Research tool for classifying Gulf War illness using survey responses: Lessons for writing replicable algorithms for...

Area of Science:

• Veterans' Health
• Epidemiology
• Computational Biology

Background:

• Gulf War illness (GWI) affects up to 30% of Gulf War Veterans, presenting a chronic symptom-based disorder.
• Current GWI case status determination relies on self-reported symptoms using varying criteria (Kansas, CDC), lacking a validated algorithm.
• Inconsistent case definitions hinder research reproducibility and accurate GWI prevalence estimation.

Purpose of the Study:

• To standardize the application of existing CDC and Kansas GWI case definitions.
• To develop and implement a reliable coding framework for complex GWI case definitions.
• To validate the developed framework using large datasets of Gulf War Veterans.

Main Methods:

• Software engineering principles were applied: pseudocode development, test case creation, and coding.
• The framework was implemented using SAS code and tested on two large Veteran cohorts (GWECB and MVP).
• Code accuracy, flexibility, reproducibility, and reusability were rigorously evaluated.

Main Results:

• Pseudocode facilitated team consensus on GWI case definition algorithms.
• The developed SAS code was successfully implemented and tested in both the GWECB and MVP datasets.
• The code was thoroughly documented, ensuring reproducibility and facilitating reuse across studies.

Conclusions:

• The developed framework offers a standardized method for applying complex case definitions, saving time and resources.
• This approach can be adapted to standardize other complex case definitions in epidemiological research.
• The documented code and test cases are publicly available via the VA Phenomics catalog, promoting open science.