Examining the role of social support and death anxiety on life expectancy among cancer patients

Abstract

BACKGROUND:

Cancer is a major global public health issue, making research into all its aspects essential. This study aimed to determine the fit of the structural equation modeling of the correlation between ‘life expectancy’ and ‘social support’ and ‘death anxiety’ in cancer patients.

MATERIALS AND METHODS:

The research method is applied survey research, characterized as descriptive-correlational, targeting the population of cancer patients visiting Omid Hospital at Isfahan University of Medical Sciences in 2023. A total of 368 individuals were selected through convenience sampling. Data were collected using a demographic information questionnaire, Snyder’s Life Expectancy Scale, Sherburne and Stewart Social Support Scale, and Templer’s Death Anxiety Scale, with confirmed validity and reliability. Data were analyzed using SPSS23 and Amos using descriptive and inferential statistics.

RESULTS:

In the study sample, no significant average differences were observed between men and women or among age groups regarding life expectancy. Furthermore, examining the relationship between the two variables, social support, and life expectancy, revealed a positive and significant correlation based on Pearson’s correlation coefficient. Additionally, the structural equation model demonstrated how social support could impact the reduction of death anxiety and the increase in life expectancy among cancer patients. In the structural equation model, the coefficients indicated a relatively good fit, suggesting that the collected data are supported by the theoretical framework of the research. In the data analysis, along with calculating descriptive indicators, inferential indicators were also computed using the structural equation modeling method, and fit indices and path coefficients were extracted for the proposed model.

CONCLUSION:

With increased social support for cancer patients, their life expectancy increases, and their death anxiety decreases. Accordingly, it is suggested that extensive studies be conducted to identify the sources of social support for cancer patients to foster their life expectancy and diminish death anxiety.

Introduction

Cancer is a leading public health problem around the globe.[1] Cancer diagnosis, more than any other disease, is a very unpleasant and distressing experience for every individual, causing a crisis for the afflicted person and affecting the occupational, economic, social, and familial life of the patient.[2] Among the challenges that these individuals face in connection with the course of their illness are the fear of a recurrence of the disease, the worry of possible complications, and the uncertainty of returning to everyday life.[3] These challenges hurt the mental health of cancer patients over time.[4] Generally speaking, cancer is one of the leading causes of mortality in the world, with a prevalence of 10 million deaths per year.[1]

Anxiety is a feeling of tension, apprehension, and physical changes such as increased blood pressure, diaphoresis, tremors, vertigo, or tachycardia.[5] When anxiety is higher than usual, it weakens the body’s immune system, and as a result, the risk of being sick increases.[6] Recent studies have indicated that anxiety is the most critical factor affecting the mental health of cancer patients, and death anxiety is considered one of the most significant psychological factors affecting them.[7] Death anxiety is highlighted at different times for cancer patients, particularly at the beginning of diagnosis, during every examination, and in cases of relapse or disease progression.[8] According to the definition provided by the International Nursing Diagnosis Association of North America, death anxiety is a vague feeling of discomfort or fear caused by the perception of a real or imaginary threat to one’s existence.[9] Failure to reduce death anxiety in cancer patients over time can lead to problems such as insomnia, pain intensification, physical discomfort, and a decreased quality of life[2,10]; this eventually causes mental disorders in people, which affect the treatment and recovery process and result in an increased mortality rate among cancer patients.[2] As mentioned before, for many patients, the diagnosis of cancer and the course of its treatment are very stressful and potentially traumatic experiences; they perceive them as threatening events that are also associated with psychological consequences.[7] Today, one of the most important topics in positive psychology is creating hope because positive psychologists believe that optimism can support people against stressful events in life.[11] Optimism refers to one’s ability to have specific goals, formulate plans to reach them, and follow them.[12,13] Thus, it can be asserted that life expectancy as an internal force enables patients to look beyond the existing situation and continue their lives purposefully,[14] while in the case of pessimism, the person finds the situation unbearable. They consider the concept and achievement of their goals impossible, which ultimately leads to depression, death wishes, and suicidal thoughts.[15] Findings of studies indicate that hope and optimism are significantly related to recovery after illness[16,17] because the higher the life expectancy of a cancer patient, the more actively they will be involved in the treatment process, the more resistant they will be to long and painful treatments, and finally the more adaptable they will be to the conditions that arise.[18,19] Another important aspect of caring for cancer patients is social support because the need for social support increases following the many changes in patients’ life processes and their quality of life.[3] Social support is defined as a person’s perception or experience of being loved, protected, respected, and having membership in a social network with all of its contributions and responsibilities.[15] It should also be noted that social support begins with social contact, communication, and safe support of the individual.[20] Social support received from family members, friends, and healthcare professionals is very important in dealing with cancer and creating adaptations in a person with cancer.[21,22] In general, it can be said that social support improves the quality of life of the patient, increases their survival time, ameliorates professional care outcomes and the economic situation, maintains the feeling of social homogeneity, facilitates self-evaluation, connects them to the community, and enables them to deal with the feeling of loneliness.[23,24] As mentioned earlier, a cancer diagnosis is a very unpleasant experience that is often associated with internal turmoil, anxiety, and depression in patients.[25] This shows, per se, the necessity of proper psychological support by the treatment team, especially nurses, as one of the first healthcare providers for cancer patients in all stages of their treatment.[26] This psychological support will cause negative feelings and states to become positive and hopeful.[27] Hence, considering the increasing number of people with cancer and their many problems, and considering the importance of life expectancy and social support components, and the role of each of these variables in death anxiety, the present research team decided to embark on conducting research in this field. Given that no study was found regarding the inter-correlations of the mentioned variables at the same time, the necessity of research in this field was felt. Therefore, the present study aims to examine the question of whether social support can lead to a reduction in death anxiety and, ultimately, an increase in life expectancy among cancer patients. Figure 1 shows the theoretical model of the research.

Figure 1.

The theoretical model of the research

Materials and Methods

Study design and setting

The research is of a descriptive-correlational type, focusing on a statistical population of cancer patients who have undergone treatment for at least one month and are over 18 years old. The research environment was Omid Hospital, chosen for its accessibility to cancer patients in Isfahan city, facilitating sample collection.

Study participants and sampling

Based on its purpose, the present study was applied research using a descriptive-correlational design. The statistical population of the research included cancer patients referred to the Omid Hospital of Isfahan University of Medical Sciences in 2023. In this study, sampling was performed using a convenience sampling method. The following formula was used to determine the sample size:

Reference was made to the standard deviation of 4.79 for the life expectancy score reported in the study by Sabaripour et al.[28] Considering the confidence interval of 95%, the test power of 80%, and the maximum estimation error of 0.7, the sample size was estimated to be 368 people. The inclusion criteria were people with cancer who had passed at least one month since the start of treatment for their disease, aged over 18 years, could read and write, were alert, and were able to respond, as well as not being in the end stage of the disease. Distorted and incomplete questionnaires were removed from the research process.

Data collection tool and technique

In the present study, data were collected using a demographic information questionnaire, Snyder’s Life Expectancy Scale, Sherburne and Stewart Social Support Scale, and Templer’s Death Anxiety Scale. Content, concurrent, and face validity were utilized for measuring validity, while Cronbach’s alpha was used for reliability. After obtaining ethical clearance and a referral from the research vice-dean of the faculty and gaining permission from the educational hospital affiliated with Isfahan University of Medical Sciences, the researcher approached patients in the hospital, explaining the study’s objectives and emphasizing information confidentiality, before providing the questionnaires. Data analysis was conducted using SPSS version 21 and AMOS software.

Demographic information questionnaire

This questionnaire contains 11 items including the patient’s age, place of residence, gender, occupation, education, marital status, number of children if married, duration of treatment, and type of cancer.

Snyder’s life expectancy scale

Snyder’s Life Expectancy Scale was developed in 1991 by Snyder et al.[29] measure life expectancy in people over 15 years old. This scale contains 12 items and consists of two subscales of agency thinking and strategic thinking. The scoring of this scale is based on an 8-point Likert scale from “completely false” with a score of 1 to “completely true” with a score of 8. Moreover, no points are assigned to the four distractors of the questionnaire, and therefore the range of points is from 8 to 64; the higher the score obtained by the subject, the higher their life expectancy. To measure the validity of the Life Expectancy Scale, the harmony of the items of this scale with Snyder’s theory indicates its good content validity.[30] Besides, in concurrent validity, there has been a significant positive correlation between this scale and positive emotion, optimism, satisfaction with life, and self-esteem; there has also been a significant negative correlation with anxiety and pessimism.[31] Snyder et al.[29] reported the reliability of the Life Expectancy Scale through the test-retest method after three weeks for the whole scale as 0.85 and for the two subscales of agency and strategic (system) thinking as 0.81 and 0.74, respectively. Also, in the research by Kermani et al.[32] (2013), using Cronbach’s α coefficient, the reliability of the scale was reported to be 0.86 for the whole scale and 0.77 and 0.79 for the two subscales of agency and strategic (system) thinking, respectively.

Sherburne and Stewart social support scale

Sherburne and Stewart’s Social Support Scale, which was developed in 1991,[33] was used to measure social support. This scale consists of 19 items and 5 subscales. These subscales are: tangible support, which measures material and behavioral assistance, emotional support, which evaluates sympathy and encouragement to express feelings, informational support, which measures guidance, giving information or feedback, affectionate support, which measures the expression of love and interest, and finally the positive social interaction that evaluates engaging in recreational activities. The scoring of each item is based on a 5-point Likert scale. The lowest score that can be obtained from this questionnaire is 19 and the highest score is 95. The higher the score obtained by the subject, the higher their social support. The face and content validity of the social support scale from the perspective of psychologists in Iran has been confirmed by Tamanaeifar and Mansoori Nik.[34] The Cronbach’s α reliability of the social support scale has been reported in the range of 0.74–0.93.[35] Besides, its reliability was reported with a Cronbach’s α coefficient of 0.97 in the study by Tamanaeifar and Mansouri Nik.[34]

Templer’s death anxiety scale

The Death Anxiety Scale was developed by Templer in 1970.[36] This scale includes 15 items with two options, “true” and “false”, and the items are scored in such a way that the score is 1 for a “true” answer and 0 for a “false” answer. The minimum and maximum scores that can be obtained in this questionnaire are 0 and 15, respectively. If the score obtained by the subject ranges from 0 to 6, it means that their death anxiety is low, if it ranges between 8 to 15, then their death anxiety is at a high level, and finally, a score of 7 is the cut-off point, meaning that the subject’s death anxiety is at an average level. The validity of the Death Anxiety Scale was estimated by calculating its correlation with the anxiety scale as 0.27 and with the depression scale as 0.40.[36] The reliability coefficient of the Death Anxiety Scale has been reported as 0.83 by the test-retest method,[36] and its Cronbach’s α coefficient has been reported as 0.88 in the study by Farahi and Khalatbari.[7]

Ethical considerations

The code of ethics (IR.MUI.NUREMA.REC.1401.089) was received from the Research Ethics Committees of Nursing, Rehabilitation, and Management Schools, Isfahan University of Medical Science. In this project, the purpose of the research was fully explained to participants who could withdraw from the study at any time. We explained that participating in or withdrawing from the study would not affect their course of treatment and that all their information would remain confidential.

Data analysis

The inferential analyses were performed using SPSS23 and structural equation modeling (SEM) was done using Amos24. In data analysis, in addition to calculating descriptive indices, inferential indices were calculated using structural equation modeling and fit indices; path coefficients were extracted for the proposed model (P = 0.05).

Results

The sample consisted of 368 cancer patients including 214 women (58.2%) and 179 (48.6%) patients over 50 years old. There were no missing values in the data. Table 1 presents a description of other measured covariates.

Table 1.

Demographic information of the statistical sample

Characteristic		Category		n (%)		Characteristic		Category		n (%)
Gender		Female		214 (58.2)		Place of residence		Urban		330 (89.9)
		Male		154 (41.8)				Rural		38 (10.1)
Age (years)		20–30		15 (4.1)		Insurance coverage		Yes		361 (98.1)
		31–40		76 (20.7)				No		7 (1.9)
		41–50		98 (26.6)		Financial situation		Sufficient		134 (36.4)
		51–60		169 (45.9)				Insufficient		234 (63.6)
		>60		10 (2.7)		Education level		Primary/secondary		161 (43.8)
Marital status		Single		43 (11.7)				Diploma		131 (35.6)
		Married		303 (82.3)				University degree		76 (20.7)
		Divorced		9 (2.4)		Cancer grade		Grade I		46 (12.5)
		Widowed		13 (3.5)				Grade II		169 (45.9)
Employment status		Student		6 (1.6)				Grade III		153 (41.6)
		Unemployed		50 (13.6)		Type of treatment		Chemotherapy		219 (59.5)
		Non-governmental		100 (27.2)				Surgery		2 (0.5)
		Government employee		20 (5.4)				Combination therapy		147 (39.9)
		Housewife		178 (48.4)		Number of children		None		40 (10.9)
		Retired		14 (3.8)				1–2		162 (44.0)
Type of cancer		Breast		113 (30.7)				3–4		115 (31.3)
		Reproductive system		33 (9.0)				>4		51 (13.9)
		Blood		94 (25.5)		------------------------------------------------------------------------------
		Digestive system		70 (19.0)
		Respiratory system		24 (6.5)
		Prostate		13 (3.5)
		Other		21 (5.7)

The study variables including life expectancy with two components (strategic thinking and operant thinking), social support with four components (kindness, tangible support, positive social interaction, and emotional-informational support), and death anxiety are reported in Table 2. The mean score of life expectancy among the respondents was obtained as 6.61, indicating that the life expectancy was higher than the average among the respondents. Moreover, according to the order of scores among the two components of life expectancy, the mean of the operant thinking dimension (6.90) was slightly higher than that of strategic thinking (6.61) among the respondents. The mean score of social support was 4.32 among the respondents, indicating higher-than-average social support among the respondents. Furthermore, according to the descending order of scores among the components of social support, the mean of the dimension of kindness was 4.52, the dimension of tangible support was 4.37, the dimension of positive social interaction was 4.31, and the dimension of emotional-informational support was 4.21. The mean score of death anxiety was 0.25 among the respondents, indicating a lower-than-average score among respondents. In other words, this score shows that the death anxiety due to cancer was less than average among the respondents, and cancer was not considered a monster of death or a dangerous thing for them.

Table 2.

Descriptive statistics of quantitative research variables

		Life expectancy						Social support										Death anxiety
		Components				The main variable		Components								The main variable
		Strategic thinking		Agent thinking		Life expectancy		Emotional Information support		Tangible support		Positive social support		Kindness		Social support
Mean		6.61		6.90		6.75		4.21		4.37		4.31		4.52		4.32		.25
Std. Error of Mean		.06		.05		.057		.045		.042		.043		.039		.039		.01
Median		6.89		7.09		6.96		4.41		4.69		4.57		4.84		4.54		.21
Mode		7.00		8.00		7.00b		5.00		5.00		5.00		5.00		5.00		.11
Std. Deviation		1.25		1.10		1.10		.86		.82		.82		.76		.76		.18

To test the difference in average life expectancy based on gender, an independent-sample t-test was used [Table 3]. This result suggests no significant difference in life expectancy between women and men. Additionally, to test the differences in average life expectancy based on age groups, type of cancer, marital status, and stage of cancer, a one-way ANOVA was conducted. The results, presented in Table 3, show no significant differences in average life expectancy among the groups for.

Table 3.

The results of descriptive statistics and the results of mean difference tests (T-Test) & (One-Way ANOVA)

Variables		Groups		n		Mean		Sig
Gender		Female		214		6.75		.992
		Male		154		6.77
Age groups		20–30		15		7.10		.508
		31–40		76		6.79
		51–60		98		6.84
		61 and up		169		6.66
Cancer types		Breast		113		6.7721		.039
		Reproduction system		33		6.8220
		Blood		94		6.8830
		Digestive system		70		6.4161
		Respiratory system		24		7.2552
		Other		21		6.7024
		Prostate		13		6.6058
Marital status		Single		43		6.6686		.191
		Married		303		6.8016
		Divorced		9		6.0694
		Widowed		13		6.5385
Grade		1		46		6.4484		.105
		2		169		6.7678
		3		153		6.8423

As shown in Table 4, Pearson’s correlation test was used to examine the relationship between social support, life expectancy, and its components, indicating a positive and significant relationship (sig. =0.000 and r = .415) between these two variables. Interestingly, among “life expectancy” and “social support” components, the “informational-emotional support” dimension had the strongest relationship with “operant thinking” as one dimension of “life expectancy” (r = 428). Likewise, the relationships among all the components were also positive and significant. Pearson’s correlation coefficient test was used to investigate the relationship between death anxiety and life expectancy and its components, and the results revealed a negative and significant relationship (sig. =0.000 and r = -.264) between these two variables. In addition, regarding the life expectancy and death anxiety components, this relationship was negative and significant.

Table 4.

Pearson correlations between variables of interest and their components

Variables				Emotion		Tangible		Positive		Kindness		Social Support		Death Anxiety
Agent		Pearson Correlation		.428**		.311**		.328**		.389**		.416**		-.292**
		Sig. (2-tailed)		.000		.000		.000		.000		.000		.000
Strategic		Pearson Correlation		.357**		.290**		.297**		.336**		.360**		-.198**
		Sig. (2-tailed)		.000		.000		.000		.000		.000		.000
Life expectancy		Pearson Correlation		.420**		.320**		.333**		.388**		.415**		-.264**
		Sig. (2-tailed)		.000		.000		.000		.000		.000		.000

An important point to consider was the ability to simultaneously examine these two independent variables and their direct and indirect relationships with the dependent variable using structural equation modeling. Therefore, this model was used to explain better and understand these relationships.

As observed in Figure 2, social support has a direct relationship with life expectancy (0.64) and has been effective in increasing life expectancy for patients by reducing death anxiety (indirect relationship (-12)). Additionally, in this model, according to the coefficients of goodness-of-fit criteria and model fit, it should be noted that the research model has a relatively good fit, and the theoretical literature and selected research components support the collected data.

Figure 2.

Experimental research model in the form of a structural equation model. P = 0.000 CMIN/DF = 4.288=; RMSEA = 0.095; PNFI = 0.554; TLI = 0.960; AGFI = 0.954

Also, in this study, several variables influencing the life expectancy of patients have been considered. Multivariate regression analysis helps us understand the impact and the order of these effects in explaining the dependent variable (life expectancy). By entering the sum of independent variables on the dependent variable, we found a coefficient of determination R² =0.24 and significance = 0.000, indicating that approximately 24% of the variance in the life expectancy variable is accurately explained by the independent variables [Table 5]. These results align with our theoretical framework and previous research.

Table 5.

Model fit criteria and acceptable fit interpretation

Model fit criteria		Acceptable level		Explanation		The resulting amount		Acceptance limit
CMIN		Chi-square value of the table		The chi-square obtained is compared with the chi-square of the table for a certain degree of freedom.		51/46		Acceptable
CMIN/DF		From 1 to 5 is acceptable, but between 2 and 3 is good		A value less than 1 indicates poor fit and a value greater than 5 reflects the need for improvement.		4/288		Acceptable
GFI		(no fit) to 1 (perfect fit)		A value close to 0.95 reflects a good fit.		0/97		Acceptable
RMSEA		Smaller than 0.10		A value less than 0.05 indicates a good fit.		0/09		Acceptable
TLI		(no fit) to 1 (perfect fit)		It tries to overcome the weakness of the Bentler-Bonnet index (NFI) in not taking into account the index penalty for adding parameters		0/96		Acceptable
IFI		(no fit) to 1 (perfect fit)		Based on the developed model or assumed by the independence model		0/97		Acceptable
PNFI		Above 0.50 or 0.60		It is obtained from the product of the model’s economy ratio in the normalized fit index		0/55		Acceptable
PCFI		Above 0.50 or 0.60		The closer the model is to a saturated one, the more it is penalized		0/55		Acceptable

It is also worth mentioning that, according to the β values, the social support variable, with (β =0.38), is the most significant influencing factor on life expectancy; meaning that a one standard deviation change in the social support variable results in a 0.38 standard deviation change in the dependent variable (life expectancy). Similarly, the insurance status variable (β =0.17), death anxiety (β =0.13), and age (β =0.12) have also played a role in explaining the dependent variable, while other variables were not significant [Table 6].

Table 6.

Correlation coefficients of regression analysis

Model		Unstandardized Coefficients				Standardized Coefficients		T		Sig.
		B		Std. Error		Beta
(Constant)		7.182		.871				8.242		.000
social support		.030		.004		.387		6.847		.000
Insurance coverage		−1.379		.382		−.171		−3.613		.000
death anxiety		−.765		.309		−.130		−2.476		.014
Age		−.150		.070		−.128		−2.135		.033
Resident type		−.346		.182		−.094		−1.906		.057
Gender		−.136		.161		−.061		−.846		.398
Type of employment		.040		.060		.042		.659		.510
Type of cancer		−.018		.040		−.027		−.439		.661
Type of treatment		.018		.044		.024		.414		.679
Marriages status		−.044		.125		−.021		−.350		.726
Number of children		−.021		.059		−.019		−.355		.722
Education		.019		.086		.013		.221		.826
Financial situation		.025		.114		.011		.222		.824
Type of Grade		.009		.096		.006		.096		.924

Discussion

This study examined the relationship between social support and life expectancy, as well as the impact of reducing death anxiety on the life expectancy of cancer patients. The findings from the data analysis reveal that the mean scores for both life expectancy and social support among the participants were higher than average. Conversely, the average score for death anxiety was found to be below average. The findings about social support in a study on breast cancer patients in Ethiopia align with our results, as they reported an overall high level of perceived social support.[37] However, these findings regarding social support contradict those of Jadidi and Ameri, who reported moderate family social support.[38] In the study by Wang et al.[39] on cancer patients during the COVID-19 pandemic, it was reported that a low level of social support was a significant predictor of distress in cancer patients. Research indicates that life expectancy in various subgroups historically affected by the disease has risen more rapidly than in the overall population, although it still lags at lower levels.[40,41] In a systematic review, the findings indicated that death anxiety in cancer patients is moderate, and subgroup analysis in this study showed that the estimated mean for death anxiety in Asian studies is higher compared to European studies and North America.[42] In explaining the reason for the contradictions in the studies, it can be said that life expectancy, social support, and death anxiety can be influenced by sociodemographic factors such as country and region, culture, type of cancer, gender, marital status, and other unknown factors. A critical aspect of this study was the simultaneous examination of two independent variables, social support and life expectancy, and their direct and indirect relationships with the dependent variable, death anxiety. To facilitate a comprehensive analysis of these relationships, structural equation modeling was employed, providing a clearer understanding of the relationships between these variables.

Based on our findings, social support has a direct relationship with life expectancy and has been effective in increasing life expectancy for patients by reducing death anxiety (indirect relationships). The research model had a relatively good fit and the theoretical literature and selected research components supported the collected data. In our study, a positive and significant relationship was obtained between life expectancy (operant thinking component) and social support (emotional-informational support component), which means that the more social support people have, the higher their life expectancy, consistent with the results of previous studies.[15,43]

Among cancer patients, receiving social support from others can protect them from the negative consequences of the disease; thus, social support has a strong correlation with the patient’s psychological functions.[44,45] In explaining this finding, it can be stated that when people with cancer receive social support in all aspects of their lives, especially the emotional aspect, and feel valuable, their hope and desire to continue life increases by improving indicators such as life satisfaction, resilience, and quality of life.[21,46,47,48] In the present study, the greatest inverse relationship was obtained between death anxiety and social support (support components of emotional information and kindness). This means that the more social support there is among the clients, the less death anxiety is seen among them; this is consistent with the study by Bibi et al.[48] In their study, they stated that patients with low social support experience higher death anxiety. Lack of social, instrumental, and emotional support causes loneliness, excessive worry, and pessimism in patients.[49,50,51] Previous studies have found that social support alleviates depression and anxiety by fostering coping mechanisms such as active engagement, positive reframing, and acceptance strategies.[52] So, in general, based on direct social support theory, it can be said that the lack or absence of social support is stressful per se; hence, social support is always useful, whether in stressful situations or not. According to this model, social support increases individual resilience in several ways. People’s health is influenced by their social support.[53] Furthermore, it is expected that the content of death concerns, such as concerns about how one dies, the effect of one’s death on others, or what happens after death, overlaps with concerns about disease recurrence or progression.[54,55,56] Death anxiety was also a predictor of fear of cancer recurrence or progression.[57] Death anxiety also has a significant negative correlation with self-efficacy.[53] Based on the mentioned issues, the importance of paying attention to social support in cancer patients to reduce death anxiety and improve the quality of life of these patients is highlighted. Another finding of this study was the inverse correlation between life expectancy and death anxiety, meaning that the higher the life expectancy among cancer patients, the less death anxiety is seen among them; this is consistent with the results of previous studies.[58] In our review of the literature, no research was found to be inconsistent with this study. It has been proposed that elevated levels of fear surrounding death and dying can harm health outcomes and life expectancy.[59] In Cohen’s study, conducted on the correlation between religiosity, belief in the afterlife, death anxiety, and life satisfaction, the results showed that religiosity moderates the correlation between death anxiety scales and afterlife beliefs; at the same time, life satisfaction has a negative correlation with death anxiety and a positive correlation with belief in resurrection and afterlife.[60] Relief of death anxiety can be considered a major outcome in palliative care.[61] In cancer populations, death anxiety is associated with physical pain and discomfort, psychiatric diseases, including general anxiety and depression, disturbance in spiritual well-being and quality of life, and psychological burden in family caregivers.[62,63,64,65] Finally, other studies show that the level of fear of cancer recurrence is a significant predictor of death anxiety and has a positive correlation with death anxiety.[66,67]

Moreover, since we considered several factors in this research that are effective in increasing life expectancy in patients, we investigated the effect of this set of factors using multiple regression analysis. Importing the sum of independent variables on the dependent variable indicates that approximately 24.0% of the variance in life expectancy is accounted for by the independent variables in the regression model. The descending order of magnitudes of the coefficients affecting the dependent variable is as follows: social support, insurance status, death anxiety, age, place of residence, gender, employment status, type of cancer, treatment methods, marital status, number of children, education, financial status, and grade of cancer. Based on the results of the regression analysis, it can be asserted that other variables can affect the life expectancy of patients, and researchers interested in this topic can investigate them.

Limitations and recommendations

Every research activity faces shortcomings and limitations during implementation, and the present study was not an exception to this rule. Among the limitations of this study, we can point out the non-cooperation of some people in completing the questionnaires, the research sample being limited to only patients referring to a hospital in Isfahan, and the lack of random sampling due to the limitation of the statistical population. The use of questionnaire tools was also another limitation of this research. Although participation in the study was completely voluntary and the confidentiality of the information was assured, the participants may have given unrealistic answers in responding to the questionnaires; this was beyond the control of the researchers. To the best of our knowledge, this was the first study to have examined the relationship between life expectancy and social support with death anxiety in cancer patients in Iran. According to the mentioned limitations and the final findings of this study, it is suggested that research projects in the future be conducted to clarify the relationship between these variables in other statistical populations and age groups. As a result, considering these limitations, the generalization of the results should be done with sufficient caution.

Conclusion

Considering the significance of the presented model, our findings showed a direct correlation between social support and life expectancy, an inverse correlation between social support and death anxiety, and an inverse correlation between life expectancy and death anxiety. Thus, interventions focused on these components become an important protective factor in maintaining mental health and improving the quality of life of cancer patients and their families, which will have a positive effect on the treatment process of patients. Clinical therapists are advised to pay attention to the important role of social support in the psychological treatments they use for people with cancer to increase life expectancy and reduce death anxiety. Also, comprehensive cancer care should include a focus on building and strengthening social support systems. Healthcare organizations need to implement approaches that foster community connections for patients and their loved ones. It’s essential that health policies emphasize mental wellness services and encourage active participation from both patients and their families in community-based programs. By integrating these elements, health systems can provide more holistic and effective care for those affected by cancer.

Conflict of interest

There are no conflicts of interest.

Funding Statement

This study received financial support from the Deputy of Research at Isfahan University of Medical Sciences, which has officially approved the research under tracking code 58015 and scientific code 1401202.