Treatment for opioid use disorder in the Florida medicaid population: Using a cascade of care model to evaluate quality.
Kimberly A JohnsonHolly HillsJifeng MaC Hendricks BrownMark McGovernPublished in: The American journal of drug and alcohol abuse (2020)
Background: A cascade of care (CoC) model may improve understanding of gaps in addiction treatment availability and quality over current single measure methods. Despite increased funding, opioid overdose rates remain high. Therefore, it is critical to understand where the health-care system is failing to provide appropriate care for people with opioid use disorder (OUD) diagnoses, and to assess disparities in receipt of medication for OUD (MOUD).Objective: Using a CoC framework, assess treatment quality and outcomes for OUD in the Florida Medicaid population in 2017/2018 by demographics and primary vs. secondary diagnosis.Methods: Data from Florida Medicaid claims for 2017 and 2018 were used to calculate the number of enrollees who were diagnosed, began MOUD, were retained on medication for a minimum of 180 days, and who died.Results: Only 28% of those diagnosed with OUD began treatment with an FDA approved MOUD (buprenorphine, methadone, or injectable naltrexone). Once on medication, 38% of newly diagnosed enrollees were retained in treatment for180 days. Those who remained on MOUD for 180 days had a hazard ratio of death of 0.226 (95% CI = 0.174 to 0.294) compared to those that did not initiate MOUD, a reduction in mortality from 10% without MOUD to 2% with MOUD.Conclusions: Initiating medication after OUD diagnosis offers the greatest opportunity for intervention to reduce overdose deaths, though efforts to increase retention are also warranted. Analyzing claims data with CoC identifies system functioning for specific populations, and suggests policies and clinical pathways to target for improvement.
Keyphrases
- healthcare
- health insurance
- quality improvement
- randomized controlled trial
- affordable care act
- emergency department
- newly diagnosed
- public health
- pain management
- cardiovascular events
- combination therapy
- cardiovascular disease
- type diabetes
- coronary artery disease
- metabolic syndrome
- artificial intelligence
- machine learning
- chronic pain
- dna methylation
- insulin resistance
- smoking cessation