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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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4 Presenting Data

• Published: July 2009
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Data presentation can greatly influence audiences. This chapter reviews principles and approaches for presenting data, focusing on whether data needs to be used. Data can presented using words alone (e.g., metaphors or narratives), numbers (e.g., tables), symbols (e.g., bar charts or line graphs), or some combination that integrates these methods. Although new software packages and advanced techniques are available, visual symbols that can most readily and effectively communicate public health data are pie charts, bar charts, line graphs, icons/icon arrays, visual scales, and maps. Perceptual cues, especially proximity, continuation, and closure, influence how people process information. Contextual cues help enhance meaning by providing sufficient context to help audiences better understand data. Effective data presentation depends upon articulating the purpose for communicating, understanding audiences and context, and developing storylines to be communicated, taking into account the need to present data ethically and in a manner easily understood.

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1.3: Presentation of Data

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Skills to Develop

• To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example \(\PageIndex{1}\)

The data obtained by measuring the age of \(21\) randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array}\]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value \(x\) is listed in the first row and its frequency \(f\), which is the number of times the value \(x\) appears in the data set, is listed below it in the second row.

Example \(\PageIndex{2}\)

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array}\]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

• Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

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Presentation and Data Response Business Studies Grade 12 Notes, Questions and Answers

Find all Presentation and Data Response Notes, Examination Guide Scope, Lessons, Activities and Questions and Answers for Business Studies Grade 12. Learners will be able to learn, as well as practicing answering common exam questions through interactive content, including questions and answers (quizzes).

Topics under Presentation and Data Response

• Preparation of a Successful Presentation
• Using Visual Aids
• Verbal and Non-verbal Information

Presentations  are an important aspect of business studies as they provide an opportunity for learners to showcase their knowledge and skills to an audience. Whether it’s a data response or a multimedia presentation, it’s essential to prepare effectively to ensure that the presentation is professional, engaging, and informative. We’ll guide grade 12 business studies learners on how to prepare for presentation and data response topics.

Factors to Consider When Preparing for a Presentation

• Purpose of the Presentation:  Understanding the purpose of the presentation is crucial in determining the content and structure of the presentation. This will help you tailor your presentation to meet the needs and interests of your audience.
• Content:  When preparing for a presentation, it’s important to gather information from a variety of sources, including textbooks, online resources, and other relevant materials. Ensure that the content is accurate, relevant, and concise.
• Structure: Developing a clear and organized structure for your presentation will make it easier for you to present the information and for the audience to follow along. Start with an introduction, then move on to the main points, and conclude with a summary.
• Timing:  Allocating enough time for each section of the presentation is crucial in ensuring that the presentation runs smoothly. Avoid rushing through the content and allow enough time for questions and feedback.

Factors to Consider While Presenting

• Eye Contact:  Maintaining eye contact with the audience is important in building a connection and keeping the audience engaged. It shows that you are confident and that you value the audience’s attention.
• Visual Aids: Using visual aids effectively can help to reinforce the information you are presenting and make the presentation more engaging. Ensure that the visual aids are clear, relevant, and easy to understand.
• Movement:  Moving around the room can help to break up the monotony of the presentation and keep the audience interested. However, avoid excessive movement as it can be distracting.
• Pacing:  Speak clearly and at a moderate pace. Avoid speaking too fast or too slow, as this can make it difficult for the audience to follow along. Use pauses effectively to emphasize important points.

Responding to Questions and Feedback

• Responding to Questions:  It’s important to be prepared for questions from the audience. Answer the questions clearly and concisely, and if you don’t know the answer, be honest and say so.
• Handling Feedback:  Feedback can be valuable in helping you to improve your presentation skills. Listen to the feedback objectively and avoid taking it personally. Respond to feedback in a non-aggressive and professional manner, and thank the person for their input.

Identifying Areas for Improvement

• Self-Reflection:  Take some time after the presentation to reflect on your performance. Consider what you did well and what you could have done better.
• Feedback:  Ask for feedback from your peers, classmates, or teacher. This can provide valuable insights into areas for improvement and help you to become a better presenter.

Recommendations for Future Improvements

• Practice:  Practice makes perfect, so it’s important to practice your presentation several times before delivering it to the audience.
• Seek Feedback:  Regularly seek feedback from others to help you identify areas for improvement and refine your presentation skills.
• Use Visual Aids Effectively:  Consider how you can use visual aids more effectively in future presentations, such as incorporating more graphs or images to reinforce the information being presented.

Non-Verbal Presentations

• Written Reports:  Written reports are a type of non-verbal presentation that can be used to provide detailed information and analysis on a particular topic. They are a useful tool for presenting information that cannot be easily conveyed through verbal or visual means.
• Scenarios:  Scenarios are a type of non-verbal presentation that use hypothetical situations to illustrate concepts or theories. They can be used to demonstrate the potential outcomes of a particular decision or action.
• Graphs:  Graphs, such as line, pie, and bar charts, are a useful tool for presenting data in a visual manner. They can help to illustrate trends, patterns, and relationships between different variables.
• Pictures and Photographs:  Pictures and photographs can be used to enhance the visual appeal of a presentation and to provide context for the information being presented.

Designing a Multimedia Presentation

• Start with the Text:  Start by developing the text for your presentation, ensuring that it is clear, concise, and relevant to the topic.
• Select the Background:  Choose a background that is simple and not distracting. Ensure that the background complements the text and images used in the presentation.
• Choose Relevant Images/Create Graphs:  Select images and create graphs that reinforce the information being presented and help to illustrate key points.
• Incorporate Visual Aids:  Incorporate visual aids, such as graphs, images, and videos, into the presentation to make it more engaging and informative.

The Effectiveness of Visual Aids

Visual aids, such as  graphs ,  images , and  videos , can be effective in reinforcing the information being presented and making the presentation more engaging. However, they can also be distracting if they are not used effectively. It’s important to consider the advantages and disadvantages of visual aids when preparing a presentation, and to use them in a manner that supports the content and purpose of the presentation.

Preparing for a presentation in business studies requires careful planning and attention to detail. By considering the factors discussed in this article, grade 12 business studies learners can improve their presentation skills and deliver effective and engaging presentations.

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Perera, C.H., Nayak, R., Nguyen, L.V.T. (2022). Quantitative Data Presentation and Analysis: Descriptive Analysis. In: Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions. Springer, Singapore. https://doi.org/10.1007/978-981-19-5017-9_5

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AI + Machine Learning , Announcements , Azure AI Content Safety , Azure AI Studio , Azure OpenAI Service , Partners

Introducing GPT-4o: OpenAI’s new flagship multimodal model now in preview on Azure

By Eric Boyd Corporate Vice President, Azure AI Platform, Microsoft

Posted on May 13, 2024 2 min read

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Microsoft is thrilled to announce the launch of GPT-4o, OpenAI’s new flagship model on Azure AI. This groundbreaking multimodal model integrates text, vision, and audio capabilities, setting a new standard for generative and conversational AI experiences. GPT-4o is available now in Azure OpenAI Service, to try in preview , with support for text and image.

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A step forward in generative AI for Azure OpenAI Service

GPT-4o offers a shift in how AI models interact with multimodal inputs. By seamlessly combining text, images, and audio, GPT-4o provides a richer, more engaging user experience.

Launch highlights: Immediate access and what you can expect

Azure OpenAI Service customers can explore GPT-4o’s extensive capabilities through a preview playground in Azure OpenAI Studio starting today in two regions in the US. This initial release focuses on text and vision inputs to provide a glimpse into the model’s potential, paving the way for further capabilities like audio and video.

Efficiency and cost-effectiveness

GPT-4o is engineered for speed and efficiency. Its advanced ability to handle complex queries with minimal resources can translate into cost savings and performance.

Potential use cases to explore with GPT-4o

The introduction of GPT-4o opens numerous possibilities for businesses in various sectors: 

• Enhanced customer service : By integrating diverse data inputs, GPT-4o enables more dynamic and comprehensive customer support interactions.
• Advanced analytics : Leverage GPT-4o’s capability to process and analyze different types of data to enhance decision-making and uncover deeper insights.
• Content innovation : Use GPT-4o’s generative capabilities to create engaging and diverse content formats, catering to a broad range of consumer preferences.

Exciting future developments: GPT-4o at Microsoft Build 2024 

We are eager to share more about GPT-4o and other Azure AI updates at Microsoft Build 2024 , to help developers further unlock the power of generative AI.

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Harmful Algal Blooms and Your Health

Harmful algal blooms are the rapid growth of algae or cyanobacteria in water that can harm people, animals, or the environment.

• Going in or near water with a harmful algal bloom can make you and your animals sick.
• Stay out if water looks discolored, has scum, or smells bad.

Algae and cyanobacteria (also called blue-green algae) are plant-like organisms that live in water. They can quickly grow out of control, or "bloom." Some of these blooms produce toxins (poisons) that make people and animals sick.

What they look like

Harmful algal blooms can look like foam, scum, mats, or paint on the surface of the water. They can also grow underneath the water, making some harmful algal blooms hard to see.

When they're harmful

Not all blooms are harmful. Blooms of algae or cyanobacteria can harm people, animals, or the environment if they:

• Make toxins
• Become too dense
• Use up oxygen in the water
• Release harmful gases

Many different types of algae can cause harmful algal blooms. However, three types —cyanobacteria, dinoflagellates, and diatoms—cause most blooms that make people and animals sick.

Where they're found

Harmful algal blooms can grow in fresh water, salt water, or brackish water (a mixture of fresh and salt water). This includes water people use for recreation and for drinking.

Harmful algal blooms occur across the world. In the United States, they have been reported in all 50 states, Puerto Rico, and the U.S. Virgin Islands.

What causes them

Algae and cyanobacteria occur naturally in water. They are more likely to grow into a harmful algal bloom when water is:

• Slow-moving
• Full of nutrients, such as nitrogen or phosphorous

Nutrients get into water when fertilizer, sewage, or runoff from cities and industrial buildings washes into lakes, rivers, or oceans. This can happen during rainstorms, for example.

Environmental changes, such as warmer water, might be making harmful algal blooms worse.

Learn more about factors that help harmful algal blooms grow .

Health impacts

Harmful algal blooms cause a variety of mild to serious illnesses. Symptoms depend on the type of harmful algal bloom you come in contact with and how you are exposed.

Harmful agal blooms can be deadly for animals‎

Risk of exposure.

People and animals can get sick from having contact with water or food that contains certain types of algae, cyanobacteria, or their toxins .

You or your animals can get sick if you:

• Go in or near water contaminated by a harmful algal bloom
• Swallow contaminated water
• Eat contaminated shellfish or fish
• Use contaminated blue-green algae dietary supplements

Preventing exposure

You can take steps to avoid getting sick from harmful algal blooms. For example:

• Stay out of discolored, scummy, or smelly water and keep pets away.
• Check for and follow swimming, fishing, and shellfish advisories.
• Follow guidance from local officials if there is a harmful algal bloom in your drinking water supply.

After exposure

If you or your animal go in water with an algal bloom, rinse off with tap water right after.

If you think you have symptoms caused by a harmful algal bloom, contact a healthcare provider or the Poison Control hotline at 1-800-222-1222.

If your pets or livestock seem sick after going in or near water, contact a veterinarian right away. You can also call the ASPCA Animal Poison Control Center at 1-888-426-4435 or the Pet Poison Helpline at 1-855-764-7661. Note that there is a fee for these calls.

What CDC is doing

CDC works with federal, state, local, and territorial partners to reduce the health impact of harmful algal blooms. This includes:

• Public health research
• Tracking harmful algal blooms and illnesses
• Developing methods to detect toxins
• Outreach and education
• Providing health departments with financial and technical support

Find data‎

• Harmful Algal Blooms | NOAA
• Harmful Algal Blooms in Water Bodies | U.S. EPA
• The Science of Harmful Algal Blooms | U.S. Geological Survey
• Blue-Green Algae Products and Microcystins | FDA

Harmful Algal Bloom (HAB)-Associated Illness

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• Association of time spent on social media with youth cigarette smoking and e-cigarette use in the UK: a national longitudinal study
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• http://orcid.org/0000-0003-3235-0454 Nicholas S Hopkinson 1 ,
• http://orcid.org/0000-0002-6187-0638 Charlotte Vrinten 2 ,
• http://orcid.org/0000-0002-4385-2153 Jennie C Parnham 2 ,
• Márta K Radó 3 ,
• http://orcid.org/0000-0002-2101-2559 Filippos Filippidis 2 ,
• Eszter P Vamos 2 ,
• http://orcid.org/0000-0003-1318-8439 Anthony A Laverty 2
• 1 National Heart and Lung Institute , Imperial College London , London , UK
• 2 Department of Primary Care and Public Health , Imperial College London School of Public Health , London , UK
• 3 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm , Sweden
• Correspondence to Dr Anthony A Laverty, Department of Primary Care and Public Health, Imperial College London School of Public Health, London, W6 8RP, UK; a.laverty{at}imperial.ac.uk

Background Social media may influence children and young people’s health behaviour, including cigarette and e-cigarette use.

Methods We analysed data from participants aged 10–25 years in the UK Household Longitudinal Study 2015–2021. The amount of social media use reported on a normal weekday was related to current cigarette smoking and e-cigarette use. Generalised estimating equation (GEE) logistic regression models investigated associations of social media use with cigarette smoking and e-cigarette use. Models controlled for possible confounders including age, sex, country of UK, ethnicity, household income and use of cigarette/e-cigarettes by others within the home.

Results Among 10 808 participants with 27 962 observations, current cigarette smoking was reported by 8.6% of participants for at least one time point, and current e-cigarette use by 2.5% of participants. In adjusted GEE models, more frequent use of social media was associated with greater odds of current cigarette smoking. This was particularly apparent at higher levels of use (eg, adjusted odds ratio (AOR) 3.60, 95% CI 2.61 to 4.96 for ≥7 hours/day vs none). Associations were similar for e-cigarettes (AOR 2.73, 95% CI 1.40 to 5.29 for ≥7 hours/day social media use vs none). There was evidence of dose–response in associations between time spent on social media and both cigarette and e-cigarette use (both p<0.001). Analyses stratified by sex and household income found similar associations for cigarettes; however, for e-cigarettes associations were concentrated among males and those from higher household income groups.

Conclusions Social media use is associated with increased risk of cigarette smoking and e-cigarette use. There is a need for greater research on this issue as well as potential policy responses.

• Tobacco control

Data availability statement

Data are available in a public, open access repository. Data available from UK Data Service https://ukdataservice.ac.uk .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/thorax-2023-220569

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WHAT IS ALREADY KNOWN ON THIS TOPIC

There is substantial use of social media among children and young people, which has had debated impacts on health outcomes. There are studies examining social media use and associations with cigarette and e-cigarette use in the US but only two such studies in the UK. One study was cross-sectional, while one previous cohort study of data from 2014 to 2018 found that social media use at age 14 years was associated with a greater likelihood of cigarette smoking at age 17 years. This study did not, however, assess the use of e-cigarettes.

WHAT THIS STUDY ADDS

This study examined daily use of social media among 10–25-year-olds from 2015 to 2021. It found that time spent on social media is associated, in a dose-dependent manner, with likelihood both of cigarette smoking and vaping. Those using social media for ≥7 hours/day were more than two and a half times more likely to use both cigarettes and e-cigarettes than those not using social media.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study highlights that more frequent social media use is associated with increased likelihood of using both cigarettes and e-cigarettes in the UK. This reinforces concerns that social media is a vector of direct and indirect marketing and promotion of these products and that policies to curtail this may be warranted.

Introduction

Understanding the mechanisms that drive uptake and use of cigarettes and e-cigarettes is key to developing strategies to prevent harm. The use of social media has been identified as a novel potential vector, with substantial increases in time spent in this activity by young people. 1–5 Social media use increases with age, and girls are more likely to spend longer periods of time on social media than boys. 6 Social media may be driving cigarette smoking and e-cigarette use through both direct, targeted advertisements and the use of paid influencers by the tobacco industry. 7 To date, most evidence on the impact of social media on cigarette and e-cigarette use has focused on America. 8–10 This has found associations with uptake, regular use and reduced perceptions of harm and has included assessment of engagement with different platforms. 11–13 The only two previous UK studies include a cohort study which found that social media use at age 14 years was associated with greater likelihood of cigarette use at age 17 years. 14 A second cross-national study from 42 countries including the UK concluded that there was a link between social media use and substance use but did not examine cigarette use separately from other substances. 15

Previous research has identified links between social media use and both cigarette and e-cigarette use. For example, analyses of Instagram have identified networks of influencers promoting e-cigarettes, often without disclosing financial relationships; while Juul has recently settled a lawsuit over marketing of e-cigarettes to teens, including on social media. 16 17 Comparative analyses in the UK have found good compliance with advertising standards for e-cigarettes on traditional media, but high levels of breaches on social media. 18 Analyses of 11 of the most popular social media platforms have highlighted high levels of tobacco promotion, with few platforms having policies to deal with novel forms of promotion such as sponsored or influencer content. 19 A systematic review of exposure to tobacco promotion and use identified 29 studies (none from the UK) and concluded that there is a need for greater regulation. 20 Any proposal to regulate social media needs to be justified and based on evidence. To contribute to this, we examined the longitudinal relationship of social media use with cigarette smoking and e-cigarette use among children and young people in the UK.

Data come from participants of the UK Household Longitudinal Study (UKHLS), also known as Understanding Society. 21 This is a longitudinal household panel study with annual surveys starting in 2009. The original sample consisted of a clustered and stratified probability sample of approximately 28 000 households in the UK. Data are collected via face-to-face interviews carried out by a trained interviewer in the respondent’s home and via online, self-completion questionnaires. Adults over the age of 16 years or above are asked to complete an individual questionnaire, including a self-completion questionnaire. Household members aged 10–15 years are asked to fill in a shorter self-completion questionnaire, with permission from their parent or carer.

We have focused on children and young adults aged 10–25 years using data from 2015/2016 to 2020/2021 (wave 7 to wave 12). Questions on e-cigarette use were added to UKHLS in 2015/2016. Participation in the panel is voluntary, with a gift voucher sent to encourage completion of questionnaires and a further gift voucher sent when these are completed. All participants provided consent to be interviewed. The University of Essex Ethics Committee approved all data collection. 22

Outcomes and exposure

We used three separate binary outcomes: current cigarette smoking, current e-cigarette use and current dual use of both products. Participants were classified as current cigarette smokers if they responded “I usually smoke between one and six cigarettes a week” or “I usually smoke more than six cigarettes a week”. All other responses were coded as non-users. The same question was used for all waves of data and for all ages.

Current e-cigarette use was first assessed in 2015/2016 with the question “Do you ever use electronic cigarettes (e-cigarettes)?” with response options “Yes” and “No”. From wave 8 (2016/2017) onwards participants were classified as current (weekly) e-cigarette users if they responded “I use e-cigarettes at least once a week”. All other responses were coded as non-users. Dual use was classified as participants currently using both products, with those using only one or no products classed as non-dual users.

The main exposure variable was social media use. Participants were first asked “Do you belong to any social networking websites?” (Yes/No), and if “Yes”, they were also asked how many hours they spend chatting or interacting with friends through a social website on a normal weekday, with the following response options: “None”, “<1 hour/day”, “1–3 hours”, “4–6 hours” and “≥7 hours”. We combined those reporting “None” along with those who were not a member of a social media website into a reference category of “Not a member or no use”. 6

We considered a range of potentially relevant sociodemographics: age, sex, country in UK, self-defined ethnic group (collapsed into White vs non-White due to low numbers in the non-White category), an indicator of living in an urban or rural areas (derived from Office for National Statistics Rural and Urban Classification of Output Areas) and equivalised household net income (based on the Organisation for Economic Co-operation and Development (OECD) equivalence scale, which was used to adjust household income by household composition 23 ).

Statistical analyses

We compared differences in sociodemographics between categories of social media use using ANOVA. We used binary generalised estimating equation (GEE) regression models (family: binomial; link: logit; correlation matrix: exchangeable) to assess relationships between social media use and product use, using separate models for each outcome: cigarette smoking, e-cigarette use and dual use. GEE models assess changes over time and account for the correlation caused by observations being from the same individuals. 24 We also present tests for trend based on frequency of social media use. Analyses were adjusted for time (categorical) as well as the sociodemographic variables listed above. Models of cigarette smoking were additionally adjusted for parental tobacco use, models of e-cigarette use were adjusted for parental e-cigarette use, and models of dual use were adjusted for both. Analyses used survey weights designed by the UKHLS survey team to account for clustered and stratified probability sampling and non-response bias. 25

We tested for interactions of social media use with age (split into above and below 18 years of age), sex and household income (in three groups). This was due to possible differences between those above and below the legal age of sale, greater social media use among women, and potentially differential effects by socioeconomic groups. All interactions were p<0.001 and so we present stratified analyses. Due to the small numbers, we did not test interactions for dual use.

Sensitivity analyses

We performed a range of sensitivity analyses to test the robustness of our findings. As it is possible that those not using social media at all are atypical, we repeated our analyses excluding these participants. Our main analyses used household income as a marker of socioeconomic status. We also performed our analyses using Index of Multiple Deprivation (IMD) (in five groups) as an alternative marker of socioeconomic status. We performed analyses categorising current e-cigarette use as participants who reported using e-cigarettes at least monthly. We also performed analyses controlling for a measure of mental health (the 12-item General Health Questionnaire (GHQ-12)) to consider whether this is a possible pathway, whereby social media impacts mental health, which is then linked to cigarette and e-cigarette use.

Finally, we used fixed effects analyses to directly test if changes in social media use corresponded to uptake of cigarette smoking and e-cigarette use. These adjusted for the time-varying variables parental cigarette/e-cigarette use and household income. These models were on a smaller subset of individuals who were not product users when entering the study and who were found to change their social media use over time.

Outcomes and covariates across categories of social media use are shown in table 1 . Overall, 8.6% of the sample reported current cigarette smoking at one or more data point, 2.5% reported current e-cigarette use, and 1.1% of participants were dual users at one or more data point. Social media use frequency broken down by covariates is shown in online supplemental appendix table 1 .

Supplemental material

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Description of sample observations by social media use 2015–2021

Cigarette smoking, e-cigarette use and dual use were all more common among participants reporting greater social media use (all p<0.001) ( figure 1 ). Some 2.0% of participants who used social media “None or not a member” reported being a current cigarette smoker compared with 15.7% among those using social media for ≥7 hours/day. Current e-cigarette use ranged from 0.8% among those not using social media to 2.5% among those using it for ≥7 hours/day.

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Cigarette smoking, e-cigarette use and dual use by social media use.

Differences between categories of social media use were apparent for all variables studied (all p<0.001). Males were less likely to be in higher social media use groups than females (57.5% of the “None or not a member” social media group compared with 39.9% of the “≥7 hours/day” group). Social media use was more frequent at older ages (mean age of “None or not a member” social media group 12.0 years vs 17.7 years for the “≥7 hours/day” group). Parental cigarette smoking was more common among those using social media more frequently (17.0% for the “None or not a member” social media group vs 25.2% for the “≥7 hours/day” group) as was parental e-cigarette use (7.3% and 10.5%, respectively).

Table 2 shows results of our GEE models of social media use and cigarette smoking. Cigarette smoking was more common among those using social media more frequently (p for trend <0.001). Those using social media for “<1 hour/day” were more likely to be current cigarette smokers than those using social media “None or not a member” (adjusted odds ratio (AOR) 1.92, 95% CI 1.43 to 2.58) ( table 2 ). Those using social media for “≥7 hours/day” were substantially more likely to be current cigarette smokers than those using social media “None or not a member” (AOR 3.60, 95% CI 2.61 to 4.96).

Associations of social media use with current cigarette use from generalised estimating equation model

Table 3 shows results for e-cigarette use. E-cigarette use was more common among those using social media more frequently (p for trend <0.001). E-cigarette use was more common among those using social media “1–3 hours per day” compared with those using it “None or not a member” (AOR 1.92, 95% CI 1.07 to 3.46). E-cigarette use was considerably more likely among participants using social media “≥7 hours/day” than those using social media “None or not a member” (AOR 2.73, CI 1.40 to 5.29).

Associations of social media use and current e-cigarette use from generalised estimating equation model

Table 4 shows results for dual cigarette and e-cigarette use. Models have wide confidence intervals reflecting low levels of dual use. Those using social media more frequently were more likely to be dual users (p for trend <0.001). Those using social media “1–3 hours per day” were more likely to be dual users compared with those using it “None or not a member” (AOR 3.28, 95% CI 1.24 to 8.70). Dual use was more likely among participants using social media “≥7 hours/day” than among those using social media “None or not a member” (AOR 4.96, 95% CI 1.71 to 14.34).

Associations of social media use with current e-cigarette and cigarette dual use from generalised estimating equation model

Interactions of social media and sex were statistically significant for both cigarettes and e-cigarettes (both p<0.001). In stratified models ( table 5 ) AORs were similar between the sexes for current cigarette smoking. For e-cigarettes, associations between social media use and e-cigarette use were statistically significant for males but not for females (AOR 4.10, 95% CI 1.90 to 8.87 for males for “≥7 hours/day” vs “None or not a member” social media use).

Associations of social media use with current e-cigarette and cigarette use from gender stratified generalised estimating equation models

Interactions with household income categories were statistically significant (p<0.001 for both cigarettes and e-cigarettes) ( table 6 ). In stratified analyses of cigarette smoking, point estimates for the richest income group were higher than for the lowest income group, although these overlapped (eg, AOR 5.22 for “≥7 hours/day”, 95% CI 2.82 to 9.67 for the richest income group vs AOR 4.17, 95% CI 2.27 to 7.65 for the lowest income group). For e-cigarette use, associations were statistically significant for the highest income groups (eg, AOR 7.85, 95% CI 1.72 to 35.82 for “≥7 hours/day” vs no social media use) but were not statistically significant for the lowest income group.

Associations of social media use with current e-cigarette and cigarette use from household income stratified generalised estimating equation models

Analyses stratified by age found similar results to main analyses for cigarettes ( online supplemental appendix table 2 ). Models for e-cigarette use were only statistically significant among those <18 years old.

GEE analyses excluding those not using any social media were similar to main analyses ( online supplemental appendix table 3 ). Analyses using IMD as a marker of socioeconomic status rather than household income also gave similar results ( online supplemental appendix table 4 ). Analyses classifying current e-cigarette use as participants using them at least monthly also gave similar results although with larger point estimates ( online supplemental appendix table 5 ). Analyses controlling for GHQ-12 as a measure of mental health were similar for cigarettes but did not find statistically significant associations between social media and e-cigarette or dual use. This may indicate that social media use impacts mental health, which in turn impacts likelihood of using cigarettes or e-cigarettes, although this result should be treated with caution ( online supplemental appendix table 6 ).

Fixed effect analyses gave similar results to main analyses for uptake of cigarette smoking ( online supplemental appendix table 7 ). It should be noted that sample size was much reduced for this model (n=864). These analyses found some evidence that changes in social media use are linked to uptake of cigarette smoking in a dose–response manner (p for trend=0.053). For example, changing to using social media for ≥7 hours/day was associated with more than double the odds of taking up cigarette smoking (AOR 2.33, 95% CI 1.28 to 4.24).

Associations between changes in social media use and uptake of e-cigarettes did not reveal associations between changes in social media use and uptake of e-cigarettes. These analysis models had even lower sample sizes (n=564). For example, AORs of e-cigarette uptake ranged from 0.71 (95% CI 0.34 to 1.48) for participants using social media “<1 hour/day” to AOR 0.84 (95% CI 0.38 to 1.85) for those using social media “≥7 hours/day”. The test for trend was not statistically significant (p=0.584).

The main finding of the present study is that in children and young adults more frequent social media use was associated with a higher likelihood of both current use of cigarettes and e-cigarettes. This association was independent of other factors associated with increasing smoking and vaping including age, gender, socioeconomic status and parental smoking and vaping. These findings were robust to sensitivity analyses, while in stratified analyses there were more consistent associations for e-cigarette use among those under the legal age of sale, males and those with higher household incomes.

While we were unable to assess use of specific social media platforms or what content was being accessed, we propose a number of possible, non-exclusive explanations for this relationship. First, and most straightforwardly, there is evidence that the corporations behind cigarette smoking and vaping make use of social media to advertise and promote their products. 8–10 16 This includes direct advertising which is algorithmically targeted and the use of paid social media influencers who present smoking and vaping as a fashionable and desirable activity. Greater time spent on social media is likely to increase exposure to these forms of influence. While cigarettes and e-cigarettes are likely promoted differently, we found association with use of both products, highlighting the need for greater understanding of such corporate behaviours. Second, social media use has been shown to have features in common with reward-seeking addictive behaviour. 26 High social media use may increase susceptibility to other addictive behaviours like smoking. Alternatively, both behaviours may be driven by a common susceptibility. Third, as a space that is largely unsupervised by parents/caregivers, social media use may encourage behaviours that are transgressive, including cigarette smoking and vaping. There is evidence that peer smoking is a strong influence on child uptake of smoking 27 and social media is one of the ways in which peer smoking and vaping will be experienced, both by seeing others’ behaviour and by sharing “influencer content” that promotes these behaviours.

Stratified analyses revealed more consistent associations for cigarettes, while for e-cigarettes statistically significant associations were only found for those under the legal age of sale, among males, and those from richer households. Analyses of cigarette smoking did not identify changes over time, which fits with other evidence that smoking prevalence has been reasonably consistent over this time frame. 28 Analyses of e-cigarette use found reduced odds of these outcomes after 2015/2016, likely caused by changes in e-cigarette use ascertainment, although our main findings were robust to reclassification to examine monthly use. Our main analyses focused on weekly use of e-cigarettes; as any health impacts are probably related to amounts of vapour inhaled, this measure of regular use is more important for health than e-cigarette experimentation.

Strengths and limitations

This study uses a nationally representative cohort to examine social media use and use of cigarettes and e-cigarettes over time. UKHLS households are sampled based on geographical areas, population densities and ethnic composition, with survey weight adjusting for differential non-response across groups. 29 We conducted a range of sensitivity analyses, although other potential factors such as education may also be important. All data are based on self-report, and specifically we do not have information about which social media platforms were being used or how individuals were using them, for example, the extent to which they are interacting socially with individuals they know or consuming content from influencers, personalities or media corporations, etc. Precise pathways remain to be fully elucidated: our sensitivity analyses point to a possible role for mental health, although it should be noted that a formal mediation analysis was outside the scope of this article. As cigarette smoking is linked to poorer mental health, these relationships could well be bidirectional. 30 This, as well as potential targeted advertising, are among pathways that should be investigated in both quantitative and qualitative research.

Policy implications

Although we do not have data on the specific platforms used or content used, there is compelling evidence that vape companies are using social media to market their products. 2–5 The content that social media users are exposed to is to a substantial extent algorithmically controlled, both through targeted advertising and by the promotion of material that maximises engagement in order to increase revenue to the platform. This can be controlled. For example, far right imagery which is otherwise widely available is largely inaccessible in Germany, as a consequence of German law which social media platforms are bound to enforce. The companies that own social media platforms have substantial power to modify exposure to material that promotes smoking and vaping if they choose to or are compelled to. Voluntary codes seem unlikely to achieve this, and the introduction and enforcement on bans on material that promote this should be considered. In general, we think that algorithms should not be promoting products to individuals that they cannot legally buy. Legislation and enforcement around this and other corporate determinants of health concerns should be considered a core part of online safety and child protection.

This longitudinal analysis of children and young people in the UK found that more frequent social media use is associated with an increased risk of cigarette and e-cigarette use.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

X @COPDdoc, @cvrinten, @anthonylav

Contributors AAL conceived the project. JCP, CV and AAL cleaned data and performed the analyses with guidance from FF and MKR. AAL and NSH wrote the first draft and all authors contributed to this process. AAL is guarantor

Funding This study was supported by Cancer Research UK (CRUK PPRCTAGPJT\100005).

Competing interests NSH is Chair of Action on Smoking and Health and Medical Director of Asthma and Lung UK. AAL is a Trustee of Action on Smoking and Health.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Optimal timing for the Modified Early Warning Score for prediction of short-term critical illness in the acute care chain: a prospective observational study
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• http://orcid.org/0000-0002-9568-0138 Lars Ingmar Veldhuis 1 , 2 ,
• Merijn Kuit 1 ,
• Liza Karim 1 ,
• Milan L Ridderikhof 3 ,
• Prabath WB Nanayakkara 4 ,
• Jeroen Ludikhuize 5 , 6
• 1 Emergency Department , Amsterdam UMC Locatie AMC , Amsterdam , The Netherlands
• 2 Department of Anaesthesiology , Amsterdam UMC Locatie AMC , Amsterdam , The Netherlands
• 3 Emergency Medicine , Amsterdam UMC - Locatie AMC , Amsterdam , The Netherlands
• 4 Section Acute Medicine, Department of Internal Medicine , Amsterdam Universitair Medische Centra , Amsterdam , The Netherlands
• 5 Department of Internal Medicine , Amsterdam UMC Locatie VUmc , Amsterdam , The Netherlands
• 6 Department of Intensive Care , Haga Hospital , Den Haag , The Netherlands
• Correspondence to Lars Ingmar Veldhuis, Emergency Department, Amsterdam UMC Locatie AMC, Amsterdam, 1105 AZ, The Netherlands; l.i.veldhuis{at}amsterdamumc.nl

Introduction The Modified Early Warning Score (MEWS) is an effective tool to identify patients in the acute care chain who are likely to deteriorate. Although it is increasingly being implemented in the ED, the optimal moment to use the MEWS is unknown. This study aimed to determine at what moment in the acute care chain MEWS has the highest accuracy in predicting critical illness.

Methods Adult patients brought by ambulance to the ED at both locations of the Amsterdam UMC, a level 1 trauma centre, were prospectively included between 11 March and 28 October 2021. MEWS was calculated using vital parameters measured prehospital, at ED presentation, 1 hour and 3 hours thereafter, imputing for missing temperature and/or consciousness, as these values were expected not to deviate. Critical illness was defined as requiring intensive care unit admission, myocardial infarction or death within 72 hours after ED presentation. Accuracy in predicting critical illness was assessed using the area under the receiver operating characteristics curve (AUROC).

Results Of the 790 included patients, critical illness occurred in 90 (11.4%). MEWS based on vital parameters at ED presentation had the highest performance in predicting critical illness with an AUROC of 0.73 (95% CI 0.67 to 0.79) but did not significantly differ compared with other moments. Patients with an increasing MEWS over time are significantly more likely to become critical ill compared with patients with an improving MEWS.

Conclusion The performance of MEWS is moderate in predicting critical illness using vital parameters measured surrounding ED admission. However, an increase of MEWS during ED admission is correlated with the development of critical illness. Therefore, early recognition of deteriorating patients at the ED may be achieved by frequent MEWS calculation. Further studies should investigate the effect of continuous monitoring of these patients at the ED.

• emergency department
• emergency care systems
• care systems
• critical care

Data availability statement

Data are available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/emermed-2022-212733

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WHAT IS ALREADY KNOWN ON THIS TOPIC

The Modified Early Warning Score (MEWS) is an effective tool to identify deteriorating patients in the acute care chain who might deteriorate.

Although it is increasingly being implemented, the optimal timing for assessing the MEWS is unknown.

WHAT THIS STUDY ADDS

This prospective multicentre study included 790 patients and found that MEWS measured at ED presentation had the highest accuracy in predicting the development of critical illness. However, the performance is moderate and not significantly better compared to MEWS based at other moments in the acute care chain.

However, an increase in MEWS during the ED encounter is highly correlated with the development of critical illness.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

As clinical deterioration and subsequent development of critical illness is highly correlated with an increase of MEWS during the ED stay, we suggest further investigation on the value of continuous monitoring of these patients at the ED

Introduction

Early recognition of the deteriorating patient is of vital importance to reduce the occurrence of serious adverse events (SAEs) including cardiopulmonary arrests, (delayed) intensive care unit (ICU) admissions and death. Prior research indicates that up to 80% of deteriorating patients show physiological abnormalities up to 24 hours before the event. 1–4 Track and trigger systems, including the Early Warning Score (EWS) were developed to recognise the early signs of deterioration. These scoring systems are relatively simple models using the patients’ vital parameters to assess the degree of illness of the patient.

In general, the higher the EWS, the more likely it is that a patient is clinically deteriorating and subsequently becomes critically ill. 5 This use of an EWS has proven to be efficient for detecting deteriorating patients on the wards. 6 When a deteriorating patient is identified, the Medical Emergency Team can be consulted, and more appropriate care can be provided. The implementation of EWS-based systems can lead to a reduction in SAEs and reduced time to ICU admission in deteriorating patients. 7

As the EWS-based system has been shown to be effective in general wards, the model has been increasingly implemented in other aspects of acute care, that is, the prehospital and ED settings. 8–11 Several studies suggest that EWS can be useful in the entire acute care chain. Prior studies showed a MEWS performance in the ED setting of area under the receiver operating characteristics curve (AUROC) 0.65. 12

However, it is unclear what moment in the acute care chain has the highest accuracy in predicting deterioration.

Timely interventions such as administration of antibiotics, and fluid challenges strongly affect vital parameters and overall survival. 13 These interventions may stabilise the patient and prevent further deterioration, which influences the EWS.

The primary aim of this study was to determine at which time point, from the first moment of contact with the EMS to admission to a nursing ward, an EWS is most accurate in detecting a deteriorating patient. Although the National EWS is generally slightly more accurate compared with the Modified Early Warning Score (MEWS), 14 we studied the performance of MEWS, as this is the tool regularly used in the Netherlands.

Study design and population

This was a prospective observational multicentre study, conducted at a university hospital, serving as a level 1 trauma centre with two locations. All adult patients (18 years and older) brought by ambulance to one of these two centres between 11 March and 28 October 2021, were included. Interhospital transfers and patients receiving prehospital cardiopulmonary resuscitation were excluded. Participants gave informed consent before taking part.

Data collection

Data were collected by a researcher present during EMS presentation between 10:00 hours and 18:00 hours on workdays, as during this period most ambulances arrive at the EDs of both centres. As we recorded data up to 3 hours after ED presentation, data were obtained until 21:00 hours. Patient characteristics, including vital parameters measured at four time points were collected on paper forms: prehospital (recorded by the ambulance); at ED admission (±15 min); at 1 hour (±15 min); and at 3 hours (±30 min) after ED arrival. Three-day outcome was obtained from the electronic patient records. All obtained data were processed using a standardised data worksheet. Collected data were anonymously processed using an online data collection system (Castor eClinical Data Management).

Endpoints and definitions

The primary outcome was the performance of MEWS in predicting critical illness for all four time points during which data were collected.

Secondary outcome was the association between the MEWS over time (ie, increase of MEWS 1 hour after ED admission compared with prehospital MEWS) and subsequent development of critical illness.

Critical illness was defined as mortality; ICU admission and/or myocardial infarction (as concluded by a cardiologist) all within 3 days after ED presentation.

Primary and secondary outcome was assessed by investigating the electronic medical records on day 4 after the initial ED admission. MEWS was thereafter calculated using the vital parameters at each time point; see figure 1 for thresholds of the MEWS.

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Modified Early Warning Score.

Missing data

Previous studies have shown that the temperature and level of consciousness of patients generally remain constant from transportation by EMS to arrival at the ED. 13 Therefore, in any cases where the temperature or level of consciousness of a patient was recorded prehospitally but missing at admission or vice versa, the recorded values for these parameters were used. MEWS was then calculated if a minimum of four out of six vital parameters was available with the one or two missing parameters considered normal. In choosing this method we acted on the assumption that diverging vital parameters would have been registered by the ED nurse. If more than two vital parameters were missing for a certain point in time, the MEWS at that time was not calculated. Patients for whom the MEWS could not be calculated were excluded from analysis for that specific point in time.

Sensitivity analysis

Model performance was tested after excluding patients with SARS-CoV-2 infection, as patients with COVID-19 are known to have relatively stable vital parameters despite being critically ill (as compared with patients without COVID-19). 15

Primary and secondary outcomes

The primary outcome was the performance of MEWS at different periods of time using the outcomes of developing critical illness (as defined above). The secondary outcome was whether an increase in MEWS over time was associated with becoming critically ill.

Statistical analysis

Descriptive and statistical analysis was performed using SPSS V.22.0 (SPSS, Chicago, Illinois, USA). Non-normally distributed continuous variables were described as medians with IQRs and were compared with the Mann-Whitney U test. Categorical variables were described as numbers and percentages and were compared by Pearson’s χ 2 test. The primary outcome was expressed as the AUROC of the MEWS for each time point. Also, for each MEWS between 0 and 5, sensitivity and specificity were calculated.

Using the AUROC derived from MEWS at the different time points, superiority in performance was assessed using the method of Hanley and McNeil. 16 In general, the AUROC is characterised using standard terms, where AUROC 0.6–0.7 is considered a poor testing method, 0.7–0.8 is considered moderate, 0.8–0.9 is good and a test with an AUROC >0.9 is considered an excellent method.

A χ 2 test was used to test whether an increase of MEWS over time had a higher incidence of becoming critically ill compared with a decreased or stable MEWS.

Sample size calculation

For the sample size calculation, the previously reported performance (AUROC 0.65) of MEWS in the ED was used. 12 For the primary outcome (the moment with the highest AUROC of MEWS) based on a 95% CI, 80% power and a 0.1 difference in MEWS, 114 patients were needed to test for statistically significant difference. These calculations were performed in nQuery tool for design of trials, link https://www.statsols.com/nquery .

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Of the 790 patients included in this study, critical illness occurred in 90 patients (11.4%). Prehospital alert calls to the ED were made significantly more often for critically ill patients (88.9% vs 69.4%, p<0.001). Additionally, these patients were assessed more often in either the resuscitation or trauma bay ( table 1 ).

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Patient characteristics

Of the 90 critically ill patients, 41 patients were directly admitted from the ED to the ICU, 16 patients were initially admitted to the ward then went to the ICU, 15 died and 17 had a myocardial infarction, all within 72 hours after ED presentation. Prior to imputing for missing values, the number of complete MEWS values was limited ( table 2 ). After imputing for missing values, the most complete moment of measurements was at ED arrival (94.8%).

Complete MEWS before and after imputing

Primary outcome

MEWS based on vital parameters measured at ED admission had the highest performance with an AUROC of 0.726 ( table 3 , figure 2 ). MEWS based on vital parameters measured 1 hour and 3 hours after ED admission had lower performance ( table 4 ). The performance of MEWS measured at ED admission was not significantly superior compared with the other time points in predicting critical illness.

AUROCs for the prediction of critical illness within 72 hours

Receiver operating characteristics (ROC) curves for prediction of critical illness within 72 hours.

Sensitivity and specificity for cut-off points of MEWS

Of the 790 patients, 82 had a proven SARS-CoV-2 infection. Excluding patients with a proven SARS-CoV-2 infection did not lead to a significant improvement of MEWS accuracy in predicting critical illness ( table 3 ).

In addition, sensitivity and specificity were calculated for each threshold ( table 4 ). For the MEWS measured at ED admission using a cut-off value of 3, sensitivity was 64.0% (95% CI 60.5% to 67.4%) and specificity was 70.1% (95% CI 66.7% to 73.3%).

Secondary outcome

To estimate the influence of a change over time in MEWS (delta MEWS) on outcome, a χ 2 test was performed. An increase in MEWS between the MEWS measured prehospitally and 1 hour after ED admission had an incidence of 25.7% of critical illness, while stable or decreasing MEWS had an incidence of 7.5%. This difference was significantly different (p<0.05) (see table 5 ).

Changes in MEWS during admission and the development of critical illness

While many studies focus on the performance of EWS in either the prehospital or ED setting, little is known about the best timing to use it in the acute care chain. 8 17 18 Therefore, this prospective multicentre study was performed to attempt to direct clinical practice to the best moment in the acute care chain to measure MEWS to identify subsequent development of critical illness in patients brought to the ED by ambulance. Although MEWS calculated based at presentation had the highest accuracy in predicting the development of critical illness, an AUROC of 0.726 was not significantly superior to MEWS measured prehospitally or 1 hour or 3 hours after ED presentation. Also, excluding patients with proven SARS-CoV-2 infection did not lead to an improvement in model performance. While the performance of MEWS found in this study in predicting critical illness is moderate, this was consistent with other studies. 19

Our secondary outcome was to test the correlation between an increase of MEWS over time and the development of critical illness. Prior studies suggest that the trend of MEWS during the first hours of ED presentation may identify clinically deteriorating patients better compared with a single MEWS calculation. 5 10 20 21 Our results indicate that an increase of MEWS between prehospital and at 1 hour after ED admission was significantly correlated with the development of critical illness, p=0.005. Therefore, we suggest that patients with an increasing MEWS during ED stay should be more intensively monitored and early consultation with the ICU consultant may be justifiable.

Limitations

The study has several limitations which may reduce the generalisability of our data and have most likely influenced our results. First, the study ran during the summer months, so season-specific diseases may have occurred. Furthermore, there was a high percentage of missing data for calculating MEWS. We have excluded patients from analysis if two vital parameters other than temperature or mental status were missing. Also, we only included patients arriving between 10:00 hours and 18:00 hours potentially leading to selection bias. To improve the quality and clinical relevance of the data, future studies should also include cases where MEWS is found to be above the cut-off point, even if there are missing variables. Additionally, it is possible that the data were not missing at random. When a patient has normal vital signs during the first check, their vitals usually do not get monitored as frequently as when a patient initially has abnormal vital signs. Therefore, only including cases with known MEWS at all time points can cause a distorted view of the predictive performance of EWS in the ED, since there is a probability that patients with abnormal vital signs are disproportionately over-represented. It is important to record the full vital parameters set needed to calculate MEWS in clinical practice.

Clinical implication

Implementation of a single standard time point for measurement of MEWS in the prehospital setting or ED is clinically not useful due to its moderate performance. However, patients with an increase of MEWS over time is highly correlated with the development of critical illness. Implementing standard repeated measurements in the acute care chain may result in better prediction of which patients are likely to become critically ill.

In conclusion, MEWS based on vital parameters measured at ED presentation has the highest accuracy in predicting the development of critical illness. However, performance is moderate and not significantly better compared with MEWS measured at other moments in the acute care chain. However, an increase in MEWS during the encounter is highly correlated with the development of critical illness. We, therefore, conclude that it would be valuable to assess MEWS over time, rather than only at a single moment.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants. The Medical Ethics Committee of both locations of Amsterdam UMC waived ethics approval for this study (Waiver: W-19_480 # 19.554). Participants gave informed consent to participate in the study before taking part.

• Goldhill DR ,
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Handling editor Kirsty Challen

Contributors LIV and MK: planning, conceptualisation, methodology, data curation and writing original draft. LK: data curation. MLR, PWBN and JL: important intellectual content and guarantor of the article.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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