Previously submitted to: JMIR Medical Informatics (no longer under consideration since Sep 12, 2021)
Date Submitted: Feb 2, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Assessing the Impact of Person-level Matching Algorithms to Identify Risk of Fatal Opioid Overdose Across Disparate Datasets: Retrospective Analysis
ABSTRACT
Background:
The opioid epidemic in the United States has precipitated a need for public health agencies to better understand risk factors associated with fatal overdoses. Matching person-level information stored in public health, medical, and human services datasets can enhance the understanding of opioid overdose risk factors and interventions. A major impediment to using datasets from separate agencies, has been the lack of a cross-organization unique identifier. Although different matching techniques that leverage patient demographic information can be used, the impact of using a particular matching approach is not well understood.
Objective:
This study compares the impact of using probabilistic versus deterministic matching algorithms to link disparate datasets together for identifying persons at risk of a fatal overdose.
Methods:
This study used statewide prescription drug monitoring program (PDMP), arrest, and mortality data matched at the person-level using a probabilistic and two deterministic matching algorithms. Impact of matching was assessed by comparing the prevalence of key risk indicators, the outcome, and performance of a multivariate logistic regression for fatal overdose using the combined datasets.
Results:
The probabilistically matched population had the highest degree of matching within the PDMP data and with arrest and mortality data, resulting in the highest prevalence of high-risk indicators and the outcome. Model performance using area under the curve (AUC) was comparable across the algorithms (probabilistic: 0.847; deterministic-basic: 0.854; deterministic+zip: 0.826), but demonstrated tradeoffs between sensitivity and specificity.
Conclusions:
The probabilistic algorithm was more successful in linking patients with PDMP data with death and arrest data, resulting in a larger at-risk population. However, deterministic-basic matching may be a suitable option for understanding high-level risk based on the model’s area under the curve (0.854). The clinical use case should be considered when selecting a matching approach, as probabilistic algorithms can be more resource-intensive and costly to maintain compared with deterministic algorithms.
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