3), the range was from 27 
percent in Florida to 47 percent in Kentucky. For the high estimate 
(estimate 5), the range was from 58 percent in Georgia to 75 percent 
in Kentucky and Massachusetts. (See table 3). For population estimates 
for the 35 states, see appendix II. 

Table 3: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
across States, Low, Midpoint, or High Estimate, 2009: 

State: All adults; 
Low estimate 1: 20; 
Midpoint estimate 3: 32; 
High estimate 5: 66. 

State: Alabama; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 69. 

State: Alaska; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Arizona; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 33; 
High estimate 5: 65. 

State: Arkansas; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 66. 

State: California; 
Low estimate 1: 18; 
Midpoint estimate 3: 29; 
High estimate 5: 60. 

State: Colorado; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 68. 

State: Connecticut; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 71. 

State: Delaware; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: District of Columbia; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 
[Empty]; 
High estimate 5: [Empty]. 

State: Florida; 
Low estimate 1: 18; 
Midpoint estimate 3: 27; 
High estimate 5: 60. 

State: Georgia; 
Low estimate 1: 21; 
Midpoint estimate 3: 32; 
High estimate 5: 58. 

State: Hawaii; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 66. 

State: Idaho; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Illinois; 
Low estimate 1: 18; 
Midpoint estimate 3: 33; 
High estimate 5: 68. 

State: Indiana; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 36; 
High estimate 5: 70. 

State: Iowa; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 74. 

State: Kansas; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 67. 

State: Kentucky; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 47; 
High estimate 5: 75. 

State: Louisiana; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 72. 

State: Maine; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Maryland; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 63. 

State: Massachusetts; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 35; 
High estimate 5: 75. 

State: Michigan; 
Low estimate 1: 21; 
Midpoint estimate 3: 41; 
High estimate 5: 74. 

State: Minnesota; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 64. 

State: Mississippi; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 74. 

State: Missouri; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 36; 
High estimate 5: 70. 

State: Montana; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Nebraska; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Nevada; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: New Hampshire; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: New Jersey; 
Low estimate 1: 17; 
Midpoint estimate 3: 30; 
High estimate 5: 71. 

State: New Mexico; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: New York; 
Low estimate 1: 17; 
Midpoint estimate 3: 31; 
High estimate 5: 64. 

State: North Carolina; 
Low estimate 1: 21; 
Midpoint estimate 3: 32; 
High estimate 5: 63. 

State: North Dakota; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Ohio; 
Low estimate 1: 25; 
Midpoint estimate 3: 35; 
High estimate 5: 69. 

State: Oklahoma; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 61. 

State: Oregon; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 66. 

State: Pennsylvania; 
Low estimate 1: 20; 
Midpoint estimate 3: 34; 
High estimate 5: 69. 

State: Rhode Island; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: South Carolina; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 60. 

State: South Dakota; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Tennessee; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 30; 
High estimate 5: 62. 

State: Texas; 
Low estimate 1: 18; 
Midpoint estimate 3: 30; 
High estimate 5: 63. 

State: Utah; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 68. 

State: Vermont; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Virginia; 
Low estimate 1: 19; 
Midpoint estimate 3: 30; 
High estimate 5: 63. 

State: Washington; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: 30; 
High estimate 5: 71. 

State: West Virginia; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

State: Wisconsin; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: 64. 

State: Wyoming; 
Low estimate 1: [Empty]; 
Midpoint estimate 3: [Empty]; 
High estimate 5: [Empty]. 

Source: GAO analysis of 2009 Medical Expenditure Panel Survey (MEPS). 

Notes: Estimate 1 is based on the methodology in Harold Pollack's 
article "High-Risk Pools for the Sick and Uninsured Under Health 
Reform: Too Little and Thus Too Late," in the Journal of General 
Internal Medicine (published online on September 2, 2010). Estimate 3 
is based on the methodology in the Families USA report "Health Reform: 
Help for Americans with Pre-Existing Conditions," published in May 
2010. Estimate 5 is based on the methodology used in the GAO report 
Health Insurance: Enrollment, Benefits, Funding, and Other 
Characteristics of State High-Risk Health Insurance Pools, GAO-09-730R 
(Washington, D.C.: July 22, 2009). The 95 percent confidence intervals 
for estimates in this table are within +/-3 percent of the estimates 
themselves. Due to insufficient sample sizes, MEPS data could not be 
used to provide any estimates for 15 states and the District of 
Columbia. Dashes in the table indicate that the sample size was not 
sufficient to produce an estimate. 

[End of table] 

We estimate that 88-89 percent of adults with pre-existing conditions 
live in states without insurance protections similar to the PPACA 
provisions, which will become effective in 2014. We classified states 
as having protections similar to PPACA if the state reported having 
(1) a guaranteed issue requirement for some or all products in the 
individual insurance market and (2) pure or adjusted community 
rating.[Footnote 23] The only five states reporting such protections 
are Maine, Massachusetts, New Jersey, New York, and Vermont. 

While the majority of states do not offer protections similar to what 
will be required nationally in 2014, many states do offer some 
protection to individuals with pre-existing conditions. Insurance 
department officials in 12 states reported having a guaranteed issue 
requirement for some or all products in the individual insurance 
market. Eight states reported either pure or adjusted community rating 
requirements. Appendix III shows the various state protections for 
individuals with pre-existing conditions in the 50 states and the 
District of Columbia. 

Pre-Existing Conditions Are More Common Among Women, Whites, and Those 
with Public Insurance: 

Regardless of the list of conditions used to generate the estimates, 
we found that more women reported a pre-existing condition than men. 
Based on the different definitions of pre-existing conditions we used, 
from 21 to 72 percent of women reported a pre-existing condition in 
2009.[Footnote 24] From 18 to 59 percent of men reported a pre-
existing condition. The midpoint estimate for women and men was 37 
percent and 28 percent, respectively. (See figure 2.) 

Figure 2: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
by Gender, for Low, Midpoint, and High Estimates, 2009: 

[Refer to PDF for image: vertical bar graph] 

Female: 
Low (estimate 1): 21%; 
Midpoint (estimate 3): 37%; 
High (estimate 5): 72%. 

Male: 
Low (estimate 1): 18%; 
Midpoint (estimate 3): 28%; 
High (estimate 5): 59%. 

Source: GAO analysis of 2009 Medical Expenditure Panel Survey (MEPS). 

Note: Estimate 1 is based on the methodology in Harold Pollack's 
article "High-Risk Pools for the Sick and Uninsured Under Health 
Reform: Too Little and Thus Too Late," in the Journal of General 
Internal Medicine (published online on September 2, 2010). Estimate 3 
is based on the methodology in the Families USA report "Health Reform: 
Help for Americans with Pre-Existing Conditions," published in May 
2010. Estimate 5 is based on the methodology used in the GAO report 
Health Insurance: Enrollment, Benefits, Funding, and Other 
Characteristics of State High-Risk Health Insurance Pools, GAO-09-730R 
(Washington, D.C.: July 22, 2009). Information on the conditions 
included in each estimate is detailed in appendix I. The 95 percent 
confidence intervals for estimates in this figure are within +/-1 
percent of the estimates themselves. 

[End of figure] 

We found that the prevalence of pre-existing conditions among adults 
increases with age (see figure 3). In the 55-to 64-year-old age group, 
from 43 (low estimate) to 84 percent (high estimate) of adults 
reported a pre-existing condition that would cause an insurance 
company to deny coverage, offer coverage at a higher-than-average 
rate, or restrict coverage. In contrast, from 6 percent (low estimate) 
to 45 percent (high estimate) of 19-24 year olds (the youngest age 
group) reported a pre-existing condition. The midpoint estimate for 55-
64 year olds was 48 percent, as compared to a midpoint estimate of 19 
percent for 19-24 year olds. 

Figure 3: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
within Age Groups for Low, Midpoint, and High Estimates, 2009: 

[Refer to PDF for image: vertical bar graph] 

Age 19-24: 
Low (estimate 1): 6%; 
Midpoint (estimate 3): 19%; 
High (estimate 5): 45%. 

Age 25-34: 
Low (estimate 1): 7%; 
Midpoint (estimate 3): 24%; 
High (estimate 5): 55%. 

Age 35-44: 
Low (estimate 1): 15%; 
Midpoint (estimate 3): 31%; 
High (estimate 5): 65%. 

Age 45-54: 
Low (estimate 1): 24%; 
Midpoint (estimate 3): 37%; 
High (estimate 5): 74%. 

Age 55-64: 
Low (estimate 1): 43%; 
Midpoint (estimate 3): 48%; 
High (estimate 5): 84%. 

Source: GAO analysis of 2009 Medical Expenditure Panel Survey (MEPS). 

Note: Estimate 1 is based on the methodology in Harold Pollack's 
article "High-Risk Pools for the Sick and Uninsured Under Health 
Reform: Too Little and Thus Too Late," in the Journal of General 
Internal Medicine (published online on September 2, 2010). Estimate 3 
is based on the methodology in the Families USA report "Health Reform: 
Help for Americans with Pre-Existing Conditions," published in May 
2010. Estimate 5 is based on the methodology used in the GAO report 
Health Insurance: Enrollment, Benefits, Funding, and Other 
Characteristics of State High-Risk Health Insurance Pools, GAO-09-730R 
(Washington, D.C.: July 22, 2009). Information on the conditions 
included in each estimate is detailed in appendix I. The 95 percent 
confidence intervals for estimates in this figure are within +/-3 
percent of the estimates themselves. 

[End of figure] 

Asian and Pacific Islanders and Hispanics have the lowest reported 
rates of pre-existing conditions in each estimate, significantly lower 
than the rate seen across all adults. For example, for the midpoint 
estimate, 15 percent of Asian and Pacific Islanders reported a pre-
existing condition, compared to 32 percent of all adults. In two of 
three estimates (the low and high estimates), white adults are 
slightly more likely to report a pre-existing condition than the adult 
population as a whole. This difference was statistically significant, 
but the difference was not statistically significant for the midpoint 
estimate. (See table 4.) 

Table 4: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
within Race and Ethnicity Groups for Low, Midpoint, and High 
Estimates, 2009: 

All adults: 
Low estimate 1: 20%; 
Midpoint estimate 3: 32%; 
High estimate 5: 66%. 

Asian/Pacific Islander, non-Hispanic: 
Low estimate 1: 11%; 
Midpoint estimate 3: 15%; 
High estimate 5: 52%. 

Hispanic: 
Low estimate 1: 13%; 
Midpoint estimate 3: 26%; 
High estimate 5: 51%. 

Black, non-Hispanic: 
Low estimate 1: 19%; 
Midpoint estimate 3: 34%; 
High estimate 5: 59%. 

White, non-Hispanic: 
Low estimate 1: 22%; 
Midpoint estimate 3: 35%; 
High estimate 5: 72%. 

Other, non-Hispanic[A]: 
Low estimate 1: [B]; 
Midpoint estimate 3: 42%; 
High estimate 5: 67%. 

Source: GAO analysis of Medical Expenditure Panel Survey (MEPS). 

Notes: Estimate 1 is based on the methodology in Harold Pollack's 
article "High-Risk Pools for the Sick and Uninsured Under Health 
Reform: Too Little and Thus Too Late," in the Journal of General 
Internal Medicine (published online on September 2, 2010). Estimate 3 
is based on the methodology in the Families USA report "Health Reform: 
Help for Americans with Pre-Existing Conditions," published in May 
2010. Estimate 5 is based on the methodology used in the GAO report 
Health Insurance: Enrollment, Benefits, Funding, and Other 
Characteristics of State High-Risk Health Insurance Pools, GAO-09-730R 
(Washington, D.C.: July 22, 2009). Information on the conditions 
included in each estimate is detailed in appendix I. The 95 percent 
confidence intervals for estimates in this table are within +/-6 
percent of the estimates themselves. 

[A] Other, non-Hispanic includes American Indians, Alaskan Natives, 
and individuals identifying with multiple, non-Hispanic races. 

[B] Sample size was insufficient to generate a reliable estimate. 

[End of table] 

Compared to individuals with other types of insurance coverage, 
uninsured adults and those with private individual insurance have the 
lowest reported rates of pre-existing conditions (see figure 4). The 
lower reporting of pre-existing conditions among the uninsured, in 
part, may reflect the fact that they are less likely than the insured 
to receive timely preventive care and some common health problems such 
as hypertension and diabetes often go undetected without routine 
checkups. In addition, the uninsured are also less likely to have 
regular preventive care, including cancer screenings. The lower 
reporting of pre-existing conditions among those with private 
individual insurance may be a reflection of the fact that insurers try 
to limit the number of people with pre-existing conditions that they 
accept into their plan.[Footnote 25] The greatest protections for 
individuals with pre-existing conditions exist in employer-sponsored 
group insurance and public insurance programs, which are the groups 
that reported the highest rates of pre-existing conditions.[Footnote 
26] As noted previously, group insurance plans are not allowed to deny 
insurance coverage or increase an individual's premiums based on 
health status. Such protections may encourage individuals in employer 
group coverage or government insurance to maintain their coverage 
should they be diagnosed with certain medical conditions. We recently 
reported that research has consistently found that after a health 
diagnosis, workers with employer-sponsored coverage were less likely 
to leave a job or reduce their hours compared to workers who did not 
rely on their employer for health coverage.[Footnote 27] 

Figure 4: Percent of Adults (Age 19-64) with Pre-Existing Conditions 
within Insurance Coverage Groups for Low, Midpoint, and High 
Estimates, 2009: 

[Refer to PDF for image: vertical bar graph] 

All adults: 
Low estimate 1: 20%; 
Midpoint estimate 3: 32%; 
High estimate 5: 66%. 

Uninsured: 
Low estimate 1: 14%; 
Midpoint estimate 3: 25%; 
High estimate 5: 48%. 

Private, employer sponsored: 
Low estimate 1: 20%; 
Midpoint estimate 3: 33%; 
High estimate 5: 71%. 

Private, individual[A]: 
Low estimate 1: 0%; 
Midpoint estimate 3: 0%; 
High estimate 5: 62%. 

Public insurance: 
Low estimate 1: 34%; 
Midpoint estimate 3: 56%; 
High estimate 5: 79%. 

Source: GAO analysis of Medical Expenditure Panel Survey (MEPS). 

Note: Public insurance category includes Medicaid and military health 
coverage. Estimate 1 is based on the methodology in Harold Pollack's 
article "High-Risk Pools for the Sick and Uninsured Under Health 
Reform: Too Little and Thus Too Late," in the Journal of General 
Internal Medicine (published online on September 2, 2010). Estimate 3 
is based on the methodology in the Families USA report "Health Reform: 
Help for Americans with Pre-Existing Conditions," published in May 
2010. Estimate 5 is based on the methodology used in the GAO report 
Health Insurance: Enrollment, Benefits, Funding, and Other 
Characteristics of State High-Risk Health Insurance Pools, GAO-09-730R 
(Washington, D.C.: July 22, 2009). Estimates are provided only where 
there was sufficient sample size to generate a reliable estimate. The 
95 percent confidence intervals for estimates in this figure are 
within +/-7 percent of the estimates themselves. 

[A] Only the high estimate for individuals with private, individual 
insurance had sufficient sample size to generate a reliable estimate. 

[End of figure] 

Adults with Pre-Existing Conditions Spend Thousands of Dollars More 
Per Year on Health Care Than Other Adults, but Pre-Existing Conditions 
Are Common across All Family Income Levels: 

Adults with pre-existing conditions, on average, spend thousands of 
dollars more for all health care--between $1,504 and $4,844 more per 
year--than other adults. Based on the set of conditions included in 
estimate 1, which included the smallest number of individuals with pre-
existing conditions, average annual health care expenditures were 
$8,535 in 2009, compared to $3,691 for all adults (see figure 5). 
[Footnote 28] The average expenditures for the midpoint estimate 
(estimate 3) were $7,296 and the average expenditures for the high 
estimate (estimate 5) were $5,195. Average medical expenditures among 
those with pre-existing conditions decreased as the number of people 
in the estimate reporting a pre-existing condition increased. As more 
individuals are included in the estimate, it is likely that the 
average severity of the medical conditions decreases, thus decreasing 
the average expenditure estimate. 

Figure 5: Average Annual Health Care Expenditures for Adults (Age 19-
64) and among the Low, Midpoint, and High Estimates of Adults with Pre-
Existing Conditions, 2009: 

[Refer to PDF for image: horizontal bar graph] 

Average annual expenditures: 

Low estimate 1: $8,535; 
Midpoint estimate 3: $7,296; 
High estimate 5: $5,195; 
All adults: $3,691. 

Source: GAO analysis of Medical Expenditure Panel Survey (MEPS). 

Note: Estimate 1 is based on the methodology in Harold Pollack's 
article "High-Risk Pools for the Sick and Uninsured Under Health 
Reform: Too Little and Thus Too Late," in the Journal of General 
Internal Medicine (published online on September 2, 2010). Estimate 3 
is based on the methodology in the Families USA report "Health Reform: 
Help for Americans with Pre-Existing Conditions," published in May 
2010. Estimate 5 is based on the methodology used in the GAO report 
Health Insurance: Enrollment, Benefits, Funding, and Other 
Characteristics of State High-Risk Health Insurance Pools, GAO-09-730R 
(Washington, D.C.: July 22, 2009). Information on the conditions 
included in each estimate is detailed in appendix I. The 95 percent 
confidence intervals for estimates in this figure are within +/-8 
percent of the estimates themselves. 

[End of figure] 

The distribution of adults across income groups, whether measured by 
total family income or income as a share of the federal poverty level, 
is similar, regardless of pre-existing condition status (see tables 5 
and 6). We found that the average annual family income for adults with 
pre-existing conditions was between $64,000 and $71,000, as compared 
to the average family income of all adults, which is about $69,000. 

Table 5: Distribution of All Adults (Age 19-64) and Those with Pre-
Existing Conditions across Total Family Income Categories, Low, 
Midpoint, and High Estimates, 2009: 

Under $25,000: 
All adults: 21%; 
Adults with pre-existing conditions: 
Low estimate 1: 25%; 
Midpoint estimate 3: 25%; 
High estimate 5: 21%. 

$25,000-$49,999: 
All adults: 24%; 
Adults with pre-existing conditions: 
Low estimate 1: 23%; 
Midpoint estimate 3: 24%; 
High estimate 5: 23%. 

$50,000-$74,999: 
All adults: 19%; 
Adults with pre-existing conditions: 
Low estimate 1: 18%; 
Midpoint estimate 3: 18%; 
High estimate 5: 19%. 

$75,000-$99,999: 
All adults: 14%; 
Adults with pre-existing conditions: 
Low estimate 1: 14%; 
Midpoint estimate 3: 14%; 
High estimate 5: 14%. 

$100,000 and above: 
All adults: 22%;
Adults with pre-existing conditions: 
Low estimate 1: 21%; 
Midpoint estimate 3: 19%; 
High estimate 5: 23%. 

Total:
All adults: 100%; 
Adults with pre-existing conditions: 
Low estimate 1: 100%; 
Midpoint estimate 3: 100%; 
High estimate 5: 100%. 

Source: GAO analysis of Medical Expenditure Panel Survey (MEPS). 

Notes: Income includes annual earnings from wages salaries and other 
sources including: bonuses, tips, child support payments, Social 
Security, worker's compensation, veterans' payments, and welfare 
payments from public assistance and related programs. It does not 
include tax refunds and capital gains. Estimate 1 is based on the 
methodology in Harold Pollack's article "High-Risk Pools for the Sick 
and Uninsured Under Health Reform: Too Little and Thus Too Late," in 
the Journal of General Internal Medicine (published online on 
September 2, 2010). Estimate 3 is based on the methodology in the 
Families USA report "Health Reform: Help for Americans with Pre-
Existing Conditions," published in May 2010. Estimate 5 is based on 
the methodology used in the GAO report Health Insurance: Enrollment, 
Benefits, Funding, and Other Characteristics of State High-Risk Health 
Insurance Pools, GAO-09-730R (Washington, D.C.: July 22, 2009). Total 
may be greater than 100 percent because of rounding. The 95 percent 
confidence intervals for estimates in this table are within +/-2 
percent of the estimates themselves. 

[End of table] 

Table 6: Distribution of All Adults (Age 19-64) and Those with Pre-
Existing Conditions across Categories of Family Income as Percent of 
Poverty Level, Low, Midpoint, and High Estimates, 2009: 

Income as a percentage of FPL: Less than100%; 
All Adults: 13%; 
Adults with pre-existing conditions: 
Low estimate 1: 15%; 
Midpoint estimate 3: 15%; 
High estimate 5: 12%. 

Income as a percentage of FPL: 100 to 199%; 
All Adults: 16%; 
Adults with pre-existing conditions: 
Low estimate 1: 16%; 
Midpoint estimate 3: 17%; 
High estimate 5: 15%. 

Income as a percentage of FPL: 200 to 399%; 
All Adults: 30%; 
Adults with pre-existing conditions: 
Low estimate 1: 28%; 
Midpoint estimate 3: 30%; 
High estimate 5: 29%. 

Income as a percentage of FPL: Greater than or equal to 400%; 
All Adults: 41%; 
Adults with pre-existing conditions: 
Low estimate 1: 42%; 
Midpoint estimate 3: 38%; 
High estimate 5: 44%. 

Income as a percentage of FPL: Total; 
All Adults: 100%; 
Adults with pre-existing conditions: 
Low estimate 1: 100%; 
Midpoint estimate 3: 100%; 
High estimate 5: 100%. 

Source: GAO analysis of Medical Expenditure Panel Survey (MEPS). 

Notes: Income includes annual earnings from wages salaries and other 
sources including: bonuses, tips, child support payments, Social 
Security, worker's compensation, veterans' payments, and welfare 
payments from public assistance and related programs. It does not 
include tax refunds and capital gains. Estimate 1 is based on the 
methodology in Harold Pollack's article "High-Risk Pools for the Sick 
and Uninsured Under Health Reform: Too Little and Thus Too Late," in 
the Journal of General Internal Medicine (published online on 
September 2, 2010). Estimate 3 is based on the methodology in the 
Families USA report "Health Reform: Help for Americans with Pre-
Existing Conditions," published in May 2010. Estimate 5 is based on 
the methodology used in the GAO report Health Insurance: Enrollment, 
Benefits, Funding, and Other Characteristics of State High-Risk Health 
Insurance Pools, GAO-09-730R (Washington, D.C.: July 22, 2009). Total 
may be greater than 100 percent because of rounding. The 95 percent 
confidence intervals for estimates in this table are within +/-2 
percent of the estimates themselves. 

[End of table] 

Agency Comments: 

We provided a draft of this report to HHS for comment, but in its 
written response HHS said that it had no substantive or technical 
comments. HHS noted that PPACA provided individuals with pre-existing 
conditions new protections, including the opportunity to enroll in the 
Pre-existing Condition Insurance Plan and the prohibition on insurers 
limiting or denying insurance coverage because of a pre-existing 
condition beginning in 2014. HHS's letter is included as appendix IV. 

As agreed with your offices, unless you publicly announce the contents 
of this report earlier, we plan no further distribution until 30 days 
from the report date. At that time, we will send copies to the 
Secretary of Health and Human Services, the Administrator of the 
Centers for Medicare & Medicaid Services, and other interested 
parties. In addition, the report will be available at no charge on the 
GAO website at [hyperlink, http://www.gao.gov]. 

If you or your staff have any questions about this report, please 
contact me at (202) 512-7114 or dickenj@gao.gov. Contact points for 
our Offices of Congressional Relations and Public Affairs may be found 
on the last page of this report. GAO staff who made key contributions 
to this report are listed in appendix V. 

Signed by: 

John E. Dicken: 
Director, Health Care: 

List of Requesters: 

The Honorable Harry Reid: 
Majority Leader: 
United States Senate: 

The Honorable Max Baucus: 
Chairman: 
Committee on Finance: 
United States Senate: 

The Honorable Tom Harkin: 
Chairman: 
Committee on Health, Education, Labor, and Pensions: 
United States Senate: 

The Honorable John D. Rockefeller IV: 
Chairman:
Subcommittee on Health Care: 
Committee on Finance: 
United States Senate: 

[End of section] 

Appendix I: Scope and Methodology: 

To answer all of our research objectives, we (1) identified different 
sets of medical conditions that researchers have used to define the 
conditions that would cause an insurance company to deny health 
insurance coverage, offer coverage at higher-than-average premiums, or 
restrict health insurance coverage; and (2) analyzed data from the 
2009 Medical Expenditure Panel Survey (MEPS). We chose the MEPS 
because it is nationally representative of the civilian, 
noninstitutionalized population, provides information on medical 
conditions, medical expenditures, and demographics, and has been used 
by other researchers to answer similar research objectives.[Footnote 
29] We limited our analysis to adults, defined as individuals age 19 
to 64, and we excluded approximately 2.7 million adults continuously 
enrolled in Medicare in all 12 months of 2009. Based on Medicare 
eligibility rules, all of these individuals would have a medical 
condition. 

Identifying Pre-Existing Conditions: 

We identified four previous studies that used different lists of pre-
existing conditions to develop five estimates of the number of 
individuals with pre-existing conditions or the number of individuals 
potentially eligible for high-risk pools. Because high-risk pools are 
designed for individuals who cannot obtain insurance in the private 
individual market because of a pre-existing condition, estimates of 
individuals potentially eligible for high-risk pools can serve as a 
proxy for individuals with pre-existing conditions. Each of the 
previous studies looked at different populations, used different years 
of data, and, in one case, a different source of data. We assigned 
each condition list an estimate number from 1 to 5 in order to more 
readily identify which set of conditions was associated with each 
estimate.[Footnote 30] For example, the condition list in the study 
authored by Harold Pollack is referred to as estimate 1. 

Estimate 1 is based on the condition list in Harold Pollack's article 
"High-Risk Pools for the Sick and Uninsured Under Health Reform: Too 
Little and Thus Too Late," in the Journal of General Internal Medicine 
(published online on September 2, 2010). Pollack considered 
individuals to be plausible high-risk pool participants if they had 
ever been told they had emphysema, diabetes, stroke, cancer, 
congestive heart failure, heart disease, angina, or heart attack. 

Estimate 2 is described below with the information on estimate 4 
because both estimates came from the same study. Additionally, 
estimate 2 is based on the methodology used in estimate 3. 

Estimate 3 is based on the condition list in the Families USA report 
"Health Reform: Help for Americans with Pre-Existing Conditions," 
published in May 2010. According to a methodology developed by 
researchers from The Lewin Group, individuals were identified as 
having a pre-existing condition if they reported, in the previous 
year, one of 67 conditions most commonly included in lists for 
determining high-risk pool eligibility across all the states with high-
risk pools. The conditions were: acquired immune deficiency syndrome 
(AIDS), alcohol/drug abuse/chemical dependency, Alzheimer's disease, 
angina pectoris, anorexia nervosa, aplastic anemia, aortic aneurysm, 
arteriosclerosis obliterans, artificial heart valve/heart valve 
replacement, ascites, brain tumor, cancer (except skin), cancer 
(metastatic), cardiomyopathy/primary cardiomyopathy, cerebral 
palsy/palsy, chronic obstructive pulmonary disease (COPD), chronic 
pancreatitis, cirrhosis of the liver, congestive heart failure, 
coronary artery disease, coronary insufficiency, coronary occlusion, 
Crohn's disease, cystic fibrosis, dermatomyositis, diabetes, 
emphysema/pulmonary emphysema, Friedreich's disease/ataxia, 
hemophilia, hepatitis, HIV positive, Hodgkin's disease, Huntington's 
chorea/disease, hydrocephalus, intermittent claudication, kidney 
failure/kidney disease with dialysis/renal failure, lead poisoning 
with cerebral involvement, leukemia, Lou Gehrig's disease/amytrophic 
lateral sclerosis/ALS, lupus erythematosus disseminate/lupus, major 
organ transplant, malignant tumor, mood and psychotic disorders 
(including depression, schizophrenia, and bipolar disorder), motor or 
sensory aphasia, multiple or disseminated sclerosis, muscular atrophy 
or dystrophy, myasthenia gravis, myocardial infarction (heart attack), 
myotonia, obesity, paraplegia or quadriplegia, Parkinson's disease, 
peripheral arteriosclerosis, polyarteritis, polycystic kidney, postero-
lateral sclerosis, rheumatoid arthritis, sickle cell anemia/sickle 
cell disease, silicosis, splenic anemia/True Banti's Syndrome/Banti's 
disease, Still's disease, stroke, syringomyelia (spina bifida or 
myelomeningocele), tabes dorsalis, thalassemia (Cooley's or 
Mediterranean anemia), ulcerative colitis, and Wilson's disease. 

Estimates 2 and 4 were based on the two condition lists in the 
Department of Health and Human Services' (HHS) report "At Risk: Pre-
Existing Conditions Could Affect 1 in 2 Americans: 129 Million People 
Could Be Denied Affordable Coverage Without Health Reform" 
(Washington, D.C.: January 2011). This report included two estimates 
of individuals with pre-existing conditions. Estimate 2 replicated the 
methodology developed by The Lewin Group for the Families USA study 
with two exceptions. First, obesity was not included in the list of 
conditions that would cause a denial because HHS researchers did not 
find obesity on the condition lists used by state high-risk pools. 
Second, HHS included individuals who reported that they had ever been 
diagnosed with the following conditions: coronary heart disease, 
myocardial infarction, other heart disease, angina pectoris, stroke, 
emphysema, cancer, and diabetes. HHS's second estimate, estimate 4, 
included the same conditions as its first estimate with additional 
conditions added based on a review of the underwriting guidelines of 
seven health insurers. In addition to the conditions included in 
estimate 2, estimate 4 included individuals reporting arthritis, 
asthma, high cholesterol, hypertension, or obesity, as well as those 
who had ever been diagnosed with arthritis, asthma, high cholesterol, 
or hypertension. Estimate 4 also includes individuals reporting 
neurotic and related disorders, stress and adjustment disorders, 
conduct disorders, and emotional disturbances. 

Estimate 5 was based on the methodology used in the GAO report Health 
Insurance: Enrollment, Benefits, Funding, and Other Characteristics of 
State High-Risk Health Insurance Pools, GAO-09-730R (Washington, D.C.: 
July 22, 2009). This report used the Healthcare Cost and Utilization 
Project's Chronic Condition Indicator to identify chronic conditions 
in the 2006 MEPS, which were assumed to make a person medically 
uninsurable and therefore potentially eligible for a state high-risk 
pool. The Chronic Condition Indicator categorizes all International 
Classification of Diseases, Ninth Edition, Clinical Modification (ICD-
9-CM) codes as chronic or not chronic.[Footnote 31] A chronic 
condition is defined as a condition that lasts 12 months or longer and 
meets one or both of the following tests: (a) it places limitations on 
self-care, independent living, and social interactions; and (b) it 
results in the need for ongoing intervention with medical products, 
services, and special equipment. Individuals reporting any condition 
considered chronic were included in this estimate. We used the three-
digit ICD-9-CM codes for medical conditions. Of 1,019 three-digit ICD-
9-CM codes, the Chronic Condition Indicator categorized 417 as chronic. 

To standardize the estimates from the different sources, we applied 
the conditions identified by each study to the same population (19 to 
64 year olds not enrolled in Medicare) and the same year and source of 
data (the 2009 MEPS) to construct estimates 1 through 5. 

Identifying the Most Common Medical Conditions and Estimating 
Treatment Costs: 

To estimate the number of adults (age 19 to 64) with medical 
conditions that would cause an insurance company to restrict or deny 
insurance coverage and the average expenditures related to these 
conditions, we analyzed MEPS data on medical conditions and 
expenditures. We reported data only for those medical conditions 
included in at least one of the condition lists used for estimates 1 
through 5. MEPS respondents report medical conditions identified as 
the reason for a medical event, the reason for a disability day, or as 
something that is "bothering" the respondent during the reference year 
(in this case, 2009).[Footnote 32] To protect respondent privacy, we 
only reported estimates based on sample sizes of 100 or more. 

We defined expenditures as payments from all sources for hospital 
inpatient care, ambulatory care provided in offices and hospital 
outpatient departments, care provided in emergency departments, and 
the purchase of prescribed medications.[Footnote 33] We did not 
include expenditures for dental expenses or other medical expenses, 
such as durable and nondurable supplies, medical equipment, 
eyeglasses, and ambulance services because these items could not be 
linked to specific conditions. We also excluded expenses for home 
health care. We classified expenditures with a condition if a visit, 
stay, or medication purchase was cited as being related to the 
specific condition. 

Demographic and Geographic Profile of Adults with Pre-Existing 
Conditions: 

To describe the demographic and geographic profile of adults with pre-
existing conditions we focused on the low, midpoint, and high 
estimates of adults with pre-existing conditions, which we call 
estimates 1, 3, and 5, respectively. We provided state estimates for 
the 35 states with sufficient sample size in the MEPS data to reliably 
report an estimate. We did not have large enough sample sizes to 
generate state-level estimates of the number of adults with pre-
existing conditions in Alaska, Delaware, Idaho, Maine, Montana, 
Nebraska, Nevada, New Hampshire, New Mexico, North Dakota, Rhode 
Island, South Dakota, Vermont, West Virginia, Wyoming, and the 
District of Columbia. 

To estimate the number of individuals living in states with insurance 
protections similar to those that will be offered under PPACA in 2014, 
we surveyed state insurance department officials in September 2011, 
asking them to confirm or correct information on guaranteed issue 
requirements and rating restrictions previously reported by the Kaiser 
Family Foundation's statehealthfacts.org website.[Footnote 34] We 
asked the state officials to confirm information on guaranteed issue 
requirements and rating restrictions in place as of September 2011. We 
received responses from all states and the District of Columbia. Based 
on those responses, we used MEPS's state-based estimates of adults age 
19 to 64 with pre-existing conditions to identify the number of 
individuals living in states reporting a guaranteed issue requirement 
for at least some policies and either pure community rating or 
adjusted community rating--insurance protections similar to those that 
will be offered under PPACA in 2014. Guaranteed issue requirements 
prohibit the denial of coverage to individuals based on pre-existing 
conditions. In some states, all products must be guaranteed issue, 
while in other states guaranteed issue requirements are only 
applicable to some individuals, for instance those with 12 months of 
continuous coverage, or some standard product, such as a basic health 
plan. Under pure community rating, insurers may not adjust premiums 
based on health status, age, or gender. Under adjusted community 
rating, insurers may not adjust premiums based on health status, but 
may adjust premiums based on characteristics such as gender or age, 
depending on the state. 

Study Limitations and Data Reliability: 

There are limitations to our analysis. First, each insurance company 
separately determines which medical conditions will result in a 
denial, limitation in coverage, or an increase in premiums. An 
individual who may be denied by one insurer could obtain coverage from 
another. Therefore, the population of individuals who would be denied 
by an insurer, offered restricted coverage, or offered coverage at 
higher-than-average premiums actually varies by each health insurer. 
Second, our estimates could overstate the adult population with pre-
existing conditions because of the way in which conditions are 
aggregated in the MEPS database. To protect survey participant 
confidentiality, five-digit ICD-9-CM codes are truncated into a three-
digit code, which means that individuals reporting other related 
conditions may also be captured. 

To determine the reliability of the MEPS data, we reviewed related 
documentation, compared our estimates to other published results, and 
spoke with an official from the Agency for Healthcare Research and 
Quality, which is the federal agency responsible for MEPS. We 
determined that the MEPS data were sufficiently reliable for the 
purposes of our engagement. The 2009 data are the most recently 
available for MEPS. We conducted this performance audit in accordance 
with generally accepted government auditing standards from July 2011 
through March 2012. Those standards require that we plan and perform 
the audit to obtain sufficient, appropriate evidence to provide a 
reasonable basis for our findings and conclusions based on our audit 
objectives. We believe that the evidence obtained provides a 
reasonable basis for our findings based on our audit objectives. 

[End of section] 

Appendix II: Number of Adults (Age 19-64) with Pre-Existing Conditions 
in States, Low, Midpoint, and High Estimate, 2009: 

State: All adults; 
Low estimate 1: Number: 36,116,647; 
Low estimate 1: Standard error: 1,056,380; 
Midpoint estimate 3: Number: 59,978,301; 
Midpoint estimate 3: Standard error: 1,734,242; 
High estimate 5: Number: 121,560,174; 
High estimate 5: Standard error: 2,950,667. 

State: Alabama; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,936,170[A]; 
High estimate 5: Standard error: 692,379. 

State: Alaska; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Arizona; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: 1,136,752; 
Midpoint estimate 3: Standard error: 304,102; 
High estimate 5: Number: 2,216,186; 
High estimate 5: Standard error: 501,837. 

State: Arkansas; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,480,153[A]; 
High estimate 5: Standard error: 755,180. 

State: California; 
Low estimate 1: Number: 4,004,774; 
Low estimate 1: Standard error: 511,259; 
Midpoint estimate 3: Number: 6,606,715; 
Midpoint estimate 3: Standard error: 857,916; 
High estimate 5: Number: 13,651,336; 
High estimate 5: Standard error: 1,435,726. 

State: Colorado; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,893,776[A]; 
High estimate 5: Standard error: 590,540. 

State: Connecticut; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,211,605[A]; 
High estimate 5: Standard error: 618,166. 

State: Delaware; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: District of Columbia; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Florida; 
Low estimate 1: Number: 1,670,935; 
Low estimate 1: Standard error: 255,058; 
Midpoint estimate 3: Number: 2,612,694; 
Midpoint estimate 3: Standard error: 384,664; 
High estimate 5: Number: 5,689,120; 
High estimate 5: Standard error: 906,300. 

State: Georgia; 
Low estimate 1: Number: 1,215,469; 
Low estimate 1: Standard error: 136,640; 
Midpoint estimate 3: Number: 1,898,146; 
Midpoint estimate 3: Standard error: 211,029; 
High estimate 5: Number: 3,461,859; 
High estimate 5: Standard error: 301,183. 

State: Hawaii; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 683,798[A]; 
High estimate 5: Standard error: 348,877. 

State: Idaho; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Illinois; 
Low estimate 1: Number: 1,179,171; 
Low estimate 1: Standard error: 159,490; 
Midpoint estimate 3: Number: 2,181,909; 
Midpoint estimate 3: Standard error: 343,397; 
High estimate 5: Number: 4,488,169; 
High estimate 5: Standard error: 575,415. 

State: Indiana; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: 1,358,572; 
Midpoint estimate 3: Standard error: 233,200; 
High estimate 5: Number: 2,623,593; 
High estimate 5: Standard error: 408,833. 

State: Iowa; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,597,729; 
High estimate 5: Standard error: 349,209. 

State: Kansas; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,047,433[A]; 
High estimate 5: Standard error: 534,405. 

State: Kentucky; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: 2,186,644[A]; 
Midpoint estimate 3: Standard error: 1,058,849; 
High estimate 5: Number: 3,527,324[A]; 
High estimate 5: Standard error: 1,699,657. 

State: Louisiana; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,436,715[A]; 
High estimate 5: Standard error: 590,999. 

State: Maine; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Maryland; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 2,161,840; 
High estimate 5: Standard error: 370,581. 

State: Massachusetts; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: 1,097,055; 
Midpoint estimate 3: Standard error: 271,483; 
High estimate 5: Number: 2,311,677; 
High estimate 5: Standard error: 550,103. 

State: Michigan; 
Low estimate 1: Number: 1,226,779; 
Low estimate 1: Standard error: 221,142; 
Midpoint estimate 3: Number: 2,453,149; 
Midpoint estimate 3: Standard error: 495,322; 
High estimate 5: Number: 4,390,757; 
High estimate 5: Standard error: 888,570. 

State: Minnesota; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 2,333,096; 
High estimate 5: Standard error: 347,476. 

State: Mississippi; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,432,693[A]; 
High estimate 5: Standard error: 730,966. 

State: Missouri; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: 1,407,053; 
Midpoint estimate 3: Standard error: 210,782; 
High estimate 5: Number: 2,736,950; 
High estimate 5: Standard error: 444,142. 

State: Montana; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Nebraska; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Nevada; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: New Hampshire; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: New Jersey; 
Low estimate 1: Number: 1,055,825; 
Low estimate 1: Standard error: 142,155; 
[Empty]; 
Midpoint estimate 3: Number: 1,914,939; 
Midpoint estimate 3: Standard error: 293,777; 
High estimate 5: Number: 4,474,967; 
High estimate 5: Standard error: 690,321. 

State: New Mexico; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: New York; 
Low estimate 1: Number: 1,952,701; 
Low estimate 1: Standard error: 524,780; 
Midpoint estimate 3: Number: 3,580,631; 
Midpoint estimate 3: Standard error: 876,213; 
High estimate 5: Number: 7,308,236; 
High estimate 5: Standard error: 1,725,171. 

State: North Carolina; 
Low estimate 1: Number: 1,131,189[A]; 
Low estimate 1: Standard error: 553,188; 
Midpoint estimate 3: Number: 1,746,407[A]; 
Midpoint estimate 3: Standard error: 891,024; 
High estimate 5: Number: 3,426,132[A]; 
High estimate 5: Standard error: 1,748,027. 

State: North Dakota; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Ohio; 
Low estimate 1: Number: 1,758,412; 
Low estimate 1: Standard error: 358,372; 
Midpoint estimate 3: Number: 2,523,454; 
Midpoint estimate 3: Standard error: 540,527; 
High estimate 5: Number: 4,945,135; 
High estimate 5: Standard error: 837,152. 

State: Oklahoma; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,230,321[A]; 
High estimate 5: Standard error: 627,715. 

State: Oregon; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 2,008,453[A]; 
High estimate 5: Standard error: 867,185. 

State: Pennsylvania; 
Low estimate 1: Number: 1,628,060; 
Low estimate 1: Standard error: 139,301; 
Midpoint estimate 3: Number: 2,777,003; 
Midpoint estimate 3: Standard error: 271,756; 
High estimate 5: Number: 5,546,111; 
High estimate 5: Standard error: 583,727. 

State: Rhode Island; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: South Carolina; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,324,442[A]; 
High estimate 5: Standard error: 675,736. 

State: South Dakota; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Tennessee; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: 772,781[A]; 
Midpoint estimate 3: Standard error: 394,276; 
High estimate 5: Number: 1,625,348[A]; 
High estimate 5: Standard error: 829,259. 

State: Texas; 
Low estimate 1: Number: 2,673,379; 
Low estimate 1: Standard error: 378,510; 
Midpoint estimate 3: Number: 4,366,855; 
Midpoint estimate 3: Standard error: 619,441; 
High estimate 5: Number: 9,045,432; 
High estimate 5: Standard error: 1,483,132. 

State: Utah; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 1,736,201[A]; 
High estimate 5: Standard error: 885,817. 

State: Vermont; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Virginia; 
Low estimate 1: Number: 1,160,804[A]; 
Low estimate 1: Standard error: 380,094; 
Midpoint estimate 3: Number: 1,895,256; 
Midpoint estimate 3: Standard error: 541,223; 
High estimate 5: Number: 3,968,986; 
High estimate 5: Standard error: 1,137,964. 

State: Washington; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: 1,509,783; 
Midpoint estimate 3: Standard error: 328,015; 
High estimate 5: Number: 3,542,050; 
High estimate 5: Standard error: 736,548. 

State: West Virginia; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

State: Wisconsin; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: 2,319,211[A]; 
High estimate 5: Standard error: 834,253. 

State: Wyoming; 
Low estimate 1: Number: [Empty]; 
Low estimate 1: Standard error: [Empty]; 
Midpoint estimate 3: Number: [Empty]; 
Midpoint estimate 3: Standard error: [Empty]; 
High estimate 5: Number: [Empty]; 
High estimate 5: Standard error: [Empty]. 

Source: GAO analysis of Medical Expenditure Panel Survey (MEPS). 

Notes: Information on the conditions included in each estimate is 
detailed in appendix I. Estimate 1 is based on the methodology in 
Harold Pollack's article "High-Risk Pools for the Sick and Uninsured 
Under Health Reform: Too Little and Thus Too Late," in the Journal of 
General Internal Medicine (published online on September 2, 2010). 
Estimate 3 is based on the methodology in the Families USA report 
"Health Reform: Help for Americans with Pre-Existing Conditions," 
published in May 2010. Estimate 5 is based on the methodology used in 
the GAO report Health Insurance: Enrollment, Benefits, Funding, and 
Other Characteristics of State High-Risk Health Insurance Pools, GAO-
09-730R (Washington, D.C.: July 22, 2009). The standard error is a 
measure of variation around the estimate, in this case, the estimated 
number of individuals in the state with pre-existing conditions. The 
95 percent confidence intervals for estimates in this table are within 
+/-3 percent of the estimates themselves. Due to insufficient sample 
sizes, MEPS data could not be used to provide any estimates for 15 
states and Washington, D.C. Dashes in the table indicate that the 
sample size was not sufficient to produce an estimate. 

[A] The relative standard error is greater than or equal to 30 
percent. Relative standard error is the proportion of the standard 
error divided by the estimate itself. 

[End of table] 

[End of section] 

Appendix III: State Protections for Individuals with Pre-Existing 
Conditions, as of September 2011: 

State: Alabama; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Alaska; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Arizona; 
Guaranteed issue[A]: Yes - Some; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Arkansas; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: California; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Colorado; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Connecticut; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Delaware; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: District of Columbia; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Florida; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Georgia; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Hawaii; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Idaho; 
Guaranteed issue[A]: Yes - Some; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Illinois; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Indiana; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Iowa; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Kansas; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Kentucky; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Louisiana; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No[F]; 
Rate bands[E]: Yes. 

State: Maine; 
Guaranteed issue[A]: Yes - All; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: Yes; 
Rate bands[E]: No. 

State: Maryland; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Massachusetts; 
Guaranteed issue[A]: Yes - All; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: Yes; 
Rate bands[E]: No. 

State: Michigan; 
Guaranteed issue[A]: Yes - Some; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Minnesota; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Mississippi; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Missouri; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Montana; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Nebraska; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Nevada; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: New Hampshire; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: New Jersey; 
Guaranteed issue[A]: Yes - All; 
Insurer of last resort[B]: No[G]; 
Pure community rating[C]: No; 
Adjusted community rating[D]: Yes; 
Rate bands[E]: No. 

State: New Mexico; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: New York; 
Guaranteed issue[A]: Yes - All; 
Insurer of last resort[B]: No; 
Pure community rating[C]: Yes; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: North Carolina; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: North Dakota; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Ohio; 
Guaranteed issue[A]: Yes - Some; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No[H]. 

State: Oklahoma; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Oregon; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: Yes; 
Rate bands[E]: No. 

State: Pennsylvania; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: Yes; 
Pure community rating[C]: No; 
Adjusted community rating[D]: Yes; 
Rate bands[E]: No. 

State: Rhode Island; 
Guaranteed issue[A]: Yes - Some; 
Insurer of last resort[B]: Yes; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: South Carolina; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: South Dakota; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Tennessee; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Texas; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Utah; 
Guaranteed issue[A]: Yes - Some; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: Yes. 

State: Vermont; 
Guaranteed issue[A]: Yes - All; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: Yes; 
Rate bands[E]: No. 

State: Virginia; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: Yes; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Washington; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: Yes; 
Rate bands[E]: No. 

State: West Virginia; 
Guaranteed issue[A]: Yes - Some; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Wisconsin; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

State: Wyoming; 
Guaranteed issue[A]: No; 
Insurer of last resort[B]: No; 
Pure community rating[C]: No; 
Adjusted community rating[D]: No; 
Rate bands[E]: No. 

Source: GAO survey of state officials pertaining to categories of 
protection previously reported on by the Kaiser Family Foundation. 

Note: We did not attempt to independently identify categories of 
protection or to validate the accuracy of the state responses. 

[A] Guaranteed issue requirements prohibit the denial of coverage to 
individuals based on pre-existing conditions. In some states, all 
products must be guaranteed issue, while in other states guaranteed 
issue requirements are only applicable to some individuals, for 
instance those with 12 months of creditable coverage, or some standard 
product, such as a basic health plan. 

[B] An insurer of last resort law requires one insurer in the state to 
issue products to individuals regardless of pre-existing conditions. 

[C] Under pure community rating, insurers may not adjust premiums due 
to health status, age, or gender. 

[D] Under adjusted community rating, insurers may not adjust premiums 
due to health status, but may adjust premiums according to 
characteristics such as gender or age, depending on the state. 

[E] Rate bands limit the extent to which premium can vary based on an 
individual's health status, for example, to a certain percentage of 
the average premium. 

[F] Louisiana reported that its community modified rating law allows 
for limited rate variation between member participants for health 
status, tier rating, and durational rating. It also allows rating for 
age, gender, industry, geographic area, family composition, group 
size, tobacco usage, plan of benefits, or other factors approved by 
the Louisiana Department of Insurance. 

[G] While New Jersey reported that it has an insurer of last resort by 
law, it does not function as such in practice because of the 
guaranteed issue requirement. 

[H] Rating bands only apply in parts of the individual market where a 
small group plan is being converted to an individual plan. 

[End of table] 

[End of section] 

Appendix IV: Comments from the Department of Health and Human Services: 

Department of Health & Human Services: 
Office of The Secretary: 
Assistant Secretary for Legislation: 
Washington, DC 20201: 

March 14, 2012: 

John E. Dicken: 
Director, Health Care: 
U.S. Government Accountability Office: 
441 G Street NW: 
Washington, DC 20548: 

Dear Mr. Dicken: 

Thank you for providing the Department the opportunity to comment on 
GAO draft report entitled "Private Health Insurance: Estimates of 
Individuals with Pre-Existing Conditions Range from 36 Million to 122 
Million" (GA0-12-439). We have no substantive or technical comments on 
the report, but appreciate the work GAO has done on this important 
issue and the opportunity to review and comment on the draft report. 

We understand the difficulties that individuals with pre-existing 
conditions experience as they navigate the health care system and 
explore their options for coverage. The Affordable Care Act has 
already taken big steps to help individuals with pre-existing 
conditions, including establishing the Pre-Existing Condition 
Insurance Plan, which allows individuals with preexisting conditions 
to enroll in comprehensive and affordable coverage, and no longer 
allowing insurance companies to limit or deny benefits to children 
with pre-existing conditions. We look forward to 2014, when insurance 
companies will no longer be able to limit or deny benefits for any 
individual seeking coverage. 

Sincerely, 

Signed by: 

Jim R. Esquea: 
Assistant Secretary for Legislation: 

[End of section] 

Appendix V: GAO Contact and Staff Acknowledgments: 

GAO Contact: 

John E. Dicken, (202) 512-7114 or DickenJ@gao.gov: 

Staff Acknowledgments: 

In addition to the contact named above, Walter Ochinko, Assistant 
Director; Lori Achman; George Bogart; Sean DeBlieck; Giselle Hicks; 
and Laurie Pachter made key contributions to this report. 

[End of section] 

Footnotes: 

[1] Medicare is a federal health care program that provides health 
insurance for those aged 65 and older, certain disabled persons, and 
persons with end-stage renal disease. Medicaid is a joint federal-
state program that finances health care coverage for certain low-
income individuals. 

[2] Percentages do not sum to 100 because estimates of coverage types 
are not mutually exclusive and individuals can have more than one type 
of coverage during the year. Data from the Census Bureau on adults is 
available for 18-64 year olds. See C. DeNavas-Walt, B.D. Proctor, and 
J.C. Smith, U.S. Census Bureau, Current Population Reports, P60-239, 
Income, Poverty, and Health Insurance Coverage in the United States: 
2010 (Washington, D.C.: 2011). 

[3] Individuals not eligible to purchase employer-sponsored group 
coverage may buy coverage in the private individual market; these 
include the self-employed; those whose employers do not offer health 
insurance coverage; individuals not in the labor force; early retirees 
who no longer have employment-based coverage and are not yet eligible 
for Medicare; and individuals who lose their jobs and are either 
ineligible for or have exhausted the right to continue their employer-
based coverage for 18 months. 

[4] Pub. L. No. 111-148, 124 Stat. 119 (Mar. 23, 2010), as amended by 
the Health Care and Education Reconciliation Act of 2010 (HCERA), Pub. 
L. No. 111-152, 124 Stat. 1029 (Mar. 30, 2010). For purposes of this 
report, references to PPACA include the amendments made by HCERA. 

[5] Pub. L. No. 111-148, § 1201, 124 Stat. 154 (adding § 2704 to the 
Public Health Service Act (PHSA)). Although a PPACA provision 
prohibiting denials of coverage or restrictions on benefits because of 
a pre-existing condition for individuals under the age of 19 became 
effective September 23, 2010, insurers can still vary premium rates 
based on health status for these individuals until 2014. See Pub. L. 
No. 111-148 § 11253 as amended by § 10103(e), 124 Stat. 162, 895. 

[6] A panel survey involves multiple interviews with respondents over 
time. 

[7] During interviews, household respondents are asked about medical 
events, and after completing the interview (and obtaining permission), 
researchers contact a sample of medical providers by telephone to 
obtain information that the respondents may not be able to provide 
accurately, such as visit dates, diagnosis and procedure codes, 
charges, and payments. 

[8] High-risk pools are designed for individuals who cannot obtain 
insurance in the individual market because of a pre-existing 
condition, and therefore estimates of individuals potentially eligible 
for high-risk pools can serve as a proxy for individuals with pre-
existing conditions. 

[9] A chronic condition is defined as one that lasts 12 months or 
longer and places limits on self-care, independent living, and social 
interactions and/or results in the need for ongoing intervention. 

[10] See GAO, Private Health Insurance: Data on Application and 
Coverage Denials, [hyperlink, http://www.gao.gov/products/GAO-11-268] 
(Washington, D.C.: Mar. 16, 2011). The information is based on data 
collected by Department of Health and Human Services (HHS). 

[11] America's Health Insurance Plans' Center for Policy and Research, 
Individual Health Insurance 2009: A Comprehensive Survey of Premiums, 
Availability, and Benefits (Washington, D.C.: October 2009). America's 
Health Insurance Plans is the national trade association representing 
the health insurance industry. According to the study, 84 percent of 
applications were medically underwritten. Just over 1 percent of 
applications were denied before going through medical underwriting, 
but those denials were unrelated to the applicant's health status. 

[12] See Pub. L. No. 104-191, Title I, 110 Stat. 1936, 1939 et. seq. 

[13] Pub. L. No.104-191, § 111, 110 Stat. 1978 (adding 2741 to the 
PHSA). Creditable coverage is defined for this purpose by §2701(c)(1) 
of the PHSA as coverage for an individual under: a group health plan, 
health insurance, Medicare Part A or B, Medicaid, TRICARE, the Indian 
Health Service or a tribal organization, a state health benefits high-
risk pool, the Federal Employees Health Benefits Program, the Peace 
Corps health benefit plan, or a public health plan as defined in 
regulations. 

[14] Thirty-five states have state high-risk pools: Alabama, Alaska, 
Arkansas, California, Colorado, Connecticut, Florida, Idaho, Indiana, 
Illinois, Iowa, Kansas, Kentucky, Louisiana, Maryland, Minnesota, 
Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, 
North Carolina, North Dakota, Oklahoma, Oregon, South Carolina, South 
Dakota, Tennessee, Texas, Utah, Washington, West Virginia, Wisconsin, 
and Wyoming. 

[15] Pub. L. No. 111-148, § 1101, 124 Stat. 141. 

[16] See GAO, Pre-Existing Condition Insurance Plans: Program 
Features, Early Enrollment and Spending Trends, and Federal Oversight 
Activities, [hyperlink, http://www.gao.gov/products/GAO-11-662] 
(Washington, D.C.: July 27, 2011). 

[17] Estimates are of individuals who reported that these conditions 
were the cause of a medical event, the reason for a disability day, or 
bothered a respondent during 2009. 

[18] Expenditure information is reported only for those with 
expenditures greater than $0 and includes expenditures from all 
sources for hospital inpatient stays, emergency room visits, 
outpatient department visits, office-based medical provider visits, 
and prescribed medicines. Expenditure information does not include 
amounts for dental expenses and other medical expenses, such as 
durable and nondurable supplies, medical equipment, eyeglasses, and 
ambulance services because these items could not be linked to specific 
conditions. We also did not include expenditures for home health care. 

[19] Mental health disorders are defined as mood and psychotic 
disorders, which include conditions such as depression, bipolar 
disorder, and schizophrenia. 

[20] The eight conditions are: angina pectoris, cancer, congestive 
heart failure, diabetes, emphysema, heart attack (myocardial 
infarction), other heart disease, and stroke. 

[21] A chronic condition is defined as a condition that lasts 12 
months or longer and meets one or both of the following tests: (a) it 
places limitations on self-care, independent living, and social 
interactions; and (b) it results in the need for ongoing intervention 
with medical products, services, and special equipment. Examples of 
chronic conditions include malignancies, diabetes, most forms of 
mental illness, hypertension, many forms of heart disease, and 
congenital anomalies. Nonchronic conditions include conditions such as 
infections, pregnancy, many neonatal conditions, nonspecific symptoms, 
and injuries. We determined which conditions were considered chronic 
by using the Healthcare Cost and Utilization Project's Chronic Care 
Indicator, which categorizes medical conditions as either chronic or 
nonchronic conditions. Out of a total of 1,019 3-digit International 
Classification of Diseases, Ninth Edition, Clinical Modification (ICD-
9-CM) codes, we identified 417 as chronic conditions. ICD-9-CM is a 
commonly used system to classify and code medical conditions. 

[22] Due to insufficient sample sizes, MEPS data could not be used to 
provide estimates for 15 states and the District of Columbia. The 15 
states are Alaska, Delaware, Idaho, Maine, Montana, North Dakota, 
Nebraska, New Hampshire, New Mexico, Nevada, Rhode Island, South 
Dakota, Vermont, Wyoming, and West Virginia. 

[23] Guaranteed issue, pure community rating, and adjusted community 
rating are defined on pages 6-7. 

[24] Pregnancy was not considered a pre-existing condition in any of 
the studies we reviewed. 

[25] Due to insufficient sample sizes, we were only able to generate 
an estimate of individuals in the private individual market reporting 
pre-existing conditions for the high estimate (estimate 5). 

[26] Public insurance programs include Medicaid and military health 
coverage. For the purposes of our report, we excluded individuals with 
Medicare from our analysis. 

[27] GAO, Health Care Coverage: Job Lock and the Potential Impact of 
the Patient Protection and Affordable Care Act, [hyperlink, 
http://www.gao.gov/products/GAO-12-166R] (Washington, D.C.: Dec. 15, 
2011). 

[28] Annual expenditures on health care included expenditures on any 
medical condition during the year. Individuals with pre-existing 
conditions can have multiple medical conditions. 

[29] During interviews, household respondents are asked about medical 
events, and after completing the interview (and obtaining permission), 
researchers contact a sample of medical providers by telephone to 
obtain information that the respondents may not be able to provide 
accurately, such as visit dates, diagnosis and procedure codes, 
charges, and payments. 

[30] One study made both a low and high estimate. 

[31] ICD-9-CM is a commonly used system to classify and code medical 
conditions. The Chronic Condition Indicator is one in a family of 
databases and software tools developed as part of the Healthcare Cost 
and Utilization Project, a federal-state-industry partnership 
sponsored by the Agency for Healthcare Research and Quality. 

[32] For MEPS, medical conditions were recorded by an interviewer as 
verbatim text, which was then coded by professional coders to five-
digit International Statistical Classification of Diseases, Ninth 
Revision codes (ICD-9). ICD-9 codes are a commonly used set of codes 
used to classify medical conditions. The five-digit ICD-9 codes were 
then aggregated into clinically meaningful categories that group 
similar conditions, known as Clinical Classification codes. In 
addition, MEPS includes the ICD-9 code, but truncates it to three-
digits to protect the confidentiality of survey participants. When 
possible, we used the Clinical Classification codes in our analysis. 
When a medical condition did not align with the conditions noted with 
the Clinical Classification codes, we used the three-digit ICD-9 code 
to identify individuals with the condition. 

[33] Sources of payment include direct payments from individuals, 
private insurance, Medicare, Medicaid, Workers' Compensation, and 
miscellaneous other sources. Payments for over-the-counter drugs are 
not included in MEPS total expenditures. Indirect payments not related 
to specific medical events are also excluded. 

[34] The Kaiser Family Foundation indicated that the information on 
this website was collected by the Center for Health Insurance Studies, 
Georgetown University Health Policy Institute and was current as of 
January 2011. 

[End of section] 

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