A Systematic Literature Review (SLR) to Evaluate the Role of Socioeconomic Factors Associated with Treatment Practices Among Oncology Patients

OBJECTIVES: Impact of lower socioeconomic status (SES) on cancer mortality has been documented globally and may stem from disparities in oncologic care. An SLR was conducted to analyze how SES factors influence treatment patterns within oncology.

METHODS: Searches were conducted in Embase and MEDLINE to identify studies assessing SES factors in patients with prostate, breast, colorectal or lung cancer. Analysis was restricted to full-text publications since 2012 reporting odds ratios (ORs) through univariate or multivariate analysis (MVA).

RESULTS: Among 1243 reviewed records, 50 studies were included. Of 33 studies (74 analyses) reporting impact of income, MVA ORs ranged from 0.48 to 4.44, with 42 (57%) revealing patients with lower income were significantly less likely to receive treatment. Among 35 studies, ORs ranged from 0.09 to 4.07, with heterogeneity in how insurance status was categorized and referenced in analysis. Among 52 analyses, 25 (48%) showed that patients without insurance were statistically less likely to receive treatment for their cancer. Among 17 studies (28 analyses), 13 (46%) showed a statistically significantly increased odds of receiving treatment with higher education. Less studies showed a significant difference between areas of residence, with only 21 (33%) analyses showing that patients living in metropolitan or urban areas were more likely to receive treatment compared to rural areas. No significant difference was found among 4 studies evaluating employment status.

CONCLUSIONS: While data is heterogeneous with differing comparisons and treatment outcomes, trends suggest that income, insurance, and education level may be associated with access to oncology treatments. The findings from the studies may also be influenced by the differences in cancer stages, comorbidities and treatment modalities. The discrepancy in findings could also be attributed to different covariates adjusted in the MVA. Understanding how SES factors influence treatment patterns is crucial for addressing and mitigating these disparities.