Leave no one behind: A global survey of the current state of geriatric oncology practice by SIOG national representatives.

INTRODUCTION: The Sustainable Development Goals of the United Nations include a commitment to "leave no one behind" as a universal goal. To achieve this in geriatric oncology (GO) worldwide, it is important to understand the current state of GO at an international level. The International Society of Geriatric Oncology (SIOG) has several National Representatives (NRs) who act as SIOG's delegates in their respective countries. The NRs took part in this international survey exploring the state of GO practice, identifying barriers and solutions. MATERIALS AND METHODS: The NRs answered open-ended questions by email from February 2020 to October 2022. The questionnaire domains included the demographic information of older adults for their countries, and the NRs' opinions on whether GO is developing, what the barriers are to developing GO, and proposed actions to remove these barriers. The demographic data of each country reported in the survey was adjusted using literature and database searches. RESULTS: Twenty-one of thirty countries with NRs (70%) participated in this questionnaire study: 12 European, four Asian, two North American, two South American, and one Oceanian. The proportion of the population aged ≥75 years varied from 2.2% to 15.8%, and the average life expectancy also varied from 70 years to 86 years. All NRs reported that GO was developing in their country; four NRs (18%) reported that GO was well developed. Although all NRs agreed that geriatric assessment was useful, only three reported that it was used day-to-day in their countries' clinical practice (14%). The major barriers identified were the lack of (i) evidence to support GO use, (ii) awareness and interest in GO, and (iii) resources (time, manpower, and funding). The major proposed actions were to (i) provide new evidence through clinical trials specific for GO patients, (ii) stimulate awareness through networking, and (iii) deliver educational materials and information to healthcare providers and medical students. DISCUSSION: This current survey has identified the barriers to GO and proposed actions that could remove them. Broader awareness seems to be essential to implementing GO. Additional actions are needed to develop GO within countries and can be supported through international partnerships.

Perceptions of COVID-19 Risk: How Did People Adapt to the Novel Risk?

BACKGROUND: There is limited understanding of how risk perceptions changed as the US population gained experience with COVID-19. The objectives were to examine risk perceptions and determine the factors associated with risk perceptions and how these changed over the first 18 mo of the pandemic. METHODS: Seven cross-sectional online surveys were fielded between May 2020 and October 2021. The study included a population-weighted sample of 138,303 US adults drawn from a market research platform, with an average 68% cooperation rate. Respondents' risk perception of developing COVID in the next 30 days was assessed at each time point. We examined relationships between 30-day risk perceptions and various factors (including sociodemographic features, health, COVID-19 experience, political affiliation, and psychological variables). RESULTS: COVID risk perceptions were stable across the 2020 surveys and showed a significant decrease in the 2021 surveys. Several factors, including older age, worse health, high COVID worry, in-person employment type, higher income, Democratic political party affiliation (the relatively more liberal party in the United States), low tolerance of uncertainty, and high anxiety were strongly associated with higher 30-d risk perceptions in 2020. One notable change occurred in 2021, in that younger adults (aged 18-29 y) had significantly higher 30-d risk perceptions than older adults did (aged 65 y and older) after vaccination. Initial differences in perception by political party attenuated over time. Higher 30-d risk perceptions were significantly associated with engaging in preventive behaviors. LIMITATIONS: Cross-sectional samples, risk perception item focused on incidence not severity. CONCLUSIONS: COVID risk perceptions decreased over time. Understanding the longitudinal pattern of risk perceptions and the factors associated with 30-d risk perceptions over time provides valuable insights to guide public health communication campaigns. HIGHLIGHTS: The study assessed COVID-19 risk perceptions at 7 time points over 18 mo of the pandemic in large samples of US adults.Risk perceptions were fairly stable until the introduction of vaccines in early 2021, at which point they showed a marked reduction.Higher COVID-19 30-d risk perceptions were significantly associated with the preventive behaviors of masking, limiting social contact, avoiding restaurants, and not entertaining visitors at home.

Developing a Smartwatch-Based Healthcare Application: Notes to Consider.

Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.

Early Ascertainment of Breast Cancer Diagnoses Comparing Self-Reported Questionnaires and Electronic Health Record Data Warehouse: The WISDOM Study.

PURPOSE: The goal of this study was to use real-world data sources that may be faster and more complete than self-reported data alone, and timelier than cancer registries, to ascertain breast cancer cases in the ongoing screening trial, the WISDOM Study. METHODS: We developed a data warehouse procedural process (DWPP) to identify breast cancer cases from a subgroup of WISDOM participants (n = 11,314) who received breast-related care from a University of California Health Center in the period 2012-2021 by searching electronic health records (EHRs) in the University of California Data Warehouse (UCDW). Incident breast cancer diagnoses identified by the DWPP were compared with those identified by self-report via annual follow-up online questionnaires. RESULTS: Our study identified 172 participants with confirmed breast cancer diagnoses in the period 2016-2021 by the following sources: 129 (75%) by both self-report and DWPP, 23 (13%) by DWPP alone, and 20 (12%) by self-report only. Among those with International Classification of Diseases 10th revision cancer diagnostic codes, no diagnosis was confirmed in 18% of participants. CONCLUSION: For diagnoses that occurred ≥20 months before the January 1, 2022, UCDW data pull, WISDOM self-reported data via annual questionnaire achieved high accuracy (96%), as confirmed by the cancer registry. More rapid cancer ascertainment can be achieved by combining self-reported data with EHR data from a health system data warehouse registry, particularly to address self-reported questionnaire issues such as timing delays (ie, time lag between participant diagnoses and the submission of their self-reported questionnaire typically ranges from a month to a year) and lack of response. Although cancer registry reporting often is not as timely, it does not require verification as does the DWPP or self-report from annual questionnaires.

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman.

BACKGROUND: Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. OBJECTIVE: To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. DESIGN: Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. SUBJECTS: Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. MAIN MEASURES: Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). KEY RESULTS: A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. CONCLUSIONS: Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Parental intentions to vaccinate children against COVID-19: Findings from a U.S. National Survey.

We examined parents' COVID-19 vaccination intentions for their children, reasons for not vaccinating, and the potential impact of a school/daycare vaccination requirement or pediatrician's recommendation on vaccination intentions. Two online surveys were conducted in June-July and September-October 2021, before pediatric COVID-19 vaccines were authorized for emergency use in children age < 12 years, with an internet-based, non-probability sample of U.S. adults. Respondents with children (age < 18 years) in the household were asked about their intention (likelihood) of vaccinating these children against COVID-19. Weighted Chi-square tests using a Rao-Scott correction were performed. Vaccinated (45.7 %) versus unvaccinated (6.9 %) parents were almost seven times more likely to have vaccinated their 12-17-year-old children against COVID-19. Approximately 58.4 % of respondents with unvaccinated children ages 2-11 years and 42.4 % of those with children < 2 years said they are "very" or "extremely likely" to vaccinate these children against COVID-19. Female parents were significantly more likely (p < .01 to p < .001) to express lower levels of COVID-19 vaccine intentions. Across all age groups of children unvaccinated against COVID-19, parental vaccine intentions increased with increased household income and education levels. COVID-19 vaccine side effects and safety concerns were primary reasons for not vaccinating children. Strategies including school vaccination requirements and recommendations from pediatricians were shown to increase parental COVID-19 vaccination intentions for some. More research is needed on factors that increase/hinder COVID-19 pediatric vaccine uptake.

Transfusing Convalescent Plasma as Post-Exposure Prophylaxis Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection: A Double-Blinded, Phase 2 Randomized, Controlled Trial.

BACKGROUND: The efficacy of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) convalescent plasma (CCP) for preventing infection in exposed, uninfected individuals is unknown. CCP might prevent infection when administered before symptoms or laboratory evidence of infection. METHODS: This double-blinded, phase 2 randomized, controlled trial (RCT) compared the efficacy and safety of prophylactic high titer (≥1:320 by Euroimmun ELISA) CCP with standard plasma. Asymptomatic participants aged ≥18 years with close contact exposure to a person with confirmed coronavirus disease 2019 (COVID-19) in the previous 120 hours and negative SARS-CoV-2 test within 24 hours before transfusion were eligible. The primary outcome was new SARS-CoV-2 infection. RESULTS: In total, 180 participants were enrolled; 87 were assigned to CCP and 93 to control plasma, and 170 transfused at 19 sites across the United States from June 2020 to March 2021. Two were excluded for screening SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) positivity. Of the remaining 168 participants, 12/81 (14.8%) CCP and 13/87 (14.9%) control recipients developed SARS-CoV-2 infection; 6 (7.4%) CCP and 7 (8%) control recipients developed COVID-19 (infection with symptoms). There were no COVID-19-related hospitalizations in CCP and 2 in control recipients. Efficacy by restricted mean infection free time (RMIFT) by 28 days for all SARS-CoV-2 infections (25.3 vs 25.2 days; P = .49) and COVID-19 (26.3 vs 25.9 days; P = .35) was similar for both groups. CONCLUSIONS: Administration of high-titer CCP as post-exposure prophylaxis, although appearing safe, did not prevent SARS-CoV-2 infection. CLINICAL TRIALS REGISTRATION: NCT04323800.

Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction.

It is critical for hospitals to accurately predict patient length of stay (LOS) and mortality in real-time. We evaluate temporal convolutional networks (TCNs) and data rebalancing methods to predict LOS and mortality. This is a retrospective cohort study utilizing the MIMIC-III database. The MIMIC-Extract pipeline processes 24 hour time-series clinical objective data for 23,944 unique patient records. TCN performance is compared to both baseline and state-of-the-art machine learning models including logistic regression, random forest, gated recurrent unit with decay (GRU-D). Models are evaluated for binary classification tasks (LOS > 3 days, LOS > 7 days, mortality in-hospital, and mortality in-ICU) with and without data rebalancing and analyzed for clinical runtime feasibility. Data is split temporally, and evaluations utilize tenfold cross-validation (stratified splits) followed by simulated prospective hold-out validation. In mortality tasks, TCN outperforms baselines in 6 of 8 metrics (area under receiver operating characteristic, area under precision-recall curve (AUPRC), and F-1 measure for in-hospital mortality; AUPRC, accuracy, and F-1 for in-ICU mortality). In LOS tasks, TCN performs competitively to the GRU-D (best in 6 of 8) and the random forest model (best in 2 of 8). Rebalancing improves predictive power across multiple methods and outcome ratios. The TCN offers strong performance in mortality classification and offers improved computational efficiency on GPU-enabled systems over popular RNN architectures. Dataset rebalancing can improve model predictive power in imbalanced learning. We conclude that temporal convolutional networks should be included in model searches for critical care outcome prediction systems.

External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence.

Importance: With a shortfall in fellowship-trained breast radiologists, mammography screening programs are looking toward artificial intelligence (AI) to increase efficiency and diagnostic accuracy. External validation studies provide an initial assessment of how promising AI algorithms perform in different practice settings. Objective: To externally validate an ensemble deep-learning model using data from a high-volume, distributed screening program of an academic health system with a diverse patient population. Design, Setting, and Participants: In this diagnostic study, an ensemble learning method, which reweights outputs of the 11 highest-performing individual AI models from the Digital Mammography Dialogue on Reverse Engineering Assessment and Methods (DREAM) Mammography Challenge, was used to predict the cancer status of an individual using a standard set of screening mammography images. This study was conducted using retrospective patient data collected between 2010 and 2020 from women aged 40 years and older who underwent a routine breast screening examination and participated in the Athena Breast Health Network at the University of California, Los Angeles (UCLA). Main Outcomes and Measures: Performance of the challenge ensemble method (CEM) and the CEM combined with radiologist assessment (CEM+R) were compared with diagnosed ductal carcinoma in situ and invasive cancers within a year of the screening examination using performance metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Results: Evaluated on 37 317 examinations from 26 817 women (mean [SD] age, 58.4 [11.5] years), individual model AUROC estimates ranged from 0.77 (95% CI, 0.75-0.79) to 0.83 (95% CI, 0.81-0.85). The CEM model achieved an AUROC of 0.85 (95% CI, 0.84-0.87) in the UCLA cohort, lower than the performance achieved in the Kaiser Permanente Washington (AUROC, 0.90) and Karolinska Institute (AUROC, 0.92) cohorts. The CEM+R model achieved a sensitivity (0.813 [95% CI, 0.781-0.843] vs 0.826 [95% CI, 0.795-0.856]; P = .20) and specificity (0.925 [95% CI, 0.916-0.934] vs 0.930 [95% CI, 0.929-0.932]; P = .18) similar to the radiologist performance. The CEM+R model had significantly lower sensitivity (0.596 [95% CI, 0.466-0.717] vs 0.850 [95% CI, 0.766-0.923]; P < .001) and specificity (0.803 [95% CI, 0.734-0.861] vs 0.945 [95% CI, 0.936-0.954]; P < .001) than the radiologist in women with a prior history of breast cancer and Hispanic women (0.894 [95% CI, 0.873-0.910] vs 0.926 [95% CI, 0.919-0.933]; P = .004). Conclusions and Relevance: This study found that the high performance of an ensemble deep-learning model for automated screening mammography interpretation did not generalize to a more diverse screening cohort, suggesting that the model experienced underspecification. This study suggests the need for model transparency and fine-tuning of AI models for specific target populations prior to their clinical adoption.

Strategies to increase the intention to get vaccinated against COVID-19: Findings from a nationally representative survey of US adults, October 2020 to October 2021.

OBJECTIVES: We examined COVID-19 vaccination status, intention, and hesitancy and the effects of five strategies to increase the willingness of unvaccinated adults (≥18 years) to get a COVID vaccine. METHODS: Online surveys were conducted between October 1-17, 2020 (N = 14,946), December 4-16, 2020 (N = 15,229), April 8-22, 2021 (N = 14,557), June 17-July 6, 2021 (N = 30,857), and September 3-October 4, 2021 (N = 33,088) with an internet-based, non-probability opt-in sample of U.S. adults matching demographic quotas. Respondents were asked about current COVID-19 vaccination status, intention and hesitancy to get vaccinated, and reasons for vaccine hesitancy. Unvaccinated respondents were assigned to treatment groups to test the effect of five strategies (endorsements, changing social restrictions, financial incentives, vaccine requirements for certain activities, and vaccine requirements for work). Chi-square tests of independence were performed to detect differences in the response distributions. RESULTS: Willingness to be vaccinated (defined as being vaccinated or planning to be) increased over time from 47.6 % in October 2020 to 81.1 % in October 2021. By October 2021, across most demographic groups, over 75 % of survey respondents had been or planned to be vaccinated. In terms of strategies: (1) endorsements had no positive effect, (2) relaxing the need for masks and social distancing increased Intention to Get Vaccinated (IGV) by 6.4 % (p < 0.01), (3) offering financial incentives increased the IGV between 12.3 and 18.9 % (p <.001), (4) vaccine requirements for attending sporting events or traveling increased IGV by 7.8 % and 9.1 %, respectively (p = 0.02), and vaccine requirement for work increased IGV by 35.4 %. The leading causes (not mutually exclusive) for hesitancy were concerns regarding vaccine safety (52.5 %) or side effects (51.6 %), trust in the government's motives (41.0 %), and concerns about vaccine effectiveness (37.6 %). CONCLUSIONS: These findings suggest that multiple strategies may be effective and needed to increase COVID-19 vaccination among hesitant adults during the pandemic.

Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation.

BACKGROUND: Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown. PURPOSE: To quantify how different attributes of AI-based cancer detection and risk prediction tools affect radiologists' intentions to use AI during screening mammography interpretation. MATERIALS AND METHODS: Through qualitative interviews with radiologists, we identified five primary attributes for AI-based breast cancer detection and four for breast cancer risk prediction. We developed a discrete choice experiment based on these attributes and invited 150 US-based radiologists to participate. Each respondent made eight choices for each tool between three alternatives: two hypothetical AI-based tools versus screening without AI. We analyzed samplewide preferences using random parameters logit models and identified subgroups with latent class models. RESULTS: Respondents (n = 66; 44% response rate) were from six diverse practice settings across eight states. Radiologists were more interested in AI for cancer detection when sensitivity and specificity were balanced (94% sensitivity with <25% of examinations marked) and AI markup appeared at the end of the hanging protocol after radiologists complete their independent review. For AI-based risk prediction, radiologists preferred AI models using both mammography images and clinical data. Overall, 46% to 60% intended to adopt any of the AI tools presented in the study; 26% to 33% approached AI enthusiastically but were deterred if the features did not align with their preferences. CONCLUSION: Although most radiologists want to use AI-based decision support, short-term uptake may be maximized by implementing tools that meet the preferences of dissuadable users.

How local partisan context conditions prosocial behaviors: Mask wearing during COVID-19.

Does local partisan context influence the adoption of prosocial behavior? Using a nationwide survey of 60,000 adults and geographic data on over 180 million registered voters, we investigate whether neighborhood partisan composition affects a publicly observable and politicized behavior: wearing a mask. We find that Republicans are less likely to wear masks in public as the share of Republicans in their zip codes increases. Democratic mask wearing, however, is unaffected by local partisan context. Consequently, the partisan gap in mask wearing is largest in Republican neighborhoods, and less apparent in Democratic areas. These effects are distinct from other contextual effects such as variations in neighborhood race, income, or education. In contrast, partisan context has significantly reduced influence on unobservable public health recommendations like COVID-19 vaccination and no influence on nonpoliticized behaviors like flu vaccination, suggesting that differences in mask wearing reflect the publicly observable and politicized nature of the behavior instead of underlying differences in dispositions toward medical care.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study.

BACKGROUND: On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation. OBJECTIVE: The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients' changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility. METHODS: From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission. RESULTS: Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (ß=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (ß=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (ß=-.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (ß=-.13; SE 0.06; P=.04) was associated with toileting activities. CONCLUSIONS: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients' progress or decline.

Depressive Symptoms and Anxiety During the COVID-19 Pandemic: Large, Longitudinal, Cross-sectional Survey.

BACKGROUND: The COVID-19 pandemic has influenced the mental health of millions across the globe. Understanding factors associated with depressive symptoms and anxiety across 12 months of the pandemic can help identify groups at higher risk and psychological processes that can be targeted to mitigate the long-term mental health impact of the pandemic. OBJECTIVE: This study aims to determine sociodemographic features, COVID-19-specific factors, and general psychological variables associated with depressive symptoms and anxiety over 12 months of the pandemic. METHODS: Nationwide, cross-sectional electronic surveys were implemented in May (n=14,636), July (n=14,936), October (n=14,946), and December (n=15,265) 2020 and March/April 2021 (n=14,557) in the United States. Survey results were weighted to be representative of the US population. The samples were drawn from a market research platform, with a 69% cooperation rate. Surveys assessed depressive symptoms in the past 2 weeks and anxiety in the past week, as well as sociodemographic features; COVID-19 restriction stress, worry, perceived risk, coping strategies, and exposure; intolerance of uncertainty; and loneliness. RESULTS: Across 12 months, an average of 24% of respondents reported moderate-to-severe depressive symptoms and 32% reported moderate-to-severe anxiety. Of the sociodemographic variables, age was most consistently associated with depressive symptoms and anxiety, with younger adults more likely to report higher levels of those outcomes. Intolerance of uncertainty and loneliness were consistently and strongly associated with the outcomes. Of the COVID-19-specific variables, stress from COVID-19 restrictions, worry about COVID-19, coping behaviors, and having COVID-19 were associated with a higher likelihood of depressive symptoms and anxiety. CONCLUSIONS: Depressive symptoms and anxiety were high in younger adults, adults who reported restriction stress or worry about COVID-19 or who had had COVID-19, and those with intolerance of uncertainty and loneliness. Symptom monitoring as well as early and accessible intervention are recommended.

The Impact of COVID-19 on Routine Medical Care and Cancer Screening.

BACKGROUND: COVID-19 restrictions and fear dramatically changed the use of medical care. Understanding the magnitude of cancelled and postponed appointments and associated factors can help identify approaches to mitigate unmet need. OBJECTIVE: To determine the proportion of medical visits cancelled or postponed and for whom. We hypothesized that adults with serious medical conditions and those with higher anxiety, depressive symptoms, and avoidance-oriented coping would have more cancellations/postponements. DESIGN: Four nationally representative cross-sectional surveys conducted online in May, July, October, and December 2020. PARTICIPANTS: 59,747 US adults who completed 15-min online surveys. 69% cooperation rate. MEASURES: Physical and mental health visits and cancer screening cancelled or postponed over prior 2 months. Plan to cancel or postpone visits over the next 2 months. Relationship with demographics, medical conditions, local COVID-19 death rate, anxiety, depressive symptoms, coping, intolerance of uncertainty, and perceived COVID-19 risk. KEY RESULTS: Of the 58% (N = 34,868) with a medical appointment during the 2 months before the survey, 64% had an appointment cancelled or postponed in May, decreasing to 37% in December. Of the 41% of respondents with scheduled cancer screening, 20% cancelled/postponed, which was stable May to December. People with more medical conditions were more likely to cancel or postpone medical visits (OR 1.19 per condition, 95% CI 1.16, 1.22) and cancer screening (OR 1.20, 95% CI 1.15, 1.24). Race, ethnicity, and income had weak associations with cancelled/postponed visits, local death rate was unrelated, but anxiety and depressive symptoms were strongly related to cancellations, and this grew between May and December. CONCLUSIONS: Cancelled medical care and cancer screening were more common among persons with medical conditions, anxiety and depression, even after accounting for COVID-19 deaths. Outreach and support to ensure that patients are not avoiding needed care due to anxiety, depression and inaccurate perceptions of risk will be important.

Remote Monitoring of Patients With Hematologic Malignancies at High Risk of Febrile Neutropenia: Exploratory Study.

BACKGROUND: Febrile neutropenia is one of the most common oncologic emergencies and is associated with significant, preventable morbidity and mortality. Most patients who experience a febrile neutropenia episode are hospitalized, resulting in significant economic cost. OBJECTIVE: This exploratory study implemented a remote monitoring system comprising a digital infrared thermometer and a pulse oximeter with the capability to notify providers in real time of abnormalities in vital signs that could suggest early clinical deterioration and thereby improve clinical outcomes. METHODS: The remote monitoring system was implemented and compared to standard-of-care vital signs monitoring in hospitalized patients with underlying hematologic malignancies complicated by a febrile neutropenia episode in order to assess the feasibility and validity of the system. Statistical analysis was performed using the intraclass correlation coefficient (ICC) to assess the consistency between the measurements taken using traditional methods and those taken with the remote monitoring system for each of the vital sign parameters (temperature, heart rate, and oxygen saturation). A linear mixed-effects model with a random subject effect was used to estimate the variance components. Bland-Altman plots were created for the parameters to further delineate the direction of any occurring bias. RESULTS: A total of 23 patients were enrolled in the study (mean age 56, SD 23-75 years; male patients: n=11, 47.8%). ICC analysis confirmed the high repeatability and accuracy of the heart rate assessment (ICC=0.856), acting as a supplement to remote temperature assessment. While the sensitivity and specificity for capturing tachycardia above a rate of 100 bpm were excellent (88% and 97%, respectively), the sensitivity of the remote monitoring system in capturing temperatures >37.8 °C and oxygen saturation <92% was 45% and 50%, respectively. CONCLUSIONS: Overall, this novel approach using temperature, heart rate, and oxygen saturation assessments successfully provided real-time, clinically valuable feedback to providers. While temperature and oxygen saturation assessments lagged in terms of sensitivity compared to a standard in-hospital system, the heart rate assessment provided highly accurate complementary data. As a whole, the system provided additional information that can be applied to a clinically vulnerable population. By transitioning its application to high-risk patients in the outpatient setting, this system can help prevent additional use of health care services through early provider intervention and potentially improve outcomes.

An Integrated, Scalable, Electronic Video Consent Process to Power Precision Health Research: Large, Population-Based, Cohort Implementation and Scalability Study.

BACKGROUND: Obtaining explicit consent from patients to use their remnant biological samples and deidentified clinical data for research is essential for advancing precision medicine. OBJECTIVE: We aimed to describe the operational implementation and scalability of an electronic universal consent process that was used to power an institutional precision health biobank across a large academic health system. METHODS: The University of California, Los Angeles, implemented the use of innovative electronic consent videos as the primary recruitment tool for precision health research. The consent videos targeted patients aged ≥18 years across ambulatory clinical laboratories, perioperative settings, and hospital settings. Each of these major areas had slightly different workflows and patient populations. Sociodemographic information, comorbidity data, health utilization data (ambulatory visits, emergency room visits, and hospital admissions), and consent decision data were collected. RESULTS: The consenting approach proved scalable across 22 clinical sites (hospital and ambulatory settings). Over 40,000 participants completed the consent process at a rate of 800 to 1000 patients per week over a 2-year time period. Participants were representative of the adult University of California, Los Angeles, Health population. The opt-in rates in the perioperative (16,500/22,519, 73.3%) and ambulatory clinics (2308/3390, 68.1%) were higher than those in clinical laboratories (7506/14,235, 52.7%; P<.001). Patients with higher medical acuity were more likely to opt in. The multivariate analyses showed that African American (odds ratio [OR] 0.53, 95% CI 0.49-0.58; P<.001), Asian (OR 0.72, 95% CI 0.68-0.77; P<.001), and multiple-race populations (OR 0.73, 95% CI 0.69-0.77; P<.001) were less likely to participate than White individuals. CONCLUSIONS: This is one of the few large-scale, electronic video-based consent implementation programs that reports a 65.5% (26,314/40,144) average overall opt-in rate across a large academic health system. This rate is higher than those previously reported for email (3.6%) and electronic biobank (50%) informed consent rates. This study demonstrates a scalable recruitment approach for population health research.

Correction: Electronic Video Consent to Power Precision Health Research: A Pilot Cohort Study.

[This corrects the article DOI: 10.2196/29123.].