Influence of loneliness and social isolation on the diagnosis and treatment of Japanese patients with advanced lung cancer: a prospective cohort study

Introduction

Social determinants of health (SDHs) are social and economic factors that influence human health (1). The College of Family Physicians of Canada categorizes SDHs as food insecurity, education, child development, social security, job security, unemployment, working environment, gender, sexual orientation, social exclusion, and access to health services (1). At an international conference in Rio de Janeiro in 2011, the World Health Organization emphasized SDHs as factors that could be addressed to promote health equality (2). The main focus of SDH research is on social interactions, which may involve issues with social isolation and loneliness and may influence the individual’s living environment, access to services, and physical activity. Among SDH factors, upstream factors are at the macro level, such as race, nationality, and socio-economic status; loneliness and social isolation are thought to be in this category. Midstream factors are intermediate ones, such as health behaviors, education, and financial status. Downstream factors are the ones directly related to health, such as prevention, diagnosis, or treatment. Upstream factors influence midstream factors, and midstream factors influence downstream factors (3). Malcolm et al. (4) defined social isolation as “the objectively quantified shortfall in an individual’s social relationships often measured in terms of social network size, diversity or frequency of contacts” and loneliness as “a perceived deficit between actual and desired quality or quantity of relationships”.

A systematic review indicated that social isolation and loneliness were associated with higher mortality rates, regardless of whether they were linked to underlying medical conditions (5). Hyland et al. also reported that loneliness might be a manifestation of depressive symptoms and low quality of life (6). Social isolation also has mental and physical effects. While various studies have evaluated SDH in the general population, few studies have evaluated cancer patients (3). Previous cohort studies have indicated that living in a high deprivation area, uninsured status, and low education level were prognostic factors for lung cancer patients (7-10). However, the effects of social isolation and loneliness, as core SDH factors, remain unclear. Pezzi et al. reported that government insurance coverage influences decreased use of radiotherapy (8). This indicates that social interactions, which are reflected in loneliness and social isolation, may decrease pathological diagnosis or active treatment. We assume that loneliness and social isolation may influence the determination of diagnosis and treatment among cancer patients because these factors are upstream relative to economic or education SDH factors (3). Therefore, this study aimed to investigate the effects of loneliness and social isolation among Japanese patients with advanced lung cancer. First, we evaluated whether loneliness or social isolation were associated with an increased clinical diagnosis rate among lung cancer suspectable patients. Second, we evaluated whether loneliness or social isolation were associated with an increased proportion of best supportive care (BSC) as the first treatment after a confirmed lung cancer diagnosis.

Methods

Study design and setting

This prospective cohort study complied with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement (available at http://dx.doi.org/10.21037/apm-21-402). The study was performed at a Japanese tertiary referral hospital (Hyogo Prefectural Amagasaki General Medical Center) between April 2018 and March 2020. The study protocol has previously been published (11) and was approved by the Hyogo Prefectural Amagasaki General Medical Center (# 29-164). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All patients provided written informed consent before their enrollment.

Evaluating the method for diagnosing lung cancer

The eligibility criteria for the analysis of diagnostic method were (I) a clinical suspicion of lung cancer based on computed tomography findings or other factors, (II) the patient was considered unsuitable for curative surgery (to avoid missing surgery-related data), (III) no treatment for lung cancer during the previous 2 months (as some cases required urgent treatment before the patient might be able to complete the questionnaire and we wanted to allow for 2 months after the start of treatment), and (IV) the patient provided written informed consent to participate in the study. The exclusion criteria were: (I) inability to complete the questionnaires (e.g., because of dementia or psychological disease) and (II) a physician’s judgment that the patient was not suitable for the study.

The exposures of interest were defined as loneliness and social isolation. Loneliness was assessed using the third Japanese version of the University of California, Los Angeles (UCLA) Loneliness Scale (3–12 points) (12). To ensure that the patients could answer the questions easily, we revised the UCLA scale into a 3-question version (13). Because that tool did not have defined cut-off values, quartiles were used to categorize the participants (low to high loneliness) to help create relatively homogenous groupings. Social isolation was assessed using the Japanese version of the Lubben Social Network Scale (LSNS-6) (14). As a cut-off value for the Japanese version has not been defined, the cut-off value from the English version was used (at 12 of 30 points) (14). All patients completed the questionnaires at the time of enrollment.

The main outcome was a clinical or pathological diagnosis of lung cancer. Potential confounding factors were evaluated at enrollment, which included sex, age, smoking status, presentation with any symptoms, dementia, Eastern Cooperative Oncology Group (ECOG) performance status, and the 8th edition of the American Joint Commission on Cancer and Union for Cancer Control tumor node metastasis (TNM) classification stage (15). The presence of dementia was assessed using the Life Function Evaluation for Care Provision (16).

Evaluating the initial treatment

Patients with pathologically diagnosed lung cancer were subsequently considered to analyze the initial treatment strategy (BSC or active treatment). Patients with a clinical diagnosis were excluded from this analysis. The exposures were defined as loneliness and social isolation. The confounding factors were defined as sex, age, smoking status, respiratory symptoms, weight loss, presentation with any symptoms, ECOG performance status, TNM classification, driver gene mutations [i.e., epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and c-ros oncogene 1 (ROS1)], and programmed death-ligand 1 (PD-L1) tumor proportion score.

Active therapy was defined as palliative chemotherapy or chemoradiotherapy with curative intent starting at <3 months after enrollment. Because every lung cancer patient receives BSC, regardless of active treatment status, we assigned patients who did not start active treatment within 3 months after enrollment to the BSC group, including palliative radiotherapy and complementary or alternative medicine.

Patients were judged for enrollment in the study by physicians with inclusion and exclusion criteria. At the time of enrollment, physicians handed the questionnaires with a consent form. The presence of dementia was assessed using the Life Function Evaluation for Care Provision (16). Patients submitted the paper to physicians or nurses. After enrollment, the patient received regular care. Treatment and diagnosis data were collected from the chart. When patients were referred to other hospitals, we requested their information from the hospital.

Statistical analysis

The protocol planned to recruit 300 patients, although recruitment was terminated after 2 years because of insufficient enrollment. The clinical research coordinator selected patients with a new pathological diagnosis and managed the collected data to limit selection bias. Odds ratio (OR) or confidence intervals (CI) were calculated using a logistic regression model. Multiple imputation was used to analyze missing data regarding the exposure and outcome variables and the five combined imputed datasets using Rubin’s rule (17). We used R software (version 3.6.3; R Core Team, R Foundation for Statistical Computing, Vienna, Austria) and the “mice” package (version 3.8.0) for the analyses. We performed sensitivity analyses comparing multivariate analysis with multiple imputation and complete case analysis. P values of <0.05 were considered statistically significant.

Results

Diagnostic method

The study enrolled 264 patients who fulfilled the inclusion criteria (Figure 1). The patients’ characteristics are summarized in Table 1. The patients were categorized into quartile 1 (≤3 points), quartile 2 (≥4, ≤6 points), quartile 3 (7 points), and quartile 4 (≥8 points) based on the UCLA loneliness scale score, and each group included 70 (26%) patients, 109 (41%) patients, 39 (15%) patients, and 41 (16%) patients. The clinically diagnosed group included 24 patients, and the pathologically diagnosed group included 240 patients. Except for driver gene mutation and PD-L1, which are not mandatory for all patients, the proportion of missing data was <3% in each confounding factor because of an incomplete questionnaire. The univariate analysis revealed that a clinical diagnosis was not significantly associated with loneliness (quartile 4 vs. quartile 1, OR: 0.19, 95% CI: 0.02–1.60; quartile 3 vs. quartile 1, OR: 1.70, 95% CI: 0.56–5.10; quartile 2 vs. quartile 1, OR: 0.61, 95% CI: 0.22–1.72). Furthermore, a clinical diagnosis was not significantly associated with social isolation (present vs. absent, OR: 0.72, 95% CI: 0.31–1.69) (Table 2). Moreover, multivariate analysis with multiple imputation revealed that a clinical diagnosis was not significantly associated with loneliness (quartile 4 vs. quartile 1, OR: 0.10, 95% CI: 0.01–1.08; quartile 3 vs. quartile 1, OR: 1.36, 95% CI: 0.35–5.37; quartile 2 vs. quartile 1, OR: 0.49, 95% CI: 0.14–1.67) or with social isolation (OR: 1.82, 95% CI: 0.60–5.56) (Table 2). The complete case analysis also revealed the same results (Table 2).

Figure 1 Patient flow chart.

Table 1 Characteristics of the study participants about clinical diagnosis
Full table

Table 2 Univariate analysis/multivariate analysis with multiple imputation/complete case analysis of clinical diagnosis with loneliness and social isolation
Full table

Initial treatment

The analysis of initial treatment included 240 patients with pathologically diagnosed lung cancer (Table 3). The loneliness scores were used to assign the patients to quartile 1 (62 patients, 26%), quartile 2 (101 patients, 42%), quartile 3 (32 patients, 13%), and quartile 4 (40 patients, 17%), while 5 patients were excluded because of incomplete answers. Social isolation was judged to be present for 79 patients (33%) and absent for 153 patients (64%), although 8 patients were excluded because of incomplete answers (Table 3).

Table 3 Characteristics of the study participants about best supportive care
Full table

BSC group included 27 patients, and the pathologically diagnosed group included 213 patients. The univariate analysis revealed that the use of BSC as the initial treatment was not significantly associated with loneliness (quartile 4 vs. quartile 1, OR: 0.96, 95% CI: 0.29–3.19; quartile 3 vs. quartile 1, OR: 0.70, 95% CI: 0.17–2.83; quartile 2 vs. quartile 1, OR: 0.82, 95% CI: 0.31–2.18) or with social isolation (present vs. absent, OR: 0.61, 95% CI: 0.27–1.37) (Table 4). Furthermore, multivariate analysis with multiple imputation revealed that BSC was not significantly associated with loneliness (quartile 4 vs. quartile 1, OR: 0.41, 95% CI: 0.06–2.72; quartile 3 vs. quartile 1, OR: 0.75, 95% CI: 0.12–4.78; quartile 2 vs. quartile 1, OR: 0.44, 95% CI: 0.10–1.89) or with social isolation (OR: 1.19, 95% CI: 0.39–3.70) (Table 4). The complete case analysis also revealed the same results (Table 4).

Table 4 Univariate analysis/multivariate analysis with multiple imputation/complete case analysis of clinical best supportive care with loneliness and social isolation
Full table

Discussion

The present study revealed that loneliness and social isolation did not appear to influence the determination of a passive clinical diagnosis and treatment of Japanese patients with advanced lung cancer. A systematic review has indicated that social isolation increases all-cause mortality (5), although we are not aware of any studies regarding the effects of loneliness and social isolation on cancer-related outcomes. Some studies have evaluated the relationships of diagnosis or treatment with education and economic status, which are downstream to loneliness and social isolation as SDH factors (7-9). Pezzi et al. reported that government insurance coverage was not related to chemotherapy use but was associated with decreased radiotherapy use (8). This may indicate that social interactions, which are reflected in loneliness and social isolation, may influence cancer diagnosis or treatment. However, the present study revealed that loneliness and social isolation were not associated with lung cancer diagnosis and treatment in Japan. This result may be related to the universal health insurance system in Japan, which may limit the effects of these factors on lung cancer diagnosis and treatment selection (18). We will follow the enrolled patients to monitor their outcomes.

The present study’s findings might not be applicable to countries without a universal health insurance system. For example, American lung cancer patients living in high deprivation areas have lower rates of surgical treatment (19). In addition, stage I–III non-small cell lung cancer patients are more likely to receive timely treatment at a private hospital than at a public hospital (20), and uninsured status is also associated with a lower initial treatment rate for small cell lung cancer in the US (8). Thus, physicians might be more aware of their patients’ socio-economic conditions if they are not covered by a universal health insurance system, and active treatment might be less common for socially isolated or lonely patients in that setting.

Most of the studies about SDHs were conducted in western countries (5-10). However, this is the first study conducted in East Asia, especially in the field of lung cancer. Furthermore, this study’s findings are strengthened by the small amount of missing data and the adjustment for clinically relevant confounding factors, which increases the reliability of our findings. However, the present study also had several limitations. First, the enrollment criteria are likely a source of selection bias. Although the clinical research coordinator selected patients with a new pathological diagnosis to limit selection bias, many patients were diagnosed clinically. Therefore, some selection bias still likely remained, such as high-income patients being more inclined to receive advanced treatment at specialized hospitals; the relationship between social factors and prognosis is likely weakened, although our region has a very small population of high-income individuals. Additionally, the patient demography may be biased based on the research derived from a single center. Furthermore, most patients are treated at a hospital near where they live, which suggests that patients at our center are representative of the local population. Second, we did not assess overall survival because of the short survey period (21). We plan to continue the follow-up of our patients for an additional 2 years in order to monitor their long-term outcomes.

In Japan, loneliness and social isolation were not significantly related to the clinical diagnosis or initial treatment of patients with advanced lung cancer. Therefore, physicians and other medical staff may not need to consider these SDH factors when diagnosing lung cancer and selecting treatment. However, further studies are needed to investigate the relationships of loneliness and social isolation with the prognosis of lung cancer patients.

Conclusions

In Japan, loneliness and social isolation were not significantly related to the clinical diagnosis or initial treatment of patients with advanced lung cancer. Therefore, physicians and other medical staff may not need to consider these SDH factors when diagnosing lung cancer and selecting treatment. However, further studies are needed to investigate the relationships of loneliness and social isolation with the prognosis of lung cancer patients.

Acknowledgments

We are grateful to Kyoko Wasai for providing professional advice concerning the planning of this research and Editage (www.editage.jp) for English language editing.

Funding: This work was supported by a grant from the Hyogo Prefectural Amagasaki General Medical Center’s fiduciary funds (for English editing and article processing charge).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at http://dx.doi.org/10.21037/apm-21-402

Data Sharing Statement: Available at http://dx.doi.org/10.21037/apm-21-402

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/apm-21-402). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the institutional ethics board of Hyogo Prefectural Amagasaki General Medical Center (# 29-164), and informed consent was taken from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. The College of Family Physicians of Canada. Best Advice Social Determinants of Health, published March 2015. Available online: http://patientsmedicalhome.ca/files/uploads/BA_SocialD_ENG_WEB.pdf#search=%27ollege+of+family+physicians+of+canada+SDH%27. Accessed February 13, 2018.
2. World Health Organization. Rio Political Declaration on Social Determinants of Health, published October 21, 2011. Available online: http://www.who.int/sdhconference/declaration/Rio_political_declaration.pdf?ua=1. Accessed February 13, 2018.
3. Alcaraz KI, Wiedt TL, Daniels EC, et al. Understanding and addressing social determinants to advance cancer health equity in the United States: A blueprint for practice, research, and policy. CA Cancer J Clin 2020;70:31-46. [Crossref] [PubMed]
4. Malcolm M, Frost H, Cowie J. Loneliness and social isolation causal association with health-related lifestyle risk in older adults: a systematic review and meta-analysis protocol. Syst Rev 2019;8:48. [Crossref] [PubMed]
5. Holt-Lunstad J, Smith TB, Baker M, et al. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci 2015;10:227-37. [Crossref] [PubMed]
6. Hyland KA, Small BJ, Gray JE, et al. Loneliness as a mediator of the relationship of social cognitive variables with depressive symptoms and quality of life in lung cancer patients beginning treatment. Psychooncology 2019;28:1234-42. [Crossref] [PubMed]
7. Finke I, Behrens G, Schwettmann L, et al. Socioeconomic differences and lung cancer survival in Germany: Investigation based on population-based clinical cancer registration. Lung Cancer 2020;142:1-8. [Crossref] [PubMed]
8. Pezzi TA, Schwartz DL, Mohamed ASR, et al. Barriers to combined-modality therapy for limited-stage small-cell lung cancer. JAMA Oncol 2018;4:e174504 [Crossref] [PubMed]
9. Tendler S, Holmqvist M, Wagenius G, et al. Educational level, management and outcomes in small-cell lung cancer (SCLC): A population-based cohort study. Lung Cancer 2020;139:111-7. [Crossref] [PubMed]
10. Forrest LF, Adams J, Wareham H, et al. Socioeconomic inequalities in lung cancer treatment: systematic review and meta-analysis. PLoS Med 2013;10:e1001376 [Crossref] [PubMed]
11. Takemura T, Yuki K, Koya O, et al. Influence of social determinants of health on patients with advanced lung cancer: a prospective cohort study. BMJ Open 2018;8:e023152 [Crossref] [PubMed]
12. Shioda A, Tadaka E, Okochi A. Reliability and validity of the Japanese version of the Community Integration Measure for community-dwelling people with schizophrenia. Int J Ment Health Syst 2017;11:29. [Crossref] [PubMed]
13. EndLonelinessUK. Measuring your impact on loneliness in later life. Available online: https://www.campaigntoendloneliness.org/wp-content/uploads/Loneliness-Measurement-Guidance1.pdf. Accessed February 13, 2018.
14. Kurimoto A, Awata S, Ohkubo T, et al. Reliability and validity of the Japanese version of the abbreviated Lubben Social Network Scale. Nihon Ronen Igakkai Zasshi 2011;48:149-57. [Crossref] [PubMed]
15. Union for International Cancer Control. TMN classification of malignant tumours, eighth edition, 2017. Available online: https://www.hoofdhalskanker.info/wpavl/wp-content/uploads/TNM-Classificationof-Malignant-Tumours-8th-edition.pdf
16. Suzuki T. Manual about life function assessment for care provision. 2009 Available online: http://www.mhlw.go.jp/topics/2009/05/dl/tp0501-1c_0001.pdf. Accessed February 13, 2018.
17. Rubin DB. Multiple imputation for nonresponse in surveys. John Wiley & Sons, 2004, vol 81.
18. . Japan: Universal Health Care at 50 Years. Lancet 2011;378:1049. [Crossref] [PubMed]
19. Johnson AM, Johnson A, Hines RB, et al. Neighborhood context and non-small cell lung cancer outcomes in Florida non-elderly patients by race/ethnicity. Lung Cancer 2020;142:20-7. [Crossref] [PubMed]
20. Yorio JT, Xie Y, Yan J, et al. Lung cancer diagnostic and treatment intervals in the United States: A health care disparity? J Thorac Oncol 2009;4:1322-30. [Crossref] [PubMed]
21. Peppercorn JM, Smith TJ, Helft PR, et al. American society of clinical oncology statement: toward individualized care for patients with advanced cancer. J Clin Oncol 2011;29:755-60. [Crossref] [PubMed]