Effect of Social Stressors and Social Determinants of Health (SDOH) on Cancer Beliefs: Analysis of a Cancer Center Catchment Area

Abstract

Objectives:

This study examines how the environments that shape an individual’s health, known as social determinants of health may play a role in cancer beliefs, examining how people with more adverse social risk factors view cancer treatment and care.

Research Approach:

Data from N=1400 survey respondents was used to calculate SDoH scores, using a cancer center’s catchment area survey. Spearman’s correlation quantified the relationship between the SDoH score and cancer belief items.

Findings:

The median SDoH score was 2. Those with higher scores were more likely to not know their cancer risk (p<0.001), think that cancer is a death sentence (p<0.001) and believe that one cannot lower their cancer risk (p<0.001).

Conclusion:

Adverse social risk factors may play a role in cancer beliefs and may influence patients’ willingness to engage in risk prevention behaviors or care. Educational efforts to alter cancer beliefs can be targeted to those with adverse social risk factors.

Introduction:

In 1991, Dr. Samuel Broder of the National Cancer Institute declared poverty as a carcinogen, since socioeconomic factors including poverty, were shown to impact cancer risk and outcomes¹. Since then, the physical and social environments in which individuals are born and live in have been recognized as social determinants of health (SDoH), which lead to premature death and disease among vulnerable populations². The U.S. Department of Health and Human Services has grouped SDoH into five categories: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social community and context³. Social risk factors have been defined as specific adverse physical or social conditions that can negatively impact an individual’s health. Examples of social risk factors include poverty, minority status, social isolation, and limited community resources⁴ These social risk factors have been associated with decreased adherence to cancer screening guidelines, delays and poor compliance with treatment^5,6. Additionally, lower socioeconomic status (SES), lower levels of educational attainment and non-white race have been associated with delays in presentation for many cancers including breast, prostate, gastrointestinal and colorectal⁷.

Another factor impacting cancer care includes cancer beliefs, which are a patient’s perception and personal feelings towards cancer as well as attitude toward treatment and outcomes. These beliefs may be important in understanding who accesses care and the decisions they make about their care. Cancer beliefs can consist of positive or negative perceptions regarding various aspects of care. Positive cancer beliefs can include believing that screening can increase the rate of survivorship, while negative cancer beliefs can consist of believing that cancer is always a death sentence^8–9. Another negative belief, known cancer fatalism or the belief that cancer risk and death is beyond our personal control, was found to be more common in medically underserved populations with limited cancer knowledge¹⁰. Findings from other studies indicated that those with fatalistic cancer beliefs were less likely to engage in preventive measures, such as regular physical activity, avoiding smoking, consuming fruits and vegetables^11,12. This finding is consistent with several studies that suggest cancer knowledge does not predict seeking care and that cancer beliefs may be a mediating factor^13–15. The International Cancer Benchmarking Partnership (ICBP) conducted a large multinational survey of over 20,000 participants and found that patients with negative cancer beliefs were more likely to delay seeking help and talking to a medical practitioner¹⁶.

It is essential to distinguish between these related but distinct constructs to better understand patient outcomes. Socioeconomic status refers to objective measures of resource access like income and education whereas health literacy involves the capacity to process and apply health information.¹⁷ Fatalism is the specific belief that cancer is an inevitable death sentence or is entirely beyond personal control.¹⁸ Cancer beliefs represent a broader category including perceptions of treatment efficacy and potential for cure. Latent profile analyses and empirical studies show that these constructs are not interchangeable despite being correlated.^19–20 Many previous works have conflated these terms which has historically complicated efforts to isolate the unique impact of each on health behavior.^21–22

Evidence suggests there is a relationship between SDoH and negative cancer beliefs. One survey found that populations who did not attain a university or college degree were less likely to endorse positive cancer beliefs, which include having positive perceptions towards cancer diagnosis, treatment, and recovery.²³. Patients with lower SES and education were more likely to have fatalistic beliefs about cancer^24–26. Other have also demonstrated that cancer beliefs can vary by location and population. One sample of a low-income and minority safety net population revealed significant differences in rates of cancer fatalism with non-Hispanic whites having the highest cancer fatalism score as compared to Hispanic and African American population²⁷. Additionally,12 NCI-designated cancer centers revealed that rural residents were much more likely to believe cancer is always fatal and prevention is not possible²⁸. Three Appalachian states also found significant variation in cancer beliefs within populations²⁹. These data show that there may be an important relationship between SDoH and cancer beliefs that warrants further investigation. We hope to further these findings by observing if the overall SDoH score is associated with increased negative cancer beliefs.

The relationship between socioeconomic conditions and cancer beliefs can be understood through established theoretical frameworks.³⁰ The Theory of Fundamental Causes posits that socioeconomic disparities persist because individuals with lower socioeconomic status have reduced access to flexible resources, such as knowledge, power, and social connections, that protect health and facilitate engagement with preventive care.³¹ Within this framework, lower socioeconomic status is associated with greater cancer fatalism and more negative beliefs, even as medical advances improve outcomes overall.²⁰ Mediation and path analyses further suggest that fatalism partially explains associations between socioeconomic conditions and attitudes toward early detection and help-seeking. Structural frameworks additionally highlight how discrimination, policy barriers, and neighborhood-level disadvantage shape both socioeconomic conditions and cancer-related beliefs over time.^31–32 Current literature demonstrates a consistent association between adverse social determinants of health and negative cancer beliefs such as fatalism and a perceived lack of control over risk.^{21–22, 33–34} However, several limitations in the existing research persist. Most studies utilize cross-sectional designs which prevent the determination of causal relationships between social factors and beliefs.²¹ Furthermore, research often focuses on restricted geographic regions or specific subpopulations which limits the generalizability of the findings.³³ Recent reports emphasize a significant gap in longitudinal data and a failure to integrate multilevel structural factors such as policy and institutional racism into the analysis.^30,35–36 This study addresses these gaps by evaluating cumulative social risk in a diverse sample while maintaining clear distinctions between socioeconomic status and psychological constructs.

The aim of this study is to examine if more adverse social risks and SDoH are associated with patients cancer beliefs. By understanding how these social factors and SDoH impact cancer beliefs, we are able to target specific individuals to potentially aid in education, screening, and prevention efforts.

Methods:

In this study, we used population-level data from a catchment area survey conducted by an NCI-designated cancer center in the Mid-Atlantic region. Survey responses were used to calculate SDoH scores and determine the association of these scores with cancer beliefs. SDoH were defined using CDC guidelines.³⁷ The SDoH and social risk factors of race, sexual identity, food insecurity, housing insecurity, socioeconomic status, health insurance status, level of education, social isolation, and discrimination were used to calculate the SDoH score.

Participants

The Sidney Kimmel Cancer Center at Thomas Jefferson University conducted a catchment area survey in 2019 to understand cancer-related knowledge, risk behaviors, screening practices, and sociodemographic characteristics of residents in our 7-county catchment area straddling Pennsylvania and New Jersey. The catchment area survey received IRB approval in 2018 by the Thomas Jefferson University Institutional Review Board (18F.598)., Participants included individuals recruited through prior survey efforts as well as respondents from an online panel, all of whom completed the same survey instrument. Previous adult participants from the Southeastern Pennsylvania Household Health Survey, a population-based survey administered by the Public Health Management Corporation, received an online invitation to participate and a network of survey panelists who resided in the Sidney Kimmel Cancer Center catchment area also received an online invitation to participate. For the present analysis, participants were included if they had complete data on cancer belief items; missingness was higher for selected social determinant variables, resulting in a reduced analytic sample. Descriptive comparisons between the full sample and the analytic subsample were conducted to assess potential differences related to item completion. A flow diagram summarizing participant inclusion and item completion is provided in Figure 1.

Figure 1.

Consort diagram.

Patient Demographics

In the survey, a wide range of demographic factors were collected, including age, sex, and race. Due to discrimination being linked to poorer health outcomes, we included race and sexual minority status as social risk factors.

Cancer Belief Items

Six cancer belief items adapted from the original Awareness and Beliefs about Cancer (ABC) were used in this survey²⁵. These items included, ‘cancer is most often caused by a person’s behavior or lifestyle’, ‘it seems like everything causes cancer’, ‘there’s not much you can do to lower your chances of getting cancer’, ‘there are so many different recommendations about preventing cancer, it’s hard to know which ones to follow’, ‘when I think about cancer, I automatically think about death’, and ‘I’d rather not know my chances of getting cancer.’ Survey participants indicated how much they agreed or disagreed with each item using a 4-point Likert scale with responses including strongly agree, agree, disagree and strongly disagree. Table 1 describes the instruments or variable used to capture this data.^37–41

Table 1.

Social Belief Instruments Used and Their Characteristics.

Variable / Instrument	Item numbers	Scoring	Reliability (Cronbach’s α)	Citation
Awareness & Beliefs about Cancer (ABC)	6 (adapted)	4-point Likert	0.70–0.84	38
Everyday Discrimination Scale (EDS)	9	6-point Likert, summed	0.77–0.88	39,40
ASPE Poverty Guidelines	Household income vs. size	Threshold classification	N/A	41
SDOH Composite Score	9	Binary (0/1), summed	Index (no α)	37

SDoH Scoring

To avoid confusion with the nine-item Everyday Discrimination Scale, the composite social determinants of health score was constructed using nine distinct domains, each measured with a single survey item: poverty status, food insecurity, housing insecurity, health insurance status, educational attainment, household size (as a proxy for social isolation), experiences of discrimination, race/ethnicity, and sexual identity. Each domain was coded as ‘1’ when the participant reported an exposure consistent with established definitions of social disadvantage and summed to generate a cumulative score, with higher values reflecting exposure to a greater number of adverse social conditions.

Race/ethnicity and sexual identity were included not as adverse characteristics, but as indicators of structural vulnerability, reflecting well-documented inequities in exposure to discrimination, underinsurance, and barriers to care. This approach aligns with population health frameworks in which identity functions as a marker of socially patterned structural exposures rather than an individual-level deficit.

Analytic Strategy:

For all analyses, Social Determinants of Health (SDoH) were measured as a cumulative count (Range 0–9), where higher scores indicate greater adversity. Cancer beliefs were measured on a 4-point Likert scale, where 1 = Strongly Disagree and 4 = Strongly Agree.

Descriptive statistics were first calculated to summarize demographic characteristics of the full sample and the analytic subsample with complete SDOH scores, using frequency counts and percentages for categorical variables and means with standard deviations for continuous variables. Group comparisons were then conducted to examine differences in cancer belief items across demographic factors such as age, sex, and race/ethnicity. These comparisons were performed using Chi-square or Kruskal–Wallis tests, depending on variable type and distribution. In addition to demographic analyses, individual SDoH items (e.g., poverty, food insecurity, housing insecurity, discrimination) were examined separately by comparing cancer belief responses between participants with and without each adverse condition. To assess the cumulative impact of social determinants, a composite SDOH score was calculated by summing nine binary indicators, with higher scores reflecting greater adversity. Analyses of the composite score included both group comparisons, where participants were divided into low (≤2) and high (>2) SDoH groups based on the median score, and correlation analyses, where Spearman’s rank correlation coefficients were calculated between continuous SDOH scores and each cancer belief item. All statistical analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC).

Findings:

A total of 1,557 adults in the Sidney Kimmel Cancer Center catchment area completed the survey. The data of 1400 respondents who had SDoH scores (i.e., no missing items to calculate SDoH score) was used for analysis.

Sample Characteristics

No significantly different demographic characteristics were found when comparing the full sample and the SDoH scored sub-population. The majority of the full sample (n= 1557) were 40 years old or younger (60.4%), female (68.1%), and non-Hispanic white (67%). Thirty percent (30%) of the population was below the poverty level. 39% of the full sample experienced housing insecurity and 16.3% had no form of health insurance. Eighty-eight (88.2%) were heterosexual, 52% were married or living with a partner and 50.4% were employed full-time. Of the 1557 participants in the full sample, 1400 (89.8%) responded to all SDoH questions and had an SDoH score. Sixty-one percent (60.5%) of the SDoH sample were 40 years old or younger, 68.9% were female and 67.8% were non-Hispanic white. 28.8% of the SDoH sample lived below the poverty level and 17% experienced housing insecurity. 15% of the SDoH sample did not have health insurance. 88.6% of this sample was heterosexual and 53.3% were married or lived with a partner. 51.7% of the SDoH sample worked full time. Table 2 shows the demographic summary of the entire sample compared to the sample for analysis and the two groups appeared similar.

Table 2.

Summary statistics of demographics and the association between cancer beliefs and social dedterminants of health (SDoH) score.

Demographic Summary
		Whole Sample (n = 1,557)		Sample w SDoH scores (n = 1,400)
		N	%	N	%
Age	<=40	792	60.4	714	60.5
	41–60	500	38.1	448	38.0
	>=61	20	1.5	18	1.5
Sex	Male	495	31.9	433	31.1
	Female	1055	68.1	961	68.9
Race/ethnicity	White	1023	67.0	949	67.8
	Black or African American	259	17.0	232	16.6
	Hispanic or Latino	155	10.2	139	9.9
	All else	89	5.8	80	5.7
Which of the following do you consider yourself?	Heterosexual or straight	1354	88.2	1240	88.6
	All else	181	11.8	160	11.4
What do you consider your marital or relationship status to be?	Married/Living with a partner	797	52.0	744	53.3
	All else	736	48.0	652	46.7
How many family members live in your household? Please include yourself and all adults and children related to you by blood, marriage, or adoption as well as any children for whom you are providing foster care	1	258	17.5	241	17.2
	2	421	28.6	404	28.9
	>=3	795	53.9	755	53.9
Which of the following income categories best describes your total 2018 family income? Please include income from anyone living at this address who is related to you by blood, marriage, or adoption Also, please be sure to include income	Less than $30,000	455	30.0	403	28.8
	$30,000 to under $58,000	376	24.8	345	24.6
	$58,000 to under $175,000	594	39.2	563	40.2
	$175,000 to $250,000 or more	92	6.1	89	6.4
Which of the following best describes your current employment situation?	Employed full-time/Full-time student or job training	785	50.4	720	51.7
	Employed part-time	218	14.0	193	13.9
	All else	529	34.0	481	34.5

SDoH Score

For the 1400 participants, SDoH scores ranged from 0 to 9 with a mean of 2.1 (SD = 1.7).

Cancer Beliefs and Demographic Factors

Cancer beliefs were treated as continuous variables and a 4-point Likert scale was used to measure how much each patient agreed or disagreed with the statement. First, the cancer belief items were analyzed individually with respect to different demographic factors. Participants aged 40 or younger were more likely to strongly agree that ‘it seems like everything causes cancer’ when compared to those aged 61 or older (20.9% vs. 11.1% p<0.001). In contrast, those aged 61 years or older were more likely to agree that ‘when I think about cancer, I automatically think about death’ (44.4%vs. 29.7% p<0.001) compared to those who were 40 years or younger. Females were more likely, when compared to male survey participants, to disagree that ‘cancer is most often caused by a person’s behavior or lifestyle’ (26.6% vs. 19.1% p<0.001) and less likely to believe that ‘it seems like everything causes cancer’ (11.3 vs. 18.6% p<0.001).

With respect to race and ethnicity, differences were found in 5 of the 6 cancer belief items. African Americans and Hispanic/Latinos were more likely to agree that ‘it seems like everything causes cancer’ when compared to non-Hispanic white participants (20.3% and 21.6% vs. 15.2% p=0.002). This was also true with regard to the cancer belief item ‘when I think about cancer, I automatically think about death’ (30.2% AA vs. 40.3% H/L vs. 19.7% W p<0.001).

Cancer Beliefs and Individual SDoH Items

Differences across all cancer belief items were observed when analyzing participants based on poverty status. Participants who were below the poverty level strongly disagreed that ‘cancer is most often caused by a person’s behavior or lifestyle’ (36.3% vs. 20.9% p<0.001) compared to those above the poverty level. Those below poverty level were also more likely to agree that ‘it seems like everything causes cancer’ (21.7% vs. 15.5% p=0.004), ‘when I think about cancer, I automatically think about death’ (31.3% vs. 21.3% p=0.001) and ‘I’d rather not know my chances of getting cancer’ (17% vs. 9.0% p=0.001).

Those experiencing food insecurity, which included 36% of the SDoH score population, responded in a similar manner. Individuals who did not have enough food to last the month were more likely to agree that ‘it seems like everything causes cancer’ (30.1% vs. 15.3% p<0.001), ‘there’s not much you can do to lower your chances of getting cancer’ (17.1% vs. 4.3% p<0.001), and ‘when I think about cancer, I automatically think about death (47.9% vs. 20.1% p<0.001) when compared to those who did not experience food insecurity.

Differences between those with and without housing insecurity were found across all cancer belief items. Those with housing insecurity were more likely to agree with the statements: ‘it seems like everything causes cancer’ (24.7% vs. 15.4% p=0.010), ‘there’s not much you can do to lower your chances of getting cancer’ (12.1% vs. 4.2% p<0.001), and ‘when I think about cancer, I automatically think about death’ (37.9% vs. 20.8% p<0.001) compared to those without housing insecurity.

SDoH Burden by Level of Belief

Cancer Belief Items and SDoH scores can be found in Table 3 and Table 4. There was a statistically significant difference between Likert-scale responses in five of the six cancer belief items included in the survey based on SDoH score. In each of these cases, SDoH scores were higher in those that strongly agreed with negative cancer belief items. For example, those who strongly agreed with the statement ‘when I think about cancer, I automatically think about death’ had mean SDoH scores of 2.7 while those that strongly disagreed had SDoH scores of 2.1 (p<0.001). This was also true for the cancer belief items, ‘It seems like everything causes cancer’ (2.4 vs. 2.3 p<0.001), ‘there’s not much you can do to lower your chances of getting cancer’ (3.1 vs. 2.0 p<0.001), ‘cancer is most often caused by a person’s behavior or lifestyle (2.6 vs. 2.5 p<0.001) and ‘I’d rather not know my chances of getting cancer’ (2.8 vs. 2.0 p<0.001).

Table 3.

Association between SDoH score group and cancer beliefs

Cancer Belief Items	Low (≤2) N=896 (64%)	High (>2) N=504 (36%)	p-value**
Cancer is most often caused by a person's behavior or lifestyle, mean (SD)	2.3 (0.9)	2.2 (1.0)	0.001
It seems like everything causes cancer, mean (SD)	2.6 (0.9)	2.7 (1.0)	0.385
There’s not much you can do to lower your chances of getting cancer, mean (SD)	1.9 (0.8)	2.1 (0.9)	<.001
There are so many different recommendations about preventing cancer, it’s hard to know which ones to follow, mean (SD)	2.8 (0.8)	2.8 (0.8)	0.835
When I think about cancer, I automatically think about death, mean (SD)	2.7 (0.9)	2.9 (1.0)	<.001
I’d rather not know my chance of getting, mean (SD)	2.1 (1.0)	2.3 (1.1)	0.002

^**The p-value is calculated by the Kruskal-Wallis test.

Table 4.

Spearman’s correlation between social determinants of health (SDoH) score and cancer beliefs.

Cancer Belief Items	N	Correlation coefficient (ρ) with SDoH score	p-value (H0:ρ=0)
Cancer is most often caused by a person's behavior or lifestyle	1396	−0.09	<.001
It seems like everything causes cancer	1396	0.03	0.348
There’s not much you can do to lower your chances of getting cancer	1398	0.13	<.001
There are so many different recommendations about preventing cancer, it’s hard to know which ones to follow	1396	−0.00	0.870
When I think about cancer, I automatically think about death	1396	0.12	<.001
I’d rather not know my chance of getting cancer	1397	0.08	0.002

Belief Differences by SDoH Grouping

The scores were then further divided into two groups, those with greater than two adverse SDoH and those with less than or exactly a score of 2 which was the median of our sample (Table 3). We observed a significant difference in distributions of Likert scale by SDoH score group in four of the six cancer belief items. Participants with an SDoH score of 2 or greater were more likely to agree that ‘there’s not much you can do to lower your chances of getting cancer’ (p<0.001), ‘when I think about cancer, I automatically think about death ‘(p<0.001) and trended towards significance for the ‘I’d rather not know my chance of getting cancer’ (p=0.002). These participants were also more likely to disagree that ‘cancer is most often caused by a person’s behavior or lifestyle’ (p=−0.001).

Table 4 shows the Spearman correlation coefficients between the SDoH score and each cancer belief item. Participants who believed that ‘cancer is most often caused by a person’s behavior or lifestyle’ were associated with lower SDoH score, although the association was weak (ρ= −0.09 p<0.001). Those that felt that ‘there’s not much you can do to lower your chances of getting cancer’ were associated with a higher SDoH score (ρ=0.13 p<0.001). Similarly, respondents that agreed with the statement ‘when I think about cancer, I automatically think about death’ were associated with higher SDoH scores (ρ=0.12 p<0.001) as were those who agreed with the statement ‘I’d rather not know my chance of getting cancer (ρ=0.08 p=0.002).

Conclusions/Interpretations:

Disparities in cancer care lead to worse outcomes for certain populations despite advances in modern medicine. SDoH and social risk factors have been implicated, but do not explain these discrepancies in full⁴². Our analysis found a strong association between adverse SDoH and negative cancer beliefs. This connection may, in part, explain why certain populations feel helpless regarding their healthcare and do not seek preventative care that can lead to early detection and treatment of cancer. These findings help to confirm other studies which have found that individuals with lower socioeconomic status have lower health literacy which can impact their comprehension of medical information and cancer outcomes.⁴³

It has been reported that about 34% of adult cancer deaths could be prevented if socioeconomic disparities were eliminated^44–45. We analyzed socioeconomic factors and found that certain populations were more likely to have negative cancer beliefs. For instance, African Americans and Latinos more frequently feel that everything causes cancer and cancer automatically means death. It is well established in the literature that certain demographics are less likely to engage in healthcare screening and intervention⁴⁶, but the motivations behind this may be tied to cancer beliefs.

Adverse social risk factors yielded similar results. Those who had housing, food, and financial insecurity, as well as those experiencing poverty all were more likely to experience negative cancer beliefs consistent with cancer fatalism. Individuals who did not identify as heterosexual or straight were more likely to associate a cancer diagnosis with death and findings were similar in those experiencing discrimination. Few intervention-based studies exist targeting these populations. In one, socioeconomically deprived areas were targeted for recruitment to clinical trial s which involved engagement of community leaders and active recruitment in community settings⁴⁷.

Population-level evidence consistently demonstrates that individuals with lower socioeconomic status are more likely to endorse fatalistic or pessimistic cancer beliefs and recognize fewer modifiable risk factors.^22,33 While limited access to preventive resources contributes to these patterns, fatalism has also been theorized as an adaptive psychological response to chronic stress, environmental uncertainty, and constrained opportunities for health-promoting action.^18–21 Structural inequities, repeated exposure to discrimination, and unstable interactions with healthcare systems may reinforce perceptions that cancer outcomes are inevitable or uncontrollable, particularly when individuals face persistent systemic barriers to timely screening and care.³⁰ Rather than reflecting individual deficits, these beliefs may serve a protective function that helps manage the psychological burden of living in high-risk environments. Importantly, recent evidence indicates that fatalistic cancer beliefs remain a significant barrier to screening interest in specific minoritized populations, underscoring the need for interventions that address structural drivers while pairing social support with culturally responsive strategies that acknowledge lived experience.³²

Along with SDoH and social risk factors, cancer beliefs have also been shown to associate with a patient’s treatment decisions and overall engagement in medical care. Cancer fatalism has been linked to engagement in cancer prevention as well as time to presentation to a medical practitioner^12–16.Those with adverse SDoH are more likely to endorse negative cancer beliefs^24–27. Our study also found that adverse SDoH predicted for negative cancer beliefs in the Sidney Kimmel Cancer Center Catchment Area population. This remained true when an aggregate SDoH score was calculated and applied to the cancer belief items. Screening these patients using validated questionnaires may aid in in determining a patient’s social risks and intervening accordingly^{30, 48–49}. Routine use of standardized SDOH screeners such as ones that are embedded in the electronic medical record could be used more routinely upon patient intake so that social workers and physicians alike have the social knowledge needed to try and increase engagement in care in those more at risk. Using the screening tools within electronic medical record can help to create a standardized screening method, which has been a common issue in the past when addressing SDoH⁵⁰.

The findings regarding the behavior and lifestyle prompts show different patterns depending on which analysis is used. The results described in the text suggest that individuals who strongly agreed with this belief reported higher mean SDoH scores. However, the group comparisons in Table 3 and the correlation in Table 4 suggest that the high SDoH adversity group had lower overall agreement. These different results may suggest that the behavior prompt measures a different psychological concept than the prompts focused on fatalism. While fatalism prompts measure the belief that cancer is unavoidable, the behavior prompt may specifically measure health agency or the sense of control a person feels over their health. Research by Galicia Pacheco et al. and Sarma et al. suggests that high SDoH adversity is often linked with a lack of perceived control over cancer risk.^33–34 This suggests that individuals with high SDoH adversity might view cancer as a result of their external environment rather than personal choices. According to the Theory of Fundamental Causes, socioeconomic disadvantage limits access to flexible resources such as information and power that help individuals act on health risks. These findings suggest that structural drivers like neighborhood disinvestment and policy barriers might erode the belief that personal lifestyle choices can overcome a person's surroundings. This may explain why the behavior item behaves differently in the models compared to traditional fatalistic beliefs. While a small group of individuals with high SDoH adversity might strongly agree that lifestyle matters to manage uncertainty, the broader data suggest that increased social stressors may lower a person's overall sense of agency. The relationship between social determinants and cancer beliefs is complex and likely depends on whether the analysis focuses on the intensity of a single belief or the average trends of an entire group.

Although prior studies have explored associations between individual SDOH factors and cancer beliefs, our study is among the first to examine the impact of the total SDOH burden on cancer beliefs, reflecting how patients experience adversity in real life. The Sidney Kimmel Cancer Center (SKCC) catchment area includes participants from seven diverse counties across Pennsylvania and New Jersey, encompassing both urban and suburban populations with varied socioeconomic and demographic profiles. While not fully generalizable, these findings may prompt other cancer centers to explore similar relationships within their own populations, potentially advancing equity-informed cancer communication and outreach strategies nationwide.

Approaches to measuring SDOH vary widely across studies, with some examining individual indicators and others using composite scores. Because of this heterogeneity, we applied equal weighting to each item in our aggregate score. However, the patterns observed in this analysis could help identify which SDOH domains are most strongly associated with negative cancer beliefs, informing the development of a more nuanced, weighted scale in future studies.^51–58

The Accountable Health Communities model launched by CMS will help elucidate how identifying and addressing SDoH will reduce healthcare costs⁵⁹. Ongoing work in this space is imperative to explicate future directions in order to decrease disparities in healthcare. Understanding the individual and structural drivers of adverse SDoH is essential in order to develop interventions at both patient and systemic levels. To fully address adverse SDoH, structural and policy interventions must be set in place to help transform underlying socioeconomic systems, such as housing education, and labor. Health care works should also take time to ensure trust and understanding when screening for SDoH Addressing these systems on an individual and system- wide level will aid in building to foundations to allow for health equity.

While offering valuable insights, this study has limitations to consider. The analysis, based on a subset of participants (1400 out of 1557), may introduce selection bias, and although demographics were similar, non-response bias cannot be ruled out. While the analytic subsample was demographically similar in many respects, there was a notable decrease in the proportion of participants experiencing housing insecurity, suggesting that the most socially vulnerable individuals may be underrepresented in the SDoH analysis. The study, conducted in the greater Philadelphia area, may limit generalizability to other regions. The equal weighting of SDoH items in the aggregate score oversimplifies their impact, and reliance on self-reported survey data introduces potential response bias. Analyses were exploratory and relied on nonparametric comparisons and correlations, including dichotomization of the composite social determinants score. While this approach facilitated interpretability, it may have reduced statistical power and limited adjustment for confounding and we would consider employing multivariable regression models using continuous social determinants measures and prespecified covariates to better isolate independent associations. Future research should explore nuanced approaches and the temporal dynamics of SDoH exposure for a comprehensive understanding of their influence on cancer beliefs and healthcare disparities.

Implications for Psychosocial Oncology:

This study underscores the clinical importance of recognizing social determinants of health (SDoH) and social risk factors in cancer care. Our study showed that having adverse SDoH, lower poverty levels, and minority status is correlated with more negative beliefs towards cancer and cancer care. These findings show the need for healthcare providers to integrate SDoH screening tools into routine practice to identify at-risk patients. Screening for at-risk patients can then enable providers to address misconceptions about cancer and cancer treatments.

Prior interventions demonstrate that cancer beliefs are modifiable and can be addressed directly through tailored educational strategies, community-based outreach, and theory-driven psychosocial approaches that challenge fatalistic attitudes and misinformation.⁶⁰ However, interventions focused solely on addressing social needs, such as housing or food insecurity, may be insufficient to shift entrenched cancer beliefs unless paired with belief-focused strategies.²¹ Our findings support a combined approach in which social needs screening and navigation are integrated with brief, culturally responsive education and counseling aimed at addressing negative cancer beliefs. Such models align with psychosocial oncology practice by addressing both contextual stressors and cognitive perceptions that shape engagement across the cancer continuum.^34,60

Because African Americans, Hispanics/Latinos, and patients who were below the poverty level were found to have more negative beliefs towards cancer, providers should use culturally sensitive approaches when addressing these topics. A multidisciplinary approach involving healthcare professionals and community workers can empower patients to overcome barriers to care, ultimately reducing healthcare disparities in cancer outcomes.

In conclusion, adverse social risk factors is associated with an increased sense of cancer fatalism and may lead to patients feeling less autonomy over their disease. In turn, this may lead to decreased engagement in cancer screening, choosing to comply with physician recommendations for cancer treatment, and completing cancer treatment promptly. Addressing cancer beliefs in patients who experience adverse social risk factors may narrow health disparities in cancer outcomes in vulnerable populations. Education about cancer beliefs delivered directly to communities with community partner participation could improve cancer outcomes if patients comply more with treatment and may also work to increase clinical trial participation so that trial data is more representative of our US population as a whole. Our study has shown for the first time, that adverse social risk factors are correlated with increased cancer fatalism. Future studies should screen patients for SDoH and social risk factors at diagnosis and determine if social support could increaseengagement in care. The results of this study can be important in understanding the motivations behind healthcare utilization regarding cancer.

Availability of Data and Materials:

The datasets generated during this study are not publicly available due to patient confidentiality.