Early Acceleration of COVID-19 in Areas with Larger Nursing Homes and Certificate of Need Laws

Nursing Home Residents' COVID-19 Infections in the United States: A Systematic Review of Personal and Contextual Factors

Background: This mixed methods systemic review synthesizes the evidence about nursing home risks for COVID-19 infections. Methods: Four electronic databases (PubMed, Web of Science, Scopus, and Sage Journals Online) were searched between January 2020 and October 2022. Inclusion criteria were studies reported on nursing home COVID-19 infection risks by geography, demography, type of nursing home, staffing and resident's health, and COVID-19 vaccination status. The Mixed Methods Appraisal Tool (MMAT) was used to assess the levels of evidence for quality, and a narrative synthesis for reporting the findings by theme. Results: Of 579 initial articles, 48 were included in the review. Findings suggest that highly populated counties and urban locations had a higher likelihood of COVID-19 infections. Larger nursing homes with a low percentage of fully vaccinated residents also had increased risks for COVID-19 infections than smaller nursing homes. Residents with advanced age, of racial minority, and those with chronic illnesses were at higher risk for COVID-19 infections. Discussion and implications: Findings suggest that along with known risk factors for COVID-19 infections, geographic and resident demographics are also important preventive care considerations. Access to COVID-19 vaccinations for vulnerable residents should be a priority.

Influenza cases in nine aged care facilities in Sydney, Australia over a three-year surveillance period, 2018-2020

BACKGROUND

Influenza outbreaks in aged care facilities are a major public health concern. In response to the severe 2017 influenza season in Australia, enhanced influenza vaccines were introduced from 2018 onwards for those over 65 and more emphasis was placed on improving vaccination rates among aged care staff. During the COVID-19 pandemic, these efforts were then further escalated to reduce the additional burden that influenza could pose to facilities.

METHODS

An observational epidemiological study was conducted from 2018 to 2020 in nine Sydney (Australia) aged care facilities of the same provider. De-identified vaccination data and physical layout data were collected from participating facility managers from 2018 to 2020. Active surveillance of influenza-like illness was carried out from 2018 to 2020 influenza seasons. Correlation and Poisson regression analyses were carried out to explore the relationship between physical layout variables to occurrence of influenza cases.

RESULTS

Influenza cases were low in 2018 and 2019, and there were no confirmed influenza cases identified in 2020. Vaccination rates increased among staff by 50.5% and residents by 16.8% over the three-year period of surveillance from 2018 to 2020. For each unit increase in total number of beds, common areas, single rooms, all types of rooms (including double occupancy rooms), the influenza cases increased by 1.02 (95% confidence interval:1.018-1.025), 1.04 (95% confidence interval: 1.019-1.073), 1.03 (95% confidence interval: 1.016-1 0.038) and 1.02 (95% confidence interval:1.005-1.026) times which were found to be statistically significant. For each unit increase in the proportion of shared rooms, influenza cases increased by 1.004 (95% confidence interval:1.0001-1.207) which was found to be statistically significant.

CONCLUSIONS

There is a relationship between influenza case counts and aspects of the physical layout such as facility size, and this should be considered in assessing risk of outbreaks in aged care facilities. Increased vaccination rates in staff and COVID-19 prevention and control measures may have eliminated influenza in the studied facilities in 2020.

Spatial Risk Factors for Pillar 1 COVID-19 Excess Cases and Mortality in Rural Eastern England, UK

Understanding is still developing about spatial risk factors for COVID-19 infection or mortality. This is a secondary analysis of patient records in a confined area of eastern England, covering persons who tested positive for SARS-CoV-2 through end May 2020, including dates of death and residence area. We obtained residence area data on air quality, deprivation levels, care home bed capacity, age distribution, rurality, access to employment centers, and population density. We considered these covariates as risk factors for excess cases and excess deaths in the 28 days after confirmation of positive Covid status relative to the overall case load and death recorded for the study area as a whole. We used the conditional autoregressive Besag-York-Mollie model to investigate the spatial dependency of cases and deaths allowing for a Poisson error structure. Structural equation models were applied to clarify relationships between predictors and outcomes. Excess case counts or excess deaths were both predicted by the percentage of population age 65 years, care home bed capacity and less rurality: older population and more urban areas saw excess cases. Greater deprivation did not correlate with excess case counts but was significantly linked to higher mortality rates after infection. Neither excess cases nor excess deaths were predicted by population density, travel time to local employment centers, or air quality indicators. Only 66% of mortality was explained by locally high case counts. Higher deprivation clearly linked to higher COVID-19 mortality separate from wider community prevalence and other spatial risk factors.

Spatial Risk Factors for Pillar 1 COVID-19 Excess Cases and Mortality in Rural Eastern England, UK

Understanding is still developing about spatial risk factors for COVID-19 infection or mortality. This is a secondary analysis of patient records in a confined area of eastern England, covering persons who tested positive for SARS-CoV-2 through end May 2020, including dates of death and residence area. We obtained residence area data on air quality, deprivation levels, care home bed capacity, age distribution, rurality, access to employment centers, and population density. We considered these covariates as risk factors for excess cases and excess deaths in the 28 days after confirmation of positive Covid status relative to the overall case load and death recorded for the study area as a whole. We used the conditional autoregressive Besag-York-Mollie model to investigate the spatial dependency of cases and deaths allowing for a Poisson error structure. Structural equation models were applied to clarify relationships between predictors and outcomes. Excess case counts or excess deaths were both predicted by the percentage of population age 65 years, care home bed capacity and less rurality: older population and more urban areas saw excess cases. Greater deprivation did not correlate with excess case counts but was significantly linked to higher mortality rates after infection. Neither excess cases nor excess deaths were predicted by population density, travel time to local employment centers, or air quality indicators. Only 66% of mortality was explained by locally high case counts. Higher deprivation clearly linked to higher COVID-19 mortality separate from wider community prevalence and other spatial risk factors.

Factors associated with SARS-COV-2 attack rates in aged care– a meta-analysis

Background

The coronavirus disease 2019 (COVID-19) pandemic has resulted in significant morbidity and mortality in aged-care facilities worldwide. The attention of infection control in aged care needs to shift towards the built environment, especially in relation to using the existing space to allow social distancing and isolation. Physical infrastructure of aged care facilities has been shown to present challenges to the implementation of isolation procedures. To explore the relationship of the physical layout of aged care facilities with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attack rates among residents, a meta-analysis was conducted.

Methods

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol (PRISMA-P), studies were identified from 5 databases using a registered search strategy with PROSPERO. Meta-analysis for pooled attack rates of SARS-CoV-2 in residents and staff was conducted, with subgroup analysis for physical layout variables such as total number of beds, single rooms, number of floors, number of buildings in the facility, and staff per 100 beds.

Results

We included 41 articles across 11 countries, reporting on 90 657 residents and 6521 staff in 757 facilities. The overall pooled attack rate was 42.0% among residents (95% CI, 38.0%–47.0%) and 21.7% in staff (95% CI, 15.0%–28.4%). Attack rates in residents were significantly higher in single-site facilities with standalone buildings than facilities with smaller, detached buildings. Staff-to-bed ratio significantly explains some of the heterogeneity of the attack rate between studies.

Conclusions

The design of aged care facilities should be smaller in size, with adequate space for social distancing.

SARS-CoV-2 and Wisconsin Nursing Homes: Temporal Dynamics During the COVID-19 Pandemic

OBJECTIVES

Evidence suggests that quality, location, and staffing levels may be associated with COVID-19 incidence in nursing homes. However, it is unknown if these relationships remain constant over time. We describe incidence rates of COVID-19 across Wisconsin nursing homes while examining factors associated with their trajectory during 5 months of the pandemic.

DESIGN

Retrospective cohort study.

SETTING/PARTICIPANTS

Wisconsin nursing homes.

METHODS

Publicly available data from June 1, 2020, to October 31, 2020, were obtained. These included facility size, staffing, 5-star Medicare rating score, and components. Nursing home characteristics were compared using Pearson chi-square and Kruskal-Wallis tests. Multiple linear regressions were used to evaluate the effect of rurality on COVID-19.

RESULTS

There were a total of 2459 COVID-19 cases across 246 Wisconsin nursing homes. Number of beds (P < .001), average count of residents per day (P < .001), and governmental ownership (P = .014) were associated with a higher number of COVID-19 cases. Temporal analysis showed that the highest incidence rates of COVID-19 were observed in October 2020 (30.33 cases per 10,000 nursing home occupied-bed days, respectively). Urban nursing homes experienced higher incidence rates until September 2020; then incidence rates among rural nursing homes surged. In the first half of the study period, nursing homes with lower-quality scores (1-3 stars) had higher COVID-19 incidence rates. However, since August 2020, incidence was highest among nursing homes with higher-quality scores (4 or 5 stars). Multivariate analysis indicated that over time rural location was associated with increased incidence of COVID-19 (β = 0.05, P = .03).

CONCLUSIONS AND IMPLICATIONS

Higher COVID-19 incidence rates were first observed in large, urban nursing homes with low-quality rating. By October 2020, the disease had spread to rural and smaller nursing homes and those with higher-quality ratings, suggesting that community transmission of SARS-CoV-2 may have propelled its spread.

Impact of COVID-19 on older adults and role of long-term care facilities during early stages of epidemic in Italy

Older adults are the main victims of the novel COVID-19 coronavirus outbreak and elderly in Long Term Care Facilities (LTCFs) are severely hit in terms of mortality. This paper presents a quantitative study of the impact of COVID-19 outbreak in Italy during first stages of the epidemic, focusing on the effects on mortality increase among older adults over 80 and its correlation with LTCFs. The study of growth patterns shows a power-law scaling regime for the first stage of the pandemic with an uneven behaviour among different regions as well as for the overall mortality increase according to the different impact of COVID-19. However, COVID-19 incidence rate does not fully explain the differences of mortality impact in older adults among different regions. We define a quantitative correlation between mortality in older adults and the number of people in LTCFs confirming the tremendous impact of COVID-19 on LTCFs. In addition a correlation between LTCFs and undiagnosed cases as well as effects of health system dysfunction is also observed. Our results confirm that LTCFs did not play a protective role on older adults during the pandemic, but the higher the number of elderly people living in LTCFs the greater the increase of both general and COVID-19 related mortality. We also observed that the handling of the crises in LTCFs hampered an efficient tracing of COVID-19 spread and promoted the increase of deaths not directly attributed to SARS-CoV-2.