System-Based Intervention to Reduce Racial Disparities in Early-Stage Lung Cancer Treatment
Results from a study published by Cykert et al in Cancer Medicine showed that a pragmatic system-based intervention within cancer treatment centers may eliminate existing disparities in treatment and outcomes for black patients with early-stage lung cancer.
“These results show promise for all cancer treatment centers,” said Samuel Cykert, MD, Professor of Medicine at the University of North Carolina School of Medicine and co-principal investigator of the trial.
Previous Research
Dr. Cykert and his colleagues previously conducted studies on why racial disparities in cancer treatment exist. They found multiple reasons that contribute to the overall reduction in treatment.
“We found there was an implicit bias with many clinicians that made them less willing to take the same risks with patients that were different from them,” Dr. Cykert said. “A black and a white patient of the same age could require the same surgery, have the same comorbidities, have the same income and insurance—yet white patients were more likely to receive the surgery and get their cancer treated.”
Dr. Cykert said they additionally found that black patients with cancer who did not have a regular source of care might have trust issues or miscommunications with physicians, leading them to drop out of treatment altogether. They also found that denial of their diagnosis played a role in patients seeking treatment.
“With that knowledge, we wanted to build a system that pointed out these lapses in care or communication in real time to help us keep track of patients who would otherwise drop off the grid,” said Dr. Cykert, who is also a member of the UNC Lineberger Comprehensive Cancer Center.
System-Based Intervention
The intervention consisted of three parts: a real-time warning system derived from electronic health records; race-specific feedback to clinical teams on treatment completion rates; and a nurse navigator to engage with patients throughout treatment.
The real-time warning system notified nurse navigators when a patient missed an appointment or treatment milestone. The navigator then reached out to the patient to reengage and bring them back in for treatment. The nurse navigators were encouraged to become familiar with patients and build trust in case of a missed appointment, miscommunication between doctor and patient, or other circumstance that created a potential barrier to care.
Dr. Cykert said he and colleagues came up with the intervention model with help from the Greensboro Health Disparities Collaborative, an academic-community partnership experienced in community-based participatory research. Their goals were to create elements of real-time transparency, race-specific accountability, and enhanced patient-centered communication.
Dr. Cykert’s team recruited patients aged 18 to 85 from two multi-institutional prospective trials using identical interventions. Nearly 240 patients were enrolled in an American Cancer Society–sponsored study, and around 120 patients with lung cancer enrolled in a study sponsored by the National Cancer Institute.
Findings
The treatment rates before this three-part intervention were 78% for white patients vs 69% for black patients (P < .001); difference by race was confirmed by a model adjusted for age, treatment site, cancer stage, gender, comorbid illness, and income-odds ratio (OR) 0.66 for black patients (95% confidence interval [CI] = 0.51–0.85; P = .001). With the intervention in place, treatment rates climbed to 95% for white patients and 96.5% for black patients (P = .56). Odds ratio for the adjusted analysis was 2.1 (95% CI = 0.41–10.4, P = .39) for black vs white patients.
Researchers are in the process of submitting a grant proposal with the National Cancer Institute to implement this intervention to cover whole cancer center populations rather than study patients alone.
The study authors' full disclosures can be found at onlinelibrary.wiley.com.
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
