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📦 NFEI: Nutrition-Sensitive Food Environment Index (Python Package) Documentation

Python License CI Docs Status Research PyPI

nfei is a Python package for computing nutrition-sensitive food environment indicators from vendor survey data, food availability data, and geospatial data. It provides reusable functions for building the indicator components used in the Nutrition-Sensitive Food Environment Index (N-FEI), including food diversity, produce color diversity, vendor availability, vendor density, spatial aggregation, scaling, and validation.

This package is designed for researchers, data scientists, public-health analysts, food-system practitioners, and policy teams who want to move from raw food environment data to transparent, reproducible indicators.

What is N-FEI?

The Nutrition-Sensitive Food Environment Index (N-FEI) is a composite food environment assessment framework developed to evaluate how the built food environment may support or constrain healthier diets and public-health outcomes. It is designed to assess food environments through a nutrition and public health lens, capturing how food availability, accessibility, infrastructure, and exposure to unhealthy foods interact to influence malnutrition risk.

The N-FEI methodology was developed and validated using multi-country food environment data. The published paper describes the conceptual basis, indicator selection, spatial logic, sensitivity analysis, validation strategy, and policy relevance of the index.

Methodology paper: https://doi.org/10.55845/jos-2025-1116

It is grounded in the definition of food environments as:

“the collective physical, economic, policy, and sociocultural surroundings, opportunities, and conditions that influence people’s food and beverage choices and nutritional status.”

The N-FEI expands food environment measurement beyond affordability alone. The index is constructed from nine indicators, normalized and aggregated into a 0–10 scale, where higher values indicate healthier food environments. It focuses on observable features of vendor environments, including:

  1. Vendor Healthy Food Diversity Score, using food groups aligned with the Minimum Dietary Diversity for Women framework.
  2. Vendor Environment Healthy Food Diversity Score, capturing what is available around vendors, not only at a single vendor.
  3. Vendor ProColor Diversity Score, Fruit and vegetable color diversity reflecting ProColor-style produce diversity.
  4. Vendor Environment ProColor Diversity Score, capturing the broader produce-color environment around vendors.
  5. Access to Water and Sanitation, especially relevant for informal and mobile food vendors.
  6. Vendor availability, measured through daily and weekly operating patterns.
  7. Vendor density per population, capturing vendor availability relative to population size.
  8. Vendor density per square kilometre, capturing geographic distribution.
  9. Unhealthy food count, capturing exposure to selected unhealthy beverages and snacks.

This package does not hide the index inside a black-box function. Instead, it exposes the building blocks used to compute the indicators, so users can adapt the workflow to their own study design, data structure, and policy context.

What this package does

nfei helps you:

  • Compute vendor-level food diversity indicators.
  • Compute produce color diversity indicators.
  • Compute unhealthy beverage, snack, and total unhealthy food counts.
  • Compute daily, weekly, and combined vendor availability.
  • Estimate population covered by radius-based vendor mapping.
  • Compute vendor density by population and land area.
  • Aggregate food environment features within spatial buffers.
  • Calculate nearest distances and spatial aggregations for enviromental exposure around a point.
  • Scale indicators to a common interpretation range.
  • Detect and correct coordinate outliers using a robust MAD-based approach.

The package is especially useful when your raw survey data contains binary food availability columns, comma-separated produce color fields, vendor operating days or hours, latitude and longitude, vendor type, population denominators, and land-area denominators.

Citation

If you use this software or the methodology in your research, please cite the following manuscript:

Akingbemisilu, T. H., Jordan, I., Asiimwe, R., Bodjrenou, S., Nabuuma, D., Odongo, N., Onyango, K. O., Teferi, E., Tokeshi, C., Lundy, M., & Termote, C. (2025). The Nutrition-Sensitive Food Environment Index: A Comprehensive Approach to Assessing Food Environments in Association with Health Risks for Policy Decision Making. Journal of Sustainability, 1(1). https://doi.org/10.55845/jos-2025-1116