The Importance of Long-term Care Populations in Models of COVID-19

A Bayesian approach to estimating COVID-19 incidence and infection fatality rates

Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.

Matching medical staff to long term care facilities to respond to COVID-19 outbreak

BACKGROUND

Staff shortage is a long-standing issue in long term care facilities (LTCFs) that worsened with the COVID-19 outbreak. Different states in the US have employed various tools to alleviate this issue in LTCFs. We describe the actions taken by the Commonwealth of Massachusetts to assist LTCFs in addressing the staff shortage issue and their outcomes. Therefore, the main question of this study is how to create a central mechanism to allocate severely limited medical staff to healthcare centers during emergencies.

METHODS

For the Commonwealth of Massachusetts, we developed a mathematical programming model to match severely limited available staff with LTCF demand requests submitted through a designed portal. To find feasible matches and prioritize facility needs, we incorporated restrictions and preferences for both sides. For staff, we considered maximum mileage they are willing to travel, available by date, and short- or long-term work preferences. For LTCFs, we considered their demand quantities for different positions and the level of urgency for their demand. As a secondary goal of this study, by using the feedback entries data received from the LTCFs on their matches, we developed statistical models to determine the most salient features that induced the LTCFs to submit feedback.

RESULTS

We used the developed portal to complete about 150 matching sessions in 14 months to match staff to LTCFs in Massachusetts. LTCFs provided feedback for 2,542 matches including 2,064 intentions to hire the matched staff during this time. Further analysis indicated that nursing homes and facilities that entered higher levels of demand to the portal were more likely to provide feedback on the matches and facilities that were prioritized in the matching process due to whole facility testing or low staffing levels were less likely to do so. On the staffing side, matches that involved more experienced staff and staff who can work afternoons, evenings, and overnight were more likely to generate feedback from the facility that they were matched to.

CONCLUSION

Developing a central matching framework to match medical staff to LTCFs at the time of a public health emergency could be an efficient tool for responding to staffing shortages. Such central approaches that help allocate a severely limited resource efficiently during a public emergency can be developed and used for different resource types, as well as provide crucial demand and supply information in different regions and/or demographics.

Nursing Home Residents' Experiences During the COVID-19 Pandemic

Long-term care facilities in the United States have accounted for 40% of deaths related to coronavirus disease 2019 (COVID-19). Restriction of in-person visitation has heightened mental health challenges among nursing home residents, and limited evidence exists in the literature concerning nursing home residents' experiences since the COVID-19 outbreak first began. The current study used a qualitative design to obtain data from residents within two nursing homes in New York. Interview data were transcribed verbatim and analyzed using reflexive thematic analysis. Four major themes emerged: Emotional Reactions to Lockdown, Mixed Feelings and Attitudes Toward Nursing Home Staff and Family, Need for Support and Connection, and Desire to Be Informed and Involved. Results show that nursing home residents are emotionally burdened and suggest a critical need to provide ongoing support to prevent mental health concerns. Future research should develop interventions to help manage adverse emotional outcomes. [Journal of Gerontological Nursing, 49(4), 27-32.].

Epidemiological, Clinical, and Phylogenetic Characteristics of the First SARS-CoV-2 Transmission in a Nursing Home of Singapore: A Prospective Observational Investigation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has resulted in a significant burden among nursing home facilities globally. This prospective observational cohort study aims to define the potential sources of introduction and characteristics of SARS-CoV-2 transmission of the first nursing home facility in Singapore. An epidemiological serial point-prevalence survey of SARS-CoV-2 was conducted among 108 residents and 56 healthcare staff (HCS). In the current study, 14 (13%) residents and two (3.6%) HCS were diagnosed with coronavirus disease 2019 (COVID-19), with a case fatality rate (CFR) of 28.6% (4/14) among the residents. The median age of the infected residents was 86.5 [interquartile range (IQR) 78.5-88] and 85.7% were women. Five residents were symptomatic (35.7%) and the others were asymptomatic (64.3%). A higher proportion of residents who succumbed to COVID-19 had hypertension than those who recovered. The SARS-CoV-2 whole-genome sequencing showed lineage B.6 which is rare globally but common regionally during the early phase of the pandemic. Household transmission is a potential source of introduction into the nursing home, with at least six epidemiologically linked secondary cases. Male residents were less implicated due to the staff segregation plan by block. Among residents, a higher proportion of the non-survivors were asymptomatic and had hypertension compared with survivors.

Technology-based interventions for nursing home residents: a systematic review protocol

INTRODUCTION

A growing number of technology-based interventions are used to support the health and quality of life of nursing home residents. The onset of COVID-19 and recommended social distancing policies that followed led to an increased interest in technology-based solutions to provide healthcare and promote health. Yet, there are no comprehensive resources on technology-based healthcare solutions that describe their efficacy for nursing home residents. This systematic review will identify technology-based interventions designed for nursing home residents and describe the characteristics and effects of these interventions concerning the distinctive traits of nursing home residents and nursing facilities. Additionally, this paper will present practical insights into the varying intervention approaches that can assist in the delivery of broad digital health solutions for nursing home residents amid and beyond the impact of COVID-19.

METHODS AND ANALYSIS

Databases including the PubMed, PsycINFO, CINAHL and Scopus will be used to identify articles related to technology-based interventions for nursing home residents published between 1 January 2010 to 30 September 2021. Titles, abstracts and full-text papers will be reviewed against the eligibility criteria. The Cochrane Collaboration evaluation framework will be adopted to examine the risk of bias of the included study. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures will be followed for the reporting process and implications for existing interventions and research evaluated by a multidisciplinary research team.

ETHICS AND DISSEMINATION

As the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

CRD 42020191880.

Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data

Estimating the coronavirus disease-2019 (COVID-19) infection fatality rate (IFR) has proven to be particularly challenging –and rather controversial– due to the fact that both the data on deaths and the data on the number of individuals infected are subject to many different biases. We consider a Bayesian evidence synthesis approach which, while simple enough for researchers to understand and use, accounts for many important sources of uncertainty inherent in both the seroprevalence and mortality data. With the understanding that the results of one's evidence synthesis analysis may be largely driven by which studies are included and which are excluded, we conduct two separate parallel analyses based on two lists of eligible studies obtained from two different research teams. The results from both analyses are rather similar. With the first analysis, we estimate the COVID-19 IFR to be 0.31% [95% credible interval (CrI) of (0.16%, 0.53%)] for a typical community-dwelling population where 9% of the population is aged over 65 years and where the gross-domestic-product at purchasing-power-parity (GDP at PPP) per capita is $17.8k (the approximate worldwide average). With the second analysis, we obtain 0.32% [95% CrI of (0.19%, 0.47%)]. Our results suggest that, as one might expect, lower IFRs are associated with younger populations (and may also be associated with wealthier populations). For a typical community-dwelling population with the age and wealth of the United States we obtain IFR estimates of 0.43% and 0.41%; and with the age and wealth of the European Union, we obtain IFR estimates of 0.67% and 0.51%.

Development of the Verbal Autopsy Instrument for COVID-19 (VAIC)

BACKGROUND

Improving accuracy of identification of COVID-19-related deaths is essential to public health surveillance and research. The verbal autopsy, an established strategy involving an interview with a decedent's caregiver or witness using a semi-structured questionnaire, may improve accurate counting of COVID-19-related deaths.

OBJECTIVE

To develop and pilot-test the Verbal Autopsy Instrument for COVID-19 (VAIC) and a death adjudication protocol using it.

METHODS/KEY RESULTS

We used a multi-step process to design the VAIC and a protocol for its use. We developed a preliminary version of a verbal autopsy instrument specifically for COVID. We then pilot-tested this instrument by interviewing respondents about the deaths of 15 adults aged ≥65 during the initial COVID-19 surge in New York City. We modified it after the first 5 interviews. We then reviewed the VAIC and clinical information for the 15 deaths and developed a death adjudication process/algorithm to determine whether the underlying cause of death was definitely (40% of these pilot cases), probably (33%), possibly (13%), or unlikely/definitely not (13%) COVID-19-related. We noted differences between the adjudicated cause of death and a death certificate.

CONCLUSIONS

The VAIC and a death adjudication protocol using it may improve accuracy in identifying COVID-19-related deaths.

COVID-19 Incidence and Mortality Among Long-Term Care Facility Residents and Staff in South Carolina

Objectives

This study explored differences in COVID-19 incidence, mortality, and timing among long-term care facility (LTCF) residents and staff with those living in the community in South Carolina (SC).

Design

Longitudinal secondary data analysis.

Setting and Participants

Adults age ≥18 in SC with confirmed COVID-19 diagnosis from 3/15/2020 and 1/2/2021 (n = 307,891).

Methods

COVID-19 data came from the SC Department of Health and Environmental Control (SCDHEC). We included all COVID-19 cases, hospitalizations, and deaths among adult residents. Residence and employment in LTCF were confirmed by SCDHEC. Descriptive statistics and trends for cases, hospitalizations, and deaths were calculated. We used Cox proportional hazards to compare COVID-19 mortality in LTCF residents and staff to community dwelling older adults and adults not employed in LTCF, respectively, controlling for age, gender, race, and pre-existing chronic health conditions.

Results

LTC residents experienced greater incidence of cases throughout the study period until the week ending on 1/2/21. LTCF residents with COVID-19 were more likely to be hospitalized compared to older adults in the community and 74% more likely to die (HR: 1.74, 95% CI: 1.59-1.90), after adjusting. LTC staff experienced greater incidence of cases compared to adults not employed in LTCF until the week ending on 12/26/2020, while experiencing similar incidence of death compared to the similar community members. After adjusting, LTC staff had 0.58 (HR = 0.58; CI: 0.39-0.88) times lower hazard of death compared to community members that did not work in a LTCF.

Conclusions and Implications

Narrowing of the gap between LTCF and community-wide infection and mortality rates over the study period suggests that early detection of COVID-19 in LTCFs could serve as a first indicator of disease spread in the greater community. Results also indicate that policies and regulations addressing staff testing and protection may help to slow or prevent spread within facilities.

"I Didn't Meet My Mother; I Saw My Mother": The Challenges Facing Long-Term Care Residents and Their Families in the Age of COVID-19

The present research examines the effects of protective measures due to the coronavirus disease 2019 (COVID-19) pandemic within long-term care (LTC) settings on residents and their family members. Open-ended qualitative interviews were conducted with 14 family members of older adults who resided in LTC settings during the first wave of the pandemic in Israel. The first theme identified is Rupture, including the physical disconnect; the disruption in routine treatment to residents; and decline in the satisfaction with the setting. The second theme is Response, including sharing viewpoints and involvement in decision-making, as well as an intense ambivalence shared by family members. Our findings highlight the distress caused to residents and family members by the isolation and restrictions in LTC settings during the pandemic and underscore values and priorities that are central to them and their family members, including maintaining continuity, transparency, and working in unison with their families, staff, and management.

Controlling COVID-19 via test-trace-quarantine

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.

Controlling COVID-19 via test-trace-quarantine

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.

Asymptomatic carriage rates and case fatality of SARS-CoV-2 infection in residents and staff in Irish nursing homes

Background

SARS-CoV-2 has disproportionately affected nursing homes (NH). In Ireland, the first NH case COVID-19 occurred on 16/03/2020. A national point-prevalence testing program of all NH residents and staff took place (18/04/2020–05/05/2020).

Aims

To examine characteristics of NHs across three Irish Community Health Organisations (CHOs), proportions with COVID-19 outbreaks, staff and resident infection rates symptom-profile, and resident case-fatality.

Methods

Forty-five NHs surveyed requesting details on occupancy, size, COVID-19 outbreak, outbreak timing, total symptomatic/asymptomatic cases, and outcomes for residents from 29/02/2020–22/05/2020.

Results

Surveys were returned from 62.2% (28/45) of NHs (2043 residents, 2,303 beds). Three-quarters (21/28) had COVID-19 outbreaks (1741 residents, 1972 beds). Median time from first COVID-19 case in Ireland to first case in these NHs was 27.0 days. Resident incidence was 43.9% (764/1741): 40.8% (710/1741) laboratory-confirmed, with 27.2% (193/710) asymptomatic, and 3.1% (54/1741) clinically-suspected. Resident case-fatality was 27.6% (211/764) for combined laboratory-confirmed/clinically-suspected COVID-19. Similar proportions of residents in NHs with “early-stage” (<28 days) versus “later-stage” outbreaks developed COVID-19. Lower proportions of residents in “early” outbreak NHs had recovered compared to those with “late” outbreaks (37.4% vs 61.7%; χ² = 56.9, P < 0.001). Of 395 NH staff across twelve sites with confirmed COVID-19, 24.7% (99/398) were asymptomatic. There was a significant correlation between the proportion of staff with symptomatic COVID-19 and resident numbers with confirmed/suspected COVID-19 (Spearman’s rho = 0.81, P < 0.001).

Conclusion

This study demonstrates the significant impact of COVID-19 on the NH sector. Systematic point-prevalence testing is necessary to reduce risk of transmission from asymptomatic carriers and manage outbreaks in this setting.

Nursing home staff networks and COVID-19

Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in US nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, WA, to other skilled nursing facilities. The full extent of staff connections between nursing homes-and the role these connections serve in spreading a highly contagious respiratory infection-is currently unknown given the lack of centralized data on cross-facility employment. We perform a large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1% of smartphone users who visited a nursing home for at least 1 h also visited another facility during our 11-wk study period-even after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7.1 other facilities. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Controlling for demographic and other factors, a home's staff network connections and its centrality within the greater network strongly predict COVID-19 cases.

Clinical Presentation, Course, and Risk Factors Associated with Mortality in a Severe Outbreak of COVID-19 in Rhode Island, USA, April-June 2020

Long-term care facilities (LTCFs) have had a disproportionally high mortality rate due to COVID-19. We describe a rapidly escalating COVID-19 outbreak among 116 LTCF residents in Rhode Island, USA. Overall, 111 (95.6%) residents tested positive and, of these, 48 (43.2%) died. The most common comorbidities were hypertension (84.7%) and cardiovascular disease (84.7%). A small percentage (9%) of residents were asymptomatic, while 33.3% of residents were pre-symptomatic, with progression to symptoms within a median of three days following the positive test. While typical symptoms of fever (80.2%) and cough (43.2%) were prevalent, shortness of breath (14.4%) was rarely found despite common hypoxemia (95.5%). The majority of patients demonstrated atypical symptoms with the most common being loss of appetite (61.3%), lethargy (42.3%), diarrhea (37.8%), and fatigue (32.4%). Many residents had increased agitation (38.7%) and anxiety (5.4%), potentially due to the restriction measures or the underlying mental illness. The fever curve was characterized by an intermittent low-grade fever, often the first presenting symptom. Mortality was associated with a disease course beginning with a loss of appetite and lethargy, as well as one more often involving fever greater than 38 °C, loss of appetite, altered mental status, diarrhea, and respiratory distress. Interestingly, no differences in age or comorbidities were noted between survivors and non-survivors. Taking demographic factors into account, treatment with anticoagulation was still associated with reduced mortality (adjusted OR 0.16; 95% C.I. 0.06–0.39; p < 0.001). Overall, the clinical features of the disease in this population can be subtle and the symptoms are commonly atypical. However, clinical decline among those who did not survive was often rapid with patients expiring within 10 days from disease detection. Further studies are needed to better explain the variability in clinical course of COVID-19 among LTCF residents, specifically the factors affecting mortality, the differences observed in symptom presentation, and rate of clinical decline.

Technology-Based Interventions for Nursing Home Residents: Implications for Nursing Home Practice Amid and Beyond the Influence of COVID-19: A Systematic Review Protocol

Background

A growing number of technology-based interventions are used to support the health and quality of life of nursing home residents. The onset of COVID-19 and recommended social distancing policies that followed led to an increased interest in technology-based solutions to provide healthcare and promote health. Yet, there are no comprehensive resources on technology-based healthcare solutions that describe their efficacy for nursing home residents. This systematic review will identify technology-based interventions designed for nursing home residents and describe the characteristics and effects of these interventions concerning the distinctive traits of nursing home residents and nursing facilities. Additionally, this paper will present practical insights into the varying intervention approaches that can assist in the delivery of broad digital health solutions for nursing home residents amid and beyond the impact of COVID-19.

Methods

Databases including PubMed, PsycINFO, CINAHL, and Scopus will be used to identify articles related to technology-based interventions for nursing home residents published between January 1^st, 2010 to December 4^th, 2020. Titles, abstracts, and full-texts papers will be reviewed against the eligibility criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures will be followed for the reporting process, and implications for existing interventions and research evaluated by a multidisciplinary research team.

Results

NA-protocol study.

Conclusions

Our study will fill critical gaps in the literature by providing a review of technology-based interventions tested in the nursing home setting. As the older adult population grows, there is an urgent need to identify effective technology-based interventions that can address the distinctive characteristics and preferences of nursing home residents. Clear and comprehensive understanding of how available technology-based health solutions facilitate healthcare for nursing home residents will shed light on the approaches open to residents to fend off the negative health consequences amid and beyond the influence of COVID-19.

Systematic Review Registrations

PROSPERO CRD 42020191880.

Multigenerational social support in the face of the COVID-19 pandemic

Research documents high levels of instrumental, financial, and expressive support exchanges within multigenerational families in the 21st century. The COVID-19 pandemic poses unique challenges to support exchanges between the generations; however, the pandemic may provide opportunities for greater solidarity within families. In this review, we draw from theoretical perspectives that have been used to study family relationships to understand the implications of the pandemic for multigenerational families: the life course perspective, the intergenerational solidarity model, and rational choice/social exchange theory. We review literature on multigenerational relationships in the United States and discuss how established social support patterns and processes may be altered by the COVID-19 pandemic. We reflect on how the impact of the COVID-19 pandemic on multigenerational relationships may vary by gender, race, ethnicity, and socioeconomic status. Finally, we provide directions for future researchers to pursue in order to understand the lasting impacts of the COVID-19 pandemic on multigenerational ties.

Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach

OBJECTIVE

Inform coronavirus disease 2019 (COVID-19) infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach.

DESIGN

This retrospective cohort study used a gradient boosting algorithm to evaluate risk of COVID-19 infection (ie, presence of at least 1 confirmed COVID-19 resident) in NHs.

SETTING AND PARTICIPANTS

The model was trained on outcomes from 1146 NHs in Massachusetts, Georgia, and New Jersey, reporting COVID-19 case data on April 20, 2020. Risk indices generated from the model using data from May 4 were prospectively validated against outcomes reported on May 11 from 1021 NHs in California.

METHODS

Model features, pertaining to facility and community characteristics, were obtained from a self-constructed dataset based on multiple public and private sources. The model was assessed via out-of-sample area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in the training (via 10-fold cross-validation) and validation datasets.

RESULTS

The mean AUC, sensitivity, and specificity of the model over 10-fold cross-validation were 0.729 [95% confidence interval (CI) 0.690‒0.767], 0.670 (95% CI 0.477‒0.862), and 0.611 (95% CI 0.412‒0.809), respectively. Prospective out-of-sample validation yielded similar performance measures (AUC 0.721; sensitivity 0.622; specificity 0.713). The strongest predictors of COVID-19 infection were identified as the NH's county's infection rate and the number of separate units in the NH; other predictors included the county's population density, historical Centers of Medicare and Medicaid Services cited health deficiencies, and the NH's resident density (in persons per 1000 square feet). In addition, the NH's historical percentage of non-Hispanic white residents was identified as a protective factor.

CONCLUSIONS AND IMPLICATIONS

A machine-learning model can help quantify and predict NH infection risk. The identified risk factors support the early identification and management of presymptomatic and asymptomatic individuals (eg, staff) entering the NH from the surrounding community and the development of financially sustainable staff testing initiatives in preventing COVID-19 infection.

Risk Factors for In-hospital Mortality from COVID-19 Infection among Black Patients - An Urban Center Experience

Background

Racial disparities are central in the national conversation about Covid-19. Black/African Americans are contracting and dying from COVID-19 disproportionately. We assessed risk factors for death from COVID-19 among black inpatients at an urban center in Detroit, MI.

Methods

This was a retrospective, single-center cohort study. We reviewed the electronic medical records of patients positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, the virus that causes COVID-19) on qualitative polymerase-chain-reaction assay, who were admitted between 3/8-5/6/2020. The primary outcome was in-hospital mortality.

Results

The case fatality rate was 29.1% (122/419). The mean duration of symptoms prior to hospitalization was 5.3 (3.9) days. Patients who died were older (mean [SD] age, 68.7 [14.8] years vs 60.3 [16.0] years; p <0.0001), had dementia (35 [28.7%] vs 34 [11.4%]; p <0.0001), hemiplegia (14 [11.5%] vs 12 [4.0%]; p=0.004), malignancy (11 [9.0%] vs 12 [4.0%]; p=0.04), and moderate-severe liver disease (4 [3.3%] vs 1 [0.3%]; p=0.01). The incidence of AMS on presentation was higher among patients who died than those who survived, 43% vs. 20.0%, respectively (p<0.0001). From multivariable analysis, the odds of death increased with age (≥60 yrs.), admission from a nursing facility, Charlson score, altered mental status, higher C-reactive protein on admission, need for mechanical ventilation, presence of shock, and acute respiratory distress syndrome.

Conclusions

These demographic, clinical and laboratory factors should help healthcare providers identify black patients at highest risk for severe COVID-19-associated outcomes. Early and aggressive interventions among this at-risk population can help mitigate adverse outcomes.

Technology-Based Interventions for Nursing Home Residents: Implications for Nursing Home Practice Amid and Beyond the Influence of COVID-19: A Systematic Review Protocol

Background: A growing number of technology-based interventions are used to support the health and quality of life of nursing home residents. The onset of COVID-19 and recommended social distancing policies that followed led to an increased interest in technology-based solutions to provide healthcare and promote health. Yet, there are no comprehensive resources on technology-based healthcare solutions that describe their efficacy for nursing home residents. This systematic review will identify technology-based interventions designed for nursing home residents and describe the characteristics and effects of these interventions concerning the distinctive traits of nursing home residents and nursing facilities. Additionally, this paper will present practical insights into the varying intervention approaches that can assist in the delivery of broad digital health solutions for nursing home residents amid and beyond the impact of COVID-19. Methods: Databases including PubMed, PsycINFO, CINAHL, and Scopus will be used to identify articles related to technology-based interventions for nursing home residents published between January 1 st , 2020 to July 7 th , 2020. Titles, abstracts, and full-texts papers will be reviewed against the eligibility criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures will be followed for the reporting process, and implications for existing interventions and research evaluated by a multidisciplinary research team. Results: NAâ€"protocol study Conclusions: Our study will fill critical gaps in the literature by providing a review of technology-based interventions tested in the nursing home setting. As the older adult population grows, there is an urgent need to identify effective technology-based interventions that can address the distinctive characteristics and preferences of nursing home residents. Clear and comprehensive understanding of how available technology-based health solutions facilitate healthcare for nursing home residents will shed light on the approaches open to residents to fend off the negative health consequences amid and beyond the influence of COVID-19. Systematic Review Registrations: PROSPERO CRD 42020191880.

Long-Term Care, Residential Facilities, and COVID-19: An Overview of Federal and State Policy Responses

The COVID-19 pandemic has disproportionately affected residents and staff at long-term care (LTC) and other residential facilities in the United States. The high morbidity and mortality at these facilities has been attributed to a combination of a particularly vulnerable population and a lack of resources to mitigate the risk. During the first wave of the pandemic, the federal and state governments received urgent calls for help from LTC and residential care facilities; between March and early June of 2020, policymakers responded with dozens of regulatory and policy changes. In this article, we provide an overview of these responses by first summarizing federal regulatory changes and then reviewing state-level executive orders. The policy and regulatory changes implemented at the federal and state levels can be categorized into the following 4 classes: (1) preventing virus transmission, which includes policies relating to visitation restrictions, personal protective equipment guidance, and testing requirements; (2) expanding facilities' capacities, which includes both the expansion of physical space for isolation purposes and the expansion of workforce to combat COVID-19; (3) relaxing administrative requirements, which includes measures enacted to shift the attention of caretakers and administrators from administrative requirements to residents' care; and (4) reporting COVID-19 data, which includes the reporting of cases and deaths to residents, families, and administrative bodies (such as state health departments). These policies represent a snapshot of the initial efforts to mitigate damage inflicted by the pandemic. Looking ahead, empirical evaluation of the consequences of these policies-including potential unintended effects-is urgently needed. The recent availability of publicly reported COVID-19 LTC data can be used to inform the development of evidence-based regulations, though there are concerns of reporting inaccuracies. Importantly, these data should also be used to systematically identify hot spots and help direct resources to struggling facilities.