key: cord-0956802-3szujh6y
authors: Zhang, Amy Y.; Koroukian, Siran; Owusu, Cynthia; Moore, Scott E.; Gairola, Richa
title: Socioeconomic correlates of health outcomes and mental health disparity in a sample of cancer patients during the COVID‐19 pandemic
date: 2022-03-01
journal: J Clin Nurs
DOI: 10.1111/jocn.16266
sha: 5e7af6ab5299fcb4dcb4d04a98b436ac78caef55
doc_id: 956802
cord_uid: 3szujh6y

AIMS AND OBJECTIVES: To investigate socioeconomic, behavioural and healthcare delivery factors that are associated with health outcomes of cancer patients during the COVID‐19 pandemic, especially among underserved cancer patients. BACKGROUND: Cancer patients are at a higher risk of adverse physical and mental health outcomes during the pandemic than those without cancer. DESIGN: Cross‐sectional online survey. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines in this study. METHODS: The sample comprised 322 individuals diagnosed with incident cancer between January 2019 and January 2020. Demographically, 64% were female, 49% had a college degree, 12% were African American, and 88% were White (77% of the Whites were from metropolitan and 23% from nonmetropolitan areas). Descriptive analysis and multivariable regression analyses of global health status, depression and irritability were performed. RESULTS: After adjusting for demographic variables and comorbidity, the feelings of loneliness, crowded living space, lower confidence in taking preventive measures and less satisfaction with telehealth visits were significantly associated with poorer global health, depression and irritability. Daily exercise was associated with better global health, and difficulty in getting medicine was associated with depression and irritability. Moreover, African Americans who felt lonely reported more depression and irritability and those who had less confidence in taking preventive measures reported more irritability than Whites. Respondents having low income and feeling lonely reported more depression than others. CONCLUSIONS: In this study, socioeconomic factors (e.g. loneliness or crowded living conditions) were as important to health outcomes during the pandemic as behavioural (e.g. prevention and exercises) and quality‐of‐care factors (e.g. telehealth, access to medicine). Disparity was more pronounced in the mental health of African Americans and those with low incomes. RELEVANCE TO CLINICAL PRACTICE: Healthcare providers should promote social support and physical activity for improving health and reducing mental health disparities among cancer patients.

Cancer patients are at a higher risk for life-threatening illness caused by COVID-19 because of older age, high comorbidity and compromised immunity (Centers for Disease control and Prevention (CDC, 2020) . They were diagnosed with and died from COVID-19 at a significantly higher rate than people without cancer (Yang et al., 2021) . Besides biological factors, socioeconomic circumstances and behavioural factors also precipitate health adversity. Inadequate preventive measures, including failure to wear a mask or practice social distancing, increase the risk of COVID exposure and infection.

Acute disruptions in cancer care such as suspending vital diagnostic, therapeutic and surveillance care due to COVID concerns (Greiner, 2020 ) may result in adverse cancer outcomes. Physical and social isolation, declining social support, and economic hardship can diminish the availability of resources and impede preventive action and healthcare seeking, thus exacerbating a decline in the mental and physical health of patients with cancer.

The World Health Organization has promoted the idea that socioeconomic determinants of health are as important as the physical environment (including the healthcare system) and individual characteristics or behaviours (e.g. prevention behaviours) in determining health outcomes (World Health Organization, 1998; World Health Organization, 2017) . Theories of socioeconomic determinants of health explain that although an individual's behavioural choices are responsible for personal health, they are determined by the individual's material condition of life; it is the economic and social condition that affects one's living condition (Bartley, 2003) and health behaviour (Townsend et al., 1992; Marmot and Wilkinson, 2005) , thus increasing vulnerability to poor health. Therefore, to understand health outcomes of cancer patients during the pandemic requires an understanding of the patient's socioeconomic and living conditions. Furthermore, the Centers for Disease Control and Prevention has defined socioeconomic determinants of health as 'resources' to be used to promote health and well-being; for example, food, housing, transportation, health care, employment or income, and social connections. Unequal distribution of resources due to existing political and power structures produces health disparities among populations (Ramirez et al., 2008) .

Conceivably, insufficient financial resources to acquire hygiene materials, less physical space for quarantine, a literacy level inadequate to understand the necessity and practice of preventive measures, a lack of social connection or support, and less access to healthcare facilities or high-quality care can heighten vulnerability to COVID-19, stress, and adverse physical and mental health consequences. The lack of these resources among African Americans has been well documented (Beyer, 2019; Hastert et al., 2019; Echeverri et al., 2018) and likely contributes to poorer health outcomes in this population. In fact, African Americans represent 13% of the population, but 34% of the total COVID-19 deaths in the United States (Holmes et al., 2020) . As of June 2021, they were hospitalised for COVID-19 at 2.9 times the rate of White Americans and died of the disease twice as often (CDC, 2021) . Among cancer patients, another vulnerable subgroup is people who live in nonmetropolitan areas and are mostly White. Rural residents are more likely to have less education, lower income, higher unemployment rates and less health insurance coverage than their urban counterparts (Henley & Jemal, 2018) . Data have shown that cancer patients living in nonmetropolitan areas have higher cancer mortality than those residing in urban areas, explained in part by the higher poverty rates and barriers to healthcare access (CDC, 2017; Blake, 2020) . Apparently, both African Americans and nonmetropolitan Whites are at a socioeconomic disadvantage compared to White counterparts living in urban and suburban areas (Yabroff et al., 2020; Hunt et al., 2019) . The relationship between unequal resource distribution among these three groups and cancer-related health disparities warrants examination.

To understand the reasons for disparities in health outcomes of cancer patients during the COVID-19 pandemic, we conducted a survey study among cancer patients to evaluate socioeconomic Relevance to clinical practice: Healthcare providers should promote social support and physical activity for improving health and reducing mental health disparities among cancer patients.

cancer, COVID-19, health disparity, mental health, socioeconomic determinants What does this paper contribute to the wider global clinical community?

• Living condition, whether it is too lonely or too crowded, significantly associates with health outcomes of cancer patients during the COVID-19 pandemic.

• Disparities are pronounced in mental health outcome.

African American and low-income cancer patients that felt lonely reported significantly more depression than other patients. determinants (e.g. unmet socioeconomic needs), health service delivery (e.g. continuity of care), individual behaviours (e.g. prevention practice) and their association with health outcomes, especially among underserved African American and nonmetropolitan White patients.

This study focused on two main questions: (a) What socioeconomic, health service and behavioural factors are associated with physical and mental health outcomes of cancer patients during the pandemic? (b) Do these associations differ across racial and urban/nonurban communities? Identifying modifiable variables (structural or behavioural) will help to inform interventions aimed at improving cancer care in underserved subgroups of the population during public health crises. and January 2020 were eligible, regardless of cancer type or stage.

Individuals were excluded if they did not speak English or had impaired cognitive ability that would interfere with survey completion due to Alzheimer's, Huntington's or Parkinson's disease, traumatic brain injury or Creutzfeldt-Jakob disease. Patient's eligibility was identified from medical records and the self-reported information provided during the online informed-consent process. This study was approved by a local Institutional Review Board (#STUDY20200582).

We identified 2,171 potentially eligible cancer patients (256 African Americans, 1,915 Whites) in the hospital's tumour registry whose electronic medical records included an email address. An invitation letter that introduced the study and included a link to the survey webpage was emailed to these patients directly from a Research Electronic Data Capture (REDCap) database, a secure web platform for building and managing online surveys and databases. This invitation was followed by three automatic email reminders once every other day over a week. The research assistant on our team monitored patient response. Because our source database had fewer African American patients available for the study and we were striving to obtain an adequate sample of them, our research assistant phoned African American patients to ensure that they had received the email invitation. In the end, a total of 322 patients consented online to participate and responded to the survey with a response rate of 14.8%.

When they opened the survey link, respondents were asked to read a consent form describing the study, followed by the survey questions to be completed using a phone or computer. Responses to any survey questions constituted the consent and were kept confidential. The responses provided online were entered directly into a secured REDCap database. Certain responses that suggested a risk for depression triggered the display of a hotline telephone number. We estimated that completing the survey required 45 minutes. Participants received a $15 Walmart gift card after completing the survey.

We used existing instruments whenever available and incorporated survey questions from the Phenotype and eXposures (PhenX Toolkit, Ver 40.1) when there were no valid measures, especially with regard to COVID-19 (Hamilton et al., 2011 (Billioux et al., 2017) . It consists of 10 core items in 5 domains (housing, food, transportation, utilities and safety) and 16 items in 8 supplemental domains (financial strain, employment, family and community support, education, physical activity, substance use, mental health and disabilities). To measure domains of interest parsimoniously, we used 6 of the 10 core items to assess housing, food, transportation and safety, and 3 supplementary items to assess financial strain and community and family support. The additional questions from measures of the PhenX Toolkit asked about stockpiling of food, medicine and cleaning products; difficulty in obtaining medicine; the frequency and means of staying in touch with others during the pandemic (e.g. phone, email, postal mail, video or in-person meeting); and the degree of neighbourhood safety for walking or exercise.

Health behaviours included preventive behaviours against COVID-19 infection and lifestyle health behaviours. The preventive behaviours were assessed by a number of items taken from the PhenX Toolkit using a 4-point scale (never, rarely, sometimes, most of the time) in three areas: protection of oneself (e.g. wearing a mask, washing hands), household protection (e.g. regular cleaning or disinfecting) and social activities (e.g. social distancing). Additionally, participants were asked to rate their confidence in taking preventive measures daily for self, household protection and social activities, using a scale from 0 ('not confident at all') to 10 ('extremely confident').

To evaluate lifestyle behaviours, we used additional three items of the AHC HRSN Screening Tool in two supplementary domains: physical activity and substance use. Physical activity was determined with a single question: 'How many days per week did you engage in moderate exercise in the past 30 days?' Substance use comprised questions about daily consumption of alcohol and tobacco: 'How many times in the past month have you had 5 or more alcoholic drinks in a day (males) or 4 or more drinks in a day (females)?' and 'How many times in the past month have you used tobacco products?' Possible answers ranged from 'never' to 'daily or almost daily'.

Cancer and medical care services were based on access to, and quality of, health care. Participants were asked whether (yes or no) they had health insurance, had cancelled a cancer care appointment or had changed an in-person visit to a telehealth visit. Additionally, they were asked to rate their satisfaction with telehealth for cancer care or general medical care, using a scale from 0 ('not satisfied at all') to 10 ('extremely satisfied').

Health outcomes were measured in terms of general health and mental health. The Patient-Reported Outcomes Measurement Information System Global Health (v1.2) measure was used to assess general health. It includes 10 items and assesses 5 core domains: physical function, pain, fatigue, emotional distress and social health.

Four items of physical and mental health (2 items each) are used to calculate a score of global health status that represents overall health. This measure of global health status has a satisfactory internal consistency reliability (0.81) and validity (r = 0.76 and −0.75 for correlation with EQ-5D and pain impact scores respectively). A higher score indicates better global health status. (Hays et al., 2009) . Two instruments measured depression, anxiety and irritability as indications of mental health. The Profile of Mood State (POMS) has been used extensively to measure psychological adjustment to cancer (McNair et al., 1992) . The shortened version of POMS (SV-POMS) has shown good internal consistency, reliability and responsiveness to change among cancer patients (Dilorenzo et al., 1999) .

Scores were calculated for its subscales of depression or dejection (8 items) and tension or anxiety (6 items), rated on a 5-point Likert scale (0-4), with a higher score indicating worsening mood. These subscale scores indicate severity of symptoms without specific cut-offs for a clinical diagnosis.

The Irritability Scale-Initial Version (TISi) was developed with cancer patients and consists of 35 items. It measures irritability in physical, mood and behavioural domains; the total score reflects the level of irritability with satisfactory psychometric properties (Cronbach's α = 0.97, test-retest reliability = 0.69 and intraclass correlation coefficient = 0.86) (Zhang & Ganocy, 2020) . Irritability has been reported by cancer patients undergoing treatments, and increasing irritability has been associated with depression (Sharpley et al., 2018; Zhang et al., 2021) .

Descriptive analyses (chi-square test and simple ANOVA) were conducted on all study variables except health outcome variables to explore differences among three groups (African Americans, metropolitan Whites and nonmetropolitan Whites). Generalised linear regression analyses were performed in two steps to identify factors associated with health outcomes. Initially, variables in each of the three areas (unmet socioeconomic needs, healthcare services and behaviours) were regressed (stepwise) separately on the dependent variables of global health status, depression, and anxiety and irritability, while controlling for individual characteristics (demographics, socioeconomic status and medical conditions). Next, significant variables with p < 0.05 from each area were identified and pooled together to be included in the final analysis. These selected variables were regressed on each dependent variable, respectively, using a threshold significance level of p < 0.05. All analyses controlled for age, gender and the number of chronic diseases as covariates.

To examine the effects of racial and nonmetropolitan status, two dummy variables of race and nonmetropolitan status (using metropolitan Whites as the reference group) were created and included in the final regression model. Interaction terms between each dummy variable and most impactful predicting variable(s) were created and added to the final regression model. An interaction term between income and the most impactful predicting variable was also included.

In the regression analyses, cases with missing values for dependent variables were excluded, while cases with missing values for independent variables or covariates were retained, and the missing values were treated in a generalised linear model without interfering consequence. For the bivariate analysis, missing data were assigned a value (e.g. 9) and counted as such in the frequency distributions. All data analyses were performed using SAS, version 9.4 (SAS Institute Inc.).

The study sample included 322 respondents; 40 were African American and 282 were White, of whom 215 lived in an urban or suburban areas and 67 lived outside urban or suburban areas (i.e. nonmetropolitan) as defined by the rural-urban continuum code (USDA, 2021) . The latter two groups are hereafter referred to as Metro Whites and non-Metro Whites respectively.

As shown in Table 1 , there were marked differences between groups in demographic traits. With regard to age, individuals 65 years of age or older had a higher representation among Metro Whites (46.98%), compared with African Americans (37.50%) or non-Metro Whites (35.82%). We also noted a higher percentage of men (35.92%) in Metro Whites than in African Americans (23.08%) or non-Metro Whites (29.69%). With regard to education and income, we found statistically significant differences across the three groups. The lowest percentage of individuals with a college degree or above was among African Americans (28.21%), followed by non-Metro Whites ( In terms of healthy lifestyle behaviours, exercising 6 to 7 days a week was reported by a significantly lower percentage of African Americans (62.86%) than either Metro or non-Metro Whites (86.5% and 72.73%, respectively). On the contrary, we found little variation among the three groups for alcohol consumption (75%-80% of respondents answered 'never') and smoking (over 90% answered 'never'). 

The results from the multivariate regression analyses are presented in Table 3 . The three outcomes of interest were global health, depression and irritability. Anxiety as a dependent variable generated similar and even less information than did depression and thus is not presented separately. As noted earlier, we included the independent variables in the models only if they were selected in stepwise regression models.

The results showed that the outcome of global health was positively associated with more satisfaction about telehealth for medical care (other than cancer care), more confidence in taking preventive measures and more days engaging in exercise. On the contrary, we observed a negative association between global health and greater comorbidity, feeling lonely, and living in a place too crowded to allow social distancing.

The results showed that depression was negatively associated with satisfaction about telehealth for medical (non-cancer) care and confidence in taking preventive measures, and positively associated with feeling lonely (sometimes, often or always), and living in a place too crowded for social distancing. Depression also had a positive association with having Medicaid only (vs. Medicare) or having difficulty obtaining medicine.

Analysing the outcome of irritability, we found that it had a significant negative association with being older, being insured through an employer or through the Affordable Care Act (ACA), being satisfied with telehealth for medical care (other than cancer care) and having confidence in taking preventive measures. Conversely, being African American, having difficulty obtaining medicine, feeling lonely and living in a place too crowded to have social distancing were positively associated with irritability.

Regarding the outcome of depression, the interaction term of African American race by loneliness was positive and statistically significant, meaning that the association between loneliness and depression was stronger in African Americans than among Metro

Whites. An interaction term between feeling lonely and income level (<$25,000 vs. ≥$25,000) was negative and statistically significant, indicating that the association between loneliness and depression was lessened with higher income levels.

Regarding the outcome of irritability, the interaction term of African American race by loneliness was positive and statistically significant, meaning that the association between feeling lonely and irritability was stronger in African Americans than among Metro

Whites. Further, the interaction term of African American race by confidence in taking preventive measures was negative and statistically significant, indicating that the association between lower confidence in taking preventive measures and irritability was stronger among African Americans than their White counterparts. We did not identify any statistically significant interaction effects regarding global health.

Our findings show that living conditions, indicated by the two variables of feeling lonely and crowded living space, were significantly associated with poorer global health status, depression and irritability. Most notably, feeling lonely stands out as the most significant associated factor of poor health outcomes during the pandemic.

Studies have shown that loneliness is significantly associated with depression and the risk of all-cause mortality (Erzen & Çikrikci, 2018; OʼSúilleabháin, et al., 2019) . A possible explanation is that feelings of loneliness correlate significantly with increasing cortisol during the day (Doane & Adam, 2010 exhaust the possibility of interaction effects between race and all other significant associates of health outcomes. Finally, this study was cross-sectional, and the findings have enlightened associations between socioeconomic, healthcare, personal, and behavioural variables and health outcomes. However, a directional relationship and a causal nature of the associations require a longitudinal study. We plan to follow-up with the study participants over several months to further investigate these associations. These shortcomings can limit the generalisability of study findings.

In conclusion, this study has shown that socioeconomic determinants of health as manifested in individual living conditions are strongly associated with health outcomes of cancer patients during the COVID-19 pandemic. We have observed remarkable mental health disparities among African American and low-income cancer patients. In particular, loneliness deserves special attention from clinicians and researchers, as it may signal worsening mental and overall health in cancer patients.

This study was supported by Case Western Reserve University COVID-19 Pilot Program and CASE Comprehensive Cancer Center.

The University Hospitals Cleveland Medical Center provided patient access.

The authors declare no conflict of interest.

Jennifer Frame and Jinglu Li assisted with project management.

Matthew McManus provided editorial assistance.

Amy Y. Zhang https://orcid.org/0000-0001-8208-0742

Scott E. Moore https://orcid.org/0000-0002-4294-1429

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