Emergency department and ‘Google flu trends’ data as syndromic surveillance indicators for seasonal influenza

2014 ◽  
Vol 142 (11) ◽  
pp. 2397-2405 ◽  
Author(s):  
L. H. THOMPSON ◽  
M. T. MALIK ◽  
A. GUMEL ◽  
T. STROME ◽  
S. M. MAHMUD
Keyword(s):  
Emergency Department ◽  
Linear Regression ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Seasonal Influenza ◽  
Linear Regression Models ◽  
Physician Visits ◽  
Influenza Activity ◽  
Ed Visits ◽  
Highly Correlated

SUMMARYWe evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data – total ED visits attributed to influenza-like illness (ILI) (‘ED ILI volume’) and percentage of visits attributed to ILI (‘ED ILI percent’) – and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

Why are people increasingly attending the emergency department? A study of the French healthcare system

2019 ◽  
Vol 36 (9) ◽  
pp. 548-553 ◽  
Author(s):  
Helene Colineaux ◽  
Fanny Pelissier ◽  
Laure Pourcel ◽  
Thierry Lang ◽  
Michelle Kelly-Irving ◽  
...  
Keyword(s):  
Emergency Department ◽  
Observational Study ◽  
Regression Models ◽  
Age Groups ◽  
Average Increase ◽  
Linear Regression Models ◽  
Clinical Classification ◽  
Therapeutic Procedures ◽  
Ed Visits

ObjectiveIt is often asserted that the crowding phenomenon in emergency departments (ED) can be explained by an increase in visits considered as non-urgent. The aim of our study was to quantify the increase in ED visit rates and to determine whether this increase was explained by non-severe visit types.MethodsThis observational study covers all ED visits between 2002 and 2015 by adult inhabitants of the Midi-Pyrénées region in France. Their characteristics were collected from the emergency visit summaries. We modelled the visit rates per year using linear regression models, and an increase was considered significant when the 95% CIs did not include zero. The severity of the patients’ condition during ED visit was determined through the ‘Clinical Classification of Emergency’ score. Non-severe visits were those where the patient was stable, and the physician deemed no intervention necessary. Intermediate-severity visits concerned patients who were stable but requiring diagnostic or therapeutic procedures.ResultsThe 37 studied EDs managed >7 million visits between 2002 and 2015. There was an average increase of +4.83 (95% CI 4.33 to 5.32) visits per 1000 inhabitants each year. The increase in non-severe visit types was +0.88 (95% CI 0.42 to 1.34) per 1000 inhabitants, while the increase in intermediate-severity visit types was +3.26 (95% CI 2.62 to 3.91) per 1000 inhabitants. This increase affected all age groups and all sexes.DiscussionIt appears that the increase in ED use is not based on an increase in non-severe visit types, with a greater impact of intermediate-severity visit types requiring diagnostic or therapeutic procedures in ED.

scholarly journals Tobacco Use Behaviors and Perceptions of Parental Smokers in the Emergency Department Setting

2019 ◽  
Vol 12 ◽  
pp. 1179173X1984139 ◽  
Author(s):  
E Melinda Mahabee-Gittens ◽  
Ashley L Merianos ◽  
Lara Stone ◽  
Meredith E Tabangin ◽  
Jane C Khoury ◽  
...  
Keyword(s):  
Emergency Department ◽  
Linear Regression ◽  
Tobacco Smoke ◽  
Regression Models ◽  
Urgent Care ◽  
Smoking Bans ◽  
Linear Regression Models ◽  
Older Children ◽  
Black And White ◽  
Child Age

Background: More information is needed about modifiable child tobacco smoke exposure (TSE) patterns in racially diverse parental smokers to tailor interventions designed to help parents quit smoking and reduce their child’s TSE. Our objectives were to determine whether there were differences in smoking and TSE patterns based on parental race and child age and whether these patterns differed based on child age within black and white parental smokers. Secondary objectives were to assess the relationship between parental perceptions about the effects of smoking and the benefits of quitting on their child based on child age, race, and reported TSE patterns and to examine biochemically verified TSE levels by child age, race, and parent-reported TSE patterns. Methods: Participants (N = 415) were non-Hispanic black and non-Hispanic white parental smokers, mean age (standard deviation [SD]) = 31.2 (7.2) years, who visited the Pediatric Emergency Department (PED) or Urgent Care (UC) with their child, mean age (SD) = 4.7 (4.6) years. Parents reported sociodemographics, smoking, and child TSE patterns. We conducted chi-square tests, independent t-tests, and general linear regression models to answer our primary objectives and linear regression models to answer our secondary objectives. Results: Parents were 56.1% non-Hispanic black; 87.5% women; mean (SD) number of cigarettes smoked/day was 10.5(6.8). A higher proportion of parents with younger children <3 years old reported smoking bans compared with parents with older children ⩾3 to <18 years old (41.3% vs 19.7%, P < .0001). Subsequent analyses revealed this pattern for both black and white parents. A total of 212 (51%) of children had biochemical assessment of TSE; 89.6% had detectable TSE. Younger children had significantly higher cotinine levels than older children independent of their race ( P < .001). Conclusions: Children of parental smokers who visit the PED/UC were highly tobacco smoke exposed. Both black and white parental smokers with younger children were more likely to enforce smoking bans, but younger children had higher TSE levels than older children. Interventions that target this group of parental smokers with younger children may be more effective than interventions geared to all parental smokers.

scholarly journals Modeling and Syndromic Surveillance for Estimating Weather-Induced Heat-Related Illness

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Alexander G. Perry ◽  
Michael J. Korenberg ◽  
Geoffrey G. Hall ◽  
Kieran M. Moore
Keyword(s):  
Time Series ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Weather Data ◽  
Hospital Data ◽  
Weather Variables ◽  
Syndromic Surveillance System ◽  
Fast Orthogonal Search ◽  
Ed Visits ◽  
Highly Correlated

This paper compares syndromic surveillance and predictive weather-based models for estimating emergency department (ED) visits for Heat-Related Illness (HRI). A retrospective time-series analysis of weather station observations and ICD-coded HRI ED visits to ten hospitals in south eastern Ontario, Canada, was performed from April 2003 to December 2008 using hospital data from the National Ambulatory Care Reporting System (NACRS) database, ED patient chief complaint data collected by a syndromic surveillance system, and weather data from Environment Canada. Poisson regression and Fast Orthogonal Search (FOS), a nonlinear time series modeling technique, were used to construct models for the expected number of HRI ED visits using weather predictor variables (temperature, humidity, and wind speed). Estimates of HRI visits from regression models using both weather variables and visit counts captured by syndromic surveillance as predictors were slightly more highly correlated with NACRS HRI ED visits than either regression models using only weather predictors or syndromic surveillance counts.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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