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Introduction: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients. Objectives: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures. Methods: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell’s C-statistic, respectively. Results: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42–3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08–2.74), 6-month mortality (HR = 2.29; 2.04–2.56), and a longer length of stay (β-coefficient = 2.00; 1.65–2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell’s C = 0.733), and 6-month mortality (Harrell’s C = 0.719). Conclusion: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

ImportancePrevious reports have suggested reductions in mortality risk from COVID-19 throughout the first wave of the COVID-19 pandemic. Mortality changes later in the pandemic and pandemic effects on other types of geriatric hospitalizations are less studied.ObjectivesTo describe the changes in hospitalizations and 30-day mortality in Stockholm for patients 70+ receiving inpatient geriatric care for COVID-19 and other causes.DesignObservational study. For patients 70 or older, we present the incidence of 30-day mortality from COVID-19 in the Stockholm region, in relationship to geriatric hospitalizations and 30-day mortality after admission for COVID-19 and other causes.SettingHospitalizations for patients 70+ from geriatric clinics in Stockholm, Sweden hospitalized for COVID-19 or other causes between March 2020 and July 31, 2021, were included.ParticipantsThe total number of geriatric hospitalizations for patients 70+ was 5,320 for COVID-19 and 32,243 for non-COVID-19 causes, corresponding to 4,565 individual COVID-19 patients and 19,308 non-COVID-19 patients.Exposure(s)The date of hospital admission to a geriatric clinic. Main Outcome(s) and Measure(s): 30-day mortality after admission.ResultsIn patients with COVID-19, the 30-day mortality rate was highest at the beginning of the first wave (29% in March-April 2020), decreased as the first wave subsided (7% July-August), increased again in the second wave (17% November-December), but failed to increase as much in the third wave (11-13% March-July 2021). In non-COVID-19 geriatric patients during the same period, the 30-day mortality presented a similar trend, but with a smaller magnitude of variation (5 to 10%). The number of persons 70 or older testing positive for COVID-19 in Stockholm reached two peaks in 2020 (April and December), fell in January 2021 and then increased again in March-April 2021.Conclusions and RelevanceDuring the first and second waves, hospital admissions and 30-day mortality after geriatric hospitalization for COVID-19 increased in periods of high community transmission, although the mortality peak was lower in wave 2 than in wave 1. The mortality for non-COVID geriatric cases was lower and more stable but also showed an increase with the pandemic peaks.KEY POINTSQuestionMultiple previous reports in different countries and settings have shown higher case fatality ratio or hospitalized case fatality ratio for COVID-19 in the first wave compared to the second wave of the pandemic. However, less is known about how the COVID-19 waves specifically affected the care of geriatric patients, including those with conditions other than COVID.FindingsThe total number of hospitalizations was 5,320 for COVID-19 and 32,243 for non-COVID-cases. In COVID-patients, the 30-day mortality rate was highest at the beginning of the first wave (29% in March-April 2020), reached 17% at the second wave peak (November-December) followed by 11-13% in the third wave (March-July 2021). The mortality in non-COVID geriatric patients showed a similar trend, but of lower magnitude (5-10%). During the incidence peaks, COVID-19 hospitalizations displaced non-COVID geriatric patients.MeaningHospital admissions and 30-day mortality after hospitalizations for COVID-19 increased in periods of high community transmission, albeit with decreasing mortality rates from wave 1 to 3, with a possible vaccination effect in wave 3. Thus, the healthcare system could not compensate for the high community spread of COVID-19 during the pandemic peaks, which also led to displacing care for non-COVID geriatric patients. These results are important for planning healthcare resources in future health emergencies.

BackgroundThe Clinical Frailty Scale (CFS) is a strong predictor for worse outcomes in geriatric COVID-19 patients, but it is less clear whether an electronic frailty index (eFI) constructed from routinely collected electronic health records (EHRs) provides similar predictive value. This study aimed to investigate the predictive ability of an eFI in comparison to other frailty and comorbidity measures, using mortality, readmission, and the length of stay as outcomes in geriatric COVID-19 patients.MethodsWe conducted a retrospective cohort study using EHRs from nine geriatric clinics in Stockholm, Sweden, comprising 3,405 COVID-19 patients (mean age 81.9 years) between 1/3/2020 and 31/10/2021. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the CFS, and Hospital Frailty Risk Score (HFRS). Comorbidity was measured using the Charlson Comorbidity Index (CCI). We analyzed in-hospital mortality and 30-day readmission using logistic regression and area under receiver operating characteristic curve (AUC). 30-day and 6-month mortality were modelled by Cox regression, and the length of stay by linear regression.ResultsControlling for age and sex, a 10% increase in the eFI was associated with higher risks of in-hospital mortality (odds ratio [OR]=2.84; 95% confidence interval=2.31-3.51), 30-day mortality (hazard ratio [HR]=2.30; 1.99-2.65), 6-month mortality (HR=2.33; 2.07-2.62), 30-day readmission (OR=1.34; 1.06-1.68), and longer length of stay (β=2.28; 1.90-2.66).The CFS, HFRS and CCI similarly predicted these outcomes, but the eFI had the best predictive accuracy for in-hospital mortality (AUC=0.775).ConclusionsAn eFI based on routinely collected EHRs can be applied in identifying high-risk geriatric COVID-19 patients.

OBJECTIVE: To describe temporal changes in treatment, care, and short-term mortality outcomes of geriatric patients during the first wave of the COVID-19 pandemic. DESIGN: Observational study. SETTING AND PARTICIPANTS: Altogether 1785 patients diagnosed with COVID-19 and 6744 hospitalized for non-COVID-19 causes at 7 geriatric clinics in Stockholm from March 6 to July 31, 2020, were included. METHODS: Across admission month, patient vital signs and pharmacological treatment in relationship to risk for in-hospital death were analyzed using the Poisson regression model. Incidence rates (IRs) and incidence rate ratios (IRRs) of death are presented. RESULTS: In patients with COVID-19, the IR of mortality were 27%, 17%, 10%, 8%, and 2% from March to July, respectively, after standardization for demographics and vital signs. Compared with patients admitted in March, the risk of in-hospital death decreased by 29% [IRR 0.71, 95% confidence interval (CI) 0.51-0.99] in April, 61% (0.39, 0.26-0.58) in May, 68% (0.32, 0.19-0.55) in June, and 86% (0.14, 0.03-0.58) in July. The proportion of patients admitted for geriatric care with oxygen saturation

This study monitored the stormwater runoff quantity and quality treatment performance of a 6.8 ha 19-year old combined pond-wetland system, located in south Sweden, over one year. The mean volume reductions for 53 storm events for the pond and wetland were 40% and 28%, respectively, while the mean flow reductions were 60% and 76%, respectively. Pollutant concentrations in the influent to the wetland were highly variable. The pond-wetland system could efficiently remove an average of 91%, 80%, 94%, 91%, 83% and 92% of TSS, TP, particulate Cd, Cu, Pb, and Zn, respectively, whereas the removal of particulate and dissolved Ni was highly variable with an average of 67% ± 62% and −5% ± 41%, respectively. The removal of TN, NH4-N and NO3 + NO2-N was highly variable with an average of 45% ± 27%, 12% ± 96% and 45% ± 43%, respectively. These removal percentages are high in comparison to other studies and underline that relatively old systems can also provide efficient treatment. Although the pond accounted for a substantial reduction of pollutant concentration, the wetland significantly enhanced both the treatment performance and the peak flow reduction. This underlines that a combined pond/wetland system is a more beneficial solution than a pond only. The pollutant removal efficiency was significantly influenced by some factors including Antecedent Dry Days, seasonal variations, air temperature, retention times, rainfall depth and duration, and peak rainfall intensity.

Constructed stormwater wetlands (CSWs) are a commonly used measure for stormwater retention and quality treatment. However, although questions have been raised about the long-term performance of CSWs, only a few studies have targeted this issue and none have evaluated the performance of CSWs more than approximately 5–10 years old. Further, most studies have not examined the development of the long-term performance of CSWs but delivered a snapshot at a certain point of time. The present study investigated the performance of a 19-year-old CSW in Vaxjo, Sweden, treating stormwater from a 320-ha urban catchment. Besides removal of sediment from the CSW’s forebay, no other maintenance had been conducted. However, regular inspections had been performed. The results of the present sampling campaign were compared to two existing datasets collected at the same CSW after three years of operation in 1997 and nine years of operation in 2003. The CSW was found to provide efficient peak flow reduction and, depending on the event characteristics, also volume reduction. It still treated stormwater effectively: removal of Cd, Cu, Pb, Zn, TSS and TP event mean concentrations were between 89 and 96%, whereas mean concentrations of TN were reduced by 59%. The load removal efficiencies were even higher. Comparative analysis of the three monitoring periods based on the load removal efficiency revealed that the CSW, despite the lack of maintenance, performed more efficiently and stably for most pollutants compared to when newly constructed. This underlines the importance of the establishment and maturation of constructed wetland systems. Overall, the results showed that CSWs are resilient systems, which if designed well and regularly inspected to prevent major issues, can work efficiently for at least two decades.

The implied volatilities for Swedish equity options show a U-shaped smile pattern when averaged within groups according to the moneyness of the options. This makes the use of at-the-money implied volatilities for valuation of out-of- or in-the-money options questionable. Using at-the-money implied volatilities as benchmark it is, therefore, investigated whether some kind of implied volatility function could lead to more accurate model values. Although the benchmark model performs best for at-the-money options, the use of a volatility function could clearly improve the valuation accuracy for deep in-the-money options. However, no model works very well for options of all moneyness levels.

Using Swedish equity option data, this study investigates how well the actual exercise behavior of American put options corresponds to the early exercise rules. The optimal exercise strategy is est ...