Income and Education Affect Prognosis and Treatment in Symptomatic Myeloma: A Population-Based Study on 8672 Multiple Myeloma Patients Diagnosed 2008-2021 from the Swedish Myeloma Registry

Introduction

While survival in myeloma has improved with new treatment modalities, a substantial variation in mortality remains. This variation cannot entirely be explained by differences in known prognostic factors, such as age and genetic risk group. A link between socioeconomic factors and prognosis has gained attention during recent years, but most studies derive from populations with unequal access to health care. We tested whether income and education affects myeloma treatment and survival also in a country where nearly all health care is publicly funded.

Materials and methods

Study population

The study base included all Swedish patients with symptomatic myeloma diagnosed between 2008 and 2021 (n=8672) and included in the Swedish Myeloma Registry. Data on stem cell transplants (HSCT) were retrieved from the 1-year follow-up form in the myeloma register. Data on lenalidomide, pomalidomide, and melphalan treatment were retrieved from the Swedish Prescribed Drug Register. Data on comorbidities were retrieved from the Swedish Patient Register and the Swedish Cancer Register and Charlson comorbidity index was constructed based on diagnoses registered within five years preceding myeloma diagnosis. Data on socioeconomic conditions were retrieved from the Integrated Database for Labor Market Research (LISA), held by Statistics Sweden. Education level was divided into three categories; primary school only (≤9 years), some college or university (≥13 years), or anything between these two (10-12 years). To avoid undue effect of outliers, personal and family income were grouped into highest quartile, lowest quartile and those in-between. Patients were followed in Swedish population statistics until death, permanent emigration or 31 ^st December 2022.

Statistical analysis

Analyses on overall survival were performed with Kaplan-Meier plots and with Cox proportional hazards models, yielding hazard ratios (HR) with 95% confidence intervals (95% CI) as measures of relative risk of death. Differences in treatment were assessed by Poisson regression. All p-values were two-sided and a value below 0.05 was considered statistically significant. All analyses were performed with SAS 9.4 statistical software (SAS Inc., Cary, NC).

Ethical considerations

The study has been approved by the Swedish Ethical Review Authority (2020-01729). All data were anonymized before analyses.

Results

Median survival was doubled in patients with higher education, compared with patients with primary school only (6.0 years vs 3.0 years). Similarly, patients in the highest family income quartile had a median survival of 7.5 years, compared to 2.8 years among patients from the lowest quartile (Figure 1). The survival differences were to some degree explained by differences at baseline, in particular age at diagnosis. In a multivariable proportional hazards regression, with adjustments for age group, sex, year of diagnosis and comorbidity index, low income patients had 40% higher risk of death than high income patients (HR 1.6, 95% CI 1.5-1.8) and patients with primary school only had 30% higher risk of death compared to patients with higher education (HR=1.4, 95% CI=1.3-1.5), p<0.0001 for both analyses.

Patients with the highest family incomes were also more likely to have been treated with a stem cell transplant, a lenalidomide containing regimen, or a pomalidomide containing regimen, while patients with the lowest family incomes were more often treated with melphalan-prednisone based regimens (Table 1).

Discussion

Education and private economy are correlated with survival in myeloma, even in a country where the cost of health care is covered almost completely by taxes. The differences are not explained by age and comorbidities alone. Our data also suggest that doctors' choices of myeloma treatment are influenced by socioeconomic factors. The data indicate that myeloma survival could increase with more than a year if we could achieve the same results for all patients as we do with the wealthiest and most educated quartile.