Skip to main content
NCBI home page
Nursing Open logoLink to Nursing Open
. 2023 May 27;10(8):5328–5337. doi: 10.1002/nop2.1770

Quality of life and associated factors among community‐dwelling adults with multimorbidity in Shanghai, China: A cross‐sectional study

Xingyue Liu 1,2, Juhua Zhang 3, Shixiang Zhang 4, Shuzhi Peng 2, Mengyun Pei 2, Chunying Dai 5, Tingting Wang 4,✉, Peng Zhang 6,7,✉
PMCID: PMC10333836  PMID: 37243492

Abstract

Aim

To compare the quality of life of patients with and without multimorbidity and investigate potential factors related to the quality of life in patients with multimorbidity.

Design

A descriptive cross‐sectional study.

Methods

This study included 1778 residents with chronic diseases, including single disease (1255 people, average age: 60.78 ± 9.42) and multimorbidity (523 people, average age: 64.03 ± 8.91) groups, who were recruited from urban residents of Shanghai through a multistage, stratified, probability proportional to size sampling method. The quality of life was measured using the World Health Organization Quality of Life Questionnaire. The socio‐demographic data and psychological states were measured using a self‐made structured questionnaire, Self‐rating Anxiety Scale, and Self‐rating Depression Scale. Differences in demographic characteristics were estimated using Pearson's chi‐squared test, and independent t‐test or one‐way ANOVA followed by S‐N‐K test was used to compare the mean quality of life. Multiple linear regression analysis was conducted to identify risk factors for multimorbidity.

Results

There were differences in age, education, income, and BMI between single‐disease and multimorbidity groups, but no differences in gender, marriage, and occupation. Multimorbidity had lower quality of life, reflected in all four domains. Multiple linear regression analyses showed that low level of education, low income, number of diseases, depression, and anxiety were negatively related to quality of life in all domains.

Keywords: cross‐sectional study, factors, multimorbidity, quality of life

1. INTRODUCTION

Multimorbidity is most prevalently defined as the simultaneous occurrence of two or more chronic diseases (Yarnall et al., 2017). About 25% of adults have at least two chronic diseases—cardiovascular, respiratory, endocrine, digestive system, tumour, neuropsychiatric diseases, and other diseases with the hidden onset and long progress after onset, and more than half of older people tend to concurrently suffer from three or more diseases. Although people aged 65 and above have a greater incidence rate of multimorbidity, more than half of those who have it are under the age of 65, making it a problem that affects people at all stages of life (Wang et al., 2017). Multiple diseases increase the risk of mortality and dysfunction, which can have a negative impact on quality of life (QoL) (Palladino et al., 2016). QoL in multimorbidity is an important area that reflects this vulnerable population's health status and well‐being, particularly in an aging society, where it has become a growing public health concern.

According to reports, multimorbidity has a high symptom burden that includes symptoms including pain, mental illness, fatigue, and trouble sleeping, which has a negative effect on QoL (Holzer et al., 2017). Additionally, high symptom load, and pain in particular, frequently results in physical activity restriction and a decline in physical functioning that is adversely associated with capacity for daily living tasks (Vaughan et al., 2016). Furthermore, among patients with multimorbidity, QoL is poorer when the patient has a greater number of diseases (Yang et al., 2020).

Given that multimorbidity has a significant negative impact on QoL, it would be expected that this relationship would have been thoroughly investigated. Existing studies tend to focus on the current situation and management mode of patients with multiple diseases (Matos et al., 2020; Newman et al., 2019; Nunes et al., 2017; Ofori‐Asenso et al., 2019), and there are not many articles on the influencing factors of the QoL in multimorbidity, which have revealed that the possible influencing factors are those sociodemographic factors such as age, education, marriage, occupation, etc., especially in the suburban areas of China. The research subjects in these studies were almost always the populations from western developed countries, these results cannot fully represent the situation of multimorbidity in China due to the existence of cultural, environmental, and demographic differences. Accordingly, it is necessary to identify factors relevant for assessing and managing appropriate supportive interventions for this vulnerable population in China.

2. OBJECTIVE

We will describe the QoL in patients with a single disease and multimorbidity in the suburbs of Shanghai and further analysed the related factors affecting the QoL in multimorbidity. We will also provide more references for clinical staffs to formulate targeted interventions based on potential factors to improve the QoL of the population in China.

3. METHODS

3.1. Design

The study was a quantitative, comparative study among patients with a single disease and multimorbidity in the suburbs of Shanghai.

3.2. Instrument with validity and reliability

3.2.1. Sociodemographic and multimorbidity

Data on the sociodemographic profiles of chronic patients were collected using a questionnaire that was developed by the authors specifically and was to be completed by patients so as to determine characteristics such as gender, age, education, marital status, employment, monthly income, body mass index (BMI) and a number of diseases among other variables widely used in epidemiological studies.

A checklist was used to assess whether the participants were diagnosed with each of the following chronic conditions: hypertension, diabetes, chronic neck/lumbar disease, heart disease, coronary heart disease, arrhythmia, COPD, stroke, depression, chronic stomach/duodenum ulcer, and cancer, geriatric syndrome (malnutrition, weak, urinary incontinence, sleep disorders, memory disorders, osteoporosis, constipation, and psychological or mental illness), chronic kidney disease, Parkinson's disease, gout, benign prostatic hyperplasia (only in the questionnaire for men), asthma or cancer. Residents with two or more diseases were included in the multimorbidity group, while those with one disease were assigned to the single‐disease group.

3.2.2. Quality of life (QoL)

The WHOQOL‐BREF instrument, a short version of the WHOQOL‐100 that has been shown to be cross‐culturally relevant, was used to evaluate the QoL (Group T W, 1998). Numerous researches have evaluated the validity and reliability of the Chinese version of WHOQOL‐BREF found that it has a Cronbach's alpha coefficient of 0.95, confirming its suitability as a QoL measurement instrument (Krageloh et al., 2011; Tsutsumi et al., 2010). The questionnaire, which consists of 26 items, is self‐administered and available in 19 different languages. The first two items independently examine overall perceptions of HRQOL, while the next 24 questions assess the four major HRQOL domains defined by the WHO; physical health (7 items), psychological health (6 items), social relationship (3 items), and environment (8 items). The tool uses a scoring method in which each item is assessed on a 5‐point Likert scale, and the values from all four areas are added together and scaled in a positive direction.

3.2.3. Anxiety and depression

The Zung self‐rating anxiety scale (SAS) and Zung self‐rating depression scale (SDS) were used, respectively, to measure anxiety and depression (Zung, 1971; Zung et al., 1965). A total standard score of 53 or 50 was established as the cut‐off point for depression or anxiety, respectively, based on the Chinese norm for the SDS and SAS, which represent the subjective experiences of people with anxiety tendencies or depression severity. They had been proven to be valid and efficient tools for screening depression and anxiety in the Chinese population (Peng et al., 2013).

3.3. Sample and recruitment

This cross‐sectional study was carried out in Fengxian District, a typical Shanghai suburb, from December 2018 to April 2019. The subjects included residents aged 30–80 who have lived in Fengxian District, Shanghai, for more than 6 months. Inclusion criteria were the public clinical diagnosis of chronic diseases such as diabetes, hypertension, stroke, hyperlipidaemia, COPD, coronary heart disease, osteoarthropathy etc., for more than 6 months. Excluded were individuals who did not reside in the region, declined to participate or did not fill out and sign the informed consent form, and those who had severe communication disorders due to severe physical and psychological illness.

The participants were chosen by a multistage, stratified, probability proportional to size sampling method. Out of the eight towns in Fengxian District, one was chosen at random for the first stage. Six neighbourhood committees/administrative villages from each town were chosen at random for the second stage. In the third stage, two resident/villager groups that included more than 100 households were randomly selected from each neighbourhood committee/administrative village. From each resident/villager group, 100 households were chosen at random for the fourth stage. According to the inclusion and exclusion criteria, 1778 residents completed the survey effectively (Figure 1). This study was approved by the Ethics Review Committee of Medicine & Health Sciences (No.ERC‐SUMHC), shanghai China.

FIGURE 1.

FIGURE 1

Open in a new tab

Flowchart of sampling method.

3.4. Data analysis

SPSS (Statistical Package for Social Sciences) version 25.0 for Windows was used to enter and evaluate the gathered data. The normality of continuous variables was examined by the Shapiro–Wilk test. The mean and standard deviation (SD) was used to describe variables that followed the normal distribution, whereas the median or inter‐quartile range (IQP) was used to describe variables that did not. Categorical variables were described as the frequency with percentage. Demographic characteristics including anxiety and depression were compared between single disease and multimorbidity groups using Pearson's chi‐square. When applicable, independent t‐test or one‐way ANOVA followed by S‐N‐K test was used to compare the mean QoL scores across the domains and multimorbidity amounts. Several multiple linear regression analyses were done with gender, age, education, marital status, employment, monthly income, BMI, number of diseases, depression and anxiety as independent variables, and QoL and its four domains as dependent variables separately to determine the independent effect of factors associated with QoL. A p‐value of 0.05 or below, used in all tests of significance, denotes a statistically significant difference.

3.5. Research ethics committee approval

This study was approved by the Ethics Review Committee of the REDACTED (REDACTED), shanghai China. Participation in this study was completely voluntary and anonymous. Prior to the commencement of the trial, all participants completed a permission form authorizing data collection. All procedures were carried out in conformity with the applicable norms and regulations. They were also told of the study's goal and the option to participate or quit at any time.

4. RESULTS

A total of 1778 (851 men and 927 women) chronic illness patients took part in the survey. There were 1255 patients in the single disease group with an average age of (60.78 ± 9.42) and 523 patients in the multimorbidity group with an average age of (64.03 ± 8.91). The detailed sociodemographic information about research participants was shown in Table 1. Meaningful differences were detected between single disease and multimorbidity groups, such as age, education, monthly income and BMI.

TABLE 1.

Sociodemographic characteristics and differences between patients with a single disease and multimorbidity.

Variable Frequency (%) / (mean ± SD) p
All Single disease Multimorbidity
(n = 1778) (n = 1255) (n = 523)
Gender
Male 851 (47.9) 590 (47.0) 261 (49.9) 0.274
Female 927 (52.1) 665 (53.0) 262 (50.1)
Age (years) (61.73 ± 9.39) (60.78 ± 9.42) (64.03 ± 8.91) <0.001
≤60 705 (39.7) 547 (43.6) 158 (30.2)
61–65 336 (18.9) 231 (18.4) 105 (20.1)
66–70 523 (29.4) 357 (28.4) 166 (31.7)
≥71 214 (12.0) 120 (9.6) 94 (18.0)
Education
Primary and below 687 (38.6) 445 (35.5) 242 (46.3) <0.001
Middle and high school 889 (50.0) 650 (51.8) 239 (45.7)
College and above 202 (11.4) 160 (12.7) 42 (8.0)
Marital status
With partner 1621 (91.2) 1153 (91.9) 468 (89.5) 0.119
Single/widow/separated 157 (8.8) 102 (8.1) 55 (10.5)
Employment
Stable 1257 (70.7) 882 (70.3) 375 (71.7) 0.999
Unstable 521 (29.3) 373 (29.7) 148 (28.3)
Monthly income (RMB)
≤2000 568 (31.9) 358 (28.5) 210 (40.2) <0.001
2000–3999 784 (44.1) 582 (46.4) 202 (38.6)
≥4000 426 (24.0) 315 (25.1) 111 (21.2)
BMI (kg/m2)
18.5–23.9 699 (39.3) 513 (40.8) 187 (35.8) 0.049
<18. 5or ≥ 24 1079 (60.7) 744 (59.2) 336 (64.2)
Open in a new tab

Table 2 shows the scores of the different QoL domains for the single disease /multimorbidity groups. A statistically significant difference (p < 0.001) was observed for all WHOQOL‐BREF domains, with multimorbidity displaying lower mean QoL values. Also, scores in all domains of QoL for patients with multimorbidity were also affected by the amount of disease—the more the number, the lower the QoL.

TABLE 2.

QoL scores and differences between single disease and multimorbidity.

Single disease Multimorbidity (n = 523) p
(n = 1255) All 2 3 ≥4
(n = 523) (n = 396) (n = 105) (n = 22)
Physical 13.51 ± 1.85 13.06 ± 1.85 13.26 ± 1.81 12.57 ± 1.89 11.82 ± 1.35 <0.001
Psychological 13.56 ± 2.12 13.16 ± 2.08 13.42 ± 2.02 12.44 ± 2.16 11.91 ± 1.66 <0.001
Social relationship 15.07 ± 2.67 14.40 ± 2.71 14.65 ± 2.63 13.78 ± 2.96 12.97 ± 1.98 <0.001
Environmental 14.60 ± 2.54 14.09 ± 2.41 14.29 ± 2.37 13.63 ± 2.47 12.68 ± 2.16 <0.001
Final scores 83.41 ± 8.52 80.61 ± 10.68 81.73 ± 9.86 77.28 ± 13.00 76.36 ± 8.34 <0.001
Open in a new tab

The relationships between QoL component scores and possible influencing factors were evaluated with univariate analysis. According to sociodemographic data and comorbidity‐related variables, the QoL total score and mean scores for the four domains are displayed in Table 3.

TABLE 3.

Analysis of possible factors affecting QoL in multimorbidity.

Variable Domains of quality of life (mean ± SD)
Physical p Psychological p Social relationship p Environmental p Final scores p
Gender
Male 13.19 ± 2.01 0.102 13.19 ± 2.16 0.719 14.49 ± 2.81 0.442 14.17 ± 2.40 0.445 80.94 ± 10.36 0.475
Female 12.93 ± 1.67 13.13 ± 2.00 14.31 ± 2.61 14.01 ± 2.42 80.27 ± 11.00
Age (years)
≤60 13.29 ± 1.81 0.037 13.31 ± 2.10 0.358 14.63 ± 2.76 0.410 14.07 ± 2.41 0.820 81.96 ± 9.97 0.063
61–65 13.10 ± 1.73 13.23 ± 2.22 14.45 ± 2.58 14.22 ± 2.46 79.61 ± 9.99
66–70 13.07 ± 1.97 13.15 ± 2.05 14.35 ± 2.71 14.14 ± 2.53 81.12 ± 9.32
≥71 12.60 ± 1.77 12.84 ± 1.95 14.04 ± 2.77 13.91 ± 2.15 78.55 ± 14.05
Education level
Primary and below 12.84 ± 1.85 0.022 12.97 ± 2.02 0.144 14.01 ± 2.72 0.002 13.89 ± 2.39 0.092 79.24 ± 11.34 0.003
Middle and high school 13.20 ± 1.79 13.33 ± 2.12 14.61 ± 2.54 14.19 ± 2.41 81.26 ± 9.90
College and above 13.54 ± 2.00 13.30 ± 2.21 15.46 ± 3.16 14.70 ± 2.46 84.79 ± 9.84
Marital status
with partner 13.12 ± 1.84 0.017 13.26 ± 2.07 0.002 14.48 ± 2.61 0.090 14.17 ± 2.37 0.028 80.87 ± 10.75 0.099
Single/widow/separated 12.50 ± 1.85 12.34 ± 2.05 13.68 ± 3.39 13.42 ± 2.64 78.36 ± 9.89
Employment
stable 13.11 ± 1.91 0.278 13.28 ± 2.10 0.031 14.56 ± 2.88 0.014 14.25 ± 2.48 0.015 81.23 ± 10.67 0.034
unstable 12.92 ± 1.68 12.85 ± 2.01 13.99 ± 2.18 13.69 ± 2.17 79.03 ± 10.58
Monthly income (RMB)
<2000 13.32 ± 1.81 0.007 13.53 ± 2.04 0.003 14.81 ± 2.60 0.010 14.54 ± 2.46 0.002 82.05 ± 10.02 0.012
2000–3999 13.02 ± 1.81 13.00 ± 2.10 14.24 ± 2.56 13.85 ± 2.38 80.35 ± 10.32
≥4000 12.64 ± 1.92 12.76 ± 2.04 13.91 ± 3.08 13.68 ± 2.23 78.36 ± 12.12
BMI (kg/m2)
18.5–23.9 13.35 ± 1.78 0.006 13.53 ± 2.04 0.002 14.70 ± 2.68 0.058 14.50 ± 2.41 0.004 82.65 ± 8.72 0.001
<18.5 or ≥ 24 12.89 ± 1.87 12.95 ± 2.08 14.23 ± 2.71 13.86 ± 2.38 79.47 ± 11.48
Number of diseases
2 13.26 ± 1.81 <0.001 13.42 ± 2.02 <0.001 14.65 ± 2.63 0.001 14.29 ± 2.37 0.001 81.73 ± 9.86 <0.001
3 12.57 ± 1.89 12.44 ± 2.16 13.78 ± 2.96 13.63 ± 2.47 77.28 ± 13.00
≥4 11.82 ± 1.35 11.91 ± 1.66 12.97 ± 1.98 12.68 ± 2.16 76.36 ± 8.34
Depression
Yes 13.81 ± 1.87 <0.001 13.87 ± 2.25 <0.001 15.37 ± 2.90 <0.001 14.82 ± 2.61 <0.001 86.31 ± 8.11 <0.001
No 12.73 ± 1.74 12.85 ± 1.93 13.98 ± 2.51 13.77 ± 2.24 78.12 ± 10.72
Anxiety
Yes 13.18 ± 1.82 <0.001 13.32 ± 2.03 <0.001 14.60 ± 2.64 <0.001 14.27 ± 2.38 <0.001 81.49 ± 10.35 <0.001
No 12.31 ± 1.85 12.16 ± 2.11 13.19 ± 2.84 13.03 ± 2.35 75.27 ± 11.15
Open in a new tab

Table 4 presents the effects of the sociodemographic factors and potential influencing factors on the QoL domains of the WHOQOL‐BREF. Multiple linear regression analysis revealed that all domains of QoL and overall mean QoL score were found to be independently affected by education level (p < 0.05), net monthly income (p < 0.05), comorbidity (p < 0.05), depression (p < 0.05) and anxiety (p < 0.05). Patients with high educational levels and good financial conditions reported significantly better scores, while patients with comorbidity, depression and anxiety were associated with lower scores in all domains of QoL. BMI was associated with psychological (p = 0.005), environmental (p = 0.005) domain and final scores (p = 0.047), while employment status affected both psychological (p = 0.032) and environmental (p = 0.032) domains score. Marital status was only associated with the psychological domain (p = 0.009). Gender and age did not have any significant role in the differences in QoL scores.

TABLE 4.

Multiple linear regression analysis of QoL in multimorbidity.

Physical Psychological Social relationship Environmental Final scores
Stβ t p Stβ t p Stβ t p Stβ t p Stβ t p
Gender −0.035 −0.838 0.402 0.020 0.472 0.637 0.013 0.314 0.754 <0.001 0.008 0.993 0.015 0.370 0.712
Age (years) −0.025 −0.558 0.577 0.007 0.163 0.870 0.028 0.634 0.526 0.062 1.384 0.167 0.021 0.489 0.625
Education 0.163 3.529 <0.001 0.132 2.824 0.005 0.226 4.857 <0.001 0.171 3.649 <0.001 0.210 4.661 <0.001
Marital status −0.067 −1.628 0.104 −0.110 −2.634 0.009 −0.070 −1.680 0.094 −0.081 −1.924 0.055 −0.049 −1.219 0.223
Employment −0.035 −0.865 0.388 −0.089 −2.156 0.032 −0.076 −1.834 0.067 −0.089 −2.146 0.032 −0.060 −1.512 0.131
Monthly income (RMB) −0.137 −3.133 0.002 −0.130 −2.943 0.003 −0.149 −3.380 0.001 −0.143 −3.215 0.001 −0.125 −2.944 0.003
BMI (kg/m2) 0.118 2.820 0.005 0.075 1.779 0.076 0.080 1.898 0.058 0.120 2.831 0.005 0.081 1.989 0.047
Number of diseases −0.166 −4.015 <0.001 −0.188 −4.496 <0.001 −0.136 −3.260 0.001 −0.145 −3.447 0.001 −0136 −3.364 0.001
Depression −0.202 −4.844 <0.001 −0.148 −3.514 <0.001 −0.170 −4.036 <0.001 −0.128 −3.010 0.003 −0.289 −7.109 <0.001
Anxiety −0.088 −2.121 0.034 −0.131 −3.139 0.002 −0.122 −2.925 0.004 −0.123 −2.943 0.003 −0.126 −3.142 0.002
R 2 0.423 0.409 0.408 0.394 0.473
Open in a new tab

5. DISCUSSION

Our results indicated that patients with MCC suffered worse QoL compared to those with single diseases in all domains and overall QoL, which is consistent with numerous studies worldwide (Ba et al., 2019; Wang et al., 2017). However, unlike in our study, the final scores reported in some previous studies were lower, which could be attributed to the fact that our study was conducted in Shanghai, which has a higher living standard in general. Moreover, our results concerning education, marital status, employment status, income, BMI, the number of diseases, depression and anxiety resulted as unambiguous, exploring factors influencing the multimorbidity people's QoL. Having higher education, higher income, fewer diseases, no depression or anxiety were associated with higher scores on all domains of WHOQOL‐BREF.

Interestingly, we found no correlation between gender and multimorbidity and their QoL across all domains. In some previous studies (Klompstra et al., 2019; Krevers et al., 2020), male patients were reported to have higher scores in the physical health domain when compared to females, and better psychological health than female patients (Lee et al., 2021). Similarly, the older people with multimorbidity were reported to present lower scores in some domains (Costa et al., 2018; Nunes et al., 2018), which was not consistent with our results, showing that age was not associated with QoL. This may be due to the fact that although the age range of surveyed subjects had a wide range span, the greatest concentration of patients was in the age range of 66–70 years old, so there were no differences in scores between different ages.

Our study revealed that education, income, number of diseases, depression and anxiety were common influencing factors in all domains of QoL in multimorbidity. Klompstra (Klompstra et al., 2019) and Ba, N. V (Ba et al., 2019) showed that depression, education and occupations were significant correlates of QoL, which is consistent with this study. We also found no evidence that gender and age were related to QoL, corroborating the finding of Ba et al (Ba et al., 2019). Moreover, each domain of QoL had other influencing factors as well, for example, those living with the partner scored higher in a physical domain; a stable job was linked to higher scores in psychological and environmental domains, and normal BMI was associated with higher scores in physical and environmental domains, and total scores.

As previously mentioned, participants who were married or cohabited scored higher in the psychological domain, which is similar to the findings of Ahn et al (Ahn et al., 2016) that confirmed positive spouse support significantly reduced the negative effect of the psychological burden on patients with multimorbidity. However, previous studies suggested that people who live alone identified a lack of social connections as a factor in their low QoL satisfaction (Chen et al., 2014). In addition, living alone was associated with institutionalization following hospital discharge, sadness and loneliness among elderly people (Stahl et al., 2017), which was not corroborated by our results.

The patients with stable jobs reported significantly higher scores for both the psychological health and the environmental domains, which was consistent with the study of Ba, N. V (Ba et al., 2019). Among the plausible explanations for this is that a stable job reduces the mental stress and worry brought by the uncertainty of the job, and the stable job is often accompanied by a good working environment that allows individuals to spend more time and energy engaging in leisure activities, thus boosting their psychological and environmental well‐being (Krevers et al., 2020; Lee et al., 2021). We found that patients with higher BMI had higher scores in physical, environmental, and total domains, which was consistent with Brazilian (de Carvalho et al., 2018) and American (Brown & Reynolds, 2019) studies. According to existing literature, excess weight or overweight and obesity are considered important predecessors to multimorbidity and important risk factors for future morbidity (Agrawal & Agrawal, 2016).

In our study, education and income were associated with all domains of QoL. In other studies, patients with higher education experienced better QoL in some domains (Gobbens et al., 2013; Gobbens & Assen, 2014). For example, in a cross‐sectional study, higher education predicted better future physical health and psychological and environmental QoL (Puth et al., 2017). Also, income was associated with each QoL domain. A study in China showed that aged persons who had adequate home resources and had access to healthcare were less likely to have several diseases and lower QoL (Li et al., 2020). Education and income are two commonly used indicators to measure socioeconomic status (SES). In addition, SES impacts health a lot, involving a variety of mechanisms, such as psychosocial factors and health behaviours, which include emotional stress and social support (Robbert et al., 2019).

The most striking result was that QoL scores decreased with an increasing number of diseases in all aspects, which has been supported by a few studies (Li et al., 2021; Wang et al., 2017). The detrimental impact of symptom burden on QoL has been reported in various chronic illnesses, including cancer, renal failure, COPD and heart failure (Cassell et al., 2018; Hayek et al., 2017). Multimorbidity frequently comes with a greater symptom load, which has been linked to a worse QoL.

The previous study showed that patients with concurrent anxiety and/or depression have poorer QoL (Tong et al., 2021). A recent prospective study demonstrated that patients with anxiety comorbidity have higher severity and poorer QoL than those without anxiety comorbidity (Yan et al., 2019). This study supports the World Health Organization's conclusions that depression is one of the most severe mental health diseases that affect older individuals. Depressive symptoms have been shown to considerably worsen QoL, impair executive function, increase disability and increase morbidity and death (Anon, Organization, W. H, 2011).

Due to the improvement of medical treatment and an aging population, the frequency of multimorbidity is increasing. The increase in chronic illness prevalence brought on by the extension of life expectancy contributes to the increase in multimorbidity in older adults. This, in turn, results in an increase in the number of people using public health services, physical and functional impairments, and decreased QoL. Due to its rising frequency, challenging management, and significant economic burden of disease, multimorbidity has become a focal point of healthcare. According to the survey, there are signs that early social and emotional support seems to be a potential alternative to improve QoL during the aging process, both physically and psychologically (Wang et al., 2017). Therefore, it is particularly important to formulate relevant policies and establish a healthy aging support and influencing factors evaluation network based on the highest possible QoL. Based on our findings, to identify people at risk for low QoL, we recommend that health professionals caring for the elders with multiple conditions assess education, marital status, employment status, income, BMI, number of illnesses, depression and anxiety. Understanding the factors associated with QoL in older adults with multimorbidity can be used to inform health service planning to meet the needs of patients and reduce the burden of disease on the elders and the health system.

It is important to be aware of the study's advantages and disadvantages. To the best of our knowledge, this study is the first to compare QoL of residents with the single disease and multimorbidity and investigate potential factors related to QoL of multimorbidity in the suburbs of Shanghai. Moreover, the study strictly abided by the multi‐stage, stratified and proportional‐to‐scale method for sampling; there were researchers' involvements at each stage, with the quality of sample data controlled by special personnel scrupulously, and the team was experienced and multidisciplinary. The present study has several limitations. First, since the current study is based on an observational study, causal investigation was not possible. In order to explore the causal relationship of influencing factors, further longitudinal researches are necessary in the future. Second, there is a large difference in sample size between the two subgroups, which may lead to a certain bias in statistical results. Future studies should pay attention to the equivalence of sample sizes in subgroups. Third, the effect of disease type, severity, and duration on patients' QoL was not considered in this study, which may have some influence on the results. Future studies could focus on the multimorbidity group and further classify and combine the number, type, severity and duration of diseases to explore their impact on patients' QoL.

6. CONCLUSIONS

In this study, we found a lower QoL in multimorbidity among the evaluated chronic disease patients. In addition, education, marital status, employment status, income, BMI, the number of diseases, depression and anxiety are important indicators for QoL in patients with multimorbidity. This means, providing adequate health education, ensuring family companionship, relieving work pressure, increasing social support, maintaining normal weight, reducing complications and reducing negative emotions such as anxiety and depression are crucial to the QoL of patients with multimorbidity. In order to better improve the QoL of this group, it is more efficient for healthcare professionals to develop more targeted clinical education and intervention programmes based on these influencing factors.

AUTHOR CONTRIBUTIONS

Conceptualization; P.Z. and T.W.; data curation: X.L., Z.S. and C.D.; investigation: M.P., and S.P.; Writing original draft: X.L., and J.Z.; writing—review and editing: P.Z. All the authors approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

FUNDING INFORMATION

This research was funded by Xinjiang Uygur Autonomous Region Natural Protection Foundation (No.2022D01A20), Kashgar Scientific Research Innovation Team Construction Plan (No.KYTD202106) and Kashgar Vocational and Technical College Key Project (No.C2202).

CONFLICT OF INTEREST STATEMENT

None of the authors have expressed any conflict of interest.

ACKNOWLEDGEMENTS

We thank all the healthcare workers and staff for their hard work in research sites including Shanghai Fengxian District Health Commission and the local primary health centres/institutes.

Liu, X. , Zhang, J. , Zhang, S. , Peng, S. , Pei, M. , Dai, C. , Wang, T. , & Zhang, P. (2023). Quality of life and associated factors among community‐dwelling adults with multimorbidity in Shanghai, China: A cross‐sectional study. Nursing Open, 10, 5328–5337. 10.1002/nop2.1770

Statistician: Peng Zhang.

Contributor Information

Tingting Wang, Email: wangtt@sumhs.edu.cn.

Peng Zhang, Email: zhangp@sumhs.edu.cn.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

  1. Agrawal, S. , & Agrawal, P. K. (2016). Association between body mass index and prevalence of multimorbidity in low‐and middle‐income countries: A cross‐sectional study. International Journal of Medicine and Public Health, 2(6), 73–83. 10.5530/ijmedph.2016.2.5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ahn, S. , Kim, S. , & Zhang, H. (2016). Changes in depressive symptoms among older adults with multiple chronic conditions: Role of positive and negative social support. International Journal of Environmental Research and Public Health, 14(1), 16. 10.3390/ijerph14010016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Anon, Organization, W. H . (2011). Global status report on noncommunicable diseases 2014. Women, 47(26), 2562–2563. [Google Scholar]
  4. Ba, N. V. , Minh, H. V. , Quang, L. B. , Chuyen, N. V. , Ha, B. , Dai, T. Q. , Duc, D. M. , Quynh, N. T. , & Khanh, P. G. (2019). Prevalence and correlates of multimorbidity among adults in border areas of the Central Highland region of Vietnam, 2017. Journal of comorbidity, 9, 2235042X19853382X. 10.1177/2235042X19853382 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Brown, C. M. , & Reynolds, R. B. (2019). Identifying obesity‐related multimorbidity combinations in the United States. Clinical Obesity, 9(6), e12336. 10.1111/cob.12336 [DOI] [PubMed] [Google Scholar]
  6. Cassell, A. , Edwards, D. , Harshfield, A. , Rhodes, K. , Brimicombe, J. , Payne, R. , & Griffin, S. (2018). The epidemiology of multimorbidity in primary care: A retrospective cohort study. The British Journal of General Practice, 68, e245–e251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen, Y. , Hicks, A. , & While, A. E. (2014). Quality of life and related factors: A questionnaire survey of older people living alone in mainland China. Quality of Life Research, 23(5), 1593–1602. 10.1007/s11136-013-0587-2 [DOI] [PubMed] [Google Scholar]
  8. Costa, C. D. S. , Flores, T. R. , Wendt, A. , Neves, R. G. , Tomasi, E. , Cesar, J. A. , Bertoldi, A. D. , Ramires, V. V. , & Nunes, B. P. (2018). Inequalities in multimorbidity among elderly: A population‐based study in a city in southern Brazil. Cadernos de Saúde Pública, 34(11), e00040718. 10.1590/0102-311X00040718 [DOI] [PubMed] [Google Scholar]
  9. de Carvalho, J. N. , de Camargo Cancela, M. , & de Souza, D. L. B. (2018). Lifestyle factors and high body mass index are associated with different multimorbidity clusters in the Brazilian population. PLoS One, 13(11), e0207649. 10.1371/journal.pone.0207649 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gobbens, R. , & Assen, M. (2014). The prediction of quality of life by physical, psychological and social components of frailty in community‐dwelling older people. Quality of Life Research, 23(8), 2289–2300. 10.1007/s11136-014-0672-1 [DOI] [PubMed] [Google Scholar]
  11. Gobbens, R. J. , Luijkx, K. G. , & van Assen, M. A. (2013). Explaining quality of life of older people in The Netherlands using a multidimensional assessment of frailty. Quality of Life Research, 22(8), 2051–2061. 10.1007/s11136-012-0341-1 [DOI] [PubMed] [Google Scholar]
  12. Group T W . (1998). Development of the World Health Organization WHOQOL‐BREF quality of life assessment. Psychological Medicine, 28(3), 551–558. 10.1017/S0033291798006667 [DOI] [PubMed] [Google Scholar]
  13. Hayek, S. , Ifrah, A. , Enav, T. , & Shohat, T. (2017). Prevalence, correlates, and time trends of multiple chronic conditions among Israeli adults: Estimates from the Israeli National Health Interview Survey, 2014–2015. Preventing Chronic Disease, 14, E64. 10.5888/pcd14.170038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Holzer, B. M. , Siebenhuener, K. , Bopp, M. , & Minder, C. E. (2017). Evidence‐based design recommendations for prevalence studies on multimorbidity: Improving comparability of estimates. Population Health Metrics, 15(1), 9. 10.1186/s12963-017-0126-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Klompstra, L. , Ekdahl, A. W. , Krevers, B. , Milberg, A. , & Eckerblad, J. (2019). Factors related to health‐related quality of life in older people with multimorbidity and high health care consumption over a two‐year period. BMC Geriatrics, 19(1), 187. 10.1186/s12877-019-1194-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Krageloh, C. U. , Henning, M. A. , Hawken, S. J. , Zhao, Y. , Shepherd, D. , & Billington, R. (2011). Validation of the WHOQOL‐BREF quality of life questionnaire for use with medical students. Education for Health (Abingdon, England), 24(2), 545. [PubMed] [Google Scholar]
  17. Krevers, B. , Ekdahl, A. , Jaarsma, T. , Eckerblad, J. , & Milberg, A. (2020). Factors associated with health‐related quality of life and burden on relatives of older people with multi‐morbidity: A dyadic data study. BMC Geriatrics, 20(1), 224. 10.1186/s12877-020-01604-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lee, E. , Cha, S. , & Kim, G. M. (2021). Factors affecting health‐related quality of life in multimorbidity. Healthcare (Basel), 9(3), 334. 10.3390/healthcare9030334 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Li, H. W. , Lee, W. J. , Lin, M. H. , Peng, L. N. , & Lu, C. C. (2021). Quality of life among community‐dwelling middle‐aged and older adults: Function matters more than multimorbidity. Archives of Gerontology and Geriatrics, 95(1), 104423. 10.1016/j.archger.2021.104423 [DOI] [PubMed] [Google Scholar]
  20. Li, X. , Cai, L. , Cui, W. L. , Wang, X. M. , Li, H. F. , He, J. H. , & Golden, A. R. (2020). Association of socioeconomic and lifestyle factors with chronic non‐communicable diseases and multimorbidity among the elderly in rural Southwest China. Journal of Public Health (Oxford, England), 42(2), 239–246. 10.1093/pubmed/fdz020 [DOI] [PubMed] [Google Scholar]
  21. Matos, J. , Dias, C. M. , & Félix, A. (2020). Work absence and multimorbidity in Portugal: Results from the 1st National Health Examination Survey. The European Journal of Public Health, 30(5), v1049. 10.1093/eurpub/ckaa166.1390 [DOI] [Google Scholar]
  22. Newman, D. , Levine, E. , Kishore, S. P. , & Busija, L. (2019). Prevalence of multiple chronic conditions in New York state, 2011–2016. PLoS One, 14(2), e0211965. 10.1371/journal.pone.0211965 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Nunes, B. P. , Batista, S. , Andrade, F. B. , Souza, J. P. , Lima‐Costa, M. F. , & Facchini, L. A. (2018). Multimorbidity: The Brazilian longitudinal study of aging (ELSI‐Brazil). Revista de Saúde Pública, 52(Suppl 2), 10s. 10.11606/S1518-8787.2018052000637 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Nunes, B. P. , Filho, A. C. , Pati, S. , Teixeira, D. C. , Flores, T. R. , Camargo‐Figuera, F. A. , Munhoz, T. N. , Thumé, E. , Facchini, L. A. , & Batista, S. R. (2017). Contextual and individual inequalities of multimorbidity in Brazilian adults: A cross‐sectional national‐based study. BMJ Open, 7(6), e15885. 10.1136/bmjopen-2017-015885corr2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ofori‐Asenso, R. , Chin, K. L. , Curtis, A. J. , Zomer, E. , Zoungas, S. , & Liew, D. (2019). Recent patterns of multimorbidity among older adults in high‐income countries. Population Health Management, 22(2), 127–137. 10.1089/pop.2018.0069 [DOI] [PubMed] [Google Scholar]
  26. Palladino, R. , Tayu, L. J. , Ashworth, M. , Triassi, M. , & Millett, C. (2016). Associations between multimorbidity, healthcare utilisation and health status: Evidence from 16 European countries. Age and Ageing, 45(3), 431–435. 10.1093/ageing/afw044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Peng, H. , Zhang, Y. , Ying, G. I. , Tang, W. , Qiang, L. I. , Yan, X. , & Zhuang, Q. (2013). Analysis of reliability and validity of Chinese version SDS scale in women of rural area. Shanghai Medical & Pharmaceutical Journal, 14, 20–23. [Google Scholar]
  28. Puth, M. T. , Weckbecker, K. , Schmid, M. , & Münster, E. (2017). Prevalence of multimorbidity in Germany: Impact of age and educational level in a cross‐sectional study on 19,294 adults. BMC Public Health, 17(1), 826. 10.1186/s12889-017-4833-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Stahl, S. T. , Beach, S. R. , Musa, D. , & Schulz, R. (2017). Living alone and depression: The modifying role of the perceived neighborhood environment. Aging & Mental Health, 21(10), 1065–1071. 10.1080/13607863.2016.1191060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Tong, L. , Pu, L. , Guo, X. , Sun, M. , & Jin, L. (2021). Multimorbidity study with different levels of depression status. Journal of Affective Disorders, 292, 30–35. 10.1016/j.jad.2021.05.039 [DOI] [PubMed] [Google Scholar]
  31. Tsutsumi, A. , Izutsu, T. , Kato, S. , Islam, M. A. , Yamada, H. S. , Kato, H. , & Wakai, S. (2010). Reliability and validity of the Bangla version of WHOQOL‐BREF in an adult population in Dhaka. Bangladesh. Psychiatry & Clinical Neurosciences, 60(4), 493–498. 10.1111/j.1440-1819.2006.01537.x [DOI] [PubMed] [Google Scholar]
  32. Vaughan, L. , Leng, X. , La Monte, M. J. , Tindle, H. A. , Cochrane, B. B. , & Shumaker, S. A. (2016). Functional Independence in late‐life: Maintaining physical functioning in older adulthood predicts daily life function after age 80. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 71, S79–S86. 10.1093/gerona/glv061 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Wang, L. , Palmer, A. J. , Cocker, F. , & Sanderson, K. (2017). Multimorbidity and health‐related quality of life (HRQoL) in a nationally representative population sample: Implications of count versus cluster method for defining multimorbidity on HRQoL. Health and Quality of Life Outcomes, 15(1), 7. 10.1186/s12955-016-0580-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Yan, R. , Xia, J. , Yang, R. , Lv, B. , Wu, P. , Chen, W. , Zhang, Y. , Lu, X. , Che, B. , Wang, J. , & Yu, J. (2019). Association between anxiety, depression, and comorbid chronic diseases among cancer survivors. Psychooncology, 28(6), 1269–1277. 10.1002/pon.5078 [DOI] [PubMed] [Google Scholar]
  35. Yang, C. , Hui, Z. , Zeng, D. , Liu, L. , & Lee, D. (2020). Examining and adapting the information‐motivation‐behavioural skills model of medication adherence among community‐dwelling older patients with multimorbidity: Protocol for a cross‐sectional study. BMJ Open, 10(3), e33431. 10.1136/bmjopen-2019-033431 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Yarnall, A. J. , Sayer, A. A. , Clegg, A. , Rockwood, K. , Parker, S. , & Hindle, J. V. (2017). New horizons in multimorbidity in older adults. Age and Ageing, 46(6), 882–888. 10.1093/ageing/afx150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Zung, W. W. (1971). A rating instrument for anxiety disorders. Psychosomatics, 12(6), 371–379. 10.1016/S0033-3182(71)71479-0 [DOI] [PubMed] [Google Scholar]
  38. Zung, W. W. , Richards, C. B. , & Short, M. J. (1965). Self‐rating depression scale in an outpatient clinic: Further validation of the SDS. Archives of General Psychiatry, 13(6), 508–515. 10.1001/archpsyc.1965.01730060026004 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Nursing Open are provided here courtesy of Wiley

RESOURCES