data analysis using excel case study pdf

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Data Analysis Using Excel Case Study

Data analysis is an essential skill in today’s business world. As organizations deal with increasing amounts of data, it becomes crucial for professionals to make sense of this information and derive useful insights. Excel is a powerful and versatile tool that can assist in analyzing and presenting data effectively, particularly through the use of case studies.

A case study is a detailed examination of a specific situation or problem in order to better understand the complexities involved. By using Excel for data analysis, individuals can explore and analyze the data related to the case study in a comprehensive and structured manner. Excel offers various tools and functionalities, such as PivotTables, slicers, and data visualization features, which allow users to assess patterns, trends, and relationships within the data.

Applying these techniques for data analysis in Excel case studies enables professionals to make well-informed business decisions and communicate their findings effectively. By leveraging the capabilities of Excel in conjunction with case studies, individuals can unlock valuable insights that drive organizational success and contribute to an enhanced understanding of the overall data landscape.

Excel Basics for Data Analysis

Dataset preparation.

When working with Excel, the first step in data analysis is dataset preparation . This process involves setting up the data in a structured format, with clearly defined headers and cells. To start, you must import or enter your data into an Excel spreadsheet, ensuring that each record is represented by a row and each variable by a column. Headers should be placed in the top row and provide descriptive labels for each column. Proper organization of your dataset helps to ensure accurate analysis and interpretation .

For example, suppose you have a dataset that contains the following information:

In this dataset, the headers are “Year,” “Category,” “Sales,” and “Profit.” Each row represents a record, and the cells contain the corresponding data.

Data Cleaning

The next step in data analysis using Excel is data cleaning . Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in your dataset. Common data cleaning tasks include:

• Removing duplicate records,
• Filling in missing values,
• Correcting data entry errors,
• Standardizing and formatting variable names and values.

To perform data cleaning in Excel, you can use various functions and tools:

• Remove duplicates: To remove duplicate records, select your dataset and navigate to the Data tab. Click the “Remove Duplicates” button and select the columns to be used for identifying duplicate rows.
• Fill in missing values: Use Excel functions such as VLOOKUP , HLOOKUP , and INDEX-MATCH to fill in missing values based on other data in your dataset. You can also use the IFERROR function to handle errors when looking up values.
• Correct data entry errors: Use Excel’s “Find and Replace” tool (Ctrl + F) to search for and correct errors in your dataset. You may need to perform this multiple times for different errors.
• Standardize and format variable names and values: Use Excel functions such as UPPER , LOWER , PROPER , and TRIM to standardize text data. Format numerical values using the Number Format options in the Home tab.

By ensuring your dataset is clean and well-organized, you can confidently move forward with more advanced data analysis tasks in Excel.

Powerful Excel Functions

Excel is a versatile tool when it comes to data analysis. There are many powerful functions that can help you perform complex calculations and analysis easily. In this section, we will explore some of the top functions in three categories: Text Functions, Date Functions, and Lookup Functions.

Text Functions

Text Functions are crucial when working with large sets of data containing text. These functions help in cleaning, extracting, and modifying text data. Some key text functions include:

• LEFT : Extracts a specified number of characters from the beginning of a text string.
• RIGHT : Extracts a specified number of characters from the end of a text string.
• MID : Extracts a specified number of characters from a text string, starting at a specified position.
• TRIM : Removes extra spaces from text, leaving a single space between words and no space at the beginning or end of the text.
• CONCATENATE : Joins multiple text strings into one single string.
• FIND : Locates the position of a specific character or text string within another text string.

Date Functions

Date Functions are essential for dealing with dates and times in data analysis. These functions help in calculating the difference between dates, extracting parts of a date, and performing various date-related calculations. Some notable date functions include:

• TODAY : Returns the current date.
• NOW : Returns the current date and time.
• DATEDIF : Calculates the difference between two dates in days, months, or years.
• DATE : Creates a date by combining individual day, month, and year values.
• WEEKDAY : Returns the day of the week corresponding to a specific date, as an integer between 1 (Sunday) and 7 (Saturday).
• EOMONTH : Returns the last day of the month for a given date.

Lookup Functions

Lookup Functions are powerful tools used to search and retrieve data from a specific range or table in Excel. These functions can save time and effort when working with large datasets. Some essential lookup functions include:

• VLOOKUP : Searches for a specific value in the first column of a range and returns a corresponding value from a specified column.
• HLOOKUP : Searches for a specific value in the first row of a range and returns a corresponding value from a specified row.
• INDEX : Returns a value from a specific cell within a range, using row and column numbers.
• MATCH : Searches for a specific value in a range and returns its relative position within that range.
• XLOOKUP : Performs a lookup by searching for a specific value in a range or table and returning a corresponding value from another column or row (available only in Excel 365 and Excel 2019).

These powerful Excel functions can help make the process of data analysis more efficient and accurate. In combination with appropriate formatting, tables, and other visual aids, these functions can greatly enhance your ability to process and understand large datasets.

Related Article: Excel Functions for Data Analysts.

Data Exploration and Visualization

In the process of data analysis using Excel, data exploration and visualization play essential roles in revealing patterns, trends, and relationships within the data. This section will cover two primary techniques for data visualization in Excel: Charts and Trends, and Pivot Tables and Pivot Charts.

Charts and Trends

Charts in Excel are a highly effective method of uncovering patterns and relationships within the dataset. There are various types of charts available in Excel that cater to different use cases, such as bar charts, line charts, and scatter plots. These chart types can be customized to suit the needs of the analysis and to emphasize specific trends or patterns.

Trends in the data can be identified with the help of charts, and Excel offers trend lines functionalities to visualize these trends more clearly. By applying a trend line, one can easily identify the overall direction (positive or negative) of the dataset and make predictions based on this information. Additionally, Excel offers built-in formatting options that can help emphasize certain data points or highlight particular trends for easier interpretation.

Pivot Tables and Pivot Charts

Pivot Tables are another powerful data analysis feature in Excel. They allow the user to summarize, reorganize, and filter data by dragging and dropping columns into different areas. This enables the user to analyze data across multiple dimensions, revealing hidden insights and patterns.

To complement Pivot Tables, Excel also offers Pivot Charts, which allow users to create dynamic visualizations derived from the Pivot Table data. Pivot Charts offer the same chart types as regular Excel charts but with the added capability to update the chart when the Pivot Table data is altered. This makes Pivot Charts ideal for creating interactive and easily updatable visualizations.

Overall, incorporating these techniques into the data analysis process can enhance understanding and unveil valuable insights from the dataset. When using Excel for data analysis, data exploration and visualization with Charts and Trends, as well as Pivot Tables and Pivot Charts, can provide a comprehensive and insightful overview of the data in question.

Case Study: Covid-19 Data Analysis

Data collection and cleaning.

The Covid-19 pandemic has generated vast amounts of data, requiring researchers and analysts to collect, clean, and organize data sets to gain valuable insights. Several sources, such as the World Health Organization and Johns Hopkins University , provide updated information on confirmed cases, recoveries, and deaths.

Data collection starts with gathering raw data from various sources. These data sets may have inconsistencies, missing values, or discrepancies, which need to be addressed to ensure accurate analysis. Data cleaning is a critical step in this process, involving tasks such as removing duplicates, filling in missing values, and correcting errors.

Exploratory Data Analysis

Once the data is clean and organized, exploratory data analysis (EDA) can be conducted using tools like Excel. EDA helps analysts understand the data, identify patterns, and generate hypotheses for further investigation.

Some useful techniques in conducting EDA in Excel include:

• Pivot Tables : These allow users to summarize and reorganize data quickly, providing aggregated views of the data.
• Charts and Graphs : Visual representations of data, such as bar charts or line graphs, can display trends, correlations, or patterns more clearly than raw numbers.
• Descriptive Statistics : Excel’s built-in functions allow easy calculation of measures such as mean, median, and standard deviation, providing a preliminary statistical analysis of the data.

In the context of Covid-19 data, EDA can help reveal important information about the pandemic’s progression. For example, analysts can:

• Compare infection rates across countries or regions
• Monitor changes in case numbers over time
• Evaluate the effectiveness of public health interventions and policies

The insights gained from exploratory data analysis can guide further research, inform decision-making, and contribute to a better understanding of the pandemic’s impact on public health.

Case Study: Stock Market Data Analysis

Data collection and preparation.

The first step in the stock market data analysis case study is collecting and preparing the data. This process involves gathering historical stock prices, trading volumes, and other relevant financial metrics from reliable sources. The data can be cleaned and organized in Excel, removing any errors or inconsistencies. It’s essential to verify the collected data’s accuracy to ensure the analysis’s validity.

After preparing the financial data, the next step is to compute essential measures and ratios. These may include:

• Price-to-Earnings (P/E) Ratio
• Dividends Yield
• Total Return
• Moving Averages

Calculating these ratios and measures provides a general overview of a company’s performance in the stock market, which can be further analyzed with Excel tools.

Profit and Loss Analysis

In this stage of the case study, profit and loss analysis is conducted to assess the stock’s performance. Using Excel PivotTables, we can summarize the data to identify trends or patterns in the stock market. For instance, we can analyze the historical profits and losses of multiple stocks during a specific state or market condition.

Analyzing profit and loss data can also be done with natural language capabilities in Excel. This feature allows us to ask questions about the dataset, and Excel will produce relevant results. For example, we could pose a question like “Which stocks had the highest profit margins in the last quarter?” or “What is the average loss for the technology sector?”

After exploring the profits and losses of the stocks, we can gain insights into which stocks or sectors are more profitable or risky. This information can help potential investors make informed decisions about their investment strategies. Additionally, the insights from the case study can serve as a reference point for future stock market analyses.

Remember, this case study only serves as an example of how to conduct stock market data analysis using Excel. By adapting and expanding on these techniques, one can harness the power of Excel to explore various aspects of financial markets and derive valuable insights.

Case Study: San Diego Burrito Ratings

Data gathering and cleaning.

The main objective of this case study is to evaluate and analyze the various factors that contribute to the ratings of San Diego burritos. The data used in this analysis is collected from different sources, which include customer reviews and ratings from Yelp, along with other relevant information about burrito sales and geographical distribution. The raw data is then compiled and cleaned to ensure that it is consistent and free from any discrepancies or errors. This process involves standardizing the fields and records, as well as filtering out any irrelevant information. The cleaned data is then organized into a structured format, which is suitable for further analysis using Excel PivotTables and Charts.

Use of Pivot Tables and Charts

After cleaning and organizing the data, Excel PivotTables are utilized to analyze the regional distribution of San Diego burrito ratings. By categorizing the data based on regions, such as East and West, it becomes convenient to identify the ratings and sales trends across these regions. The organized data is then sorted based on the ratings and popularity of burrito establishments within specific densely populated areas.

Using Pivot Charts, a graphical representation of the data is created to provide a clear and comprehensive visual of the ratings distribution in different regions of San Diego. It becomes easier to discern patterns and trends, allowing for the development of informed conclusions on the factors influencing the popularity and success of burrito establishments.

Throughout the analysis, various parameters are investigated, which include the relationship between ratings and sales, the potential impact of particular fields on popularity, and the apparent differences between densely populated regions in terms of burrito preferences. By utilizing PivotTables and Charts confidently, it is possible to draw insights and conclusions that can help optimize marketing strategies, guide customer preferences, and influence the overall success of burrito establishments across San Diego.

Case Study: Shark Attack Records Analysis

Data collection and pre-processing.

In this case study, the primary focus is on the analysis of shark attack records recorded between 1900 and 2016, consisting of just under 5,300 records or observations. To begin the analysis, the data needs to be collected from a reliable source and pre-processed to ensure its accuracy and relevance.

Data pre-processing is an essential step to prepare the dataset for analysis. It involves checking for missing values, outliers, and inconsistencies in the data. Additionally, it may also require converting the data into a suitable format, such as categorizing dates or splitting location information into separate columns (latitude and longitude).

Identifying Trends and Patterns

Once the dataset has been pre-processed, it’s time to dive into the analysis using Microsoft Excel. Excel offers a fast and central way to analyze data and search for trends and patterns within shark attack records. One powerful tool for this purpose is Excel’s PivotTables, which allows users to easily aggregate and summarize data.

Some possible trends and patterns that can be identified through the analysis of shark attack records include:

• Temporal Trends: Analyzing the frequency of shark attacks over time to identify any patterns in the occurrence of attacks, such as seasonality or specific years with higher attack rates.
• Geographical Patterns: Identifying areas with a higher concentration of shark attacks, which can provide insights into hotspots and potentially dangerous locations.
• Victim Demographics: Examining the demographics of shark attack victims, such as age, gender, and activity type, to determine if certain groups are more prone to attacks.
• Species Involved: Investigating the types of shark species responsible for attacks and their relative frequency in the dataset.

By utilizing Excel’s data analysis tools and PivotTables, researchers can confidently and clearly identify trends and patterns in the shark attack records, providing valuable insights into shark behavior and risk factors associated with shark attacks. This analysis can be helpful in understanding and managing the risks associated with shark encounters for both public safety and conservation efforts.

Related Article: How to Solve Data Analysis Real World Problems.

Additional Resources and Exercises

Kaggle and data analysis courses.

Kaggle is a popular platform that offers data science competitions, datasets, and courses to help you improve your data analysis skills in Excel. The courses are designed for various skill levels, and they cover essential concepts like PivotTables and data visualization. The comprehensive exercises and practical case studies provide a real-world context for mastering data analysis techniques.

The course reviews on Kaggle are usually quite positive, with many users appreciating the knowledgeable instructors and engaging content. If you’re looking to become a data analyst or enhance your existing skills, exploring the data analysis courses on Kaggle is a great starting point.

Power Query in Excel

Power Query is a powerful data analysis tool in Excel that enables you to import, transform, and combine data from various sources. This feature is particularly useful when working with large datasets or preparing data for analysis. There are numerous resources available to learn how to use Power Query effectively.

To practice using Power Query, consider working on exercises that focus on data cleansing, data transformation, and data integration. As you progress, you will gain a deeper understanding of the various Power Query functionalities and become more confident in your data analysis abilities.

In conclusion, engaging with additional resources like Kaggle courses and Power Query exercises will help you hone your Excel data analysis skills and enable you to tackle complex case studies with ease.

Frequently Asked Questions

How can excel be used for effective case study analysis.

Excel is a versatile tool that can be utilized for effective case study analysis. By organizing and transforming data into easily digestible formats, users can better identify trends, patterns, and insights within their data sets. Excel also offers various functions and tools, such as pivot tables, data tables, and data visualization, which enable users to analyze case study data more efficiently and uncover valuable information.

Which Excel functions are most useful for data analysis in case studies?

There are numerous Excel functions that can be highly useful for data analysis in case studies. These include:

• VLOOKUP, which allows users to search for specific information in large data sets
• INDEX-MATCH, a more advanced alternative to VLOOKUP that’s capable of handling more complex data structures
• IF, which helps in making conditional statements and decisions in data analysis
• AVERAGE, MAX, MIN, and COUNT for basic data aggregation
• SUMIFS and COUNTIFS, which allow users to perform conditional aggregation based on predefined criteria

What are some examples of data analysis projects using Excel?

Many different projects can benefit from data analysis using Excel, such as financial analysis, market research, sales performance tracking, and customer behavior analysis. Businesses across industries are known to use Excel for evaluating their case studies and forming data-driven decisions based on their insights.

How can Excel pivot tables aid in analyzing case study data?

Pivot tables in Excel are powerful, enabling users to summarize and analyze large data sets quickly and efficiently. They allow users to group and filter data based on different dimensions, making it much easier to identify trends, patterns, and relationships within the data. Additionally, pivot tables provide user-friendly drag-and-drop functionalities, allowing for easy customization and requiring minimal Excel proficiency.

In which industries is Excel data analysis most commonly applied in case studies?

Excel data analysis is widely used across various industries for case studies, including:

• Finance and banking, for analyzing investment portfolios, risk management, and financial performance
• Healthcare, for patient data analysis and identifying patterns in disease occurrence
• Marketing and sales, to analyze customer data and product performance
• Retail, for inventory management and sales forecasting
• Manufacturing, to evaluate the efficiency and improve production processes

What steps should be followed for a successful data analysis process in Excel?

A successful data analysis process in Excel typically involves the following steps:

• Data collection: Gather relevant data from various sources and consolidate it in Excel.
• Data cleaning and preprocessing: Remove any errors, duplicate records, or missing values in the data, and reformat it as necessary.
• Data exploration: Familiarize with the data, identify patterns, and spot trends through descriptive analysis and visualization techniques.
• Data analysis: Use relevant functions, formulas, and tools such as pivot tables to analyze the data and extract valuable insights.
• Data visualization: Create charts, graphs, or dashboard reports to effectively visualize the findings for improved understanding and decision-making.

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This project is done using Advanced-Excel for in-depth exploratory data analysis on loan applications, identifying patterns to differentiate loan defaulters from reliable borrowers. By addressing missing data, outliers, and imbalances, I've unveiled influential factors, aiding informed lending decisions.

Sourav-Pattanayak/Bank-Loan-Case-Study

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Bank-loan-case-study.

In this project, I have used Exploratory Data Analysis (EDA) to predict loan default among finance company customers. I addressed missing data, identified outliers, analyzed data imbalance, conducted various analyses on customer and loan attributes, and identified top correlations for loan default. These insights informed better loan approval and risk assessment decisions.

It involved comprehensive data analysis of a loan application dataset, focusing on tasks that are essential for ensuring data quality, identifying outliers, assessing data balance, and exploring relationships between variables.

A. Handling Missing Data 🧩

Identified missing data using Excel functions like COUNT, ISBLANK, and IF. Employed Excel built-in features to decide on appropriate methods for dealing with missing data, including imputation using AVERAGE or MEDIAN. B. Identifying Outliers 🚀

Detected outliers in the dataset by utilizing Excel statistical functions like QUARTILE and IQR. Employed conditional formatting and thresholds to identify potential outliers, ensuring data quality. C. Analyzing Data Imbalance ⚖️

Determined data imbalance in the loan application dataset by calculating the ratio of data imbalance using Excel functions like COUNTIF and SUM. Visualized the distribution of the target variable through pie charts or bar charts to highlight class imbalance. D. Exploring Data Attributes 📊

Conducted univariate analysis to understand variable distributions using Excel functions like COUNT, AVERAGE, and MEDIAN. Utilized Excel's sorting, filtering, and pivot tables for segmented univariate and bivariate analysis, comparing variable distributions and exploring relationships with the target variable. E. Identifying Top Correlations 🔍

Segmented the dataset based on different scenarios (e.g., clients with payment difficulties) and identified the top correlations for each segment using Excel functions like CORREL. Visualized the correlations between variables within each segment using correlation matrices. This project aimed to provide valuable insights into the factors influencing loan default, ensuring data quality, and addressing issues such as missing data, outliers, and data imbalance. The analysis results can be used to guide decision-making and develop predictive models for loan applications. 📈

The Complete Microsoft Excel Data Analysis Bootcamp

• Post published: 30 August, 2024
• Post category: StudyBullet-19
• Reading time: 6 mins read

What you will learn

• Navigating the Excel Interface
• Basic Excel Functions and Formulas
• Sorting and Filtering Data
• Data Transformation Techniques
• Standard Deviation and Variance
• Data Visualization with Charts
• Analyzing Data with Pivot Tables
• Install Data Analysis Toolpak
• Using Analysis Tools (e.g., regression, correlation)
• Creating Interactive Dashboards
• Analyzing Sales Data
• Data Analysis For Decision Making
• Sales Projection and Forecast Calculation

Why take this course?

Unlock the full potential of Microsoft Excel with “The Complete Microsoft Excel Data Analysis Bootcamp.” This comprehensive course is designed to take you from a beginner to an advanced user, equipping you with the skills to perform powerful data analysis and make data-driven decisions confidently.

Whether you’re a professional looking to enhance your data analysis skills, a student eager to learn one of the most valuable tools in the job market, or a business owner wanting to make more informed decisions, this bootcamp covers everything you need. You’ll learn to use Excel’s robust features for data cleaning, analysis, visualization, and automation, mastering tools such as PivotTables, VLOOKUP, Power Query, and advanced charting techniques.

Through step-by-step tutorials, real-world examples, and hands-on exercises, you’ll gain the knowledge to analyze large datasets, create insightful reports, and automate repetitive tasks, saving you time and effort. By the end of this course, you’ll be proficient in using Excel for data analysis, making you an invaluable asset in any data-driven role.

What You’ll Learn:

• Excel Fundamentals: Understand the basics of Excel, including navigation, functions, and formulas.
• Data Cleaning and Preparation: Learn techniques to clean and prepare datasets for analysis, ensuring accuracy and reliability.
• Advanced Formulas and Functions: Master complex functions like VLOOKUP, INDEX-MATCH, and array formulas to manipulate data efficiently.
• PivotTables and PivotCharts: Use PivotTables and PivotCharts to summarize, analyze, and visualize large datasets with ease.
• Data Visualization: Create dynamic and interactive charts and dashboards to present data insights clearly and effectively.
• Power Query and Power Pivot: Explore Excel’s powerful data modeling and analysis tools to handle large datasets and complex calculations.
• Automation with Macros and VBA: Learn to automate repetitive tasks using Excel’s macro recorder and Visual Basic for Applications (VBA).
• Real-World Projects: Apply your skills to real-world scenarios with hands-on projects that simulate actual business data challenges.

Includes This Course:

Module 1: Excel Basics

• Entering and Editing Data
• Data Validation

Module 2: Data Cleaning and Preparation

• Removing Duplicates
• Handling Missing Values
• Structuring Data
• Using Text Functions

Module 3: Data Analysis Techniques

• Mean, Median, Mode
• Creating Pivot Tables
• Using Pivot Charts for Visualization

Module 4: Advanced Excel Functions

• IF, AND, OR Functions
• Conditional Formatting
• VLOOKUP, HLOOKUP, INDEX, MATCH
• Using Dynamic Ranges

Module 5: Data Visualization

• Types of Charts and their Uses
• Customizing Chart Elements
• Using Sparklines

Module 6: Case Studies and Real-World Applications

• Monthly Budget Analysis

Who This Course is For:

• Professionals looking to enhance their Excel skills for data analysis.
• Students and graduates seeking to gain a competitive edge in the job market.
• Business analysts and managers who want to make data-driven decisions.
• Anyone interested in mastering Excel for personal or professional use.

Enrolling in this course will transform you into an Excel power user, capable of analyzing data, creating insightful reports, and automating workflows with confidence. With practical, hands-on experience and expert instruction, you’ll be ready to tackle any data challenge and drive impactful decisions in your organization. Start your journey to Excel mastery today!

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Excel Sample Data (Free Download 13 Sample Datasets)

Project Management Sample Data

A project management sample data is suitable for various types of data filtering, analyzing, and visualizing. Here are the variables that we have included in the sample data:

• Project Name
• Assigned to
• Days Required

Here is a preview of the project management dataset:

Download the Sample Workbook

Inventory Records Sample Data in Excel

Inventory management records consist of product and stock information. In our sample dataset, we have included the following fields:

• Product Name
• Opening Stock
• Purchase/Stock-in
• Number of Units Sold
• Hand-in-stock
• Cost Price Per Unit
• Cost Price Total

Here is a preview of the inventory records sample data:

Call Center Customer Satisfaction Data

Call centers deal with customer service and receive various types of feedback from customers. In our Call Center Customer Satisfaction data, we have included the following fields:

• Customer Name
• Call Timestamp
• Response Time
• Call Duration
• Call Center

Here is a preview of our sample data:

Supermarket Sales Sample Data in Excel

Supermarket sales sample data is a popular dataset for learning and practicing your Excel skills. Here is the list of variables we have included in our supermarket sales sample data:

• Retail Price
• Order Quantity

Here is a preview of the sample supermarket sales data in Excel:

Download the Practice Workbook

Employee Management Data

Employee management data contains information on all employees in an organization. In our sample employee management data in Excel, we have listed the following variables:

• Employee ID
• Designation
• Annual Salary

Here is a preview of the employee management data:

Technological Product Sample Data

Any technological product information requires various specifications. In our sample dataset, we have listed the following specifications:

• Country of Origin
• Release Date

The following image shows a preview of our sample technological product dataset:

Engineering and Manufacturing Sample Data

Engineered or manufactured products also require various specifications. Here is the list of variables we have included in our sample engineering and manufacturing sample data:

• Manufacturer
• Stock Quantity

Here is a preview of the sample dataset:

Students Marksheet Sample Data in Excel

A student mark sheet contains the student identifiers and marks in various subjects. In our sample students marksheet dataset, we have listed the following variables:

• Marks in Mathematics
• Marks in Physics
• Marks in Chemistry

Here is a preview of the sample student marksheet dataset:

2022 FIFA World Cup Performance Data

In our sample dataset, we have listed the information of each player from the World Cup-winning Argentina team. Here is the list of variables we have included:

• Player Name
• Jersey Number
• Appearances
• Goals Scored
• Assists Provided
• Dribbles per 90 Min
• Interceptions per 90 Min
• Tackles per 90 Min
• Total Duels Won per 90 Min

You can preview the sample dataset in the following image:

Tokyo Olympic Data

This sample dataset contains the team names, number of Gold, Silver, Bronze, and total medals, and ranking of teams (based on gold medal and total medal count) in the Tokyo Olympics. Here is a preview of the sample dataset:

Healthcare Insurance Sample Data in Excel

The price of healthcare insurance depends on various factors such as current age, BMI, smoking habits,  etc. In our sample healthcare insurance dataset, we have listed the following variables:

• Smoking Status
• Insurance Price

Travel Destination Distance Data in Excel

While deciding on a travel destination, we need to take the distance, available travel modes, travel duration, etc. factors into consideration. In our sample travel destination dataset, we have listed the following variables:

• Source City
• Source Latitude
• Source Longitude
• Destination City
• Destination Latitude
• Destination Longitude
• Distance (in km)
• Distance (in mile)
• Travel Mode

Netflix Movies Sample Data

The movie dataset provided in this section contains the following variables:

• IMDb Rating

You can preview the dataset in the image below:

Excel Sample Data: Knowledge Hub

• GST State Code List in Excel
• Historical Data of NSE Stocks in Excel

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What is ExcelDemy?

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Seemanto Saha graduated in Industrial and Production Engineering from Bangladesh University of Engineering and Technology. He has been with ExcelDemy for a year, where he wrote 40+ articles and reviewed 50+ articles. He has also worked on the ExcelDemy Forum and solved 50+ user problems. Currently, he is working as a team leader for ExcelDemy. His role is to guide his team to write reader-friendly content. His interests are Advanced Excel, Data Analysis, Charts & Dashboards, Power Query,... Read Full Bio

Thanks for the DATA SETS !

Hello Margit ,

You are most welcome.

Regards ExcelDemy

Thank you so much

Hello Usama Jamil ,

I needed Oil parameters Offshore Foundation design 2015 excel sheet access

Hello Ameer Ahmed ,

We don’t have any dataset you asked for and your question is not clear enough. Our datasets are given in the article.

Thank you so much for the data sets.

Hello Gaurav,

Where are the solutions to these problems? Would like to check against what I did

Hello Cynthia Satterwhite ,

Neither any problem nor any solution are given. These dataset are given to practice by yourself.

Wow Great Readymade Excel Sheets, Can You provide any excel sheet to maintain monthly calibration details for internal lab

Hello Karunesh Pandey ,

As calibration details varies with lab type we can provide you the criteria. You can fill out your information and edit the list.

nice work thank you ! I need dataset for students, subjects, teachers and departments hope U help.

Hello Fulad

Thanks for your compliment! As you requested, you can use the following dataset, which contains data with your mentioned fields:

You can download the workbook from the following link:

DOWNLOAD WORKBOOK

I hope you have found the dataset you were looking for; good luck.

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ExcelDemy good work. It is difficult to get excel datasets for predictive analysis. please can I get a comprehensive CRM (customer online journey) or Supply chain dataset (Procurement-Logistics- inventory-sales) for retail industry.

Hello Amara ,

Here I’m attaching a sample dataset for CRM and Supply chain. You can modify this dataset based on your requirements. Dataset of CRM: CRM Dataset.xlsx Dataset of Supply Chain: Supply Chain Dataset.xlsx

I need a dataset for Customs Compliance Monthly Report excel sheet to help me populate:

General data: •Import/Export numbers. [Breakdown of EC, Sanctions, IP, FC, DG, Incoterms, duty spend] •CCAR performance. •Procedures status. Declaration Accuracy: •Export (Export Control/IP/General). •Import (General/IP).

Inward Processing: •Quarterly update. •Renewal.

Audit: •Export/Export Control. •Tariff Classification. •Preferential Origin. •Finance/Tax. •Documentation. •NISR audit (high risk).

Project status updates: •Incoterms. •Plain Language Customs Descriptions. •Service Level Agreements. •D365 (Service Centre). Regards Dems

Hello Demba ,

Download the Excel file from here : Customs Compliance Monthly Report.xlsx

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eTable 1. Classification of Indications

eTable 2. Dechallenge and Rechallenge With Semaglutide

eTable 3. Dechallenge and Rechallenge With Liraglutide

eTable 4. Sex, Median Age and Dose (IQR) for Suicidal Ideation ADRs by Drug and Indication

eTable 5. Coreported Psychiatric Reactions for Semaglutide

eTable 6. Coreported Psychiatric Reactions for Liraglutide by Indication

eTable 7. Number of Cases, Noncases, Other Adverse Drug Reactions (ADRs) and Total Number of Other Reports in the Database for Semaglutide

eTable 8. Number of Cases, Noncases, Other Adverse Drug Reactions (ADRs) and Total Number of Other Reports in the Database for Liraglutide

eTable 9. Disproportionality Analysis of Semaglutide-Associated Suicidal Ideation Compared With All Other Drugs in the Database in Female and Male Patients Separately

eTable 10. Number of Semaglutide-Associated Cases of Adverse Drug Reactions (ADRs) by Year

eTable 11. Number of Liraglutide-Associated Cases of Adverse Drug Reactions (ADRs) by Year

eReferences.

Data Sharing Statement

• GLP-1 Receptor Agonists and Suicidality JAMA Network Open Invited Commentary August 20, 2024 Francesco Salvo, MD, PhD; Jean-Luc Faillie, MD, PhD

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Schoretsanitis G , Weiler S , Barbui C , Raschi E , Gastaldon C. Disproportionality Analysis From World Health Organization Data on Semaglutide, Liraglutide, and Suicidality. JAMA Netw Open. 2024;7(8):e2423385. doi:10.1001/jamanetworkopen.2024.23385

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Disproportionality Analysis From World Health Organization Data on Semaglutide, Liraglutide, and Suicidality

• 1 The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, New York
• 2 Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, Glen Oaks, New York
• 3 Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
• 4 Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
• 5 Institute of Primary Care, University of Zurich and University Hospital Zurich, Zurich, Switzerland
• 6 WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
• 7 Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
• 8 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
• Invited Commentary GLP-1 Receptor Agonists and Suicidality Francesco Salvo, MD, PhD; Jean-Luc Faillie, MD, PhD JAMA Network Open

Question   Are glucagon-like peptide-1 receptor agonists semaglutide and liraglutide, which were originally introduced for the treatment of type 2 diabetes and are frequently prescribed due to their weight loss properties, associated with disproportionately increased reporting of suicidality?

Findings   This disproportionality analysis through the case-control design based on the World Health Organization global database collecting suspected adverse drug reactions, identified a disproportionality signal of suicidal ideation with semaglutide, which remained significant when comparing semaglutide with dapagliflozin and metformin and in the subgroup of patients with coreported use of antidepressants and benzodiazepines.

Meaning   A detected signal of semaglutide-associated suicidal ideation warrants urgent clarification.

Importance   Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have gained use primarily due to their weight-reduction effects, although a regulatory review was undertaken for potential suicidality concern.

Objectives   To evaluate potential signals for suicidal and self-injurious adverse drug reactions (ADRs) associated with the GLP-1 RAs semaglutide and liraglutide.

Design, Setting, and Participants   Disproportionality analysis through the case-control design using the World Health Organization (WHO) global database of suspected ADRs. Participants were clinical patients worldwide experiencing an ADR suspectedly attributable to semaglutide or liraglutide in the database from inception to August 30, 2023. Data were analyzed from September to December 2023.

Exposure   Treatment with semaglutide or liraglutide regardless of indication or treatment duration.

Main Outcomes and Measures   Reporting odds ratio (ROR) and the bayesian information component (IC) with 95% CIs were calculated as measures of disproportionate reporting of suicidal and self-injurious ADRs associated with semaglutide and liraglutide compared with all other medications. Sensitivity analyses were conducted including patients with coreported use of antidepressants and benzodiazepines and using dapagliflozin, metformin, and orlistat as comparators. A disproportionality signal was considered when the lower limits of the ROR and IC were above 1 and 0, respectively.

Results   A total of 107 (median [IQR] age 48 [40-56] years; 59 female patients [55%]) and 162 (median [IQR] age 47 [38-60] years; 100 female patients [61%]) cases of suicidal and/or self-injurious ADRs were reported between November 2000 and August 2023 with semaglutide and liraglutide, respectively. Significant disproportionality was detected only for semaglutide-associated suicidal ideation (ROR, 1.45; 95% CI, 1.18-1.77; IC, 0.53; 95% CI, 0.19-0.78), which remained significant in patients with coreported use of antidepressants (ROR, 4.45; 95% CI, 2.52-7.86; IC, 1.96; 95% CI, 0.98-2.63) and benzodiazepines (ROR, 4.07; 95% CI, 1.69-9.82; IC, 1.67; 95% CI, 0.11-2.65), when compared with dapagliflozin (ROR, 5.56; 95% CI, 3.23-9.60; IC, 0.70; 95% CI, 0.36-0.95), metformin (ROR, 3.86; 95% CI, 2.91-5.12; IC, 1.20; 95% CI, 0.94-1.53) and orlistat (ROR, 4.24; 95% CI, 2.69-6.69; IC, 0.70; 95% CI, 0.36-0.95).

Conclusions and Relevance   This study using the WHO database found a signal of semaglutide-associated suicidal ideation, which warrants urgent clarification.

Over the past decade, obesity trends have reached epidemic standards. 1 In this context, the understanding of glucagon-like peptide-1 (GLP-1)–based mechanisms and related anorectic properties of GLP-1 receptor agonists (RAs) have revolutionized the treatment of obesity. 2 In addition to enhancing glucose-dependent insulin release, GLP-1 RAs may reduce glucagon secretion as well as gastric emptying. 2 Originally introduced for the treatment of type 2 diabetes, the effect of GLP-1 RAs on weight loss soon caught research attention. 3 The weight loss properties of liraglutide and semaglutide quickly went viral on social media, leveraging their promotion as lifestyle drugs not just for patients with diabetes 4 and leading to a global shortage. 5 Currently, it is estimated that approximately 10% of patients with type 2 diabetes in the US are prescribed GLP-1 RAs. 6 Accordingly, regulatory authorities over the world have urged health care professionals to direct available supplies to patients with type 2 diabetes who are inadequately managed with other medications over off-label prescriptions. 7 , 8 Despite the promising potential of GLP-1 RAs, serious concerns have been raised about their safety. 9 , 10 On July 3, 2023, a series of reports for suicidal or self-harming thoughts associated with liraglutide or semaglutide triggered an ongoing review by the European Medicines Agency (EMA). 11 Previously, in the approval trials, 9 of the 3384 patients treated with liraglutide (0.27%) had reported suicidal ideation compared with 2 of 1941 patients allocated to the placebo group (0.10%). 12 On the other hand, no patients using semaglutide for obesity had developed suicidal ideation, 13 , 14 and no mental health differences were observed in adolescents, with a lower percentage of participants in the semaglutide group than in the placebo group reporting psychiatric adverse events (7% vs 15%). 15

The EMA-led investigation might have a global impact, given that liraglutide and semaglutide are administered to more than 20 million people per year. 11 This investigation was expected to be completed in November 2023, but ultimately updated in April 2024 after requesting further clarifications. 16 In the meantime the British Medicines and Healthcare Products Regulatory Agency and the US Food and Drug Administration (FDA) also announced a similar investigation. 17 So far both EMA and FDA declared that they did not find any clear demonstration of a relationship between GLP-1 RAs and suicide based on the available evidence, although the FDA investigation is still ongoing. 18 , 19

Marketing companies stated that warnings about suicidal behavior and ideation are formally required for medications prescribed for chronic weight management affecting the central nervous system. 20 The first 2 pharmacovigilance studies on the topic only included partial data from the US, 21 , 22 and a report from the EMA pharmacovigilance database did not assess disproportionality. 23 Typically, patients with suicidality are excluded from clinical trials; therefore, the reports from clinical trials may be less precise in capturing the risk of suicidal or self-injurious adverse drug reactions (ADRs) in later practice. In this context, we aimed to assess suicidal and/or self-injurious ADRs associated with liraglutide or semaglutide at a global level, using a World Health Organization (WHO) database of individual case safety reports (ICSRs).

This case-control study was conceived as a disproportionality analysis of the WHO Vigibase, a consolidated tool for postmarketing surveillance. In the past, large-scale ICSR databases attracted interest for early detection and characterization of emerging safety issues. 24 - 26

All procedures and analyses adhered to the Uppsala Monitoring Centre (UMC) caveat agreement for reporting standards and were in accordance with the Helsinki declaration for ethical principles in medical research. As the data were anonymized and all analyses were descriptive, an ethical review from the Zurich Cantonal ethics board was not required. Additionally, per the Common Rule, because all data in the database are anonymized, patient informed consent was not required. This study was reported according to The Reporting of A Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV) guideline. 27 - 29

We conducted a comprehensive search for reports of suicidal or self-injurious ADRs associated with liraglutide and semaglutide within the WHO global ICSRs database, which is the largest pharmacovigilance archive worldwide containing over 28 million reports of suspected ADRs from 140 member countries. On August 30, 2023, we selected all deduplicated ICSRs recorded in the database from inception. Reports for semaglutide were recorded between July 2011 and August 2023, whereas for liraglutide, reports were collected between November 2000 and August 2023. Database services are offered by the UMC, which manages the database. 30 , 31 Drugs recorded on the reports are coded using the WHO drug dictionary 32 and ADRs are classified according to the Medical Dictionary for Regulatory Activities (MedDRA), version 23.1. 33 Two authors (C.G. and G.S.) identified ADRs involving liraglutide and/or semaglutide as suspected or interacting drugs to identify any report of suicidal and/or self-injurious ADRs as classified in the “suicide/self-injury” standardized MedDRA query (SMQ) of the MedDRA classification. Such SMQs include any ADR related to suicidal and/or self-injurious thoughts and events. 34 Cases were all reports of suicidal and/or self-injurious ADRs, whereas controls were all other reports of suspected ADRs. We included liraglutide-related or semaglutide-related reports.

Descriptive statistics on demographic and clinical characteristics (in medians and IQRs) of reported cases were provided. We compared the percentage of female patients, age, and dose between patients prescribed GLP-1 RAs for different indications. We grouped indications into the following categories: diabetes, weight management, possible off-label indication, and others. The classification of indications is detailed in eTable 1 in Supplement 1 . Comparisons of demographic and clinical characteristics were performed using χ 2 tests, Wilcox, Kruskal-Wallis, or Fisher tests. We also explored psychiatric symptoms coreported with the suicidal and/or self-injurious ADRs of interest. Two-sided P values less than .05 were considered significant.

We performed disproportionality analysis using 2 consolidated measures when at least 3 reports were recorded: first, we estimated reporting odds ratio (ROR) 35 and the bayesian information component (IC), 36 with 95% CIs. We applied well-established thresholds to define signals of disproportionate reporting, that is, lower limit of the 95% CI greater than 1 and greater than 0 for ROR and IC, respectively. Additional details about disproportionality analysis are reported in the eMethods in Supplement 1 .

For suicidal and/or self-injurious ADRs that had a signal of disproportionate reporting in the main disproportionality analysis, we performed sensitivity analyses to explore potential confounders. The following sensitivity analyses were conducted: (1) selecting only cases with coreporting of use of antidepressants and (2) only cases with coreporting of use of benzodiazepines as a proxy of depressive and anxiety disorders that can increase the risk of suicidal and/or self-injurious behaviors and ideation. Moreover, we repeated the aforementioned analyses by (3) excluding reports with coreporting of antidepressants and (4) benzodiazepines. To additionally mitigate the risk of confounding by indication and channeling bias, we performed 3 other sensitivity analyses using other drugs prescribed for the same indications (obesity and type 2 diabetes) as comparators; specifically we selected (5) dapagliflozin, a sodium-glucose cotransporter 2 inhibitor, and (6) metformin, considering their well-established role in the treatment of type 2 diabetes coupled with their favorable impact on body weight 37 , 38 ; and (7) orlistat, considering its indication for obesity and weight loss.

We also assessed the disproportionality of reporting in female and male patients separately. Last, to assess the trend of reporting over time, we reported the number of reports by year for all ADRs of interest and for each ADR with disproportionate reporting. Data were analyzed from September to December 2023.

As of August 30, 2023, of the 36 172 078 total reports in the database, we identified a total of 107 (median [IQR] age, 48 [40-56] years; 59 female patients [55%]; median [IQR] treatment duration, 24.0 [2.3-61.0] days [data from 28 reports]) unique, deduplicated cases of suicidal and/or self-injurious ADRs associated with semaglutide (107 of the 30 527 total reports [0.35%]) and 162 (median [IQR] age, 47 [38-60] years; 100 female patients [61%]; median [IQR] treatment duration, 46.0 [14.0-99.0] days [data from 33 reports]) cases associated with liraglutide (162 of 52 131 total reports [0.31%]). Demographic and clinical characteristics of the cases are reported in Table 1 by GLP-1 RA.

Regarding indications for use, the main reason for prescription was a possible off-label use (34 patients for semaglutide [31.8%] and 55 for liraglutide [33.9%]), followed by weight management (28 for semaglutide [26.2%] and 40 for liraglutide [24.7%]), diabetes (26 for semaglutide [24.3%] and 33 for liraglutide [20.4%]), and in 1 case for polycystic ovary syndrome for each GLP-1 RA (1 for semaglutide [0.9%] and 1 for liraglutide [0.6%]). Semaglutide-associated cases were reported by consumers in almost half of the reports (52 cases [48.6%]). Liraglutide-associated cases were mainly reported by health professionals (103 cases [63.6%]).

Regarding outcomes following dechallenge and rechallenge, both for semaglutide (eTable 2 in Supplement 1 ) and liraglutide (eTable 3 in Supplement 1 ), suicidal ideation resolved after drug discontinuation in 62.5% of the cases. In the semaglutide-associated reports of suicidal and/or self-injurious ADRs, the most common comedications included antidiabetics (17 patients [15.9%]) and antidepressants (14 patients [13.1%]), with higher percentages for liraglutide (49 patients [30.3%] and 30 patients [18.5%], respectively) ( Table 1 ).

Suicidal and/or self-injurious ADRs associated with semaglutide and/or liraglutide are presented in Table 2 . Suicidal ideation, intentional overdose, and suicide attempt ranked highest for semaglutide (94 patients [88%], 7 patients [6.5%], and 7 patients [6.5%], respectively), whereas for liraglutide suicidal ideation, completed suicide, and suicide attempt ranked highest (116 patients [71.6%], 19 patients [11.7%], and 16 patients [9.9%], respectively). Seven reactions (6.5%) were fatal for semaglutide and 24 (14.8%) for liraglutide. In Table 2 we report the number and percentage of each ADR by indication. For reports of suicidal ideation, we reported older age for patients prescribed liraglutide for diabetes compared with off-label and weight management, as well as a lower percentage of female patients prescribed liraglutide for diabetes compared with off-label and weight management (eTable 4 in Supplement 1 ).

The list of coreported psychiatric symptoms for semaglutide and liraglutide is reported in eTable 5 and eTable 6 in Supplement 1 . For liraglutide there were 91 cases in which suicidal and/or self-injurious ADRs were reported without any other psychiatric symptoms, 50 cases in which 1 psychiatric symptom was coreported, and 21 in which 2 or more other psychiatric symptoms were reported. Table 2 shows the number of cases in which suicidal and/or self-injurious ADRs were reported alone by indication. In half of these cases the drug was taken off-label both for semaglutide and liraglutide ( Table 2 ).

We detected a significant disproportionality only for semaglutide-associated suicidal ideation compared with all medications (ROR, 1.45; 95% CI, 1.18-1.77; IC, 0.53; 95% CI, 0.19-0.78) ( Table 3 ). Data on duration for semaglutide treatment were available in 26 patients reporting suicidal ideation, with a mean (range) duration of 80.39 (0 to 610) days between semaglutide treatment initiation and suicidal ideation occurrence. We did not find signals for any other ADR of interest ( Table 3 ). Numbers of cases and controls are reported in eTable 7 and eTable 8 in Supplement 1 .

The first sensitivity analysis including cases with comedications with antidepressants showed a disproportionate reporting of semaglutide-associated suicidal ideation compared with all medications (ROR, 4.45; 95% CI, 2.52 to 7.86; IC, 1.96; 95% CI, 0.98 to 2.63). The second sensitivity analysis including cases with comedications with benzodiazepines showed a disproportionate reporting of semaglutide-associated suicidal ideation compared with all medications (ROR, 4.07; 95% CI, 1.69 to 9.82; IC, 1.67; 95% CI, 0.11 to 2.65). Reporting was not disproportionate when excluding reports with comedications with antidepressants (ROR, 1.28; 95% CI, 1.03 to 1.60; IC, 0.36; 95% CI, −0.10 to 0.63), but remained disproportionate when excluding reports with comedications with benzodiazepines (ROR, 1.40; 95% CI, 1.13 to 1.72; IC, 0.48; 95% CI, 0.13 to 0.73). The fifth sensitivity analysis showed a disproportionate reporting of semaglutide-associated suicidal ideation compared with dapagliflozin (ROR, 5.56; 95% CI, 3.23 to 9.60; IC, 0.70; 95% CI, 0.36 to 0.95). The sixth sensitivity analysis showed a disproportionate reporting of semaglutide-associated suicidal ideation compared with metformin (ROR, 3.86; 95% CI, 2.91 to 5.12; IC, 1.20; 95% CI, 0.94 to 1.53). The seventh sensitivity analysis showed a disproportionate reporting of semaglutide-associated suicidal ideation compared with orlistat (ROR, 4.24; 95% CI, 2.69 to 6.69; IC, 0.70; 95% CI, 0.36 to 0.95). The analysis of disproportionality for suicidal ideation in female and male patients separately yielded disproportionate reporting in men (ROR, 1.51; 95% CI, 1.09 to 2.08; IC, 0.58; 95% CI, 0.03 to 0.97), whereas in female patients, although the lower limit of the 95% CI of the ROR was greater than 1 (ROR, 1.35; 95% CI, 1.02 to 1.77) the lower limit of the 95% CI of the IC was less than 0 (IC, 0.42; 95% CI,−0.05 to 0.75) (eTable 9 in Supplement 1 ).

From marketization year till August 2023, there was a slight increase in the proportion of suicidal ADRs reported for both drugs. The increase was from 0% (2017) to 0.8% (2023) for semaglutide and from 0.09% (2014) to 0.4% (2023) for liraglutide (eTable 10 and eTable 11 in Supplement 1 ).

In this disproportionality analysis of the world’s largest ICSRs database using a case-control design, we found a significant disproportionality only for semaglutide-associated suicidal ideation compared with other medications. The number of reports showed a gradual increase over the years, which may indicate a widening therapeutic scope in obesity and accumulating clinical experience.

To our knowledge, no previous reports investigated the association between semaglutide and suicidal ideation using this database. In our sensitivity analyses, the disproportionality remained significant when focusing on coreported antidepressants or benzodiazepines, suggesting that people with anxiety and depressive disorders may be at higher probability of reporting suicidal ideation when medicated with semaglutide. When repeating the analysis after excluding cases in which antidepressants were coreported, we did not detect a disproportionality signal. In contrast, when repeating the analysis after excluding cases in which benzodiazepines were coreported, the disproportionality remained significant. This is consistent with an interaction between baseline psychopathology and semaglutide effects and warrants further investigation. Although EMA stated that no update to the product information is warranted, based on these findings, we believe that a precaution of use in patients with psychiatric disorders or psychological lability could be added in the semaglutide package insert. Remarkably, the FDA label of semaglutide for obesity warned to monitor for depression or suicidal thoughts. 39

One study using the FDA pharmacovigilance database suggested disproportionate reporting for suicidal ideation and suicidal depression for semaglutide and liraglutide, 21 whereas another study did not detect an association between suicidality and GLP-1 RAs. 22 Likewise, a cohort study using electronic health records did not detect higher risks of suicidal ideation in patients with obesity or diabetes treated with semaglutide compared with non–GLP-1 RAs. 40 Compared with this study, in our analysis we also included patients with potential off-label prescription of GLP-1 RAs. Thus, our analysis may be generalized to patients receiving GLP-1 RAs without a diagnosis of diabetes or obesity, thus further confirming the complementary nature of studies based on ICSRs disproportionality analysis and longitudinal observational design.

Evidence from bariatric studies suggests that a history of depression or anxiety is a predictor of suicide risk postoperatively, providing context for this interplay 41 ; authors discussed this association in light of the emerging frustration due to high expectations of bariatric surgery outcomes in patients with limited resources to deal with mental distress. 41 Of note, the pivotal trials of semaglutide in obesity had different exclusion criteria for mental disorders, such as major depressive disorder within 2 years before screening, diagnosis of severe psychiatric disorders, and history of suicide attempts. 13 , 15 An alternative hypothesis may consider very rapid weight loss related to adjustment problems such as inability to eat as expected and ultimately exacerbated mental distress in highly vulnerable patients. 41 Due to lack of data on GLP-1 RA–related changes of baseline weight or BMI, we could not test for either hypothesis.

Although comorbidity with depression is high in patients with diabetes, 42 the coreporting between antidepressants and antidiabetics was negligible. Furthermore, the signal of semaglutide remained when comparing with dapagliflozin, metformin, and orlistat, mitigating the risk of confounding by indication for diabetes and obesity. Therefore, patients with diabetes and/or obesity without psychiatric comorbidities may not be at high risk of semaglutide-associated suicidal ideation. Although ADR incidence cannot be calculated using the spontaneous reporting system, this ADR is likely to be rare and would probably not substantially alter the benefit-risk profile of semaglutide in approved therapeutic settings. However, the observed high proportion of cases due to possible off-label use and a recently published postmarketing signal of misuse or abuse 43 call for urgent clarification of patient-related and drug-related risk factors; cohort studies and large registries should be stratified by therapeutic indication, sex, and history of mental disorders, and data from off-label use should be retrieved.

Despite the large number of cases, we did not detect any signals for liraglutide-associated suicidal and/or self-injurious ADRs. Pooled data from phase 2 and 3 trials on liraglutide vs placebo for weight management identified a potential risk for suicidal ideation. 12 Nine of 3384 participants in the liraglutide group vs 2 of 1941 in the placebo group reported suicidal ideation or behavior during the trial (0.27% vs 0.10%). 12

The results of this study should be interpreted in light of several limitations. 25 First, we need to consider barriers to reporting and missing information. Second, the well-known inability to infer causality does not allow us to attribute any reactions to the effect of a drug. Third, the lack of denominator does not allow us to estimate the incidence of ADRs. Fourth, selection and collider bias, 44 as well as confounding by indication and channeling bias, although partially mitigated by our sensitivity analyses, may have played a role as people with treatment-resistant diabetes or obesity might reflect a subgroup of patients with more severe conditions including higher risk of mental distress. Additional adjustments for potential confounders, such as alcohol or substance misuse, were limited by the relatively small number of reports found. In the absence of more details about off-label prescribing, we were not able to further qualify the extent to which prescribing was off-label and its impact on the results. Fifth, the lack of treatment outcomes, such as weight change, did not allow different hypotheses to be considered. Sixth, the high proportion of cases with missing data on medication dose precluded a dose-response analysis. Seventh, because of the absence of information on the sociodemographic profile of the reporters, it is impossible to account for volunteer bias. Eighth, it is not possible to exclude the chance that, for instance, suicidal ideation may have preexisted. Ninth, data on treatment duration until ADR were provided only in a small number of reports. Additionally, since disproportionality measures are interdependent, the lack of statistically significant disproportionality should not be automatically interpreted as a safety endorsement. Several factors may influence the reporting pattern and the ability to detect disproportionality, including known and widely reported ADRs such as gastrointestinal ADRs.

Our findings are relevant to the general reader seeking up-to-date information. This relevance arises from the expectation that personal or anecdotal reports may continue to gain popularity on social media platforms without knowledge about risks. 45 One consequence of this trend may be the increase in off-label use of semaglutide, which is a public health concern that has led to the illegal trade in semaglutide pens, some of which are counterfeit. 46 Recently, a public warning about fake counterfeit semaglutide pens was issued in the United Kingdom and the US. 46 , 47 Considering the risk of suicidal ideation in people taking semaglutide off-label, authorities should consider issuing a warning to inform about this risk.

In this disproportionality study of an ADR database, we reported a disproportionality signal of suicidal ideation with semaglutide, but not for liraglutide, particularly among patients with coreported antidepressant use, a proxy for affective disorders (a notable exclusion criteria of premarketing clinical trials).

Accepted for Publication: May 21, 2024.

Published: August 20, 2024. doi:10.1001/jamanetworkopen.2024.23385

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Schoretsanitis G et al. JAMA Network Open .

Corresponding Author: Georgios Schoretsanitis, MD, PhD, The Zucker Hillside Hospital, Behavioral Health Pavilion, 75-59 263rd St, Glen Oaks, NY 11004 ( [email protected] ).

Author Contributions: Drs Schoretsanitis and Gastaldon had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Schoretsanitis, Weiler, Raschi, Gastaldon.

Drafting of the manuscript: Schoretsanitis, Gastaldon.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Schoretsanitis, Gastaldon.

Administrative, technical, or material support: Barbui.

Supervision: Schoretsanitis, Barbui, Raschi.

Conflict of Interest Disclosures: Dr Schoretsanitis reported receiving personal fees from HLS, Dexcel, Saladax, and Thermo Fisher outside the submitted work. Dr. Weiler reported being a member of the Human Medicines Expert Committee of Swissmedic. No other disclosures were reported.

Disclaimer: The views expressed in this article are the personal views of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the European Medicines Agency or one of its committees or working parties. While the authors used data from VigiBase, the World Health Organization (WHO) global database of individual case safety reports, as a source of information, the conclusions do not represent the opinion of the Uppsala Monitoring Centre (UMC) or the WHO.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: The authors acknowledge the UMC, which provided and gave permission to use the data analyzed in the present study. The authors are also indebted to Dr Leonie Heron, PhD, Institute for Social and Preventive Medicine, University of Bern, Bern, Switzerland, who helped in editing this article. She was not compensated for her services.

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https://educationhub.blog.gov.uk/2024/08/20/gcse-results-day-2024-number-grading-system/

GCSE results day 2024: Everything you need to know including the number grading system

Thousands of students across the country will soon be finding out their GCSE results and thinking about the next steps in their education.   

Here we explain everything you need to know about the big day, from when results day is, to the current 9-1 grading scale, to what your options are if your results aren’t what you’re expecting.  

When is GCSE results day 2024?  

GCSE results day will be taking place on Thursday the 22 August.     

The results will be made available to schools on Wednesday and available to pick up from your school by 8am on Thursday morning.  

Schools will issue their own instructions on how and when to collect your results.   

When did we change to a number grading scale?  

The shift to the numerical grading system was introduced in England in 2017 firstly in English language, English literature, and maths.  

By 2020 all subjects were shifted to number grades. This means anyone with GCSE results from 2017-2020 will have a combination of both letters and numbers.  

The numerical grading system was to signal more challenging GCSEs and to better differentiate between students’ abilities - particularly at higher grades between the A *-C grades. There only used to be 4 grades between A* and C, now with the numerical grading scale there are 6.  

What do the number grades mean?  

The grades are ranked from 1, the lowest, to 9, the highest.  

The grades don’t exactly translate, but the two grading scales meet at three points as illustrated below.  

The bottom of grade 7 is aligned with the bottom of grade A, while the bottom of grade 4 is aligned to the bottom of grade C.    

Meanwhile, the bottom of grade 1 is aligned to the bottom of grade G.  

What to do if your results weren’t what you were expecting?  

If your results weren’t what you were expecting, firstly don’t panic. You have options.  

First things first, speak to your school or college – they could be flexible on entry requirements if you’ve just missed your grades.   

They’ll also be able to give you the best tailored advice on whether re-sitting while studying for your next qualifications is a possibility.   

If you’re really unhappy with your results you can enter to resit all GCSE subjects in summer 2025. You can also take autumn exams in GCSE English language and maths.  

Speak to your sixth form or college to decide when it’s the best time for you to resit a GCSE exam.  

Look for other courses with different grade requirements     

Entry requirements vary depending on the college and course. Ask your school for advice, and call your college or another one in your area to see if there’s a space on a course you’re interested in.    

Consider an apprenticeship    

Apprenticeships combine a practical training job with study too. They’re open to you if you’re 16 or over, living in England, and not in full time education.  

As an apprentice you’ll be a paid employee, have the opportunity to work alongside experienced staff, gain job-specific skills, and get time set aside for training and study related to your role.   

You can find out more about how to apply here .  

Talk to a National Careers Service (NCS) adviser    

The National Career Service is a free resource that can help you with your career planning. Give them a call to discuss potential routes into higher education, further education, or the workplace.   

Whatever your results, if you want to find out more about all your education and training options, as well as get practical advice about your exam results, visit the  National Careers Service page  and Skills for Careers to explore your study and work choices.   

You may also be interested in:

• Results day 2024: What's next after picking up your A level, T level and VTQ results?
• When is results day 2024? GCSEs, A levels, T Levels and VTQs

Tags: GCSE grade equivalent , gcse number grades , GCSE results , gcse results day 2024 , gsce grades old and new , new gcse grades

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