ABSTRACT
Background: Studies on cancer patients during the COVID-19 pandemic have shown a decrease in new diagnoses, delays in care, and a shift to later stage disease presentations. Considering that NY has been an epicenter for COVID-19 in the U.S., we investigated its impact on new cancer diagnoses at the two campuses of NYU's Perlmutter Cancer Center and hypothesized that there would be a decrease in presentations during the peak outbreaks in NY. Methods: We conducted a single center, retrospective analysis of new cancer diagnoses before, during, and after the peak of the pandemic between Dec 1, 2019, and Aug 31, 2020. Following IRB approval, subjects were identified using our cancer center database, which includes both inpatient and outpatient visits. Subjects were included regardless of their treatment plan. New diagnoses before COVID-19 (Dec to Feb), at first peak (March to May), and during the initial recovery phase (June-Aug) were assessed. No COVID-19 vaccines were available during this time. Results: As summarized in Table, during the initial COVID-19 peak, there was a substantial decrease in new patient visits with statistically significant differences seen by age and certain cancer types including breast, skin, and hematologic malignancies. In all cancers, there was a decrease in the proportion of new patient visits among those over age 75 during the peak. When confining analyses to breast, skin, and hematologic cancers, we saw a significant increase in the proportion of younger new patients at the peak period. We also observed an association between age and stage, with an increase in new stage I diagnoses in the younger (age 18-54) population at peak. Telemedicine was most utilized by the younger population during both peak and recovery periods. Conclusions: In this retrospective analysis, we found that during the initial COVID-19 peak, prior to vaccine availability, outpatient visits for hematologic and solid malignancies decreased at our cancer center in NY. The decrease in the proportion of all cancer types in elderly patients during the peak was likely related to hesitancy among this vulnerable population to seek care. The widespread use of telemedicine also likely contributed to the increased incidence in new patient visits in younger patients. Lessons learned from this experience can help guide outreach to vulnerable populations during future outbreaks, particularly by fostering telemedicine use among the elderly.
ABSTRACT
Background: Most ambulatory oncology practices utilize an on-call service that is essential for continuity of cancer care after office hours, yet there is limited literature about the dynamics of this care delivery model and how to improve it from a quality standpoint. We hypothesize that patients with more advanced cancers are more likely to require the oncall service due to acute symptoms arising during their treatment, and that this information can be used to create a risk model to predict subsequent hospitalization. Methods: We performed a singlecenter, retrospective review of sequential overnight and weekend calls received by an oncology practice with 16 physicians over a 20-week period from January-May 2020. Calls were classified as being either urgent, requiring immediate attention, or nonurgent, which could be addressed during office hours. Data were summarized using descriptive statistics, continuous and categorical variables were compared using Wilcoxon rank-sum test and Fisher's exact test, respectively. Multivariate analyses were estimated by logistic regression using the penalized maximum likelihood estimation method. Results: The data set included 236 consecutive calls among 176 patients, with 65% females and median age of 68 (range: 25-87). Of these, 185 calls (78.4%) were deemed urgent, among which 139 (75%) were symptom-related. Among the 202 calls (85%) from patients with cancer, 164 (81%) of them were urgent, mostly due to symptoms (82%). Of these urgent calls, 44 (27%) resulted in admission within 24 hours (P < 0.0001), primarily related to treatment toxicity or disease progression (81%). Patients with stage 4 cancers (42%) or hematologic malignancies (28%) were more likely to use the on-call service. There was no significant difference between call urgency and treatment regimen (P = 0.06). In a multivariable model, advanced age [OR = 1.03(1.0-1.07)] and urgent calls [OR = 33.1(2.7-401.0)] were independently associated with risk of hospitalization. Of note, there were no differences in admissions before or after the peak of the COVID-19 pandemic in New York City (P = 0.49). Conclusions: We identified an association between after-hours calls and more advanced malignancy, which was independent of treatment regimen. The majority of on-call issues were urgent and symptom-related, with advanced age and urgent calls being most likely to result in hospital admission. These results suggest that strategies can be developed to prevent hospitalizations in patients at higher-risk for adverse events based on a multivariable risk model.