Readdressing the Needs of Cancer Survivors During COVID-19: A Path Forward

Jones, Jennifer; Saeed, Hina; Katz, Matthew S.; Lustberg, Maryam B.; Forster, Victoria J.; Nekhlyudov, Larissa

doi:10.1093/jnci/djaa200

Cited by 30 publications

(35 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, these patients would not have been included in the dataset, resulting in fewer low complexity patients being represented. Furthermore, the added stressors of the pandemic may have resulted in some level of response bias regarding the PPF, for example, some patients may have reported uncharacteristically high levels of anxiety and depression [35]. Some of these patients may have been outliers, if they had highly heightened symptom burdens due to the pandemic.…”

Section: Discussionmentioning

confidence: 99%

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes

Watson

DeIure

et al. 2021

IJERPH

View full text Add to dashboard Cite

An increasing incidence of cancer has led to high patient volumes and time challenges in ambulatory oncology clinics. By knowing how many patients are experiencing complex care needs in advance, clinic scheduling and staff allocation adjustments could be made to provide patients with longer or shorter timeslots to address symptom complexity. In this study, we used predictive analytics to forecast the percentage of patients with high symptom complexity in one clinic population in a given time period. Autoregressive integrated moving average (ARIMA) modelling was utilized with patient-reported outcome (PRO) data and patient demographic information collected over 24 weeks. Eight additional weeks of symptom complexity data were collected and compared to assess the accuracy of the forecasting model. The predicted symptom complexity levels were compared with observation data and a mean absolute predicting error of 5.9% was determined, indicating the model’s satisfactory accuracy for forecasting symptom complexity levels among patients in this clinic population. By using a larger sample and additional predictors, this model could be applied to other clinics to allow for tailored scheduling and staff allocation based on symptom complexity forecasting and inform system level models of care to improve outcomes and provide higher quality patient care.

show abstract

Section: Discussionmentioning

confidence: 99%

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes

Watson

DeIure

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Long-term impacts of the pandemic within many healthcare disciplines are expected to emerge over the next years or even decades. Within cancer care and prevention, the pandemic has led to delays or deferrals of cancer-related treatments, screening, and surveillance appointments (5)(6)(7)(8)(9)(10). For example, weekly colorectal cancer screenings drastically decreased by 86% in 2020 (6,10,11).…”

Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 pandemic on exercise habits among cancer patients

Himbert

Hathaway

Daniels

et al. 2021

Preprint

View full text Add to dashboard Cite

Purpose There is limited information on how the COVID-19 pandemic has changed health behaviors among cancer patients. We examined the impact of the pandemic on changes in exercise behaviors and identified characteristics associated with these changes among cancer patients. Methods Cancer patients (n = 1,361) completed a survey from August-September 2020 to assess COVID-19 pandemic-related changes in health behaviors and psychosocial factors. Patients were categorized into 3 groups: exercising less, exercising did not change, and exercising more. Patient characteristics were compared by exercise groups. Results One-third of the patients reported a decreased amount of regular exercise, while 11% reported exercising more during the pandemic. Patients who exercised less were more likely to be unemployed/retired, undergoing active treatment, and had increased pandemic-related alcohol consumption and psychosocial stressors such as loneliness and financial stress (all p < 0.05). In contrast, patients who exercised more were younger, female, full-time employed, did not consume alcohol, and had good health status and more social interactions (all p < 0.05). Patients who were living in rural areas and did not experience changes in daily life, were also more likely not to experience changes in exercise habits (all p < 0.05). Conclusion Our results indicate that a significant proportion of cancer patients experienced changes in exercise habits during the first 6 months of the COVID-19 pandemic. Age, sex, employment status, health status, alcohol consumption, and psychosocial factors were associated with changes in exercise behaviors. Providers should monitor for changes in health behaviors, such as exercise, because of their importance in improving cancer survivorship.

show abstract

“…15 Of course, it is important to emphasize that there is no one-size-fits-all kind of solution, and individualized treatment/surveillance decisions for patients and survivors need to be made. 16 Additionally, there continue to be widespread issues with regulations regarding financial incentives and reimbursements associated with telemedicine. To address this evolving situation, the American Society for Clinical Oncology recently issued an interim position statement on telemedicine in cancer care that included recommendations on federal and state coverage, health equity, patient education, and research.…”

mentioning

confidence: 99%

“…There may also be an opportunity to expand remote monitoring of patient‐reported outcomes 15 . Of course, it is important to emphasize that there is no one‐size‐fits‐all kind of solution, and individualized treatment/surveillance decisions for patients and survivors need to be made 16 . Additionally, there continue to be widespread issues with regulations regarding financial incentives and reimbursements associated with telemedicine.…”

mentioning

confidence: 99%

Prostate cancer survivorship, deaths, and health care management

Bauer

2021

Cancer

View full text Add to dashboard Cite

Prostate cancer (PCa) remains an important public health burden. According to data from the American Cancer Society, PCa will account for an estimated 248,530 of all new cancer cases (26%) in males in the United States during 2021. 1 It is also estimated that there are currently more than 3.6 million male survivors in this country who are living with PCa, and this total number is projected to grow to more than 5 million male survivors by 2030. 2 A major context for Weiner et al's article titled "Cause of Death During Prostate Cancer Survivorship: A Contemporary, US Population-Based Analysis," which appears in this issue of Cancer, is that both the number of PCa survivors and the proportion of male PCa survivors as a part of the total male cancer survivor population are increasing. 3 Currently, approximately 4 in every 10 cancer survivors are PCa survivors, and by the end of this decade, that proportion will increase to nearly 5 in every 10. 2 Trying to determine causes of death after a diagnosis of PCa, Weiner et al 3 conducted a retrospective cohort study that was exploratory in nature, but they used a strong study design to compensate for limitations of previous studies. They assessed contemporary, population-based data for causes of death during PCa survivorship in the United States by using the National Cancer Institute's Surveillance, Epidemiology, and End Results 18 registries, which collect data on approximately 34.6% of the US population, from 2000 to 2016. 4,5 Previous work has demonstrated that the regions of the United States reported by the Surveillance, Epidemiology, and End Results program tend to be broadly generalizable to the general United States. 6 The authors also obtained additional access to the radiation/chemotherapy specialized data set, which was also used for analysis. 7 Causes of death after a PCa diagnosis were then stratified by patient characteristics and tumor stage. Finally, they compared the risk of death for each cause with the general US population.Straightforward, conservative statistical analyses were performed on the data. The causes of death for men with local/regional PCa as well as the causes of death for men with distant PCa were investigated. Additionally, there was an assessment of year of diagnosis and stage at diagnosis. Succinctly, men with early-stage PCa are 4-fold more likely to die of other causes, whereas those with advanced-stage PCa have an increased risk of several non-cancer-related causes in comparison with the general population.The work of Weiner et al 3 should be understood within the context of the coronavirus disease 2019 (COVID-19) pandemic. Health care for patients with PCa can be characterized as an acute and intense period of treatment and surveillance administered by oncology teams. The health care management of patients with PCa has become more challenging with respect to prioritization, access to treatment, multiple adjustments, and attempts to tailor approaches.Obek et al 8 published a review and synopsis of currently available recommendations ...

show abstract

Readdressing the Needs of Cancer Survivors During COVID-19: A Path Forward

Cited by 30 publications

References 80 publications

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes

Impact of the COVID-19 pandemic on exercise habits among cancer patients

Prostate cancer survivorship, deaths, and health care management

Contact Info

Product

Resources

About