Background Previous studies have demonstrated epidemiological trends in individual metastatic cancer subtypes; however, research forecasting long-term incidence trends and projected survivorship of metastatic cancers is lacking. We assess the burden of metastatic cancer to 2040 by (1) characterizing past, current, and forecasted incidence trends, and (2) estimating odds of long-term (5-year) survivorship. Methods This retrospective, serial cross-sectional, population-based study used registry data from the Surveillance, Epidemiology, and End Results (SEER 9) database. Average annual percentage change (AAPC) was calculated to describe cancer incidence trends from 1988 to 2018. Autoregressive integrating moving average (ARIMA) models were used to forecast the distribution of primary metastatic cancer and metastatic cancer to specific sites from 2019 to 2040 and JoinPoint models were fitted to estimate mean projected annual percentage change (APC). Results The average annual percent change (AAPC) in incidence of metastatic cancer decreased by 0.80 per 100,000 individuals (1988–2018) and we forecast an APC decrease by 0.70 per 100,000 individuals (2018–2040). Analyses predict a decrease in metastases to liver (APC = −3.40, 95% CI [−3.50, −3.30]), lung (APC (2019–2030) = −1.90, 95% CI [−2.90, −1.00]); (2030–2040) = −3.70, 95% CI [−4.60, −2.80]), bone (APC = −4.00, 95% CI [−4.30, −3.70]), and brain (APC = −2.30, 95% CI [−2.60, −2.00]). By 2040, patients with metastatic cancer are predicted to have 46.7% greater odds of long-term survivorship, driven by increasing plurality of patients with more indolent forms of metastatic disease. Conclusions By 2040, the distribution of metastatic cancer patients is predicted to shift in predominance from invariably fatal to indolent cancers subtypes. Continued research on metastatic cancers is important to guide health policy and clinical intervention efforts, and direct allocations of healthcare resources.

Objectives: 1) To determine the impact of COVID-19 and the corresponding increase in use of telemedicine on volume, efficiency, and burden of Electronic Health Record (EHR) usage by residents and fellows; and 2) To compare these metrics with those of attending physicians. Materials and Methods: We analyzed eleven metrics from Epic’s Signal Database of outpatient physician user logs for active residents/fellows at our institution across three 1-month time periods: August 2019 (pre-pandemic / pre-telehealth), May 2020 (mid-pandemic / post-telehealth implementation) and July 2020 (follow-up period) and compared these metrics between trainees and attending physicians. We also assessed how the metrics varied for medical trainees in primary care as compared to subspecialties. Results: Analysis of 141 residents/fellows and 495 attendings showed that after telehealth implementation, overall patient volume, Time in In Basket per day, Time Outside of 7AM-7PM, and Time in Notes decreased significantly compared to the pre-telehealth period. Female residents, fellows, and attendings had a lower same day note closure rate before and during the post-telehealth implementation period and spent greater time working outside of 7 AM-7 PM compared to male residents, fellows, and attendings (p<0.01) compared to the pre-telehealth period. Attending physicians had a greater patient volume, spent more time and were more efficient in the EHR compared to trainees (p<0.01) in both the post-telehealth and follow-up periods as compared to the pre-telehealth period. Conclusion: The dramatic change in clinical operations during the pandemic serves as an inflection point to study changes in physician practice patterns via the EHR. We observed that: 1) female physicians closed fewer notes the same day and spent more time in the EHR outside of normal working hours compared to male physicians; and 2) attending physicians had higher patient volumes and also higher efficiency in the EHR compared to resident physicians.

Objectives: Deaths from an unknown cause are difficult to adjudicate and oncologic studies of comparative effectiveness often demonstrate inconsistencies in incorporating these deaths and competing events (eg, heart disease and stroke) in their analyses. In this study, we identify cancer patients most at risk for death of an unknown cause. Methods: This retrospective, population-based study used cancer registry data from the Surveillance, Epidemiology, and End Results database (1992-2015). The absolute rate of unknown causes of death (COD) cases stratified by sex, marital status, race, treatment, and cancer site were calculated and a multivariable logistic regression model was applied to obtain adjusted odds ratios with 95% CIs. Results: Out of 7,154,779 cancer patients across 22 cancer subtypes extracted from Surveillance, Epidemiology, and End Results, 3,448,927 died during follow-up and 276,068 (7.4%) of these deaths were from unknown causes. Patients with an unknown COD had a shorter mean survival time compared with patients with known COD (36.3 vs 65.7 mo, P < 0.001). The contribution of unknown COD to total mortality was highest in patients with more indolent cancers (eg, prostate [12.7%], thyroid [12.3%], breast [10.7%]) and longer follow-up (eg, >5 to 10 y). One, 3, and 5-year cancer-specific survival (CSS) calculations including unknown COD were significantly decreased compared with CSS estimates excluding cancer patients with unknown COD. Conclusion: Of the patients, 7.4% died of unknown causes during follow-up and the proportion of death was higher with longer follow-up and among more indolent cancers. The attribution of high percentages of unknown COD to cancer or non-cancer causes could impact population-based cancer registry studies or clinical trial outcomes with respect to measures involving CSS and mortality.