FB2NEP — Nutritional Epidemiology

Practical workbooks

This page links to the FB2NEP practical workbooks.
Each notebook can be opened in two ways — choose whichever works best for you:

Google Colab — requires a Google account, but offers a reliable environment with easy file saving to Google Drive.
Binder — no account required; opens directly in your browser. May take a minute to start up.

You may also download the notebooks and run them locally in Jupyter.

How-To and sandbox (optional, but recommended)

If you have not used Python or Google Colab before, start with the How-To & Sandbox materials.
They show you how to open notebooks, run code safely, and try things out without breaking anything.

FB2NEP — How-To & Sandbox (Colab/Jupyter)

Part 1 - Nutritional Epidemiology

1.03 - Introduction to Jupyter and Google Colab

1.04 - Data collection and cleaning

1.05 - Representativeness and sampling

1.06 - Data exploration and “Table 1”

1.07 - Data transformation

1.08 - Regression and modelling (Part 1)

1.09 - Regression and modelling (Part 2)

1.09a - Confounding: causal diagrams and worked examples

Four worked examples with DAGs and synthetic data — showing clearly how age, sex, SES, and smoking distort crude associations, and what happens when you adjust for them.

1.10 - Missing data and sensitivity analysis

Part 2 - Public Health Nutrition: Policy and Evaluation

The following workbooks focus on quantitative methods for evaluating public health nutrition policies.

2.02 - DALYs and QALYs

Quantifying population health burden using Disability-Adjusted Life Years and Quality-Adjusted Life Years. Includes an interactive exercise where you set your own disability weights and compare to GBD values.

2.03 - Health inequalities

Measuring the social gradient in health using the Slope Index of Inequality (SII), Relative Index of Inequality (RII), and concentration curves. Applied to dietary intake and nutrition-related outcomes.

2.04 - Case study: Salt reduction (with objective measures)

Evaluating the UK salt reduction programme using 24-hour urinary sodium — a model example of policy evaluation with an objective biomarker. Includes health impact modelling and sensitivity analysis.

2.05 - Case study: Sugar reduction (without objective measures)

Evaluating the Soft Drinks Industry Levy (SDIL) without a biomarker — exploring the evidence hierarchy from product reformulation to health outcomes. Addresses the counterfactual problem in policy evaluation.

2.06 - Policy simulation and resource allocation

Interactive budget allocation game: distribute £50M across public health nutrition interventions. Explore trade-offs between efficiency and equity, the impact of diminishing returns, and uncertainty in cost-effectiveness estimates.

Assessment

Please find assessment details here

--- title: FB2NEP — Nutritional Epidemiology format: html: toc: false number-sections: false --- ## Practical workbooks This page links to the **FB2NEP practical workbooks**. Each notebook can be opened in two ways — choose whichever works best for you: - **Google Colab** — requires a Google account, but offers a reliable environment with easy file saving to Google Drive. - **Binder** — no account required; opens directly in your browser. May take a minute to start up. You may also download the notebooks and run them locally in Jupyter. --- ## How-To and sandbox (optional, but recommended) If you have not used Python or Google Colab before, start with the **How-To & Sandbox** materials. They show you how to open notebooks, run code safely, and try things out without breaking anything. - [FB2NEP — How-To & Sandbox (Colab/Jupyter)](howto_sandbox/) ## Part 1 - Nutritional Epidemiology --- ### 1.03 - Introduction to Jupyter and Google Colab <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.03_introduction_colab_jupyter.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.03 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.03_introduction_colab_jupyter.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.04 - Data collection and cleaning <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.04_data_collection_cleaning.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.04 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.04_data_collection_cleaning.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.05 - Representativeness and sampling <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.05_representative.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.05 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.05_representative.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.06 - Data exploration and "Table 1" <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.06_data_exploration.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.06 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.06_data_exploration.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.07 - Data transformation <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.07_data_transformation.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.07 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.07_data_transformation.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.08 - Regression and modelling (Part 1) <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.08_regression_modelling_01.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.08 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.08_regression_modelling_01.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.09 - Regression and modelling (Part 2) <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.09_regression_modelling_02.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.09 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.09_regression_modelling_02.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.09a - Confounding: causal diagrams and worked examples Four worked examples with DAGs and synthetic data — showing clearly how age, sex, SES, and smoking distort crude associations, and what happens when you adjust for them. <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.09a_confounding_dags.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.09a in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.09a_confounding_dags.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 1.10 - Missing data and sensitivity analysis <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/1.10_missing_data_and_sensitivity.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 1.10 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/1.10_missing_data_and_sensitivity.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ## Part 2 - Public Health Nutrition: Policy and Evaluation The following workbooks focus on quantitative methods for evaluating public health nutrition policies. --- ### 2.02 - DALYs and QALYs Quantifying population health burden using Disability-Adjusted Life Years and Quality-Adjusted Life Years. Includes an interactive exercise where you set your own disability weights and compare to GBD values. <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/2.02_qaly.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 2.02 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/2.02_qaly.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 2.03 - Health inequalities Measuring the social gradient in health using the Slope Index of Inequality (SII), Relative Index of Inequality (RII), and concentration curves. Applied to dietary intake and nutrition-related outcomes. <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/2.03_health_inequalities.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 2.03 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/2.03_health_inequalities.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 2.04 - Case study: Salt reduction (with objective measures) Evaluating the UK salt reduction programme using 24-hour urinary sodium — a model example of policy evaluation with an objective biomarker. Includes health impact modelling and sensitivity analysis. <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/2.04_salt_reduction.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 2.04 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/2.04_salt_reduction.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 2.05 - Case study: Sugar reduction (without objective measures) Evaluating the Soft Drinks Industry Levy (SDIL) without a biomarker — exploring the evidence hierarchy from product reformulation to health outcomes. Addresses the counterfactual problem in policy evaluation. <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/2.05_sugar_reduction.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 2.05 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/2.05_sugar_reduction.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ### 2.06 - Policy simulation and resource allocation Interactive budget allocation game: distribute £50M across public health nutrition interventions. Explore trade-offs between efficiency and equity, the impact of diminishing returns, and uncertainty in cost-effectiveness estimates. <a href="https://colab.research.google.com/github/ggkuhnle/fb2nep-epi/blob/main/notebooks/2.06_policy_simulation.ipynb" target="_blank"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open 2.06 in Colab"> </a> <a href="https://mybinder.org/v2/gh/ggkuhnle/fb2nep-epi/main?filepath=notebooks/2.06_policy_simulation.ipynb" target="_blank"> <img src="https://mybinder.org/badge_logo.svg" alt="Open in Binder (no login required)"> </a> --- ## Assessment Please find assessment details [here](assessment/index.qmd)