Racial and ethnic associations with comprehensive cancer center access and clinical trial enrollment for acute leukemia.

BACKGROUND: Clinical trial participation at Comprehensive Cancer Centers (CCC) is inequitable for minoritized racial and ethnic groups with acute leukemia. CCCs care for a high proportion of adults with acute leukemia. It is unclear if participation inequities are due to CCC access, post-access enrollment, or both. METHODS: We conducted a retrospective cohort study of adults with acute leukemia (2010-2019) residing within Massachusetts, the designated catchment area of the Dana-Farber/Harvard Cancer Center (DF/HCC). Individuals were categorized as non-Hispanic Asian (NHA), Black (NHB), White (NHW), Hispanic White (HW), or Other. Decomposition analyses assessed covariate contributions to disparities in (1) access to DF/HCC care and (2) post-access enrollment. RESULTS: Of 3698 individuals with acute leukemia, 85.9% were NHW, 4.5% HW, 4.3% NHB, 3.7% NHA, and 1.3% Other. Access was lower for HW (age- and sex-adjusted OR = 0.64, 95% CI = 0.45 to 0.90) and reduced post-access enrollment for HW (aOR = 0.54, 95% CI =0.34 to 0.86) and NHB (aOR = 0.60, 95% CI = 0.39 to 0.92) compared to NHW. Payor and socioeconomic status (SES) accounted for 25.2% and 21.2% of the +1.1% absolute difference in HW access. Marital status and SES accounted for 8.0% and 7.0% of the -8.8% absolute disparity in HW enrollment; 76.4% of the disparity was unexplained. SES and marital status accounted for 8.2% and 7.1% of the -9.1% absolute disparity in NHB enrollment; 73.0% of the disparity was unexplained. CONCLUSIONS: A substantial proportion of racial and ethnic inequities in acute leukemia trial enrollment at CCCs are from post-access enrollment, the majority of which was not explained by sociodemographic factors.

Regional Disparities in the Use and Delivery of Adjuvant Radiation Therapy after Lumpectomy for Breast Cancer in the Medicare Population.

Purpose: We examined radiation therapy (RT) use among patients with early-stage breast cancer and analyzed the contribution of patient, cancer, and regional factors to the likelihood of RT receipt across Health Service Areas. Methods and Materials: We identified 13,176 patients aged 66 to 79 years in the Surveillance, Epidemiology, and End Results (SEER) Program-Medicare database who were diagnosed with lymph node-negative breast cancer in 2007 to 2011 and were treated with breast-conserving surgery. Patients were stratified as being at high risk or low risk for recurrence based on National Comprehensive Cancer Network Guidelines. Receipt of RT was studied with 5 modeling approaches to determine whether RT use and regional variation in its use changed based on the risk level of the cohort. Multivariable mixed-effects logistic regression was performed for each outcome. Choropleth maps were used to describe patterns of RT use. Results: Among high-risk patients, 70.1% received RT, compared with 72.6% of low-risk patients (P = .002). Among patients receiving RT, 60.9% were classified as high-risk, compared with 63.0% of patients who did not receive RT (P = .002). In multivariable analyses, patients in all rural areas had lower odds of receiving RT compared with the entire cohort (odds ratio [OR], 0.73; P < .001) and had lower odds of being high-risk and receiving RT (OR, 0.69; P < .001). Black patients (OR, 0.73; P = .001) and Asian patients (OR, 0.74; P = .004) had decreased likelihood of receiving RT compared with the entire cohort. The regional interclass correlation coefficient (ICC) for the model predicting receipt of RT among all patients was 0.05 and among low-risk patients was 0.06. The regional ICC dropped to 0.02 for the model predicting being both high-risk and receiving RT among all patients. Conclusions: We observed regional and racial and ethnic disparities in RT receipt among our cohort. Reassuringly, less regional variability was observed for RT receipt among those at high risk for recurrence. Future work is needed to understand the causes of these regional disparities to better serve patients who may benefit from treatment.

Association of Oncologist Participation in Medicare's Oncology Care Model With Patient Receipt of Novel Cancer Therapies.

Importance: Medicare's Oncology Care Model (OCM) was an alternative payment model that tied performance-based payments to cost and quality goals for participating oncology practices. A major concern about the OCM regarded inclusion of high-cost cancer therapies, which could potentially disincentivize oncologists from prescribing novel therapies. Objective: To examine whether oncologist participation in the OCM changed the likelihood that patients received novel therapies vs alternative treatments. Design, Setting, and Participants: This cohort study of Surveillance, Epidemiology, and End Results (SEER) Program data and Medicare claims compared patient receipt of novel therapies for patients treated by oncologists participating vs not participating in the OCM in the period before (January 2015-June 2016) and after (July 2016-December 2018) OCM initiation. Participants included Medicare fee-for-service beneficiaries in SEER registries who were eligible to receive 1 of 10 novel cancer therapies that received US Food and Drug Administration approval in the 18 months before implementation of the OCM. The study excluded the Hawaii registry because complete data were not available at the time of the data request. Patients in the OCM vs non-OCM groups were matched on novel therapy cohort, outcome time period, and oncologist specialist status. Analysis was conducted between July 2021 and April 2022. Exposures: Oncologist participation in the OCM. Main Outcomes and Measures: Preplanned analyses evaluated patient receipt of 1 of 10 novel therapies vs alternative therapies specific to the patient's cancer for the overall study sample and for racial subgroups. Results: The study included 2839 matched patients (760 in the OCM group and 2079 in the non-OCM group; median [IQR] age, 72.7 [68.3-77.6] years; 1591 women [56.0%]). Among patients in the non-OCM group, 33.2% received novel therapies before and 40.1% received novel therapies after the start of the OCM vs 39.9% and 50.3% of patients in the OCM group (adjusted difference-in-differences, 3.5 percentage points; 95% CI, -3.7 to 10.7 percentage points; P = .34). In subgroup analyses, second-line immunotherapy use in lung cancer was greater among patients in the OCM group vs non-OCM group (adjusted difference-in-differences, 17.4 percentage points; 95% CI, 4.8-30.0 percentage points; P = .007), but no differences were seen in other subgroups. Over the entire study period, patients with oncologists participating in the OCM were more likely to receive novel therapies than those with oncologists who were not participating (odds ratio, 1.47; 95% CI, 1.09-1.97; P = .01). Conclusions and Relevance: This study found that participation in the OCM was not associated with oncologists' prescribing novel therapies to Medicare beneficiaries with cancer. These findings suggest that OCM financial incentives did not decrease patient access to novel therapies.

Patterns of Medical Care Cost by Service Type for Patients With Recurrent and De Novo Advanced Cancer.

OBJECTIVES: There is limited knowledge about the cost patterns of patients who receive a diagnosis of de novo and recurrent advanced cancers in the United States. METHODS: Data on patients who received a diagnosis of de novo stage IV or recurrent breast, colorectal, or lung cancer between 2000 and 2012 from 3 integrated health systems were used to estimate average annual costs for total, ambulatory, inpatient, medication, and other services during (1) 12 months preceding de novo or recurrent diagnosis (preindex) and (2) diagnosis month through 11 months after (postindex), from the payer perspective. Generalized linear regression models estimated costs adjusting for patient and clinical factors. RESULTS: Patients who developed a recurrence <1 year after their initial cancer diagnosis had significantly higher total costs in the preindex period than those with recurrence ≥1 year after initial diagnosis and those with de novo stage IV disease across all cancers (all P < .05). Patients with de novo stage IV breast and colorectal cancer had significantly higher total costs in the postindex period than patients with cancer recurrent in <1 year and ≥1 year (all P < .05), respectively. Patients in de novo stage IV and those with recurrence in ≥1 year experienced significantly higher postindex costs than the preindex period (all P < .001). CONCLUSIONS: Our findings reveal distinct cost patterns between patients with de novo stage IV, recurrent <1-year, and recurrent ≥1-year cancer, suggesting unique care trajectories that may influence resource use and planning. Future cost studies among patients with advanced cancer should account for de novo versus recurrent diagnoses and timing of recurrence to obtain estimates that accurately reflect these care pattern complexities.

Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data.

PURPOSE: Hospitalizations are a common occurrence during chemotherapy for advanced cancer. Validated risk stratification tools could facilitate proactive approaches for reducing hospitalizations by identifying at-risk patients. PATIENTS AND METHODS: We assembled two retrospective cohorts of patients receiving chemotherapy for advanced nonhematologic cancer; cohorts were drawn from three integrated health plans of the Cancer Research Network. We used these cohorts to develop and validate logistic regression models estimating 30-day hospitalization risk after chemotherapy initiation. The development cohort included patients in two health plans from 2005 to 2013. The validation cohort included patients in a third health plan from 2007 to 2016. Candidate predictor variables were derived from clinical data in institutional data warehouses. Models were validated based on the C-statistic, positive predictive value, and negative predictive value. Positive predictive value and negative predictive value were calculated in reference to a prespecified risk threshold (hospitalization risk ≥ 18.0%). RESULTS: There were 3,606 patients in the development cohort (median age, 63 years) and 634 evaluable patients in the validation cohort (median age, 64 years). Lung cancer was the most common diagnosis in both cohorts (26% and 31%, respectively). The selected risk stratification model included two variables: albumin and sodium. The model C-statistic in the validation cohort was 0.69 (95% CI, 0.62 to 0.75); 39% of patients were classified as high risk according to the prespecified threshold; 30-day hospitalization risk was 24.2% (95% CI, 19.9% to 32.0%) in the high-risk group and 8.7% (95% CI, 6.1% to 12.0%) in the low-risk group. CONCLUSION: A model based on data elements routinely collected during cancer treatment can reliably identify patients at high risk for hospitalization after chemotherapy initiation. Additional research is necessary to determine whether this model can be deployed to prevent chemotherapy-related hospitalizations.

Interinstitutional variation in management decisions for treatment of 4 common types of cancer: A multi-institutional cohort study.

BACKGROUND: When clinical practice is governed by evidence-based guidelines and there is consensus about their validity, practice variation should be minimal. For areas in which evidence gaps exist, greater variation is expected. OBJECTIVE: To systematically assess interinstitutional variation in management decisions for 4 common types of cancer. DESIGN: Multi-institutional, observational cohort study of patients with cancer diagnosed between July 2006 through May 2011 and observed through 31 December 2011. SETTING: 18 cancer centers participating in the formulation of treatment guidelines and systematic outcomes assessment through the National Comprehensive Cancer Network. PATIENTS: 25 589 patients with incident breast cancer, colorectal cancer, lung cancer, or non-Hodgkin lymphoma. MEASUREMENTS: Interinstitutional variation for 171 binary management decisions with varying levels of supporting evidence. For each decision, variation was characterized by the median absolute deviation of the center-specific proportions. RESULTS: Interinstitutional variation was high (median absolute deviation >10%) for 35 of 171 (20%) oncology management decisions, including 9 of 22 (41%) decisions for non-Hodgkin lymphoma, 16 of 76 (21%) for breast cancer, 7 of 47 (15%) for lung cancer, and 3 of 26 (12%) for colorectal cancer. Forty-six percent of high-variance decisions involved imaging or diagnostic procedures and 37% involved choice of chemotherapy regimen. The evidence grade underpinning the 35 high-variance decisions was category 1 for 0%, 2A for 49%, and 2B/other for 51%. LIMITATION: Physician identifiers were unavailable, and results may not generalize outside of major cancer centers. CONCLUSION: The substantial variation in institutional practice manifest among cancer centers reveals a lack of consensus about optimal management for common clinical scenarios. For clinicians, awareness of management decisions with high variation should prompt attention to patient preferences. For health systems, high variation can be used to prioritize comparative effectiveness research, patient-provider education, or pathway development. PRIMARY FUNDING SOURCE: National Cancer Institute and National Comprehensive Cancer Network.