Abstract
Rationale:
Pulmonary arterial hypertension (PAH) medications are costly for patients and often subject to insurance-related restrictions that may limit access. The impact of insurance type on PAH treatment has not been studied previously.
Objectives:
To examine whether insurance type is associated with PAH medication use, time to initiation of combination therapy, and clinical outcomes.
Methods:
We studied a prospective cohort of incident adult patients with PAH enrolled in the Pulmonary Hypertension Association Registry from 2015 to 2025. Insurance type was categorized as private insurance, Medicare, or Medicaid or similar public programs. We evaluated associations between insurance type and PAH medication regimen at the first follow-up visit, time to initiation of combination therapy, emergency department visit and hospitalization rates, and health-related quality of life outcomes using ordinal and logistic regression models and Cox proportional hazard models, as appropriate.
Results:
The study cohort included 1,219 patients, of whom 40.0% had private insurance, 36.8% Medicare, and 23.2% Medicaid or similar programs. After multivariable adjustment, insurance type was not associated with treatment intensity (compared to Medicare, private insurance: OR, 1.15; 95% CI, 0.82-1.62; Medicaid: OR, 1.28; 95% CI, 0.84-1.96). Insurance was also not associated with time to initiation of combination therapy, hospitalization rates, or health-related quality of life. Use of financial assistance programs (non-profit or pharmaceutical company-sponsored) to pay for PAH medications was associated with higher odds of receiving more intensive medication therapy (OR, 1.69; 95% CI, 1.29-2.23) and earlier initiation of combination therapy (HR, 1.54; 95% CI, 1.16-2.04).
Conclusions:
In this multicenter cohort of patients treated at accredited pulmonary hypertension centers, patients received similar PAH treatment and had comparable outcomes regardless of insurance type or income. PAH medication assistance program use was associated with earlier combination therapy and more intensive treatment.
Keywords: pulmonary hypertension, insurance, healthcare disparities, medication access
Subject Category: 9.34 Pulmonary Hypertension: Clinical-Treatment, 2.11 Social Determinants of Lung Health and Disease
BACKGROUND
Pulmonary arterial hypertension (PAH) is characterized by progressive remodeling of small pulmonary arteries, right ventricular dysfunction, and reduced functional capacity (1-3). Advances in therapy have led to significant improvements in patient-reported outcomes, functional status, and survival (4, 5) and early treatment with combination therapy is now recommended for most patients (6-8). However, medications for PAH are costly—averaging $4,590–$7,852 per month in the United States (9, 10)—and patients often face significant copayments (11). Medication costs continue to rise with evolving standards of care and the introduction of new agents, further increasing financial burden.
Prescription coverage is therefore integral for access to PAH therapies. However, insurance restrictions such as prior authorizations, formulary limits, and step therapy requirements are common and vary widely in the United States (12); such barriers can limit access to affordable PAH treatment. Although most patients with PAH have insurance, many still report financial strain. In one study, nearly half of patients required medication copayment assistance from nonprofit or pharmaceutical organizations, while 30% reported changing medications or skipping doses due to cost (13). High copayments were associated with decreased adherence to prostacyclin agents and combination therapy in a claims-based study (11), while patients with Medicaid or no insurance had lower medication adherence in a Pulmonary Hypertension Association Registry (PHAR) study (14). Additionally, Black patients with PAH have been shown to have worse survival than White patients, which appeared to be partially mediated by differences in insurance coverage (15). Insurance-related differences in access to PAH medications may explain some of this disparity but have not been studied previously. Understanding the impact of insurance coverage on PAH medication use is important to inform policy discussions and ensure equitable treatment access. We aimed to determine whether insurance type is associated with PAH medication regimen, time to initiation of combination therapy, and patient outcomes.
METHODS
Additional details are found in the online supplement.
Study Design
The PHAR is an ongoing, prospective, multicenter registry of patients with PAH and chronic thromboembolic pulmonary hypertension (CTEPH) in the U.S. that enrolls patients treated at participating accredited pulmonary hypertension (PH) care centers (University of Pennsylvania, single institutional review board 822830) (16, 17). PHAR collects data at registry enrollment and at routine clinical follow-up visits approximately every 6 months. This study included incident adult (≥18 years old) patients with PAH who enrolled in PHAR within 6 months of PAH diagnosis from 2015 to 2025 (Figure 1). CTEPH, portopulmonary hypertension, and pulmonary veno-occlusive disease were excluded due to different treatment approaches. Participants with fewer than four months of follow-up were also excluded, as were participants who were uninsured or who had unknown insurance due to small sample sizes in these subgroups. The dataset was locked on September 2, 2025.
Figure 1.
Participant flow diagram. PAH = pulmonary arterial hypertension; PHAR = Pulmonary Hypertension Association Registry; PVOD = pulmonary veno-occlusive disease; PCH = pulmonary capillary hemangiomatosis; CTEPH = chronic thromboembolic pulmonary hypertension.
In the PHAR, patients self-report insurance status and can select multiple types. We assigned participants listing more than one insurance type to a single category based on the plan most likely to provide prescription coverage (see Online Supplement). Patients with military healthcare were grouped with Medicare (n=31). The primary exposure was baseline insurance type: private insurance, Medicare, or Medicaid/similar programs. The latter group included other public or state-sponsored plans. We performed a sensitivity analysis separating out dual-eligible participants (who have both Medicare and Medicaid) as a fourth group.
Outcomes
The primary outcome was PAH medication regimen recorded at the first follow-up visit (first follow-up after registry enrollment visit). Medications included oral, inhaled, and parenteral therapies. For the primary analysis, medication regimen was classified as the total number of medications (zero, one, two, or three). We also studied medication regimen classified as no PAH therapy, single therapy, or combination therapy (two or three medications). Sotatercept was not included because few incident patients were receiving it at the time of data lock (n=5).
Secondary outcomes included patient-reported all-cause hospitalization rates, ED visit rates, and health-related quality of life (HRQoL) as measured by emPHasis-10 and SF-12 scores obtained at each visit (18, 19). We also assessed the association between insurance type and time to initiation of combination therapy, defined as the interval from PHAR enrollment to the first visit where combination therapy was recorded for a participant originally receiving zero or one medication.
Covariates
Baseline covariates (assessed at the registry enrollment visit) included age, self-reported sex at birth, self-reported race and ethnicity, highest education level, income, PAH etiology, World Health Organization (WHO) functional status, 6-minute walk distance (6MWD), hemodynamics, and REVEAL (Registry to Evaluate Early and Long-Term PAH Disease Management) Lite 2 risk score (20). PAH medication assistance program use was assessed at the first follow-up visit to reflect the status most likely to influence the medication regimen at that time point, as program enrollment typically occurs after the initial PHAR center evaluation as centers work to secure coverage for prescribed therapies.
The PHAR categorizes annual household income into one of 15 levels. For this analysis, baseline income was converted into a continuous variable using the median value for each level, as previously described (21). Income was next indexed to the Federal Poverty Level (FPL) using each participant’s household size and year of PHAR enrollment (22-24), then log-transformed for the analyses. Income was also categorized as below poverty (<100% FPL), low- (100–199% FPL), moderate- (200–399% FPL), and high-income (≥400% FPL) (Online Supplement).
Statistical Analysis
Continuous variables were summarized as mean and standard deviation or median and interquartile range (IQR), categorical variables as counts and percentages. Associations between baseline insurance type and medication regimen at the first follow-up visit were examined using the chi-square (X2) test for unadjusted analyses and ordinal logistic regression for adjusted analyses. Covariates were selected a priori based on clinical and biological considerations, informed by a causal directed acyclic graph (Figure E1): age, sex, education, etiology, REVEAL Lite 2 score, income, and medication assistance program use. We performed sensitivity analyses using inverse probability of treatment weighting and overlap weighting (methods in the Online Supplement).
Binomial logistic regression evaluated the association between baseline insurance and combination therapy versus monotherapy, adjusted for the same covariates. Effect modification by age, REVEAL Lite 2, and use of medication assistance was tested using interaction terms with insurance. We additionally evaluated an interaction using age as a binary variable (<65 vs. ≥65, based on when patients become Medicare-eligible). The association between insurance and time to initiation of combination therapy was assessed using Nelson–Aalen cumulative hazard estimates and multivariable Cox proportional hazards models. Cox models were adjusted for the same baseline covariates. Patients who did not initiate combination therapy by study end were right-censored at their last visit.
The effect of insurance type on hospitalization and ED visit rates was estimated using mixed-effects negative binomial regression with robust standard errors and participant-level random intercepts. HRQoL outcomes were evaluated using linear mixed-effects models with robust standard errors and participant-level random intercepts. To examine whether associations between insurance and clinical outcomes varied by treatment intensity, we included an interaction term between insurance type and medication regimen (no therapy, monotherapy, or combination therapy). Multivariable models were adjusted for the same baseline covariates. Analyses were performed using the lme4 and glmmTMB packages in R version 4.5.1.
RESULTS
The initial cohort included 1,632 incident adults with PAH who had at least one follow-up visit recorded in the PHAR (Figure 1). After applying exclusion criteria, 1,219 participants were included in the final analysis. There were 487 (40.0%) with private insurance, 449 (36.8%) with Medicare, and 283 (23.2%) with Medicaid or similar programs at baseline (Table 1, Figure 1). Only 74 (6.1%) patients changed insurance type before the first follow-up visit.
Table 1.
Patient Demographics by Insurance Type
| Private Insurance (N=487) |
Medicare (N=449) |
Medicaid/Similar Programs (N=283) |
P Value | SMD* | |
|---|---|---|---|---|---|
| Age, years | 51.4 [39.7, 60.3] | 69.9 [65.5, 74.8] | 46.8 [37.1, 58.1] | <0.001† | 1.00 |
| Female sex (n=1,218) | 404 (83.0) | 333 (74.2) | 208 (73.8) | 0.001‡ | 0.15 |
| Race and ethnicity | <0.001‡ | 0.43 | |||
| Asian | 26 (5.3) | 8 (1.8) | 3 (1.1) | ||
| Non-Hispanic Black | 43 (8.8) | 49 (10.9) | 48 (17.0) | ||
| Hispanic or Latino | 44 (9.0) | 21 (4.7) | 48 (17.0) | ||
| Non-Hispanic White | 359 (73.7) | 360 (80.2) | 166 (58.7) | ||
| Other or more than one race | 15 (3.1) | 11 (2.4) | 18 (6.4) | ||
| PAH etiology | <0.001‡ | 0.55 | |||
| Idiopathic or heritable | 270 (55.4) | 219 (48.8) | 109 (38.5) | ||
| Drug- or toxin-associated | 35 (7.2) | 28 (6.2) | 89 (31.4) | ||
| Connective tissue disease-associated | 158 (32.4) | 183 (40.8) | 66 (23.3) | ||
| Congenital heart disease-associated | 20 (4.1) | 16 (3.6) | 7 (2.5) | ||
| HIV-associated | 4 (0.8) | 3 (0.7) | 12 (4.2) | ||
| Highest education (n=1,198) | <0.001‡ | 0.48 | |||
| High school graduate/GED or less | 144 (30.1) | 158 (35.8) | 149 (53.6) | ||
| Some college | 93 (19.4) | 119 (27.0) | 77 (27.7) | ||
| College graduate or advanced degree | 242 (50.5) | 164 (37.2) | 52 (18.7) | ||
| Income (% of FPL)§ (n=1,042) | <0.001‡ | 0.98 | |||
| Below poverty (<100%) | 36 (8.3) | 53 (14.2) | 133 (56.4) | ||
| Low income (100–199%) | 65 (15.0) | 82 (22.0) | 51 (21.6) | ||
| Moderate income (200–399%) | 135 (31.2) | 121 (32.4) | 36 (15.3) | ||
| High income (≥400%) | 197 (45.5) | 117 (31.4) | 16 (6.8) | ||
| PAH medication assistance (n=1,050) | <0.001‡ | 0.37 | |||
| No | 271 (62.7) | 223 (56.7) | 184 (81.8) | ||
| Yes | 161 (37.3) | 170 (43.3) | 41 (18.2) | ||
| Region | <0.001‡ | 0.26 | |||
| Northeast | 90 (18.4) | 98 (21.8) | 47 (16.6) | ||
| Midwest | 124 (25.5) | 106 (23.6) | 52 (18.4) | ||
| South | 170 (34.8) | 167 (37.2) | 90 (31.8) | ||
| West | 103 (21.1) | 78 (17.4) | 94 (33.2) | ||
| Median follow-up, days | 947 [449, 1706] | 849 [421, 1459] | 865 [430, 1502] | 0.13∥ | 0.12 |
Data are presented as number (%), mean ± standard deviation, or median [interquartile range].
SMD = standardized mean difference; PAH = pulmonary arterial hypertension; FPL = Federal Poverty Level.
Standardized mean differences < 0.3 are considered small.
P values were derived using the Kruskal–Wallis test.
P values were derived using the Pearson X2 test.
Annual household income was categorized by percentage of the Federal Poverty Level based on each participant’s year of enrollment and total household size.
P value derived using one-way ANOVA.
Baseline characteristics differed across insurance groups (Table 1). Participants with Medicare were the oldest (mean 69.9 years), followed by those with private insurance (51.4 years) and Medicaid (46.8 years). Female sex was most prevalent in the private insurance group. Idiopathic/heritable PAH was the most common etiology across groups. Drug- or toxin-associated PAH was significantly more common in the Medicaid group, while connective tissue disease-associated PAH was most prevalent in patients with Medicare.
Socioeconomic indicators also varied across insurance groups. Half of Medicaid patients (53.6%) had a high school diploma or less, whereas half of those with private insurance (50.5%) were college graduates. Most patients with Medicaid (56.4%) had an annual household income below the FPL, compared with 8.3% of privately insured and 14.2% of Medicare participants. Use of PAH medication assistance programs was highest among patients with Medicare (43.3%) or private insurance (37.3%) and lowest among those with Medicaid (18.2%) (Figure E2). There were also regional differences in insurance (Figure 2).
Figure 2.
Regional distribution by insurance type.
Disease severity measures also differed by insurance type (Table 2). Medicare beneficiaries had the lowest 6MWD and the highest proportion classified as high-risk by REVEAL Lite 2, while patients with Medicare or Medicaid were more often classified as WHO functional class III/IV compared with privately insured patients. However, Medicare patients demonstrated more favorable hemodynamics, with lower mean pulmonary artery pressure, lower pulmonary vascular resistance, and higher cardiac index than patients with private insurance or Medicaid. At the first follow-up visit, most patients were receiving combination therapy, which was most common among those with private insurance (74.8%) followed by Medicaid (69.2%) and Medicare (65.0%) (P < 0.001) (Figure 3).
Table 2.
Baseline Clinical Characteristics by Insurance Type
| Private Insurance (N=487) |
Medicare (N=449) |
Medicaid/Similar Programs (N=283) |
P Value | SMD* | |
|---|---|---|---|---|---|
| Six-minute walk distance, m (n=999) | 354.0 ± 119.9 | 286.2 ± 121.8 | 335.2 ± 118.1 | <0.001† | 0.38 |
| WHO functional class (n=1,158) | <0.001‡ | 0.26 | |||
| I | 36 (7.7) | 16 (3.8) | 14 (5.1) | ||
| II | 194 (41.7) | 143 (34.0) | 81 (29.8) | ||
| III | 218 (46.9) | 229 (54.4) | 150 (55.1) | ||
| IV | 17 (3.7) | 33 (7.8) | 27 (9.9) | ||
| REVEAL Lite 2 risk category§ | <0.001‡ | 0.27 | |||
| Low | 176 (36.1) | 93 (20.7) | 90 (31.8) | ||
| Intermediate | 141 (29.0) | 120 (26.7) | 81 (28.6) | ||
| High | 170 (34.9) | 236 (52.6) | 112 (39.6) | ||
| Hemodynamics | |||||
| Right atrial pressure, mmHg (n=1,188) | 9.00 [5.00, 13.0] |
8.0 [5.00, 12.0] |
10.0 [6.25, 14.0] |
<0.001∥ | 0.18 |
| Mean pulmonary artery pressure, mmHg (n=1,203) | 49.5 [40.8, 59.0] |
43.0 [36.0, 51.0] |
51.0 [42.0, 59.0] |
<0.001∥ | 0.37 |
| Pulmonary capillary wedge pressure, mmHg (n=1,176) | 10.0 [7.00, 13.0] |
10.0 [7.00, 13.0] |
10.0 [7.00, 14.0] |
0.89∥ | 0.04 |
| Cardiac output, L/min (n=1,158) | 4.00 [3.17, 4.96] |
4.00 [3.26, 5.09] |
3.80 [3.10, 4.79] |
0.13∥ | 0.06 |
| Cardiac index, L/min/m2 (n=1,190) | 2.12 [1.68, 2.58] |
2.19 [1.78, 2.74] |
2.00 [1.67, 2.48] |
<0.001∥ | 0.17 |
| Pulmonary vascular resistance, Wood units (n=911) | 10.0 [6.90, 14.0] |
8.00 [5.20, 11.3] |
10.8 [7.68, 15.1] |
<0.001∥ | 0.24 |
| Baseline health-related QOL score | |||||
| EmPHasis-10 (n=1,210) | 24.0 [14.5, 32.0] |
26.0 [16.5, 34.0] |
29.0 [16.2, 36.0] |
0.002∥ | 0.17 |
| SF-12 Physical Component (n=1,197) | 35.8 [28.3, 42.8] |
32.9 [26.2, 39.8] |
33.0 [26.0, 41.2] |
<0.001∥ | 0.18 |
| SF-12 Mental Component (n=1,197) | 49.7 [41.0, 56.4] |
50.7 [41.4, 58.1] |
43.3 [35.8, 54.1] |
<0.001∥ | 0.31 |
Data are presented as number (%), mean ± standard deviation, or median [interquartile range].
SMD = standardized mean difference; PAH = pulmonary arterial hypertension; WHO = World Health Organization; REVEAL = Registry to Evaluate Early and Long-Term PAH Disease Management; QOL = quality of life; SF-12 = Short Form-12.
Standardized mean differences < 0.3 are considered small.
P value derived using one-way ANOVA.
P values were derived using the Pearson X2 test.
REVEAL Lite 2 scores were classified as low (0–5), intermediate (6–7), or high (≥ 8) risk.
P values were derived using the Kruskal–Wallis test.
Figure 3.
Pulmonary arterial hypertension medication regimen at the time of the first follow-up appointment, by insurance type. PAH = pulmonary arterial hypertension.
Insurance and PAH Medication Regimen
Participants with private insurance and those with Medicaid had higher odds of receiving a greater number of PAH medications compared with patients with Medicare (private insurance: odds ratio [OR], 1.66; 95% confidence interval [CI], 1.31-2.11; P <0.001; Medicaid: OR, 1.34; 95% CI, 1.02-1.79; P = 0.04) (Table 3). However, after adjusting for age, sex, PAH etiology, REVEAL Lite 2 score, education, income, and medication assistance program use, there was no significant difference in the odds of receiving a more intensive medication regimen (private insurance: OR, 1.15; 95% CI, 0.82-1.62; Medicaid: OR, 1.28; 95% CI, 0.84-1.96). Use of medication assistance was independently associated with higher odds of receiving more intensive therapy (OR, 1.69; 95% CI, 1.29-2.23; P < 0.001), while older age was associated with lower odds (OR, 0.96 per year; 95% CI, 0.95-0.97; P = 0.001). Income was not associated with treatment intensity (P = 0.95). Sensitivity analyses comparing combination therapy with monotherapy and using propensity score–weighted methods yielded similar findings, with no significant differences in treatment intensity by insurance type after covariate adjustment (Table E1 and E2).
Table 3.
Odds of Receiving Higher Number of Medications by Insurance Type
| Odds Ratio (95% CI)* | P Value† | |
|---|---|---|
| Unadjusted model (n=1,219) | ||
| Private insurance vs Medicare | 1.66 (1.31-2.11) | <0.001 |
| Medicaid/similar vs Medicare | 1.34 (1.02-1.79) | 0.04 |
| Adjusted model‡ (n=903) | ||
| Baseline insurance | ||
| Private insurance vs Medicare | 1.15 (0.82-1.62) | 0.42 |
| Medicaid/similar vs Medicare | 1.28 (0.84-1.96) | 0.25 |
| Use of PAH medication assistance program | 1.69 (1.29-2.23) | <0.001 |
CI = confidence interval; PAH = pulmonary arterial hypertension; REVEAL = Registry to Evaluate Early and Long-Term PAH Disease Management.
Medicare is the reference group for all comparisons.
P values were derived using Wald tests.
Adjusted model includes age, sex, PAH etiology, REVEAL Lite 2 risk score, education, income, and use of PAH medication assistance programs.
Age, Insurance, and Medication Use in PAH
There was no evidence of interaction between insurance type and age on the odds of receiving more intensive therapy (P for interaction = 0.41). Similarly, there was no significant difference in the effect of insurance on treatment intensity for patients younger or older than 65 years (P for interaction = 0.76).
Dual Eligibility and PAH Medication Use
Fifty-two participants (4.3%) had both Medicare and Medicaid insurance at baseline (“dual-eligible”) (Figure E3); characteristics are summarized in Table E3 and Figure E4. In unadjusted analyses, private insurance (OR, 1.97; 95% CI, 1.50-2.59; P < 0.001) and Medicaid (OR, 2.11; 95% CI, 1.50-2.97; P < 0.001) were associated with higher odds of receiving more PAH medications compared with Medicare, while there was no significant difference for the dual-eligible group (OR, 1.37; 95% CI, 0.73-2.58; P = 0.32) (Table E4). After multivariable adjustment, none of the insurance groups differed significantly from Medicare. However, medication assistance program use remained associated with higher odds of receiving more PAH medications (OR, 1.71; 95% CI, 1.30-2.25, P < 0.001), while older age was associated with lower odds (OR, 0.97 per year; 95% CI, 0.95-0.98; P < 0.001). There was no association between income and treatment intensity (P = 0.81).
Insurance and Time to Initiation of Therapy
The median follow-up time was 884 days (IQR, 434-1536) (Table 1). At enrollment, most patients (709; 58.2%) were receiving combination therapy, while 176 (14.4%) were receiving no medications and 334 (27.4%) were receiving monotherapy. The most common treatment change was escalation from zero or single therapy to combination therapy between enrollment and each participant’s last follow-up (increasing from 63.6% to 81.5% for private insurance, 51.6% to 69.7% for Medicare, and 59.0% to 78.4% for Medicaid; Figure 5). In total, 134 (76.1%) of the participants initially receiving zero medications and 138 (41.3%) of those receiving monotherapy increased to combination therapy during follow-up.
Figure 5.
Change in pulmonary arterial hypertension medication regimen over time by insurance type. Transitions in pulmonary arterial hypertension medication regimen between the first follow-up visit and the last recorded follow-up visit are shown, stratified by insurance type. PAH = pulmonary arterial hypertension.
Among the 510 participants receiving zero or one PAH medication at baseline, there was no difference in time to initiation of combination therapy by insurance type in unadjusted or adjusted analyses (Table 4, Figure 4). Older age was associated with slower initiation of combination therapy (hazard ratio [HR], 0.98 per year; 95% CI, 0.98-0.99; P = 0.003). Patients whose PAH medications were funded through an assistance program started combination therapy significantly sooner (HR, 1.54; 95% CI, 1.16-2.04; P = 0.003). Income was not associated with time to combination therapy (P = 0.78).
Table 4.
Initiation of Combination Therapy During Follow-Up by Insurance Type
| HR (95% CI)* | P Value† | |
|---|---|---|
| Unadjusted model (n=510) | ||
| Private insurance vs Medicare | 1.23 (0.94-1.60) | 0.13 |
| Medicaid/similar vs Medicare | 1.29 (0.96-1.74) | 0.09 |
| Adjusted model‡ (n=365) | ||
| Baseline insurance | ||
| Private insurance vs Medicare | 1.21 (0.86-1.71) | 0.27 |
| Medicaid/similar vs Medicare | 1.46 (0.97-2.19) | 0.07 |
| Use of PAH medication assistance program | 1.54 (1.16-2.04) | 0.003 |
CI = confidence interval; PAH = pulmonary arterial hypertension; REVEAL = Registry to Evaluate Early and Long-Term PAH Disease Management.
Hazard ratios from Cox proportional hazards models for time to initiation of combination therapy among patients who were not receiving combination therapy at their initial enrollment visit; Medicare is the reference group.
P values were derived using Wald tests.
Adjusted model includes age, sex, PAH etiology, REVEAL Lite 2 risk score, education, income, and use of PAH medication assistance programs.
Figure 4.
Time to initiation of combination therapy by insurance type. Nelson–Aalen cumulative hazard estimates of time to initiation of combination therapy over the first year among patients who were not receiving combination therapy at baseline. The P value for the log-rank test is shown.
Emergency Department Visit and Hospitalization Rates by Insurance Type
Unadjusted emergency department (ED) visit rates were 0.64 visits per person-year (IQR, 0.60-0.69) for patients with private insurance, 0.88 (IQR, 0.82-0.94) for those with Medicare, and 1.23 (IQR, 1.14-1.31) for those with Medicaid. After multivariable adjustment, participants with private insurance had significantly lower ED visit rates than Medicare beneficiaries (incidence rate ratio [IRR], 0.75; 95% CI, 0.59-0.95; P = 0.02), while rates did not differ between Medicaid and Medicare (IRR, 1.01; 95% CI, 0.76-1.35; P = 0.94) (Table 5). Hospitalization rates followed a similar pattern, with patients with private insurance having the lowest hospitalization rate (0.40 hospitalizations per person-year; 95% CI, 0.37-0.44) compared to patients with Medicare (0.52; 95% CI, 0.48-0.57) or Medicaid (0.57; 95% CI, 0.51-0.63). After multivariable adjustment, hospitalization rates did not differ significantly across insurance groups (Table 5). PAH medication assistance program use was not associated with ED visit or hospitalization rates (P > 0.05 for both). Higher income was associated with significantly lower ED visit rates (IRR, 0.84 per one-unit increase in log-income; 95% CI, 0.76-0.93; P < 0.001) but not with hospitalization rates. There was no evidence that the association between insurance and either ED visit or hospitalization rates differed by treatment intensity (P for both interactions > 0.05).
Table 5.
Emergency Department Visit Rates, Hospitalization Rates, and Health-Related Quality of Life by Insurance Type
| Unadjusted Model | Adjusted Model† | |||
|---|---|---|---|---|
| IRR (95% CI)* | P Value‡ | IRR (95% CI)* | P Value‡ | |
| ED visit rates | ||||
| Private insurance | 0.73 (0.60-0.89) | 0.002 | 0.75 (0.59-0.95) | 0.02 |
| Medicaid/similar | 1.46 (1.17-1.82) | <0.001 | 1.01 (0.76-1.35) | 0.94 |
| Hospitalization rates | ||||
| Private insurance | 0.74 (0.59-0.94) | 0.01 | 0.87 (0.65-1.16) | 0.35 |
| Medicaid/similar | 1.06 (0.81-1.39) | 0.67 | 1.06 (0.75-1.50) | 0.72 |
| Unadjusted Mean Difference (95% CI)* |
P Value‡ | Adjusted Mean Difference (95% CI)* |
P Value‡ | |
| EmPHasis-10 | ||||
| Private insurance | −2.3 (−3.8, −1.8) | 0.002 | −0.57 (−2.4, +1.2) | 0.53 |
| Medicaid/similar | +2.2 (+0.44, +4.0) | 0.02 | +1.0 (−1.2, +3.2) | 0.38 |
| SF-12 physical | ||||
| Private insurance | +4.0 (+2.8, +5.3) | <0.001 | +0.40 (−1.1, +1.8) | 0.59 |
| Medicaid/similar | +1.2 (−0.33, +2.7) | 0.13 | −1.1 (−2.9, +0.73) | 0.24 |
| SF-12 mental | ||||
| Private insurance | −0.62 (−1.9, +0.64) | 0.33 | +0.40 (−1.1, +1.9) | 0.61 |
| Medicaid/similar | −4.8 (−6.3, −3.3) | <0.001 | −1.8 (−3.8, +0.08) | 0.06 |
Data are presented as incidence rate ratios or mean differences with 95% confidence intervals.
CI = confidence interval; IRR = incidence rate ratios; PAH = pulmonary arterial hypertension; REVEAL = Registry to Evaluate Early and Long-Term PAH Disease Management; SF-12 = Short Form-12.
Estimates are from negative binomial and linear mixed-effects models (random intercept for patient), with Medicare as the reference group. Negative emPHasis-10 mean differences indicate better health-related quality of life, positive SF-12 coefficients indicate better health-related quality of life.
Adjusted models include age, sex, PAH etiology, REVEAL Lite 2 risk score, education, income, and use of PAH medication assistance programs.
P values were derived using two-sided Wald tests.
Insurance and Health-Related Quality of Life
At baseline, the median emPHasis-10 score—where higher scores indicate worse quality of life—was 24.0 (IQR, 14.5-32.0) for patients with private insurance, 26.0 (IQR, 16.5-34.0) for those with Medicare, and 29.0 (IQR, 16.2-36.0) for those with Medicaid (Table 2). For the SF-12 physical component, where higher scores indicate better quality of life, patients with private insurance reported the highest median score (35.8; 95% CI, 28.3-42.8) compared with those with Medicare (32.9; 95% CI, 26.2-39.8) or Medicaid (33.0; 95% CI, 26.0-41.2). SF-12 mental component scores were similar for participants with private insurance (49.7; 95% CI, 41.0-56.4) and Medicare (50.7; 95% CI, 41.4-58.1), while those with Medicaid had lower scores (43.3; 95% CI, 35.8-54.1). After multivariable adjustment, insurance type was not significantly associated with any of the studied HRQoL outcomes (all P > 0.05) (Table 5). PAH medication assistance program use was similarly not associated with differences in HRQoL (all P > 0.05). There was no evidence of effect modification by treatment intensity, with similar HRQoL scores among patients receiving no therapy, monotherapy, or combination therapy (all interaction P ≥ 0.05).
DISCUSSION
In this multicenter study of over 1,000 incident adult PAH patients receiving care at accredited PH care centers, we found no significant differences in PAH medication use or clinical outcomes by insurance type.
Across insurance groups, there were significant differences in most clinical and sociodemographic variables. Patients with Medicare were older and had worse disease severity, while patients with Medicaid or other public insurance were more likely to identify as racial and ethnic minorities, have lower income and education, and have drug- or toxin-associated PAH. Insurance distribution also varied by geographic region of the center: Medicaid was concentrated in the West, whereas Medicare and private insurance were more evenly distributed. These regional differences may reflect differing state Medicaid eligibility, healthcare access, or referral patterns and warrant further study.
Despite these differences, insurance was not independently associated with PAH treatment intensity, time to initiation of combination therapy, hospitalization rates, or HRQoL. There was no significant difference in the odds of receiving more PAH medications, and patients initiated combination therapy at essentially the same rate. HRQoL scores were also similar across insurance groups. However, while we observed no differences in hospitalization rates, patients with private insurance had significantly lower ED visit rates than those with Medicare. This may reflect greater comorbidity burden among Medicare beneficiaries, with patients with more comorbidities more likely to be triaged for ED evaluation for lower-acuity complaints that do not result in admission. Alternatively, privately insured patients may have greater access to prompt outpatient care and higher ED cost-sharing, reducing their likelihood of ED presentation. Finally, we found no evidence that medication regimen modified any of these associations.
Age was an important driver of treatment variation across insurance groups. Patients with Medicare were older, and older age was associated with less intensive therapy and slower escalation to combination therapy. Although patients with Medicare received fewer medications, this was accounted for by older age: age appeared to be the primary reason for reduced treatment intensity and slower initiation of combination therapy. This likely reflects higher likelihood of comorbidities including occult left heart disease among older adults with PAH, and it is consistent with current guidelines recommending cautious use of pulmonary vasodilator therapy in older patients and those with cardiopulmonary comorbidities (6-8).
Importantly, there were significant treatment differences for patients who received financial assistance. Many patients with PAH receive assistance from nonprofit organizations or pharmaceutical manufacturers to reduce out-of-pocket medication costs (25). Pharmaceutical companies, which are incentivized to develop orphan drugs, often establish such programs to help offset the high costs of these medications. In this study, patients receiving medication assistance had both higher odds of receiving combination therapy and faster initiation of combination therapy. As combination therapy is the standard of care for incident PAH, these findings suggest that assistance programs may help patients access appropriate therapy sooner. Interestingly, medication assistance use was lowest among patients insured by Medicaid, likely because Medicaid covers medications at nominal cost. Additionally, eligibility for financial assistance varies by program; patients with government-run health insurance are not eligible for some pharmaceutical company-funded programs (26-28), potentially contributing to lower utilization. Overall, medication assistance programs appear to play an important role in improving access to PAH medications.
Study participants received care at accredited PH care centers, which may limit generalizability. To receive accreditation, PH care centers must have robust multidisciplinary teams that can assist with accessing financial assistance, navigating insurance requirements, and obtaining insurance for uninsured or underinsured patients (29, 30). Individuals who can establish care at these centers may have overall better healthcare access, including greater financial resources and insurance coverage. Prior work has shown that treatment at PH care centers is associated with improved outcomes and suggested that factors like improved treatment access may be responsible (31, 32). The same support may not be available in community settings, which may have fewer resources to address medication access issues. As recent work using a national claims-based database found that a majority of patients are prescribed PAH medications outside of accredited PH care centers (33), our results may not fully reflect barriers experienced by the broader U.S. PAH population. Ultimately, current guidelines recommend that all patients be referred to a dedicated PH specialty care center, and understanding care delivery within these settings remains important. The PH community and medical societies should continue to advocate for expanding medication assistance programs, reducing drug costs, and increasing access to specialized PH care centers.
Our study has a few additional limitations. Due to small participant numbers, we were unable to study the impact of being uninsured or to separately evaluate military healthcare. Treatment intensity was defined by medication count, rather than by drug class(es). Insurance plan-level heterogeneity (e.g. different copayment amounts, deductibles, or coverage restrictions) is not captured by PHAR and may limit detection of differences between insurance groups. Our study did not incorporate medication adherence data, which was the focus of a recent work that showed a low overall prevalence of nonadherence in PHAR (~6%) (14). Finally, age and insurance type are highly correlated (most patients with Medicare qualify based on age), which might obscure relationships between insurance and treatment patterns. However, we found no differences when stratifying by age or testing for interaction.
In this study of patients with PAH enrolled in the PHAR, it is reassuring that patients received similar treatment and had comparable clinical and HRQoL outcomes regardless of insurance coverage or income. Future work should explore the impact of insurance coverage in other settings, as there may be greater barriers to medication access outside of well-resourced PH specialty care centers.
Supplementary Material
Acknowledgments:
We thank the patients enrolled in the Pulmonary Hypertension Association Registry for generously contributing their time and data to advance PAH research. We would like to thank the PHAR Data Coordinating Center and all the PHAR investigators for their dedication and contributions to the registry.
Supported by the National Institutes of Health/National Institute of General Medical Sciences (T32 GM075766 [B.E.M.]) and the National Institutes of Health/National Heart, Lung, and Blood Institute (R01 HL173533 [N.A.-N.]). Portions of this work were previously presented as an abstract at the American Thoracic Society International Conference, May 2025.
Primary Source of Funding:
National Institutes of Health/National Institute of General Medical Sciences (T32 GM075766 [B.E.M.]) and the National Institutes of Health/National Heart, Lung, and Blood Institute (R01 HL173533 [N.A.-N.]).
Footnotes
This article has an online data supplement, which is accessible from this issue's table of content online at www.atsjournals.org.
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