scholarly journals Associations Between Canada's Cannabis Legalization and Emergency Department Presentations for Transient Cannabis-Induced Psychosis and Schizophrenia Conditions: Ontario and Alberta, 2015–2019

2022 ◽  
pp. 070674372110706
Author(s):  
Russell C. Callaghan ◽  
Marcos Sanches ◽  
Robin M. Murray ◽  
Sarah Konefal ◽  
Bridget Maloney-Hall ◽  
...  
Keyword(s):  
Emergency Department ◽  
Moving Average ◽  
Step Function ◽  
Autoregressive Integrated Moving Average ◽  
Study Results ◽  
Icd 10 ◽  
Significant Step ◽  
October 17 ◽  
Comparison Series ◽  
Sarima Models

Objective Cannabis legalization in many jurisdictions worldwide has raised concerns that such legislation might increase the burden of transient and persistent psychotic illnesses in society. Our study aimed to address this issue. Methods Drawing upon emergency department (ED) presentations aggregated across Alberta and Ontario, Canada records (April 1, 2015–December 31, 2019), we employed Seasonal Autoregressive Integrated Moving Average (SARIMA) models to assess associations between Canada's cannabis legalization (via the Cannabis Act implemented on October 17, 2018) and weekly ED presentation counts of the following ICD-10-CA-defined target series of cannabis-induced psychosis (F12.5; n = 5832) and schizophrenia and related conditions (“schizophrenia”; F20-F29; n = 211,661), as well as two comparison series of amphetamine-induced psychosis (F15.5; n = 10,829) and alcohol-induced psychosis (F10.5; n = 1,884). Results ED presentations for cannabis-induced psychosis doubled between April 2015 and December 2019. However, across all four SARIMA models, there was no evidence of significant step-function effects associated with cannabis legalization on post-legalization weekly ED counts of: (1) cannabis-induced psychosis [0.34 (95% CI −4.1; 4.8; P = 0.88)]; (2) schizophrenia [24.34 (95% CI −18.3; 67.0; P = 0.26)]; (3) alcohol-induced psychosis [0.61 (95% CI −0.6; 1.8; P = 0.31); or (4) amphetamine-induced psychosis [1.93 (95% CI −2.8; 6.7; P = 0.43)]. Conclusion Implementation of Canada's cannabis legalization framework was not associated with evidence of significant changes in cannabis-induced psychosis or schizophrenia ED presentations. Given the potentially idiosyncratic rollout of Canada's cannabis legalization, further research will be required to establish whether study results generalize to other settings.

scholarly journals Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

scholarly journals Analysis of the demand for gastronomic tourism in Andalusia (Spain)

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246377
Author(s):  
Mª Genoveva Dancausa Millán ◽  
Mª Genoveva Millán Vázquez de la Torre ◽  
Ricardo Hernández Rojas
Keyword(s):  
Olive Oil ◽  
Raw Materials ◽  
Moving Average ◽  
Food Products ◽  
Southern Spain ◽  
Autoregressive Integrated Moving Average ◽  
Wine Tourism ◽  
Protected Designations Of Origin ◽  
Sarima Models

In recent years, gastronomy has become a fundamental motivation to travel. Learning how to prepare gastronomic dishes and about the raw materials that compose them has attracted increasing numbers of tourists. In Andalusia (region of southern Spain), there are many quality products endorsed by Protected Designations of Origin, around which gastronomic routes have been created, some visited often (e.g., wine) and others remaining unknown (e.g., ham and oil). This study analyses the profile of gastronomic tourists in Andalusia to understand their motivations and estimates the demand for gastronomic tourism using seasonal autoregressive integrated moving average (SARIMA) models. The results obtained indicate that the gastronomic tourist in Andalusia is very satisfied with the places he/she visits and the gastronomy he/she savours. However, the demand for this tourist sector is very low and heterogeneous; while wine tourism is well established, tourism focusing on certain products, such as olive oil or ham, is practically non-existent. To obtain a homogeneous demand, synergies or pairings should be created between food products, e.g., wine-ham, oil-ham, etc., to attract a greater number of tourists and distinguish Andalusia as a gastronomic holiday destination.

scholarly journals Forecasting Daily Volume and Acuity of Patients in the Emergency Department

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Rafael Calegari ◽  
Flavio S. Fogliatto ◽  
Filipe R. Lucini ◽  
Jeruza Neyeloff ◽  
Ricardo S. Kuchenbecker ◽  
...  
Keyword(s):  
Emergency Department ◽  
Climatic Factors ◽  
Moving Average ◽  
Tertiary Care ◽  
Percentage Error ◽  
Patient Classification ◽  
High Complexity ◽  
Porto Alegre ◽  
Model Accuracy ◽  
Autoregressive Integrated Moving Average

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System’s (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

scholarly journals Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lihong Huang ◽  
Lindsay Sullivan ◽  
Jingzhen Yang
Keyword(s):  
Moving Average ◽  
Spillover Effect ◽  
Claim Data ◽  
Care Organization ◽  
Accountable Care ◽  
Intervention Model ◽  
Autoregressive Integrated Moving Average ◽  
Regression Methods ◽  
Study Results ◽  
The Impact

Abstract Background Existing studies analyzing the impact of state concussion laws have found an increase in concussion-related medical encounters post-law, in some instances, such increases were observed during the pre-law period due to a potential “spillover” effect. This study assessed the effects of Ohio’s concussion law, while accounting for such a “spillover” effect, on the trends in monthly rates of concussion-related medical encounters in Medicaid insured children using autoregressive integrated moving average (ARIMA) analysis. Methods We analyzed claim data obtained from the Partners For Kids database, a pediatric accountable care organization in Ohio. Concussion-related medical encounters for Medicaid-insured children (ages 0–18 years) treated between April 1, 2008 to December 31, 2016 were selected and analyzed. We assessed pre- and post-law trends in concussion-related medical encounters using an ARIMA intervention model. We also used traditional regression methods to validate the study results. Results A total of 16,943 concussion-related medical encounters sustained by 15,545 unique patients were included. Monthly rates of concussion-related medical encounters significantly increased from 4.64 per 10,000 member months during the pre-law period to 6.69 per 10,000 member months in the post-law period (P < 0.0001). Three upward breaks in the monthly rates of concussion-related medical encounters were observed between 2009 and 2016, with two breaks observed during the pre-law period. Specifically, the increased breakpoint observed in July 2011 (P = 0.0186) was significantly associated with an estimated 7.3% increase (95% CI: 1.1–13.7) in the rate of concussion-related medical encounters. This finding was confirmed in the Poisson regression and curve fitting models. Furthermore, a seasonal trend in concussion-related medical encounters was observed with the highest rates in September and October of each year. Conclusions Two of the three upward breaks identified in the monthly rate of concussion-related medical encounters occurred before the enactment of Ohio’s concussion law, suggesting a potential “spillover” effect. Further research is needed to confirm such an effect in children with other types of medical insurance.

scholarly journals Acute cannabis intoxication in the emergency department: the effect of legalization

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Robert Baraniecki ◽  
Puru Panchal ◽  
Danya Deepsee Malhotra ◽  
Alexandra Aliferis ◽  
Zaka Zia
Keyword(s):  
Emergency Department ◽  
Chart Review ◽  
Primary Outcome ◽  
Age Groups ◽  
Retrospective Chart Review ◽  
Before And After ◽  
Icd 10 ◽  
Ed Visits ◽  
October 17 ◽  
Recreational Use

Abstract Background On October 17, 2018, the Cannabis Act decriminalized the recreational use of cannabis in Canada. This study seeks to determine how legalization of cannabis has impacted emergency department (ED) visits for acute cannabis intoxication. Methods We conducted a retrospective chart review at an academic ED in Hamilton, Ontario. We assessed all visits with a cannabis-related ICD-10 discharge code 6 months before and after legalization (October 17, 2018) to determine cases of acute cannabis intoxication. The primary outcome was the rate of ED visits. Secondary outcomes included number of visits distributed by age, length of stay, co-ingestions, and clinical course in the emergency department (investigations and treatment). Results There was no difference in the overall rate of ED visits following legalization (2.44 vs. 2.94 visits/1000, p = 0.27). However, we noted a 56% increase in visits among adults aged 18–29 (p = 0.03). Following legalization, a larger portion of patients required observation without interventions (25% vs 48%, p < 0.05). Bloodwork and imaging studies decreased (53% vs. 12%, p < 0.05; 29% vs. 2%, p < 0.05); however, treatment with benzodiazepines increased (24% vs. 51%, p < 0.05). Conclusions Legalization was not associated with a change in the rate of cannabis-related ED visits in our study. More research is needed regarding changing methods of cannabis ingestion and trends among specific age groups.

scholarly journals Forecasting Zambia’s Gross Domestic Product Using Time Series Autoregressive Integrated Moving Average (ARIMA) Model

2020 ◽  
Vol 5 (9) ◽  
pp. 440-447
Author(s):  
Chikumbe Evans Sankwa ◽  
Sikota Sharper
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Moving Average ◽  
Social Indicators ◽  
Arima Model ◽  
Information Criteria ◽  
Domestic Product ◽  
Data Set ◽  
Autoregressive Integrated Moving Average ◽  
Study Results

Gross Domestic Product is one of the social indicators of development. This study attempts to model Zambia’s Gross domestic product using the Autoregressive Integrated Moving Average (ARIMA) model. This model has proved to help many countries during economic recession or when there is any disruption in the economic system due to pandemics or natural disasters. The study utilized a time series dataset from 1960 to 2018. The best model that fit the data set, following the selection model criteria, was ARIMA (5,2,0) model with the lowest Akaike’s Information Criteria(AIC) and Bayesian Information Criteria (BIC) and smallest volatility. The study results showed that, on average, Zambia’s gross domestic product will continue to rise over the next eight years. However, few recession (decline) points are expected in the period 2020 to 2022. It is hoped that the forecasts would be useful for researchers in Zambia, including the fiscal and monetary policy makers.

scholarly journals Forecasting Sports Popularity: Application of Time Series Analysis

2017 ◽  
Vol 6 (2) ◽  
pp. 75-82 ◽  
Author(s):  
Ryan Miller ◽  
Harrison Schwarz ◽  
Ismael S. Talke
Keyword(s):  
Performance Measures ◽  
Historical Data ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Search Volume ◽  
Forecasting Performance ◽  
Search Data ◽  
Sports Leagues ◽  
The Times ◽  
Sarima Models

Abstract Popularity trends of the NFL and NBA are fun and interesting for casual fans while also of critical importance for advertisers and businesses with an interest in the sports leagues. Sports leagues have clear and distinct seasons and these have a major impact on when each league is most popular. To measure the popularity of each league, we used search data from Google Trends that gives real-time and historical data on the relative popularity of search words. By using search volume to measure popularity, the times of year, a sport is popular relative to its season can be explained. It is also possible to forecast how sport leagues are trending relative to each other. We compared and discussed three different univariate models both theoretically and empirically: the trend plus seasonality regression, Holt- Winters Multiplicative (HWMM), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to determine the popularity trends. For each league, the six forecasting performance measures used in this study indicated HWMM gave the most accurate predictions.

scholarly journals Emergency department visits for self-harm in adolescents after release of the Netflix series ‘13 Reasons Why’

2021 ◽  
pp. 000486742110659
Author(s):  
Mark Sinyor ◽  
Emilie Mallia ◽  
Claire de Oliveira ◽  
Ayal Schaffer ◽  
Thomas Niederkrotenthaler ◽  
...  
Keyword(s):  
Emergency Department ◽  
Health Service ◽  
Standard Error ◽  
Service Use ◽  
Moving Average ◽  
Control Period ◽  
Emergency Department Visits ◽  
Health Service Use ◽  
Autoregressive Integrated Moving Average ◽  
Self Harm

Objective: To determine whether the release of the first season of the Netflix series ‘13 Reasons Why’ was associated with changes in emergency department presentations for self-harm. Methods: Healthcare utilization databases were used to identify emergency department and outpatient presentations according to age and sex for residents of Ontario, Canada. Data from 2007 to 2018 were used in autoregressive integrated moving average models for time series forecasting with a pre-specified hypothesis that rates of emergency department presentations for self-harm would increase in the 3-month period following the release of 13 Reasons Why (1 April 2017 to 30 June 2017). Chi-square and t tests were used to identify demographic and health service use differences between those presenting to emergency department with self-harm during this epoch compared to a control period (1 April 2016 to 30 June 2016). Results: There was a significant estimated excess of 75 self-harm-related emergency department visits (+6.4%) in the 3 months after 13 Reasons Why above what was predicted by the autoregressive integrated moving average model (standard error = 32.4; p = 0.02); adolescents aged 10–19 years had 60 excess visits (standard error = 30.7; p = 0.048), whereas adults demonstrated no significant change. Sex-stratified analyses demonstrated that these findings were largely driven by significant increases in females. There were no differences in demographic or health service use characteristics between those who presented to emergency department with self-harm in April to June 2017 vs April to June 2016. Conclusions: This study demonstrated a significant increase in self-harm emergency department visits associated with the release of 13 Reasons Why. It adds to previously published mortality, survey and helpline data collectively demonstrating negative mental health outcomes associated with 13 Reasons Why.

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