Challenges in Caffeinated Energy Drink Adverse Event Surveillance, Data Capture, and Analysis

2020 ◽  
Author(s):  
André Markon ◽  
Olivia Jones-Dominic ◽  
Cecile Punzalan ◽  
Beverly Wolpert
Keyword(s):  
Adverse Event ◽  
Energy Drink ◽  
Data Capture ◽  
Surveillance Data ◽  
Data Capture And Analysis

Exploring the Impact of Demographic Factors on E-Government Services Adoption

2018 ◽  
Vol 31 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Isaac Kofi Mensah ◽  
Jianing Mi
Keyword(s):  
Demographic Factors ◽  
Data Capture ◽  
Demographic Factor ◽  
Government Services ◽  
Gender And Education ◽  
Adoption And Implementation ◽  
Willingness To Adopt ◽  
Willingness To Use ◽  
The Impact ◽  
Data Capture And Analysis

The purpose of this article is to investigate the impact of demographic factors on the adoption of e-government services. Specifically, this article sought to examine the extent to which demographic factors such as age, gender and education would influence the willingness to adopt and use e-government services. The data capture and analysis was done with SPSS. The results indicate that age as a demographic factor was significant in determining the willingness of citizens to use e-government services. The results, however, showed that gender and education as demographic factors were not positively significant in predicting the willingness to use e-government services. The implications of these findings on the adoption and implementation of e-government are further discussed.

scholarly journals Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Susannah Paul ◽  
Osaro Mgbere ◽  
Raouf Arafat ◽  
Biru Yang ◽  
Euncie Santos
Keyword(s):  
Moving Average ◽  
Forecast Accuracy ◽  
Data Capture ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Influenza Like Illness ◽  
Public Health Professionals ◽  
Influenza Activity ◽  
Diverse Data ◽  
And Control

ObjectiveThe objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms.BackgroundUsing novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to earlier detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts.Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period.ResultsForecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX.ConclusionThough the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.

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