Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection : A Prospective Cohort Study.

BACKGROUND: The performance of rapid antigen tests (Ag-RDTs) for screening asymptomatic and symptomatic persons for SARS-CoV-2 is not well established. OBJECTIVE: To evaluate the performance of Ag-RDTs for detection of SARS-CoV-2 among symptomatic and asymptomatic participants. DESIGN: This prospective cohort study enrolled participants between October 2021 and January 2022. Participants completed Ag-RDTs and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 every 48 hours for 15 days. SETTING: Participants were enrolled digitally throughout the mainland United States. They self-collected anterior nasal swabs for Ag-RDTs and RT-PCR testing. Nasal swabs for RT-PCR were shipped to a central laboratory, whereas Ag-RDTs were done at home. PARTICIPANTS: Of 7361 participants in the study, 5353 who were asymptomatic and negative for SARS-CoV-2 on study day 1 were eligible. In total, 154 participants had at least 1 positive RT-PCR result. MEASUREMENTS: The sensitivity of Ag-RDTs was measured on the basis of testing once (same-day), twice (after 48 hours), and thrice (after a total of 96 hours). The analysis was repeated for different days past index PCR positivity (DPIPPs) to approximate real-world scenarios where testing initiation may not always coincide with DPIPP 0. Results were stratified by symptom status. RESULTS: Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDTs twice 48 hours apart resulted in an aggregated sensitivity of 93.4% (95% CI, 90.4% to 95.9%) among symptomatic participants on DPIPPs 0 to 6. When singleton positive results were excluded, the aggregated sensitivity on DPIPPs 0 to 6 for 2-time serial testing among asymptomatic participants was lower at 62.7% (CI, 57.0% to 70.5%), but it improved to 79.0% (CI, 70.1% to 87.4%) with testing 3 times at 48-hour intervals. LIMITATION: Participants tested every 48 hours; therefore, these data cannot support conclusions about serial testing intervals shorter than 48 hours. CONCLUSION: The performance of Ag-RDTs was optimized when asymptomatic participants tested 3 times at 48-hour intervals and when symptomatic participants tested 2 times separated by 48 hours. PRIMARY FUNDING SOURCE: National Institutes of Health RADx Tech program.

Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities.

BACKGROUND: Many interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively. METHODS: This observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence. RESULTS: In total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran's I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana. CONCLUSIONS: Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.

The Implementation and Acceptability of a Combined Mobile Application with a COVID-19 at-Home Test Kit.

Introduction: Testing facilities for COVID-19 were stood up around the country during the pandemic, but could not handle the demand. This study aimed to combine a mobile application (App) with an at-home test kit to facilitate home-based testing. Methods: After integrating an App with an at-home testing service, we measured the time between sample collection and notification of results. We recruited 92 volunteers to utilize the platform. Results: Sixty-one percent (55/92) responded to the survey. Median sample collection-to-result time was 2.2 days (IQR = 1.3-3.2). Eighty-two percent (45/55) found the self-test kit and App easy to use. Eighty-four percent agreed that the combined solution is an acceptable way to receive health care services. Discussion: Decreasing testing time and providing timely test results improve care access and decrease the risk of infection. Combining a tailored App with an at-home testing service is a feasible solution to reaching that goal.

The Implementation and Acceptability of a Mobile Application for Screening for Atrial Fibrillation at Home.

Introduction: Although patients are able to easily record electrocardiograms using consumer devices, these are typically not shared with their clinicians. This article discusses the development and acceptability of a mobile application (app) that integrates with the electronic health record to facilitate screening for atrial fibrillation (AF). Methods: After app development and implementation, we compared workflows with and without the mobile app. Seven older adults used it during a prospective twice-daily 2-week home-based AF screening protocol and completed an acceptability survey with Likert scale responses. Results: Compliance with the screening protocol was 82%. Acceptability and usability was favorable. Patients reported confidence in the connection between the app and their medical record. Discussion: The availability of apps to capture data and facilitate a connection with health systems is critical. The app developed is a feasible solution for older patients with AF to self-monitor and report results to their health provider.

Time-Course of Cause-Specific Hospital Admissions During Snowstorms: An Analysis of Electronic Medical Records From Major Hospitals in Boston, Massachusetts.

With global climate change, more frequent severe snowstorms are expected; however, evidence regarding their health effects is very limited. We gathered detailed medical records on hospital admissions (n = 433,037 admissions) from the 4 largest hospitals in Boston, Massachusetts, during the winters of 2010-2015. We estimated the percentage increase in hospitalizations for cardiovascular and cold-related diseases, falls, and injuries on the day of and for 6 days after a day with low (0.05-5.0 inches), moderate (5.1-10.0 inches), or high (>10.0 inches) snowfall using distributed lag regression models. We found that cardiovascular disease admissions decreased by 32% on high snowfall days (relative risk (RR) = 0.68, 95% confidence interval (CI): 0.54, 0.85) but increased by 23% 2 days after (RR = 1.23, 95% CI: 1.01, 1.49); cold-related admissions increased by 3.7% on high snowfall days (RR = 3.7, 95% CI: 1.6, 8.6) and remained high for 5 days after; and admissions for falls increased by 18% on average in the 6 days after a moderate snowfall day (RR = 1.18, 95% CI: 1.09, 1.27). We did not find a higher risk of hospitalizations for injuries. To our knowledge, this is the first study in which the time course of hospitalizations during and immediately after snowfall days has been examined. These findings can be translated into interventions that prevent hospitalizations and protect public health during harsh winter conditions.

Defining Team Effort Involved in Patient Care from the Primary Care Physician's Perspective.

BACKGROUND: A better understanding of the attributes of patients who require more effort to manage may improve risk adjustment approaches and lead to more efficient resource allocation, improved patient care and health outcomes, and reduced burnout in primary care clinicians. OBJECTIVE: To identify and characterize high-effort patients from the physician's perspective. DESIGN: Cohort study. PARTICIPANTS: Ninety-nine primary care physicians in an academic primary care network. MAIN MEASURES: From a list of 100 randomly selected patients in their panels, PCPs identified patients who required a high level of team-based effort and patients they considered complex. For high-effort patients, PCPs indicated which factors influenced their decision: medical/care coordination, behavioral health, and/or socioeconomic factors. We examined differences in patient characteristics based on PCP-defined effort and complexity. KEY RESULTS: Among 9594 eligible patients, PCPs classified 2277 (23.7 %) as high-effort and 2676 (27.9 %) as complex. Behavioral health issues were the major driver of effort in younger patients, while medical/care coordination issues predominated in older patients. Compared to low-effort patients, high-effort patients were significantly (P < 0.01 for all) more likely to have higher rates of medical (e.g. 23.2 % vs. 6.3 % for diabetes) and behavioral health problems (e.g. 9.8 % vs. 2.9 % for substance use disorder), more frequent primary care visits (10.9 vs. 6.0 visits), and higher acute care utilization rates (25.8 % vs. 7.7 % for emergency department [ED] visits and 15.0 % vs. 3.9 % for hospitalization). Almost one in five (18 %) patients who were considered high-effort were not deemed complex by the same PCPs. CONCLUSIONS: Patients defined as high-effort by their primary care physicians, not all of whom were medically complex, appear to have a high burden of psychosocial issues that may not be accounted for in current chronic disease-focused risk adjustment approaches.

Building Equity Improvement into Quality Improvement: Reducing Socioeconomic Disparities in Colorectal Cancer Screening as Part of Population Health Management.

BACKGROUND: Improving colorectal cancer (CRC) screening rates for patients from socioeconomically disadvantaged backgrounds is a recognized public health priority. OBJECTIVE: Our aim was to determine if implementation of a system-wide screening intervention could reduce disparities in the setting of improved overall screening rates. DESIGN: This was an interrupted time series (ITS) analysis before and after a population management intervention. PARTICIPANTS: Patients eligible for CRC screening (age 52-75 years without prior total colectomy) in an 18-practice research network from 15 June 2009 to 15 June 2012 participated in the study. INTERVENTION: The Technology for Optimizing Population Care (TopCare) intervention electronically identified patients overdue for screening and facilitated contact by letter or telephone scheduler, with or without physician involvement. Patients identified by algorithm as high risk for non-completion entered into intensive patient navigation. MAIN MEASURES: Patients were dichotomized as ≤ high school diploma (≤ HS), an indicator of socioeconomic disadvantage, vs. >HS diploma (> HS). The monthly disparity between ≤ HS and > HS with regard to CRC screening completion was examined. KEY RESULTS: At baseline, 72% of 47,447 eligible patients had completed screening, compared with 75% of 51,442 eligible patients at the end of follow-up (p < 0.001). CRC screening completion was lower in ≤ HS vs. >HS patients in June 2009 (65.7% vs. 74.5%, p < 0.001) and remained lower in June 2012 (69.4% vs. 76.7%, p < 0.001). In the ITS analysis, which accounts for secular trends, TopCare was associated with a significant decrease in the CRC screening disparity (0.7%, p < 0.001). The effect of TopCare represents approximately 99 additional ≤ HS patients screened above prevailing trends, or 26 life-years gained had these patients remained unscreened. CONCLUSIONS: A multifaceted population management intervention sensitive to the needs of vulnerable patients modestly narrowed disparities in CRC screening, while also increasing overall screening rates. Embedding interventions for vulnerable patients within larger population management systems represents an effective approach to increasing overall quality of care while also decreasing disparities.

Rationale and Design of Healthy at Home for COPD: an Integrated Remote Patient Monitoring and Virtual Pulmonary Rehabilitation Pilot Study.

Chronic Obstructive Pulmonary Disease (COPD) is a common, costly, and morbid condition. Pulmonary rehabilitation, close monitoring, and early intervention during acute exacerbations of symptoms represent a comprehensive approach to improve outcomes, but the optimal means of delivering these services is uncertain. Logistical, financial, and social barriers to providing healthcare through face-to-face encounters, paired with recent developments in technology, have stimulated interest in exploring alternative models of care. The Healthy at Home study seeks to determine the feasibility of a multimodal, digitally enhanced intervention provided to participants with COPD longitudinally over six months. This paper details the recruitment, methods, and analysis plan for the study, which is recruiting 100 participants in its pilot phase. Participants were provided with several integrated services including a smartwatch to track physiological data, a study app to track symptoms and study instruments, access to a mobile integrated health program for acute clinical needs, and a virtual comprehensive pulmonary support service. Participants shared physiologic, demographic, and symptom reports, electronic health records, and claims data with the study team, facilitating a better understanding of their symptoms and potential care needs longitudinally. The Healthy at Home study seeks to develop a comprehensive digital phenotype of COPD by tracking and responding to multiple indices of disease behavior and facilitating early and nuanced responses to changes in participants' health status. This study is registered at Clinicaltrials.gov (NCT06000696).

Performance of and Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostics Based on Symptom Onset and Close Contact Exposure: An Analysis From the Test Us at Home Prospective Cohort Study.

Background: Understanding changes in diagnostic performance after symptom onset and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure within different populations is crucial to guide the use of diagnostics for SARS-CoV-2. Methods: The Test Us at Home study was a longitudinal cohort study that enrolled individuals across the United States between October 2021 and February 2022. Participants performed paired antigen-detection rapid diagnostic tests (Ag-RDTs) and reverse-transcriptase polymerase chain reaction (RT-PCR) tests at home every 48 hours for 15 days and self-reported symptoms and known coronavirus disease 2019 exposures immediately before testing. The percent positivity for Ag-RDTs and RT-PCR tests was calculated each day after symptom onset and exposure and stratified by vaccination status, variant, age category, and sex. Results: The highest percent positivity occurred 2 days after symptom onset (RT-PCR, 91.2%; Ag-RDT, 71.1%) and 6 days after exposure (RT-PCR, 91.8%; Ag-RDT, 86.2%). RT-PCR and Ag-RDT performance did not differ by vaccination status, variant, age category, or sex. The percent positivity for Ag-RDTs was lower among exposed, asymptomatic than among symptomatic individuals (37.5% (95% confidence interval [CI], 13.7%-69.4%) vs 90.3% (75.1%-96.7%). Cumulatively, Ag-RDTs detected 84.9% (95% CI, 78.2%-89.8%) of infections within 4 days of symptom onset. For exposed participants, Ag-RDTs detected 94.0% (95% CI, 86.7%-97.4%) of RT-PCR-confirmed infections within 6 days of exposure. Conclusions: The percent positivity for Ag-RDTs and RT-PCR tests was highest 2 days after symptom onset and 6 days after exposure, and performance increased with serial testing. The percent positivity of Ag-RDTs was lowest among asymptomatic individuals but did not differ by sex, variant, vaccination status, or age category.

Performance of Rapid Antigen Tests Based on Symptom Onset and Close Contact Exposure: A secondary analysis from the Test Us At Home prospective cohort study.

Background: The performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) in temporal relation to symptom onset or exposure is unknown, as is the impact of vaccination on this relationship. Objective: To evaluate the performance of Ag-RDT compared with RT-PCR based on day after symptom onset or exposure in order to decide on 'when to test'. Design Setting and Participants: The Test Us at Home study was a longitudinal cohort study that enrolled participants over 2 years old across the United States between October 18, 2021 and February 4, 2022. All participants were asked to conduct Ag-RDT and RT-PCR testing every 48 hours over a 15-day period. Participants with one or more symptoms during the study period were included in the Day Post Symptom Onset (DPSO) analyses, while those who reported a COVID-19 exposure were included in the Day Post Exposure (DPE) analysis. Exposure: Participants were asked to self-report any symptoms or known exposures to SARS-CoV-2 every 48-hours, immediately prior to conducting Ag-RDT and RT-PCR testing. The first day a participant reported one or more symptoms was termed DPSO 0, and the day of exposure was DPE 0. Vaccination status was self-reported. Main Outcome and Measures: Results of Ag-RDT were self-reported (positive, negative, or invalid) and RT-PCR results were analyzed by a central laboratory. Percent positivity of SARS-CoV-2 and sensitivity of Ag-RDT and RT-PCR by DPSO and DPE were stratified by vaccination status and calculated with 95% confidence intervals. Results: A total of 7,361 participants enrolled in the study. Among them, 2,086 (28.3%) and 546 (7.4%) participants were eligible for the DPSO and DPE analyses, respectively. Unvaccinated participants were nearly twice as likely to test positive for SARS-CoV-2 than vaccinated participants in event of symptoms (PCR+: 27.6% vs 10.1%) or exposure (PCR+: 43.8% vs. 22.2%). The highest proportion of vaccinated and unvaccinated individuals tested positive on DPSO 2 and DPE 5-8. Performance of RT-PCR and Ag-RDT did not differ by vaccination status. Ag-RDT detected 78.0% (95% Confidence Interval: 72.56-82.61) of PCR-confirmed infections by DPSO 4. For exposed participants, Ag-RDT detected 84.9% (95% CI: 75.0-91.4) of PCR-confirmed infections by day five post-exposure (DPE 5). Conclusions and Relevance: Performance of Ag-RDT and RT-PCR was highest on DPSO 0-2 and DPE 5 and did not differ by vaccination status. These data suggests that serial testing remains integral to enhancing the performance of Ag-RDT.

Finding a Needle in a Haystack: Design and Implementation of a Digital Site-less Clinical Study of Serial Rapid Antigen Testing to Identify Asymptomatic SARS-CoV-2 Infection.

Background: Rapid antigen tests (Ag-RDT) for SARS-CoV-2 with Emergency Use Authorization generally include a condition of authorization to evaluate the test's performance in asymptomatic individuals when used serially. Objective: To describe a novel study design to generate regulatory-quality data to evaluate serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals. Design: Prospective cohort study using a decentralized approach. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Setting: Participants throughout the mainland United States were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Ag-RDTs were completed at home, and molecular comparators were shipped to a central laboratory. Participants: Individuals over 2 years old from across the U.S. with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Measurements: Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported. Key Results: A total of 7,361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 U.S. states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. Limitations: New, complex workflows required significant operational and data team support. Conclusions: The digital site-less approach employed in the 'Test Us At Home' study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19, and can be adapted across research disciplines to optimize study enrollment and accessibility.

Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection.

Background: Performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) varies over the course of an infection, and their performance in screening for SARS-CoV-2 is not well established. We aimed to evaluate performance of Ag-RDT for detection of SARS-CoV-2 for symptomatic and asymptomatic participants. Methods: Participants >2 years old across the United States enrolled in the study between October 2021 and February 2022. Participants completed Ag-RDT and molecular testing (RT-PCR) for SARS-CoV-2 every 48 hours for 15 days. This analysis was limited to participants who were asymptomatic and tested negative on their first day of study participation. Onset of infection was defined as the day of first positive RT-PCR result. Sensitivity of Ag-RDT was measured based on testing once, twice (after 48-hours), and thrice (after 96 hours). Analysis was repeated for different Days Post Index PCR Positivity (DPIPP) and stratified based on symptom-status. Results: In total, 5,609 of 7,361 participants were eligible for this analysis. Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDT twice 48-hours apart resulted in an aggregated sensitivity of 93.4% (95% CI: 89.1-96.1%) among symptomatic participants on DPIPP 0-6. Excluding singleton positives, aggregated sensitivity on DPIPP 0-6 for two-time serial-testing among asymptomatic participants was lower at 62.7% (54.7-70.0%) but improved to 79.0% (71.0-85.3%) with testing three times at 48-hour intervals. Discussion: Performance of Ag-RDT was optimized when asymptomatic participants tested three-times at 48-hour intervals and when symptomatic participants tested two-times separated by 48-hours.

Design and implementation of a digital site-less clinical study of serial rapid antigen testing to identify asymptomatic SARS-CoV-2 infection.

Background: Rapid antigen detection tests (Ag-RDT) for SARS-CoV-2 with emergency use authorization generally include a condition of authorization to evaluate the test's performance in asymptomatic individuals when used serially. We aim to describe a novel study design that was used to generate regulatory-quality data to evaluate the serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals. Methods: This prospective cohort study used a siteless, digital approach to assess longitudinal performance of Ag-RDT. Individuals over 2 years old from across the USA with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Participants throughout the mainland USA were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported. Key Results: A total of 7361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 US states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. Conclusions: The digital site-less approach employed in the "Test Us At Home" study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19 and can be adapted across research disciplines to optimize study enrollment and accessibility.

Efficacy and cost-effectiveness of an automated screening algorithm in an inpatient clinical trial.

INTRODUCTION: Screening and recruitment for clinical trials can be costly and time-consuming. Inpatient trials present additional challenges because enrollment is time sensitive based on length of stay. We hypothesized that using an automated prescreening algorithm to identify eligible subjects would increase screening efficiency and enrollment and be cost-effective compared to manual review of a daily admission list. METHODS: Using a before-and-after design, we compared time spent screening, number of patients screened, enrollment rate, and cost-effectiveness of each screening method in an inpatient diabetes trial conducted at Massachusetts General Hospital. Manual chart review (CR) involved reviewing a daily list of admitted patients to identify eligible subjects. The automated prescreening (APS) method used an algorithm to generate a daily list of patients with glucose levels ≥ 180 mg/dL, an insulin order, and/or admission diagnosis of diabetes mellitus. The census generated was then manually screened to confirm eligibility and eliminate patients who met our exclusion criteria. We determined rates of screening and enrollment and cost-effectiveness of each method based on study sample size. RESULTS: Total screening time (prescreening and screening) decreased from 4 to 2 h, allowing subjects to be approached earlier in the course of the hospital stay. The average number of patients prescreened per day increased from 13 ± 4 to 30 ± 16 (P < 0.0001). Rate of enrollment increased from 0.17 to 0.32 patients per screening day. Developing the computer algorithm added a fixed cost of US$3000 to the study. Based on our screening and enrollment rates, the algorithm was cost-neutral after enrolling 12 patients. Larger sample sizes further favored screening with an algorithm. By contrast, higher recruitment rates favored individual CR. LIMITATIONS: Because of the before-and-after design of this study, it is possible that unmeasured factors contributed to increased enrollment. CONCLUSION: Using a computer algorithm to identify eligible patients for a clinical trial in the inpatient setting increased the number of patients screened and enrolled, decreased the time required to enroll them, and was less expensive. Upfront investment in developing a computerized algorithm to improve screening may be cost-effective even for relatively small trials, especially when the recruitment rate is expected to be low.

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study.

BACKGROUND: Public health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts' Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated. OBJECTIVE: We developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic. METHODS: MAGGI uses a server-client model-based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time. RESULTS: We created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts' spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19-positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention's social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021. CONCLUSIONS: We developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.

Association of Mass Distribution of Rapid Antigen Tests and SARS-CoV-2 Prevalence: Results from NIH-CDC funded Say Yes! Covid Test program in Michigan.

Importance: Wide-spread distribution of diagnostics is an integral part of the United States’ COVID-19 strategy; however, few studies have assessed the effectiveness of this intervention at reducing transmission of community COVID-19. Objective: To assess the impact of the Say Yes! Covid Test (SYCT!) Michigan program, a population-based program that distributed 20,000 free rapid antigen tests within Ann Arbor and Ypsilanti, Michigan in June-August 2021, on community prevalence of SARS-CoV-2. Design: This ecological study analyzed cases of SARS-CoV-2 from March to October 2021 reported to the Washtenaw County Health Department. Setting: Washtenaw County, Michigan. Participants: All residents of Washtenaw County. Interventions: Community-wide distribution of 500,000 rapid antigen tests for SARS-CoV-2 to residents of Ann Arbor and Ypsilanti, Michigan. Each household was limited to one test kit containing 25 rapid antigen tests. Main Outcome and Measures: Community prevalence of SARS-CoV-2, as measured through 7-day average cases, in Ann Arbor and Ypsilanti was compared to the rest of Washtenaw County. A generalized additive model was fitted with non-parametric trends for control and relative differences of trends in the pre-intervention, intervention, and post-intervention periods to compare intervention municipalities of Ann Arbor and Ypsilanti to the rest of Washtenaw County. Model results were used to calculate average cases prevented in the post-intervention period. Results: In the post-intervention period, there were significantly lower standardized average cases in the intervention communities of Ann Arbor/Ypsilanti compared to the rest of Washtenaw County (p<0.001). The estimated standardized relative difference between Ann Arbor/Ypsilanti and the rest of Washtenaw County was -0.016 cases per day (95% CI: -0.020 to -0.013), implying that the intervention prevented 40 average cases per day two months into the post-intervention period if trends were consistent. Conclusions and Relevance: Mass distribution of rapid antigen tests may be a useful mitigation strategy to combat community transmission of SARS-CoV-2, especially given the recent relaxation of social distancing and masking requirements.

Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure.

BACKGROUND: Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care. OBJECTIVE: We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days. METHODS: We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission. RESULTS: We found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source. CONCLUSIONS: Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.

Implementation of the Connect for Health pediatric weight management program: study protocol and baseline characteristics.

We are implementing Connect for Health, a primary care-based intervention to improve family-centered outcomes for children, ages 2-12 years, in organizations that care for low-income children. We will use the 'Reach-Effectiveness-Adoption-Implementation-Maintenance' framework to guide our mixed-methods evaluation to examine the effectiveness of stakeholder-informed strategies in supporting program adoption and child outcomes. We also describe characteristics of children, ages 2-12 years with a BMI ≥85th percentile and obesity-related care practices. During the period prior to implementation, 26,161 children with a BMI ≥85th percentile were seen for a primary care visit and a majority lacked recommended diagnosis codes, referrals and laboratory evaluations. The findings suggest the need to augment current approaches to increase uptake of proven-effective weight management programs. Clinical trial registration number: NCT04042493 (Clinicaltrials.gov), Registered on 2 August 2019; https://clinicaltrials.gov/ct2/show/NCT04042493.

Queuing theory to guide the implementation of a heart failure inpatient registry program.

OBJECTIVE The authors previously implemented an electronic heart failure registry at a large academic hospital to identify heart failure patients and to connect these patients with appropriate discharge services. Despite significant improvements in patient identification and connection rates, time to connection remained high, with an average delay of 3.2 days from the time patients were admitted to the time connections were made. Our objective for this current study was to determine the most effective solution to minimize time to connection. DESIGN We used a queuing theory model to simulate 3 different potential solutions to decrease the delay from patient identification to connection with discharge services. MEASUREMENTS The measures included average rate at which patients were being connected to the post discharge heart failure services program, average number of patients in line, and average patient waiting time. RESULTS Using queuing theory model simulations, we were able to estimate for our current system the minimum rate at which patients need to be connected (262 patients/mo), the ideal patient arrival rate (174 patients/mo) and the maximal patient arrival rate that could be achieved by adding 1 extra nurse (348 patients/mo). CONCLUSIONS Our modeling approach was instrumental in helping us characterize key process parameters and estimate the impact of adding staff on the time between identifying patients with heart failure and connecting them with appropriate discharge services.

Lessons from implementing a combined workflow-informatics system for diabetes management.

Shortcomings surrounding the care of patients with diabetes have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease. To address these shortcomings, we developed a diabetes registry population management application to change the way our staff manages patients with diabetes. Use of this new application has helped us coordinate the responsibilities for intervening and monitoring patients in the registry among different users. Our experiences using this combined workflow-informatics intervention system suggest that integrating a chronic disease registry into clinical workflow for the treatment of chronic conditions creates a useful and efficient tool for managing disease.