Program Cut Cancer Diagnostic Time by Over 62% for Underserved Populations

A preliminary analysis of individuals from historically marginalized populations receiving health care at a Federally Qualified Health Center (FQHC) over 6 years showed that colocating diagnostic services cut time to a cancer diagnosis by 62.5%.

Dana-Farber Cancer Institute began a cancer care equity program in 2012 at the FQHC in Roxbury, Massachusetts in 2012. The program integrates oncologists and primary care providers, assisted by clinical nurse navigators.

Before the program began, the average median time to diagnostic resolution (MTR) was32 days.After colocated services began, the MTR for patients was 12 days, according to the review, published in JCO Oncology Practice .¹

Cancer disparities are well known among Black, Indigenous, and people of color, but not much is known about the programs that serve them. Even though the cancer mortality rate has been improving overall with the mortality gap between Black and White patients with cancer narrowing, differences in outcomes by race are still present.

“Compared with White patients, Black patients are diagnosed at later stages for colorectal, breast, and cervical cancer,and there is substantial evidence documenting differences in treatment for Black patients in the United States compared with their White counterparts for many cancer types,” said the researchers.

In addition to seeking a resolution for cancer-related health issues within 21 days, the secondary aims of the project were to document the financial distress of these patients as well as to observe any impact on clinical trial enrollment.

A total of 497 patients were assessed through the program from January 2012 to July 2018.Of those patients, 411 were eligibleand 366 were enrolled..

Of the whole cohort, most patients were female; out of the cancer-only cohort (82), most patients were male.

Of the 366 patients, 147 were Black (non-Hispanic), 95 were Black (Hispanic), 35 were White (non-Hispanic), 57 were White (Hispanic). Twenty marked their race as “other,” 7 marked “two or more,” and 5 had missing data.

Those with a cancer diagnosis had similar demographics as the overall cohort, with from a slightly higher percentage of Hispanic and Black patients.

Most patients presented with comorbid health conditions and there was a high prevalence of self-reported financial distress.

Of the 82 patients with cancer, 10 had a new diagnosis, 28 had an existing diagnosis, and 44 had a prior diagnosis. The most common types of cancer included leukemia or lymphoma, prostate cancer, colon or rectal cancer, and breast cancer.

Treatment and monitoring plans were implemented for those with and without cancer at a MTR of 12 and 28 days, respectively.

The researchers noted that although this program was not designed to directly affect clinical trial enrollment,they were surprised to find that the clinical trial enrollment for the cohort overall was10%and was 24% for those diagnosed with cancer. Prior research has estimated clinical trial participation in this population at about 5%.

This study represented the first time the improvement of cancer diagnosis was sought via the colocation model.²

Colocating cancer services in community health settings might be one way to raise clinical trial enrollment of historically marginalized patients at cancer centers, and that future interventions and studies will analyze factors influencing clinical trial access prospectively, the researchers said Colocating these cancer evaluation services within community-based primary health care settings can improve coordination and delivery of services and reduce disparities.1

In terms of limitations, the cohort was fairly small, and there was little information on cancer care and outcomes at the FQHC prior to the program’s implementation. Cost was also not included in the study.The researchers weren’t able to retrospectively align the newer variables to historical variables, which limits the generalizability of the data. They said they “developed an infrastructure to systematically gather longitudinal data, which will be used to track project-level outcomes and to conduct a prospective case-control analysis in collaboration with another FQHC where the program has not been implemented. This will enable us to evaluate the program model for effectiveness and generalizability.”

References

1. Stockman L, Gunderson D, Gikandi A, et al. The colocation model in community cancer care: a description of patient clinical and demographic attributes and referral pathways. JCO Oncol Pract. Published online March 20, 2023. doi:10.1200/op.22.00487

2. Cancer diagnostic services in a community health center speed diagnosis for underserved populations. News release. Dana-Farber Cancer Institute.March 20, 2023. Accessed March 27, 2023. https://www.newswise.com/articles/cancer-diagnostic-services-in-a-community-health-center-speed-diagnosis-for-underserved-populations?sc=mwhr&xy=10021935