HEALTH POLICY CENTER RE S E AR CH RE P O R T Medical Debt in New York State and Its Unequal Burden across Communities Michael Karpman Fredric Blavin Dulce Gonzalez Jennifer Andre Breno Braga July 2023 AB O U T T HE U R BA N I NS T I T U TE The Urban Institute is a nonprofit research organization that provides data and evidence to help advance upward mobility and equity. We are a trusted source for changemakers who seek to strengthen decisionmaking, create inclusive economic growth, and improve the well-being of families and communities. For more than 50 years, Urban has delivered facts that inspire solutions-and this remains our charge today. Copyright © July 2023. Urban Institute. Permission is granted for reproduction of this file, with attribution to the Urban Institute. Cover image by Tim Meko. Contents Acknowledgments iv Executive Summary v Medical Debt in New York State and Its Unequal Burden across Communities 1 Results: Medical Debt in New York State 3 Results: Multivariate Model 17 Policy Implications 20 Conclusion 26 Appendix A: Data, Methods, and Limitations 27 Data 27 Analysis 29 Limitations 31 Appendix B: Regional Profiles 32 Capital District 32 Central New York 36 Finger Lakes 39 Long Island 42 Mid-Hudson 45 Mohawk Valley 48 New York City 51 North Country 60 Southern Tier 63 Western New York 66 Appendix C: Tables and Figures 70 Notes 107 References 109 About the Authors 111 Statement of Independence 113 Acknowledgments This report was funded by the New York Health Foundation (NYHealth). We are grateful to them and to all our funders, who make it possible for Urban to advance its mission. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts. Further information on the Urban Institute's funding principles is available at urban.org/fundingprinciples. The mission of NYHealth is to expand health insurance coverage, increase access to high-quality health care services, and improve public and community health. The views presented here are those of the authors and not necessarily those of the New York Health Foundation or its directors, officers, and staff. The authors gratefully acknowledge helpful comments on earlier drafts from Elisabeth Benjamin, Ali Foti, and Stephen Zuckerman, as well as careful editing by Lauren Lastowka. iv ACKNOWLEDGMENTS Executive Summary Patients who are unable to pay their medical bills often incur medical debt that health care providers seek to collect. The burden of medical debt can intensify a person's financial challenges; affect their access to health care, credit, employment, housing, and food; and worsen their health. In New York State, approximately 6 percent of consumers had medical debt in collections on their credit reports in February 2022. However, the relatively low statewide average conceals variation across regions and communities, including disparities by race and ethnicity, income, and other demographic characteristics. This report examines the geographic distribution of medical debt in collections in New York State, providing a detailed picture of its unequal impacts on local communities and residents. Our analysis draws on February 2022 data from a representative random sample of deidentified credit records for more than 600,000 consumers ages 18 and older in New York State from one of the national credit reporting agencies. We estimate the share of consumers with medical debt on their credit reports and the amounts owed. Because the credit bureau data does not contain demographic information for consumers except for their ages, we use information on the demographic and socioeconomic characteristics of consumers' communities-defined based on zip codes of residence-as a proxy for examining the disproportionate burden of medical debt on marginalized groups. We assess how the prevalence and amounts of medical debt differ based on these community characteristics, both statewide and in each of the state's 10 economic regions and their constituent counties. We also examine variation in the prevalence of medical debt at the community level. Our key findings include the following: ◼ The share of New York State consumers with medical debt varied widely across regions and communities. » Communities with high rates of medical debt were concentrated in the Central New York, Mohawk Valley, North Country, and Southern Tier regions, whereas communities in Long Island and New York City tended to have low rates of medical debt. » The share of New York State consumers with medical debt ranged from 3.2 percent or less in communities with the lowest levels of medical debt to between 9.7 percent and 37.6 percent in communities with the highest levels of medical debt. EXECUTIVE SUMMARY v ◼ Within each region, the burden of medical debt generally fell most heavily on communities of color and communities with greater economic challenges. » In most regions, communities with a majority of residents who are people of color had higher rates of medical debt than communities with residents who are predominantly white. » In all regions, communities with lower median household incomes had higher rates of medical debt than communities with higher incomes. The prevalence of medical debt was also higher in communities where more residents were uninsured. ◼ Nearly half of consumers with medical debt owed $500 or more and therefore may still have medical debt on their credit reports following a change in credit reporting practices that took effect in April 2023. » Of consumers with medical debt, 48 percent owed $500 or more, including nearly one in three (30 percent) who owed $1,000 or more and about one in eight (13 percent) who owed $2,000 or more. » In communities with the lowest incomes, more than half of consumers with medical debt owed $500 or more. ◼ Communities with the highest prevalence of medical debt faced additional challenges in confronting greater health care needs with fewer resources. » High-debt communities were more rural, had higher rates of disability, and had lower rates of educational attainment and employment than communities with the lowest prevalence of medical debt. ◼ Even after accounting for differences in local demographic and health characteristics observed in the data, living in certain regions was associated with a greater risk of having medical debt. » Many counties with high rates of medical debt have also been hotspots for hospital lawsuits against patients over unpaid medical bills (Dunker and Benjamin 2022). These findings can inform state initiatives to protect consumers from medical debt by demonstrating where such reforms are likely to have the greatest impact. The evidence presented in this report suggests the following strategies have the potential to reduce racial, economic, and regional inequities in health and financial well-being by mitigating the risk and impact of medical debt: vi EXECUTIVE SUMMARY ◼ Expanding health insurance coverage and lowering deductibles and other cost-sharing requirements would decrease the risk of incurring medical debt. ◼ Strengthening requirements for hospitals to provide financial assistance could complement health insurance reforms to reduce medical debt prevalence. ◼ Changes in credit reporting would lower the number of consumers with medical debt on their credit files and improve credit scores. These changes may also require new strategies for monitoring trends in medical debt and understanding potential provider and consumer responses to prevent unintended consequences. ◼ Additional consumer protections that place limits on extraordinary collection actions taken by health care providers can mitigate financial impacts for people who have already incurred medical debt. ◼ Further analysis is needed to understand the individual, community, and health system factors that explain the disproportionate risk of medical debt in certain regions. The unequal burden of medical debt across communities in New York State exacerbates health and economic disparities. State policies to protect residents from medical debt can advance progress toward the goal of a more affordable and equitable health care system that promotes the health, well- being, and economic security of all New York residents. EXECUTIVE SUMMARY vii Medical Debt in New York State and Its Unequal Burden across Communities Each year, millions of patients receive medical bills they cannot afford to pay when faced with the complexity and high costs of the US health care system (Cohen and Cha 2023). These bills frequently result in medical debt that health care providers seek to collect. Unlike most other forms of debt, medical debt is often unpredictable and unavoidable, saddling patients with a financial burden as they recover from illness or injury. Health insurance coverage can lower the risk of incurring medical debt (Caswell and Waidmann 2017; Finkelstein et al. 2012; Himmelstein et al. 2022; Kluender et al. 2021) but may fail to offer sufficient protection because of high deductibles, other cost-sharing requirements, or gaps in covered benefits (Karpman and Long 2015; Rabin et al. 2020). When medical bills remain overdue for an extended period, health care providers typically refer them to third-party collection agencies, which may in turn furnish information on these debts to the nation's three major credit reporting agencies (CFPB 2014; Kirsch and Eswaramoorthy 2023). In February 2022, about one in eight US consumers with a credit record, or approximately 13 percent, had medical debt in collections on their credit reports.1 The Consumer Financial Protection Bureau (CFPB) found that medical debt in collections constituted 58 percent of all debt-collection items on consumer credit reports and totaled $88 billion in 2021 (CFPB 2022). Though recent changes announced by the national credit bureaus in April 2023 will remove most medical debt collections (i.e., specific items under $500) from credit reports, this will not reduce the underlying debt that patients owe to providers.2 The impacts of medical debt can ripple across every aspect of patients' lives, affecting their health, financial stability, and access to health care, credit, employment, housing, and food. Adults with medical debt are more likely to forgo needed health care because of cost concerns, and many are denied care from providers they owe (Lopes et al. 2022; Rabin et al. 2020). Moreover, medical debt is associated with a greater risk of food insecurity, eviction and foreclosure, and bankruptcy (Dobkin et al. 2018; Gross and Notowidigdo 2011; Himmelstein et al. 2009; Himmelstein et al. 2022). People who owe large debts often have elevated levels of stress, which can worsen their physical and mental health (Richardson, Elliot, and Roberts 2013; Sweet et al. 2013). MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 1 The credit and legal consequences for overdue medical bills can intensify a person's financial challenges. Damaged credit can make it difficult to get loans and insurance and to find jobs and housing, since many employers and landlords review credit reports when conducting background checks. 3 Health care providers may also sue patients over unpaid bills, opening the door to aggressive collection practices, including placing liens on primary residences and garnishing wages (Benjamin and Dunker 2021; Dunker and Benjamin 2020, 2022). Because the burden of medical debt falls disproportionately on marginalized groups-including people of color and people with low incomes, disabilities, or poor health-these impacts exacerbate health disparities and economic inequality (Karpman, Martinchek, and Braga 2022; Lopes et al. 2022).4 This report examines the geographic distribution of medical debt in collections (hereafter referred to as "medical debt") in New York State using February 2022 data from a representative random sample of deidentified credit records from one of the national credit reporting agencies. We estimate the share of consumers with medical debt on their credit reports-including medical debt that was sent to a third- party collector or assigned to a creditor's internal collections department-and the amounts owed, both statewide and in each of the state's 10 economic regions and their constituent counties. 5 We also estimate the shares of consumers with medical debt across communities, which are defined based on zip codes of residence. We further assess disparities within the state and each region in the prevalence and amounts of medical debt based on the demographic and socioeconomic characteristics of consumers' communities, including race/ethnicity, income, and health insurance coverage. The next section of the report examines the geographic variation in medical debt across communities and regions of New York State. We then highlight results from a multivariate analysis to disentangle community- and county-level characteristics that are associated with medical debt. We conclude with a discussion of implications of the study findings for state policy decisions. The appendices of the report provide a detailed description of our data and methodology, additional tables and figures, and a series of in-depth profiles showing where medical debt is concentrated within each region, including the extent to which areas with high rates of medical debt have also been hotspots for hospital debt collection lawsuits against patients (Dunker and Benjamin 2022). This focus is highly pertinent as nearly three in four nonelderly adults with past-due medical debt report owing at least some of that debt to hospitals (Karpman 2023). A forthcoming set of supplemental tables will examine the prevalence of medical debt at other levels of geography, such as state legislative districts, Congressional districts, metropolitan areas, and large cities. 2 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN Results: Medical Debt in New York State The share of New York State consumers with medical debt varied widely across regions and communities. In February 2022, approximately 6 percent of New York State consumers with a credit record had medical debt on their credit reports, representing approximately 740,000 adults in the state (Karpman et al. 2023). Though less than half of the national average (6 percent versus 13 percent nationwide), this estimate understates the actual level of medical debt among New York residents,6 since not all medical debt is reported to credit bureaus and it is not always possible to identify the source of debt collections on credit reports. For instance, we cannot determine whether unpaid credit card accounts include debt incurred because of medical expenses. Additionally, taking a snapshot of the share of consumers with medical debt at a moment in time does not capture the dynamic risks of incurring debt over longer periods, as surveys suggest larger shares of adults have medical bills that are past due or that they are paying over time (Karpman and Caswell 2017; Lopes et al. 2022). The relatively low statewide average in New York also conceals substantial variation across regions and communities. Figure 1 shows the share of consumers with medical debt in the state's 10 economic regions, each of which represents clusters of counties. The regions with the lowest rates of medical debt were Long Island (3 percent) and New York City (4 percent). The region with the highest rate was Central New York, where about 1 in 7 consumers (14 percent) had medical debt on their credit reports. Approximately 1 in 10 consumers had medical debt in the Mohawk Valley (11 percent), North Country (11 percent), and Southern Tier (10 percent) regions. MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 3 FIGURE 1 Share of Consumers with Medical Debt in Collections in New York State, by Region, February 2022 North Country 11% Mohawk Valley 11% Central Capital Finger Lakes New York District 5% 14% 8% Western New York 8% Southern Tier 10% Mid-Hudson 7% Long Island 3% New York City 4% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Relative to the statewide average, regions with a higher prevalence of medical debt were more rural and tended to have lower educational attainment, average household incomes, and employment rates and higher shares of nonelderly adults with disabilities (see Appendix C table 1). They were predominantly composed of non-Hispanic white residents and US-born citizens compared with the rest of the state. However, racial and ethnic disparities in medical debt were found in most regions, as discussed below. Within regions, the prevalence of medical debt varied widely across communities. Figure 2 shows the share of consumers with medical debt across New York's zip code tabulation areas (ZCTAs), a geographic unit representing communities based on the US Postal Service's zip code service areas 4 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN (hereafter referred to as "communities"). Our data provided a sufficient sample size to estimate the prevalence of medical debt in 1,134 of the state's 1,754 populated communities, and the communities in our sample represent 98 percent of the state's population. We define low-debt communities as those in the lowest quartile of the distribution of medical debt prevalence across the state and high-debt communities as those in the highest quartile. Among the 282 high-debt communities, the share of consumers with medical debt was between 9.7 percent and 37.6 percent. In contrast, the share of consumers with medical debt in the 284 low-debt communities was 3.2 percent or less. FIGURE 2 Share of Consumers with Medical Debt in Collections in Communities of New York State, February 2022 Share of Consumers with Medical Debt 9.7% to 37.6% 5.6% to 9.6% 3.3% to 5.5% 0.0% to 3.2% Insufficient data URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 5 Within each region, the burden of medical debt generally fell most heavily on communities of color and communities with greater economic challenges. Figure 3 highlights racial and ethnic disparities in medical debt at the region level. Communities of color had higher rates of medical debt than predominantly white communities within most economic regions, revealing a pattern that was not observed at the state level. FIGURE 3 Share of Consumers with Medical Debt in Collections in New York State, by Racial/Ethnic Composition of Communities, Statewide and by Region, February 2022 0% to <30% people of color 30% to <50% people of color 50% or more people of color 0% 5% 10% 15% 20% 25% 30% Statewide Capital District Central New York Finger Lakes Long Island Mid-Hudson Mohawk Valley New York City North Country Southern Tier Western New York URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. No communities in the Mohawk Valley and Southern Tier regions had 50 percent or more residents who were people of color. 6 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN The starkest racial/ethnic disparity in rates of medical debt can be seen in Central New York, where more than one in four (28 percent) consumers in communities of color (i.e., where 50 percent or more of the population consists of people of color) had medical debt compared to 12 percent of consumers in predominantly white communities (i.e., where fewer than 30 percent of residents are people of color). Similar patterns in the prevalence of medical debt based on the racial and ethnic composition of communities, albeit with varying differences in magnitudes, are observed in the Capital District, Finger Lakes, Long Island, Mid-Hudson, Mohawk Valley, New York City, and Western New York regions. In two regions-Mohawk Valley and Southern Tier-there are no communities where over 50 percent of the population consists of people of color. In the Mohawk Valley region, communities with between 30 percent and 50 percent of residents who are people of color had higher rates of medical debt than predominantly white communities; the reverse was true in the Southern Tier region. We also found wide racial/ethnic disparities in many counties with larger populations. Counties in which the share of residents with medical debt in communities of color was more than twice as high as the share in predominantly white communities included Albany (14 percent versus 6 percent), Dutchess (19 percent versus 8 percent), Erie (14 percent versus 6 percent), Monroe (7 percent versus 3 percent), New York (Manhattan) (4 percent versus 2 percent), Onondaga (28 percent versus 10 percent), Schenectady (19 percent versus 6 percent), and Westchester (7 percent versus 2 percent; Appendix C table 3). Figure 4 shows that lower median household income in communities is associated with higher rates of medical debt both statewide and within each region. Statewide, consumers in the lowest-income communities-those with a median household income of less than $54,200-were three times more likely to have medical debt than consumers in the highest-income communities (with median household income above $88,500). Overall, 9 percent of consumers in the lowest-income communities had medical debt compared with only 3 percent of consumers in the highest-income communities, 5 percent in the second-highest, and 6 percent in the third-highest-income communities. This same relationship between medical debt and community-income quartile was observed in each region. MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 7 FIGURE 4 Share of Consumers with Medical Debt in Collections in New York State, by Median Household Income of Communities, Statewide and by Region, February 2022 Lowest income quartile ($2,500–$54,200) Second income quartile ($54,200–$66,400) Third income quartile ($66,400–$88,500) Highest income quartile (≥$88,500) 0% 5% 10% 15% 20% 25% 30% Statewide Capital District Central New York Finger Lakes Long Island Mid-Hudson Mohawk Valley New York City North Country Southern Tier Western New York URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Median household incomes for communities are rounded to the nearest $100. Point estimates are suppressed for sample sizes below 100. Figure 5 shows that living in a community with a higher uninsurance rate is also associated with a higher rate of medical debt. Statewide, the difference in medical debt between lower and higher uninsurance communities is small; around 6 percent of consumers in communities with an uninsurance rate above the statewide median had medical debt compared to 5 percent in the communities with uninsurance rates below the median. However, larger disparities between lower and higher insurance communities are observed within regions, with the largest absolute differences in Central New York (7 8 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN percentage points) and Western New York (5 percentage points). While differences in medical debt based on health insurance coverage exist, these differences would likely be greater if not for New York State's high rate of health insurance coverage relative to other states, particularly states that have not expanded Medicaid (Conway and Branch 2022). FIGURE 5 Share of Consumers with Medical Debt in Collections in New York State, by Community Uninsurance Rates, Statewide and by Region, February 2022 Uninsurance rates below median (0.0%–3.6%) Uninsurance rates above median (>3.6%) 0% 5% 10% 15% 20% 25% 30% Statewide Capital District Central New York Finger Lakes Long Island Mid-Hudson Mohawk Valley New York City North Country Southern Tier Western New York URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Uninsurance rates are for residents of all ages in the civilian noninstitutionalized population. MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 9 Nearly half of New York State consumers with medical debt owed $500 or more, including 30 percent of consumers with medical debt who owed $1,000 or more. Among consumers with any medical debt, the median amount of debt was $456 (figure 6). This amount was highest in four regions in which the prevalence of medical debt was above the statewide average: Mohawk Valley ($687), Southern Tier ($655), Mid-Hudson ($591), and North Country ($561). The median amount of medical debt was lowest in three regions with rates of medical debt below the statewide average: Long Island ($371), New York City ($375), and Finger Lakes ($422). These results suggest that in regions where people are more likely to have any medical debt, those individuals are also generally more likely to incur larger amounts of debt. FIGURE 6 Median Medical Debt Among Consumers with Any Medical Debt in Collections in New York State, Statewide and by Region, February 2022 Median medical debt $0 $100 $200 $300 $400 $500 $600 $700 $800 $900 $1,000 Statewide $456 Capital District $490 Central New York $463 Finger Lakes $422 Long Island $371 Mid-Hudson $591 Mohawk Valley $687 New York City $375 North Country $561 Southern Tier $655 Western New York $453 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Nearly half (48 percent) of consumers with medical debt owed $500 or more, and 52 percent owed less than $500 (figure 7). This threshold is significant because of a recent change in credit reporting 10 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN practices; on April 11, 2023, the three major national credit reporting agencies announced the removal of all medical debt collection tradelines with a balance under $500 from consumer credit reports. Consumers with total medical debt collection balances below $500 will therefore have all this debt removed from their credit files, and some consumers with total balances above this amount will benefit from the reporting change if they have one or more account balances below $500. FIGURE 7 Amount of Medical Debt Among Consumers with Any Medical Debt in Collections in New York State, Statewide and by Region, February 2022 <$500 $500–$999 $1,000–$1,999 ≥$2,000 Statewide 52% 18% 17% 13% Capital District 51% 15% 20% 15% Central New York 52% 18% 15% 15% Finger Lakes 55% 16% 16% 14% Long Island 58% 19% 13% 9% Mid-Hudson 46% 19% 19% 15% Mohawk Valley 42% 19% 19% 19% New York City 58% 18% 16% 8% North Country 48% 16% 16% 20% Southern Tier 43% 17% 15% 25% Western New York 53% 18% 18% 11% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. However, even though nearly three-quarters of consumers with medical debt nationally will have at least some of this debt removed from their credit reports, about half will continue to have medical debt appear on their credit reports (Brown and Wilson 2023). The persistence of medical debt on credit reports is especially likely for those who owe larger total amounts. Nearly one in three consumers in MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 11 New York State (30 percent) with medical debt owed a total balance of $1,000 or more, including more than one in eight (13 percent) who owed $2,000 or more. In two regions of the state, roughly 4 in 10 consumers (38 percent in Mohawk Valley and 40 percent in Southern Tier) with medical debt had total balances of at least $1,000. It is also important to note that the change in credit reporting practices does not alter the underlying debt that consumers owe to health care providers. These providers and the third-party collection agencies with which they contract can still file civil lawsuits against patients and take other actions to collect payment for past-due medical bills. Just as there were disparities in the prevalence of medical debt based on the racial/ethnic composition and income of communities within regions, we also found differences by community characteristics in median debt amounts among consumers with medical debt. For instance, the median debt amount in communities of color in the Capital District region was $899, about twice the median amount in predominantly white communities ($448; figure 8). 12 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN FIGURE 8 Median Medical Debt Among Consumers with Any Medical Debt in Collections in New York State, by Racial/Ethnic Composition of Communities, Statewide and by Region, February 2022 0% to <30% people of color 30% to <50% people of color 50% or more people of color Median medical debt $0 $100 $200 $300 $400 $500 $600 $700 $800 $900 $1,000 Statewide Capital District Central New York Finger Lakes Long Island Mid-Hudson Mohawk Valley New York City North Country Southern Tier Western New York URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. No communities in the Mohawk Valley and Southern Tier regions had 50 percent or more residents who were people of color. Point estimates are suppressed for sample sizes below 100. Median medical debt was also highest in communities with the lowest incomes-where more than half of consumers with medical debt owed $500 or more, with a median of $516-compared with a median of $390 in communities with the highest incomes (figure 9). In some regions, these income disparities were wider. For instance, the lowest-income communities in the Mid-Hudson region had a median debt amount of $737, compared with $478 in the region's highest-income communities. In all MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 13 regions, median debts were larger in communities with higher uninsurance rates (see Appendix C figure 1), possibly reflecting the greater likelihood that uninsured residents will be responsible for paying the full amount of health care provider charges out of pocket. FIGURE 9 Median Medical Debt Among Consumers with Any Medical Debt in Collections in New York State, by Median Household Income of Communities, Statewide and By Region, February 2022 Lowest income quartile ($2,500–$54,200) Second income quartile ($54,200–$66,400) Third income quartile ($66,400–$88,500) Highest income quartile (≥$88,500) Median medical debt $0 $100 $200 $300 $400 $500 $600 $700 $800 $900 $1,000 Statewide Capital District Central New York Finger Lakes Long Island Mid-Hudson Mohawk Valley New York City North Country Southern Tier Western New York URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. 14 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN Notes: Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Median household incomes for communities are rounded to the nearest $100. Point estimates are suppressed for sample sizes below 100. These findings are consistent with CFPB projections which find that, nationally, consumers who have all medical debt removed from their credit reports because of the reporting change are more likely to live in neighborhoods that are majority white and higher income (Nathe and Sandler 2022). High-debt communities faced additional challenges in confronting greater health care needs with fewer resources. Table 1 compares the average characteristics of communities across the four quartiles of medical debt. Overall, the data suggest high-debt communities were more rural and faced greater economic and health challenges. High-debt communities had a population density of 210 residents per square mile, compared with 1,930 residents per square mile in low-debt communities. Residents of high-debt communities also tended to have relatively lower levels of educational attainment, income, and employment. For instance, one in four adults ages 25 and older in high-debt communities (25 percent) had attained a bachelor's degree, compared with about one in two adults in low-debt communities (51 percent). The average household income of high-debt communities was less than half that of low-debt communities ($72,079 versus $146,895). And the employment-to-population ratio for adults ages 25 to 54-an age group referred to as prime-age workers-was about 7 percentage points lower for high-debt communities than for low-debt communities (75 percent versus 82 percent). Relative to low-debt communities, high-debt communities also had higher shares of adults ages 18 to 64 with disabilities (8 percent versus 4 percent) and nonelderly residents insured with Medicaid or other public coverage (29 percent versus 17 percent), suggesting residents in high-debt communities faced greater health challenges, health care needs, and limitations on their ability to work. In both low-debt and high-debt communities, most residents were non-Hispanic white (63 percent and 71 percent), whereas communities in the two middle quartiles of medical debt prevalence had larger shares of residents who are people of color. As noted above, however, statewide patterns conceal racial and ethnic disparities in medical debt within most regions. More than 9 in 10 residents of high- debt communities (92 percent) were US-born citizens, a higher share than in communities with a lower prevalence of medical debt. Communities with the largest immigrant populations are concentrated in New York City, a region that had low rates of medical debt even in many communities with low incomes. MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 15 TABLE 1 Characteristics of Communities in New York State, by Prevalence of Medical Debt in Collections across Communities, February 2022 Lowest Second Third Highest debt debt debt debt quartile quartile quartile quartile Number of communities with sufficient data for 284 283 285 282 medical debt estimates Total population of communities with sufficient data 6,380,573 6,084,110 3,971,743 2,628,486 Share of adults with medical debt among all 2% 4% 7% 15% communities Range of medical debt prevalence across communities 0% to 3.2% 3.3% to 5.6% to 9.7% to 5.5% 9.6% 37.6% Median medical debt among consumers with any $357 $390 $436 $580 medical debt Race/ethnicity Asian, non-Hispanic 13% 9% 3% 3% Black, non-Hispanic 6% 23% 16% 11% Hispanic 15% 24% 26% 10% White, non-Hispanic 63% 40% 51% 71% Additional races, non-Hispanic 3% 3% 3% 4% Nativity and citizenship status US born 74% 71% 81% 92% Naturalized citizen 16% 17% 10% 4% Not a citizen 10% 12% 9% 4% Highest level of educational attainment (ages 25 and older) High school degree or less 29% 41% 45% 44% Some college or associate's degree 19% 24% 28% 31% Bachelor's degree or more 51% 35% 28% 25% Family income as a percent of FPL Below 100% FPL 9% 14% 18% 17% 100–299% FPL 22% 30% 34% 36% 300–499% FPL 20% 24% 23% 24% 500% FPL or more 49% 33% 25% 23% Average household income $146,895 $95,912 $77,737 $72,079 Employment-to-population ratio (ages 25–54) 82% 78% 76% 75% Health insurance coverage (ages 0–64) Uninsured 5% 7% 7% 6% Private coverage 75% 63% 56% 59% Public coverage 17% 25% 31% 29% Both private and public coverage 3% 4% 6% 6% 16 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN Lowest Second Third Highest debt debt debt debt quartile quartile quartile quartile Share with a disability (ages 18–64) 4% 6% 7% 8% Population density (number of people/square mile of 1,930 940 362 210 land area) Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Prevalence of medical debt in collections is based on the share of consumers with credit bureau records who have medical debt in collections. Communities are defined based on zip code tabulation areas. Communities with sufficient data for medical debt estimates refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. Quartiles of medical debt prevalence are based on the prevalence of medical debt across all communities in the state. Additional races includes those who identify as American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, more than one race, or some other race. FPL is federal poverty level. Private coverage includes employer sponsored insurance, direct purchase, TRICARE/military coverage, and combinations of multiple types of private coverage. Public coverage includes Medicare, Medicaid, VA coverage, and combinations of multiple types of public coverage. Results: Multivariate Model The previous section provided a comprehensive assessment of medical debt in New York State and how it varied across regions and community characteristics. Here, we use a multivariate linear regression model, which simultaneously accounts for differences in local demographic, socioeconomic, and health characteristics observed in the data, to disentangle the associations between consumers' medical debt and each local area characteristic and to assess the extent to which community characteristics explain regional variation in medical debt. Even after accounting for differences in local demographic, socioeconomic, and health characteristics observed in the data, we find that living in certain regions was associated with a greater risk of having medical debt. Approach The primary outcome of the model is an indicator for whether the consumer had medical debt. The model controls for several demographic and socioeconomic characteristics, including individual-level indicators for the person's age; community-level controls for racial/ethnic composition, population density, employment-to-population ratio, average household income, and uninsurance rates; and indicators for each economic region. In addition, the model includes two measures to control for regional differences in the use of health care services. First, we control for the share of 18- to 64-year-olds with disabilities at the community MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 17 level. Second, we control for emergency room visits per Medicare beneficiary at the county level.7 This measure among the Original Medicare (i.e., fee-for-service) population serves as a proxy for the county's health status and its propensity to use hospital care as a whole. Additional details on the data and methods can be found in Appendix A. Findings Key findings from the regression models are described below. These estimated associations are consistent with the descriptive results in the prior section and estimates from a prior study that assessed which county characteristics predict medical debt across the US (Blavin, Braga, and Gangopadhyaya 2022). Therefore, these results confirm the correlations between medical debt and each community characteristic. ◼ Communities with a greater share of non-Hispanic Black and other or multiple-race populations had higher rates of medical debt relative to communities with a greater share of non-Hispanic white residents, even after controlling for other community characteristics such as income, insurance status, and health factors (table 2). This is consistent with past work showing that Black people are more likely to have past-due medical debt (Blavin, Braga, and Gangopadhyaya 2022; CFPB 2022; Karpman, Martinchek, and Braga 2022). We also find that communities with a higher share of Hispanic people had lower rates of medical debt than non- Hispanic white communities, holding other factors constant. ◼ People living in communities with a higher share of uninsured people were also significantly more likely to have medical debt, even after controlling for other community characteristics. This relationship is consistent with the descriptive correlations and prior causal evidence that health insurance expansions are associated with reductions in medical debt and total collection balances (Caswell and Waidmann 2017; Hu et al. 2018). ◼ Communities with lower average incomes and lower employment-to-population ratios had higher rates of medical debt. This result is not surprising because nonworkers and families with less income are less likely to have the means to pay their medical bills on time. ◼ Higher community disability rates and higher number of emergency room visits per Medicare beneficiary at the county level were associated with a higher share of residents with medical debt. For instance, a 10 percentage-point increase in the share of nonelderly adults in the community with a disability was associated with a 4.2 percentage-point increase in medical debt. ◼ Young adults ages 18–24 were least likely to have medical debt, holding other factors constant. Relative to 18- to 24-year-olds, adults in most older age groups were 2 to 3 percentage points more likely to have medical debt. Lower rates of medical debt among young adults may be 18 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN related to their lower average health care needs as well as their greater access to health insurance coverage relative to the next oldest age groups (Batty, Gibbs, and Ippolito 2018).8 For instance, 18-year-olds in New York can qualify for no-cost or low-cost coverage under Medicaid or the Children's Health Insurance Program (CHIP) if their family incomes are below 405 percent of the federal poverty level (FPL).9 Under the Affordable Care Act, dependent children can also remain covered by their parents' health insurance plans until they reach age 26. While those ages 65 or older are more likely to have medical debt than those ages 18–24, they are less likely to have medical debt than adults in other age groups, likely because of their nearly universal eligibility for Medicare (Caswell and Goddeeris 2020). ◼ Even after accounting for these differences in community demographic and health characteristics, we still find that living in certain economic regions was associated with a higher likelihood of medical debt. However, it is important to note that the characteristics in this model only explain a small portion of the differences in medical debt across individuals in New York State, as measured by the R-squared. Much of the unexplained variation in the model can be attributable to the lack of individual-level controls (besides age) available in the credit bureau data. In other words, individual age and community characteristics alone can only explain a small share of the likelihood that an individual consumer will have medical debt. Further research is needed to understand additional individual, community, and health system factors that may explain differences in medical debt across regions. TABLE 2 Association Between Consumer Age and Community and County Characteristics with Medical Debt in Collections in New York State, February 2022 Coefficient Consumer age (reference: ages 18 to 24) 25 to 34 0.030*** 35 to 44 0.030*** 45 to 54 0.029*** 55 to 64 0.022*** 65 and older 0.007*** Age not reported 0.025*** Community racial/ethnic composition (reference: share non-Hispanic white) Share non-Hispanic Black 0.012** Share non-Hispanic American Indian/Alaska Native 0.062 Share non-Hispanic Asian or Pacific Islander -0.013 Share Hispanic -0.022*** Share non-Hispanic other/multiple races 0.114** Additional community demographic and socioeconomic characteristics Employment-to-population ratio (ages 25 to 54) -0.045** MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 19 Coefficient Log average household income -0.018*** Uninsurance rate (ages 0 to 64) 0.128*** Share with a disability (ages 18 to 64) 0.418*** Population per square mile of land area (in millions) 0.073** County health characteristics Emergency room visits per Medicare fee-for-service beneficiary 0.044** Region (reference: Long Island) Capital District 0.029*** Central New York 0.084*** Finger Lakes -0.021*** Mid-Hudson 0.024*** Mohawk Valley 0.043*** New York City -0.015*** North Country 0.033*** Southern Tier 0.033*** Western New York 0.019*** Constant 0.215*** Observations 612,265 R-squared 0.026 Source: Urban Institute credit bureau data from February 2022, 2016–2020 American Community Survey 5-year estimates, and Centers for Medicare and Medicaid Services Multiple Chronic Conditions Database. Notes: Age refers to the consumer age in the credit bureau data. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. *** p<0.01, ** p<0.05, * p<0.10 Policy Implications The share of New York State consumers with medical debt is below the national average, but this share varies widely across communities and regions of the state. Within regions, the burden of medical debt is greatest in communities with a higher share of residents who are people of color and communities with lower household incomes and employment rates, higher rates of disability, and limited access to health insurance coverage. Nearly half of consumers with medical debt owe $500 or more, and almost one- third owe $1,000 or more, suggesting many will continue to have medical debt appear on their credit reports even after the change in reporting practices that took effect in April 2023. Below, we discuss the policy implications that emerge from our study findings, including recent and proposed state efforts to protect patients from medical debt. 20 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN Expanding health insurance coverage and lowering deductibles and other cost- sharing requirements would reduce the risk of medical debt. In every region of the state, communities with higher shares of residents who were uninsured also had higher shares of consumers with medical debt. Prior research provides strong evidence that expansions of Medicaid have reduced medical debt and other measures of financial distress by extending access to coverage for the uninsured (Blavin, Braga, and Gangopadhyaya 2022; Caswell and Waidmann 2017; Hu et al. 2018; Finkelstein et al. 2012; Gross and Notowidigdo 2011). Even though New York has already expanded Medicaid, further efforts to increase access to and take-up of coverage could offer new protection against medical debt for the remaining population without health insurance. In recent years, state policymakers have enacted or proposed new policies to increase the number of residents with coverage. For instance, in 2021, the state eliminated premiums for its Essential Plan, a Basic Health Program established under the Affordable Care Act that offers coverage with no deductibles and low copayments to people with incomes below 200 percent of FPL who do not qualify for Medicaid or Child Health Plus, the state's CHIP program.10 The state budget for fiscal year 2023 also eliminated Child Health Plus premiums for households with incomes of up to 222 percent of FPL. 11 A state waiver application would increase access to coverage by raising the income threshold at which residents can qualify for Essential Plan coverage to 250 percent of FPL, enabling more people to benefit from the plan's lower out-of-pocket costs.12 Other policy changes under consideration would expand coverage to groups that historically have been excluded from public programs. These include proposals for using state funds to expand Medicaid and/or Essential Plan eligibility to undocumented immigrants.13 At the federal level, the Inflation Reduction Act extended an expansion of health insurance marketplace premium subsidies through the end of 2025. A state premium-assistance program for qualified health plans sold through the New York State of Health marketplace could further lower premium costs for families with incomes above the Essential Plan and Child Health Plus eligibility thresholds and reduce the number of uninsured (Benjamin, Dunker, and Orecki 2022). Enhanced outreach and enrollment assistance can help more consumers learn about and apply for low-cost coverage. Other states are testing new automatic-enrollment strategies that could reduce the rate at which people churn on and off of coverage by creating smoother transitions between Medicaid and marketplace coverage.14 Though health insurance coverage is a key protective factor, it does not always provide sufficient protection against medical debt because of gaps in covered services and deductibles, copayments, and MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 21 other cost-sharing requirements. For example, in high-debt communities, only 6 percent of the population under age 65 did not have health insurance on average, but 15 percent of consumers had medical debt (table 1). The risk of incurring medical debt is also not limited to people with incomes below or near the poverty level. Even in communities in the highest quartile of median household income (i.e., above $88,500), 3 percent of consumers had medical debt, and this share was as high as 7 percent in some regions (figure 4). Our findings suggest that providing additional cost-sharing subsidies to people with lower incomes will be the most effective strategy for reducing the burden of medical debt; however, expanding financial assistance for people with moderate incomes is also important. Strengthening requirements for hospitals to provide financial assistance could complement health insurance reforms to reduce the prevalence of medical debt. National data show that nearly three in four adults ages 18 to 64 with past-due medical debt in 2022 reported owing at least some of that debt to hospitals, suggesting that expanding access to hospital financial assistance can make a significant impact in mitigating debt burdens (Karpman 2023). All hospitals in New York State that are nonprofit entities must provide financial assistance to patients in order to maintain their tax-exempt status under federal law, and hospitals must meet additional state requirements to receive funding from the state's Indigent Care Pool. The Affordable Care Act requires nonprofit hospitals to have written financial assistance policies for all emergency and medically necessary care provided by the facilities and prohibits hospitals from engaging in extraordinary collection actions (ECAs, which include reporting past-due debt to credit bureaus, filing civil lawsuits, or garnishing wages or bank accounts) without making reasonable efforts to determine whether patients qualify for charity care. The state's 2006 Hospital Financial Assistance Law imposes further regulations, including a requirement that hospitals must offer financial assistance on a sliding scale to uninsured patients with incomes below 300 percent of FPL and restrictions on burdensome documentation requirements for applicants (Dunker and Tracy 2023). The availability of financial assistance should offer an additional layer of protection to people who are most at risk of medical debt. For instance, more than half (52 percent) of residents in high-debt communities had family incomes below 300 percent of FPL, compared with less than one-third (31 percent) of residents in low-debt communities (table 1). However, gaps in both statute and enforcement of federal and state rules expose many consumers to medical debt and ECAs, including those who were likely eligible for but did not receive financial assistance (Dunker and Tracy 2023; Karpman 2023).15 Statewide, 9 percent of consumers in 22 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN communities in the bottom quartile of median household income (i.e., median income below $54,200- about 400 percent of FPL for a single person and just below 250 percent of FPL for a family of three) had medical debt. This share was 21 percent in communities in the bottom income quartile in Central New York and 13 percent in the Capital District, Mid-Hudson, Mohawk Valley, North Country, and Western New York regions. These findings suggest some patients who should qualify for hospital charity care are not receiving it or that the discounts they receive are insufficient to help them pay the total amount of their medical bills. Enforcing and expanding required financial assistance from hospitals and the providers that practice within them and streamlining the process of applying for assistance could reduce the burden of medical debt. The state's fiscal year 2024 budget takes a step in this direction by requiring hospitals to adopt a uniform application for financial assistance developed by the state health department. Changes in credit reporting would reduce the number of consumers with medical debt on their credit files and improve credit scores. Nearly half of consumers (48 percent) who had medical debt on their credit reports in February 2022 had total balances of $500 or more. Many of these consumers will continue to have medical debt appear on their credit reports even after a recent change in credit reporting practices. In April 2023, the three major credit reporting agencies announced the removal of all unpaid medical debt in collections with a balance of less than $500 from credit reports. This followed a previous change in July 2022 that removed paid medical collections and extended the period before unpaid medical collections could appear on credit reports from 6 months to one year. Though some consumers in New York State with total collection balances above $500 will see a portion of medical debt dropped from their credit records (i.e., if they have any individual balances below $500), others may not benefit from this change, particularly the 30 percent of consumers with medical debt whose total medical collection balances are $1,000 or more. Policies that would go further by removing all medical debt from credit reports have been proposed at both the state and federal levels.16 For instance, in 2023, the New York State legislature passed a bill, now being considered by the governor, that would prohibit health care providers and the collection entities with which they contract from furnishing information about medical debt to consumer reporting agencies and prohibit those agencies from reporting or maintaining such information on a consumer's file. One rationale for this change is that medical collections are less predictive than non- medical collections in determining a consumer's credit risk, since medical debt may reflect difficulties MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 23 navigating complex billing and reimbursement processes rather than a person's creditworthiness (Brevoort and Kambara 2014; CFPB 2014). The removal of medical debt could improve credit scores, particularly those relying on older scoring models that place more weight on medical debt relative to newer models (CFPB 2022). A recent study found that the average consumer experiences a meaningful increase in their credit scores and credit availability when medical debt is removed from their credit report, with larger gains among those who had medical debt balances over $500 (Brown and Wilson 2023). Removing medical debt may also enhance the ability of some people to find jobs and housing to the extent adverse credit reports served as a barrier. Changes in credit reporting may also require new strategies for monitoring trends in medical debt and understanding potential provider and consumer responses to prevent unintended consequences. Recent and proposed changes to the inclusion of medical debt on credit reports will not affect the underlying debt that consumers owe to providers. As a smaller portion of the total amount of medical debt held by consumers remains visible in the credit bureau data, new strategies may be needed for monitoring the prevalence of medical debt in the state, variation across communities, and disparities by race, ethnicity, and income. Federal and state surveys of households present one important method for estimating the share of the population with medical debt. Court records will also continue offering insight on the extent to which health care providers are filing civil lawsuits against patients over unpaid bills (Dunker and Benjamin 2020). Information from hospitals, other health care providers, and third- party collection agencies could provide an additional source of data, and several states have developed processes for collecting this information (Robertson, Rukavina, and Fuse Brown 2022). Since removing medical debt from credit reports limits the tools that providers have for collecting payment, it will also be important to safeguard against unintended consequences for access to care and consumer and provider finances by anticipating and monitoring how both groups respond to these changes. For instance, patients' ability to receive and afford needed care may depend on whether providers increasingly require payment at the point of service or use more aggressive collection practices rather than offering additional financial assistance to patients who are unable to pay. These questions underscore the need for further research to examine state and local trends in health care access and affordability and patient and provider experiences. 24 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN Other consumer protections can mitigate financial impacts for people who have already incurred medical debt. Restrictions on furnishing information to credit reporting agencies represent one approach to limiting ECAs in order to mitigate the impact of medical debt after people have incurred it. Other ECAs include selling medical debt to a third party (i.e., a debt buyer), denying medically necessary care due to nonpayment, or initiating legal processes that may result in property liens and foreclosures, wage garnishment, bank account seizure, and writs of body attachment or other actions that can cause a person's arrest.17 State legislation passed in November 2022 will offer new protections for consumers who have already incurred medical debt by prohibiting hospitals and health care professionals from garnishing their wages or placing liens on their primary residences over unpaid medical bills. States have also taken steps to limit ECAs until a pending appeal of a health insurance decision is resolved (Robertson, Rukavina, and Fuse Brown 2022; Wu, Bosco, and Kuehnhoff 2019). Additional analysis is needed to understand the individual, community, and health system factors that explain the disproportionate burden of medical debt in certain regions. The variation in medical debt across communities that we observed partially reflects differences in community-level demographic and socioeconomic characteristics. However, consumers in some regions faced a greater risk of medical debt, even after controlling for these differences, suggesting that individual factors that we cannot observe (except for age) and other community characteristics not included in our analysis played a role in explaining regional differences in medical debt. For instance, communities in the New York City and Long Island regions had low rates of medical debt, even in communities where large shares of residents had incomes below FPL or lacked insurance. In contrast, Central New York remained an outlier for its high prevalence of medical debt across communities. Many of the counties in New York where a large percentage of consumers had medical debt have also been hotspots for hospital lawsuits against patients over unpaid medical bills (Dunker and Benjamin 2022). These findings suggest other unobserved factors-either at the individual, community, or health system level-may contribute to local differences in medical debt and merit further inquiry. For instance, we are unable to measure and control for individual health status, propensity to use health care, health insurance generosity, and household savings. These factors can play a significant role in determining the likelihood of having medical debt. MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN 25 In addition, if hospitals and other health care providers in some regions offer limited financial assistance to patients or employ more aggressive collection practices, including furnishing information on past-due medical debt to credit reporting agencies, consumers in these regions may be more likely to incur medical debt and have it appear on their credit reports. Increased scrutiny of health system practices in regions with disproportionate rates of medical debt and ECAs can promote local system change and identify the need for new policy solutions (Paturzo et al. 2021). Conclusion The unequal burden of medical debt across communities in New York State exacerbates health and economic disparities by race, ethnicity, income, and region. Consumers who incur medical debt likely face the most serious health and financial consequences, but the challenges they face also have spillover effects on their families and communities. State policies to protect residents from medical debt can further progress toward the goal of a more affordable and equitable health care system that promotes the health, well-being, and economic security of all New York residents. 26 MEDICAL DEBT IN NEW YORK STATE AND ITS UNEQUAL BURDEN Appendix A: Data, Methods, and Limitations Data Credit Bureau Data Our primary data source is February 2022 credit bureau data, which include a 4 percent nationally representative random sample of deidentified, consumer-level records from one of the major national credit reporting agencies. Our sample includes more than 600,000 consumers ages 18 and older with a credit record in New York State. All records were stripped of personally identifiable information. The credit bureau data contain the amount of consumer medical debt held in collections, including debt held by external collection agencies and as internal collections and charge-offs. The data also contain consumer age and geographic identifiers, including zip code and county of residence. We matched consumer zip codes of residence to zip code tabulation areas (ZCTAs), a set of non-overlapping geographic units that approximate the zip codes used for mail delivery and that come closest to representing the geographic boundaries of communities. There are 1,794 ZCTAs in New York State, including 1,754 populated ZCTAs, with populations ranging from under 100 to over 108,000, and a median population of about 3,000 residents.18 Throughout this report, we refer to these ZCTAs as communities. American Community Survey Data With the exception of age, we do not observe demographic and socioeconomic characteristics of individual consumers in the credit bureau data. We instead draw on 2016–2020 American Community Survey (ACS) five-year estimates to examine the demographic and socioeconomic makeup of consumers' communities by matching ZCTA-level ACS estimates to consumers' zip codes of residence. The ACS is a nationally representative survey conducted annually by the Census Bureau that collects social, economic, housing, and demographic data on the US household population. Pooled five-year average estimates for small geographic areas are available in a series of data tables published by the APPENDIX A 27 Census Bureau.19 We group communities based on their ZCTA-level characteristics, including the following: ◼ Racial and ethnic composition. We estimate the share of community residents who are people of color, defined as those who identify as Hispanic or Latino, or who are not Hispanic or Latino and identify as American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, or more than one race or some other race, excluding those who identify as non- Hispanic and white alone. ◼ Median household income. We divide communities into quartiles based on the median household income of communities across the state. ◼ Health insurance coverage. We identify whether communities have uninsurance rates that are below or above the median uninsurance rate of communities across the state. Additional characteristics in our analysis include family income as a percentage of the federal poverty level, nativity and citizenship status, educational attainment of adults ages 25 and older, employment-to-population ratio of adults ages 25 to 54, disability status of adults ages 18 to 64 (including vision, hearing, cognitive, ambulatory, self-care, or independent living difficulties), and population density, defined as the number of people per square mile of land area (scaled to be in millions for the multivariate analysis). We also estimate average household income based on the aggregate income for the community divided by the total number of households. These characteristics are measured at the community, county, region, and state levels. When we match community-level ACS data to the credit bureau data for New York, we drop 199 credit records that are missing ZCTAs because the zip code for that record does not match a zip code in the US or the zip code for the record is not associated with a ZCTA. We drop an additional 277 credit records with zip codes that are matched to one of the 40 unpopulated ZCTAs in New York. Six ZCTAs for PO Boxes in New York are not matched to credit record zip codes. Centers for Medicare and Medicaid Services (CMS) Multiple Chronic Conditions Database We use data from the CMS Multiple Chronic Conditions dataset to obtain data on emergency room visits per Medicare fee-for-service beneficiary at the county level in our multivariate analysis. The Multiple Chronic Conditions data provide state and county information on the number of chronic conditions among Original Medicare beneficiaries. It also includes information on condition prevalence, utilization of care, and spending organized by the four categories of chronic condition counts. 20 28 APPENDIX A Analysis Aggregating to Larger Geographic Units We aggregate the community-level data to larger geographic units using consumer county of residence, which is available for 98 percent of consumers in our sample. For consumers with missing county of residence, we use the Missouri Census Data Center's Geocorr (geographic correspondence engine) application.21 This tool can be used to estimate relationships between two or more geographic areas in the US, including the portion of a ZCTA's population in counties and other geographic areas. 22 We assign ZCTAs to the county in which a plurality of the ZCTA population resides. We also manually match eight ZCTAs that were not assigned to a county in Geocorr because they are PO Box zip codes that do not appear in the MABLE database Geocorr uses to build its lists of correlations between geographies. We exclude 40 unpopulated ZCTAs from our analysis. Finally, we group counties based on the 10 economic regions defined by the Office of the State Comptroller. 23 Descriptive Analyses of Medical Debt We estimate the share of consumers in New York State with medical debt, overall and in each community, county, and region. We also estimate median medical debt among those with any medical debt in collections and the share with medical debt amounts below $500, $500 to $999, $1,000 to $1,999, and $2,000 or more. We present medical debt estimates for groups of communities in each area (state, region, and/or county) based on the community characteristics described above (i.e., race/ethnicity, median household income, and lack of health insurance). We divide communities into quartiles based on the prevalence of medical debt across all communities in the state and compare the average characteristics of low-debt and high-debt communities. Estimates of the medical debt quartile for communities are suppressed for sample sizes below 50. Our data provide a sufficient sample size to estimate the prevalence of medical debt (as a range) in 1,134 of the state's 1,754 populated communities. Communities with fewer than 50 consumers in our data account for 35 percent of the communities in New York State, but only about 2 percent of the state's population. In addition, we suppress point estimates for all geographic areas if sample sizes are below 100 due to the imprecision of those estimates. APPENDIX A 29 Hotspots for Hospital Medical Debt Lawsuits Against Patients For each regional profile in the appendix, we assess whether counties with a high prevalence of medical debt were also hotspots for hospital medical debt litigation against patients. The information on litigation hotspots is drawn from work conducted by the Community Service Society (CSS) of New York (Dunker and Benjamin 2020, 2022). CSS researchers developed a database of civil lawsuits filed by hospitals against patients in New York State between 2015 and 2020 and identified geographic litigation hotspots based on the number of lawsuits per 10,000 residents in each county. For this study, 20 counties are categorized as hotspots for hospital lawsuits against patients based on information in a CSS report on hospital lawsuits resulting in wage garnishment (Dunker and Benjamin 2022). Each of these counties had more than 50 lawsuits per 10,000 residents between 2015 and 2020 and collectively accounted for more than 80 percent of lawsuits filed in the state during that period. Multivariate Analysis We estimate a linear probability model, in which the primary outcome is an indicator for whether the consumer had medical debt. The model controls for several demographic and socioeconomic characteristics, including indicators for each economic region; individual-level indicators for the person's age (under 25, 25–34, 35–44, 45–54, 55–64, 65 and older, and age not reported); and community-level controls for racial/ethnic composition (Hispanic and non-Hispanic Black, Asian and Pacific Islander, American Indian/Alaska Native, other or multiple races, and white), population density, employment-to-population ratio among 25- to 54-year-old adults, average household income (log- transformed), and the share of the nonelderly population that is uninsured. In addition, the model includes two measures to control for regional differences in the use of health care services. First, we control for the share of 18- to 64-year-olds with disabilities at the community level. The ACS disability measure captures the presence of any one of the following six disability types: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Second, we also control for emergency room visits per Medicare beneficiary at the county level using the Centers for Medicare and Medicaid Services (CMS) Multiple Chronic Conditions dataset.24 This measure among the Original Medicare (i.e., fee-for-service) population serves as a proxy for the county's health status and propensity to use hospital care as a whole. We cluster standard errors at the community level. 30 APPENDIX A Limitations The credit bureau data have some limitations. First, these data exclude adults in New York State who do not have a credit record. At the national level, 11 percent of adults are estimated to be "credit invisible," with young adults, older adults, and people of color disproportionately represented in this group (Brevoort, Grimm, and Kambara 2015). A 2018 report estimated that nearly 15 percent of adults in New York State do not have a credit record and credit score (Hamdani et al. 2018). Second, the credit bureau data do not contain demographic or socioeconomic characteristics such as race and ethnicity, income, or health insurance coverage for individual consumers. To address this limitation, we examine medical debt based on the demographic and socioeconomic characteristics of the communities where consumers live. The credit bureau data also underestimate the prevalence of medical debt because providers do not always furnish information on medical collections to the credit bureaus and because it is not always possible to determine the industry source of collections tradelines (e.g., if a consumer pays medical bills with a credit card and is then unable to pay their credit card balance). Further, though the credit bureau data contain records for more than 600,000 consumers in New York, the number of consumers varies widely across ZCTAs. We protect the privacy of consumers by reporting medical debt prevalence for communities within ranges (i.e., by quartile across all communities in the state) rather than point estimates. Though we suppress all estimates for communities with fewer than 50 consumers and point estimates for any geographic area with fewer than 100 consumers, the level of sampling error varies based on the number of consumers within each geographic area. APPENDIX A 31 Appendix B: Regional Profiles This section contains profiles of medical debt in each of New York State's 10 economic regions (see figure 1). For each region, we highlight the share of consumers with medical debt across counties, the location of high-debt communities, and disparities by communities' racial/ethnic composition, median household income, and uninsurance rates. General trends from earlier sections of the report are borne out in these profiles, where we see an even more granular picture of the disparities and disproportionate burdens of medical debt. For instance: ◼ The estimated share of consumers with medical debt across counties ranges from 3 percent in Nassau County to 27 percent in Chemung County. ◼ All or nearly all communities in certain counties of the Central New York, North Country, and Southern Tier regions were high-debt communities (i.e., in the highest quartile of medical debt for communities across the state). ◼ None of the high-debt communities were located in Long Island and New York City, despite economic differences across communities within those regions. We also focus on the prevalence of medical debt in counties found to be hotspots for hospital lawsuits to collect medical debt from patients, based on the number of lawsuits filed per capita (Dunker and Benjamin 2020, 2022). Most but not all of these hotspots had high rates of medical debt. Capital District Variation in Medical Debt across Counties Overall, 8 percent of consumers in the Capital District region had medical debt on their credit reports (table A1). This share was highest in Washington County (11 percent), Columbia County (10 percent), and Greene County (10 percent), and lowest in Saratoga County (7 percent) and Albany County (8 percent). In counties that have been hotspots for hospital lawsuits against patients, between 8 and 9 percent of residents had medical debt. 32 APPENDIX B TABLE A1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Capital District Region, Overall and by County, February 2022 Share with Median Share with Communities medical medical medical debt with sufficient High-debt debt debt ≥$500 data communities Capital District Region 8% $490 49% 105 37 Albany County* 8% $530 51% 24 8 Columbia County 10% $584 54% 10 3 Greene County 10% $486 50% 8 3 Rensselaer County* 9% $668 56% 18 4 Saratoga County 7% $400 43% 18 6 Schenectady County 9% $435 48% 11 4 Warren County* 9% $539 52% 6 3 Washington County 11% $401 46% 10 6 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. * Indicates the county was a hospital lawsuit hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022); see the methods section for further details. Location of High-Debt Communities Of the 105 communities in the region with sufficient data, 37 communities (about one in three) were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). High- debt communities were found in all eight counties in the region. The high-debt communities with relatively large populations for the region included communities in and around Albany, Schenectady, Hudson Falls, and Glens Falls (figure A1). APPENDIX B 33 FIGURE A1 Share of Consumers with Medical Debt in Collections across Communities of the Capital District Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. 34 APPENDIX B Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was nearly twice that of predominantly white communities (15 percent versus 8 percent; figure A2). The rate of medical debt in the lowest-income communities was more than twice as high as the rate in the highest-income communities (13 percent versus 5 percent). Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (10 percent versus 7 percent). FIGURE A2 Share of Consumers with Medical Debt in Collections in the Capital District Region, by Selected Community Characteristics, February 2022 All communities 8% 0% to <30% people of color 8% 30% to <50% people of color 10% 50% or more people of color 15% Lowest income quartile ($2,500–$54,200) 13% Second income quartile ($54,200–$66,400) 10% Third income quartile ($66,400–$88,500) 7% Highest income quartile (≥$88,500) 5% Uninsurance rates below median (0.0%–3.6%) 7% Uninsurance rates above median (>3.6%) 10% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. APPENDIX B 35 Central New York Variation in Medical Debt across Counties Overall, 14 percent of consumers in the Central New York region had medical debt on their credit reports (table B1). This share was highest in Oswego (19 percent) and Onondaga Counties (14 percent) and lowest in Cayuga and Cortland Counties (12 percent). Four of the region's five counties have been hotspots for hospital lawsuits against patients, and between 12 and 14 percent of residents of these counties had medical debt. TABLE B1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Central New York Region, Overall and by County, February 2022 Communities Share with Median Share with with medical medical medical debt sufficient High-debt debt debt ≥$500 data communities Central New York 14% $463 48% 79 58 Region Cayuga County* 12% $448 49% 10 6 Cortland County* 12% $458 49% 5 3 Madison County* 13% $368 41% 10 7 Onondaga County* 14% $449 47% 38 26 Oswego County 19% $527 52% 16 16 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. * Indicates the county was a hospital lawsuit hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022); see the methods section for further details. Location of High-Debt Communities Of the 79 communities in the region with sufficient data, 58 communities (nearly three in four) were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). In each county, the majority of communities were high-debt, including all 16 communities in which a majority of 36 APPENDIX B residents lived in Oswego County. The high-debt communities with relatively large populations for the region included those in and around Syracuse, Cortland, and Oswego (figure B1). FIGURE B1 Share of Consumers with Medical Debt in Collections across Communities of the Central New York Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. APPENDIX B 37 Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was over twice that of predominantly white communities (28 percent versus 12 percent; figure B2). The rate of medical debt in the lowest-income communities was three times as high as the rate in the highest-income communities (21 percent versus 7 percent). Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (18 percent versus 11 percent). FIGURE B2 Share of Consumers with Medical Debt in Collections in the Central New York Region, by Selected Community Characteristics, February 2022 All communities 14% 0% to <30% people of color 12% 30% to <50% people of color 20% 50% or more people of color 28% Lowest income quartile ($2,500–$54,200) 21% Second income quartile ($54,200–$66,400) 14% Third income quartile ($66,400–$88,500) 11% Highest income quartile (≥$88,500) 7% Uninsurance rates below median (0.0%–3.6%) 11% Uninsurance rates above median (>3.6%) 18% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. 38 APPENDIX B Finger Lakes Variation in Medical Debt across Counties Overall, 5 percent of consumers in the Finger Lakes region had medical debt on their credit reports (table C1). This share was highest in Seneca County (15 percent), Ontario County (8 percent), and Yates County (8 percent), and lowest in Livingston, Monroe, and Wyoming Counties (4 percent). None of the counties in the Finger Lakes region have been hotspots for hospital lawsuits against patients. TABLE C1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Finger Lakes Region, Overall and by County, February 2022 Communities Share with with Share with Median medical debt sufficient High-debt medical debt medical debt ≥$500 data communities Finger Lakes Region 5% $422 45% 108 14 Genesee County 6% 9 0 Livingston County 4% 11 0 Monroe County 4% $414 43% 38 0 Ontario County 8% $456 48% 12 4 Orleans County 6% 6 0 Seneca County 15% $494 49% 5 4 Wayne County 7% $492 50% 14 4 Wyoming County 4% 10 1 Yates County 8% 3 1 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. Blank cells indicate point estimates are suppressed for sample sizes below 100. Location of High-Debt Communities Of the 108 communities in the region with sufficient data, 14 communities (about one in eight) were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). High- APPENDIX B 39 debt communities were found in five of the nine counties in the region. The high-debt communities with relatively large populations for the region included communities around Seneca Lake (figure C1). FIGURE C1 Share of Consumers with Medical Debt in Collections across Communities of the Finger Lakes Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. 40 APPENDIX B Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was higher than that of predominantly white communities (7 percent versus 5 percent; figure C2). The rate of medical debt in the lowest-income communities was nearly four times as high as the rate in the highest-income communities (7 percent versus 2 percent). Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (7 percent versus 4 percent). FIGURE C2 Share of Consumers with Medical Debt in Collections in the Finger Lakes Region, by Selected Community Characteristics, February 2022 All communities 5% 0% to <30% people of color 5% 30% to <50% people of color 4% 50% or more people of color 7% Lowest income quartile ($2,500–$54,200) 7% Second income quartile ($54,200–$66,400) 6% Third income quartile ($66,400–$88,500) 4% Highest income quartile (≥$88,500) 2% Uninsurance rates below median (0.0%–3.6%) 4% Uninsurance rates above median (>3.6%) 7% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. APPENDIX B 41 Long Island Variation in Medical Debt across Counties Overall, 3 percent of consumers in the Long Island region had medical debt on their credit reports (table D1). This share was the same in both Nassau and Suffolk Counties, both of which have been hotspots for hospital lawsuits against patients. TABLE D1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Long Island Region, Overall and by County, February 2022 Share Share with Communities with Median medical with Communities medical medical debt sufficient High-debt in third debt debt debt ≥$500 data communities quartile Long Island Region 3% $371 42% 155 0 12 Nassau County* 3% $325 38% 64 0 2 Suffolk County* 3% $416 45% 91 0 10 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state; the number of communities in the third debt quartile is also shown. * Indicates the county was a hospital lawsuit hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022); see the methods section for further details. Location of High-Debt Communities Of the 155 communities in the region with sufficient data, none were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). Yet, there was some geographic variation. Communities with relatively large populations for the region that were in the third quartile of medical debt were concentrated in and around Riverhead, Mastic, and Mastic Beach (figure D1). 42 APPENDIX B FIGURE D1 Share of Consumers with Medical Debt in Collections across Communities of the Long Island Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was slightly higher than that of predominantly white communities (4 percent versus 3 percent; figure D2). The rate of medical debt in the highest income communities was lower than communities in the second and third quartile of median household income (3 percent versus 6 percent and 4 percent). While there are some communities in Long Island in the lowest income quartile, the sample size is too low for estimation. Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (4 percent versus 2 percent). APPENDIX B 43 FIGURE D2 Share of Consumers with Medical Debt in Collections in the Long Island Region, by Selected Community Characteristics, February 2022 All communities 3% 0% to <30% people of color 3% 30% to <50% people of color 3% 50% or more people of color 4% Lowest income quartile ($2,500–$54,200) Second income quartile ($54,200–$66,400) 6% Third income quartile ($66,400–$88,500) 4% Highest income quartile (≥$88,500) 3% Uninsurance rates below median (0.0%–3.6%) 2% Uninsurance rates above median (>3.6%) 4% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. Point estimates are suppressed for sample sizes below 100. 44 APPENDIX B Mid-Hudson Variation in Medical Debt across Counties Overall, 7 percent of consumers in the Mid-Hudson region had medical debt on their credit reports (table E1). This share was highest in Sullivan County (13 percent), Ulster County (10 percent), and Dutchess County (10 percent), and lowest in Rockland County (4 percent) and Westchester County (5 percent). None of the counties in the Mid-Hudson region were hotspots for hospital lawsuits against patients. TABLE E1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Mid-Hudson Region, Overall and by County, February 2022 Communities Share with with Share with Median medical debt sufficient High-debt medical debt medical debt ≥$500 data communities Mid-Hudson Region 7% $591 54% 186 40 Dutchess County 10% $548 53% 26 11 Orange County 9% $640 55% 31 7 Putnam County 6% $543 52% 8 0 Rockland County 4% $533 51% 24 1 Sullivan County 13% $894 59% 15 10 Ulster County 10% $471 48% 21 9 Westchester County 5% $615 55% 61 2 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. Location of High-Debt Communities Of the 186 communities in the region with sufficient data, 40 communities (about one in five) were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). High- debt communities were found in six of the seven counties in the region. The high-debt communities with APPENDIX B 45 relatively large populations for the region included communities in and around Yonkers, Poughkeepsie, and other communities along the Hudson River (figure E1). FIGURE E1 Share of Consumers with Medical Debt in Collections across Communities of the Mid-Hudson Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. 46 APPENDIX B Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was higher than that of predominantly white communities (9 percent versus 6 percent; figure E2). The rate of medical debt in the lowest-income communities was over three times as high as the rate in the highest-income communities (13 percent versus 4 percent). Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (8 percent versus 5 percent). FIGURE E2 Share of Consumers with Medical Debt in Collections in the Mid-Hudson Region, by Selected Community Characteristics, February 2022 All communities 7% 0% to <30% people of color 6% 30% to <50% people of color 6% 50% or more people of color 9% Lowest income quartile ($2,500–$54,200) 13% Second income quartile ($54,200–$66,400) 9% Third income quartile ($66,400–$88,500) 8% Highest income quartile (≥$88,500) 4% Uninsurance rates below median (0.0%–3.6%) 5% Uninsurance rates above median (>3.6%) 8% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. APPENDIX B 47 Mohawk Valley Variation in Medical Debt across Counties Overall, 11 percent of consumers in the Mohawk Valley region had medical debt on their credit reports (table F1). This share was highest in Montgomery County (13 percent), Oneida County (12 percent), and Fulton County (12 percent), and lowest in Hamilton County (3 percent) and Schoharie County (6 percent). In counties that have been hotspots for hospital lawsuits against patients, between 9 and 13 percent of residents had medical debt. TABLE F1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Mohawk Valley Region, Overall and by County, February 2022 Median Share with Communities Share with medical medical with sufficient High-debt medical debt debt debt ≥$500 data communities Mohawk Valley Region 11% $687 58% 54 22 Fulton County* 12% $753 61% 5 4 Hamilton County 3% 0 0 Herkimer County* 9% $670 59% 10 2 Montgomery County* 13% $813 67% 8 5 Oneida County* 12% $645 55% 25 11 Schoharie County 6% 6 0 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. Blank cells indicate point estimates are suppressed for sample sizes below 100. * Indicates the county was a hospital lawsuit hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022); see the methods section for further details. Location of High-Debt Communities Of the 54 communities in the region with sufficient data, 22 communities (about four in ten) were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). High-debt communities were found in four of the six counties in the region. The high-debt communities with 48 APPENDIX B relatively large populations for the region included communities in and around Utica, Rome, and areas of Fulton and Montgomery Counties (figure F1). FIGURE F1 Share of Consumers with Medical Debt in Collections across Communities of the Mohawk Valley Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. APPENDIX B 49 Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities where 30 percent to less than 50 percent of residents are people of color was higher than that of predominantly white communities where less than 30 percent of residents were people of color (17 percent versus 11 percent; figure F2). There were no communities in the region in which 50 percent or more residents were people of color. The rate of medical debt in the lowest-income communities was nearly twice as high as the rate in the highest- income communities (13 percent versus 7 percent). Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (13 percent versus 10 percent). FIGURE F2 Share of Consumers with Medical Debt in Collections in the Mohawk Valley Region, by Selected Community Characteristics, February 2022 All communities 11% 0% to <30% people of color 11% 30% to <50% people of color 17% 50% or more people of color Lowest income quartile ($2,500–$54,200) 13% Second income quartile ($54,200–$66,400) 11% Third income quartile ($66,400–$88,500) 8% Highest income quartile (≥$88,500) 7% Uninsurance rates below median (0.0%–3.6%) 10% Uninsurance rates above median (>3.6%) 13% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median 50 APPENDIX B uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. There were no communities in the Mohawk Valley region where 50% or more residents were people of color. New York City Variation in Medical Debt across Counties Overall, 4 percent of consumers in the New York City region had medical debt on their credit reports (table G1). This share was highest in the Bronx (6 percent) and Queens (4 percent). Brooklyn, Staten Island, and Manhattan had similar shares of consumers with medical debt (3 percent). None of the counties in the New York City region have been hotspots for hospital lawsuits against patients. TABLE G1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the New York City Region, Overall and by County, February 2022 Share Share with Communities with Median medical with Communities medical medical debt sufficient High-debt in third debt debt debt ≥$500 data communities quartile New York City region 4% $375 42% 179 0 23 Bronx County 6% $440 46% 25 0 15 Kings County 3% $381 44% 37 0 3 New York County 3% $355 40% 45 0 0 Queens County 4% $350 39% 60 0 5 Richmond County 3% $343 40% 12 0 0 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state; the number of communities in the third debt quartile is also shown. Location of High-Debt Communities Of the 179 communities in the region with sufficient data, none were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). Relatively higher-debt communities APPENDIX B 51 (those in the third quartile of medical debt prevalence for communities across the state) were located in central and southern portions of the Bronx, the East New York, Bedford-Stuyvesant, and Brownsville neighborhoods of Brooklyn, and the Corona, Rockaway, Flushing, and Jackson Heights neighborhoods of Queens. Figures G1-1 through G1-5 show the share of consumers with medical debt across communities of New York City's five boroughs. 52 APPENDIX B FIGURE G1 Share of Consumers with Medical Debt in Collections across Communities of the New York City Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. APPENDIX B 53 FIGURE G1-1 Share of Consumers with Medical Debt in Collections across Communities of Bronx County, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. 54 APPENDIX B FIGURE G1-2 Share of Consumers with Medical Debt in Collections across Communities of Kings County, February 2022 URBAN INSTIT UTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. APPENDIX B 55 FIGURE G1-3 Share of Consumers with Medical Debt in Collections across Communities of New York County, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. 56 APPENDIX B FIGURE G1-4 Share of Consumers with Medical Debt in Collections across Communities of Queens County, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. APPENDIX B 57 FIGURE G1-5 Share of Consumers with Medical Debt in Collections across Communities of Richmond County, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was higher than that of predominantly white communities (4 percent versus 2 percent; figure G2). The rate of medical debt in 58 APPENDIX B the lowest-income communities was higher than the rate in the highest-income communities (5 percent versus 3 percent). Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (4 percent versus 3 percent). FIGURE G2 Share of Consumers with Medical Debt in Collections in the New York City Region, by Selected Community Characteristics, February 2022 All communities 4% 0% to <30% people of color 2% 30% to <50% people of color 3% 50% or more people of color 4% Lowest income quartile ($2,500–$54,200) 5% Second income quartile ($54,200–$66,400) 4% Third income quartile ($66,400–$88,500) 3% Highest income quartile (≥$88,500) 3% Uninsurance rates below median (0.0%–3.6%) 3% Uninsurance rates above median (>3.6%) 4% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. APPENDIX B 59 North Country Variation in Medical Debt across Counties Overall, 11 percent of consumers in the North Country region had medical debt on their credit reports (table H1). This share was highest in Jefferson County (14 percent), St. Lawrence County (14 percent), Lewis County (9 percent), and Franklin County (9 percent), and lowest in Essex County (5 percent) and Clinton County (7 percent). Jefferson County has been a hotspot in the region for hospital lawsuits against patients. TABLE H1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the North Country Region, Overall and by County, February 2022 Share with Communities Share with Median medical with medical medical debt sufficient High-debt debt debt ≥$500 data communities North Country Region 11% $561 52% 70 36 Clinton County 7% $250 32% 13 1 Essex County 5% 7 1 Franklin County 9% $320 45% 8 3 Jefferson County* 14% $672 56% 20 15 Lewis County 9% 6 2 St. Lawrence County 14% $777 60% 16 14 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. Blank cells indicate point estimates are suppressed for sample sizes below 100. * Indicates the county was a hospital lawsuit hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022); see the methods section for further details. Location of High-Debt Communities Of the 70 communities in the region with sufficient data, 36 communities (about half) were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). High-debt 60 APPENDIX B communities were found in all 6 counties in the region, including 15 of 20 communities of Jefferson County and 14 of 16 communities in St. Lawrence County. High-debt communities with relatively large populations for the region were located in and around Massena and Watertown (figure H1). FIGURE H1 Share of Consumers with Medical Debt in Collections across Communities of the North Country Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. APPENDIX B 61 Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was slightly lower than that of predominantly white communities (10 percent versus 11 percent; figure H2). The rate of medical debt in the lowest-income communities was higher than the rate in communities in the second and third quartiles of median household income (13 percent versus 9 percent). While there are some communities in the North Country region in the highest income quartile, the sample size is too low for estimation. Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (12 percent versus 10 percent). FIGURE H2 Share of Consumers with Medical Debt in Collections in the North Country Region, by Selected Community Characteristics, February 2022 All communities 11% 0% to <30% people of color 11% 30% to <50% people of color 13% 50% or more people of color 10% Lowest income quartile ($2,500–$54,200) 13% Second income quartile ($54,200–$66,400) 9% Third income quartile ($66,400–$88,500) 9% Highest income quartile (≥$88,500) Uninsurance rates below median (0.0%–3.6%) 10% Uninsurance rates above median (>3.6%) 12% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median 62 APPENDIX B uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. Point estimates are suppressed for sample sizes below 100. Southern Tier Variation in Medical Debt across Counties Overall, 10 percent of consumers in the Southern Tier region had medical debt on their credit reports (table I1). This share was highest in Chemung County (27 percent), Schuyler County (15 percent), and Steuben County (14 percent), and lowest in Otsego County (5 percent), Broome County (5 percent), and Delaware County (5 percent). In counties that have been hotspots for hospital lawsuits against patients, between 5 and 27 percent of residents had medical debt. TABLE I1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Southern Tier Region, Overall and by County, February 2022 Communities Median Share with with Share with medical medical debt sufficient High-debt medical debt debt ≥$500 data communities Southern Tier Region 10% $655 57% 85 33 Broome County* 5% $504 51% 16 1 Chemung County* 27% $989 65% 9 9 Chenango County* 6% 9 0 Delaware County 5% 6 1 Otsego County* 5% 10 0 Schuyler County 15% 4 4 Steuben County* 14% $797 60% 14 9 Tioga County 9% $754 59% 9 4 Tompkins County 8% $510 51% 8 5 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. Blank cells indicate point estimates are suppressed for sample sizes below 100. * Indicates the county was a hospital lawsuit APPENDIX B 63 hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022); see the methods section for further details. Location of High-Debt Communities Of the 85 communities in the region with sufficient data, 33 communities (nearly four in 10) were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). High-debt communities were found in 7 of the 9 counties in the region, including all communities in Chemung County and Schuyler County. High-debt communities with relatively large populations for the region were located in or around Elmira (figure I1). FIGURE I1 Share of Consumers with Medical Debt in Collections across Communities of the Southern Tier Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. 64 APPENDIX B Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities where between 30 percent and 50 percent of residents are people of color was lower than that of predominantly white communities where less than 30 percent of residents are people of color (7 percent versus 10 percent; figure I2). There were no communities in the region in which 50 percent or more residents were people of color. The rate of medical debt in the lowest-income communities was higher than the rate in communities in the second and third quartiles of median household income (11 percent versus 9 percent). While there are some communities in the Southern Tier region in the highest income quartile, the sample size is too low for estimation. Communities with uninsurance rates above the state median had higher shares of residents with medical debt than communities with uninsurance rates below the median (11 percent versus 9 percent). APPENDIX B 65 FIGURE I2 Share of Consumers with Medical Debt in Collections in the Southern Tier Region, by Selected Community Characteristics, February 2022 All communities 10% 0% to <30% people of color 10% 30% to <50% people of color 7% 50% or more people of color Lowest income quartile ($2,500–$54,200) 11% Second income quartile ($54,200–$66,400) 9% Third income quartile ($66,400–$88,500) 9% Highest income quartile (≥$88,500) Uninsurance rates below median (0.0%–3.6%) 9% Uninsurance rates above median (>3.6%) 11% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. There were no communities in the Southern Tier region where 50% or more residents were people of color. Point estimates are suppressed for sample sizes below 100. Western New York Variation in Medical Debt across Counties Overall, 8 percent of consumers in the Western New York region had medical debt on their credit reports (table J1). This share was highest in Cattaraugus County (13 percent) and Chautauqua County 66 APPENDIX B (11 percent). Allegany County, Erie County, and Niagara County all had similar shares of consumers with medical debt (8 percent). Chautauqua County has been a hotspot for hospital lawsuits against patients. TABLE J1 Share of Consumers with Medical Debt in Collections and Median Debt Amounts in the Western New York Region, Overall and by County, February 2022 Share with medical Communities Share with Median debt with sufficient High-debt medical debt medical debt ≥$500 data communities Western New York Region 8% $453 47% 113 42 Allegany County 8% $425 42% 9 3 Cattaraugus County 13% $630 57% 14 10 Chautauqua County* 11% $380 42% 20 8 Erie County 8% $450 46% 55 18 Niagara County 8% $565 52% 15 3 Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of all consumers with credit bureau records in the region or county who have medical debt in collections. Median medical debt and share with medical debt ≥$500 are estimated among consumers with medical debt. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Communities with sufficient data refer to those with 50 or more consumers. Estimated ranges of the share of consumers with medical debt in each community are suppressed below this sample size threshold. High-debt communities are those in the highest quartile of medical debt prevalence for communities across the state. * Indicates the county was a hospital lawsuit hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022); see the methods section for further details. Location of High-Debt Communities Of the 113 communities in the region with sufficient data, 42 communities were in the highest quartile of medical debt prevalence for communities across the state (i.e., high-debt). High-debt communities were found in all 5 counties in the region. High-debt communities with relatively large populations for the region included communities in and around Buffalo and Niagara Falls (figure J1). APPENDIX B 67 FIGURE J1 Share of Consumers with Medical Debt in Collections Across Communities of the Western New York Region, February 2022 URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. Disparities in Medical Debt by Community Characteristics The share of consumers with medical debt in communities of color was higher than that of predominantly white communities (14 percent versus 7 percent; figure J2). The rate of medical debt in the lowest-income communities was over three times higher than the rate in the highest-income 68 APPENDIX B communities (13 percent versus 4 percent). Communities with uninsurance rates above the state median had twice the share of residents with medical debt as communities with uninsurance rates below the median (12 percent versus 6 percent). FIGURE J2 Share of Consumers with Medical Debt in Collections in the Western New York Region, by Selected Community Characteristics, February 2022 All communities 8% 0% to <30% people of color 7% 30% to <50% people of color 13% 50% or more people of color 14% Lowest income quartile ($2,500–$54,200) 13% Second income quartile ($54,200–$66,400) 8% Third income quartile ($66,400–$88,500) 5% Highest income quartile (≥$88,500) 4% Uninsurance rates below median (0.0%–3.6%) 6% Uninsurance rates above median (>3.6%) 12% URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non-Hispanic and white. Median household income quartiles are based on the median household income in communities across the state and are rounded to the nearest $100. Uninsurance rate categories are based on the median uninsurance rate in communities across the state and are for residents of all ages in the civilian noninstitutionalized population. Point estimates are suppressed for sample sizes below 100. APPENDIX B 69 Appendix C: Tables and Figures TABLE C-1 Characteristics of Communities in New York State, Overall and by Region, February 2022 Central New Western Capital New Finger Long Mid- Mohawk York North Southern New Statewide District York Lakes Island Hudson Valley City Country Tier York Number of 1,754 202 116 149 176 293 115 182 168 180 173 populated communities Total 19,514,719 1,078,579 776,227 1,204,745 2,843,082 2,324,375 431,075 8,373,516 413,156 690,075 1,379,889 population of communities Race/ ethnicity Asian 8% 4% 3% 3% 7% 5% 2% 14% 1% 3% 3% Black 14% 7% 7% 10% 9% 11% 4% 21% 4% 3% 10% Hispanic 19% 5% 4% 7% 18% 20% 6% 29% 4% 4% 5% White 55% 81% 83% 77% 63% 62% 85% 32% 87% 87% 79% Additional 3% 4% 4% 3% 2% 3% 3% 4% 3% 3% 3% races Nativity and citizenship status US born 78% 92% 94% 94% 81% 82% 95% 64% 96% 94% 94% Naturalized 13% 4% 3% 4% 12% 11% 3% 21% 2% 3% 3% citizen Not a citizen 9% 3% 2% 3% 7% 8% 2% 15% 2% 3% 3% Highest level of educational attainment (ages 25+) 70 APPENDIX C Central New Western Capital New Finger Long Mid- Mohawk York North Southern New Statewide District York Lakes Island Hudson Valley City Country Tier York High school 38% 35% 39% 36% 33% 34% 44% 41% 47% 41% 38% degree or less Some college 24% 28% 31% 30% 25% 25% 32% 20% 30% 29% 31% or associate's degree Bachelor's 37% 37% 31% 34% 42% 41% 24% 39% 23% 30% 31% degree or more Family income as a percent of FPL <100% FPL 14% 10% 14% 13% 6% 10% 14% 17% 15% 15% 14% 100–299% FPL 29% 28% 32% 32% 19% 24% 35% 31% 37% 35% 32% 300–499% FPL 23% 26% 26% 26% 22% 21% 26% 20% 27% 26% 26% ≥500% FPL 35% 37% 28% 29% 53% 44% 24% 31% 22% 24% 27% Average $105,282 $91,625 $80,517 $81,342 $145,460 $128,117 $73,427 $106,987 $71,079 $73,497 $76,542 household income Employment- 79% 82% 79% 80% 82% 79% 76% 78% 69% 77% 79% to-population ratio (ages 25– 54) Health insurance coverage (ages 0–64) Uninsured 6% 4% 4% 5% 5% 6% 5% 8% 6% 5% 4% Private 65% 72% 68% 68% 79% 70% 62% 58% 63% 65% 66% Public 24% 19% 22% 22% 13% 20% 28% 30% 24% 25% 24% Both private 4% 4% 6% 5% 4% 4% 5% 4% 6% 5% 5% and public Share with a 6% 6% 7% 7% 4% 5% 7% 5% 8% 8% 7% disability (ages 18–64) APPENDIX C 71 Central New Western Capital New Finger Long Mid- Mohawk York North Southern New Statewide District York Lakes Island Hudson Valley City Country Tier York Population 417 215 215 256 2,408 508 73 28,808 44 97 280 density (people per square mile) Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Asian, Black, white, and additional races refer to individuals who are not Hispanic. Additional races includes those who identify as American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, more than one race, or some other race. FPL is federal poverty level. Private coverage includes employer sponsored insurance, direct purchase, TRICARE/military coverage, and combinations of multiple types of private coverage. Public coverage includes Medicare, Medicaid, VA coverage, and combinations of multiple types of public coverage. Population density indicates number of people per square mile of land. 72 APPENDIX C FIGURE C-1 Median Medical Debt Among Consumers with Any Medical Debt in Collections in New York State, by Community Uninsurance Rates, Statewide and by Region, 2022 Uninsurance rates below median (0.0%–3.6%) Uninsurance rates above median (>3.6%) Median medical debt $0 $100 $200 $300 $400 $500 $600 $700 $800 $900 $1,000 Statewide Capital District Central New York Finger Lakes Long Island Mid-Hudson Mohawk Valley New York City North Country Southern Tier Western New York URBAN INSTITUTE Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Uninsurance rates are for residents of all ages in the civilian noninstitutionalized population. APPENDIX C 73 TABLE C-2 Share of Consumers with Medical Debt in Collections across Communities in New York State, by Region, February 2022 Zip code Share of consumers Region tabulation area Population with medical debt Capital District 12009 7,690 3.3% to 5.5% Capital District 12015 3,347 5.6% to 9.6% Capital District 12018 7,112 5.6% to 9.6% Capital District 12019 14,244 3.3% to 5.5% Capital District 12020 33,235 5.6% to 9.6% Capital District 12023 2,376 3.3% to 5.5% Capital District 12027 3,996 0% to 3.2% Capital District 12033 8,152 5.6% to 9.6% Capital District 12037 4,489 5.6% to 9.6% Capital District 12047 21,453 9.7% to 37.6% Capital District 12051 6,769 3.3% to 5.5% Capital District 12052 1,921 3.3% to 5.5% Capital District 12053 4,611 5.6% to 9.6% Capital District 12054 16,948 0% to 3.2% Capital District 12056 2,543 5.6% to 9.6% Capital District 12057 2,176 5.6% to 9.6% Capital District 12061 9,725 3.3% to 5.5% Capital District 12062 1,601 5.6% to 9.6% Capital District 12065 42,432 3.3% to 5.5% Capital District 12074 2,957 5.6% to 9.6% Capital District 12075 2,530 5.6% to 9.6% Capital District 12077 6,488 0% to 3.2% Capital District 12083 3,830 3.3% to 5.5% Capital District 12084 4,806 3.3% to 5.5% Capital District 12090 5,931 5.6% to 9.6% Capital District 12094 2,260 5.6% to 9.6% Capital District 12106 2,299 5.6% to 9.6% Capital District 12110 21,223 3.3% to 5.5% Capital District 12118 16,467 9.7% to 37.6% Capital District 12123 5,120 9.7% to 37.6% Capital District 12137 1,993 3.3% to 5.5% Capital District 12138 3,156 3.3% to 5.5% Capital District 12140 1,681 3.3% to 5.5% Capital District 12143 5,032 5.6% to 9.6% Capital District 12144 20,604 5.6% to 9.6% Capital District 12148 4,143 0% to 3.2% 74 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Capital District 12154 3,249 0% to 3.2% Capital District 12158 6,550 3.3% to 5.5% Capital District 12159 7,971 0% to 3.2% Capital District 12168 1,993 3.3% to 5.5% Capital District 12170 5,034 9.7% to 37.6% Capital District 12173 2,139 3.3% to 5.5% Capital District 12180 53,181 9.7% to 37.6% Capital District 12182 13,886 9.7% to 37.6% Capital District 12183 2,647 9.7% to 37.6% Capital District 12184 6,762 5.6% to 9.6% Capital District 12185 2,007 9.7% to 37.6% Capital District 12186 6,190 3.3% to 5.5% Capital District 12188 11,774 5.6% to 9.6% Capital District 12189 17,945 9.7% to 37.6% Capital District 12192 1,778 5.6% to 9.6% Capital District 12193 1,888 5.6% to 9.6% Capital District 12196 4,072 0% to 3.2% Capital District 12198 7,119 5.6% to 9.6% Capital District 12202 7,753 9.7% to 37.6% Capital District 12203 30,837 3.3% to 5.5% Capital District 12204 7,497 9.7% to 37.6% Capital District 12205 26,039 5.6% to 9.6% Capital District 12206 15,537 9.7% to 37.6% Capital District 12207 2,264 9.7% to 37.6% Capital District 12208 20,517 5.6% to 9.6% Capital District 12209 10,237 5.6% to 9.6% Capital District 12210 11,057 9.7% to 37.6% Capital District 12211 11,227 3.3% to 5.5% Capital District 12302 27,665 5.6% to 9.6% Capital District 12303 29,416 5.6% to 9.6% Capital District 12304 20,929 9.7% to 37.6% Capital District 12305 6,246 9.7% to 37.6% Capital District 12306 26,934 5.6% to 9.6% Capital District 12307 7,219 9.7% to 37.6% Capital District 12308 14,038 9.7% to 37.6% Capital District 12309 30,992 3.3% to 5.5% Capital District 12413 3,290 9.7% to 37.6% Capital District 12414 9,726 9.7% to 37.6% Capital District 12451 1,425 3.3% to 5.5% APPENDIX C 75 Zip code Share of consumers Region tabulation area Population with medical debt Capital District 12463 1,700 9.7% to 37.6% Capital District 12502 1,224 5.6% to 9.6% Capital District 12523 1,810 9.7% to 37.6% Capital District 12526 3,530 5.6% to 9.6% Capital District 12529 1,919 9.7% to 37.6% Capital District 12534 17,814 9.7% to 37.6% Capital District 12801 14,330 9.7% to 37.6% Capital District 12803 8,317 9.7% to 37.6% Capital District 12804 26,112 5.6% to 9.6% Capital District 12809 3,751 9.7% to 37.6% Capital District 12816 4,642 3.3% to 5.5% Capital District 12817 2,243 5.6% to 9.6% Capital District 12822 5,962 9.7% to 37.6% Capital District 12827 3,455 5.6% to 9.6% Capital District 12828 9,690 9.7% to 37.6% Capital District 12831 17,389 5.6% to 9.6% Capital District 12832 6,500 9.7% to 37.6% Capital District 12833 4,544 5.6% to 9.6% Capital District 12834 6,101 5.6% to 9.6% Capital District 12835 2,530 9.7% to 37.6% Capital District 12839 13,011 9.7% to 37.6% Capital District 12845 4,610 3.3% to 5.5% Capital District 12846 3,248 9.7% to 37.6% Capital District 12850 2,866 3.3% to 5.5% Capital District 12859 2,425 9.7% to 37.6% Capital District 12865 3,470 9.7% to 37.6% Capital District 12866 38,732 5.6% to 9.6% Capital District 12871 4,361 5.6% to 9.6% Capital District 12885 4,437 9.7% to 37.6% Capital District 12887 4,857 9.7% to 37.6% Central New York 13021 37,528 9.7% to 37.6% Central New York 13027 33,937 9.7% to 37.6% Central New York 13029 7,360 3.3% to 5.5% Central New York 13030 3,470 9.7% to 37.6% Central New York 13031 15,484 5.6% to 9.6% Central New York 13032 12,522 9.7% to 37.6% Central New York 13033 3,807 5.6% to 9.6% Central New York 13034 1,925 9.7% to 37.6% Central New York 13035 8,707 5.6% to 9.6% 76 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Central New York 13036 9,632 9.7% to 37.6% Central New York 13037 9,775 9.7% to 37.6% Central New York 13039 17,078 5.6% to 9.6% Central New York 13040 2,370 9.7% to 37.6% Central New York 13041 11,808 9.7% to 37.6% Central New York 13044 2,742 9.7% to 37.6% Central New York 13045 28,975 9.7% to 37.6% Central New York 13057 13,126 9.7% to 37.6% Central New York 13060 3,050 9.7% to 37.6% Central New York 13063 1,827 5.6% to 9.6% Central New York 13066 12,443 5.6% to 9.6% Central New York 13069 24,111 9.7% to 37.6% Central New York 13074 4,230 9.7% to 37.6% Central New York 13076 2,037 9.7% to 37.6% Central New York 13077 6,462 9.7% to 37.6% Central New York 13078 10,940 5.6% to 9.6% Central New York 13080 3,227 9.7% to 37.6% Central New York 13081 1,203 5.6% to 9.6% Central New York 13082 4,201 9.7% to 37.6% Central New York 13083 2,027 9.7% to 37.6% Central New York 13084 3,814 9.7% to 37.6% Central New York 13088 20,926 9.7% to 37.6% Central New York 13090 29,017 9.7% to 37.6% Central New York 13092 2,484 9.7% to 37.6% Central New York 13101 2,448 5.6% to 9.6% Central New York 13104 16,038 3.3% to 5.5% Central New York 13108 6,193 3.3% to 5.5% Central New York 13110 2,198 9.7% to 37.6% Central New York 13112 1,683 9.7% to 37.6% Central New York 13114 6,594 9.7% to 37.6% Central New York 13116 3,447 9.7% to 37.6% Central New York 13118 6,130 9.7% to 37.6% Central New York 13120 2,092 9.7% to 37.6% Central New York 13126 35,369 9.7% to 37.6% Central New York 13131 3,317 9.7% to 37.6% Central New York 13132 3,557 9.7% to 37.6% Central New York 13135 5,939 9.7% to 37.6% Central New York 13140 4,534 9.7% to 37.6% Central New York 13142 6,486 9.7% to 37.6% APPENDIX C 77 Zip code Share of consumers Region tabulation area Population with medical debt Central New York 13145 1,410 9.7% to 37.6% Central New York 13152 7,841 5.6% to 9.6% Central New York 13156 1,757 9.7% to 37.6% Central New York 13159 5,303 9.7% to 37.6% Central New York 13160 2,242 5.6% to 9.6% Central New York 13164 2,607 5.6% to 9.6% Central New York 13166 5,465 5.6% to 9.6% Central New York 13167 2,756 9.7% to 37.6% Central New York 13202 6,943 9.7% to 37.6% Central New York 13203 16,876 9.7% to 37.6% Central New York 13204 19,264 9.7% to 37.6% Central New York 13205 16,746 9.7% to 37.6% Central New York 13206 16,837 9.7% to 37.6% Central New York 13207 12,552 9.7% to 37.6% Central New York 13208 22,560 9.7% to 37.6% Central New York 13209 12,364 9.7% to 37.6% Central New York 13210 27,172 9.7% to 37.6% Central New York 13211 6,117 9.7% to 37.6% Central New York 13212 21,557 9.7% to 37.6% Central New York 13214 8,392 9.7% to 37.6% Central New York 13215 14,306 5.6% to 9.6% Central New York 13219 14,819 5.6% to 9.6% Central New York 13224 8,486 9.7% to 37.6% Central New York 13302 1,483 9.7% to 37.6% Central New York 13332 2,823 3.3% to 5.5% Central New York 13346 6,225 5.6% to 9.6% Central New York 13408 3,584 9.7% to 37.6% Central New York 13409 2,255 9.7% to 37.6% Central New York 13421 13,230 9.7% to 37.6% Central New York 13493 2,318 9.7% to 37.6% Central New York 13803 4,131 5.6% to 9.6% Finger Lakes 13143 2,300 9.7% to 37.6% Finger Lakes 13146 2,433 9.7% to 37.6% Finger Lakes 13148 10,648 9.7% to 37.6% Finger Lakes 13165 9,905 9.7% to 37.6% Finger Lakes 14009 5,625 9.7% to 37.6% Finger Lakes 14011 8,864 0% to 3.2% Finger Lakes 14020 22,060 5.6% to 9.6% Finger Lakes 14024 1,408 5.6% to 9.6% 78 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Finger Lakes 14036 5,588 5.6% to 9.6% Finger Lakes 14040 1,719 3.3% to 5.5% Finger Lakes 14058 2,160 0% to 3.2% Finger Lakes 14098 3,028 3.3% to 5.5% Finger Lakes 14103 10,448 5.6% to 9.6% Finger Lakes 14125 3,514 0% to 3.2% Finger Lakes 14145 1,456 0% to 3.2% Finger Lakes 14167 1,788 0% to 3.2% Finger Lakes 14411 13,671 5.6% to 9.6% Finger Lakes 14414 6,797 3.3% to 5.5% Finger Lakes 14416 3,611 0% to 3.2% Finger Lakes 14420 21,124 0% to 3.2% Finger Lakes 14422 2,074 0% to 3.2% Finger Lakes 14423 4,479 0% to 3.2% Finger Lakes 14424 28,055 5.6% to 9.6% Finger Lakes 14425 12,230 5.6% to 9.6% Finger Lakes 14427 2,073 5.6% to 9.6% Finger Lakes 14428 7,278 0% to 3.2% Finger Lakes 14432 5,277 9.7% to 37.6% Finger Lakes 14433 3,920 5.6% to 9.6% Finger Lakes 14435 2,327 3.3% to 5.5% Finger Lakes 14437 11,274 3.3% to 5.5% Finger Lakes 14445 7,761 5.6% to 9.6% Finger Lakes 14450 42,513 0% to 3.2% Finger Lakes 14454 11,237 0% to 3.2% Finger Lakes 14456 19,539 9.7% to 37.6% Finger Lakes 14464 7,419 3.3% to 5.5% Finger Lakes 14466 1,526 3.3% to 5.5% Finger Lakes 14467 9,909 3.3% to 5.5% Finger Lakes 14468 17,994 3.3% to 5.5% Finger Lakes 14469 5,646 3.3% to 5.5% Finger Lakes 14470 7,331 3.3% to 5.5% Finger Lakes 14471 2,385 3.3% to 5.5% Finger Lakes 14472 9,271 0% to 3.2% Finger Lakes 14476 2,270 0% to 3.2% Finger Lakes 14477 1,460 5.6% to 9.6% Finger Lakes 14481 1,906 0% to 3.2% Finger Lakes 14482 8,230 5.6% to 9.6% Finger Lakes 14485 3,976 3.3% to 5.5% APPENDIX C 79 Zip code Share of consumers Region tabulation area Population with medical debt Finger Lakes 14487 5,997 0% to 3.2% Finger Lakes 14489 7,065 5.6% to 9.6% Finger Lakes 14502 10,551 0% to 3.2% Finger Lakes 14505 5,277 3.3% to 5.5% Finger Lakes 14510 4,571 3.3% to 5.5% Finger Lakes 14512 4,542 5.6% to 9.6% Finger Lakes 14513 13,749 9.7% to 37.6% Finger Lakes 14514 6,293 3.3% to 5.5% Finger Lakes 14516 2,338 9.7% to 37.6% Finger Lakes 14517 2,846 3.3% to 5.5% Finger Lakes 14519 11,537 5.6% to 9.6% Finger Lakes 14521 3,077 9.7% to 37.6% Finger Lakes 14522 9,559 5.6% to 9.6% Finger Lakes 14525 2,524 5.6% to 9.6% Finger Lakes 14526 19,001 0% to 3.2% Finger Lakes 14527 12,835 5.6% to 9.6% Finger Lakes 14530 5,500 3.3% to 5.5% Finger Lakes 14532 3,998 9.7% to 37.6% Finger Lakes 14534 31,975 0% to 3.2% Finger Lakes 14541 4,584 9.7% to 37.6% Finger Lakes 14543 3,145 0% to 3.2% Finger Lakes 14544 2,280 3.3% to 5.5% Finger Lakes 14546 4,614 3.3% to 5.5% Finger Lakes 14548 4,132 9.7% to 37.6% Finger Lakes 14550 1,256 3.3% to 5.5% Finger Lakes 14551 5,392 5.6% to 9.6% Finger Lakes 14559 18,584 0% to 3.2% Finger Lakes 14560 2,320 3.3% to 5.5% Finger Lakes 14561 2,815 5.6% to 9.6% Finger Lakes 14564 15,359 0% to 3.2% Finger Lakes 14568 6,241 3.3% to 5.5% Finger Lakes 14569 5,910 3.3% to 5.5% Finger Lakes 14580 53,593 0% to 3.2% Finger Lakes 14586 11,848 0% to 3.2% Finger Lakes 14589 7,502 0% to 3.2% Finger Lakes 14590 5,420 5.6% to 9.6% Finger Lakes 14591 1,556 0% to 3.2% Finger Lakes 14604 2,536 3.3% to 5.5% Finger Lakes 14605 11,457 5.6% to 9.6% 80 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Finger Lakes 14606 27,711 3.3% to 5.5% Finger Lakes 14607 16,880 3.3% to 5.5% Finger Lakes 14608 11,260 3.3% to 5.5% Finger Lakes 14609 40,081 5.6% to 9.6% Finger Lakes 14610 13,373 0% to 3.2% Finger Lakes 14611 17,429 5.6% to 9.6% Finger Lakes 14612 34,316 3.3% to 5.5% Finger Lakes 14613 15,375 5.6% to 9.6% Finger Lakes 14615 16,812 3.3% to 5.5% Finger Lakes 14616 26,935 5.6% to 9.6% Finger Lakes 14617 21,985 3.3% to 5.5% Finger Lakes 14618 23,252 0% to 3.2% Finger Lakes 14619 13,788 5.6% to 9.6% Finger Lakes 14620 23,912 0% to 3.2% Finger Lakes 14621 33,757 5.6% to 9.6% Finger Lakes 14622 12,906 3.3% to 5.5% Finger Lakes 14623 24,720 0% to 3.2% Finger Lakes 14624 37,873 0% to 3.2% Finger Lakes 14625 10,051 0% to 3.2% Finger Lakes 14626 29,941 0% to 3.2% Finger Lakes 14837 5,947 9.7% to 37.6% Finger Lakes 14847 2,375 5.6% to 9.6% Long Island 11001 26,542 0% to 3.2% Long Island 11003 44,712 3.3% to 5.5% Long Island 11010 25,318 0% to 3.2% Long Island 11020 6,325 0% to 3.2% Long Island 11021 18,181 0% to 3.2% Long Island 11023 9,479 0% to 3.2% Long Island 11024 7,869 3.3% to 5.5% Long Island 11030 18,450 0% to 3.2% Long Island 11040 41,523 0% to 3.2% Long Island 11050 30,699 3.3% to 5.5% Long Island 11096 8,155 5.6% to 9.6% Long Island 11501 19,675 0% to 3.2% Long Island 11507 7,051 0% to 3.2% Long Island 11509 1,790 0% to 3.2% Long Island 11510 33,949 3.3% to 5.5% Long Island 11514 4,644 0% to 3.2% Long Island 11516 7,476 3.3% to 5.5% APPENDIX C 81 Zip code Share of consumers Region tabulation area Population with medical debt Long Island 11518 9,819 0% to 3.2% Long Island 11520 43,451 3.3% to 5.5% Long Island 11530 27,669 0% to 3.2% Long Island 11542 27,666 3.3% to 5.5% Long Island 11545 12,686 0% to 3.2% Long Island 11550 57,073 3.3% to 5.5% Long Island 11552 25,169 3.3% to 5.5% Long Island 11553 26,938 3.3% to 5.5% Long Island 11554 37,992 0% to 3.2% Long Island 11557 7,950 0% to 3.2% Long Island 11558 8,435 5.6% to 9.6% Long Island 11559 8,265 3.3% to 5.5% Long Island 11560 6,850 0% to 3.2% Long Island 11561 37,759 3.3% to 5.5% Long Island 11563 21,537 0% to 3.2% Long Island 11565 8,921 0% to 3.2% Long Island 11566 33,626 0% to 3.2% Long Island 11568 4,470 0% to 3.2% Long Island 11569 1,175 0% to 3.2% Long Island 11570 27,240 0% to 3.2% Long Island 11572 28,673 0% to 3.2% Long Island 11575 16,799 3.3% to 5.5% Long Island 11576 12,606 0% to 3.2% Long Island 11577 12,120 0% to 3.2% Long Island 11579 5,183 0% to 3.2% Long Island 11580 41,930 0% to 3.2% Long Island 11581 21,847 0% to 3.2% Long Island 11590 47,468 0% to 3.2% Long Island 11596 10,276 0% to 3.2% Long Island 11598 13,977 0% to 3.2% Long Island 11701 27,264 3.3% to 5.5% Long Island 11702 13,963 0% to 3.2% Long Island 11703 16,820 0% to 3.2% Long Island 11704 39,960 0% to 3.2% Long Island 11705 8,033 0% to 3.2% Long Island 11706 67,484 3.3% to 5.5% Long Island 11709 6,720 0% to 3.2% Long Island 11710 35,473 0% to 3.2% Long Island 11713 8,247 5.6% to 9.6% 82 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Long Island 11714 22,769 0% to 3.2% Long Island 11715 4,287 5.6% to 9.6% Long Island 11716 9,748 0% to 3.2% Long Island 11717 62,888 3.3% to 5.5% Long Island 11718 3,070 0% to 3.2% Long Island 11719 3,209 3.3% to 5.5% Long Island 11720 28,773 0% to 3.2% Long Island 11721 6,258 0% to 3.2% Long Island 11722 34,139 3.3% to 5.5% Long Island 11724 3,089 0% to 3.2% Long Island 11725 29,229 0% to 3.2% Long Island 11726 21,012 0% to 3.2% Long Island 11727 29,262 3.3% to 5.5% Long Island 11729 25,597 3.3% to 5.5% Long Island 11730 13,335 0% to 3.2% Long Island 11731 30,951 0% to 3.2% Long Island 11732 3,505 0% to 3.2% Long Island 11733 17,000 0% to 3.2% Long Island 11735 33,299 0% to 3.2% Long Island 11738 19,364 3.3% to 5.5% Long Island 11740 9,321 0% to 3.2% Long Island 11741 26,245 0% to 3.2% Long Island 11742 11,968 0% to 3.2% Long Island 11743 42,636 0% to 3.2% Long Island 11746 66,518 0% to 3.2% Long Island 11747 18,795 0% to 3.2% Long Island 11749 3,315 0% to 3.2% Long Island 11751 13,399 0% to 3.2% Long Island 11752 8,821 0% to 3.2% Long Island 11753 13,092 0% to 3.2% Long Island 11754 17,861 0% to 3.2% Long Island 11755 12,514 0% to 3.2% Long Island 11756 42,856 0% to 3.2% Long Island 11757 44,130 0% to 3.2% Long Island 11758 53,034 0% to 3.2% Long Island 11762 22,263 0% to 3.2% Long Island 11763 28,769 3.3% to 5.5% Long Island 11764 13,633 0% to 3.2% Long Island 11766 11,018 0% to 3.2% APPENDIX C 83 Zip code Share of consumers Region tabulation area Population with medical debt Long Island 11767 15,051 0% to 3.2% Long Island 11768 20,903 0% to 3.2% Long Island 11769 8,299 0% to 3.2% Long Island 11771 9,517 3.3% to 5.5% Long Island 11772 46,311 3.3% to 5.5% Long Island 11776 23,578 3.3% to 5.5% Long Island 11777 9,156 0% to 3.2% Long Island 11778 11,855 3.3% to 5.5% Long Island 11779 38,091 3.3% to 5.5% Long Island 11780 15,934 0% to 3.2% Long Island 11782 15,057 0% to 3.2% Long Island 11783 21,285 0% to 3.2% Long Island 11784 25,014 3.3% to 5.5% Long Island 11786 6,225 0% to 3.2% Long Island 11787 33,837 0% to 3.2% Long Island 11788 16,693 0% to 3.2% Long Island 11789 7,496 3.3% to 5.5% Long Island 11790 16,099 0% to 3.2% Long Island 11791 25,776 0% to 3.2% Long Island 11792 8,413 0% to 3.2% Long Island 11793 30,601 0% to 3.2% Long Island 11795 25,062 0% to 3.2% Long Island 11796 4,112 0% to 3.2% Long Island 11797 8,998 0% to 3.2% Long Island 11798 15,636 3.3% to 5.5% Long Island 11801 40,179 0% to 3.2% Long Island 11803 29,208 0% to 3.2% Long Island 11804 4,639 0% to 3.2% Long Island 11901 28,760 5.6% to 9.6% Long Island 11930 618 0% to 3.2% Long Island 11931 84 3.3% to 5.5% Long Island 11932 1,099 0% to 3.2% Long Island 11933 6,475 3.3% to 5.5% Long Island 11934 7,357 5.6% to 9.6% Long Island 11935 3,008 0% to 3.2% Long Island 11937 16,672 0% to 3.2% Long Island 11940 4,963 3.3% to 5.5% Long Island 11941 1,770 3.3% to 5.5% Long Island 11942 5,053 5.6% to 9.6% 84 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Long Island 11944 4,144 5.6% to 9.6% Long Island 11946 15,058 3.3% to 5.5% Long Island 11949 13,457 0% to 3.2% Long Island 11950 16,130 5.6% to 9.6% Long Island 11951 14,756 5.6% to 9.6% Long Island 11952 5,031 0% to 3.2% Long Island 11953 14,301 0% to 3.2% Long Island 11954 3,563 3.3% to 5.5% Long Island 11955 3,149 3.3% to 5.5% Long Island 11959 571 3.3% to 5.5% Long Island 11960 721 3.3% to 5.5% Long Island 11961 13,701 0% to 3.2% Long Island 11963 6,966 0% to 3.2% Long Island 11964 2,287 0% to 3.2% Long Island 11967 29,130 3.3% to 5.5% Long Island 11968 10,778 0% to 3.2% Long Island 11971 6,073 0% to 3.2% Long Island 11976 1,847 3.3% to 5.5% Long Island 11977 2,629 5.6% to 9.6% Long Island 11978 2,981 5.6% to 9.6% Long Island 11980 5,966 0% to 3.2% Mid-Hudson 10502 5,351 0% to 3.2% Mid-Hudson 10504 8,053 0% to 3.2% Mid-Hudson 10506 5,555 3.3% to 5.5% Mid-Hudson 10507 7,152 0% to 3.2% Mid-Hudson 10509 20,230 5.6% to 9.6% Mid-Hudson 10510 10,300 0% to 3.2% Mid-Hudson 10511 2,300 0% to 3.2% Mid-Hudson 10512 24,619 5.6% to 9.6% Mid-Hudson 10514 12,543 0% to 3.2% Mid-Hudson 10516 5,449 0% to 3.2% Mid-Hudson 10520 12,885 3.3% to 5.5% Mid-Hudson 10522 11,052 0% to 3.2% Mid-Hudson 10523 9,415 3.3% to 5.5% Mid-Hudson 10524 4,317 0% to 3.2% Mid-Hudson 10526 1,860 0% to 3.2% Mid-Hudson 10528 13,395 0% to 3.2% Mid-Hudson 10530 12,925 0% to 3.2% Mid-Hudson 10532 4,739 0% to 3.2% APPENDIX C 85 Zip code Share of consumers Region tabulation area Population with medical debt Mid-Hudson 10533 7,463 0% to 3.2% Mid-Hudson 10536 11,002 0% to 3.2% Mid-Hudson 10537 2,769 0% to 3.2% Mid-Hudson 10538 16,151 0% to 3.2% Mid-Hudson 10541 26,678 5.6% to 9.6% Mid-Hudson 10543 20,999 0% to 3.2% Mid-Hudson 10547 8,034 3.3% to 5.5% Mid-Hudson 10548 4,087 5.6% to 9.6% Mid-Hudson 10549 16,121 0% to 3.2% Mid-Hudson 10550 36,382 5.6% to 9.6% Mid-Hudson 10552 21,342 5.6% to 9.6% Mid-Hudson 10553 9,747 5.6% to 9.6% Mid-Hudson 10560 4,814 5.6% to 9.6% Mid-Hudson 10562 31,847 3.3% to 5.5% Mid-Hudson 10566 24,111 3.3% to 5.5% Mid-Hudson 10567 20,094 3.3% to 5.5% Mid-Hudson 10570 12,780 3.3% to 5.5% Mid-Hudson 10573 38,749 3.3% to 5.5% Mid-Hudson 10576 5,036 3.3% to 5.5% Mid-Hudson 10577 6,387 0% to 3.2% Mid-Hudson 10579 8,250 3.3% to 5.5% Mid-Hudson 10580 17,185 0% to 3.2% Mid-Hudson 10583 41,087 0% to 3.2% Mid-Hudson 10588 3,102 5.6% to 9.6% Mid-Hudson 10589 8,080 3.3% to 5.5% Mid-Hudson 10590 7,208 3.3% to 5.5% Mid-Hudson 10591 23,059 0% to 3.2% Mid-Hudson 10594 5,587 0% to 3.2% Mid-Hudson 10595 8,439 0% to 3.2% Mid-Hudson 10596 1,051 3.3% to 5.5% Mid-Hudson 10598 28,708 0% to 3.2% Mid-Hudson 10601 10,256 3.3% to 5.5% Mid-Hudson 10603 18,862 0% to 3.2% Mid-Hudson 10604 11,132 3.3% to 5.5% Mid-Hudson 10605 19,849 0% to 3.2% Mid-Hudson 10606 16,199 3.3% to 5.5% Mid-Hudson 10607 7,162 3.3% to 5.5% Mid-Hudson 10701 61,367 9.7% to 37.6% Mid-Hudson 10703 22,636 5.6% to 9.6% 86 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Mid-Hudson 10704 31,110 3.3% to 5.5% Mid-Hudson 10705 39,848 9.7% to 37.6% Mid-Hudson 10706 8,622 0% to 3.2% Mid-Hudson 10707 9,713 0% to 3.2% Mid-Hudson 10708 22,212 0% to 3.2% Mid-Hudson 10709 9,369 0% to 3.2% Mid-Hudson 10710 25,104 3.3% to 5.5% Mid-Hudson 10801 40,445 5.6% to 9.6% Mid-Hudson 10803 12,492 0% to 3.2% Mid-Hudson 10804 14,365 0% to 3.2% Mid-Hudson 10805 21,331 3.3% to 5.5% Mid-Hudson 10901 23,860 3.3% to 5.5% Mid-Hudson 10913 5,294 0% to 3.2% Mid-Hudson 10916 4,582 5.6% to 9.6% Mid-Hudson 10917 1,650 3.3% to 5.5% Mid-Hudson 10918 12,286 3.3% to 5.5% Mid-Hudson 10920 8,919 3.3% to 5.5% Mid-Hudson 10921 3,812 3.3% to 5.5% Mid-Hudson 10923 7,753 5.6% to 9.6% Mid-Hudson 10924 13,538 5.6% to 9.6% Mid-Hudson 10925 3,886 5.6% to 9.6% Mid-Hudson 10926 3,482 3.3% to 5.5% Mid-Hudson 10927 12,076 9.7% to 37.6% Mid-Hudson 10928 4,132 5.6% to 9.6% Mid-Hudson 10930 9,789 5.6% to 9.6% Mid-Hudson 10940 49,430 9.7% to 37.6% Mid-Hudson 10941 13,384 5.6% to 9.6% Mid-Hudson 10950 53,013 5.6% to 9.6% Mid-Hudson 10952 43,374 0% to 3.2% Mid-Hudson 10954 25,870 3.3% to 5.5% Mid-Hudson 10956 30,697 3.3% to 5.5% Mid-Hudson 10958 3,236 9.7% to 37.6% Mid-Hudson 10960 15,757 3.3% to 5.5% Mid-Hudson 10962 5,927 0% to 3.2% Mid-Hudson 10963 4,367 9.7% to 37.6% Mid-Hudson 10964 1,506 0% to 3.2% Mid-Hudson 10965 15,493 0% to 3.2% Mid-Hudson 10968 2,466 5.6% to 9.6% Mid-Hudson 10970 10,433 3.3% to 5.5% APPENDIX C 87 Zip code Share of consumers Region tabulation area Population with medical debt Mid-Hudson 10973 2,510 5.6% to 9.6% Mid-Hudson 10974 3,189 0% to 3.2% Mid-Hudson 10976 2,300 5.6% to 9.6% Mid-Hudson 10977 64,677 5.6% to 9.6% Mid-Hudson 10980 13,923 3.3% to 5.5% Mid-Hudson 10983 5,597 3.3% to 5.5% Mid-Hudson 10984 2,661 3.3% to 5.5% Mid-Hudson 10986 1,836 0% to 3.2% Mid-Hudson 10987 3,499 5.6% to 9.6% Mid-Hudson 10989 8,415 3.3% to 5.5% Mid-Hudson 10990 20,735 3.3% to 5.5% Mid-Hudson 10992 8,830 5.6% to 9.6% Mid-Hudson 10993 5,044 3.3% to 5.5% Mid-Hudson 10994 7,115 3.3% to 5.5% Mid-Hudson 10996 6,342 0% to 3.2% Mid-Hudson 10998 3,428 5.6% to 9.6% Mid-Hudson 12401 34,800 9.7% to 37.6% Mid-Hudson 12404 3,679 5.6% to 9.6% Mid-Hudson 12428 6,535 9.7% to 37.6% Mid-Hudson 12443 3,721 5.6% to 9.6% Mid-Hudson 12446 4,683 9.7% to 37.6% Mid-Hudson 12449 3,208 9.7% to 37.6% Mid-Hudson 12461 1,343 0% to 3.2% Mid-Hudson 12466 2,110 5.6% to 9.6% Mid-Hudson 12477 17,870 9.7% to 37.6% Mid-Hudson 12484 3,318 5.6% to 9.6% Mid-Hudson 12487 3,370 5.6% to 9.6% Mid-Hudson 12491 1,660 5.6% to 9.6% Mid-Hudson 12498 4,713 3.3% to 5.5% Mid-Hudson 12501 2,158 3.3% to 5.5% Mid-Hudson 12508 19,812 5.6% to 9.6% Mid-Hudson 12514 2,772 9.7% to 37.6% Mid-Hudson 12515 1,657 9.7% to 37.6% Mid-Hudson 12518 5,861 5.6% to 9.6% Mid-Hudson 12520 2,970 3.3% to 5.5% Mid-Hudson 12522 4,918 9.7% to 37.6% Mid-Hudson 12524 15,608 5.6% to 9.6% Mid-Hudson 12525 2,966 3.3% to 5.5% Mid-Hudson 12528 12,767 9.7% to 37.6% 88 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Mid-Hudson 12531 2,645 9.7% to 37.6% Mid-Hudson 12533 26,361 5.6% to 9.6% Mid-Hudson 12538 14,566 9.7% to 37.6% Mid-Hudson 12540 8,882 5.6% to 9.6% Mid-Hudson 12542 5,684 9.7% to 37.6% Mid-Hudson 12543 3,586 9.7% to 37.6% Mid-Hudson 12545 4,517 5.6% to 9.6% Mid-Hudson 12546 2,824 5.6% to 9.6% Mid-Hudson 12547 2,810 5.6% to 9.6% Mid-Hudson 12549 11,453 5.6% to 9.6% Mid-Hudson 12550 54,503 9.7% to 37.6% Mid-Hudson 12553 26,665 5.6% to 9.6% Mid-Hudson 12561 18,224 5.6% to 9.6% Mid-Hudson 12563 7,579 5.6% to 9.6% Mid-Hudson 12564 7,710 5.6% to 9.6% Mid-Hudson 12566 11,886 5.6% to 9.6% Mid-Hudson 12567 2,608 5.6% to 9.6% Mid-Hudson 12569 9,838 5.6% to 9.6% Mid-Hudson 12570 6,772 3.3% to 5.5% Mid-Hudson 12571 10,037 5.6% to 9.6% Mid-Hudson 12572 8,961 5.6% to 9.6% Mid-Hudson 12575 1,930 5.6% to 9.6% Mid-Hudson 12577 1,929 3.3% to 5.5% Mid-Hudson 12578 2,100 9.7% to 37.6% Mid-Hudson 12580 4,359 9.7% to 37.6% Mid-Hudson 12581 2,231 9.7% to 37.6% Mid-Hudson 12582 6,213 9.7% to 37.6% Mid-Hudson 12583 2,160 0% to 3.2% Mid-Hudson 12586 11,774 5.6% to 9.6% Mid-Hudson 12589 17,843 9.7% to 37.6% Mid-Hudson 12590 34,823 5.6% to 9.6% Mid-Hudson 12594 4,150 9.7% to 37.6% Mid-Hudson 12601 41,037 9.7% to 37.6% Mid-Hudson 12603 42,140 9.7% to 37.6% Mid-Hudson 12701 11,278 9.7% to 37.6% Mid-Hudson 12721 5,881 9.7% to 37.6% Mid-Hudson 12723 1,561 9.7% to 37.6% Mid-Hudson 12737 1,800 9.7% to 37.6% Mid-Hudson 12740 1,927 5.6% to 9.6% APPENDIX C 89 Zip code Share of consumers Region tabulation area Population with medical debt Mid-Hudson 12748 2,042 5.6% to 9.6% Mid-Hudson 12754 7,142 9.7% to 37.6% Mid-Hudson 12758 3,699 9.7% to 37.6% Mid-Hudson 12764 1,770 5.6% to 9.6% Mid-Hudson 12771 14,408 9.7% to 37.6% Mid-Hudson 12775 2,151 9.7% to 37.6% Mid-Hudson 12776 2,361 3.3% to 5.5% Mid-Hudson 12779 1,701 9.7% to 37.6% Mid-Hudson 12780 2,165 9.7% to 37.6% Mid-Hudson 12783 1,605 9.7% to 37.6% Mid-Hudson 12788 2,909 5.6% to 9.6% Mid-Hudson 12790 3,933 9.7% to 37.6% Mohawk Valley 12010 27,556 9.7% to 37.6% Mohawk Valley 12025 5,331 9.7% to 37.6% Mohawk Valley 12043 8,202 0% to 3.2% Mohawk Valley 12068 3,029 9.7% to 37.6% Mohawk Valley 12072 3,065 9.7% to 37.6% Mohawk Valley 12078 22,956 9.7% to 37.6% Mohawk Valley 12086 1,379 3.3% to 5.5% Mohawk Valley 12093 1,602 5.6% to 9.6% Mohawk Valley 12095 11,986 9.7% to 37.6% Mohawk Valley 12117 3,163 9.7% to 37.6% Mohawk Valley 12122 4,137 5.6% to 9.6% Mohawk Valley 12134 4,232 5.6% to 9.6% Mohawk Valley 12149 2,358 5.6% to 9.6% Mohawk Valley 12157 4,597 3.3% to 5.5% Mohawk Valley 12166 1,145 9.7% to 37.6% Mohawk Valley 13042 2,383 5.6% to 9.6% Mohawk Valley 13304 1,726 9.7% to 37.6% Mohawk Valley 13308 3,867 9.7% to 37.6% Mohawk Valley 13309 5,672 5.6% to 9.6% Mohawk Valley 13316 6,168 9.7% to 37.6% Mohawk Valley 13317 3,847 5.6% to 9.6% Mohawk Valley 13323 11,585 5.6% to 9.6% Mohawk Valley 13324 2,195 9.7% to 37.6% Mohawk Valley 13329 4,211 0% to 3.2% Mohawk Valley 13339 6,382 5.6% to 9.6% Mohawk Valley 13340 8,023 9.7% to 37.6% Mohawk Valley 13350 9,727 5.6% to 9.6% 90 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Mohawk Valley 13354 3,777 5.6% to 9.6% Mohawk Valley 13357 10,748 5.6% to 9.6% Mohawk Valley 13363 2,358 9.7% to 37.6% Mohawk Valley 13365 8,472 5.6% to 9.6% Mohawk Valley 13403 7,485 5.6% to 9.6% Mohawk Valley 13407 4,830 5.6% to 9.6% Mohawk Valley 13413 15,750 5.6% to 9.6% Mohawk Valley 13416 2,079 3.3% to 5.5% Mohawk Valley 13417 3,066 0% to 3.2% Mohawk Valley 13424 2,375 3.3% to 5.5% Mohawk Valley 13425 2,143 5.6% to 9.6% Mohawk Valley 13431 1,902 5.6% to 9.6% Mohawk Valley 13438 2,944 9.7% to 37.6% Mohawk Valley 13440 41,342 9.7% to 37.6% Mohawk Valley 13452 4,671 9.7% to 37.6% Mohawk Valley 13456 4,039 5.6% to 9.6% Mohawk Valley 13459 2,343 5.6% to 9.6% Mohawk Valley 13461 2,987 9.7% to 37.6% Mohawk Valley 13471 3,045 9.7% to 37.6% Mohawk Valley 13476 3,617 9.7% to 37.6% Mohawk Valley 13478 3,257 5.6% to 9.6% Mohawk Valley 13480 3,235 5.6% to 9.6% Mohawk Valley 13491 3,330 5.6% to 9.6% Mohawk Valley 13492 11,118 5.6% to 9.6% Mohawk Valley 13495 2,035 5.6% to 9.6% Mohawk Valley 13501 37,132 9.7% to 37.6% Mohawk Valley 13502 32,437 9.7% to 37.6% New York City 10001 25,026 0% to 3.2% New York City 10002 74,363 3.3% to 5.5% New York City 10003 54,671 0% to 3.2% New York City 10004 3,310 3.3% to 5.5% New York City 10005 8,664 0% to 3.2% New York City 10006 3,260 0% to 3.2% New York City 10007 7,566 0% to 3.2% New York City 10009 58,267 3.3% to 5.5% New York City 10010 34,763 0% to 3.2% New York City 10011 50,228 0% to 3.2% New York City 10012 23,034 0% to 3.2% New York City 10013 28,657 0% to 3.2% APPENDIX C 91 Zip code Share of consumers Region tabulation area Population with medical debt New York City 10014 29,071 0% to 3.2% New York City 10016 52,912 0% to 3.2% New York City 10017 16,065 0% to 3.2% New York City 10018 8,135 0% to 3.2% New York City 10019 45,521 0% to 3.2% New York City 10021 42,253 0% to 3.2% New York City 10022 31,574 0% to 3.2% New York City 10023 62,228 0% to 3.2% New York City 10024 60,714 0% to 3.2% New York City 10025 92,162 0% to 3.2% New York City 10026 38,937 3.3% to 5.5% New York City 10027 64,728 3.3% to 5.5% New York City 10028 47,962 0% to 3.2% New York City 10029 74,617 3.3% to 5.5% New York City 10030 30,996 3.3% to 5.5% New York City 10031 60,280 3.3% to 5.5% New York City 10032 59,527 3.3% to 5.5% New York City 10033 58,648 3.3% to 5.5% New York City 10034 41,933 3.3% to 5.5% New York City 10035 36,999 3.3% to 5.5% New York City 10036 28,231 0% to 3.2% New York City 10037 19,177 3.3% to 5.5% New York City 10038 24,205 0% to 3.2% New York City 10039 27,090 3.3% to 5.5% New York City 10040 45,197 0% to 3.2% New York City 10044 12,770 0% to 3.2% New York City 10065 30,339 0% to 3.2% New York City 10069 6,504 0% to 3.2% New York City 10075 22,591 0% to 3.2% New York City 10128 57,993 0% to 3.2% New York City 10162 1,240 0% to 3.2% New York City 10280 9,372 0% to 3.2% New York City 10282 6,086 0% to 3.2% New York City 10301 38,001 3.3% to 5.5% New York City 10302 17,942 3.3% to 5.5% New York City 10303 27,164 3.3% to 5.5% New York City 10304 42,133 0% to 3.2% New York City 10305 42,968 3.3% to 5.5% New York City 10306 54,318 0% to 3.2% 92 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt New York City 10307 15,053 0% to 3.2% New York City 10308 28,901 0% to 3.2% New York City 10309 33,896 0% to 3.2% New York City 10310 24,168 3.3% to 5.5% New York City 10312 61,114 0% to 3.2% New York City 10314 89,938 0% to 3.2% New York City 10451 49,423 5.6% to 9.6% New York City 10452 74,379 5.6% to 9.6% New York City 10453 78,858 5.6% to 9.6% New York City 10454 37,807 3.3% to 5.5% New York City 10455 40,300 3.3% to 5.5% New York City 10456 90,479 5.6% to 9.6% New York City 10457 74,679 5.6% to 9.6% New York City 10458 85,893 5.6% to 9.6% New York City 10459 48,391 5.6% to 9.6% New York City 10460 57,514 5.6% to 9.6% New York City 10461 51,081 5.6% to 9.6% New York City 10462 75,441 5.6% to 9.6% New York City 10463 70,236 3.3% to 5.5% New York City 10464 4,322 3.3% to 5.5% New York City 10465 45,066 3.3% to 5.5% New York City 10466 74,519 3.3% to 5.5% New York City 10467 100,867 5.6% to 9.6% New York City 10468 78,778 5.6% to 9.6% New York City 10469 72,550 3.3% to 5.5% New York City 10470 15,500 3.3% to 5.5% New York City 10471 22,577 3.3% to 5.5% New York City 10472 67,989 5.6% to 9.6% New York City 10473 59,627 5.6% to 9.6% New York City 10474 11,579 5.6% to 9.6% New York City 10475 43,791 3.3% to 5.5% New York City 11004 15,297 0% to 3.2% New York City 11005 2,249 0% to 3.2% New York City 11101 32,537 0% to 3.2% New York City 11102 28,359 3.3% to 5.5% New York City 11103 35,419 0% to 3.2% New York City 11104 25,037 0% to 3.2% New York City 11105 37,452 0% to 3.2% New York City 11106 37,094 3.3% to 5.5% APPENDIX C 93 Zip code Share of consumers Region tabulation area Population with medical debt New York City 11109 6,127 0% to 3.2% New York City 11201 64,798 0% to 3.2% New York City 11203 77,589 3.3% to 5.5% New York City 11204 75,840 0% to 3.2% New York City 11205 47,866 3.3% to 5.5% New York City 11206 87,599 3.3% to 5.5% New York City 11207 90,867 5.6% to 9.6% New York City 11208 102,626 3.3% to 5.5% New York City 11209 68,368 0% to 3.2% New York City 11210 62,423 0% to 3.2% New York City 11211 104,561 0% to 3.2% New York City 11212 74,037 3.3% to 5.5% New York City 11213 67,382 3.3% to 5.5% New York City 11214 90,372 0% to 3.2% New York City 11215 69,995 0% to 3.2% New York City 11216 59,567 3.3% to 5.5% New York City 11217 42,461 3.3% to 5.5% New York City 11218 73,280 0% to 3.2% New York City 11219 87,812 0% to 3.2% New York City 11220 90,657 0% to 3.2% New York City 11221 85,582 3.3% to 5.5% New York City 11222 36,710 0% to 3.2% New York City 11223 80,500 0% to 3.2% New York City 11224 45,587 3.3% to 5.5% New York City 11225 56,072 3.3% to 5.5% New York City 11226 96,332 3.3% to 5.5% New York City 11228 43,878 0% to 3.2% New York City 11229 81,909 0% to 3.2% New York City 11230 87,188 0% to 3.2% New York City 11231 37,796 0% to 3.2% New York City 11232 26,501 0% to 3.2% New York City 11233 79,796 5.6% to 9.6% New York City 11234 90,372 0% to 3.2% New York City 11235 76,921 0% to 3.2% New York City 11236 98,821 3.3% to 5.5% New York City 11237 46,772 3.3% to 5.5% New York City 11238 55,102 0% to 3.2% New York City 11239 12,832 5.6% to 9.6% New York City 11354 52,351 3.3% to 5.5% 94 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt New York City 11355 81,358 3.3% to 5.5% New York City 11356 24,178 5.6% to 9.6% New York City 11357 40,118 3.3% to 5.5% New York City 11358 36,665 0% to 3.2% New York City 11360 18,892 3.3% to 5.5% New York City 11361 28,531 3.3% to 5.5% New York City 11362 18,694 0% to 3.2% New York City 11363 6,834 0% to 3.2% New York City 11364 35,866 0% to 3.2% New York City 11365 43,730 3.3% to 5.5% New York City 11366 14,109 3.3% to 5.5% New York City 11367 40,141 3.3% to 5.5% New York City 11368 108,661 5.6% to 9.6% New York City 11369 30,693 3.3% to 5.5% New York City 11370 29,366 3.3% to 5.5% New York City 11372 63,107 0% to 3.2% New York City 11373 96,495 0% to 3.2% New York City 11374 43,173 0% to 3.2% New York City 11375 72,615 0% to 3.2% New York City 11377 82,021 3.3% to 5.5% New York City 11378 39,033 0% to 3.2% New York City 11379 37,780 3.3% to 5.5% New York City 11385 105,025 3.3% to 5.5% New York City 11411 20,473 3.3% to 5.5% New York City 11412 37,987 3.3% to 5.5% New York City 11413 41,810 3.3% to 5.5% New York City 11414 27,672 3.3% to 5.5% New York City 11415 18,464 0% to 3.2% New York City 11416 26,087 0% to 3.2% New York City 11417 31,156 0% to 3.2% New York City 11418 37,666 3.3% to 5.5% New York City 11419 47,955 3.3% to 5.5% New York City 11420 47,266 3.3% to 5.5% New York City 11421 39,654 0% to 3.2% New York City 11422 32,786 3.3% to 5.5% New York City 11423 29,532 3.3% to 5.5% New York City 11426 20,222 0% to 3.2% New York City 11427 25,096 3.3% to 5.5% New York City 11428 19,830 0% to 3.2% APPENDIX C 95 Zip code Share of consumers Region tabulation area Population with medical debt New York City 11429 27,808 3.3% to 5.5% New York City 11432 64,282 3.3% to 5.5% New York City 11433 36,929 3.3% to 5.5% New York City 11434 62,590 3.3% to 5.5% New York City 11435 59,392 3.3% to 5.5% New York City 11436 18,955 3.3% to 5.5% New York City 11691 68,454 5.6% to 9.6% New York City 11692 22,394 5.6% to 9.6% New York City 11693 13,029 5.6% to 9.6% New York City 11694 21,258 3.3% to 5.5% New York City 11697 3,527 3.3% to 5.5% North Country 12883 4,790 5.6% to 9.6% North Country 12901 33,048 5.6% to 9.6% North Country 12903 1,306 3.3% to 5.5% North Country 12910 2,190 5.6% to 9.6% North Country 12912 1,903 3.3% to 5.5% North Country 12916 2,436 5.6% to 9.6% North Country 12918 2,641 5.6% to 9.6% North Country 12919 2,805 5.6% to 9.6% North Country 12920 2,387 5.6% to 9.6% North Country 12921 2,835 9.7% to 37.6% North Country 12926 2,380 3.3% to 5.5% North Country 12928 2,029 5.6% to 9.6% North Country 12944 3,683 5.6% to 9.6% North Country 12946 4,819 0% to 3.2% North Country 12953 15,469 5.6% to 9.6% North Country 12956 1,180 9.7% to 37.6% North Country 12958 2,020 3.3% to 5.5% North Country 12962 5,084 5.6% to 9.6% North Country 12966 2,732 9.7% to 37.6% North Country 12972 6,611 5.6% to 9.6% North Country 12979 2,435 5.6% to 9.6% North Country 12981 2,401 5.6% to 9.6% North Country 12983 7,719 3.3% to 5.5% North Country 12986 5,928 9.7% to 37.6% North Country 12992 3,565 5.6% to 9.6% North Country 12993 1,486 5.6% to 9.6% North Country 12996 2,084 3.3% to 5.5% North Country 13327 1,762 3.3% to 5.5% 96 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt North Country 13343 1,572 9.7% to 37.6% North Country 13367 8,893 3.3% to 5.5% North Country 13601 36,982 9.7% to 37.6% North Country 13602 4,383 3.3% to 5.5% North Country 13603 9,274 9.7% to 37.6% North Country 13605 5,173 9.7% to 37.6% North Country 13606 2,456 9.7% to 37.6% North Country 13607 1,445 3.3% to 5.5% North Country 13612 2,233 9.7% to 37.6% North Country 13613 2,698 9.7% to 37.6% North Country 13616 2,202 9.7% to 37.6% North Country 13617 11,556 9.7% to 37.6% North Country 13618 1,525 9.7% to 37.6% North Country 13619 10,439 9.7% to 37.6% North Country 13620 2,592 5.6% to 9.6% North Country 13622 2,138 9.7% to 37.6% North Country 13624 5,297 9.7% to 37.6% North Country 13625 1,470 5.6% to 9.6% North Country 13626 2,271 9.7% to 37.6% North Country 13634 3,706 5.6% to 9.6% North Country 13637 3,720 9.7% to 37.6% North Country 13642 10,055 9.7% to 37.6% North Country 13646 1,879 9.7% to 37.6% North Country 13648 2,056 3.3% to 5.5% North Country 13650 1,559 5.6% to 9.6% North Country 13652 1,544 9.7% to 37.6% North Country 13654 2,603 9.7% to 37.6% North Country 13655 3,418 9.7% to 37.6% North Country 13656 2,834 9.7% to 37.6% North Country 13658 2,414 9.7% to 37.6% North Country 13660 1,641 9.7% to 37.6% North Country 13662 16,179 9.7% to 37.6% North Country 13667 2,781 9.7% to 37.6% North Country 13668 3,196 9.7% to 37.6% North Country 13669 15,870 9.7% to 37.6% North Country 13673 2,231 9.7% to 37.6% North Country 13676 16,754 5.6% to 9.6% North Country 13679 1,686 9.7% to 37.6% North Country 13685 2,238 3.3% to 5.5% APPENDIX C 97 Zip code Share of consumers Region tabulation area Population with medical debt North Country 13691 3,016 9.7% to 37.6% North Country 13694 1,328 9.7% to 37.6% North Country 13697 1,776 9.7% to 37.6% Southern Tier 12116 1,506 3.3% to 5.5% Southern Tier 12155 1,979 5.6% to 9.6% Southern Tier 12167 2,663 5.6% to 9.6% Southern Tier 12197 2,139 3.3% to 5.5% Southern Tier 12455 1,553 3.3% to 5.5% Southern Tier 13053 4,861 5.6% to 9.6% Southern Tier 13068 5,184 9.7% to 37.6% Southern Tier 13073 6,729 9.7% to 37.6% Southern Tier 13320 2,119 5.6% to 9.6% Southern Tier 13326 5,165 0% to 3.2% Southern Tier 13335 1,386 5.6% to 9.6% Southern Tier 13411 3,092 3.3% to 5.5% Southern Tier 13439 3,720 3.3% to 5.5% Southern Tier 13460 4,122 5.6% to 9.6% Southern Tier 13730 2,630 3.3% to 5.5% Southern Tier 13732 7,602 0% to 3.2% Southern Tier 13733 5,112 5.6% to 9.6% Southern Tier 13734 1,828 5.6% to 9.6% Southern Tier 13736 2,086 5.6% to 9.6% Southern Tier 13743 3,630 9.7% to 37.6% Southern Tier 13746 1,578 3.3% to 5.5% Southern Tier 13748 3,766 3.3% to 5.5% Southern Tier 13753 5,511 3.3% to 5.5% Southern Tier 13754 2,883 3.3% to 5.5% Southern Tier 13760 42,103 3.3% to 5.5% Southern Tier 13778 5,458 3.3% to 5.5% Southern Tier 13783 2,659 9.7% to 37.6% Southern Tier 13787 3,039 5.6% to 9.6% Southern Tier 13790 17,763 5.6% to 9.6% Southern Tier 13795 3,093 5.6% to 9.6% Southern Tier 13797 2,262 9.7% to 37.6% Southern Tier 13809 1,761 5.6% to 9.6% Southern Tier 13811 3,988 5.6% to 9.6% Southern Tier 13812 2,258 9.7% to 37.6% Southern Tier 13815 12,901 3.3% to 5.5% Southern Tier 13820 22,157 5.6% to 9.6% 98 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Southern Tier 13825 2,888 0% to 3.2% Southern Tier 13827 11,538 5.6% to 9.6% Southern Tier 13830 5,119 3.3% to 5.5% Southern Tier 13833 3,890 3.3% to 5.5% Southern Tier 13838 3,873 5.6% to 9.6% Southern Tier 13843 2,241 5.6% to 9.6% Southern Tier 13849 4,449 3.3% to 5.5% Southern Tier 13850 22,392 0% to 3.2% Southern Tier 13856 5,999 3.3% to 5.5% Southern Tier 13862 4,053 5.6% to 9.6% Southern Tier 13865 6,203 5.6% to 9.6% Southern Tier 13901 18,786 5.6% to 9.6% Southern Tier 13903 17,905 3.3% to 5.5% Southern Tier 13904 9,789 3.3% to 5.5% Southern Tier 13905 26,151 5.6% to 9.6% Southern Tier 14572 5,017 0% to 3.2% Southern Tier 14801 5,234 9.7% to 37.6% Southern Tier 14807 3,070 0% to 3.2% Southern Tier 14809 2,676 9.7% to 37.6% Southern Tier 14810 11,673 9.7% to 37.6% Southern Tier 14812 3,200 9.7% to 37.6% Southern Tier 14814 1,883 9.7% to 37.6% Southern Tier 14817 2,508 9.7% to 37.6% Southern Tier 14818 2,029 9.7% to 37.6% Southern Tier 14821 3,141 9.7% to 37.6% Southern Tier 14823 3,467 0% to 3.2% Southern Tier 14826 2,271 5.6% to 9.6% Southern Tier 14830 19,138 9.7% to 37.6% Southern Tier 14838 2,039 9.7% to 37.6% Southern Tier 14840 2,730 9.7% to 37.6% Southern Tier 14843 12,447 5.6% to 9.6% Southern Tier 14845 20,401 9.7% to 37.6% Southern Tier 14850 64,224 5.6% to 9.6% Southern Tier 14865 2,784 9.7% to 37.6% Southern Tier 14867 5,431 9.7% to 37.6% Southern Tier 14870 10,202 9.7% to 37.6% Southern Tier 14871 5,081 9.7% to 37.6% Southern Tier 14873 2,328 9.7% to 37.6% Southern Tier 14879 2,257 9.7% to 37.6% APPENDIX C 99 Zip code Share of consumers Region tabulation area Population with medical debt Southern Tier 14882 3,998 9.7% to 37.6% Southern Tier 14883 4,808 9.7% to 37.6% Southern Tier 14886 6,490 5.6% to 9.6% Southern Tier 14889 1,580 9.7% to 37.6% Southern Tier 14891 4,536 9.7% to 37.6% Southern Tier 14892 7,748 9.7% to 37.6% Southern Tier 14901 15,581 9.7% to 37.6% Southern Tier 14903 7,186 9.7% to 37.6% Southern Tier 14904 15,109 9.7% to 37.6% Southern Tier 14905 8,330 9.7% to 37.6% Western New York 14001 9,324 5.6% to 9.6% Western New York 14004 12,240 3.3% to 5.5% Western New York 14006 9,823 5.6% to 9.6% Western New York 14012 2,374 0% to 3.2% Western New York 14025 3,784 3.3% to 5.5% Western New York 14031 10,158 3.3% to 5.5% Western New York 14032 10,084 0% to 3.2% Western New York 14033 1,344 5.6% to 9.6% Western New York 14034 2,428 5.6% to 9.6% Western New York 14042 4,359 9.7% to 37.6% Western New York 14043 25,058 5.6% to 9.6% Western New York 14047 6,525 9.7% to 37.6% Western New York 14048 14,414 9.7% to 37.6% Western New York 14051 19,178 0% to 3.2% Western New York 14052 17,664 0% to 3.2% Western New York 14055 1,337 5.6% to 9.6% Western New York 14057 8,192 0% to 3.2% Western New York 14059 10,446 3.3% to 5.5% Western New York 14062 3,331 5.6% to 9.6% Western New York 14063 13,891 5.6% to 9.6% Western New York 14067 4,971 3.3% to 5.5% Western New York 14068 7,169 0% to 3.2% Western New York 14070 6,958 9.7% to 37.6% Western New York 14072 21,287 3.3% to 5.5% Western New York 14075 42,958 3.3% to 5.5% Western New York 14080 4,323 5.6% to 9.6% Western New York 14081 2,844 9.7% to 37.6% Western New York 14085 7,605 3.3% to 5.5% Western New York 14086 32,653 3.3% to 5.5% 100 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Western New York 14092 10,819 3.3% to 5.5% Western New York 14094 49,792 5.6% to 9.6% Western New York 14105 4,745 3.3% to 5.5% Western New York 14108 5,196 5.6% to 9.6% Western New York 14111 3,146 3.3% to 5.5% Western New York 14120 43,738 3.3% to 5.5% Western New York 14127 29,964 3.3% to 5.5% Western New York 14131 5,056 5.6% to 9.6% Western New York 14132 5,999 5.6% to 9.6% Western New York 14136 4,574 5.6% to 9.6% Western New York 14138 1,786 3.3% to 5.5% Western New York 14139 2,020 3.3% to 5.5% Western New York 14141 7,534 9.7% to 37.6% Western New York 14150 41,144 5.6% to 9.6% Western New York 14170 2,350 3.3% to 5.5% Western New York 14171 2,029 9.7% to 37.6% Western New York 14172 2,781 0% to 3.2% Western New York 14174 5,843 3.3% to 5.5% Western New York 14201 11,192 9.7% to 37.6% Western New York 14202 3,749 5.6% to 9.6% Western New York 14203 1,819 5.6% to 9.6% Western New York 14204 8,003 9.7% to 37.6% Western New York 14206 20,064 9.7% to 37.6% Western New York 14207 23,773 9.7% to 37.6% Western New York 14208 10,346 9.7% to 37.6% Western New York 14209 7,807 9.7% to 37.6% Western New York 14210 14,148 9.7% to 37.6% Western New York 14211 22,206 9.7% to 37.6% Western New York 14212 10,973 9.7% to 37.6% Western New York 14213 22,132 9.7% to 37.6% Western New York 14214 19,754 9.7% to 37.6% Western New York 14215 41,340 9.7% to 37.6% Western New York 14216 21,464 5.6% to 9.6% Western New York 14217 23,017 5.6% to 9.6% Western New York 14218 18,935 9.7% to 37.6% Western New York 14219 11,391 5.6% to 9.6% Western New York 14220 22,641 9.7% to 37.6% Western New York 14221 53,727 3.3% to 5.5% Western New York 14222 15,103 5.6% to 9.6% APPENDIX C 101 Zip code Share of consumers Region tabulation area Population with medical debt Western New York 14223 21,734 5.6% to 9.6% Western New York 14224 40,903 3.3% to 5.5% Western New York 14225 33,360 9.7% to 37.6% Western New York 14226 29,920 3.3% to 5.5% Western New York 14227 20,540 5.6% to 9.6% Western New York 14228 23,406 5.6% to 9.6% Western New York 14301 11,838 9.7% to 37.6% Western New York 14303 5,652 9.7% to 37.6% Western New York 14304 29,001 5.6% to 9.6% Western New York 14305 17,026 9.7% to 37.6% Western New York 14701 39,214 9.7% to 37.6% Western New York 14706 6,110 9.7% to 37.6% Western New York 14710 3,244 5.6% to 9.6% Western New York 14711 1,874 5.6% to 9.6% Western New York 14712 3,051 5.6% to 9.6% Western New York 14715 2,585 9.7% to 37.6% Western New York 14716 2,640 9.7% to 37.6% Western New York 14718 1,899 9.7% to 37.6% Western New York 14719 3,283 9.7% to 37.6% Western New York 14724 2,723 5.6% to 9.6% Western New York 14727 5,385 5.6% to 9.6% Western New York 14731 1,291 5.6% to 9.6% Western New York 14733 3,655 9.7% to 37.6% Western New York 14735 2,900 3.3% to 5.5% Western New York 14737 4,272 9.7% to 37.6% Western New York 14738 3,319 5.6% to 9.6% Western New York 14739 2,421 9.7% to 37.6% Western New York 14743 1,938 9.7% to 37.6% Western New York 14747 2,015 9.7% to 37.6% Western New York 14750 4,277 3.3% to 5.5% Western New York 14755 3,146 5.6% to 9.6% Western New York 14757 3,371 3.3% to 5.5% Western New York 14760 17,567 9.7% to 37.6% Western New York 14767 2,224 9.7% to 37.6% Western New York 14770 2,761 9.7% to 37.6% Western New York 14772 3,995 5.6% to 9.6% Western New York 14775 2,520 5.6% to 9.6% Western New York 14779 6,537 9.7% to 37.6% Western New York 14781 1,782 3.3% to 5.5% 102 APPENDIX C Zip code Share of consumers Region tabulation area Population with medical debt Western New York 14782 2,272 9.7% to 37.6% Western New York 14787 4,759 3.3% to 5.5% Western New York 14806 2,060 5.6% to 9.6% Western New York 14813 2,264 9.7% to 37.6% Western New York 14880 1,847 5.6% to 9.6% Western New York 14895 8,957 5.6% to 9.6% Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. Estimated ranges of the share of consumers with medical debt in each community are suppressed for sample sizes below 50. APPENDIX C 103 TABLE C-3 Share of Consumers with Medical Debt in Collections across Counties in New York State, by Region, February 2022 Share of Consumers with Medical Debt, Overall and by Community Race/Ethnicity Communities Communities Communities with 0% to with 30% to with 50% or Median Share with <30% people of <50% people of more people of medical medical debt Region County All communities color color color debt ≥$500 Capital District Albany* 8% 6% 7% 14% $530 51% Capital District Columbia 10% 10% $584 54% Capital District Greene 10% 10% 4% $486 50% Capital District Rensselaer* 9% 8% 12% $668 56% Capital District Saratoga 7% 7% $400 43% Capital District Schenectady 9% 6% 11% 19% $435 48% Capital District Warren* 9% 9% $539 52% Capital District Washington 11% 11% $401 46% Central New York Cayuga* 12% 12% $448 49% Central New York Cortland* 12% 12% $458 49% Central New York Madison* 13% 13% $368 41% Central New York Onondaga* 14% 10% 20% 28% $449 47% Central New York Oswego 19% 19% $527 52% Finger Lakes Genesee 6% 6% Finger Lakes Livingston 4% 4% Finger Lakes Monroe 4% 3% 4% 7% $414 43% Finger Lakes Ontario 8% 8% $456 48% Finger Lakes Orleans 6% 6% Finger Lakes Seneca 15% 16% $494 49% Finger Lakes Wayne 7% 7% $492 50% Finger Lakes Wyoming 4% 4% 104 APPENDIX C Share of Consumers with Medical Debt, Overall and by Community Race/Ethnicity Communities Communities Communities with 0% to with 30% to with 50% or Median Share with <30% people of <50% people of more people of medical medical debt Region County All communities color color color debt ≥$500 Finger Lakes Yates 8% 8% Long Island Nassau* 3% 2% 2% 4% $325 38% Long Island Suffolk* 3% 3% 4% 4% $416 45% Mid-Hudson Dutchess 10% 8% 9% 19% $548 53% Mid-Hudson Orange 9% 7% 8% 13% $640 55% Mid-Hudson Putnam 6% 6% 7% $543 52% Mid-Hudson Rockland 4% 3% 5% 6% $533 51% Mid-Hudson Sullivan 13% 11% 14% 20% $894 59% Mid-Hudson Ulster 10% 8% 13% $471 48% Mid-Hudson Westchester 5% 2% 3% 7% $615 55% Mohawk Valley Fulton* 12% 12% $753 61% Mohawk Valley Hamilton 3% 3% Mohawk Valley Herkimer* 9% 9% $670 59% Mohawk Valley Montgomery* 13% 13% $813 67% Mohawk Valley Oneida* 12% 11% 17% $645 55% Mohawk Valley Schoharie 6% 6% New York City Bronx 6% 4% 6% $440 46% New York City Kings 3% 2% 2% 4% $381 44% New York City New York 3% 2% 3% 4% $355 40% New York City Queens 4% 4% 3% 4% $350 39% New York City Richmond 3% 2% 3% 4% $343 40% North Country Clinton 7% 7% $250 32% North Country Essex 5% 5% APPENDIX C 105 Share of Consumers with Medical Debt, Overall and by Community Race/Ethnicity Communities Communities Communities with 0% to with 30% to with 50% or Median Share with <30% people of <50% people of more people of medical medical debt Region County All communities color color color debt ≥$500 North Country Franklin 9% 8% 13% $320 45% North Country Jefferson* 14% 15% 12% $672 56% North Country Lewis 9% 9% North Country St. Lawrence 14% 14% $777 60% Southern Tier Broome* 5% 5% $504 51% Southern Tier Chemung* 27% 27% $989 65% Southern Tier Chenango* 6% 6% Southern Tier Delaware 5% 5% Southern Tier Otsego* 5% 5% Southern Tier Schuyler 15% 15% Southern Tier Steuben* 14% 14% $797 60% Southern Tier Tioga 9% 9% $754 59% Southern Tier Tompkins 8% 11% 7% $510 51% Western New York Allegany 8% 8% $425 42% Western New York Cattaraugus 13% 12% 17% $630 57% Western New York Chautauqua* 11% 11% 14% $380 42% Western New York Erie 8% 6% 7% 14% $450 46% Western New York Niagara 8% 6% 16% $565 52% Source: Authors' tabulations of Urban Institute credit bureau data from February 2022 and 2016–2020 American Community Survey 5-year estimates. Notes: Share with medical debt in collections is defined as the share of consumers with credit bureau records who have medical debt in collections. Regions are based on groups of counties in the state's 10 economic regions. Communities are defined based on zip code tabulation areas. People of color include those who identify as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, more than one race, or some other race, excluding those who identify as non- Hispanic and white. No communities in the Mohawk Valley and Southern Tier regions had 50 percent or more residents of color. Other blank cells indicate point estimates are suppressed for sample sizes below 100. * Indicates the county was a hospital lawsuit hotspot between 2015–2020 based on work by Dunker and Benjamin (2020, 2022). 106 APPENDIX C Notes 1 Urban Institute, "Debt Interactive Map," last updated May 11, 2022, accessed May 5, 2023, https://github.com/UrbanInstitute/debt-interactive-map/blob/master/data/20220501- update/state_national_medical.csv. 2 On April 11, 2023, the three credit bureaus announced the removal of all unpaid medical collections tradelines (i.e., specific items) under $500 from consumer credit reports. This followed earlier changes made on July 1, 2022, when the credit bureaus expanded the period before unpaid medical collections appear on a credit report from 6 to 12 months and removed paid medical collections from credit reports. 3 Brianna McGurran, "Do Employers Look at Credit Reports?" Experian (blog), August 6, 2019, https://www.experian.com/blogs/ask-experian/do-employers-look-at-credit-reports/; Consumer Financial Protection Bureau, "Errors in your tenant screening report shouldn't keep you from finding a place to call home," updated April 19, 2022, https://www.consumerfinance.gov/about-us/blog/errors-in-your-tenant-screening- report-shouldnt-keep-you-from-finding-a-place-to-call-home/; Federal Trade Commission, "Using Consumer Reports: What Landlords Need to Know," updated October 2016, https://www.ftc.gov/business- guidance/resources/using-consumer-reports-what-landlords-need-know. 4 Neil Bennett, Jonathan Eggleston, Laryssa Mykyta, and Briana Sullivan, "19% of U.S. Households Could Not Afford to Pay for Medical Care Right Away," America Counts: Stories (blog), April 7, 2021, https://www.census.gov/library/stories/2021/04/who-had-medical-debt-in-united-states.html. 5 Our analysis of economic regions is based on a definition used by the Office of the New York State Comptroller, in which the classifications of Hamilton and Otsego counties differ from the economic development regions used by the state's labor department. See Office of the New York State Comptroller, "OSC Economic Regions of New York State," accessed May 5, 2023, https://www.osc.state.ny.us/files/local-government/publications/pdf/osc- economic-regions.pdf. 6 One statewide survey found that 16 percent of New Yorkers reported a collection agency contacted them in the past 12 months due to the cost of medical bills. See Perry Undem, "How New Yorkers Feel about Affordability and Healthcare Reform," https://smhttp-ssl-58547.nexcesscdn.net/nycss/Affordability_NY_D4.pdf. 7 Centers for Medicare and Medicaid Services (CMS), "Multiple Chronic Conditions," last updated 2018, accessed April 25, 2023, https://data.cms.gov/medicare-chronic-conditions/multiple-chronic-conditions. 8 US Census Bureau, "Selected Characteristics of Health Insurance Coverage in the United States," 2021: ACS 5- Year Estimates Subject Tables, accessed April 25, 2023, https://data.census.gov/table?q=Health+Insurance&g=040XX00US36&tid=ACSST5Y2021.S2701. 9 Kaiser Family Foundation, "Medicaid and CHIP Income Eligibility Limits for Children as a Percent of the Federal Poverty Level," updated January 2023, https://www.kff.orgs/health-reform/state-indicator/medicaid-and-chip- income-eligibility-limits-for-children-as-a-percent-of-the-federal-poverty-level. 10 New York State of Health, "Health Insurance Coverage Update: September 2021," accessed April 25, 2023, https://info.nystateofhealth.ny.gov/sites/default/files/Health%20Insurance%20Coverage%20Update%20- %20September%202021_0.pdf. NOTES 107 11 New York State Department of Health, "2022-23 Executive Budget Briefing and Questions & Answers," February 2022, accessed May 12, 2023, https://www.health.ny.gov/health_care/medicaid/redesign/2022/docs/2022-23_exec_budget_presentation.pdf. 12 New York State Department of Health, "New York State Department of Health Will Ask Federal Government to Expand Essential Plan to Further Reduce Rate of Uninsured and Improve Health Equity," February 2023, accessed April 25, 2023, https://www.health.ny.gov/press/releases/2023/2023-02- 10_expand_essential_plan.htm. 13 Kaufman, Maya, "Lawmakers continue fight to extend health insurance to undocumented New Yorkers," Politico, February 13, 2023, https://www.politico.com/newsletters/weekly-new-york-health- care/2023/02/13/lawmakers-continue-fight-to-extend-health-insurance-to-undocumented-new-yorkers- 00082412. 14 Murphy, Natasha, and Sarah Millender, "How States Can Build Bridges by Smoothing Medicaid-to-Marketplace Coverage Transitions," February 14, 2023, Center for American Progress, https://www.americanprogress.org/article/how-states-can-build-bridges-by-smoothing-medicaid-to- marketplace-coverage-transitions/. 15 Jordan Rau, "Patients Eligible for Charity Care Instead Get Big Bills," Kaiser Health News, October 14, 2019. 16 The New York State Senate, "Senate Bill S4907," accessed May 5, 2023, https://www.nysenate.gov/legislation/bills/2023/S4907. Chopra, Rohit, "Prepared Remarks of Director Rohit Chopra on New CFPB Medical Debt Report," March 1, 2022, https://www.consumerfinance.gov/about- us/newsroom/prepared-remarks-of-director-rohit-chopra-on-new-cfpb-medical-debt-report/; Hannah Metzger, "Colorado legislature approves removing medical debt from credit scores, reports," Colorado Politics, April 3, 2023, https://www.coloradopolitics.com/legislature/colorado-remove-medical-debt-credit- score/article_0382a16c-d241-11ed-ae3b-d3d130976491.html. 17 Internal Revenue Service, "Billing and Collections – Section 501(r)(6)," last updated July 15, 2022, accessed May 11, 2023, https://www.irs.gov/charities-non-profits/billing-and-collections-section-501r6. 18 ZCTA population estimates are based on 2016–2020 American Community Survey 5-year estimates. 19 US Census Bureau, "American Community Survey 5-Year Data (2009–2021)," December 08, 2022, https://www.census.gov/data/developers/data-sets/acs-5year.2020.html#list-tab-1036221584. 20 Centers for Medicare and Medicaid Services (CMS), "Multiple Chronic Conditions," last updated 2018, accessed April 25, 2023, https://data.cms.gov/medicare-chronic-conditions/multiple-chronic-conditions. 21 University of Missouri Center for Health Policy, "Geocorr Applications," accessed April 21, 2023, https://mcdc.missouri.edu/applications/geocorr.html. 22 Missouri Census Data Center, "Geocorr 2022 Help," revised April 21, 2022, accessed April 26, 2023, https://mcdc.missouri.edu/applications/docs/geocorr2022-help.html. 23 "OSC Economic Regions of New York State," https://www.osc.state.ny.us/files/local- government/publications/pdf/osc-economic-regions.pdf. 24 Centers for Medicare and Medicaid Services (CMS), "Multiple Chronic Conditions," last updated 2018, accessed April 25, 2023, https://data.cms.gov/medicare-chronic-conditions/multiple-chronic-conditions. 108 NOTES References Batty, Michael, Christa Gibbs, and Benedic Ippolito. 2018. 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Boston, MA: National Consumer Law Center. 110 REFERENCES About the Authors Michael Karpman is a principal research associate in the Health Policy Center at the Urban Institute. His work focuses on quantitative analysis related to health insurance coverage, access to and affordability of health care, use of health care services, and health status. His work includes overseeing and analyzing data from the Urban Institute's Health Reform Monitoring Survey and Well-Being and Basic Needs Survey. Before joining Urban in 2013, Karpman was a senior associate at the National League of Cities Institute for Youth, Education, and Families. He received his MPP from Georgetown University. Fredric Blavin is a principal research associate in the Health Policy Center at the Urban Institute. His areas of expertise include hospital finances, telehealth and health information technology, health care reform, private health insurance markets, provider supply, health care spending, child and maternity health, and Medicaid policy. He has published in peer-reviewed journals on topics including hospital finances, the impact of the Affordable Care Act on income inequality, the health effects of the earned income tax credit, Express Lane Eligibility programs in Medicaid, health care financial burdens, prescription drug spending, the adoption and use of electronic health records, value-based insurance design, and the cost and coverage implications of various state and national health reform policies. Blavin received his PhD in managerial science and applied economics from the University of Pennsylvania. Dulce Gonzalez is a research associate in the Health Policy Center. She forms part of a team working on the Urban Institute's Well-Being and Basic Needs Survey. Gonzalez conducts quantitative and qualitative research focused primarily on the social safety net, immigration, and barriers to health care access. Her work has also focused on the impacts of the COVID-19 pandemic on nonelderly adults and their families. Before joining Urban, Gonzalez worked at the Georgetown University Center for Children and Families and at the nonprofit organization Maternal and Child Health Access. Gonzalez holds a BA in economics from California State University, Long Beach, and a master's degree in public policy from Georgetown University. Jennifer Andre is a data scientist in the Center on Labor, Human Services, and Population at the Urban Institute, focusing on financial well-being research. Before joining Urban, she worked as an economic consulting analyst at Charles River Associates in the antitrust and competition practice. Andre holds a ABOUT THE AUTHORS 111 BA in economics from the University of Notre Dame and an MS in public policy and management‒data analytics from Carnegie Mellon University. Breno Braga is a labor economist and principal research associate in the Center on Labor, Human Services, and Population at the Urban Institute. His research has covered topics such as the effects of high-skilled immigration on labor markets, the role of local conditions in asset accumulation, and the local factors associated with debt in collections. His articles have been published in academic journals including the Journal of Labor Economics. Braga received his MA in economics from the Pontifical Catholic University of Rio de Janeiro and his PhD in economics from the University of Michigan. 112 ABOUT THE AUTHORS STATEMENT OF INDEPENDENCE The Urban Institute strives to meet the highest standards of integrity and quality in its research and analyses and in the evidence-based policy recommendations offered by its researchers and experts. We believe that operating consistent with the values of independence, rigor, and transparency is essential to maintaining those standards. As an organization, the Urban Institute does not take positions on issues, but it does empower and support its experts in sharing their own evidence-based views and policy recommendations that have been shaped by scholarship. Funders do not determine our research findings or the insights and recommendations of our experts. 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