Assignment 2: Introduction and Revised PICOT QuestionRevise your PICOT question on the basis of the feedback you received on Week 3 Assignment 2. Submit your introduction and revised PICOT question as

Assignment 2: Introduction and Revised PICOT QuestionRevise your PICOT question on the basis of the feedback you received on Week 3 Assignment 2. Submit your introduction and revised PICOT question as
WEEK 4 DISCUSSION/ASSIGNMENTS Assignment 2: Introduction and Revised PICOT Question Revise your PICOT question on the basis of the feedback you received on Week 3 Assignment 2. Submit your introduction and revised PICOT question as a 5- to 6-page Microsoft Word document. The assignment 2 paper in this week will be a draft for your week 5 paper. Recall that your introduction will include: Purpose of or rationale for the scholarly project: Provide an evidence-based explanation of why it is necessary to complete your scholarly project and what benefit will be gained (health promotion, fiscal, and efficiency). Background on the problem or population of interest: Using primary sources, provide data on your topic. Providing the background will demonstrate the focused need for your project. Significance of the problem to nursing and health care: State how your problem or population of interest aligns with the larger interest of health care in the community. Create a context to why your topic is important. Benefit of the project to nursing practice: State what will be gained from your project. Describe the expected outcomes of your project to practice within your population and setting. Relate the outcomes to evidence-based guidelines and outcomes. Describe how your project may influence other populations or settings. Submission Details: Submission Details: Name your document SU_NSG8100_W4_A2_LastName_FirstInitial.doc. Email your documents to Dr. B by the due date assigned.(4/30) Use the criteria below in addition to your directives for this assignment. Assignment 2 Grading Criteria Maximum Points The introduction is comprehensive, is evidence based, captures the reader’s interest, and provides an overall framework for the scholarly project. 30 The purpose and rationale for the scholarly project are evidence based and clearly described. 20 The background of the problem or population of interest is sufficient enough to create a compelling need for the project and is obtained from primary sources. 15 The significance of the project to nursing and healthcare is clearly described. The benefit of the project to practice at the local, regional, national, or global level is explored. 15 Included a PICOT question in the appropriate format and applicable to the problem or population of interest. 10 Used correct spelling, grammar, and professional vocabulary. Cited all sources using APA format. 10 Total: 100 Assignment 3: Literature Review: Five Articles Abstracted onto the Evaluation Table This week, you will submit another evaluation table document with five research articles abstracted for your literature review. Although it is expected that a majority of the articles will be part of your final literature review, some may not necessarily be included depending on your evaluation. For example, out of the thirty articles you evaluate during this course, you may only end up including twenty in your final review. The total number of articles you include may also depend on the amount of existing research. Try to shoot for twenty to twenty-five articles as a target for your final review. You will organize the information obtained from the review by paraphrasing and organizing concepts, variables, themes, and data from each article according to the nine headings in the evaluation table. Click here to download a blank evaluation table. SEE ATTACHMENTS PROVIDED Click here to view a sample evaluation table that contains the required data. SEE ATTACHMENTS PROVIDED . Submission Details: Submission Details: Name your document SU_NSG8100_W4_A3_LastName_FirstInitial.doc. Email your documents to Dr. B by the due date assigned.(5/2) On a separate page, cite all sources using APA format. Use the criteria below in addition to your directives for this assignment. Assignment 3 Grading Criteria Maximum Points The evaluation table contains five new appropriate articles with each table heading having a comprehensive abstraction or justification about why the section is not applicable. 20 Used correct spelling, grammar, and professional vocabulary. Cited all sources using APA format. Total: 25 *****************************************************************************
Assignment 2: Introduction and Revised PICOT QuestionRevise your PICOT question on the basis of the feedback you received on Week 3 Assignment 2. Submit your introduction and revised PICOT question as
EDITORIAL Medication-assisted treatment for opioid dependence in Twelve Step–oriented residential rehabilitation settings Marc Galanter, MD a, Marvin Seppala, MD b, and Audrey Klein, PhD b aDepartment of Psychiatry, New York University School of Medicine, New York, New York, USA; bHazelden/Betty Ford Foundation, Center City, Minnesota, USA Noncommunicable diseases are a major focus of attention of the World Health Organization, 1and drug misuse is among the 25 leading causes of risk for mortality worldwide. 2One recent example of this problem is evident in the United States, with a rise in the last 15 years in opioid addiction as a cause of death. 3The use of medication for treatment for opioid addic- tion, most recently in the form of buprenorphine and depot naltrexone, has led to a focus on the employment of medica- tion-assisted treatment along with psychosocial approaches for opioid-dependent patients. Attention to this approach was underlined by the recent announcement by the US president of a major initiative to expand access to medication-assisted treat- ment for opioid use disorders. 4One area where consideration of this approach can be addressed is the widespread use of free- standing residential rehabilitation programs for substance use treatment. In the United States, there are 3450 such programs oriented to addiction rehabilitation that are not hospital- affiliated. Contemporary residential rehabilitation programs for alcohol and drug use disorders originated in the 1940s with the wedding of professional care to Twelve Step–based recovery. At that time, this approach was directed to dependence on alcohol, for which the fel- lowship of Alcoholics Anonymous(AA) had originally been devel- oped. It was only later that people addicted to other drugs came to be treated in Twelve Step–based facilities. With the increase of addiction to narcotic analgesics and heroin in recent years, the issue can be raised as to whether medication-assisted treatment (MAT) can be adopted in the Twelve Step–oriented rehabilitation settings to best address the needs of opioid-dependent patients who may be admitted. This has raised concerns among some clinicians committed to AA- based recovery about the compatibility of the Twelve Step model with opioid maintenance on a dependency-producing agent such as buprenorphine, or on an opioid antagonist. This relates to a fundamental issue for them, of how the biomedi- cally oriented and the Twelve Step approaches can be combined to yield an outcome that can be superior to either approach alone. We write here to discuss the feasibility of implementing such a combined approach in established Twelve Step–based residential rehabilitation settings, in order to achieve improved clinical outcome for opioid addicts. Magnitude of the problem There have been 2 related trends in the United States in recent years regarding opioid use disorders. Thefirst relates to non- medical use of prescription narcotics, yielding a marked increase in the prevalence of high frequency use of these anal- gesics and related substance use disorders. 5This has generated public health efforts geared at cutting back on the excessive pre- scribing of opioid analgesic medications by means such as state-based prescription monitoring and the development of guidelines for proper treatment of pain in substance-abusing patients. 6 A second trend has been the transition of many people from dependence on opioid analgesics to heroin. 7Among people with substance use disorders surveyed, exclusive use of heroin more than doubled between 2008 and 2014, with nearly half of those sur- veyed reporting having moved on to heroin from nonmedical use of narcotic analgesics. 8Indeed, the portion of admissions for opioid dependence among substance abuse treatment admissions increased from 11% in 1992 to 19% in 2013. 2 Treating opioid dependence with MAT in the rehabilitation setting Inpatient rehabilitation unitsareamajorresourceformanaging substance-dependent people in the United States, and most make use of a Twelve Step–oriented approach; some see engagement in theTwelveStepformatasaprimarygoal following discharge. Prior to the current increase in opioid problems, the outcome of treat- ment in a systematically managed Twelve Step–oriented inpatient rehabilitation program had been reported to be positive. 9This approach has been useful for alcohol use disorders, for which medi- cationshavebeenfoundtobeofsomebenefit, 10and for cocaine- related disorders, for which there are no medications that have a material impact on clinical outcome. On the other hand, we do have medications that provide clear-cut benefit for opioid use dis- orders, and their use in the rehabilitation setting is understandably warranted. Methadone, the most widely used medication for opioid dependence, can be prescribed in state-regulated clinics. Bupre- norphine, on the other hand, has been shown to be comparable CONTACTMarc Galanter, MD [email protected] Department of Psychiatry, New York University School of Medicine, 550 First Avenue, New York, NY 10016, USA. © 2016 Taylor & Francis Group, LLC SUBSTANCE ABUSE 2016, VOL. 37, NO. 3, 381–383 http://dx.doi.org/10.1080/08897077.2016.1187241 in effectiveness to methadone as a maintenance medication 11 and can be prescribed by certified practitioners. Patient drop- out while on buprenorphine maintenance, however, particu- larly in early stages of treatment, does remain a problem. 12 Extended-release naltrexone, also employed for opioid depen- dence, administered intramuscularly, has been shown to pro- duce significantly longer treatment retention than placebo, 13 but long-term outcome studies on this modality after the early stages of treatment have yet to be conducted. Findings among members of Narcotics Anonymous suggest that Twelve Step membership can be beneficial in achieving abstinence among opioid-dependent people. One recent survey showed that those whose primary drug problem was that of heroin-dependent individuals constituted 28% of NA members, and those with a primary problem of“other opioids,”13%. 14 Combining Twelve Step and medication approaches for MAT, however, can raise certain problems. A particularly salient issue is the difference in orientation between clinical staff who deliver Twelve Step model, abstinence-based treat- ment, 15 many of whom are in that very mode of recovery. Additionally, there is a relative lack of experience with Twelve Step approaches within the maintenance-oriented medical community. Despite this, there have been attempts to adapt the Twelve Step model for patients in methadone clinics. For exam- ple, Methadone Anonymous 16 has drawn on the Twelve Step model for rehabilitating methadone-maintained patients, but operates independent of either the AA or NA fellowship struc- tures. Given the large number of opioid-dependent people maintained on buprenorphine, some have begun to attend Twelve Step groups, and in such cases, a positive correlation between the level of group attendance and ongoing abstinence has been reported. 17 Medication-assisted treatment in inpatient rehabilitation There has been a marked increase in opioid-related admis- sions in recent years in Twelve Step–oriented residential rehabilitation settings. In one such setting, Hazelden–Betty Ford in Minnesota, the portion of admissions that were opi- ate-related between 2001 and 2011 had increased from 19% to 30% of adults, and from 15% to 41% of adolescents. Opi- oid-dependent patients, many of whom had transitioned from narcotic analgesics to heroin addiction, were found to experience considerable morbidity and mortality after dis- charge from inpatient care, as clinically observed in numer- ous settings, including Hazelden. Combining MAT and Twelve Step facilitation The value of employing MAT in such residential settings, which employ a Twelve Step–oriented format, despite potential conflicts in treatment orientation, appears to be clinically indicated. Twelve Step facilitation (TSF), a manual-guided treatment for alcohol and substance use disorders, is a systematic way of integrating AA attendance into professional care. It has been shown to yield clinical results similar to those of motivational enhancement and cognitive behavioral therapy in the individual therapeutic setting. 18 Addi- tionally, Twelve Step approaches are employed in many, but notall, alcoholism treatment programs, and TSFfindings are relevant here. In one naturalistic study, a Twelve Step–oriented program was found to provide better outcomes than one oriented to cogni- tive-behavioral therapy (CBT). 19It is relevant to rehabilitation in that greater duration of patient retention in TSF-based treatment has been found to be associated with better outcomes. 20 Thesefindings suggest that promoting Twelve Step atten- dance over the course of an extended residential stay may enhance treatment outcomes because it continues to be rein- forced over the course of residence there. Such residential set- tings may therefore be suitable for introducing MAT into an extended Twelve Step–oriented stay. A strong therapeutic alli- ance between patient and staff members has been shown to enhance TSF outcome, 21and with proper training, staff in resi- dential settings could be oriented so as to maximize their alli- ance with patients to support combining a Twelve Step approach and MAT over the course of a residential stay. Fur- thermore, a group-based format for patients has been demon- strated to be clinically useful. 22 Its use in residential settings may also be adapted for MAT. The potential benefit in terms of long-term substance use outcome for MAT in“rehab”settings remains to be deter- mined. The Hazelden–Betty Ford Center for Research, for example, is currently conducting a study of patients treated in this manner. The successful initiation of such a program, with appropriate orientation, depends on Twelve Step–oriented staff accepting the introduction of buprenorphine or depot naltrex- one for opioid-dependent patients and on patients being willing to engage with this type of treatment. Given this, the need for reducing postdischarge morbidity and mortality of opioid- dependent people from residential rehabilitation settings, it would be advisable to implement such programmatic options more widely. References [1] World Health Organization.Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013–2020. Geneva, Switzerland: World Health Organization; 2013. [2] Lozano R, Naghavi M, Foreman K, et al. Global and regional mortal- ity from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;280:2095–2128. [3] Maxwell JC. The pain reliever and heroin epidemic in the United States: shifting winds in the perfect storm.J Addict Dis. 2015;34:127–140. [4] Botticelli M. Addressing the epidemic of prescription opioid abuse and heroin use.https://www.whitehouse.gov/blog/2016/02/01/preventing- epidemic-opioid-abuse-and-heroin-use. Accessed March 1, 2014. [5] Han B, Compton WM, Jones CM, Cai R. Nonmedical prescription opioid use and use disorders among adults aged 18 through 64 years in the United States, 2003–2013.JAMA. 2015;314:1468–1478. [6] Cheatle M, Comer D, Wunsch M, Skoufalos A, Reddy Y. Treating pain in addicted patients: recommendations from an expert panel. Popul Health Manag. 2014;17:79–89. [7] Mars SG, Fessel JN, Bourgois P, Montero F, Karandinos G, Ciccarone D. Heroin-related overdose: the unexplored influences of markets, market- ing and source-types in the United States.Soc Sci Med. 2015;140:44–53. [8] Cicero TJ, Ellis MS, Harney J. Shifting patterns of prescription opi- oid and heroin abuse in the United States.N Engl J Med. 2015;373:1789–1790. [9] Stinchfield R, Owen P. Hazelden’s model of treatment and its out- come.Addict Behav. 1998;23:669–683. 382 M. GALANTER ET AL. [10] Jonas DE, Amick HR, Feitner C, et al. Pharmacotherapy for adults with alcohol use disorders in outpatient settings: a systemic review and meta-analysis.JAMA. 2014;311:1889–1900. [11] Bell J, Trinh L, Butler B, Randall D, Rubin G. Comparing retention in treatment and mortality in people after initial entry to methadone and buprenorphine treatment.Addiction. 2009;104:1193–1200. [12] Weiss RD, Potter JS, Fiellin DA, et al. Adjunctive counseling during brief and extended buprenorphine-nalaxone treatment for prescrip- tion opioid dependence: a 2-phase randomized controlled trial.Arch Gen Psychiatry. 2011;68:1238–1246. [13] Comer SD, Sullivan MA, Yu E, et al. Injectable, sustained-release nal- trexone for the treatment of opioid dependence: a randomized, pla- cebo-controlled trial.Arch Gen Psychiatry. 2006;63:210–218. [14] Galanter M, Dermatis H, Post S, Santucci C. Abstinence from drugs of abuse in community-based members of Narcotics Anonymous.J Stud Alcohol Drugs. 2013;74:349–352. [15] Volkow ND, Frieden TR, Hyde PS, Cha SS. Medication-assisted therapies: tackling the opioid-overdose epidemic.NEnglJMed. 2014;370:2063–2066. [16] Gilman S, Galanter M, Dermatis H. Methadone Anonymous: a 12- step program for methadone maintained heroin addicts.Subst Abus. 2001;22:247–256.[17] Monico LB, Gryczynski J, Mitchell SG, Schwartz RP, O’Grady KE, Jaffe JH. Buprenorphine treatment and 12-step meeting atten- dance: conflicts, compatibilities, and patient outcomes.JSubst Abuse Treat. 2015;57:89–95. [18] Project Match Research Group. Matching alcoholism treatments to client heterogeneity: Project MATCH post treatment drinking out- comes.J Stud Alcohol. 1997;58:7–29. [19] Moos R, Moos B, Andrassy J. Outcomes of four treatment approaches in community residential programs for patients with substance use disorders.Psychiatr Serv. 1999;50:1577– 1583. [20] Timko C, DeBenedetti A, Billow R. Intensive referral to 12-Step self- help groups and 6-month substance use disorder outcomes.Addic- tion. 2006;101:678–688. [21] Campbell BK, Guydish J, Le T, Wells EA, McCarty D. The relation- ship of therapeutic alliance and treatment deliveryfidelity with treat- ment retention in a multisite trial of Twelve-Step facilitation.Psychol Addict Behav. 2015;29:106–113. [22] Kaskutas LA, Subbaraman MS, Witbrodt J, Zemore SE. Effec- tiveness of making Alcoholics Anonymous easier: a group for- mat 12-step facilitation approach.JSubstAbuseTreat. 2009;37:228–239. SUBSTANCE ABUSE 383 Copyright ofSubstance Abuseisthe property ofTaylor &Francis Ltdand itscontent maynot be copied oremailed tomultiple sitesorposted toalistserv without thecopyright holder’s express writtenpermission. However,usersmayprint, download, oremail articles for individual use.
Assignment 2: Introduction and Revised PICOT QuestionRevise your PICOT question on the basis of the feedback you received on Week 3 Assignment 2. Submit your introduction and revised PICOT question as
Impact of Medicaid Expansion on Access to Opioid Analgesic Medications and Medication-Assisted Treatment Alana Sharp, MPH, Austin Jones, MA, Jennifer Sherwood, MSPH, Oksana Kutsa, BS, Brian Honermann, JD, and Gregorio Millett, MPH Objectives.To assess the impact of the expansion of Medicaid eligibility in the United States on the opioid epidemic, as measured through increased access to opioid analgesic medications and medication-assisted treatment. Methods.Using Medicaid enrollment and reimbursement data from 2011 to 2016 in all states, we evaluated prescribing patterns of opioids and the 3 Food and Drug Admin- istration–approved medications used in treating opioid use disorders by using 2 sta- tistical models. We used difference-in-differences and interrupted time series models to measure prescribing rates before and after state expansions. Results.Although opioid prescribing per Medicaid enrollee increased overall, we observed no statistical difference between expansion and nonexpansion states. By contrast, per-enrollee rates of buprenorphine and naltrexone prescribing increased more than 200% after states expanded eligibility, while increasing by less than 50% in states that did not expand. Methadone prescribing decreased in all states in this period, with larger decreases in expansion states. Conclusions.The Medicaid expansion enrolled a population no more likely to be prescribed opioids than the base Medicaid population while significantly increasing uptake of 2 drugs used in medication-assisted treatment. (Am J Public Health.2018;108: 642–648. doi:10.2105/AJPH.2018.304338) See also Humphreys, p. 589. T he United States is in the midst of an epidemic of opioid drug use, constituting one of the worst public health crises in recent history. In 2015, more than 52 000 people died from drug overdoses, and early estimates suggest continuing increases in mortality in 2016 and 2017. 1,2 Today, drug overdose is the leading cause of accidental death in the country and contributes to more deaths than do motor vehicle accidents. 3,4 The response to rising opioid use and mortality will require increased access to evidence-based treatment options for people who use drugs. Currently, methadone, buprenorphine, and naltrexone are the only Food and Drug Administration (FDA)– approved medications produced for and used in the treatment of opioid dependence. According to the World Health Organization and Centers for Disease Control and Pre- vention (CDC), medication-assistedtreatment (MAT) is the most effective regi- men for reducing drug use and is effective in reducing overdose rates, HIV transmission, and criminal activity, while increasing treat- ment retention. 5,6 Yet nearly 9 out of 10 people with substance use disorders do not access treatment services, and lack of health insurance is cited as a primary barrier to accessing treatment by nearly one third of those with an identified need for treatment. 6 Medicaid is a major funder of substance use treatment programs and in 2015 covered services for 17% of all adults with substance use disorder. 7,8 The Patient Protection and Affordable Care Act (ACA) included severalprovisions that increased access to substance use disorder treatment. In addition to en- abling states to expand Medicaid eligibility to low-income adults, the ACA established guidance such that state benchmark plans must include a specified set of essential health benefits, including mental health and sub- stance use disorder services. 9In addition, the Mental Health Parity and Addiction Equity Act, which mandates that mental health services be offered at parity with other types of medical care, is expanded to apply to plans in the expansion. Previous workfinds that the number of Medicaid-reimbursed pre- scriptions for buprenorphine increased in states that expanded Medicaid, although many low-income adults with substance use disorders in all states continue to have limited access to affordable treatment. 10In addition, Medicaid expansion may increase access by increasing health system capacity, as shown by documented increases in the number of Drug Addiction Treatment Act of 2000–waived physicians eligible to prescribe buprenor- phine in states that expanded Medicaid. 11 While the Medicaid expansion has in- creased access to MAT, it may have similarly increased access to opioid analgesic medica- tions. Historical analyses have found opioid prescribing rates for the Medicaid population to be more than double the rates for non- Medicaid enrollees, raising concerns that Medicaid expansion may inadvertently act as a driver of opioid abuse and addiction. 12 Citing increased access to pharmaceuticals as a potential driver of opioid use and addiction, both in the Medicaid population and also ABOUT THE AUTHORSAll of the authors are with amfAR, The Foundation for AIDS Research, Washington, DC. Correspondence should be sent to Alana Sharp, 1100 Vermont Ave NW, Suite 600, Washington, DC 20005 (e-mail: alana. [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the“Reprints”link. This article was accepted January 14, 2018. doi: 10.2105/AJPH.2018.304338 642ResearchPeer ReviewedSharp et al.AJPHMay 2018, Vol 108, No. 5 AJPHPOLICY generally, several states have instituted poli- cies to reduce access to opioids, including setting limits on the number of opioid pills that can be prescribed or requiring prior authorization before prescribing. 13–17 However, despite the role of Medicaid as a major payer of substance use disorder treatment, 18the expansion’simpactonthe opioid epidemic has not been sufficiently quantified. Recently, at least 1 analysis has refuted the assertion that Medicaid expansion has contributed to rising opioid use and mor- tality, but no previous work has quantitatively described the impact of the ACA’schanges to Medicaid eligibility on both opioid drug use and treatment. 19Using both a difference-in- differences model and an interrupted time series model, this analysis describes prescribing pat- terns for opioids and the 3 FDA–approved medications for opioid use disorder treatment before and after Medicaid expansion, providing an important body of evidence on the role of Medicaid programs in the opioid epidemic. METHODS We used data on prescriptions reimbursed by Medicaid from Medicaid’s State Drug Utilization Data for years 2011 through 2016. 20We calculated the number of pre- scriptions reimbursed quarterly by the Med- icaid program by state for opioid analgesics, buprenorphine–naloxone (hereafter, bupre- norphine), methadone, and naltrexone (oral and injectable formulations) used for substance use treatment, by using the FDA’s National Drug Code Directory and the CDC’s datafile of oral morphine milligram equivalent (MME) conversion factors. 21,22 Where the number of prescriptions of a medication was fewer than 11 per state per quarter, data were suppressed and therefore were unavailable. These sup- pressed values, on average of per-quarter, per-drug observations across all years, com- prised 46.2% of observations for opioids, 38.6% for buprenorphine, 49.3% for methadone, and 40.4% for naltrexone. At the maximum number requiring suppression, the overall count of prescriptions reimbursed was de- creased by only 0.020% because of suppression. We measured quarterly rates of prescribing of each medication per annual count of Medicaid enrollees by using Centers for Medicare and Medicaid Services enrollment reports. 23,24 Wemeasured both the number of prescriptions and the number of units prescribed, for both opioids and MAT drugs. A“unit”is defined by drug, typically representing a capsule, gram, milliliter, tablet, or transdermal patch. To assess trends in the strength of opioid drugs prescribed over time, we calculated the number of MMEs, a standardized measure of opioid dosage, by using CDC conversion factors. 22We excluded states in which Medicaid does not reimburse methadone (n = 17) or naltrexone (n = 1) for substance use disorders from those drug-specific analyses; all states reimburse buprenorphine. 25 We categorized all states and the District of Columbia as“expansion”states if they eventually expanded Medicaid (n = 32) and “nonexpansion”if they did not expand by the endof2016(n=19).Wetookthedateof Medicaid expansion for each state from the Kaiser Family Foundation’s database. 26In both expansion and nonexpansion states, we cen- tered dates at thefirst quarter of 2014, the date when Medicaid expansion went into effect for most states. For the states that expanded eligibility after January 1, 2014 (n = 7), we centered dates at the state-specificdateofex- pansion. We measured the impact of Medicaid expansion on opioid and medications for substance use disorders with 2 models: (1) a difference-in-differences model with state- levelfixed effects and (2) an interrupted time series method with state-levelfixed effects, with temporal interaction terms, on the period 2011 to 2016. We selected these models to both assess the overall effect size of the expansion onprescribingaswellastoprovideamore nuanced assessment of both the pre–post changes and the temporal changes in trends in this period. These models are described as follows: ð1ÞRx it¼b 0þb 1Ptþb 2EiPtþg iþo´ it ð2ÞRx it¼b 0þb 1Ptþb 2tþb 3Eit þb 4ptþb 5EiPttþb 6EiPtt þg iþo´ it wherebcoefficients measure the estimated effect size on prescribing for the following indicators:E iis an indicator for Medicaid expansion states (0 = states that did not ex- pand; 1 = states that did expand),P tis an indicator for the postexpansion time period (0 = preexpansion; 1 = postexpansion), and tis a continuous measure of time at 3-month quarterly intervals (from 2011 to 2016). Theg iis state-levelfixed effects and o´ itis an error term. The models are assessed whereRx it represents the number of prescriptions, the count of units prescribed, and the MMEs. To measure the overall impact of the Medicaid expansion on prescribing rates in this period, we tested the following hypotheses: for the difference-in-differences model (Equation 1), we measured the impact by testing the significance of theb 2coefficient, while in the interrupted time series model (Equation 2), we assessed the impact of the expansion byb 5and b 6,whereb 5measures changes in prescribing rates at the transition from before expansion to after expansion, and whereb 6represents changes in the trend in prescribing in the postexpansion period. We measured the overall trends in prescribing rates by postestimation tests of linear combinations of coefficients. For example, we tested the difference in trends after expansion in states that expanded Medicaid relative to those that did not by ð3ÞH 0:b 3þb 4þb 5þb 7 ðÞ b 3þb 5 ðÞ¼0;a¼0:05 We performed all analyses with Stata ver- sion 15 (StataCorp LP, College Station, TX). RESULTS A total of 181 485 806 opioid prescriptions, totaling 10 745 379 857 units (e.g., pills, tablets, sprays, milligrams) of opioids, were reimbursed by Medicaid from 2011 to 2016. In 2013, the year preceding the Medicaid expansion, Med- icaid expansion states had an average of 12 138 opioid prescriptions per 100 000 Medicaid enrollees (Table 1). In nonexpansion states, opioid prescribing rates were 10 861 per 100 000 enrollees in 2013. Similarly, both the number of units prescribed and the MMEs per 100 000 were higher in expansion states than in non- expansion states during the preexpansion period (Figure A, available as a supplement to the online version of this article at http://www.ajph.org). Postexpansion Opioid Prescription Differences On average, in all states, opioid prescribing rates increased from preexpansion to post- expansion periods (Figure 1, Table 1). According to the difference-in-differences model, average prescription rates increased by AJPHPOLICY May 2018, Vol 108, No. 5AJPHSharp et al.Peer ReviewedResearch643 5985 per 100 000 in nonexpansion states and by 4366 per 100 000 in expansion states (Table 2). This difference in the differences in prescribing rates in expansion relative to nonexpansion states was not statistically sig- nificant. We saw similar patterns in rates of opioid units prescribed and MME prescribing (Figure A, Table A, and Table B, available as supplements to the online version of this article at http://www.ajph.org). In the interrupted time series model, the mean level of opioid prescriptions per 100 000 enrollees increased in all states between the preexpansion and postexpansion period, again with no significant difference between ex- pansion and nonexpansion states (Table 3). In the period before the expansion, quarterly prescribing rates increased by an average of 62 prescriptions per 100 000 each quarter in expansion states and by 217 per quarter in nonexpansion states, although these trends were not significant, nor were theysignificantly different from each other. After an increase in prescribing at the time of the ex- pansion, prescribing rates began to decline slightly in all states, with the rate of prescribing decreasing by 448 prescriptions per 100 000 every quarter in the nonexpansion states and by 230 per quarter in expansion states; again, this trend was not significant in either group. Overall, this model showed an increase in opioid prescribing in this time period, with the expansion of Medicaid eligibility not differ- entially impacting the overall growth in opioid prescribing rates nor the quarterly trends. We observed the same pattern for the number of units prescribed per 100 000 enrollees (Tables A and C, available as supplements to the online version of this article at http://www.ajph.org) and the MME prescribing rate (Table B). Medication-Assisted Treatment From 2011 to 2016, 11 166 525 pre- scriptions of buprenorphine, 3 267 551 ofmethadone, and 892 402 of naltrexone were reimbursed by Medicaid. The number of MAT prescriptions per 100 000 Medicaid enrollees was higher in 2013, the year before the expansion, for all 3 drugs in states that expanded Medicaid relative to those that did not expand (Table 1). In the difference-in-differences model, buprenorphine prescribing increased across all states from the period before the expansion to the period after, with significantly larger in- creases in expansion states (P<.001). In ex- pansion states, the rate of buprenorphine prescriptions increased by 1211 prescriptions per 100 000 from the preexpansion period to the postexpansion period, while in states that did not expand prescribing, they increased by 214 prescriptions per 100 000 (Table 2). Overall, expansion states increased bupre- norphine prescribing rates by 997 units per 100 000 more than did nonexpansion states. In the analysis of units prescribed, we TABLE 1—Prescribing Characteristics in the First Quarter (January–March) of 2013 and in the First Quarter of 2015, in States That Expanded and Did Not Expand Medicaid Eligibility: United States Characteristics Did Not Expand, Mean (SE) Expanded, Mean (SE) Difference (95% CI) Q1 2013 Prescriptions per 100 000 enrollees Opioids 10 861.0 (4 476.8) 12 138.1 (3 978.1)–1 277.1 (–3 703.1, 1 148.9) Buprenorphine 536.6 (1 142.0) 969.0 (1 527.2)–432.4 (–1 263.4, 398.7) Methadone 196.8 (143.8) 318.8 (224.2)–122.0 (–237.5,–6.5) Naltrexone 42.7 (35.1) 49.9 (48.6)–7.2 (–34.8, 20.4) Units reimbursed per 100 000 enrollees Opioids 607 987.0 (276 481.6) 773 705.1 (448 303.3)–165 718.1 (–395 028.5, 63 592.2) Buprenorphine 18 100.6 (31 750.1) 26 943.1 (28 774.5)–8 842.5 (–26 532.7, 8 847.5) Methadone 25 816.5 (19 378.9) 45 315.3 (33 034.3)–19 498.8 (–36 245.0, 2 747.7) Naltrexone 1 296.2 (974.8) 1 427.5 (1 248.4)–131.3 (–853.3, 590.5) MMEs per 100 000 enrollees: opioids 647 158.7 (370 754.1) 907 012.1 (535 205.4)–259 853.4 (–540 019.0, 20 312.2) Q1 2015 Prescriptions per 100 000 enrollees Opioids 22 719.9 (40 602.0) 20 001.5 (24 714.9) 2 718.4 (–15 613.1, 21 049.9) Buprenorphine 621.9 (1 064.6) 1 756.0 (2 579.9)–1 134.1 (–2 418.5, 150.1) Methadone 315.4 (568.9) 300.0 (291.9) 15.4 (–229.4, 260.2) Naltrexone 101.3 (205.8) 104.2 (136.6)–2.9 (–102.5, 96.8) Units reimbursed per 100 000 enrollees Opioids 1 331 492.5 (227 620.4) 1 168 961.8 (1 450 756.1) 162 530.7 (–888 073.4, 1 213 135.4) Buprenorphine 19 660.8 (24 042.5) 56 499.6 (93 709.3)–36 838.8 (–82 248.6, 8571.1) Methadone 38 489.9 (61 004.0) 38 421.2 (39 997.9) 68.7 (–28 603.0, 28 740.5) Naltrexone 3 437.2 (8 258.4) 2 547.3 (3 225.2) 889.9 (–2 465.3, 4 245.2) MMEs per 100 000 enrollees: opioids 1 380 219.6 (2 184 246.7) 1 472 694.4 (2 040 427.4)–92 474.8 (–1 311 461.3, 1 126 511.0) Note.CI = confidence interval; MME = morphine milligram equivalents. Prescribing rates are per 100 000 Medicaid enrollees. AJPHPOLICY 644ResearchPeer ReviewedSharp et al.AJPHMay 2018, Vol 108, No. 5 observed no significant change in the level of units of buprenorphine prescribed per 100 000 in nonexpansion states, while the number of units prescribed increased signif- icantly for expansion states (Table A). In the interrupted time series analysis of MAT, before the expansion, the quarterly rate of buprenorphine prescribing was in- creasing in both expansion (72.3 units per 100 000 per quarter) and nonexpansion states (43.5 units per 100 000 per quarter). Thisdifference was not statistically significant (Table 3). After the expansion, the rate of buprenor- phine prescribing leveled off slightly in the nonexpansion states, while the prescribing rate increased by 136.3 units per 100 000 every quarter in the expansion states, a statistically significant rate increase (P<.001). We ob- served a similar trend in the number of units prescribed per 100 000 enrollees (Table C). By contrast, methadone prescribing exhibited an overall declining trend in the2011–2016 period. In the difference-in- differences model, the number of methadone prescriptions per 100 000 did not significantly change in nonexpansion states but decreased by 65 per 100 000 in expansion states (P= .001; Table 2). The number of units of methadone prescribed decreased in both expansion and nonexpansion states, with a larger decrease in expansion than in nonexpansion states (–6551 per 100 000 in nonexpansion,–13 313 per 100 000 in expansion states;P<.001; Table A). 0 500 1 000 1 500 MAT Reimbursed per Enrollee 0 3 000 6 000 9 000 12 000 15 000 Opioid Reimbursed per Enrollee –10 –5 0 5 10 Quarters Before and After Expansion 0 500 1 000 1 500 MAT Reimbursed per Enrollee 0 3 000 6 000 9 000 12 000 15 000 Opioid Reimbursed per Enrollee –10 –5 0 5 10 Quarters Before and After Expansion Opioids Buprenorphine Naltrexone Methadone a b Note. MAT = medication-assisted treatment. FIGURE 1—Number of Prescriptions per 100 000 Medicaid Enrollees From 2011 to 2016 in States That (a) Expanded and (b) Did Not Expand Medicaid: United States AJPHPOLICY May 2018, Vol 108, No. 5AJPHSharp et al.Peer ReviewedResearch645 Temporal trends in the quarterly number of per-enrollee methadone prescriptions revealed a significant downward trend in the preexpansion period in both expansion (–9.50 units per 100 000 per quarter) and nonexpansion (–9.51 units per 100 000 per quarter) states (Table 3), as well as in units prescribed (Table C). The trends were not significantly different in this period. In the postexpansion period, we observed a similar downward trend, with the rate of change again not significantly different between the 2 groups. Finally, the rate of naltrexone prescribing increased by 75 prescriptions per 100 000 in expansion states and by 36 prescriptions per 100 000 in nonexpansion states in the difference-in-differences model, with the increased prescribing significantly higher in expansion states (P<.001; Table 2). The number of units of naltrexone prescribed per 100 000 similarly increased in this period, although we observed no difference in ex- pansion relative to nonexpansion states (Table Ag). In the interrupted time series model, the trend in the rate of naltrexone prescriptions per 100 000 enrollees wasflat in all groups and periods, with the exception of a significant increasing trend in prescribing rates (10.5prescriptions per 100 000 per quarter) in expansion states after the expansion (Table 3). The trend in the rate of units prescribed similarly increased by 141.91 units per 100 000 every quarter in expansion states after the expansion (Table C) but did not change in any other period or group. DISCUSSION States that did and did not expand Med- icaid had similar growth in opioid prescribing rate per Medicaid enrollee after expansion, while per-enrollee rates of Medicaid- reimbursed MAT increased significantly more in expansion states. The results from both models showed an overall increase in the rate of per-enrollee opioid prescribing in the period after January 2014, or after the date of Medicaid expansion for states that expanded later, through the end of 2016 in all states. This increase in opioid prescribing per state number of enrollees was not statistically different in states that ex- panded Medicaid to low-income adults under the ACA relative to those that did not expand. Although there was an overall increase in the quarterly trend of opioid prescribing in the preexpansion period, the trends in opioidprescribing rates were not different in ex- pansion and nonexpansion states. As with opioid prescribing, the per- enrollee rates of prescribing for all 3 MAT drugs was higher at the preexpansion baseline in states that ultimately expanded Medicaid relative to those that did not. For bupre- norphine, the quarterly growth in prescribing was significantly higher in expansion states after the expansion, while the trend in nonexpansion states did not change. For methadone, we found overall declining rates of prescribing, with larger decreases in ex- pansion states across this period. Naltrexone prescribing was higher in expansion states in the pre- and postexpansion period, and both the growth in prescribing after the expansion as well as the change in the rate of prescribing were significantly greater in states that ex- panded than in those that did not. Thesefindings suggest that the population of low-income adults newly eligible for Medicaid as part of the ACA’s expansion were no more likely to be prescribed opioid medications than the base preexpansion population, while being more likely to access treatment of substance use disorders. States that expanded Medicaid had historically higher rates of per-enrollee opioid prescribing in the Medicaid population, although this did TABLE 3—Regression Table for Interrupted Time Series Model With State-Level Fixed Effects for Number of Prescriptions: United States, 2011–2016 Opioids, b (95% CI) Buprenorphine, b (95% CI) Methadone, b (95% CI) Naltrexone, b (95% CI) Post (1 = postexpansion; 0 = preexpansion) 7 376.54 (2 769.77, 11 983.3)–105.74 (–484.92, 273.43) 131.42 (26.78, 236.06) 25.49 (–3.01, 54.00) Post·expanded–2 090.27 (–7 884.19, 3 703.65)–107.95 (–366.44, 582.34)–78.90 (–206.34, 48.53)–18.75 (–54.21, 16.70) Time (quarters) 216.55 (–193.69, 626.79) 43.47 (8.98, 77.95)–9.51 (–18.83,–0.19) 0.59 (–1.93, 3.12) Time·post–664.25 (–1 331.90, 3.40)–32.54 (–87.52, 22.43)–4.28 (–19.46, 10.90) 0.63 (–3.48, 4.75) Time·expanded–154.61 (–643.18, 333.96) 28.84 (–12.45, 70.13)–0.02 (–11.05, 11.08) 0.74 (–2.24, 3.73) Time·expanded·post 372.33 (–463.47, 1 208.13) 96.49 (27.74, 165.24) 3.66 (–14.79, 22.11) 8.56 (3.47, 13.64) (Constant) 12 967.47 (11 268.62, 14 666.32) 1 135.77 (995.69, 1 275.85) 277.47 (240.71, 314.24) 55.62 (45.23, 66.02) Note.CI = confidence interval. All prescribing rates are number of prescriptions reimbursed by Medicaid per 100 000 Medicaid enrollees. TABLE 2—Regression Table for Difference-in-Differences Model With State-Level Fixed Effects for Number of Prescriptions: United States, 2011–2016 Opioids, b (95% CI) Buprenorphine, b (95% CI) Methadone, b (95% CI) Naltrexone, b (95% CI) Post (1 = postexpansion; 0 = preexpansion) 5 985.31 (3 700.49, 8 270.13) 214.07 (15.79, 412.35)–8.37 (–60.54, 43.80) 36.44 (21.98, 50.90) Post·expanded–1 619.79 (–4 541.75, 1 302.16) 996.67 (744.71, 1 248.64)–56.30 (–120.32, 7.73) 38.99 (20.69, 57.28) (Constant) 12 226.25 (11 291.60, 13 160.91) 760.71 (679.29, 842.14) 335.69 (315.56, 355.82) 48.87 (43.06, 54.67) Note.CI = confidence interval. All prescribing rates are number of prescriptions reimbursed by Medicaid per 100 000 Medicaid enrollees. AJPHPOLICY 646ResearchPeer ReviewedSharp et al.AJPHMay 2018, Vol 108, No. 5 not appear to be driven by the population of newly eligible enrollees. Rather, the expan- sion of Medicaid eligibility appears to have primarily served as an important source of access to MAT of low-income adults with substance use disorders. Thefinding that the rate of MME pre- scribing increased in this time period contrasts with a recent analysis of per-capita MME prescribing in the total population, which found that national opioid prescribing rates have declined from 2010 to 2015. 27Our findings are corroborated by this analysis, which found that opioid prescribing rates were highest in communities with large populations without health insurance or en- rolled in Medicaid. It is outside of the scope of this analysis to determine why prescribing has increased among Medicaid enrollees yet de- clined nationally; as the indication for each prescription is not publicly available, neither the appropriateness of each prescription nor the condition each was intended to treat can be evaluated. It is nonetheless apparent that high prescribing in the Medicaid population predates the ACA, and we observed no dif- ferential effect on prescribing in states that expanded Medicaid relative to those that did not. Thesefindings are corroborated by other analyses that have found that the Medicaid expansion had no differential impact on drug-related mortality. 19 Trends in the number of opioid pre- scriptions, the units of opioids prescribed, and the number of MMEs prescribed were similar. As such, we observed no distinct trends in the number of units per prescription or the rel- ative potency of prescriptions over time, suggesting that changes in the number of prescriptions were neither offset nor com- pounded by changes in the number of units prescribed or the comparative potency of the drugs prescribed. However, the number of MMEs per capita observed in these data was considerably higher than that described in the total population; thisfinding may be attrib- utable to the comparatively high proportion of Medicaid enrollees who have disabilities requiring long-term pain management or who are aged 65 years or older. 27,28 In addition, we found a general trend toward the use of buprenorphine in MAT and a shift away from methadone. Although naltrexone occupies a smaller proportion of the MAT prescribing in this population, itsuse increased in this period. This trend, whether driven by provider or patient, may be driven by the lower risk for addiction or overdose in buprenorphine and naltrexone relative to methadone. In addition, metha- done is typically administered via daily in-person clinic visits, whereas buprenor- phine and naltrexone can be administered by larger“take-home”doses or through long- term injectable formulations. The burden of patient visits, which may incurfinancial costs, opportunity costs, and increased stigma, may also contribute toward the shift away from methadone. This analysis was limited by several factors. First, the suppression of drugs or quarters with low prescribing rates limited our ability to fully describe all trends in prescribing, al- though this analysis nonetheless describes trends of the most commonly prescribed drugs in the Medicaid program. Second, the use of state-level data may mask microtrends within states or mask differential impacts by urbanicity, distribution of substance use treatment programs or services, or health facility practices. In future research, more granular assessments that measure subnational enrollment and prescribing patterns will fa- cilitate a more complete assessment of the impact of the Medicaid expansion. Third, the prescribing data did not include the indication nor the patient diagnosis, which may confound thefindings. Indeed, methadone is used not just for treating opioid dependence, but is also used as an analgesic, and naltrexone is also approved for the treatment of alcohol addiction. Fourth, we identified 5 states where the proportion of data from managed care organizations was lower than would be predicted by the pro- portion of Medicaid enrollees in managed care organizations. The mainfindings are unchanged by excluding these states, except that in the difference-in-differences model opioid prescribing rates did expand signifi- cantly in expansion states, while remaining nonsignificant for units, MMEs, and in the interrupted time series model. Finally, we were not able to measure trends in the number of days associated with each pre- scription, an important metric that can signify patterns of overprescribing. Nonetheless, the use of 3 distinct metrics of prescribing (number of prescriptions, number of units prescribed, and MMEs) providea multifaceted assessment that triangulates state-level trends in prescribing patterns. This analysis of Medicaid prescribing data suggests that the expansion of insurance cov- erage in expansion states provided health in- surance coverage to a population no more likely to be prescribed opioids than the pre- expansion population, while significantly in- creasing access to treatment for people with substance use disorders. Continuing to address the opioid epidemic should maintain a focus on increasing access to health care and increasing health system capacity to provide substance abuse treatment of all those in need. CONTRIBUTORSA. Sharp, B. Honermann, and G. Millett originated the research question. A. Sharp, A. Jones, J. Sherwood, and O. Kutsa contributed to data analysis and statistical design. A. Sharp wrote the article. All authors contributed to discussions and analysis of results and review of the article. HUMAN PARTICIPANT PROTECTIONThis analysis did not involve human participants or per- sonally identifiable information. Institutional review board approval was not required. REFERENCES1. Results from the 2015 National Survey on Drug Use and Health: Detailed Tables. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2016. 2. Katz J. Drug deaths in America are rising faster than ever.New York Times. June 5, 2017. Available at: https:// www.nytimes.com/interactive/2017/06/05/upshot/ opioid-epidemic-drug-overdose-deaths-are-rising- faster-than-ever.html. Accessed December 1, 2017. 3. NCHS data on drug-poisoning deaths. Atlanta, GA: National Center for Health Statistics; 2017. 4. All injuries. National Center for Health Statistics. 2017. Available at: https://www.cdc.gov/nchs/fastats/injury. htm. Accessed December 1, 2017. 5.How to Improve Opioid Substitution Therapy Imple- mentation. Geneva, Switzerland: World Health Organi- zation; 2014. 6. Rudd RA, Seth P, David F, Scholl L. Increases in drug and opioid-involved overdose deaths—United States, 2010–2015.MMWR Morb Mortal Wkly Rep. 2016; 65(5051):1445–1452. 7. Burns RM, Pacula RL, Bauhoff S, et al. Policies related to opioid agonist therapy for opioid use disorders: the evolution of state policies from 2004 to 2013.Subst Abus. 2016;37(1):63–69. 8. Zur J, Musumeci M, Garfield R. Medicaid’srolein financing behavioral health services for low-income in- dividuals. Menlo Park, CA: Kaiser Family Foundation; 2017. 9. Mann C. Essential health benefits in the Medicaid program. Baltimore, MD: Department of Health and Human Services; 2012. 10. Clemans-Cope L, Epstein M, Kenney G. Medicaid coverage of effective treatment for opioid use disorder trends in state buprenorphine prescriptions and spending since 2011. Washington, DC: Urban Institute; 2017. 11. Wen H, Schackman BR, Aden B, Bao Y. States with prescription drug monitoring mandates saw a reduction AJPHPOLICY May 2018, Vol 108, No. 5AJPHSharp et al.Peer ReviewedResearch647 in opioids prescribed to Medicaid enrollees. Health Aff (Millwood) . 2017;36(4):733 –741. 12. Centers for Disease Control and Prevention. Over- dose deaths involving prescription opioids amongMedicaid enrollees, 2004 –2007. Washington, DC: Department of Health and Human Services; 2009. 13. Baker-White A. A look at state legislation limiting opioid prescriptions. Arlington, VA: Association of Stateand Territorial Health Of ficials; 2017. 14. Dube N. Connecticut ’s opioid drug abuse law. Hartford, CT: Connecticut Of fice of Legislative Research; 2016. 15. Vestal C. Maryland moves to limit opioid painkillers. The Pew Charitable Trusts. 2017. Available at: http://www.pewtrusts.org/en/research-and-analysis/blogs/stateline/2017/01/25/maryland-moves-to-limit-opioid-painkillers. Accessed December 1, 2017. 16. Opioid prior authorization requirements. Medstar Family Choice. 2017. Available at: http://www.medstarfamilychoice.com/maryland-healthchoice/for-maryland-healthchoice-physicians/pharmacy/opioid/ #q={}. Accessed December 1, 2017. 17. Senate Bill No.: 226, General Assembly of the State of Indiana, 120 Sess (2017). 18. Use of opioid recovery medications: recent evidence on state level buprenorphine use and payment types. IMSInstitute for Healthcare Informatics. 2016. Available at: https:// www.iqvia.com/-/media/iqvi a/pdfs/institute-reports/ use-of-opioid-recovery-med ications.pdf?la=en&hash= A0AEB9DE498D46FED720F37C41515330504F4DC3.Accessed December 1, 2017. 19. Goodman-Bacon A, Sandoe E. Did Medicaid expansion cause the opioid epidemic? There ’s little evidence that it did. Health Affairs Blog. 2017. Available at: https://www. healthaffairs.org/action/showDoPubSecure?doi=10.1377%2Fhblog20170823.061640&format=full&. AccessedDecember 1, 2017. 20. State drug utilization data. Centers for Medicare and Medicaid Services. Available at: https://www.medicaid.gov/medicaid/prescription-drugs/state-drug-utilization- data/index.html. Accessed August 8, 2017. 21. National Drug Code Directory . US Food and Drug Administration. 2017. Available at: https://www.fda. gov/Drugs/InformationOnDrugs/ucm142438.htm. Accessed August 8, 2017. 22. Analyzing prescription data and morphine milligram equivalents. Centers for Disease Control and Prevention. 2017. Available at: https://www.cdc.gov/drugoverdose/media/index.html. Accessed August 8, 2017. 23. Medicaid Enrollment Report Centers for Medicare & Medicaid Services. Centers for Medicare & MedicaidServices. 2015. Available at: https://www.medicaid.gov/medicaid/managed-care/enrollment/index.html.Accessed August 8, 2017. 24. Medicaid enrollment data collected through MBES. Centers for Medicare and Medicaid Services. 2016.Available at: https://www.medicaid.gov/medicaid/program-information/medicaid-and-chip-enrollment- data/enrollment-mbes/index.html. Accessed August 8, 2017. 25. Advancing access to addiction medications: implica- tions for opioid addiction treatment. American Society of Addiction Medicine. 2013. Available at: https://www. asam.org/docs/default-source/advocacy/aaam_implications-for-opioid-addiction-treatment_ final. Accessed August 8, 2017. 26. Status of state action on the Medicaid expansion decision. The Kaiser Family Foundation. 2017. Available at: http://www.kff.org/health-reform/state-indicator/ state-activity-around-expanding-medicaid-under-the-affordable-care-act/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:% 22asc%22%7D. Accessed August 8, 2017. 27. Guy GP Jr, Zhang K, Bohm M, et al. Vital signs: changes in opioid prescribing in the United States, 2006 – 2015. MMWR Morb Mortal Wkly Rep. 2017;66(26): 697 –704. 28. Medicaid enrollees by enrollment group. The Kaiser Family Foundation. 2014. Available at: http://www. kff.org/medicaid/state-indicator/distribution-of- medicaid-enrollees-by-enrollment-group/?dataView=1&currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22s ort%22:%22asc%22%7D. Accessed August 8, 2017. AJPH POLICY 648 Research Peer Reviewed Sharp et al. AJPH May 2018, Vol 108, No. 5 Copyright ofAmerican JournalofPublic Health isthe property ofAmerican PublicHealth Association anditscontent maynotbecopied oremailed tomultiple sitesorposted toa listserv without thecopyright holder’sexpresswrittenpermission. However,usersmayprint, download, oremail articles forindividual use.
Assignment 2: Introduction and Revised PICOT QuestionRevise your PICOT question on the basis of the feedback you received on Week 3 Assignment 2. Submit your introduction and revised PICOT question as
References Sharp, A., Jones, A., Sherwood, J., Kutsa, O., Honermann, B., & Millett, G. (2018). Impact of Medicaid Expansion on Access to Opioid Analgesic Medications and Medication-Assisted Treatment. American Journal Of Public Health, 108(5), 642-648. doi:10.2105/AJPH.2018.304338 Jones, C. M., Campopiano, M., Baldwin, G., & McCance-Katz, E. (2015). National and State Treatment Need and Capacity for Opioid Agonist Medication-Assisted Treatment. American Journal Of Public Health, 105(8), e55-63. doi:10.2105/AJPH.2015.302664 Cavaiola, A. A., Fulmer, B. A., & Stout, D. (2015). The Impact of Social Support and Attachment Style on Quality of Life and Readiness to Change in a Sample of Individuals Receiving Medication-Assisted Treatment for Opioid Dependence. Substance Abuse, 36(2), 183-191. doi:10.1080/08897077.2015.1019662 Mittal, M. L., Vashishtha, D., Sun, S., Jain, S., Cuevas-Mota, J., Garfein, R., & … Werb, D. (2017). History of medication-assisted treatment and its association with initiating others into injection drug use in San Diego, CA. Substance Abuse Treatment, Prevention & Policy, 121-5. doi:10.1186/s13011-017-0126-1 Galanter, M., Seppala, M., & Klein, A. (2016). Medication-assisted treatment for opioid dependence in Twelve Step-oriented residential rehabilitation settings. Substance Abuse, 37(3), 381-383. doi:10.1080/08897077.2016.1187241
Assignment 2: Introduction and Revised PICOT QuestionRevise your PICOT question on the basis of the feedback you received on Week 3 Assignment 2. Submit your introduction and revised PICOT question as
Evaluation Table— The Practice of Monitoring Sleep Duration to Aid in Weight Loss First Author (Year) Conceptual Framework Design/Method Sample and Setting Major Variables Studied (and Their Definitions) Measurement Data Analysis Findings Appraisal: Worth to Practice Kobayashi, et al., 2012 none 3 year Retrospective cohort study 21,469 healthy adults > 20 yrs with BMI > 25 and weight gain who presented for health checkups; Tokyo, Japan BMI, sleep duration, weight gain Self reported average nightly sleep duration Regression analysis < 5 hrs sleep per night may facilitate weight gain; optimal duration to mitigate weight gain is 7 hrs Strengths: size, length of study Weaknesses: Japanese population only; no qualitative data (sleep habits, quality), potential confounder: OSA; 7,649 not included in analysis due to loss data Alfaris, et al., 2015 none 2 year RCT 390 obese (BMI 30-50) men & women divided in 3 weight loss intervention programs (differed in consultation intensity); University of Pennsylvania Sleep duration and quality, mood, BMI, weight PSQI survey; Patient Health Questionnaire-8 (PHQ-8) mood assessment; administered at baseline, 6 and 24 months P value losing 5% of initial weight is associated with short-term improvement in sleep duration and quality & favorable short- and long-term changes in mood Favorable results in all 3 intervention groups; 1/3 to 1/5 of all 3 groups lost 5% of initial weight Strengths: 86% retention rate Weaknesses: absence of larger mean weight losses Knox, 2015 none Systematic review 67 studies narrowed to 2 (RCT and cohort) Sleep duration and quality, BMI, weight PSQI survey, BMI, weight P value, RR Inconclusive; whether PCPs should emphasize sleep in the treatment of obese patients remains unanswered Strengths: Higher level data Weakness: Only two studies Coughlin & Smith, 2014 none Thematic review of four 2008 systematic review 4 systematic reviews Sleep duration, BMI, weight Self- reports, PSQI Pooled analysis, OR Support for an association between short sleep duration & weight gain There is good experimental & substantive longitudinal data linking short sleep to increased weight gain & obesity as well as to behaviors implicated in weight gain Filitrault, et al., 2014 none RCT, dietary intervention of 12-16 weeks n = 150 with avg BMI 33.3 BMI, anthropometric measurements, eating behavior traits (Three-Factor Eating Questionnaire), PSQI score & sleep duration @ baseline & post intervention Self reports, PSQI P value changes in strategic dieting behavior were constantly negatively associated with changes in body weight and fat mass (P<0.05) for recommended duration sleepers Eating behavior traits & sleep may act together to influence the outcome of weight-loss programs OSA: obstructive sleep apnea PSQI: Pittsburgh Sleep Quality Index References Alfaris, N., Wadden, T., Sarwer, D., Diwald, L., Volger, S., Hong, P.,…Chittams, J. (2015). Effects of a 2-year behavioral weight loss intervention on sleep and mood in obese individuals treated in primary care practice. Obesity, 23, 558-564. doi: http://dx.doi.org/10.1002/oby.20996 Coughlin, J. & Smith, M. (2014). Sleep, obesity, and weight loss in adults: Is there a rationale for providing sleep interventions in the treatment of obesity? International Review of Psychiatry, 26(2), 177-188. doi: http://dx.doi.org/10.3109/09540261.2014.911150 Filiatrault, M., Chaput, J., Drapeau, V., & Tremblay, A. (2014). Eating behavior traits and sleep as determinants of weight loss in overweight and obese adults. Nutrition and Diabetes, 2014(4), 1-8. doi: http://dx.doi.org/10.1038/nutd.2014.37 Knox, K. (2015). Should primary care physicians address sleep to improve weight loss in obese patients? A Clin-IQ. Journal of Patient Centered Research and Reviews, 2015(2), 197-200. doi: http://dx.doi.org/10.17294/2330-0698.1205 Kobayashi, D., Takahashi, O., Deshpande, G. A., Shimbo, T., & Fukui, T. (2012). Association between weight gain, obesity, and sleep duration: A large-scale 3-year cohort study. Sleep and Breathing, 16(3), 829-33. doi: http://dx.doi.org/10.1007/s11325-011-0583-0 Page 4 of 4 NSG8100 Capstone in Applied Practice I©2014 South University