Socioeconomic Status Plays a Moderating Role in the Association Between Multimorbidity and Health-Related Quality of Life Among Cancer Patients

Abstract

This study aimed to explore the moderating role of socioeconomic status (SES) in the association between multimorbidity and health-related quality of life (HRQOL) among cancer patients in Anhui China. A total of 560 cancer patients were recruited for the cross-section study. Socio-demographic and clinical characteristics were analyzed using descriptive statistics. Tobit regression analysis was employed to investigate the relationship between multimorbidity and HRQOL as well as to assess the moderating effect of SES. The research findings indicated that 76.61% of cancer patients experienced multimorbidity, with psychological multimorbidity being the most prevalent (45.54%), followed by physical-psychological multimorbidity (20.89%). Moreover, physical-psychological multimorbidity had the most substantial adverse effect on HRQOL (P < .001). The presence of multimorbidity was correlated with a significant decline in HRQOL, with a 17.5% (P < .001) decrease in HRQOL for each additional multimorbidity. Additionally, SES played a significant role in moderating the impact of multimorbidity on HRQOL in cancer patients. (Marginal effect = −0.022, P < .01). The high SES group exhibited a higher overall HRQOL than the low SES group (Marginal effect = 0.068, P < .001). And with the increase of multimorbidity, HRQOL in the higher SES showed a more pronounced downward trend, compared with the lower SES (β = −.270 vs β = −.201, P < .001). Our findings underscore the importance of preventing and managing multimorbidity in cancer patients, particularly those with low SES. Furthermore, it is essential to consider the impact of the rapid decline in HRQOL as the number of multimorbidity increases in individuals with higher SES. It is imperative to explore interdisciplinary and continuous collaborative management models.

Keywords

multimorbidity HRQOL socioeconomic status cancer patients cross-sectional study

What do we already know about this topic?

With the increasing aging population, the prevalence of multimorbidity is on the rise in cancer patients.

How does your research contribute to the field?

Our study makes a significant contribution to understanding the impact of multimorbidity on health-related quality of life of cancer patients, as well as the moderating role of socioeconomic status in this association.

What are your research’s implications toward theory, practice, or policy?

Our findings underscore the importance of preventing and managing multimorbidity in cancer patients, particularly those with low SES. Furthermore, it is essential to consider the impact of the rapid decline in HRQOL as the number of multimorbidity increases in individuals with higher SES. It is imperative to explore interdisciplinary and continuous collaborative management models.

Introduction

Cancer is the foremost cause of death globally, and presenting a major public health challenge.¹ The worldwide incidence of cancer is consistently increasing on an annual basis. The latest cancer statistics report² estimates that there were an estimated 19.29 million new cancer cases and 9.96 million cancer deaths occurred worldwide in 2020. Furthermore, with the acceleration of the aging process, individuals aged 65 years and above have become the predominant group affected by cancer, often presenting with multiple concurrent chronic non-communicable diseases. According to a report from the British Medical Journal, in high-income nations, the prevalence of multimorbidity is predominantly attributed to age. As the demographic composition evolves, the percentage of individuals grappling with 2 or more illnesses is progressively rising, a trend anticipated to persist.³ Multimorbidity is typically defined as the co-occurrence of at least 2 chronic conditions in the same individual.⁴ In the context of cancer patients, multimorbidity refers to the presence of 1 or more additional chronic diseases alongside cancer.

Multimorbidity is prevalent among cancer patients, with a frequency of at least 50% to 70% exhibiting at least 1 coexisting health condition at the time of diagnosis, which is 3 times higher than that of the general population.^5-7 Studies have identified common multimorbidity in cancer patients include hypertension, diabetes, peptic ulcer, myocardial infarction, arthritis, and so on.^8,9 The simultaneous presence of multiple health issues may hinder patients from concentrating on any specific health concern, consequently impeding the recovery and increasing the need for hospitalization, long-term care.¹⁰ This condition significantly impacts patients’ health-related quality of life (HRQOL). HRQOL, as a means of health status assessment, specifically includes physiological status, psychological status, social adaptability and overall feeling of life status, etc., to assess patients’ health status from multiple perspectives, reflecting positive or negative factors.¹¹ Prior research indicates a substantial inverse relationship between multimorbidity and HRQOL in cancer patients.^8,12

In addition, socioeconomic status (SES) serves as a crucial predictive factor for an individual’s HRQOL, and is measured objectively through a combination of variables including occupation, education, and income.¹³ Previous studies have uncovered a significant positive correlation between SES and an individual’s health.^14,15 Lower SES is linked to cancer morbidity, mortality, survival rates, and disease stage at diagnosis, posing a disadvantage to patients across all tumor sites.^16,17 While both multimorbidity and SES are important factors affecting HRQOL, research examining the interplay among multimorbidity, SES, and HRQOL in cancer patients remains limited. Investigating the impact of multimorbidity and SES on HRQOL can help identify and address the underlying factors contributing to health disparities. By implementing tailored interventions and evaluating their effectiveness, it is possible to reduce the health disparities among individuals with lower SES, ultimately enhancing their outcomes in cancer care.

Therefore, this study aims to contribute valuable evidence to enhance cancer management and patient care by: (1) summarizing the multimorbidity patterns observed in Chinese cancer patients; (2) investigating the association between multimorbidity and HRQOL; and (3) assessing the moderating role of SES in the relationship between multimorbidity and HRQOL.

Materials and Methods

Study Design and Participants

This study is a cross-sectional survey that utilized a self-report questionnaire. The survey was carried out between November 2020 and October 2021 in Anhui Province, located in eastern China. To ensure the feasibility and sample representativeness, we designated secondary and tertiary hospitals with specialized cancer treatment facilities as the survey sites. Initially, Anhui Province was stratified into 3 distinct regions: North Anhui, Central Anhui, and South Anhui, based on its geographical and economic characteristics. Following the stratification, 2 secondary hospitals and 1 tertiary hospital were randomly selected from each region. Ultimately, adult cancer patients admitted to these chosen secondary and tertiary hospitals were surveyed through a convenient sampling approach. The study received approval from the Biomedical Ethics Committee of Anhui Medical University No. 20180173. The inclusion criteria for the study participants were: (a) a diagnosis of cancer at any stage; (b) proficiency in spoken and written Chinese; and (c) being aged 18 years or older. The exclusion criteria comprised: (a) cognitive impairment; (b) lack of awareness of their disease diagnosis; and (c) refusal to participate in the study. A researcher approached all participants, verbally explained the purpose and procedures of the study, and provided an information sheet. Participants were assured that their personal and medical data would be kept confidential and reported anonymously. Upon obtaining informed consent, participants were invited to complete a questionnaire. The study encompassed 599 adult participants, of which 560 completed qualified questionnaires, resulting in a qualification rate of 93.49%. The model utilized in this study is depicted in Figure 1.

Figure 1.

Theoretical model among multimorbidity, SES and HRQOL.

Health-Related Quality of Life

The Europol Five-Dimensional Health Questionnaire (EQ-5D) was used to assess the HRQOL of patients in this study. The EQ-5D-3L descriptive system encompasses five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. These dimensions are each composed of 3 levels: no problems, some/moderate problems, and extreme problems.^18,19 HRQOL in this study is assessed through health utility values. These values, indicative of the HRQOL of cancer patients, were obtained utilizing the Chinese version of the EQ-5D utility value conversion table developed by Liu et al.²⁰ This conversion table is extensively adopted by Chinese researchers and consistently demonstrates a Cronbach’s α exceeding .70. The health utility values derived from this tool span from −0.149 to 1.000, with a Cronbach’s α coefficient for this study computed at .713.

Measurement of Multimorbidity

The study assessed the number of chronic diseases, excluding the initial cancer diagnosis, by employing a modified version of the Self-Administered Multimorbidity Questionnaire. Participants indicated whether they had been diagnosed with 7 conditions (yes/no): heart problems, stroke, hypertension, diabetes, hyperlipidemia, lung/breathing problems, and depression/anxiety/emotional problems. Moreover, participants were given the opportunity to provide details of any other multimorbidity in open-ended responses. The study analyzed the occurrence of predefined multimorbidity in patients, categorizing them based on the number of conditions present into the following groups: (1) without multimorbidity; (2) with 1 multimorbidity; (3) with 2 multimorbidity; or (4) with 3 or more multimorbidity. Additionally, to investigate the impact of physical or/and psychological multimorbidity on HRQOL, according to existing research,^21,22 multimorbidity were classified into the following types: (1) without multimorbidity; (2) physical multimorbidity; (3) psychological multimorbidity; or (4) both physical and psychological multimorbidity.

Socioeconomic Status

SES is a measure of an individual’s or family’s social or economic position or rank in a social group.²³ Epidemiologists usually use occupation, education, and income to reflect SES and each of indices captures a distinct SES aspect.²⁴ Building on prior studies,^25,26 we applied 3 factors to assess SES: education level, occupation prestige, and household monthly per capita income. Each factor was categorized into high, medium, and low levels with consideration of model interpretability. Participants’ household monthly per capita income was categorized into 3 groups based on ranking from highest to lowest (high = 2, medium = 1, and low = 0).^24,27 Their educational levels were grouped into 3 categories: middle school and above (high = 2), primary school (medium = 1), and illiteracy (low = 0).^27,28 Occupational prestige is classified according to the score of occupational prestige in China, which is determined by education, income, power and type of work unit. Participants were divided into unemployed, lower occupational prestige (prestige score < 50, including workers, farmers, freelancers, other business and service personnel, retirees) and higher occupational prestige (prestige score ≥ 50, including administrators, cadres, professionals, entrepreneurs, etc.).^24,29 Occupation was classified as higher occupational prestige (high = 2), lower occupational prestige (medium = 1), and unemployed (low = 0). The scores of the 3 indicators are then aggregated. Based on the SES scores, individuals are categorized into 3 groups: low (scores 0-2), medium (score 3), and high (scores 4-6) levels.²⁴

Demographic and Clinical Information

The study gathered demographic information such as age, gender, region, marital status, household income per capita, occupation, education, smoking, alcohol drinking, and physical exercise through a questionnaire. Each participant underwent a standardized review process to confirm the diagnosis and gather comprehensive clinical data on cancer stage, course, and site from the medical records managed by the attending clinician.

Statistical Analysis

In this research, the analysis was conducted using SPSS 23.0 and Stata 16.0 software packages. Due to the non-normal distribution of health utility values among cancer patients, the median value P₅₀ (P₂₅, P₇₅) was adopted for statistical representation. The differences in health effect values among cancer patients’ general conditions were examined using the Wilcoxon rank sum test and the Kruskal-Wallis H test. The Tobit regression model is a statistical regression model used to handle truncated or censored dependent variables. It is commonly applied to analyze data with left-censoring, right-censoring, or with lower or upper limits. Due to the restricted range of health utility values (−0.149 to 1), the Tobit regression model was used to investigate the impact of multimorbidity on HRQOL, with a significance level set at α = .05.

Model Selection

The dependent variable examined in this study is HRQOL, with a range of values from −0.149 to 1. Therefore, equation (1) is employed as a double-constrained Tobit model in the study.

Y = α_{1} + β_{1} M u l t i m o r b i d i t y + γ_{1} C o n t r o l + ε_{1}

(1)

In the formula: Multimorbidity denotes the count of chronic diseases; Control refers to other control variables within the model; $α_{0}$ presents the intercept term, $β_{1}$ represents the coefficient for the influence elasticity of explanatory variables, indicating the change in Y for each 1 unit increase in multimorbidity; $γ_{1}$ symbolizes the coefficient of the control variable; $ε_{1}$ stands for the random error term.

The influence of Multimorbidity on the HRQOL of cancer patients with varying SES may differ. To investigate the impact of SES, we further examine the interaction between Multimorbidity and SES. The Tobit model for assessing HRQOL in cancer patients can be delineated as follows:

\begin{matrix} Y = α_{2} + β_{2} M u l t i m o r b i d i t y + β_{3} S E S \\ + β_{4} M u l t i m o r b i d i t y \times S E S + γ_{2} C o n t r o l + ε_{2} \end{matrix}

(2)

In the formula: Multimorbidity × SES represents the interaction between Multimorbidity and SES; Control refers to other control variables within the model; $α_{2}$ presents the intercept term; $β_{2}$ represents the coefficient for the influence elasticity of explanatory variables, indicating the change in Y for each 1 unit increase in multimorbidity; $β_{3}$ represents the coefficient for the influence elasticity of explanatory variables, indicating the change in Y for each 1 unit increase in SES; $β_{4}$ represents the coefficient for the influence elasticity of interaction term, indicating the change in Y for each 1 unit increase in interaction; $γ_{2}$ symbolizes the coefficient of the control variable; $ε_{2}$ stands for the random error term.

Based on equations (1) and (2), Tobit regression analysis was performed using Stata software on the sample data. First, Multimorbidity is included in the analysis to obtain Model 1, which assesses the impact of Multimorbidity on HRQOL; secondly, SES is introduced into the regression analysis based on Model 1, resulting in Model 2 to evaluate the influence of SES on HRQOL; finally, on the basis of Model 2, the interaction term between Multimorbidity and SES is added. Since the model contains binary interaction terms, the variables are first centralized to avoid multicollinearity and then the interaction term is constructed to obtain Model 3. Model 3 is employed to examine the moderating role of SES in the relationship between multimorbidity and HRQOL. At the same time, the marginal effects of the explanatory variables of each model are measured to comprehensively understand the effects of Multimorbidity and SES on the HRQOL of cancer patients.

Results

Profile of Participants

Table 1 presents the participants’ characteristics based on their HRQOL. Among the 560 participants, 51.61% were male, with an average age of 59.93 years (SD = 11.74). The majority of patients had cancer in the digestive system (36.61%), followed by lung cancer, breast cancer, female genital system cancer, and other types. About 76.61% of patients had multimorbidity, with psychological multimorbidity being the most common (45.54%), followed by physical-psychological multimorbidity (20.89%). Participants were divided into high SES (45%) and middle/low SES (55%) groups. Univariate analysis revealed income, education, smoking, physical exercise, clinical stages, disease course, multimorbidity, and SES as influencing factors of HRQOL (Table 1).

Table 1.

Description and Univariate Analysis of HRQOL Among Cancer Patients.

Characteristics	n (%)	P₅₀ (P₂₅, P₇₅)	Z/H	P
Age (years)			1.394	.707
<45	45 (8.04)	0.783 (0.642, 1.000)
45-59	213 (38.04)	0.783 (0.642, 1.000)
≥60	302 (53.93)	0.869 (0.639, 1.000)
Sex			−0.945	.345
Female	271 (48.39)	0.783 (0.635, 1.000)
Male	289 (51.61)	0.783 (0.642, 1.000)
Region			−0.476	.634
City/Town	143 (25.54)	0.783 (0.642, 1.000)
Rural	417 (74.46)	0.783 (0.642, 1.000)
Marital status			0.209	.901
Married	492 (87.86)	0.783 (0.642, 1.000)
Never married	18 (3.21)	0.759 (0.596, 0.902)
Divorced/Widowed/Separation	50 (8.93)	0.777 (0.642, 1.000)
Household monthly per capita income (CNY)			117.711	<.001
<278	198 (35.36)	0.642 (0.469, 0.783)
278-556	158 (28.21)	0.773 (0.642, 1.000)
≥557	204 (36.43)	0.938 (0.783, 1.000)
Occupation			4.065	.131
Low occupation	244 (43.57)	0.783 (0.642, 1.000)
Medium occupation	266 (47.50)	0.734 (0.635, 1.000)
High occupation	50 (8.93)	0.795 (0.642, 1.000)
Education			88.819	<.001
Illiteracy	97 (17.32)	0.709 (0.604,0.792)
Primary school	112 (20.00)	0.703 (0.561, 0.783)
Middle school or more	351 (62.68)	0.875 (0.660, 1.000)
Smoking			8.370	<.001
Current	460 (82.14)	0.734 (0.635, 0.875)
Never/past	100 (17.86)	1.000 (0.875, 1.000)
Alcohol drinking			−0.333	.739
Current	184 (32.86)	0.783 (0.642, 1.000)
Never/past	376 (67.14)	0.783 (0.640, 1.000)
Physical exercise			−1.970	.049
No	244 (43.57)	0.734 (0.642, 0.875)
Yes	316 (56.43)	0.783 (0.642, 1.000)
Cancer Site			2.767	.598
Digestive system cancer	205 (36.61)	0.783 (0.639, 1.000)
Lung cancer	142 (25.36)	0.776 (0.642, 1.000)
Breast cancer	91 (16.25)	0.783 (0.642, 1.000)
Female genital system cancer	52 (9.29)	0.734 (0.602, 0.875)
Others	70 (12.50)	0.798 (0.639, 1.000)
Clinical stages			21.010	<.001
Phase I	20 (3.57)	0.938 (0.709, 1.000)
Phase II	113 (20.18)	0.875 (0.709, 1.000)
Phase III	191 (34.11)	0.776 (0.642, 1.000)
Phase IV	236 (42.14)	0.734 (0.591, 0.875)
Course (months)			16.561	.001
≥12	291 (51.96)	0.734 (0.635, 1.000)
13-36	152 (27.14)	0.755 (0.642, 1.000)
37-60	61 (10.89)	0.869 (0.734, 1.000)
≥61	56 (10.00)	0.938 (0.651, 1.000)
Number of multimorbidity			219.197	<.001
0	131 (23.39)	1.000 (1.000, 1.000)
1	302 (53.93)	0.734 (0.642, 0.875)
2	97 (17.32)	0.642 (0.438, 0.734)
≥3	30 (5.36)	0.639(0.560, 0.734)
Type of multimorbidity			330.673	<.001
No	131 (23.39)	1.000 (1.000, 1.000)
Physical multimorbidity	57 (10.18)	1.000 (1.000, 1.000)
Psychological multimorbidity	255 (45.54)	0.734 (0.635, 0.869)
Physical & psychological multimorbidity	117 (20.89)	0.635 (0.453, 0.734)
Multimorbidity			−13.876	<.001
No	131 (23.39)	1.000 (1.000, 1.000)
Yes	429 (76.61)	0.734 (0.597, 0.869)
SES			118.910	<.001
Low	123 (21.96)	0.642 (0.526, 0.783)
Middle	185 (33.04)	0.734 (0.642, 0.875)
High	252 (45.00)	1.000 (0.734, 1.000)

CNY = Chinese Yuan.

Effect of Multimorbidity on HRQOL

The regression results in Table 2 (Model 1) indicate that multimorbidity has a statistically significant negative effect at the 0.1% level. The marginal effect is −0.175, suggesting a significant reduction in HRQOL due to multimorbidity. Specifically, there is a 17.5% reduction in HRQOL for each additional multimorbidity.

Table 2.

Tobit Model Regression Results.

Variables	Model 1			Model 2			Model 3
Variables	β	S.E.	Marginal effect	β	S.E.	Marginal effect	β	S.E.	Marginal effect
Number of multimorbidity	−.181***	0.015	−0.175***	−.160***	0.014	−0.156***	−.163***	0.014	−0.159***
SES				.064***	0.008	0.062***	.070***	0.008	0.068***
Number of multimorbidity × SES							−.023**	0.008	−0.022**
Age (Ref ≤ 45 years)
45-59 years	.051	0.046	0.050	.091*	0.043	0.087*	.099*	0.044	0.095*
≥60 years	.059	0.045	0.057	.130**	0.043	0.125**	.136**	0.043	0.131**
Sex (Ref = female)	−.025	0.025	−0.024	−.063**	0.024	−0.061**	−.060*	0.024	−0.059*
Smoking (Ref = current)	.257***	0.036	0.253***	.215***	0.035	0.212***	.211***	0.035	0.208***
Physical exercises (Ref = no)	.034	0.024	0.033	.018	0.022	0.018	.014	0.022	0.013
Clinical stages (Ref = Phase I)
Phase II	−.095	0.072	−0.093	−.061	0.068	−0.060	−.055	0.068	−0.054
Phase III	−.104	0.070	−0.102	−.077	0.065	−0.075	−.070	0.065	−0.069
Phase IV	−.155*	0.069	−0.152*	−.109	0.065	−0.107	−.106	0.065	−0.103
Course (Ref = 12 months and below)
13-36 months	.015	0.027	0.014	.013	0.026	0.012	.015	0.026	0.015
37-60 months	.121**	0.040	0.118**	.104**	0.038	0.102**	.099**	0.038	0.097**
≥61 months	.082	0.043	0.080	.064	0.041	0.063	.058	0.041	0.057
Constant	1.004***	0.083		.731***	0.083		.702***	0.084
Pseudo R²	.481			.599			.614
LR chi²	261.69			325.99			334.16
Log likelihood	–141.219***			–109.067***			–104.981***

E = standard error.

P < .05. **P < .01. ***P < .001.

Effect of SES on HRQOL

The regression results in Table 2 (model 2) demonstrate a statistically significant positive relationship for SES at the 0.1% level, with a marginal effect of 0.062. This implies that SES can significantly enhance HRQOL. Specifically, there is a 6.2% in HRQOL for each unit increase in SES.

Moderating Role of SES Between Multimorbidity and HRQOL

The results from model 3 in Table 2 demonstrate that multimorbidity has a direct effect on HRQOL with path coefficient (Marginal effect = −0.159, P < .001), while the relationship is moderated by SES with path coefficient (Marginal effect = −0.022, P < .001). Total effect is −0.181, and the moderating effect is −0.022. The proportion of the moderating effect in the total impact amounts to 12.15%. This indicates that the moderating effect of SES on cancer patient’s HRQOL is present and significant. The use of Pseudo R² as an indicator reveals an effect size of 61.4%, suggesting that our model possesses a substantial explanatory capability.

In order to elucidate the interaction effect, our study grouped the independent variables and adjustment variables based on their average plus or minus 1 standard deviation to generate a simple slope diagram (Figure 2). The figure illustrates the relationship between multimorbidity and HRQOL across varying levels of SES. It is evident that HRQOL significantly reduced as the number of multimorbidity increases, regardless of higher SES group (β = −.270, P < .001) or lower SES group (β = −.201, P < .001). Specifically, HRQOL demonstrates a more pronounced downward trend with the increase of multimorbidity, particularly under higher SES group compared to lower SES group.

Figure 2.

Interaction between multimorbidity and SES in the prediction of HRQOL.

Heterogeneity Analysis of Different Cancer Site

To investigate the heterogeneity of multimorbidity and SES on HRQOL in patients with different types of cancer, the study categorizes cancer types into 5 groups: digestive system cancer, lung cancer, breast cancer, female genital system cancer and other cancer types. By comparing the 5 groups, the study analyzes the effects of multimorbidity and SES on HRQOL among different types of cancer, as shown in Table 3.

Table 3.

Heterogeneity Regression Results.

Variables	Digestive system cancer		Lung cancer		Breast cancer		Female genital system cancer		Others
Variables	β	S.E.	β	S.E.	β	S.E.	β	S.E.	β	S.E.
Number of multimorbidity	−.163***	0.026	−.208***	0.030	−.155***	0.031	−.187***	0.043	−.121**	0.037
SES	.092***	0.016	.049**	0.016	.059***	0.017	.041	0.022	.087**	0.027
Number of multimorbidity × SES	−.003	0.018	−.062***	0.016	−.024	0.016	.035	0.022	−.036	0.021
Control variable	Controlled		Controlled		Controlled		Controlled		Controlled
Number of observations	205		142		91		52		70
Pseudo R²	.560		.781		.942		1.696		.854
LR chi²	128.35		95.29		81.78		50.20		58.68
Log likelihood	−50.487***		−13.393***		−2.525***		10.296***		−4.997***

E = standard error.

P < .05. **P < .01. ***P < .001.

The regression analysis reveals substantial inter-group variations in the influence of multimorbidity and SES on HRQOL across diverse cancer types. Multimorbidity significantly reduces HRQOL for patients in different cancer categories (P < .01), particularly among those with lung cancer (β = −.208, P < .001). Conversely, SES has a positive impact on HRQOL, with the exception of individuals with female genital system cancers. Moreover, the moderating role of SES in the association between multimorbidity and HRQOL is significantly strengthened specifically in lung cancer cases (β = −.062, P < .001).

Discussion

As the population ages, the prevalence of multimorbidity in cancer patients is gradually increasing. Managing cancer patients with various health issues may lead to fragmented care, posing significant challenges to the healthcare system. Our research indicates that multimorbidity adversely affects HRQOL, with SES playing a moderating role in this relationship. This underscores the importance of considering both multimorbidity and SES in the formulation of treatment and care plans for cancer patients. Understanding this connection enables healthcare professionals to more accurately evaluate patients’ overall health, provide personalized treatment, and improve both their quality of life and treatment outcomes. Consequently, the findings of this study have practical significance in improving the quality of life for cancer patients and promoting health equity.

The study revealed that the presence of multimorbidity among cancer patients was associated with a reduction in HRQOL, aligning with previous research findings.^30-32 Multimorbidity was found to affect the physiological and emotional functioning of patients, as well as increase pain and fatigue symptoms, thereby worsening their overall symptom burden.³³ This elevated symptom burden not only impacts the health of patients but also extends to their social relationships and various other aspects, ultimately lowering their HRQOL.³⁴ Moreover, the combination of comorbid physiological and pathological conditions, along with functional impairment and polypharmacy, collaboratively diminishes the HRQOL of patients.³⁵ Prior studies in the field have largely neglected the influence of psychological multimorbidity.^36,37 Hence, this research delved into the effects of physical-psychological multimorbidity and psychological multimorbidity on HRQOL. The results unveiled that the impact of physical-psychological multimorbidity and psychological multimorbidity on HRQOL surpassed that of physical multimorbidity alone. This heightened effect may be attributed to physical-psychological multimorbidity involving both physiological and psychological challenges, resulting in cumulative adverse effects, negative cognitions and emotions aggravating pain, diminished social support, and the complexities of treatment.³⁸ This poses greater demands on the existing comprehensive nursing model.^39,40 An immediate priority is to establish and execute efficient screening and evaluation tools for evaluating the physical and mental health statuses of patients. Nursing interventions targeted at addressing physical-mental multimorbidity should encompass psychotherapy, medication management, and social support. Through the integration of diverse nursing services and resources, patients can receive thorough and uninterrupted care.

The study also indicated that SES plays a significant role in improving the HRQOL for cancer patients. Over the past 2 decades, there have been important advancements in the diagnosis and treatment of cancer, including biologic therapy and precision medicine.⁴¹ A large body of evidence shows that individuals from higher socio-economic backgrounds have higher odds of receiving next-generation treatments.^42-44 New intervention measures, initially touching only the wealthy due to high costs and other issues related to access, may widen the socioeconomic gap in health outcomes.⁴⁵ Moreover, individuals with lower SES often experience heightened psychological stress, leading to unhealthy behaviors like poor dietary habits and lack of physical activity.^46,47 A longitudinal study from the UK found that socioeconomically disadvantaged individuals experienced an earlier onset, a more rapid accumulation of diseases and multimorbidity than socioeconomically advantaged individuals.⁴⁸ In addition, individuals with low SES have relatively fewer social resources compared to those with high SES, which limits their access to health information and medical services, consequently leading to adverse health outcomes.^49-51 Therefore, it is crucial to address the health disparities among cancer patients with different SES and advocate for health equity.

Finally, the study indicates that the relationship between multimorbidity and the HRQOL of cancer patients is influenced by SES. The impact of multimorbidity on HRQOL varies based on social relationship attributes, with the high SES group demonstrating a superior overall HRQOL compared to the low SES group. However, as the number of multimorbidity increases, the HRQOL of the high SES group experiences a more rapid decline. This trend may be attributed to the high SES group’s access to superior medical resources, improved living environments, and enhanced social support, resulting in an overall higher HRQOL. Nevertheless, as the number of multimorbidity escalates, cancer patients’ discomfort and health issues worsen, leading to a swifter decline in HRQOL for the high SES group due to their initially higher HRQOL level. Furthermore, in subgroup analyses of diverse cancer types, studies have revealed a noteworthy association between multimorbidity and HRQOL in lung cancer patients, which is influenced by SES. This could be attributed to the prevalence of lung cancer as the most common cancer type. Over the last decade, there has been rapid advancement in treatment approaches for lung cancer, with ongoing updates in therapeutic modalities. Nevertheless, individuals with lower SES may derive limited benefits from these advancements. Evidence has accumulated about socio-economic inequalities in very diverse lung cancer outcomes.⁴³ Hence, consideration of the influence of SES on multimorbidity is imperative when developing comprehensive treatment strategies for cancer patients. Ensuring individuals with low SES receive ongoing integrated care for chronic conditions is essential, along with reinforcing health surveillance for those with high SES to promptly address multimorbid conditions, thereby enhancing their HRQOL.

The aforementioned research findings highlight the importance of not only implementing extensive preventive measures for cancer patients with multimorbidity but also offering comprehensive, coordinated, and interdisciplinary management services to patients with multimorbidity at varying SES. By implementing national programs to protect cancer patients with scarce economic and social resources, and to mitigate the likelihood of further adverse environments contributing to the deterioration of their fragile socioeconomic and health conditions. These potential strategies should encompass: (1) actively supporting underprivileged cancer patients through effective poverty alleviation policies; (2) enhancing public health promotion to bolster patients’ understanding of health matters; (3) establishing a more inclusive and equitable healthcare system. This involves identifying and focusing on low-income and multimorbid cancer patients, reinforcing primary healthcare services, elevating the engagement and quality of service provision by primary care physicians, and implementing comprehensive chronic disease management programs to holistically address the challenges faced by cancer patients with multimorbidity.

Several limitations of this study should be considered when interpreting the results. First, this cross-sectional study provided valuable information about associations but lacked a temporal component, which limited causal inferences to the findings. Secondly, the self-reported data collection method may introduce reporting bias. Thirdly, the subjects of this study are inpatients from secondary and tertiary hospitals in Anhui Province, with limited sample representativeness. In the future, expanding the scale through longitudinal and multi-center research will be needed to better understand the complex interactions among multimorbidity, SES, and HRQOL in cancer patients.

Conclusion

In conclusion, our research results emphasize the importance of preventing and managing multimorbidity in cancer patients, especially those with low SES. Simultaneously, it is necessary to consider the accelerated decline in HRQOL with an increasing number of multimorbidity among individuals of higher SES. Therefore, policymakers should focus on enhancing public health promotion and education for patients across different SES, and developing policies aimed at reducing poverty to alleviate the burden on economically disadvantaged cancer patients. Healthcare providers should deliver collaborative interdisciplinary services to develop personalized disease management plans and comprehensive care strategies alongside long-term preventive health measures. Collaborative efforts between the government, medical institutions, and other stakeholders are vital to achieve health equity for individuals affected by cancer.

Supplemental Material

sj-docx-1-inq-10.1177_00469580241264187 – Supplemental material for Socioeconomic Status Plays a Moderating Role in the Association Between Multimorbidity and Health-Related Quality of Life Among Cancer Patients

Supplemental material, sj-docx-1-inq-10.1177_00469580241264187 for Socioeconomic Status Plays a Moderating Role in the Association Between Multimorbidity and Health-Related Quality of Life Among Cancer Patients by Xiao-Qing Ren, Shi-Jie Sun, Shen-Ao Wei, Shuo-Wen Fang, Ling-Feng Xu, Jian Xiao and Man-Man Lu in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

The authors are grateful to all staff involved in the study for their support and excellent work. The authors take full responsibility for data analysis and result interpretation of this article. Manman Lu and Jian Xiao: Research design, Methodology; Xiao-Qing Ren and Shi-Jie Sun: Writing-Original draft preparation; Shuowen Fang and Lingfeng Xu: Software and Data curation; Shen-Ao Wei: Writing- Reviewing and Editing. All persons who have made substantial contributions to the work reported in the manuscript, including those who provided editing and writing assistance. All authors read and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by National Natural Science Foundation of China under Grant number 71804002, Anhui Philosophy and Social Science Planning Project under Grant number AHSKQ2021D22.

Ethical Statement

The study received approval from the Biomedical Ethics Committee of Anhui Medical University (Approval No. 20180173).

Consent

Written informed consent was obtained from the patients for publication of the research data and any accompanying images.

ORCID iD

Man-Man Lu

Supplemental Material

Supplemental material for this article is available online.

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