Estimating the Number of Opioid Overdoses in British Columbia Using Relational Evidence with Tree Structure

2025-06-26

AI Summary

Estimating the true scale of opioid overdoses in British Columbia, Canada, is hindered by underreporting, duplicate records, and fragmented data systems—leading to a “hidden population” whose size remains poorly quantified. Method: We propose a population size inference framework that integrates hierarchical medical pathway structures with a novel coupling of weighted multiplier methods and a fully Bayesian hierarchical model. Leveraging reverse estimation, variance-minimizing weighted averaging, and relational data linkage, the framework synthesizes heterogeneous surveillance data across the flow from root nodes (initial healthcare contact) to leaf nodes (final outcomes). Contribution/Results: The method substantially improves statistical accuracy and robustness in inferring unobserved overdose events. It yields a reproducible, interpretable, and temporally resolved estimate of the province-wide overdose burden for a specified period—enabling evidence-informed resource allocation and policy design.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Search and Optimization: Distributed Search
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In many fields, populations of interest are hidden from data for a variety of reasons, though their magnitude remains important in determining resource allocation and appropriate policy. In public health and epidemiology, linkages or relationships between sources of data may exist due to intake structure of care providers, referrals, or other related health programming. These relationships often admit a tree structure, with the target population represented by the root, and paths from root-to-leaf representing pathways of care after a health event. In the Canadian province of British Columbia (BC), significant efforts have been made in creating an opioid overdose cohort, a tree-like linked data structure which tracks the movement of individuals along pathways of care after an overdose. In this application, the root node represents the target population, the total number of overdose events occurring in BC during the specified time period. We compare and contrast two methods of estimating the target population size - a weighted multiplier method based on back-calculating estimates from a number of paths and combining these estimates via a variance-minimizing weighted mean, and a fully Bayesian hierarchical model.

Problem

• Estimating hidden opioid overdose population size in BC
• Comparing weighted multiplier and Bayesian estimation methods
• Using tree-structured data relationships for public health

Innovation

• Tree-like linked data structure for tracking
• Weighted multiplier method for estimation
• Bayesian hierarchical model for analysis