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Chronic Disease Monitoring: Methodology for Classification Error and Self-Selection Bias Correction in Clinical
Jesuan Betancourt1, Efrain Betancourt1, Abiel Roche-Lima2
1Abartys Health, San Juan, PR 00907-3913, USA.
Healthcare (Basel, Switzerland)
|December 11, 2025
Summary
This study developed a bias-corrected method using laboratory data to accurately monitor chronic diseases like diabetes. The new approach provides timely, representative prevalence estimates, improving public health surveillance.
Area of Science:
- Public Health
- Epidemiology
- Biostatistics
Background:
- Chronic diseases, including diabetes mellitus, represent a significant global health burden and healthcare cost.
- Current diabetes surveillance methods in Puerto Rico, relying on infrequent studies and self-reports, lack accuracy, segmentation, and timeliness.
- A novel methodology is needed to enhance chronic disease monitoring using routinely collected data.
Purpose of the Study:
- To develop a generalizable methodology for monitoring chronic disease prevalence using routinely collected laboratory data.
- To correct for systematic biases and diagnostic errors inherent in laboratory and self-reported data.
- To provide accurate, timely, and demographically representative prevalence estimates for public health decision-making.
Main Methods:
- Analysis of de-identified laboratory test results from a large, island-wide network in Puerto Rico (2020-2024).
- Application of a mathematical correction framework to address classification errors (using confusion matrices) and self-selection bias (using demographic reweighting).
- Ensuring representativeness by aligning estimates with census demographic data.
Main Results:
- Corrected adult diabetes prevalence in Puerto Rico for 2024 was estimated at 18.0%, significantly higher than the 14.1% from raw laboratory data.
- The methodology enabled high-resolution prevalence estimates stratified by age, sex, and geographic location.
- Fine-grained detection of demographic and geographic disparities in chronic disease burden was achieved.
Conclusions:
- Bias-corrected laboratory surveillance offers an accurate, timely, and representative approach to monitoring chronic disease prevalence.
- The developed methodology is scalable, cost-effective, and applicable to various chronic conditions.
- This approach lays the groundwork for advanced public health surveillance and targeted interventions.
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