Background The impact of COVID-19 on pre-hospital and hospital services and hence on the prevalence and outcomes of out-of-hospital cardiac arrests (OHCA) remain unclear. The review aimed to evaluate the influence of the COVID-19 pandemic on the incidence, process, and outcomes of OHCA. Methods A systematic review of PubMed, EMBASE, and pre-print websites was performed. Studies reporting comparative data on OHCA within the same jurisdiction, before and during the COVID-19 pandemic were included. Study quality was assessed based on the Newcastle-Ottawa Scale. Results Ten studies reporting data from 35,379 OHCA events were included. There was a 120% increase in OHCA events since the pandemic. Time from OHCA to ambulance arrival was longer during the pandemic (p = 0.036). While mortality (OR = 0.67, 95%-CI 0.49-0.91) and supraglottic airway use (OR = 0.36, 95%-CI 0.27-0.46) was higher during the pandemic, automated external defibrillator use (OR = 1.78 95%-CI 1.06-2.98), return of spontaneous circulation (OR = 1.63, 95%-CI 1.18-2.26) and intubation (OR = 1.87, 95%-CI 1.12-3.13) was more common before the pandemic. More patients survived to hospital admission (OR = 1.75, 95%-CI 1.42-2.17) and discharge (OR = 1.65, 95%-CI 1.28-2.12) before the pandemic. Bystander CPR (OR = 1.08, 95%-CI 0.86-1.35), unwitnessed OHCA (OR = 0.84, 95%-CI 0.66-1.07), paramedic-resuscitation attempts (OR = 1.19 95%-CI 1.00-1.42) and mechanical CPR device use (OR = 1.57 95%-CI 0.55-4.55) did not defer significantly. Conclusions The incidence and mortality following OHCA was higher during the COVID-19 pandemic. There were significant variations in resuscitation practices during the pandemic. Research to define optimal processes of pre-hospital care during a pandemic is urgently required. Review registration PROSPERO (CRD42020203371).

No abstract

Applications of exponential smoothing to forecast time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to select the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected exponential smoothing: for sub-annual series it is the seasonal adaptation of trend corrected exponential smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with exponential smoothing to estimate the smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approach appears to provide the best basis for an automated approach to method selection, provided that it is based on Akaike's information criterion.

Despite interventions being mostly successful in retarding progression to overt diabetes, this did not result in reductions in all-cause or cardiovascular mortality, or myocardial infarction, with the possible exception of stroke.

No abstract