ABSTRACT
The COVID-19 pandemic has drastically affected how higher education operates, but relatively little is known about its effects on students enrolled in remote online classes. Across two data collection timepoints, we sought to examine college students' experiences, focusing particularly on their sense of belonging/loneliness, their course formats, and their experiences in the pandemic. Though some findings differed between data collected in fall 2020 and in spring 2021, we generally found that students' belonging/loneliness was linked with their class format, aspects of their virtual classes, social contact, and experiences in the pandemic. This research demonstrates the importance both of understanding students' experiences in general and of continuing to study students' experiences as we progress from one stage of the pandemic to the next. © 2022, The Online Learning Consortium. All rights reserved.
ABSTRACT
Introduction Measures taken to mitigate infection spread during the 2020 COVID-19 pandemic are considered to have caused significant unintended consequences on other diseases. Large decreases in the numbers of symptomatic and asymptomatic people presenting for diagnosis of heart disease, diabetes and cancer have been observed. A recent analysis of solid tumors showed up to 70% reduction in the number of patients presenting for diagnosis. The potential exists for significantly increased morbidity and mortality for these missed or delayed presenting patients. Further, it is important to determine whether infection spread mitigation measures affected the diagnostic testing and treatment decisions for these patients. This study aimed to determine whether pandemic control measures affected presentation, testing and treatment of patients across eight different hematologic cancers. Methods CMS claims data were analyzed for the presence of diagnostic (DX) ICD 10 codes indicative of hematologic cancer. Patients with a DX code first appearing in 2019 or in 2020 were selected to provide newly diagnosed pre-COVID-19 and during COVID-19 cohorts for comparison, with unique patient counts being calculated for each month. A “COVID-19 dip” i.e. a decrease in the number of patients was calculated as the change in number of patients diagnosed in a given month relative to the number for JAN2020. Dip duration was calculated only when the decrease was >10% of the JAN2020 figure. Patients who received treatment via a “J” code Healthcare Common Procedure Coding System (HCPCS) code were extracted from the cohorts and the time taken from initial diagnosis to first treatment calculated. Results Eight hematologic cancers: AML, CLL, CML, HEME (a group of different hematologic cancers), Hodgkins (HOG), Myelodysplasia (MDS), Non-Follicular Lymphomas (NFL), and Non-Hodgkins Lymphoma (NHL) showed a decrease in the number of patients being diagnosed during the early part of 2020 (Fig.1) Fig.1. Change in new patient diagnoses for selected hematologic cancers as a proportion of their JAN2020 value There was some variation in the depth and duration of the COVID-19 dip (Table 1) with MDS having both the longest and deepest dip. Median depth and duration of the dip was 33% and 3.5 months, respectively, with all dips starting either in FEB or MAR2020. Table 1. Duration and depth of COVID-19 dips for selected hematological cancers The proportions of patients receiving therapy via J HCPCS code (JRX) are shown in Table 2 Table 2. Proportions of patients receiving J code therapy Conclusions The decline in new patient diagnoses for heme cancers during the period when COVID-19 control measures were implemented is similar to that seen with solid tumors, although the depth of the COVID-19 dip was generally larger in the latter. There is no evidence of “catch up” diagnosis occurring i.e. patients missing from Q2 2020 are not reappearing en masse in subsequent quarters. The decline for MDS patients has, except for SEP to OCT2020, remained. Collectively, (depending on the calculation method), the COVID-19 dip for these eight heme cancers represents 16,584-33,671 patients who will likely have significantly increased rates of morbidity and mortality due to delayed diagnosis. Analysis of J code treatments show little difference between the proportions of patients receiving these treatments in 2020 compared to 2019 suggesting that at least some aspects of treatment e.g. infused chemotherapy, IO drugs for these patients was relatively unchanged by pandemic control measures. It also suggests that the main cause for decreased patient numbers treated is due to decreased testing for diagnosis, rather than not being treated once diagnosed. This aligns with findings from studies in the US and UK. The results of this study indicate that there may be a “backlog” of tens of thousands of people with cancer whose diagnosis has been significantly delayed and who urgently need to be identified in order to get on proper treatment to lessen the impact of that delay. [F rmula presented] Disclosures: No relevant conflicts of interest to declare.
ABSTRACT
Background and Aim: Public hospital outpatient departments are a critical interface between acute and specialist hospital services and primary care. Failure of patients to attend is an expensive and persistent issue worldwide, with reported did-not-attend (DNA) rates of up to 30% in some centers. Non-attendance is influenced by many factors, such as logistics in getting to the hospital, work commitments, financial hardship, transportation access, and competing health interests. Telehealth has been available for some years, but its implementation and uptake have been limited. Telehealth is defined as “information and communications technologies to deliver health and transmit health information over both long and short distances,”1 and it can be conducted via videoconferencing or telephone. It represents an attractive model to increase outpatient clinic appointments, which is important given the long waiting times for many clinics. Telehealth also provides avenues to continue critical outpatient management during the coronavirus disease 2019 (COVID-19) pandemic and for ongoing clinical management for furloughed or isolated staff who can still be engaged in outpatient care. At our institution, the COVID-19 pandemic stimulated the immediate and almost universal implementation of the telehealth model of care for outpatient appointments. We aimed to evaluate the experience of the telehealth model in the first 3 months of the COVID-19 pandemic in Victoria, focusing on the impact of telehealth on the number of scheduled appointments and clinic DNA rates. Methods: Over a 9-week period during the first COVID-19 lockdown in Melbourne, scheduled appointment numbers and patient attendance rates at 13 gastroenterology and hepatology outpatient clinics at a single tertiary hospital were evaluated through the hospital's online patient administration system, following rapid implementation of the telehealth model of outpatient care. Appointment numbers and attendance were compared with the average attendance rate over the same period in the preceding 5 years. Data collected included patient DNA rates for every scheduled clinic and appointment type (videoconferencing, telephone, or face-to-face consultation). Results: A total of 2626 outpatient clinic appointments were scheduled during the first 9-week COVID-19 lockdown, with 2237 appointments (85%) attended and 389 DNAs (15%), an improvement of 2.2% in attendance rate compared with the average attendance rate during the same 9-week period in the preceding 5 years (P = 0.035). Of the 2626 appointments, 1319 (50%) were video consultations, and 1307 (50%) were telephone consultations. In the preceding 5 years, an average of 2304 outpatient clinic appointments (322 fewer appointments) were scheduled during the same 9-week period, with 1912 appointments (83%) attended and 392 (17%) not attended. Of these 2304 appointments, 2271 (99%) were face-to-face consultations and only 33 (1%) were video consultations. Attendance rates differed according to clinic type. Compared with previous years, outpatient clinics with significantly lower DNA rates during COVID-19 included combined general gastroenterology (15% vs 20%, P = 0.014), satellite inflammatory bowel disease (2% vs 10%, P = 0.033), satellite liver clinic (20% vs 28%, P = 0.198), and privatized liver clinic (13% vs 18%, P = 0.051). Clinics with higher numerical DNA rates included hepatoma (18% vs 12%, P = 0.731) and weight management (20% vs 15%, P = 0.343). When evaluating the appointment type, we found that consultations carried out by telephone resulted in a significantly lower DNA rate, compared with video consultations (9% vs 21%;P < 0.001). Furthermore, an additional 37 clinic lists occurred during this 9-week period, equivalent to four additional lists per week, compared with the average number in the preceding 5 years. Conclusion: Despite the upheaval of clinical services during the COVID-19 pandemic, the major and rapid systems change to overhaul outpatient clinics to an almost exclusively telehealth model was highly succes ful. A total of 1319 video consultations occurred during the 9-week period, compared with just 43 in the preceding year, demonstrating the rapid and widespread implementation of telehealth. Importantly, there was a significant overall reduction in DNA rates, by 2.2%, using the telehealth model. Phone calls were particularly effective for clinic consultations, with DNA rates of only 9.0%. Telehealth has the potential to improve outpatient clinic attendance and efficiency, and our data strongly advocate for ongoing support for telehealth models, including both video and telephone consultation, beyond the COVID-19 era.