Annals of the American Thoracic Society

Rationale: Medical treatment can improve quality of life and avert exacerbations for those with chronic obstructive pulmonary disease (COPD). High-deductible health plans (HDHPs) can increase exposure to medical costs, and might compromise healthcare access and financial well-being for patients with COPD.

Objectives: To examine the association of HDHPs with healthcare access, utilization, and financial strain among individuals with COPD.

Methods: We analyzed privately insured adults aged 40–64 years with COPD in the 2011–2017 National Health Interview Survey, which uses Internal Revenue Service–specified thresholds to classify health plans as “high” or “traditional” deductible coverage. We assessed the association between enrollment in an HDHP and indicators of cost-related impediments to care, financial strain, and healthcare utilization, adjusting for potential confounders.

Results: Our sample included 803 individuals with an HDHP and 1,334 with a traditional plan. The two groups’ demographic and health characteristics were similar. Individuals enrolled in an HDHP more frequently reported delayed or foregone care, cost-related medication nonadherence, medical bill problems, and financial strain. They also more frequently reported out-of-pocket healthcare spending in excess of $5,000 a year. Although the two groups’ office visit rates were similar, those enrolled in an HDHP were more likely to report a hospitalization or emergency room visit in the past year.

Conclusions: For patients with COPD, enrollment in an HDHP was associated with cost-related barriers to care, financial strain, and more frequent emergency room visits and hospitalizations.

More than 13 million Americans have been diagnosed with chronic obstructive pulmonary disease (COPD) (1), a disease that compromises both quality of life and longevity (2). A number of medical therapies can alleviate symptoms and improve outcomes, and possibly reduce mortality, for those with this condition (2, 3). To benefit from such treatments, however, individuals with COPD must be able to afford them.

The financial burden of healthcare is increasing, especially for persons with chronic disease (4). Insurance deductibles for those with employer-sponsored coverage have risen more than 50% over the past 5 years (5). In the individual market, deductibles are even higher—more than $8,000, on average, for a 2018 family Silver plan available through the Affordable Care Act’s Health Insurance Marketplace (6). Overall, 46% of the nonelderly, privately insured adult population was covered by a high-deductible health plan (HDHP) in early 2018, up from 25% in 2010 (7). By erecting financial barriers to care, HDHPs may adversely affect both the health and economic well-being of those with COPD.

The cost of inhaled medicines is a particular concern for patients with COPD. Media reports have described patients forgoing prescribed inhalers because of costs (8), and the American Thoracic Society has identified medication affordability as a priority issue for patients with chronic lung disease (9). Observational studies suggest that high inhaler costs may reduce adherence for those with COPD (10), which could lead to worse outcomes, including more hospitalizations (11).

For the general population, transition to an HDHP reduces utilization of virtually every type of healthcare, including emergency department (ED) and hospital care (12, 13). However, for individuals with chronic diseases, such as COPD, enrollment in an HDHP might deter outpatient visits or daily medication use, leading to worse disease control and hence more ED visits and hospitalizations (14, 15). Although the effects of HDHPs have been studied for patients with some chronic conditions, such as diabetes (1618), few studies have examined their impact on those with COPD. In addition, whether persons with COPD face “financial toxicity” from the costs of their care, similar to patients with cancer (19, 20), is unclear.

We used nationally representative data to investigate the association of HDHPs with financial well-being and healthcare access and utilization among privately insured individuals with COPD. Some study results were presented in abstract form at the 2019 American Thoracic Society conference (21).

Data and Population

We conducted a cross-sectional study using data from the National Health Interview Survey (NHIS), a nationally representative survey of the civilian, noninstitutionalized population that is conducted annually by the National Center for Health Statistics (NCHS). The NHIS is a household-based survey conducted face to face in participants’ homes, with a response rate of 66.5% in 2017 (22). It uses a multistage sampling approach involving selection of households in nested, geographically defined strata and clusters; sample weights provided by the NCHS allow analysts to produce nationally representative estimates (22). Within each family surveyed, one “sample adult” is selected at random, and asked a more extensive set of questions. In 2011, a number of new questions on healthcare access were added, including some on cost-related medication nonadherence. Our study population was drawn from 2011–2017 sample adults.

Respondents answered three separate questions pertaining to COPD: whether they had ever been told by a physician or another healthcare professional that they had: 1) emphysema; 2) chronic bronchitis; and (from 2012 onward) 3) COPD. Self-reported physician diagnosis of COPD is commonly used in epidemiologic studies of this illness, including in analyses of the NHIS (1, 23, 24). Following recommendations from NCHS investigators (23), we considered individuals to have COPD if they answered affirmatively to any of these three questions. However, following other studies, we excluded individuals under age 40 years, as COPD is unusual among younger individuals (23, 25). Finally, we only included individuals who were privately insured without another form of coverage (i.e., we excluded individuals who additionally had Medicaid, Medicare, or other public coverage, together with the uninsured), who knew their deductible, and who reported their family income. We also excluded persons 65 years of age and over, almost all of whom are eligible for Medicare (see Figure E1 in the online supplement for details on study population formation).

Exposure

The NHIS categorizes persons as having HDHP coverage if their annual medical deductible exceeds an inflation-adjusted threshold set annually by the Internal Revenue Service. In 2017, the threshold was $1,300 for an individual, and $2,600 for a family (see Table E1). We considered persons with private insurance who reported lower (or no) deductibles using the year-specific threshold to have a “traditional plan.” These deductible amounts exclude separate deductibles for hospital care and prescription drugs, but in 2017, only 1% and 15% of individuals with employer-sponsored plans had separate deductibles for these items, respectively (26).

Outcome Measures

We analyzed multiple indicators of access to care, financial strain, and healthcare utilization that we grouped in to three broad categories. Table E2 provides details of the NHIS variables used for each indicator, the wording of the relevant questions, and how the indicator variable was constructed.

The first category, cost-related impediments to care, included foregone care, delayed care, inability to see a specialist, and foregone follow-up due to cost. It also included four questions about cost-related medication nonadherence, such as skipping doses of a prescription drug due to cost.

The second category, “financial strain,” included family out-of-pocket medical spending (excluding spending on premiums), three outcomes related to medical bill problems, and four outcomes (available only since 2013) related to financial concerns, including worries about the costs of: “regular healthcare,” a serious illness or accident, monthly bills, and “maintaining one’s standard of living.” For this and for the cost-related impediments to care outcomes, we also created composite outcomes for groups of related problems (e.g., having one or more medical bill problem vs. having none).

The third category, “healthcare utilization,” included use of office visits, the ED, and hospitalizations. Note that office visits and ED visits are reported in the NHIS in categories that we converted to counts to facilitate interpretation of the results; however, we retained them as categorical variables for a sensitivity analysis, as described subsequently here. We examined both the proportion of those with any use in the past year, and the number of visits or hospitalizations.

Analysis Plan

We first tabulated the characteristics of the study population, stratified by high versus low deductible.

We next tabulated the proportion of individuals having each outcome stratified by deductible level, as well as the mean utilization of each type of care for each group. We used logistic regression models to test differences between the deductible groups for binary outcomes, and STATA’s “margins” command to estimate absolute percentage point differences, as opposed to odds ratios, facilitating interpretation of results. For number of office visits, emergency room (ER) visits, and hospitalizations, we used linear regression models.

We then performed multivariable regressions. Covariates included: demographic variables, including age, race, and marital status; socioeconomic status variables, including education, income, and employment status; health behavior variables, including smoking and alcohol use; health status variables, including self-reported health status, overweight status, having a functional limitation, and diagnoses of kidney disease, liver disease, any cardiovascular disease, stroke, diabetes, and cancer history (Table E3 provides details on the construction of each covariate). Income was treated as a proportion of the year-specific federal poverty level (FPL), which accounts for both inflation and family size. We repeated analyses examining our main composite outcomes, as well as the utilization outcomes, stratified by income category (family income, <400% FPL vs. ≥400% FPL). Finally, we performed sensitivity analyses using ordinal logistic regression, in which we retained the number of office visits and ED visits as categorical variables; these analyses yielded results that were consistent with our main analyses and are thus not presented further.

The small number of individuals with missing data for covariates, noted in Table E3, were excluded from adjusted regressions; Table E4 provides the number of observations for each analysis.

We used STATA/SE 15.1 (StataCorp LLC) for all analyses. Using the svy procedure together with weights, primary sampling units, and strata provided by the NCHS, we report nationally representative estimates that account for the NHIS’s complex design. The study was deemed exempt from review by the Cambridge Health Alliance Institutional Review Board.

Our sample consisted of 2,137 privately insured adults aged 40–64 years with a diagnosis of COPD; 803 with an HDHP, and 1,334 with a traditional plan (Figure E1 diagrams the formation of the study population). The proportion of respondents in our study population reporting HDHPs rose over time, from 25.7% in 2011 to 45.1% in 2017, as illustrated in Figure E2.

The demographic, socioeconomic, and health status characteristics of persons with traditional and HDHP coverage were similar (Table 1). They were closely matched in age (53.5 yr for those in traditional plans vs. 53.2 yr for the HDHP group) and sex (61.3% vs. 63.4% female). Income was also similar, as was self-reported health, smoking status, overweight condition, alcohol use, functional limitation, and the rates of six comorbidities. A substantial majority of individuals in both groups was employed. The HDHP group was slightly more likely to be white, while those with traditional plans were slightly more likely to have a higher education.

Table 1. Characteristics of persons with diagnosis of chronic obstructive pulmonary disease with high-deductible or traditional private health insurance (N = 2,137)

 Traditional Insurance (n = 1,334)High-Deductible Insurance (n = 803)
Age, yr53.5 ± 0.353.2 ± 0.3
Female sex61.363.4
Family size  
 One19.620.7
 Two42.441.8
 Three18.817.9
 Four +19.219.6
Race  
 Hispanic7.54.8
 White78.686.7
 Black11.26.3
 Asian1.91.5
 Other0.70.8
Married64.367.0
Education  
 <HS8.26.3
 HS/GED61.469.8
 4-yr degree19.616.7
 Postgraduate10.87.2
Family income ($)  
 <$35,00015.613.6
 $35,000-$74,99938.341.5
 $75,000-$99,99916.615.4
 ≥$100,00029.529.6
Family income (% FPL)  
 <100%2.62.5
 100%–199%12.910.8
 200%–299%14.618.7
 300%–399%16.619.6
 400%–499%14.113.3
 500%+39.235.2
Employed79.481.5
Fair/worse health23.823.8
Smoking  
 Current28.729.1
 Former29.132.1
 Never42.138.9
Weight  
 Normal weight23.526.2
 Overweight33.026.2
 Obese43.547.6
Alcohol use: moderate to heavy28.725.5
Functional limitation58.564.0
Comorbidities  
 Heart disease19.721.3
 Stroke4.26.0
 Cancer history14.712.8
 Kidney disease2.53.6
 Liver disease4.74.1
 Diabetes20.318.4

Definition of abbreviations: FPL = federal poverty level; GED = general equivalency diploma; HS = high school.

There were 3 individuals with missing data for marital status; 1 for health status; 4 for functional limitation; 2 for education; 5 for income group ($), 4 for smoking status; 74 for overweight; 25 for alcohol use; 1 for heart disease; 2 for cerebrovascular disease; 2 for cancer history; 1 for liver disease; and 1 for diabetes. No individuals had missing data on income as a proportion of the FPL in our study population, as this was an inclusion criterion for the study, as described in the main text (see also Figure E1). Values are weighted percentages or means (±SE)

Persons with an HDHP reported significantly higher rates of each indicator of “cost-related impediments to care,” as presented in Table 2. For instance, 5.3% of those with a traditional plan couldn’t afford to see a specialist versus 10.2% of those with an HDHP, an adjusted difference of 5.1 percentage points (95% confidence interval [CI] = 2.2–8.0; P = 0.001); 10.7% of those with a traditional plan skipped doses of their medication to save money, versus 18.4% of those with an HDHP, an adjusted difference of 6.3 percentage points (95% CI = 2.6–10.1; P = 0.001). Overall, 16.0% of those with a traditional plan had any of the four access-to-care problems, versus 28.9% of those with an HDHP (adjusted P < 0.001). Similarly, 19.6% with a traditional plan had any of the four cost-related medication nonadherence outcomes, versus 30.1% of those with an HDHP (adjusted P < 0.001).

Table 2. Percentage of persons with diagnosis of chronic obstructive pulmonary disease and private insurance reporting cost-related impediments to care in the past 12 months: traditional versus high-deductible coverage

 UnadjustedAdjusted*
Traditional Coverage (%)High-Deductible Coverage (%)P Value
Absolute % (95% CI)P Value
Forgone/delayed care due to costs     
 Care delayed due to cost11.724.2<0.00111.5 (7.5–15.6)<0.001
 Care not obtained due to cost7.315.4<0.0016.9 (3.8–10.0)<0.001
 Couldn’t afford specialist5.310.20.0015.1 (2.2–8.0)0.001
 Couldn’t afford follow up3.97.90.0023.2 (0.8–5.7)0.01
 Any of four access issues16.028.9<0.00112.4 (7.9–16.9)<0.001
Cost-related medication nonadherence     
 Couldn’t afford drugs13.020.50.0016.5 (2.5–10.5)0.001
 Skipped doses to save money10.718.4<0.0016.3 (2.6–10.1)0.001
 Took less medicine to save money11.020.2<0.0018.2 (4.3–12.0)<0.001
 Delayed filling Rx to save money14.724.5<0.0018.2 (3.9–12.5)<0.001
 Any of four cost-related medication nonadherence outcomes19.630.1<0.0019.3 (4.7–14.0)<0.001

Definition of abbreviations: CI = confidence interval; Rx = prescriptions.

*Logistic regressions adjusted for age, sex, race, marital status, education, income as a proportion of the federal poverty level, employment status, health status, smoking status, overweight status, alcohol use, absence or presence of a functional limitation, kidney disease, liver disease, cardiovascular disease, stroke, diabetes, and cancer history (see Table E2 for details on covariate treatment, and Table E4 for number of observations included in each analysis). P values and adjusted absolute percentage point differences are for marginal effects, which were generated using STATA’s “margins” command.

The financial burden of care was higher for those with HDHPs compared with traditional plans, as shown in Table 3 and Figure E3. Individuals with HDHPs had higher out-of-pocket medical spending: 21.3% of them had family out-of-pocket spending of greater than $5,000 (excluding premiums), versus 8.0% of those with a traditional plan, an adjusted difference of 13.1 percentage points (95% CI = 8.9–17.3; P < 0.001). Notably, a majority of all persons in both groups reported one or more medical bill problems, although those with HDHPs reported them more frequently (P < 0.001). Finally, those with HDHPs had higher rates of financial worries, although, again, rates were high in both groups. For instance, 43.2% of those with an HDHP worried about paying their monthly bills, versus 33.7% of those with a traditional plan (adjusted P = 0.04).

Table 3. Indicators of financial strain among persons with diagnosis of chronic obstructive pulmonary disease and private coverage: traditional versus high-deductible insurance

 UnadjustedAdjusted*
Traditional Coverage (%)High-Deductible Coverage (%)P ValueAbsolute % Difference (95% CI)P Value
Family out-of-pocket medical spending     
 >$2,00035.959.4<0.00122.9 (17.5 to 28.4)<0.001
 >$5,0008.021.3<0.00113.1 (8.9 to 17.3)<0.001
Medical bill issues     
 Medical bill worries51.865.9<0.00111.8 (6.2 to 17.4)<0.001
 Problems paying family medical bills20.732.9<0.00111.3 (6.4 to 16.2)<0.001
 Family medical bills being paid off over time31.649.6<0.00115.6 (9.9 to 21.4)<0.001
 Any of three medical bill issues63.077.9<0.00112.7 (7.5 to 17.8)<0.001
Financial worries     
 Worried about cost of serious illness/accident53.268.2<0.00111.5 (5.3 to 17.7)<0.001
 Worried about costs of normal healthcare37.150.4<0.00111.4 (5.4 to 17.4)<0.001
 Worried about paying monthly bills33.743.20.0066.7 (0.4 to 13.0)0.04
 Worried about maintaining standard of living52.160.90.016.1 (−0.5 to 12.8)0.07
 Any of four financial worries66.375.90.0027.4 (1.5 to 13.3)0.01

Definition of abbreviation: CI = confidence interval.

*Logistic regressions adjusted for age, sex, race, marital status, education, income as a proportion of the federal poverty level, employment status, health status, smoking status, overweight status, alcohol use, absence or presence of a functional limitation, kidney disease, liver disease, cardiovascular disease, stroke, diabetes, and cancer history (see Table E2 for details on covariate treatment, and Table E4 for number of observations included in each analysis). P values and adjusted absolute percentage point differences are for marginal effects, which were generated using STATA’s “margins” command.

Outpatient utilization was similar for the two groups, but those with HDHPs had more ED and hospital utilization (Table 4). Those with traditional plans had 5.9 office visits per person per year, versus 6.1 among those with HDHPs (adjusted P = 0.70). However, 33.3% of those with an HDHP had one or more ED visits in the previous year, versus 26.4% of those with a traditional plan (adjusted P = 0.03), although the mean number of visits was not significantly different. Individuals with HDHPs were also more likely to be hospitalized in the past year (17.1% vs. 11.8%; adjusted P = 0.03), and had a higher mean number of hospitalizations (31.3 vs. 19.1 hospitalizations/100 person-years; adjusted P = 0.02).

Table 4. Healthcare utilization in the past 12 months among persons with diagnosis of chronic obstructive pulmonary disease and private coverage: traditional versus high-deductible coverage

 UnadjustedAdjusted*
Traditional CoverageHigh Deductible CoverageP ValueAbsolute Difference (95% CI)P Value
Office visits     
 % with 1+96.196.00.93−0.4 (−2.2 to 1.4)0.64
 Visits/100 person-years585.1609.20.4511.3 (−45.6 to 68.2)0.70
ER visits     
 % with 1+26.433.30.015.7 (0.5 to 10.9)0.03
 Visits/100 person-years52.062.40.195.8 (−9.1 to 20.6)0.45
Hospitalizations     
 % with 1+11.817.10.0084.1 (0.3 to 7.9)0.03
 Hospitalizations/100 person-years19.131.30.0211.4 (1.8 to 21.0)0.02

Definition of abbreviations: CI = confidence interval; ER = emergency room.

*Logistic (% with 1+ use) or linear (mean use/100 person-years) regressions adjusted for age, sex, race, marital status, education, income as a proportion of the federal poverty level, employment status, health status, smoking status, overweight status, alcohol use, absence or presence of a functional limitation, kidney disease, liver disease, cardiovascular disease, stroke, diabetes, and cancer history (see Table E2 for details on co-variate treatment, and Table E4 for number of observations included in each analysis). For logistic models, P values and adjusted absolute percentage point differences are for marginal effects, which were generated using STATA’s “margins” command.

Tables E5 and E6 provide results of analyses stratified by family income. Compared with individuals from higher-income families, individuals from lower-income families more frequently reported problems affording care, and had more ER visits and hospitalizations. HDHP enrollment was associated with reduced access, more frequent financial problems, and more ER and hospital use for those in both income groups, without a consistent differential effect by income; not all of these subgroup analyses reached statistical significance.

In this nationally representative sample of privately insured persons with a diagnosis of COPD, high deductibles were associated with adverse financial and clinical outcomes, including medical bill problems and financial worries, cost-related medication nonadherence, reports of foregone specialist and follow-up care, and greater ED use and hospitalizations. These findings suggest that HDHPs, which today cover nearly half of the privately insured, may be particularly ill suited to the needs of patients with COPD.

Foregone medical care, particularly medication nonadherence, can have clinical consequences for patients with COPD. Although the disease has no cure, treatment can help. Inhaled long-acting bronchodilators and steroids improve lung function, reduce symptoms, prevent exacerbations, and possibly lower mortality (2, 27). Other therapies—including chronic macrolides (28) and nocturnal noninvasive ventilation (29)—may prevent exacerbations, whereas oxygen therapy reduces mortality in those with advanced disease (30). Quality ambulatory care also plays an important role in disease control: studies have found that the regularity (31), availability (32), and continuity (33) of primary care may help avert hospitalizations. However, much of this care—especially inhaled medications—is expensive. For instance, seniors with COPD who require two or three inhalers have estimated annual out-of-pocket medication costs of $1,622–$2,811 (34). Not surprisingly, such costs often compel individuals with modest incomes to skip doses or delay filling prescriptions, as we observed in our sample.

Many factors can affect adherence to prescribed medications, including side effects, patients’ understanding of their disease, and treatment complexity (35). Poor inhaler technique also commonly contributes to poor disease control (36). However, for many patients, cost is a major barrier to compliance, and it is plausible that cost-related medication nonadherence contributed to the higher ED and hospital utilization that we observed among those with HDHPs. After the imposition of drug deductibles on residents of the Canadian province of British Columbia in 2002, use of controller inhalers fell (37), whereas hospital admissions for obstructive airways disease rose (15). Other studies have linked higher medication costs with more cost-related nonadherence in older adults with airways disease (10), and with more hospitalizations among children with asthma (38).

Our data, however, did not include respondents’ diagnoses during their ED visits or hospitalizations, which might have been for COPD or for other illnesses, especially because our COPD cohort had a high burden of comorbidities, particularly cardiovascular disease. High deductibles have been associated with worse outcomes for those with several other chronic conditions. They lead to more ED visits for acute complications of diabetes (16), and to delays in the diagnosis and treatment of breast cancer (39). HDHPs are also associated with reduced adherence to preventive cardiac medications (40), whereas a randomized trial found that, in patients with a recent myocardial infarction, imposing drug copayments, compared with full coverage, led to more vascular complications (41).

We also found that “financial toxicity,” well documented in patients with cancer (20) and cardiovascular disease (42), is common in patients with COPD. A majority of all privately insured individuals with COPD worried about maintaining their standard of living and covering their medical costs, despite the fact that few lived in poverty, most were employed, and all were privately insured. Substantial proportions also struggled with paying their monthly bills, a problem that was more prevalent among persons enrolled in HDHPs. Overall, our findings indicate that COPD—an illness affecting 6 (1, 23, 43) to 20% (44) of the population—is already a frequent cause of financial toxicity, a problem likely to increase in light of rising drug costs and deductibles (5).

Although we found, as expected, that low-income individuals with COPD more frequently experienced problems affording medical care relative to those with higher incomes, HDHPs surprisingly affected individuals in both income groups similarly. However, our higher-income group includes individuals from middle-income families, a population sensitive to the price of healthcare (12). In addition, we had limited power to examine effects of HDHPs by income subgroup.

Our study has limitations. The NHIS provides detailed data on the health and healthcare of the U.S. population, but it relies on self-report from participants, and it lacks examination or spirometry data. Hence, we identified individuals with COPD based on self-report of a diagnosis from a medical professional. This approach has been used in a range of epidemiologic studies (23, 24, 43), although it is thought to underestimate the prevalence of COPD (1). A reported diagnosis of COPD or emphysema was found to have high specificity for airway obstruction in one study of a general population (45). However, it is possible that some individuals in our cohort had a chronic respiratory condition other than COPD, particularly because more of our COPD sample (∼40%) were never-smokers than in some previous studies (46).

Data on healthcare utilization is based on self-report, and hence subject to recall bias. However, this would affect individuals in both coverage groups. We also lacked details on utilization, such as the type of provider seen in particular outpatient visits. Hence, it is unclear why those with HDHPs reported more problems accessing specialists and follow-up care, despite having similar numbers of outpatient visits. Complications arising from medication nonadherence may have led to a greater need for care among those with HDHPs; alternatively, patients with HDHPs may have been able to afford some types of visits, but not other, more expensive ones (e.g., to pulmonologists or other specialists).

The cross-sectional design of the NHIS limits our ability to draw causal inferences. In particular, confounding by economic status or severity of illness is a concern, particularly for our utilization outcomes. However, it is reassuring that the high- and low-deductible groups had very similar demographic and health characteristics. Moreover, multivariable adjustment produced little change in the association of HDHPs with utilization, making residual confounding by socioeconomic or comorbidities less likely. Confounding by unmeasured variables nevertheless remains a possibility in any observational study. Because we lacked data on forced expiratory volume in 1 second or other metrics of lung function, the relationship between HDHPs and increased ED or hospital use should be interpreted cautiously.

Finally, we lacked data on reasons why individuals were in HDHPs (i.e., because their employer provided only one option or because they chose it [from multiple offerings] to lower their premiums). For instance, those with a choice of plans who anticipate high health care utilization might be incentivized to select traditional plans, whereas (presumably healthier) persons anticipating less utilization might prefer HDHPs. This prediction is supported by empirical research (47). However, such biased selection would, if anything, produce an effect on utilization opposite what we observed. We similarly lacked data on overall spending on healthcare for the individuals in our population (i.e., inclusive of payments to providers by insurers), although this was not the focus of the study.

Our findings should raise clinicians’ awareness of the financial constraints and hardships faced by their patients with COPD. They also have important implications for ongoing healthcare reform debates. For instance, a white paper (48) released by the Trump Administration in 2018 proposed an expansion of HDHPs. Our results suggest that such a policy—or similar proposals that would raise out-of-pocket costs or give individuals more “skin in the game”—could harm patients with COPD. Conversely, removing financial barriers to care might benefit them (49), and patients with other chronic diseases (17, 39).

In conclusion, privately insured patients with COPD in the United States—especially those with higher deductibles—frequently face financial strain related to the costs of their healthcare, and report forgoing needed care because they cannot afford it. Although advances in treatment have the potential to improve the lives of patients with lung disease, reforms in healthcare financing are also needed to ensure that such therapies actually make it to our patients.

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Correspondence and requests for reprints should be addressed to Adam Gaffney, M.D., 1493 Cambridge Street, Cambridge, MA 02138. E-mail: .

Author Contributions: A.G. performed the analyses; all authors were involved in interpretation of study results. A.G., A.W., L.H., D.H., S.W., D.C.C., and D.M. were involved in study design, and participated in either drafting or critical review of the manuscript.

This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.

Author disclosures are available with the text of this article at www.atsjournals.org.

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Annals of the American Thoracic Society
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