Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19: A multicenter retrospective cohort study.

ABSTRACT: As severe acute respiratory syndrome coronavirus 2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals.In this retrospective observational study, we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for severe acute respiratory syndrome coronavirus 2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model.Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our final model includes the patient's age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital's COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively.We conclude that the risk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter.

Simple methods for early warnings of COVID-19 surges: Lessons learned from 21 months of wastewater and clinical data collection in Detroit, Michigan, United States.

Wastewater-based epidemiology (WBE) has drawn great attention since the Coronavirus disease 2019 (COVID-19) pandemic, not only due to its capability to circumvent the limitations of traditional clinical surveillance, but also due to its potential to forewarn fluctuations of disease incidences in communities. One critical application of WBE is to provide "early warnings" for upcoming fluctuations of disease incidences in communities which traditional clinical testing is incapable to achieve. While intricate models have been developed to determine early warnings based on wastewater surveillance data, there is an exigent need for straightforward, rapid, broadly applicable methods for health departments and partner agencies to implement. Our purpose in this study is to develop and evaluate such early-warning methods and clinical-case peak-detection methods based on WBE data to mount an informed public health response. Throughout an extended wastewater surveillance period across Detroit, MI metropolitan area (the entire study period is from September 2020 to May 2022) we designed eight early-warning methods (three real-time and five post-factum). Additionally, we designed three peak-detection methods based on clinical epidemiological data. We demonstrated the utility of these methods for providing early warnings for COVID-19 incidences, with their counterpart accuracies evaluated by hit rates. "Hit rates" were defined as the number of early warning dates (using wastewater surveillance data) that captured defined peaks (using clinical epidemiological data) divided by the total number of early warning dates. Hit rates demonstrated that the accuracy of both real-time and post-factum methods could reach 100 %. Furthermore, the results indicate that the accuracy was influenced by approaches to defining peaks of disease incidence. The proposed methods herein can assist health departments capitalizing on WBE data to assess trends and implement quick public health responses to future epidemics. Besides, this study elucidated critical factors affecting early warnings based on WBE amid the COVID-19 pandemic.

Perception of Local COVID-19 Transmission and Use of Preventive Behaviors Among Adults with Recent SARS-CoV-2 Infection - Illinois and Michigan, June 1-July 31, 2022.

During the early stages of the COVID-19 pandemic, use of preventive behaviors was associated with perceived risk for contracting SARS-CoV-2 infection (1,2). Over time, perceived risk has declined along with waning COVID-19-related media coverage (3,4). The extent to which communities continue to be aware of local COVID-19 transmission levels and are implementing recommended preventive behaviors is unknown. During June 1-July 31, 2022, health departments in DuPage County, Illinois and metropolitan Detroit, Michigan surveyed a combined total of 4,934 adults who had received a positive test result for SARS-CoV-2 during the preceding 3 weeks. The association between awareness of local COVID-19 transmission and use of preventive behaviors and practices was assessed, both in response to perceived local COVID-19 transmission levels and specifically during the 2 weeks preceding SARS-CoV-2 testing. Both areas had experienced sustained high COVID-19 transmission during the study interval as categorized by CDC COVID-19 transmission levels.* Overall, 702 (14%) respondents perceived local COVID-19 transmission levels as high, 987 (20%) as substantial, 1,902 (39%) as moderate, and 581 (12%) as low; 789 (16%) reported they did not know. Adjusting for geographic area, age, gender identity, and combined race and ethnicity, respondents who perceived local COVID-19 transmission levels as high were more likely to report having made behavioral changes because of the level of COVID-19 transmission in their area, including wearing a mask in public, limiting travel, and avoiding crowded places or events. Continued monitoring of public perceptions of local COVID-19 levels and developing a better understanding of their influence on the use of preventive behaviors can guide COVID-19 communication strategies and policy making during and beyond the pandemic.

Seroprevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection among Veterans Affairs healthcare system employees suggests higher risk of infection when exposed to SARS-CoV-2 outside the work environment.

OBJECTIVE: The seroprevalence of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) IgG antibody was evaluated among employees of a Veterans Affairs healthcare system to assess potential risk factors for transmission and infection. METHODS: All employees were invited to participate in a questionnaire and serological survey to detect antibodies to SARS-CoV-2 as part of a facility-wide quality improvement and infection prevention initiative regardless of clinical or nonclinical duties. The initiative was conducted from June 8 to July 8, 2020. RESULTS: Of the 2,900 employees, 51% participated in the study, revealing a positive SARS-CoV-2 seroprevalence of 4.9% (72 of 1,476; 95% CI, 3.8%-6.1%). There were no statistically significant differences in the presence of antibody based on gender, age, frontline worker status, job title, performance of aerosol-generating procedures, or exposure to known patients with coronavirus infectious disease 2019 (COVID-19) within the hospital. Employees who reported exposure to a known COVID-19 case outside work had a significantly higher seroprevalence at 14.8% (23 of 155) compared to those who did not 3.7% (48 of 1,296; OR, 4.53; 95% CI, 2.67-7.68; P < .0001). Notably, 29% of seropositive employees reported no history of symptoms for SARS-CoV-2 infection. CONCLUSIONS: The seroprevalence of SARS-CoV-2 among employees was not significantly different among those who provided direct patient care and those who did not, suggesting that facility-wide infection control measures were effective. Employees who reported direct personal contact with COVID-19-positive persons outside work were more likely to have SARS-CoV-2 antibodies. Employee exposure to SARS-CoV-2 outside work may introduce infection into hospitals.

COVID-19 in the workplace: Self-reported source of exposure and availability of personal protective equipment by industry and occupation in Michigan.

OBJECTIVES: Fragmented industry and occupation surveillance data throughout the COVID-19 pandemic has left public health practitioners and organizations with an insufficient understanding of high-risk worker groups and the role of work in SARS-CoV-2 transmission. METHODS: We drew sequential probability samples of noninstitutionalized adults (18+) in the Michigan Disease Surveillance System with COVID-19 onset before November 16, 2020 (N = 237,468). Among the 6000 selected, 1839 completed a survey between June 23, 2020, and April 23, 2021. We compared in-person work status, source of self-reported SARS-CoV-2 exposure, and availability of adequate personal protective equipment (PPE) by industry and occupation using weighted descriptive statistics and Rao-Scott χ2 tests. We identified industries with a disproportionate share of COVID-19 infections by comparing our sample with the total share of employment by industry in Michigan using 2020 data from the US Bureau of Labor Statistics. RESULTS: Employed respondents (n = 1244) were predominantly female (53.1%), aged 44 and under (54.4%), and non-Hispanic White (64.0%). 30.4% of all employed respondents reported work as the source of their SARS-CoV-2 exposure and 78.8% were in-person workers. Work-related exposure was prevalent in Nursing and Residential Care Facilities (65.2%); Justice, Public Order, and Safety Activities (63.3%); and Food Manufacturing (57.5%). By occupation, work-related exposure was highest among Protective Services (57.9%), Healthcare Support (56.5%), and Healthcare Practitioners (51.9%). Food Manufacturing; Nursing and Residential Care; and Justice, Public Order, and Safety Activities were most likely to report having adequate PPE "never" or "rarely" (36.4%, 27.9%, and 26.7%, respectively). CONCLUSIONS: Workplaces were a key source of self-reported SARS-CoV-2 exposure among employed Michigan residents during the first year of the pandemic. To prevent transmission, there is an urgent need in public health surveillance for the collection of industry and occupation data of people infected with COVID-19, as well as for future airborne infectious diseases for which we have little understanding of risk factors.

Trends and outcomes of percutaneous coronary intervention during the COVID-19 pandemic in Michigan.

BACKGROUND: The COVID-19 pandemic has severely impacted healthcare delivery and patient outcomes globally. AIMS: We aimed to evaluate the influence of the COVID-19 pandemic on the temporal trends and outcomes of patients undergoing percutaneous coronary intervention (PCI) in Michigan. METHODS: We compared all patients undergoing PCI in the BMC2 Registry between March and December 2020 ("pandemic cohort") with those undergoing PCI between March and December 2019 ("pre-pandemic cohort"). A risk-adjusted analysis of in-hospital outcomes was performed between the pre-pandemic and pandemic cohort. A subgroup analysis was performed comparing COVID-19 positive vs. negative patients during the pandemic. RESULTS: There was a 15.2% reduction in overall PCI volume from the pre-pandemic (n = 25,737) to the pandemic cohort (n = 21,822), which was more pronounced for stable angina and non-ST-elevation acute coronary syndromes (ACS) presentations, and between February and May 2020. Patients in the two cohorts had similar clinical and procedural characteristics. Monthly mortality rates for primary PCI were generally higher in the pandemic period. There were no significant system delays in care between the cohorts. Risk-adjusted mortality was higher in the pandemic cohort (aOR 1.26, 95% CI 1.07-1.47, p = 0.005), a finding that was only partially explained by worse outcomes in COVID-19 patients and was more pronounced in subjects with ACS. During the pandemic, COVID-19 positive patients suffered higher risk-adjusted mortality (aOR 5.69, 95% CI 2.54-12.74, p<0.001) compared with COVID negative patients. CONCLUSIONS: During the COVID-19 pandemic, we observed a reduction in PCI volumes and higher risk-adjusted mortality. COVID-19 positive patients experienced significantly worse outcomes.

Using trajectory modeling of spatio-temporal trends to illustrate disparities in COVID-19 death in flint and Genesee County, Michigan.

COVID-19's rapid onset left many public health entities scrambling. But establishing community-academic partnerships to digest data and create advocacy steps offers an opportunity to link research to action. Here we document disparities in COVID-19 death uncovered during a collaboration between a health department and university research center. We geocoded COVID-19 deaths in Genesee County, Michigan, to model clusters during two waves in spring and fall 2020. We then aggregated these deaths to census block groups, where group-based trajectory modeling identified latent patterns of change and continuity. Linking with socioeconomic data, we identified the most affected communities. We discovered a geographic and racial gap in COVID-19 deaths during the first wave, largely eliminated during the second. Our partnership generated added and immediate value for community partners, including around prevention, testing, treatment, and vaccination. Our identification of the aforementioned racial disparity helped our community nearly eliminate disparities during the second wave.

Perceived Severity of COVID-19 in a Longitudinal Study in Detroit, Michigan.

Objective: To slow down the spread of SARS-CoV-2, many countries have instituted preventive approaches (masks, social distancing) as well as the distribution of vaccines. Adherence to these preventive measures is crucial to the success of controlling the pandemic but decreased perceptions of disease severity could limit adherence. The aim of our study was to observe changes in perceived personal severity and perceived community severity; the study also explored their predictors. Methods: In a longitudinal study from an address-based probability survey in Detroit, we asked participants to rate their perceived severity of COVID-19 for themselves and for their community. In our analysis, 746 participants were queried across 5 waves of the Detroit Metro Area Communities Study surveys from March 31 to October 27 in 2020. We tested for trends in changes of self-reported perceived severity for themselves and for their community; we assessed the effects of different predictors of the two severities through mixed effects logistic regression models. Results: Our results highlight that the overall levels of perceived community and personal severity were decreasing over time even though both severities were fluctuating with rising confirmed case counts. Compared with non-Hispanic (NH) White Detroiters, NH Black Detroiters reported a higher perceived personal severity (OR: 5.30, 95% CI: 2.97, 9.47) but both groups reported similar levels of perceived community severity. We found steeper declines in perceived severity in NH White than NH Black Detroiters over time; the impact of education and income on perceived severity was attenuated in NH Black Detroiters compared with NH White Detroiters. Conclusions: Our findings suggested that perceived severity for COVID-19 decreased through time and was affected by different factors among varied racial/ethnic groups. Future interventions to slow the pace of the pandemic should take into account perceived personal and community severities among varied ethnic/racial subgroups.

Stress and Health Status Among Members of a Disadvantaged Community in Flint, Michigan in the Early Phase of the COVID-19 Pandemic.

The COVID-19 pandemic creates psychological concerns and stress and its impacts are more extreme for those with health concerns residing in socially and economically disadvantaged communities, such as residents of Flint, Michigan. This study assesses the stress level among people who received community assistance in the first 3 months of COVID lockdowns. Further, it examines associations between stress and physical and mental health status. We measured perceived stress, health concerns, mental distress, and perceived physical and mental health from 106 survey respondents. Comparisons of stress levels by demographics showed that females, high school graduates, and homeowners had higher stress levels than its counterparts. Results from general linear models showed that stress was highest among those with high levels of psychological distress, perceived poor mental health, and more health concerns. The associations between poor perceived physical health and stress were marginal. Homeowners and high school diploma holders showed lower stress levels. This research suggests community health practices tailored to community characteristics and culture will have the greatest impact on stress and health problems in underserved communities.

The variant-specific burden of SARS-CoV-2 in Michigan: March 2020 through November 2021.

Accurate estimates of the total burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to inform policy, planning, and response. We sought to quantify SARS-CoV-2 cases, hospitalizations, and deaths by age in Michigan. Coronavirus disease 2019 cases reported to the Michigan Disease Surveillance System were multiplied by age and time-specific adjustment factors to correct for under-detection. Adjustment factors were estimated in a model fit to incidence data and seroprevalence estimates. Age-specific incidence of SARS-CoV-2 hospitalization, death, vaccination, and variant proportions were estimated from publicly available data. We estimated substantial under-detection of infection that varied by age and time. Accounting for under-detection, we estimate the cumulative incidence of infection in Michigan reached 75% by mid-November 2021, and over 87% of Michigan residents were estimated to have had ≥1 vaccination dose and/or previous infection. Comparing pandemic waves, the relative burden among children increased over time. In general, the proportion of cases who were hospitalized or who died decreased over time. Our results highlight the ongoing risk of periods of high SARS-CoV-2 incidence despite widespread prior infection and vaccination. This underscores the need for long-term planning for surveillance, vaccination, and other mitigation measures amidst continued response to the acute pandemic.

Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance.

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concentrations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preceding COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5-week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by wastewater flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we evaluated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time.

Collaboration between two CF centers; one in USA and one in Turkey before and during CoV2 pandemic.

To address the discrepancy in the quality of care and outcomes between cystic fibrosis centers (CFCs) in high-income countries and limited resources countries (LRCs), a collaboration between our team at the University of Michigan CFC (UMCFC) and a CF center in Turkey (Marmara University CFC [MUCFC], Istanbul) was established. The collaboration included evaluation of all aspects of care and initiation of quality improvement (QI) measures. Teaching and implementing QI tools has led to start of improvement in MUCFC care. Close monitoring and sharing resources like UMCFC algorithms, protocols, and QI processes were done.

Trajectory Modeling of Spatio-Temporal Trends in COVID-19 Incidence in Flint and Genesee County, Michigan.

PURPOSE: The establishment of community-academic partnerships to digest data and create actionable policy and advocacy steps is of continuing importance. In this paper, we document COVID-19 racial and geographic disparities uncovered via a collaboration between a local health department and university research center. METHODS: We leverage individual level data for all COVID-19 cases aggregated to the census block group level, where group-based trajectory modeling was employed to identify latent patterns of change and continuity in COVID-19 diagnoses. RESULTS: Linking with socioeconomic data from the census, we identified the types of communities most heavily affected by each of Michigan's two waves (in spring and fall of 2020). This includes a geographic and racial gap in COVID-19 cases during the first wave, which is largely eliminated during the second wave. CONCLUSIONS: Our work has been extremely valuable for community partners, informing community-level response toward testing, treatment, and vaccination. In particular, identifying and conducting advocacy on the sizeable racial disparity in COVID-19 cases during the first wave in spring 2020 helped our community nearly eliminate disparities throughout the second wave in fall 2020.

A descriptive study on multisystem inflammatory syndrome in children in a single center in West Michigan.

BACKGROUND: Multisystem Inflammatory Syndrome in Children (MIS-C) is a rare hyperinflammatory condition that occurs following SARS-CoV-2 infection. There is a paucity of research describing risk factors, optimal management, and outcomes of this life-threatening condition. METHODS: This is a case series of 26 patients diagnosed with MIS-C in a West Michigan pediatric tertiary care center from April 2020 to February 2021. We describe the clinical, imaging, and laboratory characteristics of these patients and detail their treatments and outcomes with comparisons between Pediatric Intensive Care Unit (PICU) and non-PICU patients. Categorical testing utilized Chi-square and Fisher's Exact tests. Comparison between groups used T-tests or Kruskal-Wallis. RESULTS: Fifteen patients (57%) required intensive care. There was no statistically significant difference in demographics between PICU and non-PICU patients, however all Black patients required intensive care. Gastrointestinal symptoms were present in 22 patients (84%). Seventeen patients (65%) had Kawasaki-like features and 12 (46%) developed coronary artery dilation. Patients requiring intensive care were less likely to have a reported history of COVID-19 disease or exposure (p = 0.0362). Statistically significant differences were also noted in peak ferritin (p = 0.0075), procalcitonin, and BNP in those who required intensive care. CONCLUSIONS: Although overlap exists with other hyperinflammatory conditions, our study provides further evidence that MIS-C is a distinct, albeit heterogenous, disorder with various degrees of cardiac involvement. Anakinra, in conjunction with steroid use, appears to be effective and safe in the treatment of MIS-C. This report identifies procalcitonin, peak ferritin, and BNP as potentially useful biomarkers for severity of disease.

Use of Telehealth by Surgical Specialties During the COVID-19 Pandemic.

Importance: While telehealth use in surgery has shown to be feasible, telehealth became a major modality of health care delivery during the COVID-19 pandemic. Objective: To assess patterns of telehealth use across surgical specialties before and during the COVID-19 pandemic. Design, Setting, and Participants: Insurance claims from a Michigan statewide commercial payer for new patient visits with a surgeon from 1 of 9 surgical specialties during one of the following periods: prior to the COVID-19 pandemic (period 1: January 5 to March 7, 2020), early pandemic (period 2: March 8 to June 6, 2020), and late pandemic (period 3: June 7 to September 5, 2020). Exposures: Telehealth implementation owing to the COVID-19 pandemic in March 2020. Main Outcomes and Measures: (1) Conversion rate defined as the rate of weekly new patient telehealth visits divided by mean weekly number of total new patient visits in 2019. This outcome adjusts for a substantial decrease in outpatient care during the pandemic. (2) Weekly number of new patient telehealth visits divided by weekly number of total new patient visits. Results: Among 4405 surgeons in the cohort, 2588 (58.8%) performed telehealth in any patient care context. Specifically for new patient visits, 1182 surgeons (26.8%) used telehealth. A total of 109 610 surgical new outpatient visits were identified during the pandemic. The median (interquartile range) age of telehealth patients was 46.8 (34.1-58.4) years compared with 52.6 (38.3-62.3) years for patients who received care in-person. Prior to March 2020, less than 1% (8 of 173 939) of new patient visits were conducted through telehealth. Telehealth use peaked in April 2020 (week 14) and facilitated 34.6% (479 of 1383) of all new patient visits during that week. The telehealth conversion rate peaked in April 2020 (week 15) and was equal to 8.2% of the 2019 mean weekly new patient visit volume. During period 2, a mean (SD) of 16.6% (12.0%) of all new patient surgical visits were conducted via telehealth (conversion rate of 5.1% of 2019 mean weekly new patient visit volumes). During period 3, 3.0% (2168 of 71 819) of all new patient surgical visits were conducted via telehealth (conversion rate of 2.5% of 2019 new patient visit volumes). Mean (SD) telehealth conversion rates varied by specialty with urology being the highest (14.3% [7.7%]). Conclusions and Relevance: Results from this study showed that telehealth use grew across all surgical specialties in Michigan in response to the COVID-19 pandemic. While rates of telehealth use have declined as in-person care has resumed, telehealth use remains substantially higher across all surgical specialties than it was prior to the pandemic.

Influenza A(H3N2) Outbreak on a University Campus - Michigan, October-November 2021.

On November 10, 2021, the Michigan Department of Health and Human Services (MDHHS) was notified of a rapid increase in influenza A(H3N2) cases by the University Health Service (UHS) at the University of Michigan in Ann Arbor. Because this outbreak represented some of the first substantial influenza activity during the COVID-19 pandemic, CDC, in collaboration with the university, MDHHS, and local partners conducted an investigation to characterize and help control the outbreak. Beginning August 1, 2021, persons with COVID-19-like* or influenza-like illness evaluated at UHS received testing for SARS-CoV-2, influenza, and respiratory syncytial viruses by rapid multiplex molecular assay.† During October 6-November 19, a total of 745 laboratory-confirmed influenza cases were identified.§ Demographic information, genetic characterization of viruses, and influenza vaccination history data were reviewed. This activity was conducted consistent with applicable federal law and CDC policy.¶.

Can changes in social contact (frequency and mode) mitigate low mood before and during the COVID-19 pandemic? The I-CONECT project.

BACKGROUND/OBJECTIVES: The coronavirus disease 2019 (COVID-19) global outbreak allowed a natural experiment to observe how older adults changed social patterns and how it affected their emotional well-being. We studied the frequency and modes of social contact and their effects on older adults' mood before and during the COVID-19 pandemic. DESIGN: Phone-based surveys were administered weekly before and during the COVID-19 pandemic. SETTING: Participants were recruited from Portland, Oregon, and Detroit, Michigan. PARTICIPANTS: Older adults ≥75 years old (n = 155, age = 81.0 ± 4.5, 72.3% women) were included in a randomized controlled trial, the Internet-Based Conversational Engagement Clinical Trial (I-CONECT). MEASUREMENTS: Low mood was self-reported as feeling downhearted or blue for three or more days in the past week. Social contact was self-reported by the amount of time spent in interactions, with whom (family, friends, others), and via which modes (in-person, phone/video call, text/email/letter). RESULTS: A total of 5525 weeks of data were derived from 155 participants. Before the COVID-19 pandemic, average social interaction time spent in-person, on phone/video call, and via text/email/letter was 406, 141, and 68 min/week, respectively. During the COVID-19 pandemic, time spent in-person was reduced by 135 min/week, while time spent via phone/video call and writing increased by 33 and 26 mins/week, respectively. In-person family contact was associated with less low mood regardless of the pandemic (odds ratio = 0.92, p < 0.05). There was a COVID-19 × text/email/letter with friends interaction (odds ratio = 0.77, p = 0.03), suggesting that during the COVID-19 pandemic, an increase of 1 h of writing with friends per week was associated with a 23% decrease in the likelihood of experiencing low mood. CONCLUSION: The lost in-person time relating to COVID-19 restrictions tended to be partially compensated for with increased calls and writing time, although overall social interaction time decreased. During the COVID-19 pandemic, at least two types of social interactions (writing to friends and in-person family time) showed promise for mitigating low mood for older adults with limited social resources.

Patterns of vascular access device use and thrombosis outcomes in patients with COVID-19: a pilot multi-site study of Michigan hospitals.

Venous thromboembolism (VTE) is an important complication of coronavirus disease 2019 (COVID-19). To date, few studies have described vascular access device use and VTE risk in this cohort. To examine the use of vascular access devices and incidence of VTE in patients hospitalized with COVID-19. We performed a retrospective, multi-center cohort study of patients hospitalized with COVID-19 who received a midline catheter, peripherally inserted central catheter (PICCs), tunneled or non-tunneled central venous catheter (CVC), hemodialysis (HD) catheter or a port during hospitalization. Mixed-effects multivariable logit models adjusting for VTE risk factors in the Caprini risk score were fit to understand the incremental risk of VTE in patients with vascular access devices vs. those that did not receive devices. Management of VTE was determined by examining anticoagulant use pre- vs. post-thrombosis. Results were expressed using odds ratios (ORs) and associated 95% confidence intervals (CI). A total of 1228 hospitalized COVID-19 patients in 40 hospitals, of which 261 (21.3%) received at least one vascular access device of interest, were included. The prevalence of acute, non-tunneled CVCs was 42.2%, acute HD catheters 18.4%, midline catheters 15.6%, PICCs 15.6%, tunneled CVCs 6.8%, and implanted ports 1.4%. The prevalence of VTE was 6.0% in the study cohort, and 10.0% among patients with vascular access devices. After adjusting for known VTE risk factors, patients that had a vascular access device placed were observed to have a four-fold greater odds of VTE than those that did not (OR 4.17, 95% CI 2.33-7.46). Patients who received multiple different catheters experienced more VTE events compared with patients that received only one type (21.5% vs. 6.1%, p < .001). Among the 26 patients with VTE, only 8 (30.8%) survived to discharge and among these, only 5 were discharged on therapeutic doses of anticoagulation. Hospitalized patients with COVID-19 that receive vascular access devices experienced higher rates of VTE than those that do not. Future studies to evaluate the nexus between COVID-19, vascular device use, and thrombosis appear are warranted.