Disparities in cancer care lead to worse outcomes for certain populations despite advances in modern medicine. SDoH have been implicated, but do not explain these discrepancies in full. Determining the drivers of these disparities is vital in order to address them on a systemic level. One such driver may be negative cancer beliefs which has been associated with less healthcare engagement in some patients9,10. 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.
It has been reported that about 34% of adult cancer deaths could be prevented if socioeconomic disparities were eliminated26,27. 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. Findings were similar in older participants and women. It is well established in the literature that certain demographics are less likely to engage in healthcare screening and intervention, but the motivations behind this may be tied to cancer beliefs.
Adverse SDoH 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. SGM were more likely to associate a cancer diagnosis with death; 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 settings28.
Along with SDoH, cancer beliefs have also been shown to impact treatment and engagement in medical care. Cancer fatalism has been shown to impact engagement in cancer prevention as well as time to presentation to a medical practitioner10–14. As mentioned previously, those with adverse SDoH are more likely to endorse negative cancer beliefs16–19. Our study also found that adverse SDoH predicted for negative cancer beliefs in the SKCC 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 accordingly29–31.
Quantifying SDoH has varied in the published literature32–34. Many studies report individual SDoH, while others use a scoring system. Due to the variability in the literature, we weighted each SDoH item equally and added them to calculate an aggregate score. Future analyses could consider weighting certain items known to be drivers of cancer disparities, which may increase the accuracy and specificity of the findings.
In order to address negative cancer beliefs and, in turn, increase healthcare engagement, at-risk populations should be screened and directly connected with the appropriate healthcare professionals. A dedicated team of patient navigators, social workers and community health workers can then assess barriers to care and recommend intervention35. Current approaches vary and published literature cites multiple methods of SDoH screening36. One study suggests that electronic screening may be superior to face-to-face for sensitive topics such as financial security37. Another suggests evaluating a family unit as a whole instead of the individual patient38. The ongoing Accountable Health Communities model launched by CMS will help elucidate how identifying and addressing SDoH will reduce healthcare costs39. Ongoing work in this space is imperative to explicate future directions in order to decrease disparities in healthcare.
There are several limitations to this study. First, only 859 of 1557 participants had responses to all 9 of the SDoH items, allowing us to calculate an SDoH score. However, the full sample and SDoH scored sub-population were compared and were not found to have significantly different demographic characteristics. Next, the survey was fielded in the greater Philadelphia area and may not be generalizable to other areas of the U.S. In addition, our SDoH score considered each item equally in the score, although some SDoHs may be more or less impactful than others. We were unable to account for the duration of exposure to the SDoH items and cannot account for the impact of each SDoH on a respondent’s life or experiences. Lastly, all survey items were self-reported.