Trends and Insights in Global Non-Communicable Disease Risk Factors

Context

There is substantial global interest in trends in variables such as obesity, diabetes prevalence, blood pressure and cholesterol, as they are risk factors for non-communicable diseases such as cardiovascular conditions and cancers. The Non-Communicable Disease Risk Factor Collaboration (NCD-RisC) produces estimates of these variables at national level, and shows how they have varied over time. Patterns observed in the data include a general increase in body-mass index and obesity, and complex trends in blood pressure driven by diet and medication use. There are also interesting patterns in diabetes prevalence and cholesterol levels that can be explored.

Task Description

The task for this project is to investigate aspects of the NCD-RisC data that you feel might be of interest. There are no restrictions on what you can investigate, and you will not be assessed on the complexity of your investigation, the level of sophistication of your analysis, or how “successful” your investigation is. The focus of this assignment is on the use of the tools and techniques from the module to support efficient and reproducible data analysis, rather than the data analysis itself. Feedback on this assessment will be based on the approach to the analysis that you take, and how well you observe best practice in your approach.

• You will not be judged on how sophisticated your investigation is. You should choose an area to investigate that you think would be interesting, but that is achievable within the (short!) timeframe of this project.
• We are primarily assessing how well you follow the best practice principles outlined in the module: using CRISP-DM to structure the investigation, using ProjectTemplate to structure the analysis and ensure that it is reproducible, using dplyr and ggplot to carry out the analysis, and R Markdown to produce the analysis report.
• For this formative assessment, the data files you are given do not have data quality issues as they are not raw data, although there is an issue that different variables are available for different ranges of years.

Submission Requirements

For the Analysis Report submission, you should submit a maximum 6-page report (made using R Markdown) describing your analysis, with reference to the phases of CRISP-DM. Your report should describe two consecutive cycles of CRISP-DM.

Note: that we will see ProjectTemplate in Week 5 of the module, so don’t panic that we haven’t seen this yet! For the ProjectTemplate Directory submission, you should submit a single zip file containing:

• Your complete ProjectTemplate directory. This is the directory that is created when you run create.project at the start of your work, and will contain your code and output from your research into the NCD-RisC data. Typically, the main subdirectories used in this project will be:
  • Data: containing the NCD-RisC data files.
  • Munge: containing the R scripts which handle data preprocessing.
  • Cache: containing the cached preprocessed data.
  • Config: containing the settings for ProjectTemplate running the analysis.
  • Report: containing the .Rmd file used to create your analysis report.
• README file: a plaintext file (called README) which outlines the steps needed to run your analysis (any extra packages required, etc.), together with the location of the deliverables in your submitted directory.

Learning Outcome Alignment

• You will be able to identify and apply best practices in data handling, exploratory data analysis, data visualisation and reproducibility, ensuring robust and reliable research.
• You will apply the scientific method in framing research questions and interpreting results.
• You will be aware of the software and data lifecycles.
• You will use advanced techniques for statistical analysis, and will apply best practices in programming.
• You will know how to ensure that analyses can be repeated, modified and shared in a transparent and collaborative manner.

AI Policy

The assessment requires careful consideration of a specific dataset, which you should carry out yourself rather than using AI. You should only use AI in a very limited way, such as for debugging of code or spell-checking of your report.

Summary of Assessment Requirements 

Assessment Requirements Summary

This assessment focuses on the exploration of global health trends using data from the Non-Communicable Disease Risk Factor Collaboration (NCD-RisC). The dataset contains long-term estimates of variables such as obesity, BMI, diabetes prevalence, cholesterol levels, and blood pressure—key indicators linked to non-communicable diseases including cardiovascular disease and cancers.

The core objective is not to produce an advanced or highly sophisticated data analysis. Instead, the emphasis is on adopting best practices in data investigation, reproducibility, and analytical documentation. To achieve this, students are expected to:

1. Select any health-related variable or trend of interest from the NCD-RisC dataset.

2. Conduct a structured investigation following two complete cycles of the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework.

3. Use ProjectTemplate to ensure a reproducible workflow, where data, preprocessing scripts, cached files, and configuration settings are properly organised.

4. Apply dplyr for data manipulation and ggplot2 for visualisation.

5. Produce a maximum 6-page R Markdown report, fully reproducible and clearly linked to the CRISP-DM phases.

6. Submit a ZIP file containing the entire ProjectTemplate directory, including:

   • Data folder

   • Munge (preprocessing) scripts

   • Cache files

   • Config settings

   • Report folder with the .Rmd file

   • README explaining how to run the analysis

The assessment aligns with learning outcomes related to data handling, reproducibility, visualisation, scientific reasoning, software lifecycle awareness, and transparent analysis.

How the Academic Mentor Guided the Student

The Academic Mentor assisted the student throughout the assignment by breaking down the task into manageable steps and ensuring that the student followed best practices throughout the workflow.

Understanding the Context and Defining an Investigation Area

The mentor first explained the importance of NCD risk factors and how they change over time. The student was encouraged to select one variable—such as BMI trends, diabetes prevalence, or cholesterol levels that was interesting yet manageable for the limited timeframe.

The mentor emphasised:

• Choosing a clear and simple research question.

• Ensuring that the analysis remains focused and aligned with available years of data

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