Profiling multiple provider prescribing of opioids, benzodiazepines, stimulants, and anorectics

Drug and Alcohol Dependence 112 (2010) 99–106 Contents lists available at ScienceDirect Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep Profiling multiple provider prescribing of opioids, benzodiazepines, stimulants, and anorectics B.L. Wilsey a,b,∗ , S.M. Fishman b , A.M. Gilson c , C. Casamalhuapa d , H. Baxi d , H. Zhang e , C.S. Li f a VA Northern California Health Care System, Sacramento, CA 95655, United States Department of Anesthesiology and Pain Medicine, UC Davis Medical Center, Sacramento, CA 95817, United States Pain & Policy Studies Group, Paul P. Carbone Comprehensive Cancer Center, University of Wisconsin School of Medicine and Public Health, Ann Arbor, MI 53711, United States d Clinical and Translational Science Center, UC Davis Health System, Sacramento, CA 95817, United States e Department of Statistics, University of California, Davis, CA 95616, United States f Department of Public Health Sciences, Division of Biostatistics, University of California, Davis, CA 95616, United States b c a r t i c l e i n f o Article history: Received 1 December 2009 Received in revised form 12 May 2010 Accepted 24 May 2010 Available online 20 June 2010 Keywords: Drug prescriptions Narcotics Benzodiazepines Central nervous system stimulants Appetite depressants a b s t r a c t Background: The main objective of this study was to determine the prevalence of multiple providers for different controlled substances using the largest electronic prescription monitoring program (PMP) in the United States. A secondary objective was to explore patient and medication variables associated with prescriptions involving multiple providers. PMPs monitor the final allocation of controlled substances from pharmacist to patient. The primary purpose of this scrutiny is to diminish the utilization of multiple providers for controlled substances. Methods: This is a secondary data analysis of the California PMP, the Controlled Substance Utilization Review and Evaluation System (CURES). The prevalence of multiple provider episodes was determined using data collected during 2007. A series of binomial logistic regressions was used to predict the odds ratio (OR) of multiple prescriber episodes for each generic type of controlled substance (i.e., opioid, benzodiazepine, stimulant, or diet pill (anorectic) using demographic and prescription variables. Results: Opioid prescriptions (12.8%) were most frequently involved in multiple provider episodes followed by benzodiazepines (4.2%), stimulants (1.4%), and anorectics (0.9%), respectively. The greatest associations with multiple provider episodes were simultaneously receiving prescriptions for different controlled substances. Conclusions: Opioids were involved in multiple provider prescribing more frequently than other controlled substances. The likelihood of using multiple providers to obtain one class of medications was substantially elevated as patients received additional categories of controlled substances from the same provider or from multiple practitioners. Polypharmacy represents a signal that requires additional vigilance to detect the potential presence of doctor shopping. Published by Elsevier Ireland Ltd. 1. Introduction The international effort to control the quandary posed by drug addiction and dependence had been considered a notable success (Musto and Korsmeyer, 2002). Legislation was implemented in the United States as the Controlled Substances Act (CSA) conforming to international treaties (Gilson, 2010). These controls established provisions for the identification of new medications with abuse liability, labeling products with significant abuse potential, and controlling the manufacture and distribution of such products in ∗ Corresponding author at: Pain Academic Office, UC Davis Medical Center, Ellison Ambulatory Care Center, 4860 Y Street, Suite 3200, Sacramento, CA 95817, United States. Tel.: +1 916 734 7836; fax: +1 916 734 6827. E-mail address: blwilsey@ucdavis.edu (B.L. Wilsey). 0376-8716/$ – see front matter. Published by Elsevier Ireland Ltd. doi:10.1016/j.drugalcdep.2010.05.007 medicinal use (Wright et al., 2008). However, newer methods of control are being advocated as the non-medical use of prescription medications has placed a considerable burden on society (Gilson and Kreis, 2009). Many regulatory authorities are now attempting to minimize the abuse of controlled substances by establishing prescription monitoring programs (PMPs). The most common PMP goals involve education, delivering information, preventing diversion, and investigating abuse (Joranson et al., 2002; Kasprak, 2003). PMPs originally operated by requiring healthcare practitioners to transmit paper copies of prescriptions to law enforcement or government health agencies. In the last decade, PMPs have utilized computerized monitoring called “electronic data transmission (EDT) systems,” which is a more efficient type of PMP for controlling prescription medication diversion (Brushwood, 2003; Fishman et al., 2004). Reports are provided, either solicited or unsolicited, to medical providers alerting them of prescription acquisitions 100 B.L. Wilsey et al. / Drug and Alcohol Dependence 112 (2010) 99–106 potentially indicating medication misuse. Electronic surveillance is intended to reduce the utilization of multiple providers, also pejoratively known as “doctor shopping,” when individuals engage prescribers who, unbeknownst to each other, write additional controlled substance prescriptions. The individual often visits several pharmacies, receiving more of a medication than intended by any single provider. Despite the growing prevalence of prescription monitoring systems, there is very little empirical data on the prevalence of multiple provider episodes for controlled substances. In a recent study, PMP data were utilized to retrospectively analyze doctor shopping for buprenorphine in France (Pradel et al., 2009). The authors found that 6% of patients utilized multiple providers in 2005. Similarly, a study in the United States found that over 93.1% of individuals had no early refills for Schedule II opioids during 2006 using the Massachusetts PMP database (Katz et al., 2009). To address this question further, we analyzed data from the California Department of Justice (DOJ) PMP database, the Controlled Substance Utilization Review and Evaluation System (CURES). This program began collecting prescription information related to Schedule II and Schedule III opioids in 1998 and 2005, respectively. Stimulant and benzodiazepine prescription data were added to the registry by legislative mandate in 2007. This study also examined the extent that variables related to patient (i.e., age, gender, and geographic location) and medication (e.g., amount and duration of prescribing, and the co-prescribing of different controlled substances) characteristics were associated with the occurrence of multiple provider episodes. 2. Methods 2.1. Administrative issues A member of the investigative team was granted access to the CURES database following a background check by the California DOJ. SQL ServerTM was used within the DOJ to de-identify the database using a record linkage methodology (Newcombe, 1988) to permit identification of sequential prescriptions for each patient. Unique computer-generated identifiers were devised for each provider and pharmacy. As a result, no patient, provider, or pharmacy-level information was contained in this report. The Institutional Review Board of the University of California-Davis Medical Center and the VA Northern California Health Care System Research and Development Committee granted approvals to conduct this research. 2.2. Study sample: CURES prescriptions The data sets for the different controlled substances contained identical fields corresponding to the information obtained from the CURES database. All entries from January 1, 2007 through December 31, 2007 were used for the analyses. This represented all prescriptions written in California for Schedule II, Schedule III, or Schedule IV controlled substances as reported to the Department of Justice and entered into the CURES database on a weekly basis by the dispensing pharmacy or clinic (California Health and Safety Code Section 11165, 2010). Just under 4% of the original prescriptions (1,117,088 out of 28,893,404) were excluded for the following reasons: (a) missing or incomplete patient or provider identification, (b) implausible prescriptions, such as prescription of the same formulation recorded for the same time interval or transactions, (c) commercial transactions whereby the prescription was written for more than 700 pills or 50 patches in a given 30-day period (personal communication: Carla Watkins, Bureau of Narcotic Enforcement, Controlled Substance Utilization Review and Evaluation System, California Department of Justice) and (d) use of medications not suggestive of standard delivery systems employed by most patients (e.g., rectal suppositories, intravenous preparations, syrups, solutions, etc.). 2.3. Study design A case–control study design was utilized to identify all California residents who used more than one provider for opioids, benzodiazepine, and amphetamine prescriptions, and then randomly selected control cases who did not receive prescriptions from multiple providers for these same drug classes. The amphetamine category was further subdivided into stimulants and anorectics (i.e., diet pills) to determine if the results varied between these two classes. Non-amphetamine stimulants (e.g., methylphenidate and modafinal) and anorectics (e.g., phentermine and phendimetrazine) were included for comparative purposes. Table 1 Controlled substances in 2007 CURES database. Rx count Opioids Hydrocodone Codeine IR Oxycodone CR Oxycodone Fentanyl Patch CR Morphine Methadone Hydromorphone IR Morphine Fentanyl lozenges or tablets Meperidine Levorphanol Benzodiazepines Alprazolam Lorazepam Clonazepam Diazepam Temazepam Triazolam Flurazepam Oxazepam Chlordiazepoxide Clorazepate & Comb. Estazolam Quazepam 11,888,536 1,908,222 1,090,311 543,190 517,618 455,026 328,917 210,708 80,286 32,562 13,837 2,800 2,344,024 2,299,623 1,404,641 1,168,350 1,056,685 178,543 109,164 73,830 55,625 28,916 10,858 497 Stimulants Methylphenidate Amphetamine & Comb. Modafinil Dexmethylphenidate Dextroamphetamine Lisdexamfetamine Methamphetaminea 657,515 590,601 189,957 83,145 81,465 19,100 2,438 Anorectics Phentermine Diethylpropion Phendimetrazine Benzphetamine 297,135 32,029 17,356 2,803 a Methamphetamine hydrochloride (Desoxyn), a Schedule II amphetamine for ADHD. 2.3.1. Dependent variable: multiple provider episodes. The database was searched for multiple provider episodes, which was defined when an individual received a prescription for the same medication from two or more practitioners filled by two or more pharmacies within a 30-day period. This recognized the potential for patients to use multiple providers because they either (1) substituted clinicians, (2) obtained medications from a practitioner covering for the patient’s customary provider, or (3) received treatment from another practitioner that could be entirely appropriate (dentist, emergency room doctor, etc.). This assumed that the pharmacy would act as a “gatekeeper” and not knowingly allow multiple provider episodes. 2.3.2. Independent variables. 2.3.2.1. Numbers and types of controlled substances. In 2007, the CURES database contained prescriptions for 17,070,418 opioids, 8,729,561 benzodiazepines, 1,624,156 stimulants, and 349,212 anorectics. The prescriptions represented generic types of 12 opioids, 12 benzodiazepines, 6 stimulants, and 4 anorectics (Table 1). 2.3.2.2. Socio-demographic variables. Socio-demographic variables include age, gender, and geographic location. Geographic location was stratified into six ordinal categories by separating California counties into groups, based upon published population estimates (California State Association of Counties, 2007), so that rurality could be defined in terms of the Office of Management and Budget’s definition of metropolitan and nonmetropolitan populations (Hart et al., 2005; Ricketts et al., 1998). In this manner, we could determine the impact of varying geographic sizes on the occurrence of multiple provider episodes for each class of controlled substances. 2.3.2.3. Amount and duration of prescribing. Differences in risk associated with the amount of medication dispensed (i.e., amount of prescribed medication multiplied by the number of pills or patches dispensed) were represented using a continuous variable. Total doses for opioids, benzodiazepines, stimulants, and anorectics were B.L. Wilsey et al. / Drug and Alcohol Dependence 112 (2010) 99–106 converted into morphine (Korff et al., 2008; Vieweg et al., 2005), diazepam (Giannini, 1997), dextroamphetamine (Bridges, 2007) or phentermine (WebMD, 2008) equivalents, respectively permitting an omnibus comparison of the effect of a unit change in dose on the dependent variable. Duration of prescribing (in days) was a continuous variable used to examine the effect of longer treatment durations. This variable was calculated by finding the difference between the date of the first controlled substance prescription and the end date of the last prescription. The “last observation carried forward method” (Mallinckrodt et al., 2001; Streiner, 2002) was used to impute the interval for the last prescription because the second anchor (i.e., end date) was nonexistent. In instances in which only one prescription was provided to a patient, the duration of treatment was assumed to be 30 days. A special case was made when 30 or less pills of a shortacting opioid (e.g., codeine, or hydrocodone- or oxycodone-combination products) were prescribed only once, where the duration of treatment was assumed to be 5 days. This assumption derives from qualitative research suggesting that opioids are provided in emergency rooms as a bridge prescription pending referral to an office-based physician (Wilsey et al., 2008), as well as the observation in the CURES database that nearly all of the single prescriptions of controlled substances with 30 or less pills were for these short-acting opioids. 2.3.2.4. Simultaneous prescribing of different controlled substances. This study sought to determine the impact of the simultaneous prescribing of different classes of controlled substances on the likelihood of multiple provider episodes for an individual controlled substance class. In the case of opioids, the following categorization was used: • No simultaneous prescribing over a 30-day period of either benzodiazepines or stimulants/anorectics, • simultaneous prescribing of benzodiazepines only, • simultaneous prescribing of stimulants/anorectics only, and • simultaneous prescribing of both stimulants/anorectics and benzodiazepines. Similar categorizations were performed for the other three classes of controlled substances. 2.3.2.5. Simultaneous multiple provider prescribing for different controlled substances. This study also examined the association between multiple provider prescribing for one controlled substance class and the occurrence of multiple provider episodes involving different classes of controlled substances within 30 days after a prescription is dispensed. Operationalizing this independent variable reflects a similar multinomial categorization to the one described above for the simultaneous prescribing of other medications. 2.4. Statistical analyses Control and multiple provider cases were drawn from the CURES database using a random sampling procedure to reduce the size of the data used for statistical analysis, to ensure a minimum of 20 cases in all categories of each independent variable, and to maintain adequate power (Taylor, 1986). The random sample that was selected for opioids was 101,651 prescriptions, which included 1651 records of multiple provider prescribing of both stimulants/anorecticss and benzodiazepines. The sample size selected for benzodiazepines was 101,445 prescriptions and contained 1445 records of multiple provider prescribing of both opioids and stimulants/anorectics. Each sample of amphetamines comprised 100,000 prescriptions. A binomial logistic regression was used to predict the odds ratio (OR) of multiple prescriber episodes for each generic type of controlled substance (i.e., opioids, benzodiazepines, and stimulant or anorectics). For each category of controlled substance, the estimated OR measures the odds by which 30 days of treatment with that medication alters the risk of multiple provider episodes relative to a patient who obtained the same medication but did not utilize multiple providers. Statistical analyses were performed using the R statistical software package (http://cran.us.r-project.org/). A p-value < 0.05 was considered statistically significant. 101 3.2. Regression analysis 3.2.1. Opioids (Table 2). With the exception of codeine, most of the opioids studied had a higher involvement in multiple provider episodes than hydrocodone. The opioids with the largest association with multiple provider prescribing were hydromorphone and fentanyl lozenges or buccal tablets while codeine and methadone demonstrated the smallest positive associations. Younger age was associated with the use of multiple providers. Specifically, there was approximately a 1% decrease in the odds of this behavior for every year of age. No difference was found between genders. Relative to metropolitan areas with greater than 1 million people, smaller populated counties were associated with a decreased likelihood of having multiple prescribers. Although the medication amount in morphine equivalents and the duration of prescribing were significantly associated with the probability of a multiple provider episode, the point estimates of these variables were negligible. Compared to individuals receiving only a single prescribed controlled substance, there was an approximate doubling of the OR for multiple provider prescribing if another controlled substance class was being prescribed. More striking was the 13-fold increase in the OR if both a stimulant/anorectic and benzodiazepine were prescribed along with opioids during the same 30-day window of observation (compared to patients with multiple providers who were prescribed opioids and no additional controlled substance). Furthermore, individuals who obtained stimulants/anorectics and/or benzodiazepines from multiple providers were associated with an elevated likelihood of being involved in this activity to procure opioids (a 10–21-fold increase in the OR). 3.2.2. Benzodiazepines (Table 3). With the exception of chlordiazepoxide and estazalam, all of the benzodiazepines had less association with multiple provider episodes than diazepam. Older age was associated with a lower incidence of multiple providers, with an almost 2% decrease in the odds of this behavior for every year of age. Males were 10% more likely than females to obtain prescriptions from two or more providers over a given 30-day period. As with opioids, multiple provider episodes involving benzodiazepines were more prevalent in geographic areas with the largest populations. Both medication amount and duration of prescribing were associated with multiple provider episodes. Again, these independent variables had an inconsequential effect on the odds of a multiple provider episode. Compared to those patients not receiving simultaneous prescriptions of controlled substances, there was a doubling to tripling of the OR for multiple provider prescribing if another controlled substance class was being prescribed. More striking was the 25-fold increase in multiple prescribing for benzodiazepines if stimulants/anorectics and opioids were prescribed to the same patient during the same 30-day window of observation. As with opioids, individuals involved in multiple provider prescribing of benzodiazepines were more likely to be involved in this type of activity to attain controlled substances from other classes (13–38-fold increase). 3. Results 3.1. Descriptive statistics Overall, the incidence of doctor shopping, as defined in this study, was 8.4%. Opioid prescriptions (12.8%) were most frequently involved in multiple provider episodes followed by benzodiazepines (4.2%), stimulants (1.4%), and anorectics (0.9). These percentages represent the proportion of prescriptions from each controlled substance class written by two or more practitioners filled by two or more pharmacies within a 30-day period. 3.2.3. Stimulants (Table 4). The largest association with multiple provider prescribing was that of the reference category, dextroamphetamine. Dexmethylphenidate and modafinil were associated with the largest negative association, representing the least multiple provider episodes. Older age was associated with an increased likelihood of using multiple providers to obtain stimulants. Specifically, there was an approximate 1% increase in the OR of this behavior for every year of age. Male patients were 23% less likely than female patients to obtain a prescription for stimulants from two or more providers over any 30-day period. When examining 102 B.L. Wilsey et al. / Drug and Alcohol Dependence 112 (2010) 99–106 Table 2 Opioid associations with independent correlates of multiple provider episodes (only statistically significant parameter estimates are shown). Independent variables Type of opioid (reference category is hydrocodone) Methadone CR Morphine CR Oxycodone Codeine Fentanyl lozenges/buccal tablets Hydromorphone IR Morphine IR Oxycodone Estimate of coefficient 0.150 0.183 0.361 −0.401 0.562 0.748 0.259 0.312 Patient demographics Age −0.011 Population (reference category is > 1,000,000 population) <20,000 −0.570 20,000–50,000 −0.536 50,000–100,00 −0.367 100,000–500,000 −0.195 500,000–1,000,000 −0.154 Prescriptions Medication amount Duration of treatment 0.000021 0.006 SE OR 95% CI for OR 0.064 0.055 0.048 0.034 0.178 0.067 0.126 0.035 1.162 1.201 1.436 0.670 1.755 2.113 1.295 1.366 (1.025, 1.319) (1.079, 1.336) (1.307, 1.578) (0.626, 0.716) (1.238, 2.486) (1.854, 2.408) (1.012, 1.657) (1.275, 1.464) 0.001 0.989 (0.988, 0.990) 0.194 0.096 0.061 0.025 0.027 0.566 0.585 0.693 0.823 0.857 (0.386, 0.828) (0.485, 0.706) (0.615, 0.780) (0.783, 0.865) (0.813, 0.904) 0.000004 0.000092 1.00002 1.006 Simultaneous prescribing of different controlled substances (reference category is no simultaneous Rx) Opioids and benzodiazepines (BZD) 0.847 0.022 Opioids and stimulants/anorectics 0.583 0.092 Opioids, BZD, and stimulants/anorectics 2.574 0.045 2.333 1.791 13.117 (1.00001, 1.00003) (1.006, 1.006) (2.236, 2.435) (1.495, 2.146) (12.008, 14.328) Simultaneous multiple provider episodes of different controlled substances (reference category is no simultaneous multiple provider episodes) Opioids and BZD 2.743 0.054 15.541 (13.982, 17.274) Opioids and stimulants/anorectics 2.357 0.342 10.557 (5.403, 20.626) Opioids, BZD, and stimulants/anorectics 3.063 0.057 21.400 (19.134, 23.933) the effects of population by county, counties with 20,000–50,000 inhabitants had slightly more than one-half the OR for multiple provider episodes as counties with greater than a million inhabitants. The medication amount, expressed as dextroamphetamine equivalents, was not associated with multiple provider episodes. The duration of prescribing, although statistically significant, was only minimally associated with the probability of a multiple Table 3 Benzodiazepine associations with independent correlates of multiple provider episodes (only statistically significant parameter estimates are shown). Independent variables Estimate of coefficient Type of benzodiazepine (reference category is diazepam) Alprazolam −0.161 Chlordiazepoxide 0.360 Clonazepam −0.201 Clorazepate & Comb. −1.156 Flurazepam −0.479 Lorazepam −0.313 Oxazepam −0.512 Temazepam −0.398 Triazolam −0.355 SE OR 95% CI for OR 0.046 0.148 0.051 0.383 0.155 0.047 0.187 0.059 0.117 0.851 1.433 0.818 0.315 0.619 0.731 0.600 0.672 0.701 (0.778, 0.931) (1.072, 1.916) (0.740, 0.905) (0.148, 0.667) (0.457, 0.839) (0.666, 0.802) (0.416, 0.864) (0.598, 0.754) (0.557, 0.882) Patient demographics Age −0.018 Gender (reference category is females) 0.099 Population (reference category is > 1,000,000 population) 20,000–50,000 −0.553 50,000–100,00 −0.626 100,000–500,000 −0.166 500,000–1,000,000 −0.134 0.001 0.030 0.982 1.104 (0.980, 0.984) (1.041, 1.172) 0.186 0.123 0.042 0.043 0.575 0.535 0.847 0.874 (0.400, 0.829) (0.420, 0.680) (0.780, 0.920) (0.803, 0.952) Prescriptions Medication amount Duration of treatment 0.00003 0.00016 1.0004 1.004 0.00039 0.004 Simultaneous prescribing of different controlled substances (reference category is no simultaneous Rx) Benzodiazepines (BZD) and opioids 1.245 0.033 BZD and stimulants/anorectics 0.815 0.121 BZD, stimulants/anorectics, and opioids 3.223 0.051 3.472 2.259 25.103 (1.0004, 1.0005) (1.004, 1.005) (3.256, 3.704) (1.782, 2.865) (22.733, 27.721) Simultaneous multiple provider episodes of different controlled substances (reference category is no simultaneous multiple provider episodes) BZD and opioids 2.568 0.040 13.039 (12.065, 14.091) BZD and stimulants/anorectics 3.025 0.312 20.601 (11.166, 38.007) BZD, stimulants/anorectics, and opioids 3.643 0.058 38.219 (34.140, 42.785) B.L. Wilsey et al. / Drug and Alcohol Dependence 112 (2010) 99–106 103 Table 4 Stimulant associations with independent correlates of multiple provider episodes (only statistically significant parameter estimates are shown). Independent variables Estimate of coefficient SE Type of stimulant (reference category is dextroamphetamine) Dexmethylphenidate −0.824 Methylphenidate −0.536 Modafinil −0.354 Patient demographics Age 0.012 Gender (reference category is females) −0.254 Population (reference category is > 1,000,000 population) 500,000–1,000,000 −0.246 Prescriptions Duration of treatment 0.005 OR 95% CI for OR 0.170 0.105 0.121 0.439 0.585 0.702 (0.314, 0.612) (0.476, 0.719) (0.554, 0.890) 0.001 0.050 1.012 0.776 (1.010, 1.014) (0.704, 0.855) 0.077 0.782 (0.672, 0.910) 0.00034 1.005 (1.005, 1.006) 2.659 2.758 4.000 (2.278, 3.103) (2.359, 3.225) (3.380, 4.732) Simultaneous prescribing of different controlled substances (reference category is no simultaneous Rx) Stimulants and benzodiazepines (BZD) 0.978 0.079 Stimulants and opioids 1.015 0.080 Stimulants, BZD, and opioids 1.386 0.086 Simultaneous multiple provider episodes of different controlled substances (reference category is no simultaneous multiple provider episodes) Stimulants and BZD 2.977 0.136 19.623 (15.043, 25.597) Stimulants and opioids 2.222 0.102 9.229 (7.550, 11.282) Stimulants, BZD, and opioids 3.289 0.208 26.825 (17.861, 40.287) provider episode. An approximate doubling of the OR for multiple provider prescribing was observed if another controlled substance class was being prescribed, rather than when patients were not simultaneously prescribed controlled substances. A quadrupling in the OR for multiple provider prescribing occurred if all three classes of controlled substances (i.e., stimulants/anorectics, benzodiazepines, and opioids) were prescribed during the same timeframe. As with opioids and benzodiazepines, individuals involved in multiple provider prescribing of stimulants were more likely to be involved with obtaining controlled substances from other classes (9–26-fold). 3.2.4. Anorectics (Table 5). Benzphetamine and phendimetrazine had a higher involvement in multiple provider episodes than the comparison medication, phentermine. Older age was more likely to be associated with the use of multiple providers involving anorectics. Specifically, there was an approximate 1% increase in the OR of this behavior for every year of age. The other demographic variables, gender and population, did not achieve statistical significance. Medication amount, expressed as phenteramine equivalents, was not associated with multiple provider episodes. Although the duration of prescribing achieved statistical significance, its association with multiple provider prescribing of anorectics was inconsequential. Compared to those patients not receiving simultaneous prescriptions for controlled substances, there was more than a doubling of the likelihood for multiple provider prescribing if another controlled substance class was being prescribed concurrently with an anorectic. A quadrupling in the OR occurred when all three classes of controlled substances (i.e., stimulants/anorectics, benzodiazepines, and opioids) were being prescribed during the same 30-day timeframe. As with all other controlled substance classes in the study, individuals involved with multiple provider prescribing of anorectics were more likely to acquire controlled substances from each of the other classes (10–27-fold increase). 4. Discussion The present results suggest that opioids are more commonly involved in multiple provider events than are other controlled sub- Table 5 Anorectic associations with independent correlates of multiple provider episodes (only statistically significant parameter estimates are shown). Independent variables Estimate of coefficient Type of diet amphetamines (reference category is phentermine) Benzphetamine 1.589 Phendimetrazine 0.652 SE OR 95% CI for OR 0.190 0.121 4.900 1.919 (3.375, 7.111) (1.514, 2.433) Patient demographics Age 0.009 Population (reference category is > 1,000,000 population) <20,000 0.145 100,000–500,000 −0.208 0.003 1.009 (1.004, 1.014) 0.712 0.092 1.156 0.812 (0.286, 4.669) (0.679, 0.972) Prescriptions Duration of treatment 0.000352 1.006 (1.005, 1.007) 2.322 2.811 4.024 (1.838, 2.933) (2.359, 3.349) (3.222, 5.026) 0.006 Simultaneous prescribing of different controlled substances (reference category is no simultaneous Rx) Anorectics and benzodiazepines (BZD) 0.843 0.119 Anorectics and opioids 1.033 0.089 Anorectics, BZD, and opioids 1.392 0.113 Simultaneous multiple provider episodes of different controlled substances (reference category is no simultaneous multiple provider episodes) Anorectics and BZD 2.297 0.247 9.945 (6.126, 16.144) Anorectics and opioids 2.403 0.107 11.056 (8.960, 13.641) Anorectics, BZD, and opioids 3.302 0.207 27.158 (18.093, 40.766) 104 B.L. Wilsey et al. / Drug and Alcohol Dependence 112 (2010) 99–106 stances. This was not entirely unanticipated, given the observation that a recent National Survey on Drug Use and Health, measuring non-medical drug use, found that the misuse of prescription opioids was second in prevalence only to the illicit use of marijuana (Office of Applied Studies, 2006). Somewhat unexpectedly, hydrocodone, despite being the most commonly prescribed opioid in California during 2007 (Table 1), had a lower association with multiple prescriber episodes than any other opioid studied, with the exception of codeine. A possible explanation for this finding is that prescriptions for hydrocodone (as well as codeine) can be refilled, unlike prescriptions for all other study opioids which are contained in Schedule II. Having refillable prescriptions can reduce the need for patients to seek multiple providers for the same medication. Alternatively, illicit users of prescription opioids who utilize multiple providers may be more motivated to seek Schedule II opioids because they are perceived as more potent or desirable. In this regard, two of the opioids with high associations with multiple provider episodes in the present study, hydromorphone and sustained-release oxycodone, were favored by respondents when asked to rate attractiveness for abuse of prescription opioids (Butler et al., 2009). The relatively low frequency in California of multiple providers for opioids (12.8%) in 2007 was, in part, corroborated by PMP data from another state, Massachusetts. During 1996–2006, most patients in the Commonwealth of Massachusetts (87.5%) used 1–2 prescribers, 1–2 pharmacies, and had no early refills (Katz et al., 2009). Taken together, the two studies indicate that doctor shopping for opioids is relatively infrequent from the perspective of a prescriber searching for abusive behavior. These incidences are not unlike the levels of aberrant drug-related behaviors in chronic pain patients associated with opioid prescribing where the incidence of patients with aberrant behavior has been estimated to be 11.5% (Fishbain et al., 2008). This similarity is complemented by the need for pharmacovigilance of both behaviors. Just as universal precautions (i.e., using assessment tools to determine the risk for addictive disorders, treatment agreements, and conducting ongoing monitoring of patients for the development of abuse related problems) (Gourlay and Heit, 2006) has become the norm to reduce prescription opioid abuse, PMPs have now emerged as a potentially important tool for addressing prescription drug diversion (Katz et al., 2007). Given the sheer magnitude of the problem (i.e., 12.8% of 17.1 million equating to 2.2 million multiple provider prescriptions), PMP monitoring will be indispensable to reduce abuse and diversion of prescription opioids. Demographic characteristics associated with multiple provider episodes were found to vary among the different classes of controlled substances (Tables 2–5). Younger age was associated with the use of multiple providers for opioids and benzodiazepines. Specifically, there was approximately a 1–2% decrease in the odds of this behavior for every year of age. This is consistent with the public health problem of a significant increase in the non-medical use and abuse of prescription medications among adolescents and young adults (Riggs, 2008). Contrariwise, older age was associated with an increased likelihood of using multiple providers to obtain stimulants and anorectics. Specifically, there was an approximate 1% increase in the OR of this behavior for every year of age. This might be an early manifestation of a demographic trend. As the number of persons 65 years of age and older escalates with the aging of the baby boomers, experts predict that prescription drug abuse among the elderly, for certain medications, also will rise significantly (Martin, 2008). Gender effects also varied with the class of controlled substance. Males were more likely to use multiple providers for benzodiazepines but less so for stimulants, while gender showed no relationship between anorectics or opioids. The latter is somewhat contradictory to previous modeling using PMP and managed care organization data in which males were more likely to be involved in prescription opioid abuse (White et al., 2009). Another discrepancy between our data and that of others involved the distinctly higher likelihood of multiple provider episodes in large urban geographic areas compared to rural settings. Assuming that some of this activity represents illicit use, this conflicts with reports suggesting that rural areas of Appalachia experience more misuse than nearby urban centers (Havens et al., 2007; Wunsch et al., 2009). One plausible explanation would be that the relative lack of prescribers in rural California reduces opportunities for finding multiple providers. Alternative reasons might arise from cultural differences in Appalachian regions versus those in California and/or the sampling of probationers rather than the general population (Havens et al., 2007; Wunsch et al., 2007). The duration of prescribing and the amount of medication were generally statistically significant but clinically inconsequential determinants for using multiple providers. It is likely that these variables had very small point estimates but achieved statistical significance due to the large sample sizes. Patients neither appear to seek multiple providers as time passes nor as they require higher dosages. This finding may help reassure clinicians that these aberrant drug-related behaviors are not a likely consequence of chronic therapy with controlled substances. Of all the independent variables, simultaneously receiving prescriptions for different controlled substances and concurrent use of multiple prescribers to obtain other controlled substances revealed the greatest associations with multiple provider episodes. The explanatory significance of acquiring numerous controlled substances from either the same or multiple providers suggests the importance of polypharmacy in the occurrence of non-medical opioid use. Several years ago, an expert panel (Parente et al., 2004) convened to consider factors that serve to identify patients with the potential for controlled substance misuse or mismanagement, and came to a similar conclusion. Controlled substance polypharmacy, although not necessarily a direct measure of misuse, warrants heightened vigilance. Controlled substance polypharmacy is under scrutiny because of its association with overdose deaths and other adverse events (Wunsch et al., 2009). Recently, prescription drugs have replaced heroin and cocaine as the leading drugs involved in unintentional and undetermined fatal overdoses (Paulozzi and Xi, 2008). Between 1999 and 2002, the number of opioid analgesic poisonings on death certificates increased 91.2%, while heroin and cocaine poisonings increased 12.4% and 22.8%, respectively (Paulozzi et al., 2006). Although it is often unclear how people are obtaining the substances involved in poisoning mortality, both pharmaceutical diversion and multiple provider prescribing have been implicated in this increased mortality from prescription opioids (Hall et al., 2008). As a means to address this public health issue, the Food and Drug Administration (FDA) recently has begun to require pharmaceutical manufacturers to adopt broad risk evaluation and mitigation strategies (REMS) for long-acting opioids to ensure that the benefits of these drugs outweigh the risks (Food and Drug Administration, 2009). Unfortunately, an unduly restrictive REMS for a certain class of opioids has the potential to deter clinicians from using these substances, even when they are medically warranted or, as has been seen before, to even stimulate excessive use of other abusible drugs not encompassed under the regulatory umbrella. Conversely, PMPs have come to represent balanced methods to address some aspects of prescription opioid abuse while ensuring access for patients who need them and for which safe and effective use can be demonstrated. Although not investigated in the present study, curbing multiple providers seems a reasonable objective for helping reduce illicit use of controlled substances. As a result, it is logical that maintaining and enhancing ongoing state PMP efforts, so that pre- B.L. Wilsey et al. / Drug and Alcohol Dependence 112 (2010) 99–106 scribers have direct access to PMP data at the point of care, would promote responsible prescribing and represent an integral part of the solution to the societal abuse and diversion issues inspiring the FDA’s conceptualization of a REMS approach (Food and Drug Administration, 2009). This study is characterized by a number of limitations that affect the research questions that can be evaluated, as well as how the results can be interpreted. For example, the CURES database was conceptualized as a state drug control program designed to monitor provider prescribing to reduce the societal consequences of the non-medical use of medications. It was never intended as a research tool. Consequently, the data contain a limited amount of information that can be used as explanatory factors for multivariate analyses. Although this study benefited from operationalizing essential variable domains (e.g., patient and prescription characteristics, and the identification of multiple provider episodes), future research investigating the phenomenon of multiple provider prescribing should attempt to elaborate the present model with other relevant predictive characteristics. Additionally, little empirical evidence exists to stratify multiple provider prescribing in order to differentiate propensity for prescription drug abuse according to the extent that multiple providers are used. An expert panel concluded that greater then five prescriptions for the same medication over a 1-year period was likely indicative of misusing controlled substances (Parente et al., 2004) but there has been no further attempt to determine the clinical validity of this consensus conclusion. A related, but nevertheless substantial, limitation is the inability to determine the association between multiple provider prescribing and actual non-medical use of these medications, as the CURES database contained no information documenting problems with diversion or abuse. At this time, the explicit motivations for seeking multiple providers for controlled substances remain uncertain, as we are in the early stages of this type of investigation and have yet to develop an understanding of this potentially illicit drug-seeking activity. Interviewing subjects under anonymous and confidential circumstances may enable further probing into this phenomenon. Although not feasible using a secondary data analysis containing de-identified PMP data, similar studies have been performed involving the unlawful channeling of regulated pharmaceuticals from legal sources to the illicit marketplace by interviewing club drug users, street-based illicit drug users, methadone maintenance patients, and HIV positive individuals who abuse and/or divert drugs (Inciardi et al., 2007). In addition to the limitations above, the definition of a multiple provider episode utilized in this study was arbitrary and may have produced spurious results in the case of a person who refilled the prescription before the 30-day period using both a new provider and a new pharmacy but who had a legitimate reason to do so. Finally, there was also the issue of data entry into the CURES database, which was limited to civilian practices thereby excluding Veteran Affairs, military, and prison facilities. In addition, the prescriptions in the database had to be filled by pharmacists in California in order to be processed. We, therefore, could not detect multiple provider episodes if they involved the crossing of state (e.g., Oregon, Nevada, and Arizona) or international (e.g., Mexico or via the internet) borders. Although efforts are underway to utilize PMPs to improve health care (Katz et al., 2008), there may be problems that undermine their clinical effectiveness. For instance, a survey of providers indicated some reluctance to utilize these programs (Barrett and Watson, 2005). Although a majority of the surveyed physicians believed that these programs would be useful for monitoring prescription histories and decreasing doctor shopping, very few (11%) actually requested information from the database (Barrett and Watson, 2005). 105 The incidence of multiple provider episodes was relatively low for all controlled substances ranging from a high of 12.8% for opioids to a low of 0.9% for anorectics. Although only a small fraction of prescriptions were involved, the consequences of the abuse and diversion of controlled substances demand better ways of reducing doctor shopping. Ideally, this must be accomplished without interfering with appropriate medical practice. There are indications that PMPs offer a balanced approach. In a recent European study, the diversion through doctor shopping for buprenorphine decreased after a prescription monitoring program was instituted without notable impact on the access to treatment (Pradel et al., 2009). Although this finding awaits replication elsewhere, such results portend the clinical and public health utility of PMPs, providing an impetus for their widespread adoption. Findings from the current study suggest that controlled substance polypharmacy requires special consideration in the clinical setting. Notwithstanding its present shortcomings, PMP information is rapidly becoming an essential therapeutic tool to provide appropriate medical care. Clinicians who provide prescription medications to patients with chronic pain may find that the availability of PMP information at the point of care to identify multiple provider episodes, as well as access to longitudinal records of PMP prescribing data, will help support more responsible prescribing of controlled substances. Role of funding source Funding for this study was provided by the Robert Wood Johnson Foundation (RWJF). The RWJF had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Contribution Authors Wilsey, Fishman, and Gilson developed the study concept and design. The acquisition of data was conducted by authors Casamalhuapa and Baxi. Analysis and interpretation of data was conducted by authors Li, Zhang, Gilson, and Wilsey. Drafting of the manuscript was performed by authors Wilsey, Fishman, and Gilson. Critical revision of the manuscript for important intellectual content was performed by authors Wilsey, Fishman, Gilson, and Li. All authors approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest. Acknowledgements We gratefully acknowledge informatics and biostatistical support for this project from the UC Davis Clinical Translational Science Center. This publication was made possible by Grant Number UL1 RR024146 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov/. Information on Re-engineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.gov/clinicalresearch/overviewtranslational.asp. 106 B.L. Wilsey et al. / Drug and Alcohol Dependence 112 (2010) 99–106 References Barrett, K., Watson, A., 2005. Physician perspectives on a pilot prescription monitoring program. J. Pain Palliat. Care Pharmacother. 19, 5–13. Bridges, S., 2007. In: Program, A.M.P. (Ed.), Memorandum: Clinical Edits and Revised Dose Edits for ADD/ADHD Drugs. Arkansas Department of Health and Human Services, Little Rock, AR, pp. 72203–81437. 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