Mapping Pre-existing Conditions across the U.S.

The future of protections for people with pre-existing conditions has once again become a focus of debate following recent legal and policy developments.

The Affordable Care Act (ACA) contained a number of new rules related to pre-existing conditions, including:

  • Guaranteed access to insurance in the individual market regardless of health. Previously, insurers typically used medical underwriting to deny coverage to people with pre-existing conditions, and also excluded coverage of any pre-existing conditions for people who were accepted.
  • Community rating in the individual and small business markets, prohibiting insurers from varying premiums based on people’s health, which was common before the ACA.
  • Required coverage of essential benefits. Prior to the ACA, insurers in the individual market often excluded coverage of maternity, mental health, and substance abuse.

Congressional efforts to repeal and replace the ACA during 2017 would have weakened these protections. For example, the bill passed by the House would have allowed states to alter the essential benefit requirement and waive community rating for people with gaps in coverage. The so-called Graham-Cassidy-Heller-Johnson bill drafted in the Senate would have allowed states to determine what factors insurers could use in setting rates, except for gender and genetic information, and also let states change the essential benefits.

While those repeal efforts failed, changes pursued by the Trump administration through regulation and the courts have implications for people with pre-existing conditions. For example:

  • New regulations would expand the availability of short-term insurance plans, which are not required to follow any of the ACA’s requirements, including guaranteed access for people with pre-existing conditions, community rating, or coverage of essential benefits. By siphoning off healthier people, these short-term plans would leave ACA-compliant plans in the individual market with a sicker pool and higher premiums. While people eligible for ACA premium subsidies would be protected from those higher premiums, those with pre-existing conditions and incomes too high to qualify for the subsidies will see higher premiums.
  • A group of state attorneys general has filed a lawsuit arguing that the ACA should be thrown out. The case argues that the ACA’s individual mandate – which was previously upheld by the Supreme Court based on the federal government’s power to tax – is unconstitutional now that Congress has set the tax penalty associated with the mandate to zero. According to the suit, the rest of the ACA is not severable from the mandate and should therefore be overturned. The Trump administration has filed a brief in the case agreeing that the individual mandate is unconstitutional, and that the ACA’s protections for pre-existing conditions should be invalidated along with it.

Polling by the Kaiser Family Foundation (KFF) finds that 64% of the public does not want the Supreme Court to overturn the ACA’s protections for people with pre-existing conditions, and that continuing those protections tops the list of health issues registered voters say they’ll consider in supporting candidates as the midterm election approaches.

Estimated Share of Adults with Pre-Existing Conditions by Metropolitan or Micropolitan Statistical Area

We previously estimated using 2015 data from the Centers for Disease Control and Prevention that 27% of adults age 18-64 (52 million people) have a pre-existing condition that would have led to a denial of insurance in the individual market before the ACA. A larger share of nonelderly women (30%) than men (24%) have declinable preexisting conditions. The share of people with pre-existing conditions also varies by age, ranging from 15% for 18-24 year-olds to 47% for 60-64 year-olds.

The share of non-elderly adults with a declinable pre-existing condition ranges from one-third or more in some states (Kentucky, Alabama, Mississippi, and West Virginia) to 23% or less in others (Alaska, District of Columbia, New Jersey, Utah, Colorado, and Minnesota). Estimates for all states are available here.

New estimates show the variation in the prevalence of pre-existing conditions across communities in the U.S., with substantial differences within states in some cases.

The share of non-elderly adults with a declinable pre-existing condition ranges from 41% in Kingsport, Tennessee to 20% in Logan, Utah and Rochester, Minnesota. The prevalence of pre-existing conditions can vary by 10% or more between cities in the same state. For example, in Kansas 32% of Topeka’s population has a pre-existing condition, as compared to 21% of Manhattan’s population. Similarly, in South Carolina 34% of Florence residents have a pre-existing condition, compared to 24% in Charleston or Hilton Head.

Our estimates are based on a review of pre-ACA underwriting manuals used by insurers in the individual market. Medical conditions that commonly led to a decline of coverage are listed in Table 1.

Table 1: Examples of Declinable Conditions In the Medically Underwritten Individual Market, Before the Affordable Care Act
Condition Condition
Alcohol abuse/ Drug abuse with recent treatment Mental disorders (severe, e.g. bipolar, eating disorder)
Alzheimer’s/dementia Multiple sclerosis
Arthritis (rheumatoid), fibromyalgia, other inflammatory joint disease Muscular dystrophy
Cancer within some period of time (e.g. 10 years, often other than basal skin cancer) Obesity, severe
Cerebral palsy Organ transplant
Congestive heart failure Paraplegia
Coronary artery/heart disease, bypass surgery Paralysis
Crohn’s disease/ ulcerative colitis Parkinson’s disease
Chronic obstructive pulmonary disease (COPD)/emphysema Pending surgery or hospitalization
Diabetes mellitus Pneumocystic pneumonia
Epilepsy Pregnancy or expectant parent
Hemophilia Sleep apnea
Hepatitis (Hep C) Stroke
Kidney disease, renal failure Transsexualism
Source: Kaiser Family Foundation review of field underwriting guidelines from Aetna (GA, PA, and TX), Anthem BCBS (IN, KY, and OH), Assurant, CIGNA, Coventry, Dean Health, Golden Rule, Health Care Services Corporation (BCBS in IL, TX) HealthNet, Humana, United HealthCare, Wisconsin Physician Service.  Conditions in this table appeared on declinable conditions list in half or more of guides reviewed.

Note: Many additional, less-common disorders also appearing on most of the declinable conditions lists were omitted from this table.

While a large share of people with pre-existing conditions have coverage through an employer or public coverage where they do not face medical underwriting, our estimates quantify how many people could be ineligible for individual market insurance under pre-ACA practices if they were to ever lose their current coverage. The individual market is often a temporary source of insurance for people who are between jobs or too sick to work, so the ramifications of the lawsuit extend beyond the people who are currently purchasing individual coverage at any given point in time. Ours is a conservative estimate, as the survey data we used do not include sufficient detail on several conditions that would have been declinable before the ACA (such as HIV/AIDS, or hepatitis C). Additionally, millions more have other conditions that could lead to higher premiums rather than a denial of coverage if the ACA’s community rating requirement were eliminated.


This analysis of selected metropolitan and micropolitan statistical areas (MMSAs) extends our prior methodology to the CDC’s 2015 Behavioral Risk Factor Surveillance System (BRFSS) SMART City and County dataset. For a detailed description of our calculation of pre-existing conditions, see the Methods section of our previous analysis.1  Since the 2015 BRFSS SMART dataset includes only a subset of the U.S. population, we applied the Generalized Regression Estimator (GREG) scaling factors used to calibrate the nationwide 2015 BRFSS to the 2015 National Health Interview Survey (NHIS). The CDC’s National Center for Health Statistics (NCHS) relies on the medical condition modules of the annual NHIS for many of its core publications on the topic; therefore, we consider this survey to be the most accurate means to estimate both the nationwide rate and weighted population. After applying this calibration, we estimated pre-existing condition prevalence rates and population estimates for the 130 geographic areas with sufficient sample size.

The programming code, written using the statistical computing package R v.3.5.1, is available upon request for people interested in replicating this approach for their own analysis.

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