scholarly journals The Impact of the COVID-19 Epidemic on Patterns of Attendance at Emergency Departments in Two Large London Hospitals: An Observational Study

2020 ◽  
Author(s):  
Michaela A C Vollmer ◽  
Sreejith Radhakrishnan ◽  
Mara D Kont ◽  
Seth Flaxman ◽  
Samir J Bhatt ◽  
...  
Keyword(s):  
Emergency Services ◽  
Moving Average ◽  
Considerable Change ◽  
National Data ◽  
Emergency Admissions ◽  
Health Measure ◽  
Crude Mortality ◽  
Auto Regressive ◽  
The Impact ◽  
Emergency Health Services

Abstract Background Hospitals in England have undergone considerable change to address the surge in demand imposed by the COVID-19 epidemic. The impact of this on emergency department (ED) attendances is unknown, especially for non-COVID-19 related emergencies. Methods We calibrated auto-regressive integrated moving average time-series models of ED attendances to Imperial College Healthcare NHS Trust (ICHNT) using historic (2015–2019) data. Forecasted trends were compared to present year ICHNT data for the period between March 12 (when England implemented the first COVID-19 public health measure) and May 31. We compared ICHTN trends with publicly available regional and national data. Lastly, we compared emergency admissions and in-hospital mortality at ICHNT during the present year to a historic 5-year average. Results ED attendances at ICHNT decreased by 35%, in keeping with the trend for ED attendances across all England regions, which fell by approximately 50%. For ICHNT, the decrease in attendances was mainly amongst those aged < 65 years and those arriving by their own means (e.g. personal or public transport). Increasing distance from postcode of residence to hospital was a significant predictor of reduced attendances. Non-COVID related emergency admissions to hospital after March 12 fell by 48%; there was an indication of a non-significant increase in non-COVID-19 crude mortality risk (RR 1.13, 95%CI 0.94–1.37, p = 0.19). Conclusions Our study finds strong evidence that emergency healthcare seeking has drastically changed across the population in England. At ICHNT, we find that a larger proportion arrived by ambulance and that hospitalisation outcomes of non-COVID patients did not differ from previous years. The extent to which these findings relate to ED avoidance behaviours compared to having sought alternative emergency health services outside of hospital remains unknown. National analyses and strategies to streamline emergency services in England going forward are urgently needed.

scholarly journals The impact of the COVID-19 pandemic on patterns of attendance at emergency departments in two large London hospitals: an observational study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michaela A. C. Vollmer ◽  
Sreejith Radhakrishnan ◽  
Mara D. Kont ◽  
Seth Flaxman ◽  
Samir Bhatt ◽  
...  
Keyword(s):  
Observational Study ◽  
Emergency Services ◽  
Hospital Admissions ◽  
Moving Average ◽  
Time Frame ◽  
Considerable Change ◽  
Crude Mortality ◽  
Spatial Dependencies ◽  
Auto Regressive ◽  
The Impact

Abstract Background Hospitals in England have undergone considerable change to address the surge in demand imposed by the COVID-19 pandemic. The impact of this on emergency department (ED) attendances is unknown, especially for non-COVID-19 related emergencies. Methods This analysis is an observational study of ED attendances at the Imperial College Healthcare NHS Trust (ICHNT). We calibrated auto-regressive integrated moving average time-series models of ED attendances using historic (2015–2019) data. Forecasted trends were compared to present year ICHNT data for the period between March 12, 2020 (when England implemented the first COVID-19 public health measure) and May 31, 2020. We compared ICHTN trends with publicly available regional and national data. Lastly, we compared hospital admissions made via the ED and in-hospital mortality at ICHNT during the present year to the historic 5-year average. Results ED attendances at ICHNT decreased by 35% during the period after the first lockdown was imposed on March 12, 2020 and before May 31, 2020, reflecting broader trends seen for ED attendances across all England regions, which fell by approximately 50% for the same time frame. For ICHNT, the decrease in attendances was mainly amongst those aged < 65 years and those arriving by their own means (e.g. personal or public transport) and not correlated with any of the spatial dependencies analysed such as increasing distance from postcode of residence to the hospital. Emergency admissions of patients without COVID-19 after March 12, 2020 fell by 48%; we did not observe a significant change to the crude mortality risk in patients without COVID-19 (RR 1.13, 95%CI 0.94–1.37, p = 0.19). Conclusions Our study findings reflect broader trends seen across England and give an indication how emergency healthcare seeking has drastically changed. At ICHNT, we find that a larger proportion arrived by ambulance and that hospitalisation outcomes of patients without COVID-19 did not differ from previous years. The extent to which these findings relate to ED avoidance behaviours compared to having sought alternative emergency health services outside of hospital remains unknown. National analyses and strategies to streamline emergency services in England going forward are urgently needed.

Can police shootings be reduced by requiring officers to document when they point firearms at citizens?

2021 ◽  
pp. injuryprev-2020-043932
Author(s):  
John A Shjarback ◽  
Michael D White ◽  
Stephen A Bishopp
Keyword(s):  
Policy Change ◽  
Moving Average ◽  
Police Department ◽  
Organisational Change ◽  
Policy Implications ◽  
Average Analysis ◽  
Before And After ◽  
Auto Regressive ◽  
Serious Injuries ◽  
The Impact

ObjectiveTo examine the impact of a novel firearm ‘pointing’ policy that requires officers to document when they directly point their guns at citizens.MethodsSixteen years (2003–2018) of narrative officer-involved shooting (OIS) reports from the Dallas Police Department were qualitatively coded to explore both the total frequency and specific characteristics of OIS before and after the policy change in 2013.Resultsχ2 tests found that the firearm pointing policy was associated with a reduction in the proportion of ‘threat perception failure’ shootings (ie, those where an officer mistakes an item for a gun). Auto Regressive Integrated Moving Average analysis found that the policy change was associated with a gradual, permanent reduction in total OIS; however, that impact was not immediate.ConclusionsFirearm pointing policies have the potential to alter organisational behaviour, particularly in highly discretionary shootings. It is unclear whether the specific mechanisms for the changes include more accountability through constrained discretion, reduced options to handle situations once officers’ guns are drawn and pointed, or an effect on officers’ timing and vision during ambiguous scenarios.Policy implicationsAlthough organisational change may be a long and complex process, reductions in OIS can prevent serious injuries and death. The policy change did not lead to an increase in the proportion of officers injured during OIS incidents.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals Does admission prevalence change after reconfiguration of inpatient services? An interrupted time series analysis of the impact of reconfiguration in five centres

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Joanne Martin ◽  
Edwin Amalraj Raja ◽  
Steve Turner
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Emergency Admission ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Emergency Admissions ◽  
Series Analysis ◽  
Inpatient Services ◽  
The Impact ◽  
Admission Prevalence

Abstract Background Service reconfiguration of inpatient services in a hospital includes complete and partial closure of all emergency inpatient facilities. The “natural experiment” of service reconfiguration may give insight into drivers for emergency admissions to hospital. This study addressed the question does the prevalence of emergency admission to hospital for children change after reconfiguration of inpatient services? Methods There were five service reconfigurations in Scottish hospitals between 2004 and 2018 where emergency admissions to one “reconfigured” hospital were halted (permanently or temporarily) and directed to a second “adjacent” hospital. The number of emergency admissions (standardised to /1000 children in the regional population) per month to the “reconfigured” and “adjacent” hospitals was obtained for five years prior to reconfiguration and up to five years afterwards. An interrupted time series analysis considered the association between reconfiguration and admissions across pairs comprised of “reconfigured” and “adjacent” hospitals, with adjustment for seasonality and an overall rising trend in admissions. Results Of the five episodes of reconfiguration, two were immediate closure, two involved closure only to overnight admissions and one with overnight closure for a period and then closure. In “reconfigured” hospitals there was an average fall of 117 admissions/month [95% CI 78, 156] in the year after reconfiguration compared to the year before, and in “adjacent” hospitals admissions rose by 82/month [32, 131]. Across paired reconfigured and adjacent hospitals, in the months post reconfiguration, the overall number of admissions to one hospital pair slowed, in another pair admissions accelerated, and admission prevalence was unchanged in three pairs. After reconfiguration in one hospital, there was a rise in admissions to a third hospital which was closer than the named “adjacent” hospital. Conclusions There are diverse outcomes for the number of emergency admissions post reconfiguration of inpatient facilities. Factors including resources placed in the community after local reconfiguration, distance to the “adjacent” hospital and local deprivation may be important drivers for admission pathways after reconfiguration. Policy makers considering reconfiguration might consider a number of factors which may be important determinants of admissions post reconfiguration.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Relationship between Changes over Time in Factors, Including the Impact of Meteorology on Photochemical Oxidant Concentration and Causative Atmospheric Pollutants in Kawasaki

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 446
Author(s):  
Akinori Fukunaga ◽  
Takaharu Sato ◽  
Kazuki Fujita ◽  
Daisuke Yamada ◽  
Shinya Ishida ◽  
...  
Keyword(s):  
Moving Average ◽  
Atmospheric Pollutants ◽  
Important Indicator ◽  
Photochemical Oxidant ◽  
Oxidant Concentration ◽  
Using Data ◽  
Changes Over Time ◽  
The Impact ◽  
The Relationship ◽  
The City

To clarify the relationship between changes in photochemical oxidants’ (Ox) concentrations and their precursors in Kawasaki, a series of analyses were conducted using data on Ox, their precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), and meteorology that had been monitored throughout the city of Kawasaki for 30 years from 1990 to 2019. The trend in air temperature was upward, wind speed was downward, and solar radiation was upward, indicating an increasing trend in meteorological factors in which Ox concentrations tend to be higher. Between 1990 and 2013, the annual average Ox increased throughout Kawasaki and remained flat after that. The three-year moving average of the daily peak increased until 2015, and after that, it exhibited a slight decline. The amount of generated Ox is another important indicator. To evaluate this, a new indicator, the daytime production of photochemical oxidant (DPOx), was proposed. DPOx is defined by daytime averaged Ox concentrations less the previous day’s nighttime averaged Ox concentrations. The trend in DPOx from April to October has been decreasing since around 2006, and it was found that this indicator reflects the impact of reducing emissions of NOx and VOCs in Kawasaki.

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

Mechanical Properties of Equal-Channel Angular Extrusion-Processed Al-20Zn Alloy

2012 ◽  
Vol 445 ◽  
pp. 195-200
Author(s):  
Murat Aydin ◽  
Yakup Heyal
Keyword(s):  
Mechanical Properties ◽  
Impact Toughness ◽  
Equal Channel Angular Extrusion ◽  
Considerable Change ◽  
Fatigue Properties ◽  
Dendritic Microstructure ◽  
Two Phase ◽  
Angular Extrusion ◽  
Alloy Increase ◽  
The Impact

The mechanical properties mainly tensile properties, impact toughness and high-cycle fatigue properties, of two-phase Al-20Zn alloy subjected to severe plastic deformation (SPD) via equal-channel angular extrusion (ECAE) using route A up to 2 passes were studied. The ECAE almost completely eliminated as-cast dendritic microstructure including casting defects such as micro porosities. A refined microstructure consisting of elongated micro constituents, α and α+η eutectic phases, formed after ECAE via route A. As a result of this microstructural change, mechanical properties mainly the impact toughness and fatigue performance of the as-cast Al-20Zn alloy increased significantly through the ECAE. The rates of increase in fatigue endurance limit are approximately 74 % after one pass and 89 % after two passes while the increase in impact toughness is 122 %. Also the yield and tensile strengths of the alloy increase with ECAE. However, no considerable change occurred in hardness and percentage elongation of the alloy. It was also observed that the ECAE changed the nature of the fatigue fracture characteristics of the as-cast Al-20Zn alloy.

Sign in / Sign up

Export Citation Format

Share Document