The dialysis facility levels and sizes are associated with outcomes of incident hemodialysis patients

The outcomes of patients with incident kidney failure who start hemodialysis are influenced by several factors. Whether hemodialysis facility characteristics are associated with patient outcomes is unclear. We included adults diagnosed as having kidney failure requiring hemodialysis during January 1, 2001 to December 31, 2013 from the Taiwan National Health Insurance Research Database to perform this retrospective cohort study. The exposures included different sizes and levels of hemodialysis facilities. The outcomes were all-cause mortality, cardiovascular death, infection-related death, hospitalization, and kidney transplantation. During 2001–2013, we identified 74,406 patients and divided them in to three groups according to the facilities where they receive hemodialysis: medical center (n = 8263), non-center hospital (n = 40,008), and clinic (n = 26,135). The multivariable Cox model demonstrated that a larger facility size was associated with a low mortality risk (hazard ratio [HR] 0.991, 95% confidence interval [95% CI] 0.984–0.998; every 20 beds per facility). Compared with medical centers, patients in non-center hospitals and clinics had higher mortality risks (HR 1.13, 95% CI 1.09–1.17 and HR 1.11, 95% CI 1.06–1.15, respectively). Patients in medical centers and non-center hospitals had higher risk of hospitalization (subdistribution HR [SHR] 1.11, 95% CI 1.10–1.12 and SHR 1.22, 95% CI 1.21–1.23, respectively). Patients in medical centers had the highest rate of kidney transplantation among the three groups. In patients with incident kidney failure, a larger hemodialysis facility size was associated with lower mortality. Overall, medical center patients had a lower mortality rate and higher transplantation rate, whereas clinic patients had a lower hospitalization risk.

The Impact of Organisational Characteristics of Staff and Facility on Infectious Disease Outbreaks in Care Homes: a Systematic Review

Background: Infectious disease outbreaks are common in care homes, often with substantial impact on the rates of infection and mortality of the residents, who primarily are older people vulnerable to infections. There is growing evidence that organisational characteristics of staff and facility might play a role in infection outbreaks however such evidence have not previously been systematically reviewed. Therefore, this systematic review aims to examine the impact of facility and staff characteristics on the risk of infectious disease outbreaks in care homes.Methods: Five databases were searched. Studies considered for inclusion were of any design reporting on an outbreak of any infectious disease in one or more care homes providing care for primarily older people with original data on: facility size, facility location (urban/rural), facility design, use of temporary hired staff, staff compartmentalizing, residence of staff, and/or nursing aides hours per resident. Retrieved studies were screened, assessed for quality, and analysed employing a narrative synthesis.Results: Sixteen studies (8 cohort studies, 6 cross-sectional studies, 2 case-control) were included from the search which generated 10,424 unique records. COVID-19 was the most commonly reported cause of outbreak (n=11). The other studies focused on influenza, respiratory and gastrointestinal outbreaks. Most studies reported on the impact of facility size (n=11) followed by facility design (n=4), use of temporary hired staff (n=3), facility location (n=2), staff compartmentalizing (n=2), nurse aides hours (n=2) and residence of staff (n=1). Findings suggest that urban location and larger facility size may be associated with greater risks of an infectious outbreak. Additionally, the risk of a larger outbreak seems lower in larger facilities. Whilst staff compartmentalizing may be associated with lower risk of an outbreak, staff residing in highly infected areas may be associated with greater risk of outbreak. The influence of facility design, use of temporary staff, and nurse aides hours remains unclear.Conclusions: This systematic review suggests that larger facilities have greater risks of infectious outbreaks, yet the risk of a larger outbreak seems lower in larger facilities. Due to lack of robust findings the impact of facility and staff characteristics on infectious outbreaks remain largely unknown.PROSPERO: CRD42020213585

The Interplay Between Infection Risk Factors of SARS-CoV-2 and Prognosis: A Cross-Sectional Study from a Cohort of Long-Term Care Nursing Home Residents

Background: Covid-19 pandemic has particularly affected older people living in Long-term Care settings. Methods: We carried out a cross-sectional analysis of a cohort of Long-term care nursing home residents between March first and June thirty, 2020, who were ≥ 65 years old and on whom at last one PCR test was performed. Socio-demographic, comorbidities, and clinical data were recorded. Facility size and community incidence of SARS-CoV-2 were also considered.Results: A total of 8021 participants were included from 168 facilities. Mean age was 86.4 years (SD = 7.4). Women represented 74.1%. SARS-CoV-2 infection was detected in 27.7% of participants, and the overall case fatality rate was 11.3% (24.9% among those with a positive PCR test). Epidemiological factors related to risk of infection were larger facility size (pooled aOR 1.73; P < .001), higher community incidence (pooled aOR 1.67, P = .04), leading to a higher risk than the clinical factor of low level of functional dependence (aOR 1.22, P = 0.03). Epidemiological risk factors associated with mortality were male gender (aOR 1.75; P < .001), age (pooled aOR 1.16; P < .001), and higher community incidence (pooled aOR 1.19, P = < .001). There was evidence of clustering for facility and health area when considering the risk of infection and mortality (P < .001). Conclusions: Our results suggest a complex interplay between structural and individual factors regarding Covid-19 infection and its impact on mortality in nursing-home residents.

Facility Characteristics and Quality Deficiencies of Florida Nursing Homes With COVID-19 Cases and Deaths

The Coronavirus disease 2019 (COVID-19) has been disproportionately affecting nursing homes throughout the United States, resulting elevated risk for COVID-19 morbidity and mortality to nursing home residents. Given the high percentage of aging population, large number of nursing homes, and staggering surge of COVID-19 cases in Florida, it’s critical to understand factors that may affect Florida nursing homes’ vulnerability to the COVID-19 pandemic. Using Nursing Home COVID-19 Dataset as of July 26, 2020 obtained through Centers for Medicare and Medicaid Services (CMS), and Provider Info Dataset and Health Deficiencies Dataset available through CMS Nursing Home Compare data, we constructed a database of Florida nursing facilities with confirmed COVID-19 cases and deaths, with corresponding facility characteristics and quality deficiencies. We examined the facility characteristics (e.g. facility size, ownership state, chain affiliation, staffing level) and quality deficiencies (e.g. infection control deficiencies) of Florida nursing homes with and without publicly reported COVID-19 cases and deaths. Results indicated that, as of July 26, 2020, 73.3% and 40.8% of Florida nursing homes had resident COVID-19 cases and death, respectively (N=701). Findings also suggested that Florida nursing homes of large facility size, chain affiliated, and for profit, were significantly more likely to have documented resident COVID-19 cases (p&lt;.05). Larger facility size (120 beds or more), staff shortage, and having prior infection control deficiency citation, were significantly related to the odds of having resident COVID-19 deaths (p&lt;.05). Policy and practice implications and future research directions will be addressed to better protect the at-risk nursing home residents.

Modeling the frequency and number of persons to test to detect and control COVID-19 outbreaks in congregate settings

BackgroundCongregate settings are at risk for coronavirus disease 2019 (COVID-19) outbreaks. Diagnostic testing can be used as a tool in these settings to identify outbreaks and to control transmission.MethodsWe used transmission modeling to estimate the minimum number of persons to test and the optimal frequency to detect small outbreaks of COVID-19 in a congregate facility. We also estimated the frequency of testing needed to interrupt transmission within a facility.ResultsThe number of people to test and frequency of testing needed depended on turnaround time, facility size, and test characteristics. Parameters are calculated for a variety of scenarios. In a facility of 100 people, 26 randomly selected individuals would need to be tested at least every 6 days to identify a true underlying prevalence of at least 5%, with test sensitivity of 85%, and greater than 95% outbreak detection sensitivity. Disease transmission could be interrupted with universal, facility-wide testing with rapid turnaround every three days.ConclusionsTesting a subset of individuals in congregate settings can improve early detection of small outbreaks of COVID-19. Frequent universal diagnostic testing can be used to interrupt transmission within a facility, but its efficacy is reliant on rapid turnaround of results for isolation of infected individuals.

Lindahl vs. Lindahl: Optimal siting and sizing of a noxious facility

The provision of projects generating net benefits for several communities except for the host community poses two problems: where to locate the unpleasant facility, and how large this facility should be. We propose a mechanism that combines some market-like properties with a modified second-price auction. We elicit prices per unit as a host and as a contributor to the facility, the desired quantity (i.e., facility size), and an auction's bid defining the hosting community. Regardless of whom is selected as the host, any equilibrium outcome of this mechanism is a Lindahl allocation. If, in addition, every community truthfully reveals its gain from becoming the host (with respect to being a contributor), the selected Lindahl allocation is globally optimal.

Unpacking the eagle collision risk model: practical guidance for wind energy

ABSTRACTClimate change is one of the greatest threats facing biodiversity, and solutions to reduce carbon emissions are needed to conserve species. Renewable energies are a prominent means to achieve this goal, but the potential for direct harm to wildlife has raised concerns as these technologies proliferate. To protect biodiversity, approaches that facilitate renewable energy development while protecting species are needed. In the United States wind energy developers must obtain a permit for any Bald or Golden eagles that might be killed at a facility. The U.S. Fish & Wildlife Service estimates fatalities using a Bayesian modeling framework, which combines pre-construction eagle surveys with prior information. The ways in which prior information is incorporated and how pre-construction monitoring affects model outcomes can be unclear to regulated entities and other stakeholders, creating uncertainty in the permitting process and retarding both the build-out of renewable energy and conservation measures for eagles. We conducted a simulation study quantifying the differences in predicted eagle fatalities obtained by incorporating prior information and using only site-specific survey data across a range of scenarios, evaluating the impact of survey effort on the magnitude of this effect. We identified predictable relationships between survey effort, eagle activity, facility size and discrepancies between estimates. We also translated these patterns into real-world financial costs, illustrating the interaction between pre-construction surveys, fatality estimates, and compensatory mitigation obligations in determining permit timing and expense.

P0822ESTIMATED GFR DECLINE OF PKD PATIENTS IN CKD G3B-5 WAS AS FAST AS THAT OF DKD PATIENTS: A RESULT FROM A JAPANESE COHORT STUDY FOR PATIENTS WITH ADVANCED CKD, THE REACH-J STUDY

Background and Aims CKD progression in Japanese patients with advanced chronic kidney disease (CKD)—an estimated glomerular filtration rate (eGFR) &lt;45 ml/min/1.73m2—has remained largely unexamined. Method We conducted a nationwide cohort study of Japanese patients with advanced CKD. We recruited 2,249 advanced CKD patients (eGFR&lt;45/ml/min/1.73m2) receiving nephrologist care from a national sample of 31 facilities throughout Japan, randomly selected with stratification by region and facility size, aligned with the international CKD Outcomes and Practice Patterns Study (CKDopps). From baseline data, we calculated annual eGFR decline by CKD stage and causes of CKD over 4 years before enrollment. Variability of eGFR decline was calculated from standard error of the regression. Results The reported causes of CKD were 552(25%) had diabetic kidney diseases (DKD), 131(6%) had PKD, 591(26%) had nephrosclerosis, 299(13%) had glomerulonephritis, and 676(30%) had other renal diseases. Of 1939 eligible patients with eGFR data more than two years, median (IQR) annual eGFR declines (ml/min/1.73m2/year) in PKD and DKD patients were 2.30 (1.16, 3.38) and 1.18 (0.23, 3.69) in G3b, 2.60 (1.81, 3.40) and 1.97 (0.20, 4.75) in G4, and 4.00 (2.00, 5.60) and 3.94 (2.05, 7.05) in G5, respectively. These eGFR declines were significantly faster than those of other kidney diseases. On the other hand, the variability of the decline in PKD patients was significantly smaller than that of DKD patients (0.43 vs 0.71, p&lt;0.001). This trend was consistent in all CKD stages. Conclusion Our study clarified that, similar to DKD patients, annual eGFR decline of PKD patients was significantly faster than those of other kidney diseases throughout all stages. Furthermore, the variability of the decline in PKD patients was smaller than those of others. These data suggest that comprehensive nephrology care should be needed especially for these patients.