Trajectories of dispensed prescription opioids among beneficiaries enrolled in a Medicaid controlled substance “lock‐in” program

“Lock‐in” programs (LIPs) are used by health insurers to address potential substance (eg, opioid) misuse among beneficiaries. We sought to (1) examine heterogeneity in trajectories of dispensed opioids (in average daily morphine milligram equivalents (MMEs)) over time: prior to, during, and following release from a LIP, and (2) assess associations between trajectory patterns and beneficiary characteristics.

[1]  Chris Ringwalt,et al.  Controlled Substance Lock-In Programs: Examining An Unintended Consequence Of A Prescription Drug Abuse Policy. , 2016, Health affairs.

[2]  R. Chou,et al.  CDC Guideline for Prescribing Opioids for Chronic Pain--United States, 2016. , 2016, JAMA.

[3]  B. Williams,et al.  Patient Outcomes in a Medicaid Managed Care Lock-In Program , 2015, Journal of managed care & specialty pharmacy.

[4]  Mei-Chen Hu,et al.  Pathways to change: Use trajectories following trauma-informed treatment of women with co-occurring post-traumatic stress disorder and substance use disorders. , 2015, Drug and alcohol review.

[5]  Tamara M Haegerich,et al.  What we know, and don't know, about the impact of state policy and systems-level interventions on prescription drug overdose. , 2014, Drug and alcohol dependence.

[6]  D. Nagin.  Group-Based Trajectory Modeling: An Overview , 2014, Annals of Nutrition and Metabolism.

[7]  A. Skinner,et al.  Assessing the Present State and Potential of Medicaid Controlled Substance Lock-In Programs , 2014, Journal of managed care & specialty pharmacy.

[8]  J. Gagne,et al.  Estimation using all available covariate information versus a fixed look‐back window for dichotomous covariates , 2013, Pharmacoepidemiology and drug safety.

[9]  M. Pletcher,et al.  Trajectories of Drug Use and Mortality Outcomes Among Adults Followed Over 18 Years , 2012, Journal of General Internal Medicine.

[10]  M. Schechter,et al.  Health related quality of life trajectories of patients in opioid substitution treatment. , 2011, Drug and alcohol dependence.

[11]  Keh‐ming Lin,et al.  Five-year trajectories of long-term benzodiazepine use by adolescents: patient, provider, and medication factors. , 2011, Psychiatric services.

[12]  Daniel S Nagin,et al.  Group-based trajectory modeling in clinical research. , 2010, Annual review of clinical psychology.

[13]  D. Hedeker,et al.  Evaluation of Longitudinal Intervention Effects: An Example of Latent Growth Mixture Models for Ordinal Drug-Use Outcomes , 2010 .

[14]  M. Brecht,et al.  Influences of Covariates on Growth Mixture Modeling , 2010, Journal of drug issues.

[15]  D. Boudreau,et al.  Opioid use behaviors, mental health and pain--development of a typology of chronic pain patients. , 2009, Drug and alcohol dependence.

[16]  Daniel J. Bauer.  Observations on the Use of Growth Mixture Models in Psychological Research , 2007 .

[17]  Daniel J Bauer,et al.  The integration of continuous and discrete latent variable models: potential problems and promising opportunities. , 2004, Psychological methods.

[18]  Daniel S. Nagin,et al.  Analyzing developmental trajectories: A semiparametric, group-based approach , 1999 .

[19]  Nisha C. Gottfredson,et al.  Evaluating short- and long-term impacts of a Medicaid "lock-in" program on opioid and benzodiazepine prescriptions dispensed to beneficiaries. , 2018, Drug and alcohol dependence.