"Cancer's a demon": a qualitative study of fear and multilevel factors contributing to cancer treatment delays

PURPOSE

Delays initiating cancer therapy are increasingly common, impact outcomes, and have implications for health equity. However, it remains unclear (1) whether patients' beliefs regarding acceptable diagnostic to treatment intervals align with current guidelines, and (2) to what degree psychological factors contribute to longer intervals. We conducted a qualitative study with patients and cancer care team members ("providers").

METHODS

We interviewed patients with several common solid tumors as well as providers. Interviews were analyzed using an interpretive approach, guided by modified grounded theory.

RESULTS

Twenty-two patients and 12 providers participated. Half of patients had breast cancer; 27% waited >60 days between diagnosis and treatment. Several themes emerged. (1) Patients felt treatment should begin immediately following diagnosis, while providers' opinion on the goal timeframe to start treatment varied. (2) Patients experienced psychological distress while waiting for treatment. (3) Participants identified logistical, social, and psychological sources of delay. Fear related to multiple aspects of cancer care was common. Emotion-driven barriers could manifest as not taking steps to move ahead, or as actions that delayed care. (4) Besides addressing logistical challenges, patients believed that education and anticipatory guidance, from their care team and from peers, may help overcome psychological barriers to treatment and facilitate the start of therapy.

CONCLUSIONS

Patients feel an urgency to start cancer therapy, desiring time frames shorter than those included in guidelines. Psychological distress is frequently both a contributor to, and a consequence of, treatment delays. Addressing multilevel barriers, including psychological ones, may facilitate timely treatment and reduce distress.

Development of a Multilevel Model to Identify Patients at Risk for Delay in Starting Cancer Treatment

Importance

Delays in starting cancer treatment disproportionately affect vulnerable populations and can influence patients' experience and outcomes. Machine learning algorithms incorporating electronic health record (EHR) data and neighborhood-level social determinants of health (SDOH) measures may identify at-risk patients.

Objective

To develop and validate a machine learning model for estimating the probability of a treatment delay using multilevel data sources.

Design, Setting, and Participants

This cohort study evaluated 4 different machine learning approaches for estimating the likelihood of a treatment delay greater than 60 days (group least absolute shrinkage and selection operator [LASSO], bayesian additive regression tree, gradient boosting, and random forest). Criteria for selecting between approaches were discrimination, calibration, and interpretability/simplicity. The multilevel data set included clinical, demographic, and neighborhood-level census data derived from the EHR, cancer registry, and American Community Survey. Patients with invasive breast, lung, colorectal, bladder, or kidney cancer diagnosed from 2013 to 2019 and treated at a comprehensive cancer center were included. Data analysis was performed from January 2022 to June 2023.

Exposures

Variables included demographics, cancer characteristics, comorbidities, laboratory values, imaging orders, and neighborhood variables.

Main Outcomes and Measures

The outcome estimated by machine learning models was likelihood of a delay greater than 60 days between cancer diagnosis and treatment initiation. The primary metric used to evaluate model performance was area under the receiver operating characteristic curve (AUC-ROC).

Results

A total of 6409 patients were included (mean [SD] age, 62.8 [12.5] years; 4321 [67.4%] female; 2576 [40.2%] with breast cancer, 1738 [27.1%] with lung cancer, and 1059 [16.5%] with kidney cancer). A total of 1621 (25.3%) experienced a delay greater than 60 days. The selected group LASSO model had an AUC-ROC of 0.713 (95% CI, 0.679-0.745). Lower likelihood of delay was seen with diagnosis at the treating institution; first malignant neoplasm; Asian or Pacific Islander or White race; private insurance; and lacking comorbidities. Greater likelihood of delay was seen at the extremes of neighborhood deprivation. Model performance (AUC-ROC) was lower in Black patients, patients with race and ethnicity other than non-Hispanic White, and those living in the most disadvantaged neighborhoods. Though the model selected neighborhood SDOH variables as contributing variables, performance was similar when fit with and without these variables.

Conclusions and Relevance

In this cohort study, a machine learning model incorporating EHR and SDOH data was able to estimate the likelihood of delays in starting cancer therapy. Future work should focus on additional ways to incorporate SDOH data to improve model performance, particularly in vulnerable populations.

Willingness to Travel for Cellular Therapy: The Influence of Follow-Up Care Location, Oncologist Continuity, and Race

PURPOSE

Patients weigh competing priorities when deciding whether to travel to a cellular therapy center for treatment. We conducted a choice-based conjoint analysis to determine the relative value they place on clinical factors, oncologist continuity, and travel time under different post-treatment follow-up arrangements. We also evaluated for differences in preferences by sociodemographic factors.

METHODS

We administered a survey in which patients with diffuse large B-cell lymphoma selected treatment plans between pairs of hypothetical options that varied in travel time, follow-up arrangement, oncologist continuity, 2-year overall survival, and intensive care unit admission rate. We determined importance weights (which represent attributes' value to participants) using generalized estimating equations.

RESULTS

Three hundred and two patients (62%) responded. When all follow-up care was at the center providing treatment, plans requiring longer travel times were less attractive (v 30 minutes, importance weights [95% CI] of -0.54 [-0.80 to -0.27], -0.57 [-0.84 to -0.29], and -0.17 [-0.49 to 0.14] for 60, 90, and 120 minutes). However, the negative impact of travel on treatment plan choice was mitigated by offering shared follow-up (importance weights [95% CI] of 0.63 [0.33 to 0.93], 0.32 [0.08 to 0.57], and 0.26 [0.04 to 0.47] at 60, 90, and 120 minutes). Black participants were less likely to choose plans requiring longer travel, regardless of follow-up arrangement, as indicated by lower value importance weights for longer travel times.

CONCLUSION

Reducing travel burden through shared follow-up may increase patients' willingness to travel to receive cellular therapies, but additional measures are required to facilitate equitable access.

Trends in Patient Volume by Hospital Type and the Association of These Trends With Time to Cancer Treatment Initiation

Importance

Increasing demand for cancer care may be outpacing the capacity of hospitals to provide timely treatment, particularly at referral centers such as National Cancer Institute (NCI)-designated and academic centers. Whether the rate of patient volume growth has strained hospital capacity to provide timely treatment is unknown.

Objective

To evaluate trends in patient volume by hospital type and the association between a hospital's annual patient volume growth and time to treatment initiation (TTI) for patients with cancer.

Design, Setting, and Participants

This retrospective, hospital-level, cross-sectional study used longitudinal data from the National Cancer Database from January 1, 2007, to December 31, 2016. Adult patients older than 40 years who had received a diagnosis of 1 of the 10 most common incident cancers and initiated their treatment at a Commission on Cancer-accredited hospital were included. Data were analyzed between December 19, 2019, and March 27, 2020.

Exposures

The mean annual rate of patient volume growth at a hospital.

Main Outcomes and Measures

The main outcome was TTI, defined as the number of days between diagnosis and the first cancer treatment. The association between a hospital's mean annual rate of patient volume growth and TTI was assessed using a linear mixed-effects model containing a patient volume × time interaction. The mean annual change in TTI over the study period by hospital type was estimated by including a hospital type × time interaction term.

Results

The study sample included 4 218 577 patients (mean [SD] age, 65.0 [11.4] years; 56.6% women) treated at 1351 hospitals. From 2007 to 2016, patient volume increased 40% at NCI centers, 25% at academic centers, and 8% at community hospitals. In 2007, the mean TTI was longer at NCI and academic centers than at community hospitals (NCI: 50 days [95% CI, 48-52 days]; academic: 43 days [95% CI, 42-44 days]; community: 37 days [95% CI, 36-37 days]); however, the mean annual increase in TTI was greater at community hospitals (0.56 days; 95% CI, 0.49-0.62 days) than at NCI centers (-0.73 days; 95% CI, -0.95 to -0.51 days) and academic centers (0.14 days; 95% CI, 0.03-0.26 days). An annual volume growth rate of 100 patients, a level observed at less than 1% of hospitals, was associated with a mean increase in TTI of 0.24 days (95% CI, 0.18-0.29 days).

Conclusions and Relevance

In this cross-sectional study, from 2007 to 2016, across the studied cancer types, patients increasingly initiated their cancer treatment at NCI and academic centers. Although increases in patient volume at these centers outpaced that at community hospitals, faster growth was not associated with clinically meaningful treatment delays.

Time to Treatment Initiation for Breast Cancer During the 2020 COVID-19 Pandemic

PURPOSE

The COVID-19 pandemic has posed significant pressures on healthcare systems, raising concern that related care delays will result in excess cancer-related deaths. Because data regarding the impact on patients with breast cancer are urgently needed, we aimed to provide a preliminary estimate of the impact of COVID-19 on time to treatment initiation (TTI) for patients newly diagnosed with breast cancer cared for at a large academic center.

METHODS

We conducted a retrospective study of patients with newly diagnosed early-stage breast cancer between January 1, 2020, and May 15, 2020, a time period during which care was affected by COVID-19, and an unaffected cohort diagnosed between January 1, 2018 and May 15, 2018. Outcomes included patient volume, TTI, and initial treatment modality. Adjusted TTI was compared using multivariable linear regression.

RESULTS

Three hundred sixty-six patients were included. There was an 18.8% decrease in patient volume in 2020 (n = 164) versus 2018 (n = 202). There was no association between time of diagnosis (pre-COVID-19 or during COVID-19) and adjusted TTI (P = .926). There were fewer in situ diagnoses in the 2020 cohort (P = .040). There was increased use of preoperative systemic therapy in 2020 (43.9% overall, 20.7% chemotherapy, and 23.2% hormonal therapy) versus 2018 (16.4% overall, 12.4% chemotherapy, and 4.0% hormonal therapy) (P < .001).

CONCLUSION

TTI was maintained among patients diagnosed and treated for breast cancer during the COVID-19 pandemic at a single large academic center. There was a decrease in patient volume, specifically in patients with in situ disease and a shift in initial therapy toward the use of preoperative hormonal therapy.

Assessing Quality of Care for the Myelodysplastic Syndromes

Measuring the quality of care for patients with chronic cancers is difficult, especially for heterogeneous malignancies such as the myelodysplastic syndromes (MDS). Recent work suggests that improvements may be needed in the quality of diagnostic, treatment, and end-of-life care for patients with these syndromes. Moreover, rigorous assessment of factors that are necessary to deliver high-quality care such as preferred method of decision-making and pre-treatment quality of life are often overlooked. Finally, a key component of quality care is that it is received equitably across different patient populations, yet several recent studies suggest that there are financial, educational, race-ethnic, and age-related barriers to equitable MDS care.