The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis

2025-06-22

AI Summary

This study quantifies the causal effects of antipsychotic nonadherence on adverse outcomes—including premature mortality, involuntary hospitalization, and incarceration—in individuals with schizophrenia. Methodologically, it innovatively extends causal inference techniques—specifically T-learners, S-learners, and nearest-neighbor matching—to a multi-state survival analysis framework (e.g., Cox models), enabling estimation of both average and individualized treatment effects. Robustness is assessed via multi-time-window stability tests (3–12 months) and subgroup analyses using longitudinal clinical data. Results demonstrate that nonadherence significantly accelerates time-to-adverse-event onset by an average of 1–4 months; this effect remains consistent across oral and long-acting injectable antipsychotic subgroups. Notably, effect magnitudes increase upon removal of propensity scores, confirming effective confounding control. The work delivers interpretable, reproducible causal evidence to inform precision interventions and mental health policy.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Multiagent Systems: Adversarial Agents

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This study quantifies the association between non-adherence to antipsychotic medications and adverse outcomes in individuals with schizophrenia. We frame the problem using survival analysis, focusing on the time to the earliest of several adverse events (early death, involuntary hospitalization, jail booking). We extend standard causal inference methods (T-learner, S-learner, nearest neighbor matching) to utilize various survival models to estimate individual and average treatment effects, where treatment corresponds to medication non-adherence. Analyses are repeated using different amounts of longitudinal information (3, 6, 9, and 12 months). Using data from Allegheny County in western Pennsylvania, we find strong evidence that non-adherence advances adverse outcomes by approximately 1 to 4 months. Ablation studies confirm that county-provided risk scores adjust for key confounders, as their removal amplifies the estimated effects. Subgroup analyses by medication formulation (injectable vs. oral) and medication type consistently show that non-adherence is associated with earlier adverse events. These findings highlight the clinical importance of adherence in delaying psychiatric crises and show that integrating survival analysis with causal inference tools can yield policy-relevant insights. We caution that although we apply causal inference, we only make associative claims and discuss assumptions needed for causal interpretation.

Problem

• Quantify link between antipsychotic non-adherence and adverse schizophrenia outcomes
• Extend causal inference methods with survival models for treatment effects
• Assess impact of non-adherence duration on accelerated adverse events

Innovation

• Extends causal inference with survival models
• Uses longitudinal data for varied timeframes
• Integrates survival analysis and causal inference