Education Level as a Predictor of Survival in Patients with Multiple Myeloma

doi:10.21203/rs.3.rs-18271/v1

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Education Level as a Predictor of Survival in Patients with Multiple Myeloma

https://doi.org/10.21203/rs.3.rs-18271/v1

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published 08 Aug, 2020

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Background Disparities in multiple myeloma (MM) prognosis based on sociodemographic factors may exist. We investigated whether education level at diagnosis influenced Chinese MM patient outcomes.

Methods We performed a multicenter retrospective analysis of data from 773 MM patients across 9 centers in China from 2006 to 2019. Sociodemographic and clinical factors at diagnosis and treatment regimens were recorded, and univariate and multivariate analyses were performed.

Results Overall, 69.21% of patients had low education levels. Patients with low education levels differed from those with high education levels in that they were more likely to be older, and a higher proportion lived in rural areas, were unemployed, had lower annual incomes and lacked insurance. Additionally, compared to patients with high education levels, patients with low education levels had a higher proportion of international staging system (ISS) stage III classification and elevated lactate dehydrogenase (LDH) levels and underwent transplantation less often. Patients with high education levels had better progression-free survival (PFS) and overall survival (OS) than did patients with low education levels (67.50±8.10 versus 30.60±1.64 months; 122.27±0 versus 55.83±3.55 months, log-rank test, p<0.001; respectively). In the multivariable analysis, patients with high education levels had lower relapse and mortality rates than did those with low education level in terms of PFS and OS (hazard ratio (HR)=0.50 [95% confidence interval (CI): 0.34-0.72], p<0.001; HR=0.31 [0.18-0.53], p<0.001, respectively).

Conclusions Low education levels may independently predict poor survival in MM patients in China. To reduce healthcare disparities for universal healthcare reform, it is critical to improve citizens’ education levels.

Oncology

education level

sociodemographic status

multiple myeloma

survival prognosis

Multiple myeloma (MM) is characterized by the clonal proliferation of malignant plasma cells, causing lytic skeletal lesions, renal failure, hypercalcemia, and anemia, and patients typically present with monoclonal protein in the serum and/or urine[1, 2]. Currently, MM is the second-most common malignancy of the blood in many countries and has been estimated to account for 1.82% of all malignancies and 18% of all hematological malignancies, according to data from the United States[3, 4].

In recent years, with the continuous advent of new drugs and new treatments, the prognosis of patients with MM has been greatly improved. However, not all MM patients benefit equally from these improvements[5]. To explore the causes of this difference, a few studies from the American Cancer Registry and the SEER database have shown the impact of racial and socioeconomic status (SES) disparities on the prognosis of patients with multiple myeloma[6–9]. Some studies have reported a significant increase in the risk of MM in individuals with low SES[8, 9]. In addition, some studies reported differences in the clinical characteristics, incidence and survival prognosis among patients with MM across racial and ethnic groups[6], while some studies showed no consistent association between race/ethnicity or SES and survival outcomes after adjustment for confounders[7, 10, 11].

Globally, compared with the United States and other high-income countries, low- or middle-income countries have slower regulatory approval of drugs, fewer types of drugs available, and higher drug prices when adjusted for gross domestic product per capita; thus, the chances of effective treatment for these MM patients are greatly reduced[12, 13]. However, the mortality of MM in China, a country with a large population, has increased in recent years, especially in rural areas[14]. The impact of demographic and socioeconomic factors on the prognosis and survival of patients with MM has not been reported in developing countries such as China.

Education level is an important factor in patients' demography. To understand the relationship between the education level and survival prognosis of Chinese MM patients, demographic factors (e.g., education level, occupational status, income, place of residence, marital status) and clinical characteristics (e.g., initial disease staging, lactate dehydrogenase (LDH) level, cytogenetics, comorbidities) at diagnosis and treatment regimens (e.g., underwent transplantation) were recorded and analyzed.

Patients

This retrospective, multicenter study was conducted in 9 centers across several provinces in China. A total of 773 newly diagnosed MM patients were enrolled in this study from January 2006 to July 2019 at each of the participating institutions. In accordance with the diagnostic criteria for multiple myeloma and disease progression, eligible patients were defined according to standard International Myeloma Working Group criteria[15, 16]. The treatment of patients was divided into transplantation and nontransplantation. Progression-free survival (PFS) was calculated from the time of the initial diagnosis of MM to disease progression, death or the last follow-up, and overall survival (OS) was calculated from the time of the initial diagnosis of MM until death or the last follow-up.

Sociodemographic and clinical variables

We analyzed the personal information and clinical information of each patient at the time of the first visit, including age, sex, smoking status (yes or no), marital status (married, single, divorced or widowed), place of residence (urban or rural), the distance between place of residence and the hospital (in the same or different provinces), insurance status (insured or uninsured), and annual household income (<$42,500 USD, ≥$4,2500 USD). As it costs approximately $42,500 USD to receive regular induced chemotherapy for 4 cycles and subsequent autologous stem cell transplantation (ASCT) for MM patients, we set $42,500 USD as the cut-off for annual household income. The education level was divided into two classes based on records of formal schooling: secondary school or lower was defined as a low education level, and a bachelor’s degree or higher was defined as a high education level. Occupational status was divided into employed and unemployed.

Clinical data included initial symptoms, comorbidity at the time of MM diagnosis, time to diagnosis (< 1 month, ≥ 1 month), international staging system (ISS) stage (Ⅰ, Ⅱ, Ⅲ), LDH level, and cytogenetic abnormality by fluorescence in situ hybridization (FISH). Briefly, translocation 4;14 [t (4;14)] and/or del[17p] and/or t [14;16] determined by FISH was defined as high risk; not carrying these mutations was defined as standard risk[17]. The treatment includes whether to transplant or not. The initial symptoms included bone pain, anemia, infection, anesthesia, and renal insufficiency.

Statistical analysis

SPSS Statistics version 23 was utilized for statistical analysis. Patient baseline characteristics were analyzed using Student’s t-test or the chi-square test. The Kaplan-Meier method was performed for survival analysis, and differences were analyzed using the log-rank test. Univariate and multivariate analyses of features predicting survival were examined using hazard ratios (HRs) and corresponding 95% confidence intervals (95% CIs) calculated from Cox proportional hazards models. P < 0.05 was considered to be statistically significant.

Baseline demographic and clinical characteristics of the MM patients

The main demographic, socioeconomic, and clinical features of the patients are listed in Table 1. Our cohort included 773 patients: 56.92% were male, 53.30% were under the age of 60, and 28.20% had a history of smoking. Most patients were married (95.99%), and most lived in the same province as their treating hospital and in urban areas (86.42% and 71.41%). Additionally, 69.21% of patients had low education levels, and only 18.63% were still employed during treatment. A total of 76.97% of patients had lower incomes (≤ $42,500 USD), and no insurance was listed for 69.34% of patients. The initial symptoms of the patients were mainly bone pain, followed by anemia and renal function impairment. Additionally, 38.55% of patients had cardiovascular disease and/or metabolic syndrome, and 4.66% had other tumors. The time from onset to definite diagnosis varied, most of the patients received a definite diagnosis after more than one month (76.58%), and 29.24% of the patients had ISS stage Ⅲ disease at the time of onset. A total of 18.63% of the patients had LDH levels greater than 240 U/L, and 17.59% of the patients had high-risk FISH. Moreover, 36.61% of patients underwent transplantation.

Table 1

Characteristics of the patients with multiple myeloma N = 773
Variables	N (%)	Variables (continued)	N (%)
Sex		Any insurance	174 (22.51)
Male	440 (56.92)	No insurance	536 (69.34)
Female	333 (43.08)	Unknown	63 (8.15)
Age		Initial symptoms
< 60	412 (53.30)	Bone pain	508 (65.72)
≥ 60	361 (46.70)	Anemia	118 (15.27)
Smoking		Infection	34 (4.40)
Yes	218 (28.20)	Anesthesia	11(1.42)
No	555 (71.80)	Renal insufficiency	47 (6.08)
Marital status		Others	55 (7.11)
Married	742 (95.99)	Comorbidity
Unmarried	9 (1.16)	None	274 (35.45)
Divorced	13 (1.69)	Cardiovascular disease and/or metabolic syndrome	298 (38.55)
Widowed	9 (1.16)	Other tumors	36 (4.66)
Residential area		Other	198 (25.61)
Urban	552 (71.41)	Time to diagnosis
Rural	221 (28.59)	≤ 1 month	181 (23.42)
Distance to hospital		> 1 month	592 (76.58)
In the same province	668 (86.42)	ISS stage
In a different province	105 (13.58)	Ⅰ/Ⅱ	531 (68.69)
Education level		Ⅲ	226 (29.24)
Low education level	535(69.21)	Unknown	16 (2.07)
High education level	226 (29.24)	LDH level
Unknown	12 (1.55)	< 240 U/L	565 (73.09)
Occupational status		≥ 240 U/L	144 (18.63)
Employed	144 (18.63)	Unknown	64 (8.28)
Unemployed	605 (78.26)	Cytogenetic abnormality by FISH
Unknown	24 (3.11)	High risk	136 (17.59)
Average annual income		Standard risk	429 (55.50)
≤ $42,500 USD	595 (76.97)	Unknown	208 (26.91)
> $42,500 USD	117 (15.14)	Receipt of transplant
Unknown	61 (7.89)	Yes	283 (36.61)
Insurance status		No	490 (63.39)
ISS international staging system, LDH lactate dehydrogenase, FISH fluorescence in situ hybridization.

Comparison between MM patients with a high vs low education level

Information on education level was available for 98.5% of the patients (761/773). Patients with low education levels were more likely to be older (≥ 60 years, 51.21% vs 36.28%, p < 0.001), and a higher proportion were female (46.92% vs 34.96%, p = 0.002), lived in rural areas (39.07% vs 5.31%, p < 0.001), were unemployed (86.95% vs 66.22%, p < 0.001), had a lower income (94.50% vs 59.28%, p < 0.001), lacked insurance (82.15% vs 60.37%, p < 0.001) and had comorbidities (32.34% vs 43.81%, p = 0.003). Additionally, time to diagnosis > 1 month was more frequent in patients with low education levels (81.31% vs 65.04%, p < 0.001), and they consistently had a higher ISS stage (Ⅲ, 32.50% vs 23.56%, p = 0.014) and elevations in LDH levels (≥ 240 U/L, 23.09% vs 13.00%, p = 0.003). However, there was no difference in cytogenetics between the two groups. In addition, patients with high education levels were more likely to be treated via transplantation (59.29% vs 27.85%, p < 0.001) (Table 2).

Table 2

Comparison of demographic and clinical characteristics between patients with high and low education levels
Variables	Low education level		High education level		P
Variables	N = 535	(%)	N = 226	(%)	P
Age					< 0.001
< 60	261	48.79	144	63.72
≥ 60	274	51.21	82	36.28
Sex					0.002
Male	284	53.08	147	65.04
Female	251	46.92	79	34.96
Smoking					0.183
Yes	158	29.53	56	24.78
No	377	70.47	170	75.22
Marital status					0.657
Married	515	96.26	216	95.58
Other	20	3.74	10	4.42
Residential area					< 0.001
Urban	326	60.93	214	94.69
Rural	209	39.07	12	5.31
Distance to hospital					0.287
Same province	458	85.61	200	88.50
Different province	77	14.39	26	11.50
Occupational status					< 0.001
Employed	68	13.05	76	33.78
Unemployed	453	86.95	149	66.22
Average annual income					< 0.001
<$42,500 USD	464	94.50	131	59.28
≥ $42,500 USD	27	5.50	90	40.72
Insurance status					< 0.001
Any insurance	88	17.85	86	39.63
No insurance	405	82.15	131	60.37
Comorbidity
Yes	362	67.66	127	56.19	0.003
No	173	32.34	99	43.81
Time to diagnosis					< 0.001
≤ 1 month	100	18.69	79	34.96
> 1 months	435	81.31	147	65.04
ISS stage					0.014
Ⅰ/Ⅱ	351	67.50	172	76.44
Ⅲ	169	32.50	53	23.56
LDH
≥ 240 U/L	115	23.09	26	13.00	0.003
< 240 U/L	383	76.91	174	87.00
FISH					0.768
High risk	96	24.49	38	23.31
Standard risk	296	75.51	125	76.69
Receipt of transplant					< 0.001
Yes	149	27.85	134	59.29
No	386	72.15	92	40.71
ISS international staging system, LDH lactate dehydrogenase, FISH fluorescence in situ hybridization.

Univariate analyses for PFS and OS

The median follow-up for the entire cohort was 29.6 months (range, 0.1 months to 162.8 months) from the start of diagnosis. Kaplan-Meier analyses showed that the median PFS and OS for all patients were, respectively, 39.93 ± 2.11 months and 79.63 ± 10.58 months (Fig. 1).

In this study, univariate Cox regression analyses were performed to explore the association between the baseline factors of patients and PFS and OS. The sociodemographic factors associated with worse PFS and OS in the univariate Cox regression model included age (hazard ratio [HR] = 1.04, p < 0.001; HR = 1.03, p < 0.001, respectively), residence in a rural setting (HR = 1.48, p = 0.003; HR = 1.47, p = 0.023, respectively), living in a different province from the treating hospital (HR = 1.18, p = 0.039; HR = 1.15, p = 0.175, respectively), being unemployed (HR = 1.67, p = 0.002; HR = 2.53, p < 0.001, respectively), and a lack of insurance (HR = 1.54, p = 0.004; HR = 2.16, p < 0.001, respectively). Additional clinical factors associated with worse PFS and OS included complications at diagnosis (HR = 1.72, p < 0.001; HR = 2.54, p < 0.001, respectively), time to diagnosis > 1 month (HR = 1.47, p = 0.004; HR = 1.96, p < 0.001, respectively), ISS stage Ⅲ disease (HR = 1.23, p = 0.001; HR = 1.38, p < 0.001, respectively), elevations in LDH levels (HR = 1.87, p < 0.001; HR = 1.85, p < 0.001, respectively), high-risk FISH (HR = 1.68, p < 0.001; HR = 1.98, p < 0.001, respectively), and no transplantation (HR = 2.98, p < 0.001; HR = 2.53, p < 0.001, respectively).

In addition, sociodemographic factors associated with better PFS and OS in the univariate Cox regression model included a high education level (HR = 0.39, p < 0.001; HR = 0.25, p < 0.001, respectively) and a high annual income (i.e., ≥ $42,500; HR = 0.51, p < 0.001; HR = 0.36, p < 0.001, respectively) (Table 3).

Table 3

Univariate analysis of the baseline parameters associated with PFS and OS
Variable	PFS		OS
Variable	HR (95% CI)	P	HR (95% CI)	P
Sex Male vs female	1.04 (0.83–1.30)	0.74	0.97 (0.73–1.29)	0.823
Age (per year of age)	1.03 (1.02–1.04)	< 0.001	1.04 (1.02–1.05)	< 0.001
Smoking Yes vs no	0.95 (0.74–1.23)	0.71	0.93 (0.68–1.29)	0.671
Marital status Married vs other	0.78 (0.44–1.34)	0.41	0.85 (0.40–1.81)	0.672
Residential area Rural vs urban	1.48 (1.14–1.93)	0.003	1.47 (1.06–2.05)	0.023
Distance to hospital Different province vs the same province	1.18 (1.01–1.37)	0.039	1.15 (0.94–1.41)	0.175
Education level High vs low education level	0.39 (0.30–0.52)	< 0.001	0.25 (0.17–0.38)	< 0.001
Occupational status Unemployed vs employed	1.67 (1.22–2.30)	0.002	2.53 (1.55–4.13)	< 0.001
Average annual income ≥ $42,500 vs < $42,500 USD	0.51 (0.37–0.70)	< 0.001	0.36 (0.23–0.55)	< 0.001
Insurance status No insurance vs any insurance	1.54 (1.15–2.06)	0.004	2.16 (1.43–3.29)	< 0.001
Comorbidity Yes vs no	1.72 (1.35–2.18)	< 0.001	2.54 (1.81–3.56)	< 0.001
Time to diagnosis > 1 vs ≤ 1 month	1.47 (1.13–1.91)	0.004	1.96 (1.37–2.81)	< 0.001
ISS stage Ⅲ vs Ⅰ/Ⅱ	1.23 (1.09–1.39)	0.001	1.38 (1.19–1.60)	< 0.001
LDH level ≥ 240 vs < 240 U/L	1.87 (1.43–2.46)	< 0.001	1.85 (1.32–2.60)	< 0.001
FISH High risk vs standard risk	1.68 (1.26–2.25)	< 0.001	1.98 (1.38–2.82)	< 0.001
Receipt of transplant No vs yes	2.98 (2.34–3.80)	< 0.001	2.53 (1.87–3.44)	< 0.001
PFS progression-free survival, OS overall survival, HR hazard ratio, CI confidence interval, ISS international staging system, LDH lactate dehydrogenase, FISH fluorescence in situ hybridization.

Multivariate analyses for PFS and OS

To further analyze the influence of sociodemographic factors on patient survival, multivariate Cox regression analyses were conducted. Since age is an important factor affecting survival and we found that education and age have interactive effects on survival, we analyzed the effects of demographic and clinical factors on PFS and OS in patients with MM by dividing them into groups of patients < 60 years old and ≥ 60 years old. We found that in different age groups, education level, elevations in LDH levels, high-risk FISH and receipt of transplant were independently associated with PFS (Table 4). In addition, education level, elevations in LDH levels, high-risk FISH and complications at diagnosis were independently associated with OS in patients older than 60 years old, but in patients younger than 60 years old, only education level and high-risk FISH were independent prognostic factors for OS (Table 5).

Table 4

Multivariate analysis of baseline parameters associated with PFS
Variables	PFS
Variables	HR (95% CI)	P
All patients
Education level: high vs low	0.50 (0.34–0.72)	< 0.001
LDH: ≥240 vs < 240 U/L	2.08 (1.48–2.94)	< 0.001
FISH: high risk vs standard risk	1.77 (1.28–2.45)	0.001
Receipt of transplant: no vs yes	2.70 (1.95–3.74)	< 0.001
Patients < 60 years
Education level: high vs low	0.45 (0.28–0.73)	0.001
LDH: ≥240 vs < 240 U/L	2.30 (1.43–3.70)	0.001
FISH: high risk vs standard risk	1.83 (1.17–2.86)	0.009
Receipt of transplant: no vs yes	2.67 (1.73–4.12)	< 0.001
Patients ≥ 60 years Education level: high vs low	0.53 (0.29–0.98)	0.043
LDH: ≥240 vs < 240 U/L	1.81 (1.10-3.00)	0.020
FISH: high risk vs standard risk	1.68 (1.03–2.72)	0.037
Receipt of transplant: no vs yes	2.38 (1.36–4.17)	0.002
PFS progression-free survival, HR hazard ratio, CI confidence interval, LDH lactate dehydrogenase, FISH fluorescence in situ hybridization.

Table 5

Multivariate analysis of baseline parameters associated with OS
Variables	OS
Variables	HR (95% CI)	P
All patients
Age (per year of age)	1.04 (1.01–1.06)	0.003
Education level: high vs low	0.31 (0.18–0.53)	< 0.001
LDH: ≥240 vs < 240 U/L	1.88 (1.19–2.98)	0.007
FISH: high risk vs standard risk	1.96 (1.29-3.00)	0.002
Comorbidity: yes vs no	2.04 (1.27–3.27)	0.003
Patients < 60 years
Education level: high vs low	0.27 (0.13–0.57)	0.001
FISH: high risk vs standard risk	2.33 (1.28–4.23)	0.005
Patients ≥ 60 years Education level: high vs low	0.26 (0.11–0.62)	0.002
LDH: ≥240 vs < 240 U/L	2.27 (1.24–4.18)	0.008
FISH: high risk vs standard risk	1.84 (1.01–3.33)	0.045
Comorbidity: yes vs no	3.16 (1.32–7.55)	0.010
OS overall survival, HR hazard ratio, CI confidence interval, LDH lactate dehydrogenase, FISH fluorescence in situ hybridization.

Patients with high education levels had better PFS than patients with low education levels (67.50 ± 8.10 versus 30.60 ± 1.64 months, log-rank test, p < 0.001, Fig. 2A). Similarly, patients with high education levels had better OS than patients with low education levels (122.27 ± 0 versus 55.83 ± 3.55 months, log-rank test, p < 0.001, Fig. 2B). In the analysis of age stratification, for patients younger than 60 years old, those with high education levels had better PFS and OS than did patients with low education levels (79.63 ± 13.31 versus 37.23 ± 3.91 months, log-rank test, p < 0.001; not reached versus 61.27 ± 5.09 months, log-rank test, p < 0.001; Figs. 2C, D). Similarly, for patients younger than 60 years old, those with high education levels had better PFS and OS than did patients with low education levels (51.13 ± 6.71 versus 24.47 ± 3.19 months, log-rank test, p < 0.001; 93.63 ± 14.91 versus 37.73 ± 3.70 months, log-rank test, p < 0.001; Figs. 2E, F).

To the best of our knowledge, this study is the first to examine the relationship between sociodemographic factors and survival in patients with MM in China. The prognostic factors of MM mainly include host factors, tumor characteristics and treatment methods[18]. A single factor is often not enough to determine the prognosis. Among the tumor factors, we usually evaluate the prognosis of patients by ISS stage, LDH level and cytogenetics. Moreover, in terms of treatment, we also found that hematopoietic stem cell transplantation in patients with MM can significantly improve the survival prognosis[19]. However, there is no consensus on the impact of patient host factors on prognosis. To date, the prognosis of patients has not been evaluated with these three factors at the same time. Therefore, we included demographic factors (e.g., age, sex, education level, income, work, insurance) tumor characteristics (e.g., ISS stage, cytogenetics, LDH level) and treatment methods in the analysis.

SES is often measured by income, education or occupation, either as singular variables or in combination, which is a strong predictor for survival prognoses in MM as well as other diseases[6, 8, 20, 21]. It can be assumed that the education level covaries with SES. In our study, we set income, education level and occupational status as independent factors. As age and educational level of these patients have an interactive effect on survival, we conducted a hierarchical analysis of age. The results of multivariate Cox regression analyses showed that education level was an independent factor affecting the prognosis of MM patients after adjustments were made for potential confounders.

Cancer death rates vary considerably by level of education[22]. Kyrollis Attalla et al found that penile cancer patients with low education levels were more likely to be diagnosed with a worse pathologic T stage[23]. Ki-Tae Hwang et al found that high education levels conferred a superior prognosis for breast cancer patients in the subgroup aged > 50 years; these patients had a lower mean age at the first diagnosis and more favorable biological features[24]. Gustav Linder et al found that high education levels were associated with a greater probability of being offered curative treatment and improved survival in esophageal and gastroesophageal junctional cancer in Sweden; the reason may be communication difficulties and a lack of understanding of treatment, which were more commonly reported in groups with low education levels[25]. Irene Biasoli et al found that the median time from the onset of symptoms to diagnosis was longer in patients with fewer years of schooling, and a higher number of fatal events occurred in patients with low education levels[26].

In our study, patients with high education levels were more likely to have a longer PFS and OS. The underlying mechanisms may be the following: patients with high education levels are younger, the proportion of male patients is higher, and more people in this subgroup live in urban areas. Younger patients and male patients are more likely to visit a doctor quickly when they are not feeling well. Similarly, urban patients are more likely to seek medical attention than their rural counterparts are due to the convenience of having medical institutions nearby[12].

In addition, patients with high education levels have financial and work support, and they tend to have more stable employment and income, which could motivate them to overlook financial factors. Of course, insurance is also a very important economic factor, and we found that patients with high education levels are more likely to have insurance coverage. Several studies have reported that insurance status was associated with overall survival, and patients who were uninsured had poorer survival than those who were insured[7, 27, 28]. We found that the time to diagnosis > 1 month was more frequent in patients with low education levels and that they consistently had higher ISS stage (Ⅲ) disease and elevated LDH levels, which may be related to the time of their visit. In addition, patients with low education have more complications. Low education levels might undermine the patient’s initiative to seek healthcare services, leading to a delay in the diagnosis of a primary disease or a life-threatening complication[26, 29].

Moreover, MM patients with high education levels show better treatment compliance, and they may be more receptive to stem cell transplantation, which could provide more benefit to patients than nontransplant treatments[30, 31].

Our research has some limitations owing to its retrospective nature. In addition, some of the values were missing, but the proportion of missing values for most variables was less than 10%.

With continuous advancements in the treatment of multiple myeloma, the prognosis of patients has greatly improved. However, not all patients benefit equally. By analyzing the relationship between sociodemographic factors and the survival of patients with multiple myeloma in China, we found that education level is an independent factor affecting survival outcomes. In particular, MM patients with high education levels have a better economic foundation, can seek medical treatment in a more timely manner, can choose the best treatment regimens and can be treated more regularly. Therefore, the results of this study indicate that we can use the education level of newly diagnosed patients to evaluate the prognosis of these patients and to create more reasonable treatment plans. In addition, improving the education level of citizens should be a primary strategy for developing countries such as China. It is very important to reduce healthcare disparities through universal healthcare reform to ensure that all patients have equal access to medical services.

Abbreviations

MM: multiple myeloma; ISS: international staging system; LDH: lactate dehydrogenase; PFS: progression-free survival; OS: overall survival; SES: socioeconomic status; HR: hazard ratio; 95% Ci: 95%confidence interval; FISH: fluorescence in situ hybridization.

Ethics approval and consent to participate

This study was reviewed and approved by the first affiliated hospital of Sun Yat-sen university (IRB:［2019］341). Due to retrospective design of the study, the requirement for informed consent was waived.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available as presently we have not been granted permission by the institutional review board to do so. However, data can be made available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

Not Applicable.

Authors’ contributions

JL came up with the study concept and design. She was also involved in editing and review. LX collected the data, prepared and edited the manuscript and performed statistical analysis. XW, XP, XW, QW, BW, JC, YZ, LC, WL helped with data acquisition. All authors read and approved of the final manuscript.

Acknowledgments

This work was supported by Sun Yat-sen University medical clinical trial “5010 Plan” 2017005.

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Education Level as a Predictor of Survival in Patients with Multiple Myeloma

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Journal Publication

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Abstract

Figures

Background

Methods

Patients

Sociodemographic and clinical variables

Statistical analysis

Results

Baseline demographic and clinical characteristics of the MM patients

Comparison between MM patients with a high vs low education level

Univariate analyses for PFS and OS

Multivariate analyses for PFS and OS

Discussion

Conclusions

Declarations

References

Status:

Journal Publication

Version 1