Exploring Racial/Ethnic Disparities in Multimorbidity Among Cancer Patients at a Safety-Net Health System

Purpose: Multimorbidity, defined as having two or more chronic health conditions, can substantially impact quality of life and mortality. Multimorbidity also increases the complexity of healthcare and treatment decision-making, particularly during cancer treatment. Racial/ethnic minorities may have higher risk for multimorbidity, potentially attributed to socioeconomic or other health-related factors. Therefore, we aimed to investigate racial/ethnic disparities in multimorbidity and potential explanatory factors among newly diagnosed cancer patients at a safety-net health system. Methods: We used electronic health record and cancer registry data from JPS Health Network, an urban safety-net health system in North Texas. Our eligible population included patients ≥18 years of age diagnosed with a first primary cancer in 2016–2020 (excluding in situ cases), whose initial diagnosis or at least part of the first course cancer treatment was received at JPS. Our outcomes were prevalence of multimorbidity (≥2 chronic conditions) and severe multimorbidity (≥3 chronic conditions). We included 30 chronic conditions defined by the Centers for Medicare & Medicaid Services and used up to a 2-year lookback period before cancer diagnosis. We used descriptive statistics to summarize sociodemographic characteristics and assess the proportions of multimorbidity by race/ethnicity. We used logistic regression to assess unadjusted and adjusted associations between race/ethnicity and multimorbidity and severe multimorbidity. Results: Our study included5,019 patients newly diagnosed with cancer. The most common cancer types were breast (13%), lung (12%), and colorectal (11%). The median age was 58 years (interquartile range: 50 – 64) and the majority were females (51%), racial/ethnic minority (59%), and uninsured (51%).Overall, 79% had multimorbidity and 62% had severe multimorbidity. Non-Hispanic Black (NHB) patients had the highest proportion of multimorbidity (86%) and severe multimorbidity (73%),whereas Hispanic patients had the lowest proportions (74% and 54%, respectively). In our unadjusted logistic regression model for multimorbidity, NHB patients had 1.75[95% CI: 1.44, 2.12]times higher odds and Hispanic patients had 0.80[95% CI: 0.69, 0.94]times the odds of multimorbidity compared with non-Hispanic White (NHW) patients. After adjusting for age, sex, body mass index, insurance status, and marital status, NHB patients maintained higher odds of multimorbidity (1.77 times [95% CI: 1.45, 2.17]), but Hispanic patients had no substantial difference (OR: 0.91[95% CI: 0.79, 1.07]) compared with NHW patients. In the unadjusted model for severe multimorbidity, NHB patients had 1.68 [95% CI: 1.44, 1.96]times higher odds and Hispanic patients had 0.71 [95% CI: 0.62, 0.81] times the odds of severe multimorbidity compared with NHW patients. In our adjusted model, NHB patientshad1.66 [95% CI: 1.41, 1.96] times higher odds and Hispanic patients had 0.77 [95% CI: 0.66, 0.89]times the odds of severe multimorbidity compared with NHW patients. Conclusions: We observed substantial racial/ethnic disparities in multimorbidity and severe multimorbidity among newly diagnosed cancer patients at a safety-net health system. Racial/ethnic disparities did not decrease after controlling for sociodemographic factors. Additional factors that are currently undocumented in electronic health records (e.g., social determinants of health) may help explain racial/ethnic disparities in multimorbidity and identify targetable points for interventions.