Original Article

# Economic Burden in Non-Hodgkin Lymphoma Survivors:
The French Lymphoma Study Association SIMONAL
Cross-Sectional Study

Virginie Nerich, PharmD, PhD ID ¹,²; Christophe Guyeux, PhD³; Michel Henry-Amar, MD, PhD ID ⁴; Raphaël Couturier, PhD³; Catherine Thieblemont, MD, PhD⁵; Vincent Ribrag, MD, PhD⁶; Hervé Tilly, MD, PhD⁷; Corinne Haioun, MD, PhD⁸;

René-Olivier Casasnovas, MD, PhD⁹; Franck Morschhauser, MD, PhD¹⁰; Pierre Feugier, MD, PhD¹¹; David Sibon, MD, PhD¹²; Loic Ysebaert, MD, PhD ID ¹³; Emmanuelle Nicolas-Virelizier, MD, PhD¹⁴; Florence Broussais-Guillaumot, MD¹⁵;

Gandhi L. Damaj, MD, PhD¹⁶; Jean-Philippe Jais, MD, PhD¹⁷; Gilles Salles, MD, PhD ID ¹⁸,¹⁹;

Macha Woronoff-Lemsi, PharmD, PhD²,²⁰; and Nicolas Mounier, MD, PhD²¹

**BACKGROUND:** No study has focused on the economic burden in non-Hodgkin lymphoma (NHL) survivors, even though this knowledge is essential. This study reports on health care resource use and associated health care costs as well as related factors in a series of 1671 French long-term NHL survivors. **METHODS:** Health care costs were measured from the payer perspective. Only direct medical costs (medical consultations, outpatient treatments, hospitalizations, and medical transport) in the past 12 months were included (reference year 2015). Multiple linear regression was used to search for explanatory factors of health care costs. **RESULTS:** In total, 1100 survivors (66%) reported having used at least 1 health care resource, and 867 (52%) reported having used at least 1 outpatient treatment. After the authors accounted for missing data, the mean health care cost was estimated at €702 ± €2221. Hospitalizations and outpatient treatments were the main cost drivers. Sensitivity analyses confirmed the robustness of the results. For the 1100 survivors who reported using at least 1 health care resource, the mean health care cost was €1067 ± €2268. Several factors demonstrated statistically significant relationships with health care costs. For instance, cardiovascular disorders increased costs by 66% ± 16%. In contrast, rituximab or autologous stem cell transplantation as initial therapy had no effect on health care costs. **CONCLUSIONS:** The consideration of economic constraints in health care is now a reality. This retrospective study reports on a better understanding of health care resource use and associated health care costs as well as related factors. It may help health care professionals in their ongoing efforts to design person-centered health care pathways. **Cancer 2022;128:519-528. © 2021 American Cancer Society.**

**KEYWORDS:** cancer survivors, explanatory factors, health care cost, health care resource use, non-Hodgkin lymphoma, rituximab.

## INTRODUCTION

Non-Hodgkin lymphomas (NHLs) are the most common hematologic malignancies in France, with more than 27,000 new cases diagnosed in 2018.¹ The modern approach with intensive chemotherapy, including autologous stem cell transplantation (ASCT) combined with targeted therapy such as rituximab, has contributed to improving patients' prognosis and survival while increasing NHL-related health care expenditures.²⁻⁴

**Corresponding Author:** Virginie Nerich, PharmD, PhD, Department of Pharmacy, University Hospital, Boulevard Fleming, 25030 Besançon Cedex, France (v1nerich@chu-besancon.fr).

¹Department of Pharmacy, University Hospital, Besançon, France; ²INSERM, EFS-BFC, UMR1098, University of Franche-Comté, Besançon, France; ³Femto-ST Institute, UMR 6174 CNRS, University of Bourgogne Franche-Comté, Besançon, France; ⁴French Center on eHealth, North-West Region Data Processing Center and French National League Against Cancer Clinical Research Platform, CCC François Baclesse, Caen, France; ⁵Hematology-Oncology Unit, Saint-Louis University Hospital Center, Public Hospital Network of Paris, Paris, France; ⁶Hematology Unit, Gustave Roussy Cancer Campus, Villejuif, France; ⁷Hematology Department and French Institute of Health and Medical Research Unit 1243, Henri Becquerel Center, Rouen, France; ⁸Lymphoid Malignancies Unit, Henri Mondor University Hospital Center, Public Hospital Network of Paris, Créteil, France; ⁹Hematology Unit and French Institute of Health and Medical Research Unit 1231, Bocage Hospital, Dijon Bourgogne Regional University Hospital Center, Dijon, France; ¹⁰Hematology Transfusion Institute, Claude Huriez Hospital, Lille Regional University Hospital Center, Lille, France; ¹¹Hematology Unit, Brabois Hospital, Nancy University Hospital Center, Vandoeuvre-lès-Nancy, France; ¹²Hematology Unit, Necker University Hospital for Sick Children, Public Hospital Network of Paris, Paris, France; ¹³Oncopole, Toulouse University Cancer Institute, Toulouse, France; ¹⁴Hematology Unit, Léon Bérard Center, Lyon, France; ¹⁵Clinical Research Department-Lymphoma Study Association, Lyon South Hospital Center, Pierre-Bénite, France; ¹⁶Basse-Normandy Hematology Institute, Côte de Nacre Regional University Hospital Center, Caen, France; ¹⁷Laboratory of Biostatistics, Paris V University-Descartes, Paris, France; ¹⁸Faculty of Medicine, Claude Bernard University, Lyon, France; ¹⁹Hematology Department, Lyon South Hospital Center, Pierre-Bénite, France; ²⁰Department of Clinical Research and Innovation, University Hospital, Besançon, France; ²¹Onco-Hematology Unit, l'Archet Hospital, Nice University Hospital Center, University of Côte d'Azur, Nice, France

We are grateful to all the survivors who participated in the survey, and we gratefully acknowledge Sabine Anthony (Clinical Research and Innovation Office, Cimiez Hospital, Nice University Hospital Center, University of Côte d'Azur, Nice, France) and Lilian Laborde (Paoli-Calmettes Institute-Provence-Alpes-Côte d'Azur Data Processing Center, Marseille, France) for their involvement in the management of the survey.

Additional supporting information may be found in the online version of this article.

**DOI:** 10.1002/cncr.33938 **Received:** June 8, 2021; **Revised:** August 19, 2021; **Accepted:** August 24, 2021, **Published online** October 4, 2021 in Wiley Online Library (wileyonlinelibrary.com)
Cancer is the leading cause of death in France and accounted for 29% of all deaths in 2016; its economic burden for the general health insurance fund (covering 57.6 million individuals, ie, nearly 90% of the population) has been estimated at 15.6 billion euros (B€) for 2.637 million patients (ie, more than 11% of medical expenses reimbursed in 2017).⁵,⁶ It represents the second cost item: its comes after psychiatric diseases (B€16.6 for 2.190 million patients) but before cardiovascular/neurovascular diseases (B€14.0 for 4.016 million patients). Reimbursed medical expenses for 1.189 million patients with cancer under treatment have been estimated at B€14.0 (ie, an annual average of €11,754 per patient), whereas expenses have been estimated at B€1.6 (ie, an annual average of €1058 per patient) for those (1.529 million) undergoing posttreatment follow-up. Among all health insurance schemes and therapeutic indications combined, rituximab expenditures reached €263.4 million in 2017, that is, almost 8% of the total expensive antineoplastic drug expenditure for inpatients (B€3.5; https://www.atih.sante.fr/).

The NHL-associated economic burden remains little known. Its evaluation is difficult because of the nature of the disease and its treatment variability; it is also difficult to compare the results of studies from different countries.⁷⁻¹⁹ To our knowledge, no study has focused on the economic burden in NHL survivors (who have at least 5 years of follow-up after treatment initiation), even though this knowledge is essential. Most survivors are elderly with a potential risk of comorbidity development related to their age and the disease itself (persistent and/or long-term adverse events such as fatigue, relapse, and secondary or new primary cancer).²⁰ Beyond the potential impact on survival, the health condition of NHL survivors may also affect health care resource use, and this translates into an economic burden.

In this context and in the context of rational decision-making in health care, we report on health care resource use (except for use related to relapsed NHL) and associated health care costs as well as related factors in a series of 1671 French long-term NHL survivors.²⁰

# MATERIALS AND METHODS

## Study Population

In 2015, the Lymphoma Study Association conducted a cross-sectional study of long-term (>5-year) survivors enrolled in 12 clinical studies conducted from 1993 to 2010. Of the 4361 French patients alive at their last examination, 3317 were eligible for the survey, and 1671

(50.4%) participated with a median follow-up of 11 years (range, 5-22 years) after the initial treatment of their lymphoma.¹⁹ The study was approved by French legal authorities, namely the Consultative Committee for Data Processing in Health Research and the National Commission on Informatics and Liberty (DR-204-252).

## Data Collection

Three self-administered questionnaires were used: the Life Situation Questionnaire to collect sociodemographic and social situation data, health situation data, post-treatment health disorders, and current treatments; the 20-item Multidimensional Fatigue Inventory to evaluate fatigue levels in 2015; and a questionnaire specifically designed to collect health care resource use by each long-term survivor in the past 12 months before the questionnaire was sent (eg, medical consultations, outpatient treatments, hospitalizations, and medical transport). Data were collected prospectively and stored in a secured database at the Lymphoma Study Association Academic Research Organization. To guarantee the quality of the analysis, the authors analyzed health care resource use by reviewing questionnaires to select resources fully provided and reimbursed by the French payer. Drugs were classified into therapeutic groups according to the Anatomical Therapeutic and Chemical (ATC) first- and second-level classification system.²¹

## Direct Health Care Cost

All expenses reimbursed by French Health Insurance for patients with cancer are covered at 100%. Health care costs were measured from the perspective of the French Public Healthcare System. Only direct medical costs in the past 12 months before the questionnaire was sent (a 1-year horizon) were included. Indirect medical and intangible costs were not analyzed. Costs are expressed in euros (reference year 2015; €1 = US $1.086).

## Medical consultations

Each medical consultation was valued according to the specialty of the physician (€23 for a general practitioner and €28 for a specialist physician except for a cardiologist [€47.73], a neurologist [€37], or a psychiatrist [€37]; https://www.ameli.fr/).

## Outpatient treatments

For each reimbursed drug administered in ambulatory settings, we determined the exact number of units used per survivor and then multiplied this quantity by its unit purchase price. Unit prices of drugs were obtained from
wholesale price lists from official tariffs (https://www. ameli.fr/). We hypothesized that all drugs were reim- bursed at 100%.

Hospitalizations

Hospital resource costs were based on the French public
diagnosis-related group (DRG) database, which is used
to fund each hospital stay (https://atih.sante.fr/). We hy-
pothesized the hospitalization cost of level 1 for each hos-
pital stay.

Medical transport

The cost for each medical transport was based on 1 re-
turn trip only by a light medical vehicle, whatever the
medical reason was (eg, a consultation or hospitalization),
with conventional applicable tariffs applied (https://atih.
sante.fr/). Personal transport was not taken into account
because it is not reimbursed by French Health Insurance.

Statistical Analyses

Descriptive analysis

Continuous variables were described by mean ± standard
deviation and range, while size in percentage was con-
sidered for qualitative ones. The χ² or Fisher exact test
was used to compare categorical variables. The Wilcoxon
rank-sum test (Mann-Whitney *U* test) or the Kruskal-
Wallis test was used to compare continuous variables.
Associations between quantitative variables were meas-
ured with the Spearman rank-order correlation, a non-
parametric measure of association based on ranks of data
values (the Spearman correlation coefficient).

Base-case and sensitivity analyses to measure
direct health care costs

We estimated the economic burden of survivors by cal-
culating the total direct health care cost via summation
of the cost of each reimbursed health care resource use
in the past 12 months. Because direct medical costs were
measured over a period of 12 months, no annual discount
rate was applied.²²

The robustness of the results was assessed through
2 deterministic 1-way sensitivity analyses focused on cost
drivers. First, the cost of each reimbursed drug was re-
duced by 30% (and also increased by 30%) to take into
account the potential advent of generic drugs into the
European market in future years as well as intracountry
price variability or underreporting by survivors. Second,
the cost of each hospitalization was reduced by 30% (and
also increased by 30%) to take into account over- and
underreporting.

Explanatory factors of health care costs

Multiple linear regression was used to search for explana-
tory factors of health care costs. To determine which vari-
ables were candidates for explanatory factors in multiple
linear regression, their association with health care costs
was previously explored in a univariate way via the Mann-
Whitney *U* test and the Spearman rank-order correlation
(like the correlation between candidate variables). All var-
iables with *P* < .15 were entered into the multiple linear
regression. Data were tested for the assumption of nor-
mality for the variable of health care cost with the Shapiro-
Wilk test. If the distribution were found to be skewed,
this might lead to problems with statistical estimation
such as heteroscedasticity in the model. Thus, the natural
logarithm of health care cost was used as the dependent
variable for further analyses. Because the dependent vari-
able was log-transformed, the coefficient estimates were
semi-elasticities, for which coefficient interpretation re-
quired some care. For dichotomous explanatory variables
such as actual age (≥60 years = 1 and <60 years = 0), the
coefficient estimate represented the factor by which costs
would be higher or lower for ≥60 years versus <60 years.
By exponentiating the estimated coefficient and subtracting
1, we could interpret the result as a percentage change
from the reference group. Using the delta method, we es-
timated and reported the standard error associated with
the estimated coefficient for the switching status variable
(the explanatory variable).²³,²⁴

Several patient- and treatment-related variables were
studied to determine their impact on health care costs:

1. Patient characteristics: gender (female vs male), actual age (ie, at time of survey) (according to the mean and median actual age: <60 vs ≥ 60 years and <65 vs ≥ 65 years; only the most significant dichotomous explanatory variable will be potentially retained in multivariate analysis), and actual obesity (body mass index ≥ 30 kg/m²; no or yes).

2. Histologic type: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and T-cell lymphoma.

3. Initial therapy: induction regimen intensity (conven-
tional vs high-dose chemotherapy), administration of
rituximab (yes or no), consolidation ASCT (yes or no),
radiotherapy (yes or no), upfront grade 3/4 treatment-
related toxicity, and relapse (no or yes).

4. Self-reported health disorders that occurred at least 6 months after the end of lymphoma treatment: alimentary tract disorders (including acid-related disorders,
functional gastrointestinal disorders, dental prostheses, ageusia, very dry mouth, and tooth extraction-related complications), cardiovascular disorders (heart failure, heart valve diseases, cardiac arrhythmia, stroke, thrombosis, and peripheral artery diseases), psychiatric disorders (anxiety, depression, and attempted suicide), severe infections (herpes zoster, hepatitis B, hepatitis C, and tuberculosis), musculoskeletal disorders (necrosis of bone, muscular fibrosis, and severe osteoarthritis), pulmonary disorders (pleuritis, impaired pulmonary function, and chronic obstructive pulmonary disease), diabetes, kidney failure, and second cancers.

5. Actual fatigue level (no or yes). Individuals were categorized as having fatigue if at least 1 score (each ranging from 0 to 100) was ≥40.

Because follow-up duration and actual age were correlated (Spearman correlation coefficient ρ = 0.08; *P* < .001), the follow-up duration variable was not analyzed in the univariate analysis.

All tests were 2-tailed and significant at an α threshold of 5% (*P*). Statistical analysis was performed with SAS software (version 9.4).

## RESULTS

### Participants' Characteristics

Overall, 1671 individuals with a mean age of 63 ± 13 years (range, 24-95 years) participated (Table 1).²⁰ The histologic types were DLBCL, FL, and T-cell lymphoma in 1176 (70%), 472 (28%), and 23 (2%), respectively. NHL treatment consisted of conventional chemotherapy, intensive chemotherapy alone, and intensive chemotherapy plus ASCT in 811 survivors (49%), 518 survivors (31%), and 342 survivors (20%), respectively. One survivor in 2 received a rituximab regimen (n = 829). Follow-up ranged from 5 to 9 years, from 10 to 14 years, and from 15 to 22 years in 527 participants (32%), 570 participants (34%), and 574 participants (34%), respectively. More than 60% of the survivors (n = 1030) reported at least 1 health disorder that occurred at least 6 months after the end of treatment. The 3 main health disorders were alimentary tract (n = 458 [27%]), cardiovascular (n = 336 [20%]), and psychiatric disorders (n = 279 [16%]).

### Health Care Resource Use in the Past 12 Months

In total, 1100 survivors (66%) reported having used at least 1 health care resource in the past 12 months. At least 1 medical consultation, hospitalization, and medical transport were reported by 52% (n = 867), 11% (n = 179), and

6% (n = 97), respectively; that is, the means were 4.3 ± 6.9 (range, 0-78), 0.3 ± 1.9 (range, 0-49), and 0.4 ± 5.2 (range, 0-200) per survivor, respectively (Fig. 1).

Fifty-two percent of the survivors (n = 871) reported having used at least 1 outpatient treatment (ie, a cumulative number of 3545 and a mean of 2.1 ± 3.0 drugs per survivor [range, 0-21 drugs]; Fig. 2). Sixty-four percent of these (n = 2283) were related to 3 first levels of the ATC classification system: C-Cardiovascular System (n = 1108 [31%]), N-Nervous System (n = 642 [18%]), and A-Alimentary Tract and Metabolism (n = 533 [15%]; Supporting Table 1). Fifteen second levels of the ATC classification system represented 75% of all used outpatient treatments (n = 2650): C09, B01 (antithrombotic agents), C10, N02, C07, A02, A10, J01 (antibacterials for systemic use), N05, H03 (thyroid therapy), N06, C03, R03 (antiasthmatics), C08, and C01 (in descending order).

Health care resource use according to histologic type, actual age, and rituximab and ASCT as initial therapy is presented in Figures 1 and 2. Survivors older than 60 years reported using more health care resources (*P* < .001) than those younger than 60 years. Survivors who did not receive ASCT tended to use more health care resources than those who did. In contrast, no statistical differences were observed whether survivors received or did not receive rituximab or were or were not given ASCT. Similar findings were observed according to histologic types (except for hospitalization).

### Direct Health Care Costs in the Past 12 Months

Medical consultations, hospitalizations, and medical transport could be valued for all survivors, but outpatient treatments (total reported = 2889) could be valued for 81.5% of the survivors only. Unvalued outpatient treatments mainly concerned the following: A-Alimentary Tract and Metabolism (n = 242 [37%]; mainly A02 [n = 192]), N-Nervous System (n = 169 [26%]; mainly N02 [n = 105]), and J-General Anti-Infectives for Systemic Use (n = 93 [14%]; mainly J01 [n = 48]) of the first level of the ATC classification system corresponding to treatments for acute diseases.

After we accounted for missing data, the mean health care cost was estimated at €702 ± €2221 (range, €0-€43,054). Hospitalizations and outpatient treatments were the main cost drivers (ie, 46% and 33% of this cost, respectively). Sensitivity analyses confirmed the robustness of the results (Table 2).

For the 1100 survivors who reported using at least 1 health care resource in the past 12 months, the mean
<table><caption>TABLE 1. Characteristics of 1671 Survivors of Non-Hodgkin Lymphoma Included in the SIMONAL Study</caption><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">Total</th><th colspan="2">Rituximab as Initial Therapy</th><th rowspan="2">P</th></tr><tr><th>Yes</th><th>No</th></tr></thead><tbody><tr><td>Male, No. (%)</td><td>906 (54.2)</td><td>445 (53.7)</td><td>461 (54.8)</td><td>.70</td></tr><tr><td>Age, mean ± SD (range), y</td><td></td><td></td><td></td><td></td></tr><tr><td>At randomization</td><td>50 ± 14 (15-87)</td><td>54 ± 13 (18-87)</td><td>46 ± 12 (16-73)</td><td>&lt;.001</td></tr><tr><td>Actual</td><td>63 ± 13 (24-95)</td><td>63 ± 14 (24-95)</td><td>62 ± 12 (31-90)</td><td>.002</td></tr><tr><td>Actual obesity, No. (%)</td><td>272 (16.3)</td><td>135 (16.7)</td><td>137 (16.8)</td><td>.97</td></tr><tr><td>Histologic type, No. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Diffuse large B-cell</td><td>1176 (70.4)</td><td>502 (60.6)</td><td>674 (80.0)</td><td></td></tr><tr><td>Follicular</td><td>472 (28.2)</td><td>324 (39.1)</td><td>148 (17.6)</td><td></td></tr><tr><td>T-cell</td><td>23 (1.4)</td><td>3 (0.4)</td><td>20 (2.4)</td><td></td></tr><tr><td>Chemotherapy intensity, No. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Conventional chemotherapy</td><td>811 (48.5)</td><td>589 (71.0)</td><td>222 (26.4)</td><td>&lt;.001</td></tr><tr><td>Intensive chemotherapy</td><td>860 (51.5)</td><td>240 (29.0)</td><td>620 (73.6)</td><td></td></tr><tr><td>Rituximab as initial therapy, No. (%)</td><td>829 (49.6)</td><td></td><td></td><td></td></tr><tr><td>Other targeted therapy as initial therapy, No. (%)</td><td>203 (12.1)</td><td>59 (7.1)</td><td>144 (17.1)</td><td>&lt;.001</td></tr><tr><td>Autologous stem cell transplantation as initial therapy, No. (%)</td><td>346 (20.7)</td><td>137 (16.5)</td><td>209 (24.8)</td><td>&lt;.001</td></tr><tr><td>Radiotherapy as initial therapy, No. (%)</td><td>84 (5.0)</td><td>0 (0.0)</td><td>84 (10.0)</td><td>&lt;.001</td></tr><tr><td>Medical history, No. (%)</td><td>903 (54.0)</td><td>598 (72.1)</td><td>305 (36.2)</td><td>&lt;.001</td></tr><tr><td>Upfront grade 3/4 treatment-related toxicity, No. (%)</td><td>729 (43.6)</td><td>490 (59.1)</td><td>239 (28.4)</td><td>&lt;.001</td></tr><tr><td>Relapse, No. (%)</td><td>294 (17.6)</td><td>145 (17.5)</td><td>149 (17.7)</td><td>.95</td></tr><tr><td>Follow-up duration, mean ± SD (range), y</td><td>13 ± 5 (5-23)</td><td>9 ± 2 (6-23)</td><td>16 ± 4 (6-23)</td><td>&lt;.001</td></tr><tr><td>Actual fatigue, No. (%)<sup>a</sup></td><td>1036 (62.0)</td><td>511 (61.6)</td><td>525 (62.3)</td><td>.80</td></tr><tr><td>At least 1 self-reported health disorder that occurred at least 6 mo after the end of lymphoma treatment, No. (%)</td><td>1030 (61.6)</td><td>484 (58.4)</td><td>546 (64.9)</td><td>.01</td></tr><tr><td>Alimentary tract</td><td>458 (27.4)</td><td>209 (25.2)</td><td>249 (29.6)</td><td>.05</td></tr><tr><td>Cardiovascular</td><td>336 (20.1)</td><td>144 (17.4)</td><td>192 (22.8)</td><td>.006</td></tr><tr><td>Psychiatric</td><td>279 (16.1)</td><td>124 (15.0)</td><td>155 (18.4)</td><td>.07</td></tr><tr><td>Severe infections</td><td>204 (12.2)</td><td>99 (11.9)</td><td>105 (12.5)</td><td>.77</td></tr><tr><td>Musculoskeletal</td><td>196 (11.7)</td><td>89 (10.7)</td><td>107 (12.7)</td><td>.22</td></tr><tr><td>Pulmonary</td><td>165 (9.9)</td><td>80 (9.7)</td><td>85 (10.1)</td><td>.81</td></tr><tr><td>Second cancer</td><td>130 (7.8)</td><td>52 (6.3)</td><td>78 (9.3)</td><td>.03</td></tr><tr><td>Diabetes</td><td>104 (6.2)</td><td>44 (5.3)</td><td>60 (7.1)</td><td>.13</td></tr><tr><td>Kidney failure</td><td>71 (4.2)</td><td>30 (3.6)</td><td>41 (4.9)</td><td>.23</td></tr></tbody></table>

health care cost was €1067 ± €2268 (range, €4-€43,054). Hospitalizations and outpatient treatments remained the main cost drivers with the corresponding costs unchanged.

## Factors Explaining Health Care Costs of the Past 12 Months

All factors studied were significantly associated with health care costs in the univariate analysis except for sex, obesity, rituximab as initial therapy, and upfront grade 3/4 treatment-related toxicity (Supporting Table 2). High relationships were observed for the following: actual age older than 60 years, follicular histologic type, and self-reported posttreatment health disorders.

In multivariate analyses, several factors demonstrated statistically significant relationships with health care costs (Table 3). Cardiovascular disorders increased the cost by 66% ± 16%; a second cancer, diabetes, and an actual age older than 60 years increased the cost by 40% to 50%; pulmonary disorders and a medical history increased the cost by 30 to 40%; and psychiatric disorders and relapse increased the cost by 20% and 30%, respectively. In

contrast, rituximab or ASCT as initial therapy had no effect on health care costs. Combining all information available, we found that yearly health care costs varied from €176 (no factors present) to €2803 (8 factors present).

## DISCUSSION

Despite its incidence rate, studies evaluating the economic burden of NHL are sparse.⁷⁻¹⁸ They mainly concern DLBCL and FL, initial and disease progression treatments, according to various perspectives and time horizon. Although it is essential for the health care costs of primary cancer treatments to be estimated, the costs of patients in the observation step gain significance as survival rates increase. Most studies, mainly conducted in North America and Asia, have focused on the economic burden among survivors of solid tumors. To our knowledge, none concern NHL survivors or survivors of hematologic malignancies.²⁵⁻²⁸

In the current study, under the hypothesis that most cancer survivors benefit from 100% health care coverage by

<footnote>aAt least 1 score on the 20-item Multidimensional Fatigue Inventory questionnaire ≥ 40.</footnote>
<figure><img src="image_1.png" /><figcaption>**Figure 1.** Percentages of health care resource used by non-Hodgkin lymphoma survivors according to histologic type, actual age, and rituximab and ASCT as initial therapy. ASCT indicates autologous stem cell transplantation; DLBCL, diffuse large B-cell lymphoma; GP, general practitioner.</figcaption></figure>

French social security, the economic burden of long-term NHL survivors in the 12 months before the questionnaire was sent was estimated at €702 ± €2221 (range, €0-€43,054) from the payer's perspective. It was estimated at €1067 ± €2268 (range, €4-€43,054) in the 1100 survivors who reported using at least 1 health care resource. Confirmed by sensitivity analyses, this result is consistent with the reimbursed medical expenses for 1.529 million French cancer survivors (any localizations) in the observation step, which have been estimated at €1058 per patient per year.⁵,⁶ The

observed difference can be explained by the survey characteristics (a declarative survey) and a potential recall bias.

It is always difficult to apply economic estimations to other situations or countries because of differences in medical practice and health care system organizations, especially in the absence of comparative data. However, it seems reasonable to assume that long-term cancer survivors have higher health care resource consumption than individuals without a cancer history but with similar sociodemographic characteristics.
<figure><img src="image_2.png" /><figcaption>**Figure 2.** Mean numbers of health care resources used by non-Hodgkin lymphoma survivors according to histologic type, actual age, and rituximab and ASCT as initial therapy. ASCT indicates autologous stem cell transplantation; DLBCL, diffuse large B-cell lymphoma; GP, general practitioner.</figcaption></figure>

However, the economic burden of NHL survivors is underestimated, regardless of the missing data inherent in any real life study of treatment or hospitalization. Moreover, out-of-pocket, indirect, and intangible medical costs were not considered as costs in this study because of the assumed weak weight. However, 7% of the participants reported having used unreimbursed outpatient treatments. Unsurprisingly, be that as it may, the overall

largest health care cost drivers were hospitalization (46% of the total) and outpatient treatments (33%). Both cost drivers are usually identified as such in economic evaluations. Medical consultations and transport represented only 16% and 5%, respectively, of the direct health care costs measured.

Despite the limitations of self-administered questionnaires, including item misunderstanding and missing
<table><caption>**TABLE 2.** Base-Case Results and Deterministic 1-Way Sensitivity Analysis of Health Care Costs of 1671 Survivors of Non-Hodgkin Lymphoma Included in the SIMONAL Study</caption><thead><tr><th></th><th>Health Care Cost, Mean ± SD (Range), €</th></tr></thead><tbody><tr><td>Base-case analysis</td><td>702 ± 2222 (0-43,054)</td></tr><tr><td>Medical consultations</td><td>114 ± 187 (0-2252)</td></tr><tr><td>Outpatient treatments</td><td>233 ± 1274 (0-42,863)</td></tr><tr><td>Hospitalizations</td><td>318 ± 1629 (0-33,650)</td></tr><tr><td>Medical transport</td><td>37 ± 283 (0-7884)</td></tr><tr><td>Sensitivity analyses</td><td></td></tr><tr><td>30% decrease of outpatient treatment cost</td><td>632 ± 2012 (0-37,443)</td></tr><tr><td>30% increase of outpatient treatment cost</td><td>632 ± 2012 (0-37,443)</td></tr><tr><td>30% decrease of hospitalization cost</td><td>772 ± 2475 (0-55,913)</td></tr><tr><td>30% increase of hospitalization cost</td><td>607 ± 1860 (0-43,054)</td></tr><tr><td></td><td>798 ± 2627 (0-47,863)</td></tr></tbody></table>

<table><caption>**TABLE 3.** Health Care Costs of 1671 Survivors of Non-Hodgkin Lymphoma Included in the SIMONAL Study: A Multivariate Analysis</caption><thead><tr><th>Explanatory Factor</th><th>Parameter Estimate: β</th><th>Standard Error: S<sub>β</sub></th><th>P</th><th>% Increase in Cost for Percentage difference q<sup>a</sup></th><th>Standard Error of % increase in Cost for Percentage difference q<sup>b</sup></th></tr></thead><tbody><tr><td>Intercept</td><td>5.17160</td><td>0.10311</td><td>&lt;.0001</td><td>—</td><td>—</td></tr><tr><td>Cardiovascular disorder</td><td></td><td></td><td>&lt;.0001</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.50490</td><td>0.09518</td><td></td><td>66</td><td>16</td></tr><tr><td>Actual age</td><td></td><td></td><td>&lt;.0001</td><td></td><td></td></tr><tr><td>≥60 vs &lt;60 y</td><td>0.37310</td><td>0.09059</td><td></td><td>45</td><td>13</td></tr><tr><td>Medical history</td><td></td><td></td><td>.0009</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.29496</td><td>0.08876</td><td></td><td>34</td><td>12</td></tr><tr><td>Second cancer</td><td></td><td></td><td>.0026</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.40575</td><td>0.13450</td><td></td><td>50</td><td>20</td></tr><tr><td>Pulmonary disorder</td><td></td><td></td><td>.0105</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.32286</td><td>0.12596</td><td></td><td>38</td><td>17</td></tr><tr><td>Diabetes</td><td></td><td></td><td>.0115</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.39203</td><td>0.15483</td><td></td><td>48</td><td>23</td></tr><tr><td>Psychiatric disorder</td><td></td><td></td><td>.0185</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.25686</td><td>0.10887</td><td></td><td>29</td><td>14</td></tr><tr><td>Relapse</td><td></td><td></td><td>.0445</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.21119</td><td>0.10500</td><td></td><td>24</td><td>13</td></tr><tr><td>Alimentary tract disorder</td><td></td><td></td><td>.0659</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.16797</td><td>0.09125</td><td></td><td>18</td><td>11</td></tr><tr><td>Musculoskeletal disorder</td><td></td><td></td><td>.0751</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>0.20685</td><td>0.11611</td><td></td><td>23</td><td>14</td></tr><tr><td>Rituximab as initial therapy</td><td></td><td></td><td>.0872</td><td></td><td></td></tr><tr><td>Yes vs no</td><td>-0.15199</td><td>0.08879</td><td></td><td>-14</td><td>8</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.11</td><td></td><td></td><td></td><td></td></tr></tbody></table>

data, our study represents real-life evaluation. Nowadays, real-world data and real-world evidence tend to play a major role in health care decisions and represent essential issues, especially in oncology. They contribute to better knowledge of the management of patients. With easier access to the national health insurance database, it will soon be possible to compare our results with reimbursement data registered in this database, and this will allow refinement of the present results.

Our study is the first to identify factors linked to economic burden in long-term NHL survivors. Unsurprisingly, with high external but low internal

validity, the identified factors are sociodemographics (age), medical history (events before the NHL diagnosis, second cancers, and lymphoma relapse), and actual comorbidities. Over time, in addition to ASCT, the advent of rituximab into the European market in 1998 has brought new hope and challenges for patients with newly diagnosed NHL. The benefit-risk profile appears favorable after 2 decades of experience with this drug in the short term and in the long term. Moreover, no differences in health care resource use and associated costs have been observed between survivors receiving rituximab and survivors not receiving rituximab or between

<footnote>a$\exp(\beta) - 1$.</footnote>

<footnote>b$\exp(\beta) \times S_{\beta}$.</footnote>
those treated with ASCT and rituximab and those treated with ASCT but without rituximab. The recent advent of obinutuzumab and chimeric antigen receptor T cells into the European market could introduce changes in these results in the future.

In conclusion, the consideration of economic constraints in health care is now a reality. Our retrospective study reports on a better understanding of health care resource use and associated health care costs as well as related factors in a series of 1671 French long-term NHL survivors.²⁰ It may help health care professionals in their ongoing efforts to design person-centered health care pathways.

## FUNDING SUPPORT

This study was supported by a grant from the National Agency for the Safety of Medicines (AAP-2012-20). Michel Henry-Amar is supported by the French National League Against Cancer.

## CONFLICT OF INTEREST DISCLOSURES

Franck Morschhauser reports payments or honoraria from Roche and Janssen and participation on boards for Roche, Janssen, Epizyme, Gilead, Novartis, BMS, and AbbVie. Florence Broussais-Guillaumot reports consulting fees from Gilead. Gilles Salles reports consulting fees from BMS/Celgene, Incyte, Ipsen, Janssen, Kite/Gilead, Loxo, Miltenyi, Morphosys, Novartis, and Rapt; payments or honoraria from AbbVie, Amgen, Bayer, Epizyme, Regeneron, Roche, Morphosys, Kite/Gilead, and Novartis; and participation on boards for AbbVie, BeiGene, BMS/Celgene, Debiopharm, Epizyme, Genentech/Roche, Genmab, Incyte, Kite/Gilead, Miltenyi, Morphosys, Takeda, and VelosBio. The other authors made no disclosures.

## AUTHOR CONTRIBUTIONS

**Virginia Nerich:** Conception and design, data analysis and interpretation, writing, and approval of the final version. **Christophe Guyeux:** Conception and design, data analysis and interpretation, writing, and approval of the final version. **Michel Henry-Amar:** Conception and design, data analysis and interpretation, writing, and approval of the final version. **Raphaël Couturier:** Conception and design, data analysis and interpretation, writing, and approval of the final version. **Catherine Thieblemont:** Writing and approval of the final version. **Vincent Ribrag:** Writing and approval of the final version. **Hervé Tilly:** Writing and approval of the final version. **Corinne Haioun:** Writing and approval of the final version. **René-Olivier Casasnovas:** Writing and approval of the final version. **Franck Morschhauser:** Writing and approval of the final version. **Pierre Feugier:** Writing and approval of the final version. **Loic Ysebaert:** Writing and approval of the final version. **Emmanuelle Nicolas-Virelizier:** Writing and approval of the final version. **Florence Broussais-Guillaumot:** Collection and assembly of data, writing, and approval of the final version. **Gandhi L. Damaj:** Writing and approval of the final version. **Jean-Philippe Jais:** Conception and design, data analysis and interpretation, writing, and approval of the final version. **Gilles Salles:** Writing and approval of the final version. **Macha Woronoff-Lemsi:** Conception and design, data analysis and interpretation, writing, and approval of the final version. **Nicolas Mounier:** Conception and design, data analysis and interpretation, writing, and approval of the final version.

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