The American Psychiatric Association (APA) has updated its Privacy Policy and Terms of Use, including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy and Terms of Use, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.

Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.

ArticlesFull Access

Effects of County-Level Opioid Dispensing Rates on Individual-Level Patterns of Prescription Opioid and Heroin Consumption: Evidence From National U.S. Data



The authors examined directly whether county-level changes in opioid dispensing rates affect individual-level prescription opioid misuse, frequency of use, and dependence, as well as the same outcomes for heroin.


Using data from the restricted-access National Survey on Drug Use and Health, the Centers for Disease Control and Prevention’s retail opioid prescription database, the Prescription Drug Abuse Policy System, and the U.S. Census, the authors applied fixed-effects models to determine whether county-level dispensing rates affected prescription opioid outcomes as intended and whether changes in rates adversely affected heroin use outcomes. Bayes factors were used to confirm evidence for null findings.


The sample included 748,800 respondents age 12 and older from 2006 to 2016. The odds of prescription opioid misuse, increased frequency of misuse, and dependence were 7.2%, 3.5%, and 10.4% higher, respectively, per standard deviation increase in the county-level opioid dispensing rate per 100 persons. There was no evidence for any association between opioid dispensing rates and the three heroin outcomes. The odds ratio was nonsignificant according to frequentist techniques in fixed-effects models, and Bayesian techniques confirmed very strong support for the null hypothesis.


County-level opioid dispensing rates are directly associated with individual-level prescription opioid misuse, frequency of misuse, and dependence. Changes in dispensing were not associated with population shifts in heroin use. Reductions in opioid dispensing rates have contributed to stemming prior increases in prescription opioid misuse while not adversely affecting heroin use. Physicians and other health care providers can take action to minimize opioid dispensing for tangible benefits regarding prescription opioid misuse without adverse effects on heroin use.

During the first decade of the 21st century, the United States experienced a dramatic increase in the number of controlled substances prescribed and dispensed, particularly opioids. The number of opioid prescriptions dispensed from retail pharmacies increased from 174.1 million in 2000 to 256.9 million in 2009 (1, 2). Over the same period, prescription opioid misuse increased dramatically (27), along with a simultaneous rise in rates of overdoses due to these substances (8). As dispensing rates began to decrease in the early 2010s (9), rates of fatal overdoses due to prescription opioids plateaued. As this plateau began, heroin overdoses rose from 2010 to 2016 before also plateauing, followed by increases in overdoses from illicit synthetic opioids such as fentanyl (8). These overdose trends suggest that dispensing trends affected prescription opioid misuse, and potentially the rise of heroin and fentanyl use. Yet, little research directly links local-level dispensing rates to individual-level use and dependence using nationally representative data, despite connections between dispensing and patterns of misuse and dependence as an often hypothesized pathway to overdose trends. In this study, we examined directly whether county-level dispensing rates affected individual-level prescription opioid and heroin misuse, frequency, and dependence in a nationally representative data set.

A body of research demonstrates a relationship between opioid dispensing patterns and adverse outcomes from opioid consumption. According to administrative and clinical records, for individuals with valid prescriptions for pain, patients with higher prescribed doses and increased days of supply are more likely to develop an opioid use disorder (10) or die of an overdose (1114). However, studies show that a substantial proportion of prescription opioid overdose deaths are associated with nonmedical use of opioids obtained via diversion (15, 16), making evidence from patient behaviors less well extendable to nonmedical use. In the aggregate, increases and decreases in opioid dispensing are associated with concomitant deaths from prescription opioids (1719). Although few national studies directly consider the relationship between dispensing and prescription opioid misuse, we can draw inferences from studies on prescription drug monitoring programs (PDMPs) as an indirect measure of opioid dispensing, as these systems of surveillance are intended to reduce unnecessary prescribing and dispensing. The evidence on whether PDMPs reduce prescriptions or reduce overdoses is mixed (2025). A study of use patterns using this measure found little evidence that PDMPs affect nonmedical opioid use and dependence (26). Studies with broader geographic scope have tended to use state-level measures, despite evidence that the quantity of prescriptions dispensed and acute outcomes such as overdose are more localized (22, 27). For example, it is not clear that high rates of dispensing in the western counties of New York would significantly affect pharmaceutical opioid consumption in relatively low-dispensing counties of Queens and Brooklyn several hundred miles away, particularly since opioid diversion commonly occurs with medication diverted from friends and family (28). In sum, past studies tend to focus on the effect of dispensing on pain patient outcomes, aggregate patterns of overdoses, or indirect measures such as state-level PDMPs and also obscure trends occurring at a more local level. Despite considerable attention given to the opioid crisis, analyses that build on this earlier work to examine the nuances of the relationship between opioid dispensing and opioid misuse and dependence remain much needed. Here, we consider the alternative hypothesis that dispensing rates are associated with changes in prescription opioid misuse outcomes; as we expect an increase in misuse and dependence when the dispensing rate was increasing and a decrease as it declined, this represents a two-sided hypothesis and a test against a null hypothesis of no association.

An additional concern about decreasing the number of available prescriptions is that reductions in the circulation of prescription opioids could push individuals with opioid dependence to illicit opioids such as heroin. While there is some evidence that many heroin users transitioned from prescription opioids (2933), the relationship between this transition and the availability of prescription opioids has not been thoroughly explored using national data. In fact, Schuchat et al., from the Centers for Disease Control and Prevention (CDC), have argued that there is no evidence that prescribing practices have resulted in increased heroin use (1). However, studies making this claim often rely on indirect evidence of the effect of dispensing rates on overdoses (17) or a measure of dispensing such as PDMPs (26). As Compton et al. have remarked, “Some persons certainly use heroin when they are unable to obtain their preferred prescription opioid; however, whether the increases in heroin trends in the overall population are driven by changes in policies and practices regarding prescription opioids is much less clear” (33, p. 155). Again, the central piece of the argument, namely, whether changes in dispensing rates affected patterns of heroin use, remains uncertain. On the one hand, reductions in dispensing may have had a direct effect on transitions to heroin use. In such circumstances, we would expect to see growth in heroin use in locations that reduced opioid dispensing. On the other hand, the growth in heroin use may be an ancillary component of the wider opioid crisis, but not directly attributable to patterns of opioid dispensing. In examining heroin use outcomes, then, our alternative hypothesis is that decreases in county-level opioid dispensing are associated with increases in heroin use outcomes, while the null hypothesis is that of no relationship.

Relying on national data from the CDC and the Substance Abuse and Mental Health Services Administration (SAMHSA), this study builds on previous research to examine directly the relationship between opioid dispensing rates at the county level and individual-level pharmaceutical opioid misuse and heroin use as well as dependence. The findings may have implications not only for prevention and intervention but also for clinical practice.


Individual-Level Data

We used data from the National Survey on Drug Use and Health (NSDUH), a national survey on substance use conducted annually by SAMHSA since 1971. In a stratified, multistage area probability sampling design to produce representative national data, approximately 70,000 individuals age 12 and older are surveyed annually. Via a U.S. Census Bureau Research Data Center, we used restricted NSDUH data, which contain state and county Federal Information Processing Standard codes, from 2006 to 2016 to examine individual-level use, frequency of use, and dependence related to prescription opioids and heroin as dispensing rates shifted. The included years represent those for which all dispensing data and covariates were available.

First, we examined a binary indicator (created by the NSDUH) of any prescription opioid misuse or heroin use in the past year. Second, for both substances, given that the sparsity of data for specific counts precluded the use of count models, we used an ordinal measure of past-year frequency of use. The categories coded from the number of days are “none,” “infrequent” (1–11 days), “intermittent” (12–59 days), “regular but nondaily” (60–300 days), and “daily” (>300 days). Because the measure of prescription opioid frequency changed in 2015 from a past-year to a past-month reference period, the model specific to prescription opioid frequency does not include 2015 and 2016 data as the other models do. Finally, we examined a binary indicator of dependence related to both substances, which the NSDUH defines by DSM-IV criteria.

Within the NSDUH, we included appropriate individual-level covariates within the models described below. Specifically, we accounted for birth year (including a squared term to account for nonlinearity), race/ethnicity, gender, income, educational attainment, health insurance status, employment status, marital status, and U.S. nativity.

Dispensing Data

We used county-level opioid dispensing data from the CDC, including years 2006 through 2016. Our key variable was retail opioid prescriptions dispensed for 100 people per county (34), which does not include administration of opioids in hospitals or other treatment settings. Although there were a total of 3,149 unique U.S. counties as of 2016, the number of counties in our analysis ranges from 2,637 in 2012 to 2,851 in 2015, with the number in other years falling in between. Missing counties primarily result from incomplete dispensing data within the CDC database, which contains data for 87.6% to 94.0% of counties in any given year. According to the CDC (34), missing data “may indicate that the county had no retail pharmacies and/or prescribers, the county had no retail pharmacies and/or prescribers sampled, or the prescription volume was erroneously attributed to an adjacent, more populous county according to the sampling rules used.”

Policy Data

To determine the effect of county-level dispensing rates independent of the effect of opioid-related policies placed into effect during the period of observation, we used the Prescription Drug Abuse Policy System for a comprehensive listing of policy passage in each state. These measures included state-level variables for any PDMP, any expanded naloxone access to the public, Good Samaritan laws absolving criminal or civil liability in reporting an overdose, any pain clinic prescribing restrictions, and presence of medical marijuana laws. We note that medical marijuana laws—any laws that permit the use of cannabis for medical purposes—were not passed because of the opioid crisis but remain an important policy covariate pertinent to pain management and general patterns of substance use (35).

County- and State-Level Covariates

The U.S. Census Bureau’s American Community Survey (ACS) and decennial censuses provided county-level time-varying covariates—the unemployment rate, the median household income, and percentages of households in poverty, foreign born, female-headed households, non-Hispanic Black, Hispanic, and over age 25 with a bachelor’s degree. We also accounted for urban/rural location using the National Center for Health Statistics urban-rural classification scheme. We used the 5-year ACS estimates because estimates for shorter time frames are available only for larger counties. Because these begin in 2009, we linearly interpolated between the 2000 census and 2009, using the interpolated values for 2006 to 2008.

Statistical Analysis

We used logistic regression models with fixed effects for state and year to determine the effect of dispensing rates (36). The strength of including fixed effects is the elimination of unobserved heterogeneity. Fixed-effects estimators are robust to any observed or unobserved time-invariant omitted variables, which removes any constant state-level effects (36). We included a fixed effect for state to account for unobserved differences across locales over time. We also included a standard error cluster correction for county to account for dependencies between individuals within counties. Finally, to provide estimates of the effect of dispensing rates independent of changes over time, all models included year fixed effects. All analyses included statistical weights to account for the stratified multistage probability sampling design. Since we use logistic regression, all model results are presented as odds ratios with 95% confidence intervals. Given the nature of the variable, we used ordinal logistic regression to model frequency of use. The variance inflation factor indicated no issues with collinearity.

To provide additional evidence for findings, we also present Bayes factors (37). In particular, Bayes factors are useful for providing evidence for null hypotheses, given that frequentist techniques (i.e., the parameter tests in the fixed-effects models) can only conclude that the null cannot be rejected, rather than providing evidence for the null. A Bayes factor of less than 1/3 indicates evidence for the null hypothesis, with the following levels: 1/3–1/10=moderate; 1/10–1/30=strong; 1/30–1/100=very strong; <1/100=extreme (37). Bayes factors also support the alternative hypothesis when the value exceeds 3. When a theoretical prior distribution is unknown, the Bayes factor can be computed from a uniform distribution with a plausible maximum (37). We take this approach here by using a uniform[0,1] prior distribution, but also note that our conclusions were identical using normal(0,1).

Across the years of observation, the entire NSDUH sample considered had about 748,800 respondents. All analytic sample sizes, in accordance with the restricted data agreement, are rounded to the nearest 100.


Table 1 presents descriptive statistics. For the outcomes, 4.6% of the sample in all years reported prescription opioid misuse. Regarding frequency, 95.6% reported no misuse, 2.3% infrequent misuse, 1.3% intermittent misuse, 0.5% regular but nondaily misuse, and 0.3% daily misuse. Across all years, 0.5% of respondents reported dependence on prescription opioids. For heroin, only 0.3% of respondents reported any use. Not surprisingly, among heroin users the rates for each of the frequency categories was below 0.1%, and only 0.2% reported dependence on heroin. While several of these percentages are quite low, the substantial size of the NSDUH ensures that there are still no small cell sizes. For our main predictor of interest, the average county-level opioid dispensing rate across all respondents and years was 78.4 per 100 persons. Temporally, average county-level opioid dispensing rates rose from 80.5 per 100 persons in 2006 to a high of 96.1 per 100 persons in 2012, at which point rates began to steadily decline.

TABLE 1. Weighted descriptive statistics in a study of the effects of county-level opioid dispensing rates on individual-level patterns of prescription opioid and heroin consumption (N=748,800)

Prescription opioid outcomes
Past-year prescription opioid misuse4.62
Past-year frequency of prescription opioid misuse
 Infrequent (1–11 days)2.30
 Intermittent (12–59 days)1.25
 Regular but nondaily (60–300 days)0.52
 Almost daily (>300 days)0.34
Prescription opioid dependence0.54
Heroin outcomes
Past-year heroin use0.26
Past-year frequency of heroin use
 Infrequent (1–11 days)0.07
 Intermittent (12–59 days)0.02
 Regular but nondaily (60–300 days)0.01
 Almost daily (>300 days)0.01
Heroin dependence0.15
Opioid Dispensing
County-level opioid dispensing rate per 100 persons78.3734.55
State-level policy covariates
Prescription drug monitoring program86.88
Good Samaritan policy22.67
Any naloxone access expansion35.78
Pain management clinic laws15.41
Medical marijuana laws33.77
County-level covariates
Percent Hispanic15.3715.30
Percent Black12.7412.63
Percent unemployed5.141.43
Percent living in poverty10.524.59
Percent over age 25 with a bachelor’s degree20.698.72
Percent foreign born12.5510.76
Percent female-headed households7.242.04
Household income (thousands of $)54.4814.50
Metropolitan area types
 Large central29.84
 Large fringe25.63
Individual-level covariates
Year of birth1967.2719.52
Health insurance86.34
U.S. born84.94
 Native American0.52
 Native Hawaiian/Pacific Islander0.35
Income bracket
Educational attainment
 Less than high school13.29
 High school diploma26.37
 Some college24.37
 Bachelor’s degree or higher26.29
 12- to 17-year-olds9.68
Employment status
 Other or not in labor force27.33
 12- to 17-year-olds9.68
Marital status
 Divorced or separated12.43
 Never married33.91

TABLE 1. Weighted descriptive statistics in a study of the effects of county-level opioid dispensing rates on individual-level patterns of prescription opioid and heroin consumption (N=748,800)

Enlarge table

Table 2 summarizes the results of our logistic regression models. Models 1 through 3 show the outcomes for prescription opioid misuse. The county-level dispensing rate was significant and positively associated with all three outcomes. From model 1, a one-unit increase in the county-level opioid dispensing rate per 100 persons is associated with a 0.2% increase in the odds of individual-level past-year prescription opioid misuse (p<0.001), net of opioid-related policies, county-level sociodemographic measures, individual-level correlates, and state and year fixed effects. While this magnitude appears low, it only reflects the magnitude of a one-unit increase. For example, we can consider an increase of a standard deviation in the dispensing rate (SD=34.6; see Table 1). A one-standard-deviation increase in the dispensing rate is associated with a 7.2% increase in the odds of past-year prescription opioid misuse. Model 2 considers frequency of prescription opioid misuse. A one-unit increase in the county-level opioid dispensing rate is associated with a 0.1% increase in the odds of being in a higher frequency category (p<0.01). For a one-standard-deviation increase in the dispensing rate, the associated odds are 3.5% higher of being in a higher frequency category. Finally, according to model 3, a one-unit increase in the county-level opioid dispensing rate is associated with a 0.3% increase in the odds of individual-level prescription opioid dependence (p<0.001). For a one-standard-deviation increase in the dispensing rate, the associated odds of dependence are 10.4% higher. While we interpret the increase given this expected positive relationship, we may also consider a one-standard-deviation decrease given that dispensing rates began to decline. In this case, a one-standard-deviation decrease in dispensing rates is associated with a 6.7% decrease in the odds of past-year prescription opioid misuse, a 3.4% decrease in the odds of being in a higher frequency category, and a 9.6% decrease in the odds of dependence. We also note that the dispensing effects presented are nearly identical in models without the policy variables (not presented here). Further, although we have strong support using a frequentist approach, we also note that Bayes factors for the effect of dispensing on each of these three outcomes indicate support for the alternative.

TABLE 2. Fixed-effects results for effect of county-level opioid dispensing rates on individual-level prescription opioid and heroin outcomes in the NSDUH from 2006 to 2016a

Opioid Type and Analytic ModelOdds Ratio95% CI
Prescription opioids
Model 1: past-year misuse1.002***1.001, 1.002
Model 2: frequency of past-year misuse1.001**1.001, 1.002
Model 3: dependence1.003***1.001, 1.004
Model 4: past-year use0.9990.996, 1.001
Model 5: frequency of past-year use0.9980.994, 1.002
Model 6: dependence0.9990.996, 1.003

aExponentiated coefficients from logistic regression are displayed (binary for past-year misuse/use and dependence; ordinal for frequency). All models include state and year fixed effects and cluster-corrected standard errors for county and utilize sampling weights. Models include all covariates shown in Table 1 (see Table S1 in the online supplement for full models). Regression coefficients represent the effect of a one-unit increase in county-level opioid dispensing rate; see the text for the magnitude of a one-standard-deviation increase. Bayes factors indicate very strong support for the null hypothesis for the heroin coefficients. NSDUH=National Survey on Drug Use and Health.

**p<0.01. ***p<0.001.

TABLE 2. Fixed-effects results for effect of county-level opioid dispensing rates on individual-level prescription opioid and heroin outcomes in the NSDUH from 2006 to 2016a

Enlarge table

Models 4 through 6 in Table 2 show the same three outcomes for heroin. There were no significant associations between the county-level opioid dispensing rate and these individual-level heroin outcomes. Using the alternative hypothesis that decreases in opioid dispensing increases the heroin outcomes, Bayes factors from the three models were 0.0050, 0.0084, and 0.0052, respectively, demonstrating very strong support for the null hypothesis.


The substantial rise and subsequent decrease in prescription opioid misuse and dependence is often attributed to changes in opioid dispensing practices (19). This study builds on past studies to move beyond indirect outcomes such as overdose (1719) and predictors such as PDMPs (2026), geographically broad measures of dispensing, and administrative and clinical data on patient populations (1016). In using a direct measure of county-level opioid dispensing linked to a nationally representative individual-level data set in the NSDUH, we find evidence of an association between higher levels of opioid dispensing and increased odds of past-year prescription opioid misuse, past-year frequency of use, and dependence. Thus, increases in dispensing at the local level enabled growth in misuse early on, while efforts to curb opioid prescriptions during more recent years appear to have had an effect on reducing prescription opioid misuse and dependence. The results thus support the conclusion that efforts to reduce opioid prescriptions have directly affected nonmedical use of pharmaceutical opioids.

Beyond the linkages of dispensing patterns to prescription opioid misuse, we find no evidence that shifts in local-level opioid dispensing affected odds of heroin use, frequency of heroin use, or heroin dependence, with the frequentist fixed-effects models confirmed by Bayesian techniques. This suggests that trends in heroin use may be an ancillary component of the opioid crisis rather than directly attributable to patterns of opioid dispensing. Given that many heroin users have transitioned from prescription opioid misuse (2933), there has been a reasonable fear that well-meaning attempts to curb opioid prescriptions could result in additional opioid misusers making this transition. However, we found no evidence that this occurred at the population level, such that efforts should continue to reduce opioid prescriptions to levels required for patient care without fears that reductions may drive up heroin use. These results speak directly to Compton and colleagues’ contention that increases in heroin trends in the overall population may not have been driven by changes in policies and practices regarding prescription opioids (33). These findings also cohere with research on discontinuation of opioids, as recent work shows that discontinuation without tapering was the norm for long-term opioid therapies and that changes in prescribing due to PDMPs do not increase abrupt discontinuation (38). Nonetheless, as reductions in prescribing and dispensing continue, expansion of substance abuse treatment programs should occur, and such programs should remain available for those who misuse prescription opioids to prevent transitions to heroin (or synthetic opioids), which have been noted in the literature (2933).

Notably, since an effect of dispensing on prescription opioid misuse remains after accounting for opioid-related policies, there are likely other mechanisms operating in the link between dispensing and opioid misuse beyond state policy implementation. We contend that these effects are attributable to more general shifts within the institution of medicine beyond those defined by public policy. General attention to the opioid and overdose crisis may have made providers more cognizant of their own prescribing behaviors (1). For example, physicians may alter their own prescribing behavior after becoming aware of overdoses within the community (39). Moreover, as the crisis became apparent, emphasis on physicians considering nonopioid pain management alternatives increased (40). Thus, although other research has shown that policies such as PDMPs and pain clinic regulations reduce opioid dispensing (17, 26), our results suggest that the effect of changes in dispensing on patterns of opioid misuse are not solely attributable to such policies and suggest broader changes in prescriber behavior.

Although this study has numerous strengths, we must consider some limitations. First, counties are an imperfect measure of geographic space, and measuring dispensing rates at this level can obfuscate important within-county differentiation. County size and number also vary considerably across states. However, use of county-level data constitutes a substantial improvement over measuring dispensing at the state level given the local nature of opioid prescription dispensing. Second, while we included a large battery of individual-, county-, and state-level controls as well as state and year fixed effects, we recognize that other factors may affect patterns of use and dependence. The use of fixed effects provides results robust to observable and unobservable static state-level factors, increasing confidence in our results. However, unmeasured time-varying factors, including those related to survey methodology, remain a possible source of confounding given the observational nature of the data. Third, with repeated cross-sectional data, we cannot examine within-person change. National-population geocoded individual longitudinal data on prescription opioid misuse and heroin, particularly of a sample size that allows the examination of these uncommon outcomes, remain unavailable. Lastly, we recognize that changes in opioid dispensing may also affect illicit fentanyl use; however, we cannot make any conjectures about fentanyl or fentanyl-adulterated heroin. NSDUH only recently began collecting this information, which limits examinations of fentanyl’s relationship with opioid dispensing. The results for heroin provide some promise that such a shift for fentanyl might not accompany dispensing changes, but this remains an open question.


Our findings indicate that county-level rates of opioid dispensing had a direct effect on individual-level opioid misuse and dependence, but reductions in dispensing did not have any adverse effects on heroin use. Institutionally driven changes among prescribers, potentially shaped by both professional recognition of the problem and policy implementation, may have helped curb the prescription opioid crisis; however, these changes do not appear to have altered heroin use (in either direction) following shifts in dispensing at the county level. We recommend that medical providers continue to monitor patterns of prescribing and dispensing and that states continue to pursue policies that temper unnecessary opioid prescriptions.

Department of Sociology, Ohio State University, Columbus (Vuolo); Department of Sociology, Purdue University, West Lafayette, Ind. (Kelly).
Send correspondence to Dr. Kelly ().

Supported by NIDA grant R21DA046447.

The authors are grateful to Karon Lewis at the National Center for Health Statistics (NCHS) and Charles Hokayem at the University of Kentucky Data Research Center of the U.S. Census Bureau for their generous assistance with data access, as well as Laura Frizzell for research assistance. This study used NCHS restricted-access data.

The results reported in this article represent those of the authors and are not necessarily representative of the funding agency, NCHS, SAMHSA, or the U.S. Census Bureau.

The authors report no financial relationships with commercial interests.


1 Schuchat A, Houry D, Guy GP Jr: New data on opioid use and prescribing in the United States. JAMA 2017; 318:425–426Crossref, MedlineGoogle Scholar

2 Maxwell JC: The prescription drug epidemic in the United States: a perfect storm. Drug Alcohol Rev 2011; 30:264–270Crossref, MedlineGoogle Scholar

3 Atluri S, Sudarshan G, Manchikanti L: Assessment of the trends in medical use and misuse of opioid analgesics from 2004 to 2011. Pain Physician 2014; 17:E119–E128Crossref, MedlineGoogle Scholar

4 Birnbaum HG, White AG, Schiller M, et al.: Societal costs of prescription opioid abuse, dependence, and misuse in the United States. Pain Med 2011; 12:657–667Crossref, MedlineGoogle Scholar

5 Blanco C, Alderson D, Ogburn E, et al.: Changes in the prevalence of non-medical prescription drug use and drug use disorders in the United States: 1991–1992 and 2001–2002. Drug Alcohol Depend 2007; 90:252–260Crossref, MedlineGoogle Scholar

6 Compton WM, Volkow ND: Major increases in opioid analgesic abuse in the United States: concerns and strategies. Drug Alcohol Depend 2006; 81:103–107Crossref, MedlineGoogle Scholar

7 Dart RC, Surratt HL, Cicero TJ, et al.: Trends in opioid analgesic abuse and mortality in the United States. N Engl J Med 2015; 372:241–248Crossref, MedlineGoogle Scholar

8 Ciccarone D: The triple wave epidemic: supply and demand drivers of the US opioid overdose crisis. Int J Drug Policy 2019; 71:183–188Crossref, MedlineGoogle Scholar

9 Guy GP Jr, Zhang K, Bohm MK, et al.: Vital signs: changes in opioid prescribing in the United States, 2006–2015. MMWR Morb Mortal Wkly Rep 2017; 66:697–704Crossref, MedlineGoogle Scholar

10 Edlund MJ, Martin BC, Russo JE, et al.: The role of opioid prescription in incident opioid abuse and dependence among individuals with chronic noncancer pain: the role of opioid prescription. Clin J Pain 2014; 30:557–564Crossref, MedlineGoogle Scholar

11 Bohnert AS, Valenstein M, Bair MJ, et al.: Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA 2011; 305:1315–1321Crossref, MedlineGoogle Scholar

12 Rose AJ, Bernson D, Chui KKH, et al.: Potentially inappropriate opioid prescribing, overdose, and mortality in Massachusetts, 2011–2015. J Gen Intern Med 2018; 33:1512–1519Crossref, MedlineGoogle Scholar

13 Dunn KM, Saunders KW, Rutter CM, et al.: Opioid prescriptions for chronic pain and overdose: a cohort study. Ann Intern Med 2010; 152:85–92Crossref, MedlineGoogle Scholar

14 Paulozzi LJ, Kilbourne EM, Shah NG, et al.: A history of being prescribed controlled substances and risk of drug overdose death. Pain Med 2012; 13:87–95Crossref, MedlineGoogle Scholar

15 Hall AJ, Logan JE, Toblin RL, et al.: Patterns of abuse among unintentional pharmaceutical overdose fatalities. JAMA 2008; 300:2613–2620Crossref, MedlineGoogle Scholar

16 Lanier WA, Johnson EM, Rolfs RT, et al.: Risk factors for prescription opioid-related death, Utah, 2008–2009. Pain Med 2012; 13:1580–1589Crossref, MedlineGoogle Scholar

17 Dowell D, Zhang K, Noonan RK, et al.: Mandatory provider review and pain clinic laws reduce the amounts of opioids prescribed and overdose death rates. Health Aff (Millwood) 2016; 35:1876–1883Crossref, MedlineGoogle Scholar

18 Modarai F, Mack K, Hicks P, et al.: Relationship of opioid prescription sales and overdoses, North Carolina. Drug Alcohol Depend 2013; 132:81–86Crossref, MedlineGoogle Scholar

19 Paulozzi LJ, Ryan GW: Opioid analgesics and rates of fatal drug poisoning in the United States. Am J Prev Med 2006; 31:506–511Crossref, MedlineGoogle Scholar

20 Bao Y, Pan Y, Taylor A, et al.: Prescription drug monitoring programs are associated with sustained reductions in opioid prescribing by physicians. Health Aff (Millwood) 2016; 35:1045–1051Crossref, MedlineGoogle Scholar

21 Meara E, Horwitz JR, Powell W, et al.: State legal restrictions and prescription-opioid use among disabled adults. N Engl J Med 2016; 375:44–53Crossref, MedlineGoogle Scholar

22 Chang HY, Lyapustina T, Rutkow L, et al.: Impact of prescription drug monitoring programs and pill mill laws on high-risk opioid prescribers: a comparative interrupted time series analysis. Drug Alcohol Depend 2016; 165:1–8Crossref, MedlineGoogle Scholar

23 Centers for Disease Control and Prevention: Prescription drug monitoring programs (PDMPs): What states need to know. Scholar

24 Fink DS, Schleimer JP, Sarvet A, et al.: Association between prescription drug monitoring programs and nonfatal and fatal drug overdoses: a systematic review. Ann Intern Med 2018; 168:783–790Crossref, MedlineGoogle Scholar

25 Rhodes E, Wilson M, Robinson A, et al.: The effectiveness of prescription drug monitoring programs at reducing opioid-related harms and consequences: a systematic review. BMC Health Serv Res 2019; 19:784Crossref, MedlineGoogle Scholar

26 Ali MM, Dowd WN, Classen T, et al.: Prescription drug monitoring programs, nonmedical use of prescription drugs, and heroin use: evidence from the National Survey of Drug Use and Health. Addict Behav 2017; 69:65–77Crossref, MedlineGoogle Scholar

27 Frizzell LC, Vuolo M, Kelly BC: State pain management clinic policies and county opioid prescribing: a fixed effects analysis. Drug Alcohol Depend 2020; 216:108239Crossref, MedlineGoogle Scholar

28 Han B, Compton WM, Blanco C, et al.: Prescription opioid use, misuse, and use disorders in US adults: 2015 National Survey on Drug Use and Health. Ann Intern Med 2017; 167:293–301Crossref, MedlineGoogle Scholar

29 Cerdá M, Santaella J, Marshall BD, et al.: Nonmedical prescription opioid use in childhood and early adolescence predicts transitions to heroin use in young adulthood: a national study. J Pediatr 2015; 167:605–612.e2Crossref, MedlineGoogle Scholar

30 Mars SG, Bourgois P, Karandinos G, et al.: “Every ‘never’ I ever said came true”: transitions from opioid pills to heroin injecting. Int J Drug Policy 2014; 25:257–266Crossref, MedlineGoogle Scholar

31 Cicero TJ, Ellis MS, Surratt HL, et al.: The changing face of heroin use in the United States: a retrospective analysis of the past 50 years. JAMA Psychiatry 2014; 71:821–826Crossref, MedlineGoogle Scholar

32 Jones CM: Heroin use and heroin use risk behaviors among nonmedical users of prescription opioid pain relievers: United States, 2002–2004 and 2008–2010. Drug Alcohol Depend 2013; 132:95–100Crossref, MedlineGoogle Scholar

33 Compton WM, Jones CM, Baldwin GT: Relationship between nonmedical prescription-opioid use and heroin use. N Engl J Med 2016; 374:154–163Crossref, MedlineGoogle Scholar

34 Centers for Disease Control and Prevention: US opioid prescribing rate maps. Scholar

35 Theriault BM, Schlesinger JJ: Potential impact of medical marijuana on nonmedical opioid use. Am J Psychiatry 2018; 175:284LinkGoogle Scholar

36 Allison P: Fixed Effects Regression Models. Thousand Oaks, Calif, Sage, 2009CrossrefGoogle Scholar

37 Beard E, Dienes Z, Muirhead C, et al.: Using Bayes factors for testing hypotheses about intervention effectiveness in addictions research. Addiction 2016; 111:2230–2247Crossref, MedlineGoogle Scholar

38 Bao Y, Zhang H, Wen K, et al.: Robust prescription monitoring programs and abrupt discontinuation of long-term opioid use. Am J Prev Med 2021; 61:537–544Crossref, MedlineGoogle Scholar

39 Doctor JN, Nguyen A, Lev R, et al.: Opioid prescribing decreases after learning of a patient’s fatal overdose. Science 2018; 361:588–590Crossref, MedlineGoogle Scholar

40 Volkow ND, Collins FS: The role of science in addressing the opioid crisis. N Engl J Med 2017; 377:391–394Crossref, MedlineGoogle Scholar