A multi-state Markov model for analyzingpatterns of use of Opioid treatments                         Hassan Fouayzi, MS; R...
Acknowledgements and Conflict of Interest statement   No conflicts of interest to disclose.   Preparation of this study wa...
Introduction   A multi-state model (MSM) is a stochastic model that allows individuals to    move among a finite number o...
The multi-state Markov model   Markov modeling is a form of stochastic modeling that    describes a process as a series o...
Objective of the study   The aim of this study was to highlight the    importance of using multi-state Markov    models t...
Background   In 2009, over 1.7 million people in the US abused or were    dependent on prescription opioids and 399,000 o...
Study population and design   Retrospective longitudinal study using    Massachusetts Medicaid Enrollment and Claims    d...
The multi-state Markov model                           8
Results          9
Results (Cont.)   Frequency table of pairs of consecutive states: for each state p and    q, the number of times a patien...
Results (Cont.)   The probabilities of being in any one state after 6 months and 24    months of follow up are reported h...
Results (Cont.)                  12
Results (Cont.)                  13
Results (Cont.)Figure 2 shows the predicted probability of survival (staying on therapy) for opioid usersfor monthly times...
Next steps   Tests such as exponential sojourn times assumption    test   Model comparison using likelihoods / AIC for a...
Conclusion   Results of this study describe the dynamics of    opioid therapy enrollment and the effects of    several co...
Thank You            17
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A Multi State Markov Model for Analyzing Patterns of Use of Opiod Treatments FOUAYZI

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  • Chronic Illness Disability Payment System (CDPS) Primary care physician plan (PCCP)Managed care plan (MCO)
  • A Multi State Markov Model for Analyzing Patterns of Use of Opiod Treatments FOUAYZI

    1. 1. A multi-state Markov model for analyzingpatterns of use of Opioid treatments Hassan Fouayzi, MS; Robin Clark, PhD; and Jeffrey Baxter, MD Hassan Fouayzi, MS, Meyers Primary Care Institute (Reliant Medical Group, Fallon Community Health Plan, and University of Massachusetts Medical School), Worcester, MA, USA Robin E. Clark, PhD, Center for Health Policy and Research, University of Massachusetts Medical School, Worcester, MA, USA Jeffrey D. Baxter, MD, Center for Health Policy and Research, University of Massachusetts Medical School, Worcester, MA, USA 1
    2. 2. Acknowledgements and Conflict of Interest statement No conflicts of interest to disclose. Preparation of this study was assisted by grant #64752 from the Robert Wood Johnson Foundation’s Substance Abuse Policy Research Program (SAPRP). Acknowledgements:  Meyers Primary Care Institute (Reliant Medical Group, Fallon Community Health Plan, and University of Massachusetts Medical School), Worcester, MA, USA  Center for Health Policy and Research at University of Massachusetts Medical School, Worcester, MA, USA
    3. 3. Introduction A multi-state model (MSM) is a stochastic model that allows individuals to move among a finite number of states In medical studies, multi-state models (MSM) are mainly used to investigate clinical symptoms , biological markers, scales of a disease, or complications of the course of an illness (1) In pharmacoepidemiology studies, survival analysis methods are usually used to investigate use of medications using 2 states only:  Adherence vs non adherence, persistence vs discontinuation... MSM models can be used to evaluate multiple states simultaneously, accommodate more information from censored subjects, and can also be used to analyze competing risks.1 Luís Meira-Machado et al (2009) .”Multi-state models for the analysis of time-to-event data”. Stat Methods Med Res;18(2):3195–
    4. 4. The multi-state Markov model Markov modeling is a form of stochastic modeling that describes a process as a series of probable transitions between states. Composed of mutually exclusive set of health states (e.g., alive or dead) Transitions among states occur at regular intervals or cycles (e.g., monthly) based on transition probabilities. The process is memoryless (one state depends only on preceding state but independent of all former states). 4
    5. 5. Objective of the study The aim of this study was to highlight the importance of using multi-state Markov models to assess use and switching patterns of three leading treatments for opioid dependence 5
    6. 6. Background In 2009, over 1.7 million people in the US abused or were dependent on prescription opioids and 399,000 on heroin (1) Methadone and buprenorphine have been shown to be effective in retaining patients in treatment and decreasing opioid use (2,3). Drug-free outpatient psychosocial behavioral health interventions alone are also sometimes used although they appear less effective than pharmacological therapy1 NSDUH 20092 Mattick, R., J. Kimber, et al. (2009). "Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence." Cochrane Database of Systematic Reviews.3 Connock, M., A. Juarez-Garcia, et al. (2007). "Methadone and buprenorphine for the management of opioid dependence: a systematic review and economic evaluation." Health Technology Assessment 11(9). 6
    7. 7. Study population and design Retrospective longitudinal study using Massachusetts Medicaid Enrollment and Claims data. 30,080 Medicaid beneficiaries aged between 16 and 65 years old and diagnosed with opioid dependence between 2003 and 2007 were followed until discontinuation of opioid treatment or end of observation, whatever comes first.  41.6 % women  mean age= 33.4, SD=9.8  mean follow up =11.1 months, SD=12.2 7
    8. 8. The multi-state Markov model 8
    9. 9. Results 9
    10. 10. Results (Cont.) Frequency table of pairs of consecutive states: for each state p and q, the number of times a patient was in state p followed by a state q. For instance, there were 600 transitions from the transient state methadone to the transient state buprenorphine. 10
    11. 11. Results (Cont.) The probabilities of being in any one state after 6 months and 24 months of follow up are reported here. A typical patient in state methadone has a probability of 0.20 of discontinuing therapy after 6 months. 11
    12. 12. Results (Cont.) 12
    13. 13. Results (Cont.) 13
    14. 14. Results (Cont.)Figure 2 shows the predicted probability of survival (staying on therapy) for opioid usersfor monthly times in the future. For instance, the 30 months survival probability of beingon Methadone is 0.3, on Buprenorphine is 0.09, and on other treatments is 0.05. 14
    15. 15. Next steps Tests such as exponential sojourn times assumption test Model comparison using likelihoods / AIC for a parsimonious final model and to potentially gain more power Goodness of fit assessment 15
    16. 16. Conclusion Results of this study describe the dynamics of opioid therapy enrollment and the effects of several covariates on the transition intensities. In addition to other methods for assessing compliance to and persistence with treatments, Markov Multi-state models are very helpful for investigating patterns of use and switching between treatments over a long time period . 16
    17. 17. Thank You 17
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