Abstract
Background and Objectives
Indolent non-Hodgkin’s lymphomas (NHL) are associated with relapses necessitating recurrent treatment and lifelong care. However, contemporary real-world evidence of the disease burden among patients with indolent NHLs remains limited. This study describes treatment patterns, healthcare resource use (HRU), and costs among patients with follicular lymphoma (FL), Waldenström macroglobulinemia (WM), and marginal zone lymphoma (MZL) in the USA.
Methods
Adults with newly diagnosed FL, WM, and MZL were identified in claims data (October 2015−June 2021). The study period spanned from the index date (first observed diagnosis code) until end of continuous enrollment or data availability. Treatment patterns were described during the study period by lines of therapy (LOTs). HRU and costs per-patient-per-month (PPPM) were described overall and by LOTs.
Results
This study included 2805 patients with FL, 791 with WM, and 2207 with MZL with a median follow-up of > 20 months each. Bendamustine-based therapies and rituximab monotherapy were most common. In later lines, bendamustine-based therapies declined while rituximab monotherapy and ibrutinib-based therapies increased. Mean all-cause total costs were US $12,402, US $8445, and US $9580 PPPM for patients with FL, WM, and MZL, respectively. Disease-related medical costs constituted > 50% of all-cause total costs, with most hospitalization costs being disease-related. In FL, HRU and costs tended to increase by LOT, driven by FL-related hospitalizations. Trends in HRU and costs across LOTs were less clear for WM and MZL.
Conclusions
Our study suggests that indolent NHLs are associated with a significant economic burden that tends to increase across LOTs in FL, highlighting the need for better therapeutic options.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1007/s40261-026-01592-6.
Key Points
| This retrospective analysis suggests that the indolent non-Hodgkin’s lymphoma (NHL) subtypes follicular lymphoma (FL), Waldenström macroglobulinemia (WM), and marginal zone lymphoma (MZL) are associated with a significant economic burden. |
| Among patients with FL, there was also a trend toward increase in medical resource usage and healthcare costs with each successive line of treatment. |
| These findings highlight the need for better management practices and more effective therapeutic options for patients with indolent NHLs. |
Introduction
Non-Hodgkin’s lymphoma (NHL) is a diverse group of blood cancers composed of several subtypes, which are characterized by the cell of origin. NHL originates in the lymphocytes, with 85–90% of cases originating from B cells [1]. Clinically, NHL has been divided into aggressive (rapidly growing) subtypes, of which diffuse large B-cell lymphoma is the most common, and indolent (slowly growing) lymphomas, which include follicular lymphoma (FL), marginal zone lymphoma (MZL), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), and Waldenström macroglobulinemia (WM), among others [2]. In regard to the indolent subtypes, FL is the most common (~ 20–25% of newly diagnosed NHL cases) [3, 4] followed by MZL and CLL/SLL (each accounting for ~ 7% of cases), while rarer indolent subtypes, such as WM, account for < 2% of cases [5, 6].
Unlike the more aggressive subtypes, most indolent NHLs are usually considered incurable, although they often have a higher survival rate (e.g., about 80–90% 10 years after diagnosis) [4, 7, 8]. Patients may therefore require care throughout the lifespan, which poses unique challenges in clinical practice [2]. Patients with indolent lymphomas who do not require therapy at diagnosis (i.e., watchful waiting) or who respond favorably to initial treatment have a life expectancy similar to healthy individuals [3]. However, patients with indolent NHLs such as FL often experience repeated relapses between disease-free periods, which necessitates recurrent treatment [3, 9]. Furthermore, the most common cause of death remains the lymphoma itself, followed by treatment-related toxicities and complications associated with the disease (e.g., infections or other malignancies) [3, 9]. Finally, early treatment relapse and transformation into more aggressive lymphomas are associated with worse prognosis. For example, patients with FL who experience early relapse (~ 20% of patients) or histological transformation (8–10% of patients) have a higher incidence of death, primarily due to lymphoma-related mortality [3, 7]. In one study of the FL population, there was a more than fivefold increase in the 10-year cumulative incidence of lymphoma-related mortality among patients who experienced an event (e.g., relapse or retreatment) within 24 months of diagnosis versus those who did not (36.1% versus 6.7%), as well as among patients with versus without transformation (45.9% versus 8.1%) [7].
The current treatment landscape for these malignancies varies depending on various factors including patient age, performance status, stage of disease, genetic subtype, and treatment history [7, 9]. In routine clinical practice, an estimated 35% of patients with FL adopt a wait and watch strategy [10], which may be suitable in the early stages of the disease when symptoms are minimal. However, most patients eventually experience disease progression requiring systemic therapy [3, 7]. Per the 2024 National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology (NCCN Guidelines®), preferred treatment options for patients with indolent NHLs in the first line (1L) and the second line (2L) include chemoimmunotherapy regimens such as bendamustine- and rituximab-based regimens; cyclophosphamide, doxorubicin, vincristine, prednisolone (CHOP)-based regimens; and cyclophosphamide, vincristine, prednisone (CVP)-based regimens (in FL and MZL), and rituximab monotherapy for patients who are elderly or infirm [11, 12]. In later lines, newer therapies are also considered viable options. For instance, chimeric antigen receptor T-cell (CAR-T) therapy is recommended for patients with FL and MZL in the third line (3L), while certain Bruton’s kinase inhibitors (BTKs) are recommended for MZL and WM in 2L [11].
Despite a broad range of available treatment options, prior retrospective insurance-claims-based studies suggest that indolent NHLs are associated with a considerable economic burden that may increase as the disease progresses [13–22]. Many prior real-world studies assessing the treatment patterns and economic burden of indolent NHLs, such as FL, MZL, and WM have relied on claims data from older study periods (prior to 2015) [13, 15–17, 21, 22] that might not reflect the economic burden associated with newer therapies and clinical management practices. Therefore, there is a need to assess the current treatment patterns and economic burden of these particular indolent NHLs using more recent claims data. Additionally, although the treatment patterns and economic burden of FL have been better characterized, there is a need for further information regarding less prevalent indolent malignancies, such as WM and MZL. Accordingly, the present study aimed to describe real-world treatment patterns, healthcare resource use (HRU), and costs in patients with indolent NHLs, specifically FL, WM, and MZL, in the USA.
Methods
Data Source
This study was conducted using data from Optum’s deidentified Clinformatics® Data Mart Database (CDM) from October 2015 to March 2021 (FL) or June 2021 (WM and MZL). The CDM database is derived from a database of administrative health claims for members of large commercial and Medicare Advantage health plans. The database covers 17–19 million annual lives among a nationally representative population spanning all 50 states in the USA. The claims in the database contain information regarding member eligibility and outpatient encounters, hospital stays, as well as standard pricing for all medical and pharmacy claims and hospital stays. Data were deidentified and comply with the patient requirements of the Health Insurance Portability and Accountability Act (HIPAA) of 1996; therefore, no review by an institutional review board was required.
Study Design and Sample Selection
In this retrospective cohort study design, patients were identified and assigned to one of three separate, mutually exclusive study samples—FL, WM, or MZL (see Supplementary Figs. S1−3). Eligible patients newly diagnosed with FL or WM were required to have ≥ 2 International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes for FL (C82.xx) or WM (C88.0), respectively. Diagnoses were required to be on ≥ 2 distinct dates within 12 months between 1 October 2015 and 31 December 2020. Since there is no ICD-10-CM code for MZL, this malignancy was identified by requiring ≥ 2 ICD-10-CM codes for small cell B-cell lymphoma (C83.0x) or extranodal marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALT-lymphoma, C88.4) on ≥ 2 distinct dates within 12 months between 1 October 2015 and 31 December 2020.
The index date for each cohort was defined as the date of the first observed diagnosis codes for FL, WM, or MZL (i.e., the first diagnosis code for either small cell B-cell lymphoma or MALT-lymphoma), respectively, in claims data, and the baseline period was defined as the 12-month period preceding the index date. Patients were required to be aged ≥ 18 years at the index date and have no diagnoses for any malignancy during baseline. Continuous enrollment was required for ≥ 12 months pre-index and ≥ 1 month post-index. Patients were considered newly diagnosed if they had no diagnosis codes for FL, WM, or MZL during the 1-year period prior to the index date. For MZL, this was defined based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 200.3 before 1 October 2015, and ICD-10-CM codes C83.0x and C88.4 after this date. The study period was defined as the period from the index date until the earliest of the end of continuous eligibility or end of data availability.
For the treatment sequence analysis, a subset of patients meeting the following additional criteria were included: no claims for treatments (defined below in algorithm to identify lines of therapy) during the baseline period, ≥ 1 claim for treatments during the study period, and sufficient visibility (i.e., ≥ 28 days of enrollment) following their first treatment claim.
Algorithm to Identify Lines of Therapy
For this study, a line of therapy (LOT) algorithm was developed to capture treatment patterns in patients after their initial diagnosis with indolent NHLs. In this algorithm, a LOT could consist of a single drug or a combination of multiple drugs, which could be further combined with other therapies, including stem cell transplantation and radiation.
The LOT algorithm comprised a sequence of steps that are described in detail in Supplementary Text S1. First, individual treatments for inclusion in the LOT algorithm were selected based on the NCCN Guidelines® recommended systemic therapies for FL, WM, and MZL (see Supplementary Text S1 for the full list of treatments). Discontinuation of a treatment was defined as a gap of ≥ 90 days without any dispensation of a treatment of interest. Second, LOTs were described based on the individual treatments included. Specifically, LOTs were characterized based on drugs taken in the first 28 days following the prescription of the first drug. Discontinuation of all drugs in the LOT for 90 days was defined as discontinuation of the LOT. For LOTs including stem cell transplant (SCT) or CAR-T, any pharmacologic treatments initiated during predefined windows before and after the SCT or CAR-T therapy were considered part of the same LOT (see Supplementary Text S1 for full details). The final step involved the classification of LOTs. In particular, LOTs were categorized into treatment types according to a hierarchy, where patients could be categorized into a treatment category only if they did not meet the criteria of categories higher in the hierarchy. For each treatment category (e.g., “ibrutinib-based therapies”), the defining treatment (e.g., ibrutinib) could be taken with or without other treatments, including rituximab, unless otherwise specified. The complete hierarchy of treatment categories is described in Supplementary Text S1.
Study Variables and Outcomes
Patient characteristics measured during the baseline period or at the index date included age, sex, index year, insurance category and type, geographic region, race/ethnicity, Charlson Comorbidity Index (CCI), and the National Cancer Institute Comorbidity Index (NCICI). Treatment patterns were described during the study period for up to three LOTs per patient, including 1L, 2L, and 3L. Specific treatment pattern outcomes included the duration of each LOT and the types of regimens used by patients within each LOT. To visualize treatment patterns across lines for patients who received at least two LOTs, alluvial plots were created. All-cause and disease-related HRU and healthcare costs during the overall study period and stratified by LOT (i.e., during 1L, 2L, and 3L or subsequent line [3L+]) were described as follows. Any visit with a diagnosis code for the disease in any position was considered disease-related and used to calculate disease-related HRU and costs. HRU (all-cause and disease-related) for the following settings were described: hospitalizations and length of stay, outpatient (OP) visits, emergency department (ED) visits, and other visits. Total and medical all-cause costs and disease-related costs were described, and stratified further into hospitalization, OP, ED, and other visit costs. All-cause total pharmacy costs were also described.
Statistical Analysis
Means, standard deviations (SD), medians, and interquartile ranges (IQR) were reported for continuous variables; frequencies and percentages were reported for categorical variables. In particular, treatment patterns were summarized using frequencies and percentages; in cases where the N for cells were < 5, the values were redacted and only the directions of the results were described. HRU outcomes were reported as incidence rates per patient-month (PPM) with 95% confidence intervals (CI). Healthcare costs were reported per-patient-per-month (PPPM) in terms of estimated paid amounts and adjusted to September 2022 US dollars using the medical care component of the Consumer Price Index (CPI). These outcomes were summarized monthly to adjust for the variable lengths of follow-up observed during the study period for individual patients. All analyses were performed using Statistical Analysis System (SAS version 9.4) and R (version 3.6.3) statistical software.
Results
Based on the study eligibility criteria, the present analysis included 2805, 791, and 2207 patients with FL, WM, and MZL, respectively. The sample selection flowcharts for FL, WM, and MZL are presented in Supplementary Figs. S1, S2, and S3, respectively.
Baseline Characteristics
Baseline characteristics of patients in the FL, WM, and MZL cohorts are presented in Table 1. The median duration of the study follow-up period was > 20 months for all three cohorts (FL: 21.5 months; WM: 20.9 months; MZL: 21.5 months). Patients in the FL, WM, and MZL cohorts had a mean (SD) age of 68.4 (12.7) years, 73.8 (10.3) years, and 70.6 (11.5) years, respectively. The majority of patients in each cohort were white (FL: 68.3%; WM: 80.4%; MZL: 75.1%), while ≤ 10% were Hispanic, Black, or Asian, respectively. Across all three cohorts, most patients were covered by Medicare Advantage plans (FL: 67.8%; WM: 82.4%; MZL: 74.5%) and roughly half were female (FL: 52.0%; WM: 42.4%; MZL: 53.2%). Patients across the three cohorts were similar in terms of their mean CCI scores (FL: 1.0; WM: 1.2; MZL: 1.1) and mean NCICI scores (FL: 1.8; WM: 2.2; MZL: 2.1). Cardiovascular disease, diabetes, chronic obstructive pulmonary disease, peripheral vascular disease, and renal disease were common comorbidities across all three cohorts.
Table 1.
Baseline demographic and clinical characteristics among patients with indolent malignancies
| FL | WM | MZL | |
|---|---|---|---|
| (N = 2805) | (N = 791) | (N = 2207) | |
| Demographics | |||
| Age at the index date (years), mean ± SD | 68.4 ± 12.7 | 73.8 ± 10.3 | 70.6 ± 11.5 |
| Female, n (%) | 1458 (52.0%) | 335 (42.4%) | 1175 (53.2%) |
| Insurance category | |||
| Medicare | 1901 (67.8%) | 652 (82.4%) | 1645 (74.5%) |
| Commercial | 904 (32.2%) | 139 (17.6%) | 562 (25.5%) |
| Insurance typea | |||
| POS | 640 (22.8%) | 95 (12.0%) | 381 (17.3%) |
| Multiple | 380 (13.5%) | 122 (15.4%) | 317 (14.4%) |
| HMO | 374 (13.3%) | 99 (12.5%) | 312 (14.1%) |
| PPO | 152 (5.4%) | 59 (7.5%) | 126 (5.7%) |
| EPO | 89 (3.2%) | 10 (1.3%) | 62 (2.8%) |
| IND | 27 (1.0%) | 9 (1.1%) | 21 (1.0%) |
| Otherb | 1143 (40.7%) | 397 (50.2%) | 988 (44.8%) |
| Regionc | |||
| South | 1097 (39.1%) | 267 (33.8%) | 807 (36.6%) |
| Midwest | 715 (25.5%) | 178 (22.5%) | 585 (26.5%) |
| West | 647 (23.1%) | 229 (29.0%) | 544 (24.6%) |
| Northeast | 333 (11.9%) | 116 (14.7%) | 268 (12.1%) |
| Unknown | 13 (0.5%) | 1 (0.1%) | 3 (0.1%) |
| Race/ethnicity | |||
| White | 1917 (68.3%) | 636 (80.4%) | 1658 (75.1%) |
| Hispanic | 286 (10.2%) | 50 (6.3%) | 201 (9.1%) |
| Black | 169 (6.0%) | 50 (6.3%) | 180 (8.2%) |
| Asian | 81 (2.9%) | 23 (2.9%) | 65 (2.9%) |
| Unknown | 352 (12.5%) | 32 (4.0%) | 103 (4.7%) |
| Comorbidities | |||
| CCI, mean ± SD | 1.0 ± 1.4 | 1.2 ± 1.4 | 1.1 ± 1.5 |
| NCICI, mean ± SD | 1.8 ± 2.1 | 2.2 ± 2.2 | 2.1 ± 2.3 |
| Cardiovascular disease | 728 (26.0%) | 274 (34.6%) | 677 (30.7%) |
| Diabetes | 717 (25.6%) | 175 (22.1%) | 568 (25.7%) |
| Chronic obstructive pulmonary disease | 559 (19.9%) | 219 (27.7%) | 576 (26.1%) |
| Peripheral vascular disease | 426 (15.2%) | 167 (21.1%) | 415 (18.8%) |
| Renal disease | 409 (14.6%) | 175 (22.1%) | 347 (15.7%) |
CCI Charlson Comorbidity Index, EPO exclusive provider organization, FL follicular lymphoma, HMO health maintenance organization, IND indemnity, MZL marginal zone lymphoma, NCICI National Cancer Institute Comorbidity Index, POS point of service, PPO preferred provider organization, SD standard deviation, WM Waldenström macroglobulinemia
aExclusive Provider Organization (EPO): services are covered, but only if patients use healthcare providers and facilities in the plan’s network (except in an emergency); Health Maintenance Organization (HMO): limits coverage to care from doctors who work for or contract with the HMO (except in an emergency), requires you to live or work in its service area to be eligible for coverage, and provides integrated care and focus on prevention and wellness; Indemnity (IND): also referred to as “fee-for-service,” this plan will reimburse an enrollee for a portion of their healthcare costs, regardless of what medical provider the member uses or how much the provider bills; Point of Service (POS): patients pay less for health care providers and facilities that belong to the plan’s network, but require a referral from a primary care doctor to see a specialist; Preferred Provider Organization (PPO): patients pay less for providers in the plan’s network, and can use doctors, hospitals, and providers outside of the network without a referral for an additional cost [23, 24]
bPatients with an insurance type of “other” are primarily Medicare Advantage patients
cRegions were defined based on the Census Regions and Divisions of the USA [25]
Treatment Patterns
Across all three cohorts, approximately 20–40% of the total sample received treatments in 1L (FL: 39.0%, N = 1095; WM: 24.8%, N = 196; MZL: 23.1%, N = 510; Fig. 1, Supplementary Table S1). Of these eligible patients, a third or fewer had treatments in 2L (FL: 21.6%; WM: 33.2%; MZL: 22.7%), while 10% or fewer had treatments in 3L (FL: 4.8%; WM: 10.2%; MZL: 7.3%). The median treatment duration decreased by LOT for FL (134, 99, and 63 days in 1L, 2L, and 3L, respectively), while there was no clear pattern for WM (97, 119, and 84 days in 1L, 2L, and 3L, respectively) or MZL (109, 112, and 56 days in 1L, 2L, and 3L, respectively).
Fig. 1.
Treatment patterns, including regimens used and duration of treatment lines by LOT among patients with indolent malignanciesa,b,c. 1L first line, 2L second line, 3L third line, CVP cyclophosphamide-vincristine with or without prednisone, LOT line of therapy, R-CHOP rituximab-cyclophosphamide-hydroxydaunorubicin hydrochloride-vincristine with or without prednisone, SCT stem cell transplant, SD standard deviation. aDuration by line summarizes the days of treatment observed in the data and does not account for censoring. bRadiation and corticosteroid claims were not used to define lines of treatment. cThe five most common regimens in each LOT are listed for each malignancy.
The most common regimens across all three cohorts, particularly in 1L, were rituximab monotherapy (FL: 25.9%; WM: 30.1%; MZL: 48.4%) and bendamustine-based therapies (FL: 41.0%; WM: 28.1%; MZL: 24.3%). R-CHOP or similar regimens were more common in FL (especially in 1L: 21.5%), while ibrutinib-based therapies were more common in WM (especially in 2L: 20.0%) and MZL (especially in 3L: 16.2%). Bortezomib-based therapies were most common in WM (especially in 2L: 20.0% and 3L: redacted due to small sample size). Among patients with FL in later LOTs, there was a relative increase in the use of rituximab monotherapy in 2L and 3L (47.0% and 50.9%, respectively), and a decrease in the use of bendamustine-based therapies (2L: 14.4%; 3L: 13.2%) and R-CHOP (2L: 9.7%; 3L: redacted due to small sample size).
Among patients with WM in later LOTs, the use of bendamustine-based therapies decreased. Instead, the predominant regimens in 2L were rituximab monotherapy (33.8%), ibrutinib-based therapies (20.0%), and bortezomib-based therapies (20.0%), while the most common regimen in 3L was rituximab monotherapy (40.0%). Among patients with MZL in later LOTs, rituximab monotherapy continued to be the most common regimen (2L: 33.6%; 3L: 51.4%). The use of bendamustine-based therapies decreased across lines (1L: 24.3%; 2L: 20.7%; 3L: 13.5%), while the use of ibrutinib-based therapies became more common (1L: 9.2%; 2L: 12.9%; 3L: 16.2%).
HRU and Healthcare Costs
Overall Study Period
In terms of the number of visits PPM, outpatient visits were the most common type of all-cause visit (FL: 2.48; WM: 2.52; MZL: 2.4) and disease-related visit (FL: 0.82; WM: 0.66; MZL: 0.54) across all three cohorts (Table 2). All-cause total healthcare costs were a mean ± SD [median] of US $12,402 ± $25,021 [US $5766] PPPM for patients with FL, US $8445 ± $14,881 [US $3435] PPPM for patients with WM, and US $9580 ± 20,545 [US $3833] PPPM for patients with MZL (Table 2). While disease-related HRU constituted only a fraction of all-cause HRU across all three cohorts, disease-related medical costs constituted > 50% of all-cause total healthcare costs (Table 2). Across all three cohorts, the majority of hospitalization costs were disease related.
Table 2.
All-cause and disease-related HRU and costs among patients with indolent malignancies during the study period
| FL | WM | MZL | |
|---|---|---|---|
| (N = 2805) | (N = 791) | (N = 2207) | |
| All -cause HRU rates per patient-month (95% CI) | |||
| Hospitalizations | 0.07 (0.07, 0.07) | 0.08 (0.07, 0.08) | 0.06 (0.06, 0.07) |
| Hospitalization days | 0.32 (0.32, 0.33) | 0.35 (0.35, 0.36) | 0.31 (0.31, 0.31) |
| Outpatient visits | 2.48 (2.47, 2.49) | 2.52 (2.50, 2.54) | 2.4 (2.39, 2.41) |
| ED visits | 0.05 (0.05, 0.05) | 0.05 (0.05, 0.06) | 0.05 (0.05, 0.05) |
| Other visitsa | 0.18 (0.18, 0.18) | 0.25 (0.24, 0.26) | 0.19 (0.18, 0.19) |
| Disease-related HRU rates per patient-month (95% CI) | |||
| Hospitalizations | 0.03 (0.03, 0.03) | 0.03 (0.03, 0.03) | 0.02 (0.02, 0.02) |
| Hospitalization days | 0.17 (0.17, 0.17) | 0.17 (0.17, 0.18) | 0.13 (0.12, 0.13) |
| Outpatient visits | 0.82 (0.82, 0.83) | 0.66 (0.65, 0.67) | 0.54 (0.54, 0.55) |
| ED visits | 0.01 (0.01, 0.01) | 0.01 (0.01, 0.01) | 0 (0.00, 0.00) |
| Other visitsa | 0.03 (0.03, 0.03) | 0.05 (0.04, 0.05) | 0.02 (0.02, 0.02) |
| All-cause costs (USD), PPPM, mean ± SD [median] | |||
| Total overall costs | 12,402 ± 25,021 [5766] | 8445 ± 14,881 [3435] | 9,580 ± 20,545 [3833] |
| Total medical costs | 12,060 ± 24,954 [5430] | 7782 ± 14,616 [2902] | 9089 ± 20,460 [3462] |
| Hospitalization costs | 5837 ± 22,621 [0] | 4222 ± 12,283 [45] | 5132 ± 18,404 [0] |
| Outpatient costs | 5820 ± 7666 [3,003] | 3101 ± 5323 [1195] | 3630 ± 6458 [1789] |
| ED costs | 256 ± 1088 [0] | 243 ± 864 [0] | 178 ± 634 [0] |
| Other costs | 148 ± 980 [3] | 216 ± 1796 [7] | 149 ± 1148 [4] |
| Pharmacy costs | 341 ± 1165 [39] | 663 ± 2006 [29] | 491 ± 1690 [24] |
| Disease-related costs, (USD), PPPM, mean ± SD [median] | |||
| Total medical costs | 8291 ± 21,807 [2606] | 4669 ± 11,755 [564] | 4969 ± 15,731 [978] |
| Hospitalization costs | 4159 ± 20,514 [0] | 2732 ± 10,604 [0] | 3100 ± 15,243 [0] |
| Outpatient costs | 4005 ± 6052 [1457] | 1856 ± 4112 [261] | 1813 ± 3488 [504] |
| ED costs | 95 ± 907 [0] | 68 ± 733 [0] | 46 ± 483 [0] |
| Other costs | 33 ± 235 [0] | 13 ± 65 [0] | 10 ± 68 [0] |
CI confidence interval, ED emergency department, FL follicular lymphoma, HRU healthcare resource utilization, MZL marginal zone lymphoma, PPPM per-patient-per-month, SD standard deviation, USD United States dollars, WM Waldenström macroglobulinemia
aThe most common places of service for visits classified as “other” are independent laboratory, unspecified, other unlisted facility, skilled nursing facility, and nursing facility
Stratified by LOT
All-Cause and Disease-Related HRU
In FL, all-cause and disease-related hospitalizations and hospital length of stay tended to increase with increasing lines (Table 3), while other HRU categories were the highest in either 1L or 2L and tended to be the lowest in 3L+. In WM, all-cause HRU was the highest in 1L, while disease-related HRU was highest in 2L and lowest in 3L+. In MZL, HRU tended to be highest in 2L and lowest in 3L+, except disease-related hospitalizations and other visits, which were the same across lines, and disease-related hospital length of stay, which was highest in 1L.
Table 3.
All-cause and disease-related HRU per patient-month among patients with indolent malignancies, stratified by LOT
| Follicular lymphoma | 1L | 2L | 3L+ |
|---|---|---|---|
| (N = 1095) | (N = 236) | (N = 53) | |
| All-cause HRU rates per patient-month (95% CI) | |||
| Hospitalizations | 0.06 (0.06, 0.07) | 0.09 (0.08, 0.09) | 0.09 (0.07, 0.10) |
| Hospitalization days | 0.27 (0.26, 0.28) | 0.46 (0.44, 0.48) | 0.48 (0.45, 0.51) |
| Outpatient visits | 2.70 (2.68, 2.72) | 2.83 (2.78, 2.88) | 2.63 (2.55, 2.71) |
| ED visits | 0.05 (0.05, 0.05) | 0.07 (0.06, 0.08) | 0.06 (0.05, 0.07) |
| Other visitsa | 0.17 (0.17, 0.18) | 0.14 (0.13, 0.16) | 0.12 (0.10, 0.14) |
| Disease-related HRU rates per patient-month (95% CI) | |||
| Hospitalizations | 0.02 (0.02, 0.03) | 0.03 (0.03, 0.04) | 0.03 (0.02, 0.03) |
| Hospitalization days | 0.13 (0.13, 0.14) | 0.24 (0.22, 0.25) | 0.30 (0.27, 0.33) |
| Outpatient visits | 0.94 (0.93, 0.95) | 0.83 (0.80, 0.86) | 0.65 (0.61, 0.69) |
| ED visits | 0.01 (0.01, 0.01) | 0.01 (0.01, 0.01) | 0.00 (0.00, 0.01) |
| Other visitsa | 0.03 (0.03, 0.03) | 0.02 (0.02, 0.03) | 0.02 (0.01, 0.03) |
| Waldenström macroglobulinemia | 1L | 2L | 3L+ | ||
|---|---|---|---|---|---|
| (N = 196) | (N = 65) | (N = 20) | |||
| All-cause HRU rates per patient-month (95% CI) | |||||
| Hospitalizations | 0.07 (0.06, 0.08) | 0.08 (0.06, 0.09) | 0.13 (0.10, 0.16) | ||
| Hospitalization days | 0.34 (0.33, 0.36) | 0.33 (0.30, 0.36) | 0.58 (0.52, 0.64) | ||
| Outpatient visits | 2.74 (2.70, 2.79) | 2.77 (2.68, 2.85) | 3.62 (3.48, 3.77) | ||
| ED visits | 0.04 (0.04, 0.05) | 0.03 (0.02, 0.04) | 0.09 (0.07, 0.11) | ||
| Other visitsa | 0.25 (0.24, 0.26) | 0.27 (0.24, 0.29) | 0.87 (0.80, 0.94) | ||
| Disease-related HRU rates per patient-month (95% CI) | |||||
| Hospitalizations | 0.03 (0.03, 0.04) | 0.04 (0.03, 0.05) | 0.02 (0.01, 0.03) | ||
| Hospitalization days | 0.20 (0.19, 0.22) | 0.25 (0.22, 0.27) | 0.12 (0.09, 0.14) | ||
| Outpatient visits | 0.88 (0.86, 0.91) | 1.01 (0.96, 1.06) | 0.83 (0.76, 0.90) | ||
| ED visits | 0.01 (0.01, 0.01) | 0.01 (0.00, 0.01) | 0 (0.00, 0.01) | ||
| Other visitsa | 0.05 (0.04, 0.05) | 0.10 (0.09, 0.12) | 0.04 (0.02, 0.05) | ||
| Marginal zone lymphoma | 1L | 2L | 3L+ | ||
|---|---|---|---|---|---|
| (N = 510) | (N = 116) | (N = 37) | |||
| All-cause HRU rates per patient-month (95% CI) | |||||
| Hospitalizations | 0.06 (0.05, 0.06) | 0.07 (0.06, 0.08) | 0.03 (0.02, 0.04) | ||
| Hospitalization days | 0.28 (0.27, 0.29) | 0.35 (0.33, 0.38) | 0.17 (0.15, 0.19) | ||
| Outpatient visits | 2.56 (2.53, 2.58) | 2.98 (2.92, 3.05) | 2.39 (2.31, 2.47) | ||
| ED visits | 0.04 (0.04, 0.04) | 0.05 (0.04, 0.06) | 0.03 (0.02, 0.04) | ||
| Other visitsa | 0.17 (0.16, 0.18) | 0.25 (0.23, 0.27) | 0.20 (0.18, 0.22) | ||
| Disease-related HRU rates per patient-month (95% CI) | |||||
| Hospitalizations | 0.01 (0.01, 0.02) | 0.01 (0.01, 0.01) | 0.01 (0.00, 0.02) | ||
| Hospitalization days | 0.10 (0.10, 0.11) | 0.08 (0.07, 0.09) | 0.06 (0.04, 0.07) | ||
| Outpatient visits | 0.54 (0.53, 0.56) | 0.56 (0.53, 0.59) | 0.49 (0.45, 0.53) | ||
| ED visits | 0 (0.00, 0.00) | 0.01 (0.00, 0.01) | 0 (0.00, 0.00) | ||
| Other visitsa | 0.02 (0.02, 0.03) | 0.02 (0.01, 0.02) | 0.02 (0.01, 0.03) | ||
1L first line, 2L second line, 3L+ third and subsequent lines, CI confidence interval, ED emergency department, HRU healthcare resource utilization, LOT line of therapy
aThe most common places of service for visits classified as “other” are independent laboratory, unspecified, other unlisted facility, skilled nursing facility, and nursing facility
All-Cause and Disease-Related Healthcare Costs
Across all diseases, all-cause hospitalization costs tended to increase with increasing lines, contributing to a larger share of total costs from 1L to 3L (Fig. 2). Disease-related hospitalization costs varied across diseases, generally increasing with LOTs in FL, being highest in 2L in WM and in 1L in MZL (Fig. 3). In FL, disease-related hospitalization costs constituted greater proportions of total disease-related medical costs with increasing lines (27.2–62.2%) (Figs. 2, 3 and Supplementary Table S2). Both all-cause and disease-related outpatient and ED costs were lowest in 3L for all diseases (Figs. 2, 3, and Supplementary Tables S2–4). All-cause pharmacy costs tended to increase with increasing LOTs for FL and WM (Fig. 2, Supplementary Tables S2 and S3). For MZL, they were highest in 2L and lowest in 1L (Fig. 2, Supplementary Table S4).
Fig. 2.
Line-specific all-cause costs for indolent malignanciesa. 1L first line, 2L second line, 3L+ third and subsequent lines, ED emergency department, FL follicular lymphoma, MZL marginal zone lymphoma, OP outpatient, WM Waldenström macroglobulinemia. aThe most common places of service for visits classified as “other” are independent laboratory, unspecified, other unlisted facility, skilled nursing facility, and nursing facility
Fig. 3.
Line-specific disease-related costs for indolent malignanciesa. 1L first line, 2L second line, 3L+ third and subsequent lines, ED emergency department, FL follicular lymphoma, MZL marginal zone lymphoma, OP outpatient, WM Waldenström macroglobulinemia. aThe most common places of service for visits classified as “other” are independent laboratory, unspecified, other unlisted facility, skilled nursing facility, and nursing facility
Discussion
The present study demonstrated a high HRU and cost burden among patients with three types of indolent NHLs in the USA, including FL, WM, and MZL. Across all these indolent NHLs, rates of HRU and healthcare costs were substantial during the overall study period. Notably, outpatient visits were frequent while more than half of total healthcare costs were driven by disease-related medical costs, primarily due to hospitalizations. Among patients with FL, there was a trend toward incremental increase in this HRU and cost burden by LOT. In particular, disease-related hospitalization costs were higher during each successive LOT and constituted increasing proportions of total all-cause costs. Trends in HRU and costs across LOTs were less clear for WM and MZL. Potential reasons for this may include the clinical heterogeneity of the patient populations and the presence of more outliers for costs during the 1L period relative to subsequent lines. Additionally, results for later lines were based on small sample sizes (e.g., 20 patients with WM and 37 patients with MZL received three or more lines), making it difficult to draw meaningful conclusions.
The demographic and clinical characteristics of patients in the present study appeared generally consistent with the prior literature. For instance, in a Surveillance, Epidemiology and End Results (SEER) database study of 68,059 adult patients with indolent NHLs diagnosed in the USA between 2000 and 2017 [26], the median age at diagnosis was 64 years (IQR 54–73 years), most patients were white (86%), and half of patients were female. In claims-based studies among patients with FL in the USA (study periods spanning between 2010 and 2016), patients were in their 60s on average, about half of patients were female, and common comorbidities included other malignancies, diabetes, chronic obstructive pulmonary disease, and cardiovascular diseases [10, 15]. However, recent studies describing the characteristics of patients with FL, MZL, and WM in the USA remain limited. Accordingly, the present study offers a contemporary description of these three patient populations in real-world clinical practice in the USA.
In terms of treatment patterns, the study results highlight the wide range of NCCN-recommended systemic therapies currently used among patients with indolent NHLs in real-world clinical practice. Nonetheless, the economic burden observed in our study suggests that current treatments may be suboptimal. In FL, our results reveal a possible gap, as only three treatments dominate, namely bendamustine-based therapies, rituximab monotherapy, and R-CHOP regimens. In WM and MZL, we see more use of additional treatment options, such as ibrutinib-based and bortezomib-based therapies, although there is still scope for novel treatments given the high burden among patients. The trend toward increasing medical costs across LOTs, particularly among patients with FL, likely reflects the fact that these slow-growing malignancies are difficult to cure with systemic therapy and often relapse after remission [14, 27]. With each relapse, disease progression and complications may necessitate further HRU, which is likely to impact costs [14]. Thus, our findings highlight the need for more effective treatment options among patients with indolent NHLs that may improve clinical outcomes and mitigate the economic burden of disease from a healthcare payer perspective. That said, we acknowledge that other factors besides the efficacy of NCCN-recommended systemic therapies might have contributed to the high HRU and costs observed among patients in the present study. For instance, patients with indolent NHLs often require chronic systemic therapy, which is associated with complications that are costly to manage [28].
Among patients with FL in the present study, rituximab monotherapy and bendamustine-based therapies were the most common regimens in 1L and 2L followed by R-CHOP, which is generally consistent with the results of prior retrospective studies of FL populations [13, 15]. This treatment pattern in turn reflects preferred 1L and 2L regimens for FL per the NCCN Guidelines, including multiagent chemoimmunotherapy regimens for patients who are able to tolerate it and rituximab monotherapy for patients who are elderly/infirm and in extended therapy [9]. Among patients with WM overall in the present study, the most common regimens were rituximab monotherapy, bendamustine-based therapies, ibrutinib-based therapies, and bortezomib-based therapies, which is consistent with the NCCN Guidelines [12]. These treatment patterns are also consistent with prior real-world studies showing that the proportion of patients with WM receiving bendamustine or bortezomib has significantly increased between 2008 and 2014, replacing classical chemoimmunotherapy [29]. Nonetheless, prior real-world studies have also observed a strong reliance on rituximab monotherapy, particularly among older patients with WM [22, 29]. Among the MZL sample, the most common regimen in 1L and 2L was rituximab monotherapy followed by bendamustine-based therapies, with an increasing use of ibrutinib-based therapies in later lines. These patterns are generally consistent with the recommended treatments for MZL per NCCN Guidelines [11] as well as a small number of real-world studies [19, 30].
Since the end of the current study period, there have been advances in therapeutics for FL, WM, and MZL that may not have been fully captured by the present analysis. For instance, recent advances in FL therapeutics include the approval of the CAR-T agents axicabtagene ciloleucel in 2021 [31] and tisagenlecleucel in 2022 [32] for relapsed/refractory (R/R) patients who have failed on ≥ 2 LOTs. Since CAR-T and other novel agents (e.g., ibrutinib) were scarcely utilized in the present FL study population (i.e., by < 1.0% of the sample altogether, who may have received them in the context of clinical trials), further research is needed to assess their impact in contemporary FL clinical practice. More recently, several novel agents have also become available for the treatment of R/R MZL, including lenalidomide in combination with rituximab, the second-generation covalent BTK inhibitor, zanubrutinib, and oral kinase inhibitor, umbralisib [33]. However, we note that the approval of umbralisib for MZL was subsequently withdrawn by the US Food and Drug Administration (FDA) in 2022 due to safety concerns [34]. Despite these recent therapeutic advances for FL, WM, and MZL, the consensus is that there remains an unmet need for treatments that are of fixed duration and can provide long-term efficacy while minimizing toxicities and costs among patients [35, 36].
The current study observed a substantial economic burden of FL overall that is generally in agreement with previous reports [13–16]. In one study by Ren et al. [13] using MarketScan (Truven) administrative claims data (January 2007–December 2015), total all-cause healthcare costs were considerable (US $10,192 PPPM on average) among patients with FL within 1 year after initial diagnosis [13]. In a pattern consistent with the present study, medical costs accounted for the largest portion (56%) of these total costs (US $5731 PPPM on average). Also consistent with the present findings, a study by Morrison et al. [16] using data from the CDM database (January 2008 to October 2015) observed substantial mean all-cause healthcare costs among newly diagnosed patients with FL over ≤ 2 years of follow-up (US $10,460 PPPM), which were largely composed of medical costs (US $10,147 PPPM). Likewise, FL-related medical costs accounted for almost 63% of mean medical costs (US $6390 PPPM) over the follow-up period.
Although very few studies have assessed the economic burden of FL by LOT, our results are generally aligned with a small existing body of literature, which observed increasing costs among patients over the course of their disease. For instance, a targeted literature review of the economic burden of FL across different countries found that the monthly cost of treating FL without disease progression was US $860 while that of treating FL with disease progression was US $3527 [17]. A US-based study by Fowler et al. [15] using data from MarketScan® databases (January 2010 to December 2013) reported an incremental increase in all-cause total healthcare costs PPPM with each successive LOT (1L: US $8093; 2L: US $10,466; 3L: US $19,935; 4L: US $30, 883; 5L: US $35,397) among patients with FL over ≥ 4 years of follow-up. Moreover, these substantial costs were largely driven by medical costs. Another study by Albarmawi et al. [14] used the linked SEER-Medicare dataset to identify patients aged 66 years or older who were diagnosed with NHL between 2007 and 2013. From a Medicare payer perspective (inflated to 2015 US dollars), elderly patients with FL in this study incurred high direct medical costs in the first 3 years after diagnosis. Furthermore, FL was found to be less costly than other NHL subtypes in the first year only and became more expensive in the second and third years. This is generally aligned with our findings that high total healthcare costs among patients with FL were driven by medical costs, and these costs tended to increase over time with each successive LOT.
The specific drivers of high medical costs among patients with FL in previous studies were largely consistent with those of the present study. In prior studies, a large portion of medical costs were attributable to reimbursements for the dispensing and administration of chemotherapy and other injectable drugs, which were largely performed in an outpatient setting [14–16]. In our study, outpatient visits accounted for the greatest proportion of HRU visits among patients with FL across LOTs and were the main cost drivers in 1L and 2L. We note that in 3L, hospitalization costs became the predominant costs in FL in the present study. Although the reasons for this are unclear, chemotherapy may also be administered in a hospital setting should a patient be at greater risk of adverse events and complications. Thus, the high hospitalization costs in 3L may reflect a greater number of patients who required chemotherapy administration in a hospital setting or who required inpatient care for complications associated with FL in later LOTs.
To date, a small number of prior claims-based studies have assessed the economic burden of WM overall in the USA [18, 21, 22]. In a SEER study of Medicare beneficiaries diagnosed with WM, the mean annual treatment cost within 1 year of diagnosis among those initiated on chemotherapy was US $48,199 from 2009 to 2011 [21], which is consistent with the high economic burden in 1L in the present study. Likewise, another retrospective US-based claims study of patients with newly diagnosed WM (January 2007–October 2013) [22] reported mean total all-cause healthcare costs in the 1L setting that were generally aligned with those observed in the present sample (US $13,589 PPPM versus US $15,834 PPPM). In contrast, Yang et al. [18] reported an increase in mean total all-cause healthcare costs across LOTs among patients with WM (IBM MarketScan data from 2014 to 2019), whereas the present study observed a decrease in all-cause total costs across LOTs among patients with WM.
With regard to MZL, the overall HRU and costs in our sample appear to differ from those reported in the prior literature. In a retrospective, observational study by Yang et al. [19] (IBM MarketScan® data from 2017–2020), newly diagnosed adult patients with MZL exhibited high rates of HRU, particularly for outpatient services. However, mean total all-cause healthcare costs (US $19,896 PPPM) were higher than those obtained in the present study (US $9580 PPPM). When assessing patients with MZL across LOTs among commercial and Medicare Advantage plan members from 2008 to 2018, Clancy et al. [30] found that the mean total healthcare costs were substantial (ranging from US $13,000 to US $18,000 PPPM depending on LOT and regimen), with no clear pattern across LOTs due to highly variable results (large IQRs).
Overall, the results obtained among patients with WM and MZL in the present study display higher variability than in FL. Furthermore, the findings among patients with WM and MZL are less consistent across the present study and prior studies. Aside from the smaller sample sizes, several other factors may have contributed to these discrepancies. First, WM and MZL are both clinically heterogeneous diseases with different subtypes [37]. Thus, future research is needed to better understand the treatment patterns and economic burden among these different subtypes. Second, the identification of patients with MZL is more complex since there are no disease-specific claims codes for it. Therefore, differences in identification methods across study might have contributed to divergent results.
Taken together, the present study adds to the literature by summarizing the current treatment landscape for these indolent NHLs using more recent claims data. Prior studies rely on older claims data that may not accurately reflect the current burden of indolent NHLs or the contemporary treatment landscape for these conditions. In addition, the current study reports all-cause- and disease-specific costs, whereas the prior literature only reports the all-cause burden for patients. For WM and MZL, the current results expand on recent treatment patterns and contribute important findings regarding the economic burden of disease to the sparse body of literature on these two rare malignancies.
However, the present study was also subject to certain limitations. First, because the study period spanned from 2015 to 2021, the results do not reflect the impact of newer treatments (e.g., CAR-T, Bruton’s tyrosine kinase inhibitors) that have been increasingly adopted in recent years. Second, since the analysis relied on administrative claims data, which are intended for reimbursement purposes rather than research use, coding practices that cater to reimbursement may have led to misclassification of patients or healthcare encounters. This is especially relevant for MZL, as it does not have a specific ICD-10-CM code. While the proxies used to identify patients with MZL were informed by the literature, there is a possibility of misclassification and under- or over-diagnosis of MZL in this study. Similarly, disease-related HRU and costs may have been overestimated if the specific indolent NHL wasn’t the primary reason for the HRU visit identified based on its ICD codes. Conversely, any FL-, WM-, or MZL-related encounters that were miscoded would not be captured in this study, potentially underestimating HRU and cost burden. Third, claims data typically do not include lab values, imaging, treatment outcomes, or the reasons for absence of treatment or treatment discontinuation. Reasons for absence of treatment are especially pertinent given that the watchful wait approach may be recommended for patients with low-grade tumors with a slow progression.[9] Additionally, pharmacy dispensing may not include all drugs prescribed or represent the actual drug utilization of the patient. Fourth, payer paid amounts were derived based on “allowed” costs standardized by Optum, and not actual exact amounts paid by the payer. Therefore, paid amounts do not reflect costs from the payer’s perspective precisely, although they serve as an estimation. Fifth, the LOT-specific results should be interpreted with caution, as patients had to survive or remain insured to be able to initiate subsequent LOTs, and hence may be different from the broader disease population in terms of demographic and clinical characteristics and disease trajectories. Lastly, analyses are limited to patients with commercial or Medicare insurance coverage from 2015 to 2021, and thus findings may not be generalizable to other patient populations, such as those without health insurance.
Conclusions
The results of this analysis suggest that the indolent NHL subtypes, FL, WM, and MZL, are associated with a significant burden on patients and payers, in both medical resource usage and healthcare costs, which tends to increase with each successive LOT among patients with FL. These findings speak to the need for more effective therapeutic options that could be curative or slow disease progression among patients with indolent NHLs. In addition, a better understanding of prognostic factors and optimal treatment sequencing is warranted to improve clinical management practices for patients with indolent NHLs [9]. For WM and MZL, the current results expand on recent treatment patterns and contribute important findings regarding the economic burden of disease to the sparse body of literature. Future studies are needed to address other potential research gaps such as describing HRU and costs for specific treatments by LOT and conducting adjusted comparisons of economic burden across treatments.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
Medical writing assistance was provided by professional medical writer, Mona Lisa Chanda, PhD, a former employee of Analysis Group, Inc., a consulting company that provided paid consulting services to Merck & Co. Inc. The authors thank Katherine Gaburo, who was an employee of Analysis Group, Inc. at the time this study was conducted, for her assistance with data analysis. We also thank Samhita Chakraborty, who was an employee of Merck & Co. Inc. at the time of this study and manuscript development, for her clinical input.
Funding
This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co. Inc., Rahway, NJ, USA. The sponsor provided funding for the conduct of this study and the preparation and publication of this manuscript.
Declarations
Conflict of Interest
Shravanthi Gandra is an employee of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, and owns stock/stock options in Merck & Co., Inc., Rahway, NJ, USA. At the time this study was conducted, Mahek Garg was an employee of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, and owned stock/stock options in Merck & Co., Inc., Rahway, NJ, USA. Ambika Satija, Benjamin Meade, Yan Song, and James Signorovitch are employees of Analysis Group, Inc., a consulting company that provided paid consulting services to Merck & Co., Inc., which funded the development and conduct of this study and manuscript.
Ethics Approval
Data were deidentified and comply with the patient requirements of the HIPAA of 1996. No review by an institutional review board was required per Title 45 of CFR, Part 46.101(b)(4) (https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/#46.101). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.
Consent to Participate
Not applicable. Data were deidentified and comply with the patient requirements of the HIPAA of 1996, therefore, consent to participate was not required.
Consent for Publication
Not applicable. Data were deidentified and comply with the patient requirements of the HIPAA of 1996, therefore, consent for publication was not required.
Data Availability Statement
The data that support the findings of this study are available from Optum. Restrictions apply to the availability of these data, which were used under license for this study. Permission to access these data must be requested from Optum (https://www.optum.com/).
Code Availability
Not applicable.
Author Contributions
Mahek Garg and Shravanthi Gandra contributed to study conception and design, data analysis, and interpretation. Ambika Satija, Yan Song, and James Signorovitch contributed to study conception and design, collection and assembly of data, data analysis, and interpretation. Benjamin Meade contributed to collection and assembly of data, data analysis, and interpretation. All authors read and approved the final version and agree to be accountable for the work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from Optum. Restrictions apply to the availability of these data, which were used under license for this study. Permission to access these data must be requested from Optum (https://www.optum.com/).