The Relationship Between Illness Uncertainty and Social Support Among Cancer Patients: A Meta-analysis.

In recent years, with the aging of the world’s population, the burden of cancer has been increasing. According to the International Agency for Research on Cancer, 1 there were approximately 19.3 million new cancer cases worldwide in 2020. Most importantly, the International Agency for Research on Cancer has projected the cancer burden to be 28.4 million new cancer cases worldwide by 2040, representing an increase of 47% from 2020. Cancer poses a significant challenge to global public health. With the development of some emerging technologies (ie, genomic technology, the cross-application of big data science and biological information) and the progress of specialty care, the survival rate of cancer is increasing year by year. 2 – 4 While receiving complex treatment and care, patients will face a variety of uncomfortable symptoms caused by not only the disease and its treatment but also a lack of disease-related information. In the face of an unknown outcome of the disease, the ability of the patient to judge things related to the disease (such as how to choose treatment methods and how to relieve economic pressure through social help) is insufficient, which can contribute to the manifestation of illness uncertainty. 5 However, some researchers have found through interviews that illness uncertainty is a common experience among patients with cancer. 6 – 8

Illness uncertainty refers to a psychological state that occurs when an individual is unable to neither classify nor organize an event or situation because of a lack of sufficient information thereof when the disease causes related stimuli (eg, unclear symptoms of the disease, complex treatment and care, expensive medical expenses, and unpredictable disease course and prognosis). 9 As a cognitive state, illness uncertainty can lead to psychological pressure on patients and subsequently affect the outcome of the disease and psychological adaptability of patients. 10 , 11 Mishel’s Uncertainty in Illness Scale is the most commonly used tool for measuring uncertainty. It is divided into 4 dimensions: uncertainty, complexity, lack or inconsistency of disease-related information, and unpredictability of disease prognosis, with a total of 33 items, each of which is scored on a 5-point Likert scale. 12 In addition, the higher the score, the higher the degree of uncertainty in illness.

Studies have shown that illness uncertainty, to a large extent, interferes with the ability of patients with cancer to seek disease-related information 13 and reduces their treatment compliance 14 and quality of life. 15 Compared with patients with low levels of illness uncertainty, the coping style of patients with high levels of illness uncertainty is mainly avoidance and passive acceptance, rather than actively seeking solutions. Importantly, illness uncertainty does not completely disappear in illness, instead fluctuates over time, and can be accompanied by positive or negative emotional states. 16 Therefore, it is necessary to actively identify indicators that regulate uncertainty in the illness of patients with cancer and support them in dealing with the high level of illness uncertainty according to these factors.

To better explain the persistent uncertainty in the illness of patients with chronic diseases, Mishel 16 redefined the theory of uncertainty in illness in 1990. The revised theory indicates that patients’ perceptions of illness uncertainty are influenced by their cognitive abilities and available resources. These resources, including the educational level of patients, social support, and reliable authority, can help individuals explain symptom patterns, event familiarity, and event consistency, which can directly or indirectly reduce illness uncertainty. In the past few years, researchers have actively explored the causes and mechanisms of uncertainty in illness among patients with cancer and found that social support is an important external factor affecting these patients. 17 , 18

Social support refers to the process by which individuals obtain spiritual support and material help through their social relationships (ie, colleagues, friends, family, and organizations) to relieve tension, reduce psychological stress, and improve adaptability. 19 The Multidimensional Scale of Perceived Social Support (MSPSS), 20 Perceived Social Support Scale, 21 and Social Support Rating Scale 22 are commonly used to evaluate the social support level reported by patients with cancer.

A growing number of studies have investigated the correlation between illness uncertainty and social support reported by adults with cancer. However, inconsistent conclusions have been drawn regarding this association. Most studies have found that illness uncertainty is negatively correlated with social support. 17 , 18 This finding is consistent with Mishel’s uncertainty theory of illness, proposed in 1988. According to this theory, the available resources that help individuals interpret symptom patterns, event familiarity, and event consistency, namely, social support, can directly or indirectly reduce illness uncertainty. However, some studies have come to the opposite conclusion. 23 In their view, patients’ contact with friends or relatives with cancer is the first step for them to generate the cognition of cancer. However, inaccurate information provided by social media and the Internet can mislead patients, causing them to have a false perception of the disease. 24 , 25 Owing to the mixed results of these studies, this meta-analysis was conducted to integrate previous empirical studies on illness uncertainty and social support in patients with cancer, thus further clarifying the relationship between illness uncertainty and social support.

Furthermore, we investigated whether the association between these 2 variables was affected by the publication year, country, proportion of women among participants, cancer type, and instruments used to measure social support. Cancer care differs according to a country’s economic development, and countries with faster economic development tend to have higher levels of healthcare. In these countries, patients with cancer have channels to obtain disease-related information to fully understand the occurrence and development of the disease and may therefore have a lower level of illness uncertainty. 26 , 27 Given this geographical difference, it is reasonable to speculate that different regions may vary in the association between illness uncertainty and social support. Second, different types of cancers have significantly different treatment methods and prognoses. According to global survey data, lung cancer is the leading cause of death. 1 Compared with patients with cancer with high survival rates, those with high mortality rates may worry more about disease deterioration and prognosis, which could affect their level of social support and increase their illness uncertainty. 28 , 29 Thus, the relationship between illness uncertainty and social support in patients with cancer may vary according to the type of cancer. Third, previous studies have found that older patients with cancer tend to exhibit higher levels of illness uncertainty. 18 Therefore, age may be a factor affecting illness uncertainty and social support relationships in patients with cancer. Finally, several studies have used different instruments to measure social support. Owing to differences in the items and dimensions of each instrument, the measurement of social support in these patients may affect the relationship between illness uncertainty and social support.

To comprehensively evaluate the relationship between social support and illness uncertainty in patients with cancer, this study used meta-analysis to combine relevant studies to clarify the correlation between the 2 variables and explore the potential moderating factors affecting this relationship.

Methods

Search Strategy

The following databases were searched comprehensively and systematically: PubMed, Web of Science, Cochrane Library, EMBASE, PsycINFO, China Science and Technology Journal Database (VIP), Wanfang Data, and China National Knowledge Infrastructure. The starting and ending times of the literature search ranged from the founding of the journal to the year 2022. On the basis of a careful review of previous research, the search terms used in this study for cancer included “Neoplasm,” “Neoplasms,” “Tumor,” “Tumors,” “Cancer,” “Cancers,” “Malignancy,” and “Malignancies.” The search terms used for illness uncertainty included “Uncertainty,” “The Uncertainty,” “Uncertainty in Illness,” “Illness Uncertainty,” “Uncertainty of Disease,” “Uncertainty of Illness,” “Uncertainty in Disease,” and “Disease Uncertainty.” Search terms used for social support included “Social Support,” “Social Supports,” “Social Care,” “Social Cares,” “Online Social Support,” “Online Social Supports,” “Perceived Social Support,” and “Perceived Social Supports.”

Study Selection Criteria

The literature retrieved from each database was imported into EndNote to eliminate duplicates. Two researchers independently screened the literature based on the inclusion and exclusion criteria. The inclusion criteria for the literature included the following: (1) the Pearson correlation coefficient r between illness uncertainty and social support was reported in the literature, or other indicators that could be transformed into effect sizes such as F , t , χ 2 , and β; (2) study participants had histologically confirmed cancer; (3) measurement tools were clearly described; and (4) data could not be reused in a different publication. If the research data sources were the same, the main study was selected. The exclusion criteria were (1) studies not published in Chinese or English, (2) conference reports, (3) studies with obvious data errors, and (4) low-quality studies assessed using the 9-item Joanna Briggs Institute critical assessment checklist. 30

Data Extraction

The literature search, screening, data extraction, and literature coding were conducted independently by 2 researchers. If a disagreement occurred, the researchers discussed it or consulted a third researcher to reach a consensus. The coding content included the first author, year of publication, country, publication type, measurement instrument, sample size, mean age, type of cancer, and correlation coefficient between illness uncertainty and social support. Importantly, in longitudinal studies, only the correlation coefficient between illness uncertainty and social support, as measured from the first data point, was extracted. If the correlation coefficient r was not reported directly in the study, but F , t , χ 2 , and β were reported, they were converted to r values using the formula ( r = t 2 t 2 + df , r = F F + d f e , r = χ 2 χ 2 + N , r = β × 0.98 + 0.05 (β ≥ 0), r = β × 0.98 − 0.05 (β < 0)[−0.5 < β < 0.5]). 31

Quality Appraisal

According to the Joanna Briggs Institute’s critical appraisal checklist, the quality of the included literature was assessed by 2 researchers. This checklist includes 9 items, and each item has 4 options: “yes” counts for 1 point, whereas “no,” “unclear,” or “not applicable” counts for 0 points. Higher scores indicate higher quality of the reviewed literature. 30

Statistical Analysis

The effect indicator for this study was the Pearson correlation coefficient r between illness uncertainty and social support extracted from the articles. Stata 17 was used to perform the meta-analysis. First, the extracted data were transformed to determine the magnitude of the correlation between illness uncertainty and social support. Specifically, equation 1 was used to convert the extracted correlation coefficient r into a Fisher’s Z value, and the SE was calculated from equation 2. Using Fisher’s Z value and SE, 95% confidence intervals (CIs) were calculated via equation 3. Finally, the summary r coefficient was calculated using equation 4. According to Lipsey and Wilson, 32 effect sizes of r = 0.10, 0.25, and 0.40 represent low, moderate, and strong correlations, respectively.

(1) Fisher’s Z = 0.5 χ ln 1 + r 1 − r

(2) SE = 1 / n − 3

(3) 95% CI = Z ± 1.96 (SE z )

(4) Summary r = e 2 z − 1 e 2 z + 1 ( Z = summary Fisher’s Z )

Second, P values and I 2 tests were used to determine heterogeneity between studies. 33 Notably, if P > .1 and I 2 < 50%, this indicated that there was no statistical heterogeneity among the studies, and the fixed effects model was used for meta-analysis. 34 On the contrary, if P ≤ .1 or I 2 ≥ 50%, there was statistical heterogeneity among the studies, and the random-effects model was used for meta-analysis. Third, if the results of the data analysis showed heterogeneity between the studies, we further explored for potential moderating effects. In detail, meta-regression analysis was used to test the significance of the results when the moderator variable was continuous. Conversely, if the moderator was a categorical variable, subgroup analysis was used to test for significance. Fourth, sensitivity analyses were performed to examine the robustness of the relationship between illness uncertainty and social support. Finally, funnel plots and egg tests were used to determine whether there was publication bias. 35

Results

Study Characteristics and Quality Assessment

After a careful search, 1268 studies were identified without duplicates. On the basis of the abstract and title, 1033 studies were excluded. Finally, 132 studies were reviewed in full. After excluding articles published in other languages (n = 9), conference reports (n = 6), studies with insufficient data (n = 63), low-quality studies (n = 2), and those with repeated samples (n = 11), 41 studies involving 5403 participants were included in the meta-analysis (Figure 1 , Table 1 ). Among these studies, 1 was from Norway, 1 was from Thailand, 5 were from Korea, 6 were from the United States, and 28 were from China.

Meta-analysis Results

The homogeneity test revealed significant heterogeneity ( I 2 = 74.20, P < .01) among the 41 included studies (Table 2 ). Therefore, a random-effects model was used to analyze the correlation r value between illness uncertainty and social support. The results showed that illness uncertainty was negatively correlated with social support ( r = −0.33; 95% CI, −0.38 to −0.27; P < .000). According to the suggestion of Lipsey and Wilson, 32 there is a moderately negative relationship between illness uncertainty and social support among patients with cancer.

Because of the heterogeneity of the included studies, regression and subgroup analyses were performed to examine the sources of heterogeneity in depth. Subgroup analyses were performed according to the country, year of publication, type of publication, type of cancer, and measurement tool for social support (Table 3 ).

Country, year of publication, cancer type, and the scale measuring social support significantly moderated this association (all P s < .05). Specifically, the correlation between illness uncertainty and social support from developed countries ( r = −0.37; 95% CI, −0.42 to −0.32) was stronger than that for cancer patients from developing countries ( r = −0.30; 95% CI, −0.33 to −0.27). Among the 4 groups of years, the correlation between illness uncertainty and social support in cancer patients from 2014 to 2022 was the strongest ( r = −0.36; 95% CI, −0.39 to −0.32). In contrast, the weakest correlation was observed between 1987 and 1995 ( r = −0.19; 95% CI, −0.34 to −0.04). For different types of cancer, the relationship between uncertainty in illness and social support in patients with ovarian cancer ( r = −0.43; 95% CI, −0.60 to −0.26) was the strongest, and the relationship between uncertainty in illness and social support in patients with bladder cancer ( r = 0.04; 95% CI, −0.07 to 0.14) was the weakest and positively correlated. Compared with the Social Support Rating Scale ( r = −0.28; 95% CI, −0.32 to −0.24), Perceived Social Support Scale ( r = −0.38; 95% CI, −0.45 to −0.30), SSQ ( r = −0.27; 95% CI, −0.35 to −0.20), NSSQ ( r = −0.26; 95% CI, −0.46 to −0.07), and other measures ( r = −0.37; 95% CI, −0.45 to −0.29), the MSPSS ( r = −0.43; 95% CI, −0.51 to −0.36) had a greater impact on the social support of cancer patients.

The moderating effect of different cancer types on patients’ disease uncertainty and social support was statistically significant. Regression analysis of the original sample according to the percentage of female participants and age of the participants showed that the moderating effect of both on the sense of uncertainty about illness and social support of cancer patients was not statistically significant (Table 4 ).

Publication Bias and Sensitivity Analysis

Funnel plots were used to determine publication bias. The results showed that the effect size of the correlation between social support and illness uncertainty in patients with cancer was almost uniform on both sides of the overall effect size ( P = .533) (Figure 2 ). This indicated that there was no significant publication bias in this study. Sensitivity analyses were also conducted to assess the robustness of our findings. After eliminating the included studies individually and recalculating the summary correlation coefficient, the correlation coefficient of illness uncertainty and social support changed only slightly. This suggests that the results of this study are stable.

Discussion

In the past, there have been inconsistent conclusions regarding the association between illness uncertainty and social support; however, no research has clarified this inconsistency. This study was the first to integrate the relationship between disease uncertainty and social support in patients with cancer through a meta-analysis of 41 studies that met the inclusion criteria. The findings indicated that the results of this study had good stability without publication bias, and the overall correlation coefficient between disease uncertainty and social support was −0.33. This moderately negative correlation between disease uncertainty and social support in cancer patients is consistent with the results of most previous studies. In a cognitive state, when patients with cancer cannot fully understand the origin and prognosis of the disease and the adverse reactions caused by various treatment methods, illness uncertainty can occur. 9 Social support, as the antecedent event of illness uncertainty in the theoretical model of disease uncertainty, 16 can help patients judge and understand disease-related events and symptom patterns, improve event familiarity, and thus directly or indirectly reduce the level of uncertainty. Therefore, improving the level of social support can be expected to reduce the illness uncertainty of patients and its negative impact on the prognosis of the disease, 71 quality of life, 72 and psychological state of patients. 73

The publication year influenced the negative correlation between illness uncertainty and social support in patients, and this correlation increased over time. The increase in the negative correlation may be because, with the development of science and technology and advancement of cancer care, patients’ uncertainty may have decreased. Meanwhile, patients commonly experience fatigue, pain, financial difficulties, depression, body image disorder, and role disorder during or after treatment, 74 , 75 which can lead to psychological problems, such as worry about the treatment outcome and uncertainty about the future. The small number of studies published between 1987 and 2004 may have influenced the findings to some extent.

The relationship between illness uncertainty and social support of patients with cancer differs by country. This negative correlation is higher in developed countries than in developing countries. Considering that most of the included studies are from developing countries, we speculate that an imbalance in the number of studies by country may have contributed to the unstable results. Compared with developing countries, developed countries have a more complete assistance system and charitable organizations that can provide financial subsidies for patients with cancer and more disease-related information. The level of social support of patients can be improved with the help of institutions and organizations.

The measures of social support moderated this correlation. Specifically, social support measured using the NSSQ ( r = −0.26) was minimally associated with illness uncertainty. In contrast, social support measured by the MSPSS ( r = −0.43) had the largest relationship with patient uncertainty about illness. These differences may be related to the varying numbers of items and dimensions across instruments. The MSPSS (including 12 items) subdivides social support by source, that is, family, friends, and significant others, 20 whereas the NSSQ divides social support into total function, total network, and total loss. 76 The scoring level of each instrument also differs; the MSPSS adopts a 7-point Likert scale, and the NSSQ uses a 4-point Likert scale. Finally, few studies have used the NSSQ to measure social support, which may have had an impact on the correlation between illness uncertainty and social support.

Different types of cancer significantly moderate the negative correlation between these 2 variables in patients with cancer. Mortality and survival rates differ by cancer type, with lung cancer ranking first in incidence and mortality rates. 1 The negative correlation between illness uncertainty and social support in patients with lung cancer is higher than that in patients with other cancers. For patients with a high mortality rate, the threat of the disease to the patient could lead to a high level of psychological distress and a strong sense of uncertainty. 29 Cancers with high mortality rates are also the focus of staff attention and research. To reduce the threat of lung cancer to human health and protect people’s health, each country has formulated corresponding health policies to further improve its medical and health service systems. For example, the United States launched the National Lung Cancer Screening Trial in 2003, 77 and China also included the screening of high-risk populations in the national major public health project in 2012, providing free screening for eligible high-risk residents. 78 Therefore, although patients with this type of cancer have higher levels of illness uncertainty, they also receive increased social support. Another issue that needs to be considered is that there are few studies on cancers other than breast cancer, which may have affected our results.

The gender and age of patients did not play a moderating role in illness uncertainty and social support, suggesting that this correlation was stable across genders and age. Considering that women are more emotionally rich and delicate, thoughtful, sensitive, and often confused by diseases, they may be more likely to perceive disease uncertainty, and the level is higher. 79 , 80 Therefore, among the 39 studies that reported gender, 23 focused on female patients. In contrast, none of the studies involved all men. This difference likely affected the results of the data analysis. In terms of age, studies have shown that older people have higher levels of disease uncertainty compared with younger people. 48 However, older patients may have different forms of social support, so the moderating effect of age on these 2 studied variables was not significant. This finding may be related to the fact that most of the participants in the included studies were middle-aged to elderly patients with cancer.

Limitations and Prospects

Through the integration and analysis of previous studies, this study explored and clarified the correlation between illness uncertainty and social support among patients with cancer. However, this study has some limitations. First, only articles published in Chinese and English were included in this study, and none of the included studies were from Africa or South America. Therefore, the sample scope of our study has certain limitations, and the universality of the conclusions obtained will be affected to some extent. In addition, this study focused on the relationship between illness uncertainty and social support of patients with cancer, and the influence of some moderating variables on this association; however, other potential influencing factors were not considered. Finally, some studies did not report the information needed to calculate the correlation coefficient, resulting in their exclusion. These exclusions may have had a subtle effect on the results. Therefore, larger sample, more detailed, and inclusive studies with other potential variables are needed to further verify the association between illness uncertainty and social support in patients with cancer and the mediating or moderating variables.

Clinical Implications

Our findings indicated a moderate negative correlation between illness uncertainty and social support in patients with cancer. This relationship was moderated by multiple factors, including country, publication year, cancer type, and instrument used to measure social support. Oncology providers, including oncology nurses, can use certain intervention methods to reduce illness uncertainty among patients. Providers should explain disease-related information to patients in detail and help them to interpret the relevant care policies and systems provided by the state to reduce patients’ anxiety and fear. Focusing on patients with cancers that have higher mortality rates may be important for prioritization. Certain countries could also focus on improving their support systems to enhance humanistic care. While taking steps to improve the level of social support for patients, providers should also pay attention to the individual differences in patients with cancer, carefully pay attention to the psychological distress of each patient, and select interventions to promote the physical and mental health of patients.

Conclusion

There is a moderately negative correlation between uncertainty in illness and social support in patients with cancer. Country, publication year, the measurement of social support, and cancer type significantly moderated this association. However, the relationship between illness uncertainty and social support was moderated by neither the gender and age of the participants nor publication. Future longitudinal studies can further verify this relationship.

Footnotes

The authors have no funding or conflicts of interest to disclose.

Xiajun Guo is the co-first author of the article.

Contributor Information

Sijia Wu, Email: wsj211902@henu.edu.cn.

Xiajun Guo, Email: gxj18238802469@126.com.

Haishan Tang, Email: 2660951034@qq.com.

Yuanyuan Li, Email: lyy202206@126.com.

Wanglin Dong, Email: 184783779@qq.com.

Guangli Lu, Email: kfwangli0915@126.com.

Cui Hou, Email: houcui@henu.edu.cn.

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