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Drivers of Excess Costs of Opioid Abuse Among a Commercially Insured Population
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State Prescription Drug Monitoring Programs and Fatal Drug Overdoses
Young Hee Nam, PhD; Dennis G. Shea, PhD; Yunfeng Shi, PhD; and John R. Moran, PhD
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Gerald Cochran, PhD; Adam J. Gordon, MD, MPH; Walid F. Gellad, MD, MPH; Chung-Chou H. Chang, PhD; Wei-Hsuan Lo-Ciganic, PhD, MS, MSPharm; Carroline Lobo, MS; Evan Cole, PhD; Winfred Frazier, MD; Ping

State Prescription Drug Monitoring Programs and Fatal Drug Overdoses

Young Hee Nam, PhD; Dennis G. Shea, PhD; Yunfeng Shi, PhD; and John R. Moran, PhD
This study investigates the impact of state prescription drug monitoring programs on drug overdose mortality rates for all drug categories.
Overall, mortality rates from prescription drug overdoses increased from 1999 to 2014, in both PDMP and non-PDMP states. The PDMP coefficients from our regression models (Tables 1 and 2) capture the difference between the mortality rates expected to arise in the absence of a PDMP, as predicted by all other covariates in the model, and the rates that occur when a PDMP is present. We organized the results to show the PDMP effect in: 1) overall drug overdose mortality rates, from the underlying-cause-of-death data and the multiple-cause-of-death data, for 1999 to 2014 and 1999 to 2010, separately (Table 1); and 2) subcategories of multiple-cause-of-death data in 2 ways: a) subcategories, which are not mutually exclusive, but collectively comprehensive and b) subcategories with high mortality rates (mortality rates >1 per 100,000 for both the PDMP and non-PDMP states) for 1999 to 2010 (Table 2). Figures 2 and 3 illustrate the results in Tables 1 and 2, respectively.

For the overall overdose mortality rates, our estimates from the publicly available data (1999-2014) were 0.08 (95% confidence interval [CI], –0.89 to 1.04) and 0.02 (95% CI, –0.97 to 1.00) for underlying cause of deaths and multiple cause of deaths, respectively. The estimates from the unsuppressed data (1999-2010) were 0.02 (95% CI, –1.05 to 1.08) and 0.02 (95% CI, –1.07 to 1.11), respectively. In the extended model, the PDMP coefficients slightly increased, but were statistically insignificant (eAppendix Table 2 and eAppendix Figure 1). These results suggest that PDMP implementation had little impact on overall overdose mortality rates. Throughout the primary and extended models, all PDMP coefficients pertaining to overall overdose death rates were positive; however, their magnitude and statistical significance varied across model specifications.

In the subcategory analysis, PDMP coefficients were 0.02 (95% CI, –0.81 to 0.84) for legal narcotics and 0.85 (95% CI, –0.08 to 1.78) for other and unspecified drugs (Table 2). In the extended model, PDMPs were associated with significantly increased mortality rates for illicit drugs (0.92; 95% CI, 0.15-1.69) and cocaine (0.71; 95% CI, 0.11-1.31) (eAppendix Table 3 and eAppendix Figure 2).

Based on the subsample analysis for states with a PDMP in place for 5 or more years, all PDMP coefficients were positive for overall mortality rates and were significant for 1999 to 2010 (Table 1 and eAppendix Table 1). In the subcategory analysis, longer-standing PDMPs were associated with significantly increased mortality rates in several categories, including legal narcotics, illicit drugs, cocaine, other and unspecified drugs (Table 2), and illicit drugs and cocaine (eAppendix Table 2).

As shown in the extended model results, none of the preprogram indicators (PRE1 and PRE2) were significant, lending support to the model specification employed in our initial analysis. The PEPO indicator was positive and significant for some categories, which suggests an increase in mortality rates for those categories in the post enactment and preoperation periods. 

DISCUSSION

This study investigated PDMP effects on fatal drug overdoses in the United States from 1999 to 2014. We found that PDMPs were not associated with a reduction in either overall or prescription opioid drug overdose mortality rates. Moreover, during the period from 1999 to 2010, for which we conducted the subcategory analysis, PDMPs were often associated with increased mortality rates in drug categories other than prescription opioids, such as illicit drugs or other and unspecified drugs, particularly among the states with longer-standing PDMPs. Although our study period was shorter when examining mortality rates for different drug categories, our results may reflect an unintended consequence of PDMPs, at least up through 2010, whereby reduced access to prescription drugs may have led some individuals with addictive disorders to seek out substitute drugs.34,35

Our findings have several policy and clinical implications. First, PDMPs do not seem to have been successful in reducing drug overdose mortality rates, even in the target categories of prescription opioids (T40.2) and legal narcotics (T40.2-T40.4). This is consistent with some previous studies.23-25 There are many possible reasons for this outcome. For example, PDMPs may not be able to fully address prescription diversions, doctor shopping, or other abusive behaviors, and under these programs, potential drug-related illegal activities are only detectable through prescription fillings. The rapidly growing online pharmaceutical sale space may have also increased the opportunities for individuals to evade state or federal regulations. (More than 90% of internet pharmacies are estimated to be illegal.36) Further, PDMPs may drive patients away from doctors who could help them address drug abuse or dependence. 

Second, our results imply that PDMPs might be related to increases in drug overdose mortality rates attributable to illicit drugs or other and unspecified drugs. The existing literature has raised the possibility of these unintended consequences of PDMPs, although there has been little empirical evidence to date.23,34 By analyzing overdose deaths for different drug categories, including illicit drugs and other and unspecified drugs, our study provides some evidence for such a possibility. Future research is needed to further explore the unintended consequences of PDMPs and the potential mechanisms contributing to them (eg, PDMPs’ influence on clinical practices and individual behaviors). If the underlying problems of drug addiction or drug abuse are not effectively addressed, PDMPs might trigger some people to obtain illicit drugs as potential substitutes. 

Third, our findings suggest that PDMPs may need to be combined with more comprehensive and prevention-oriented approaches to address drug overdose deaths. Examples of prevention-oriented approaches include: 1) improving patient education on the appropriate use of drugs, 2) ensuring proper access to prescription drugs for those with medical needs, 3) expanding treatment programs for those with drug abuse problems, and 4) improving provider education and clinical practices for pain management. Such approaches are consistent with the recent program, "Prescription Drug Overdose: Prevention for States," funded by the CDC in 16 states.37

Fourth, our findings suggest that policy makers, insurers, and managed care organizations might need to consider the effects of PDMPs when designing health plans, including such features as reimbursement, overall benefit design, and coverage criteria for specialty treatment of drug abuse. Currently, CMS provides the Medicare Part D Opioid Prescriber Summary File on its website, which includes the individual provider’s National Provider Identifier, last name, zip code, and number/percentage of prescription claims for opioid drugs.38 Although this information is based on Medicare claims data, physicians might be concerned about the potential effects the PDMP data could have on reimbursement when combined with administrative data.39 Reimbursement policies based on the number of opioid prescriptions, without consideration of medical need or value, might negatively affect quality of care and, ultimately, increase costs for payers if physicians limit opioid prescriptions in a way that runs counter to optimal patient care. In addition, there may also need to be better coordination between primary and specialty care providers for patients with drug problems. 

Finally, this study suggests that restricting attention to overdose deaths caused by opioids or prescription drugs might not fully capture the impact of PDMPs. Researchers and policy makers may need to be cautious about the heterogeneous effects across different drug categories, partly due to drug substitutability. The results also call attention to the incomplete information on the cause of death in mortality data. The “other and unspecified drugs” category (T50.9) has a larger number of fatal overdoses than any other category (eAppendix Figure 3) and warrants further investigation. 

Limitations

First, our analysis of overdose mortality rates by drug category only extended to 2010. Second, although our empirical approach mechanically eliminated many potential confounders and our preprogram tests provided support for the common trends assumption necessary for its use, we cannot rule out all sources of bias, such as those created by the time-varying factors we were unable to control for. Finally, our analysis uses binary indicators for PDMP implementation status, thus only estimating an average PDMP effect. The existing literature has documented heterogeneity in the design and implementation of PDMPs across states.40 Hence, the effects of PDMPs on drug overdose deaths may also differ across states. Previous studies examined the effects of some PDMP characteristics on overdose mortality. These studies focused on factors such as the type of governing agency, statutory authority to monitor noncontrolled substances, the requirement for committee oversight, exempting practitioners from the obligation to access PDMP data, and the provision of unsolicited reports to healthcare practitioners and law enforcement agencies, and did not find significant protective effects of any of these features.23,24 Another study reported that monitoring 4 or more drug schedules and more frequent updating of PDMP data were associated with greater reductions in overdose deaths.26 Future studies are warranted to evaluate the effectiveness of other PDMP characteristics. 

CONCLUSIONS

PDMPs were not associated with a decrease in drug overdose mortality rates, even in the target category of prescription opioids. They may be associated with increased mortality rates in categories other than prescription opioids, especially in states where PDMPs have been operating for longer periods of time. Further research is needed to better understand the heterogeneous impacts of PDMPs. More comprehensive, prevention-oriented approaches, including improvement in patient education and clinical practices for pain management, may be needed to effectively reduce mortality caused by drug overdoses.

Acknowledgments

The authors are grateful to the National Center for Health Statistics, the CDC, and the various vital statistics jurisdictions for providing data used in this study.

Author Affiliations: University of Pennsylvania (YN), Philadelphia, PA; The Pennsylvania State University (DGS, YS, JRM), University Park, PA.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (YN, DGS, YS, JRM); acquisition of data (YN, DGS); analysis and interpretation of data (YN, DGS, YS, JRM); drafting of the manuscript (YN, DGS, YS, JRM); critical revision of the manuscript for important intellectual content (YN, DGS, YS, JRM); statistical analysis (YN, JRM). 

Address Correspondence to: Young Hee Nam, PhD, Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 826 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104. E-mail: ynam@mail.med.upenn.edu. 
REFERENCES

1. Deaths from prescription opioid overdose. CDC website. http://www.cdc.gov/drugoverdose/data/overdose.html. Accessed May 20, 2015.

2. Rudd RA, Aleshire N, Zibbell JE, Gladden RM. Increases in drug and opioid overdose deaths—United States, 2000-2014. MMWR Morb Mortal Wkly Rep. 2016;64(50):1378-1382. doi: 10.15585/mmwr.mm6450a3.

3. Warner M, Chen LH, Makuc DM, Anderson RN, Miniño AM. Drug poisoning deaths in the United States, 1980-2008. CDC website. https://www.cdc.gov/nchs/data/databriefs/db81.pdf. Published December 2011. Accessed August 25, 2016. 

4. Kochanek KD, Murphy SL, Xu J, Tejada-Vera B. Deaths: final data for 2014. National Vital Statistics Reports. CDC website. http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf. Published June 30, 2016. Accessed August 23, 2016.

5. National Center for Health Statistics. NCHS data on drug-poisoning deaths. CDC website. https://www.cdc.gov/nchs/data/factsheets/factsheet_drug_poisoning.pdf. Published March 2016. Accessed August 23, 2016.

6. Case A, Deaton A. Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proc Natl Acad Sci USA. 2015;112(49):15078-15083. doi: 10.1073/pnas.1518393112. 

7. CDC. Vital signs: overdoses of prescription opioid pain relievers—United States, 1999-2008. MMWR Morb Mortal Wkly Rep. 2011;60(43):1487-1492. 

8. Jones CM, Mack KA, Paulozzi LJ. Pharmaceutical overdose deaths, United States, 2010. JAMA. 2013;309(7):657-659. doi: 10.1001/jama.2013.272.

9. White AG, Birnbaum HG, Mareva MN, et al. Direct costs of opioid abuse in an insured population in the United States. J Manag Care Pharm. 2005;11(6):469-479.

10. Florence CS, Zhou C, Luo F, Xu L. The economic burden of prescription opioid overdose, abuse, and dependence in the United States, 2013. Med Care. 2016;54(10):901-906. doi: 10.1097/MLR.0000000000000625.

11. Prescription painkiller overdoses in the US. CDC website. http://www.cdc.gov/VitalSigns/pdf/2011-11-vitalsigns.pdf. Published November 2011. Accessed September 10, 2013.

12. Tkacz J, Pesa J, Vo L, et al. Opioid analgesic-treated chronic pain patients at risk for problematic use. Am J Manag Care. 2013;19(11):871-880.

13. Finklea KM, Bagalman E, Sacco LN. Prescription drug monitoring programs. Federation of American Scientists website. https://fas.org/sgp/crs/misc/R42593.pdf. Published January 2013. Accessed August 25, 2013.

14. Epidemic: responding to America’s prescription drug abuse crisis. Obama White House archives website. https://obamawhitehouse.archives.gov/sites/default/files/ondcp/policy-and-research/rx_abuse_plan.pdf. Published 2011. Accessed September 10, 2013.

15. Briefing on PMP effectiveness. Prescription monitoring programs: an effective tool in curbing the prescription drug abuse epidemic. PMP Center of Excellence website. https://www.bja.gov/publications/brandeis_pmp_effectiveness_brief.pdf. Published March 2012. Accessed September 10, 2013.

16. 2010 KASPER satisfaction survey: executive summary. Commonwealth of Kentucky website. http://chfs.ky.gov/NR/rdonlyres/BDC0DFC9-924B-4F11-A10A-5EB17933FDDB/0/2010KASPERSatisfactionSurveyExecutiveSummary.pdf. Published December 31, 2010. Accessed August 25, 2013.

17. Blumenschein K, Fink JL, Freeman PR, Kirsh KL, Steinke DT, Talbert J. Independent evaluation of the impact and effectiveness of the Kentucky All Schedule Prescription Electronic Reporting Program (KASPER). Kentucky Cabinet for Health and Family Services and Kentucky Injury Prevention and Research Center website, http://www.chfs.ky.gov/NR/rdonlyres/24493B2E-B1A1-4399-89AD-1625953BAD43/0/KASPEREvaluationFinalReport10152010.pdf. Published October 2010. Accessed September 10, 2013.

18. Wang J, Christo PJ. The influence of prescription monitoring programs on chronic pain management. Pain Physician. 2009;12(3):507-515.

19. Reifler LM, Droz D, Bailey JE, et al. Do prescription monitoring programs impact state trends in opioid abuse/misuse? Pain Med. 2012;13(3):434-442. doi: 10.1111/j.1526-4637.2012.01327.x.

20. Twillman R. Impact of prescription monitoring programs on prescription patterns and indicators of opioid abuse. J Pain. 2006;7(4):S6. doi: 10.1016/j.jpain.2006.01.430. 

21. Baehren DF, Marco CA, Droz DE, Sinha S, Callan EM, Akpunonu P. A statewide prescription monitoring program affects emergency department prescribing behaviors. Ann Emerg Med. 2010;56(1):19-23.e1-e3. doi: 10.1016/j.annemergmed.2009.12.011.

22. 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(6):1045-1051. doi: 10.1377/hlthaff.2015.1673.

23. Li G, Brady JE, Lang BH, Giglio J, Wunsch H, DiMaggio C. Prescription drug monitoring and drug overdose mortality. Inj Epidemiol. 2014;1(9):1-8. doi: 10.1186/2197-1714-1-9.

24. Paulozzi LJ, Kilbourne EM, Desai HA. Prescription drug monitoring programs and death rates from drug overdose. Pain Med. 2011;12(5):747-754. doi: 10.1111/j.1526-4637.2011.01062.x.

25. Paulozzi LJ, Stier DD. Prescription drug laws, drug overdoses, and drug sales in New York and Pennsylvania. J Public Health Policy. 2010;31(4):422-432. doi:10.1057/jphp.2010.27.

26. Patrick SW, Fry CE, Jones TF, Buntin MB. Implementation of prescription drug monitoring programs associated with reductions in opioid-related death rates. Health Aff (Millwood). 2016;35(7):1324-1332. doi: 10.1377/hlthaff.2015.1496.

27. Wide-ranging Online Data for Epidemiologic Research (WONDER). CDC website. https://wonder.cdc.gov. Accessed August 25, 2016.

28. State profiles reports [2013]. Alliance of States With Prescription Monitoring Programs website. http://www.pmpalliance.org/content/state-profiles-reports. Accessed September 10, 2013. 

29. Prescription Drug Monitoring Program Training and Technical Assistance Center. http://www.pdmpassist.org/content/state-profiles. Accessed August 25, 2013. 

30. Current Population Survey (CPS). US Census Bureau website. https://www.census.gov/cps/data/cpstablecreator.html. Accessed August 25, 2016. 

31. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. 2nd ed. Cambridge, MA: MIT Press; 2010.

32. Rosenbaum PR, Rubin DB. Difficulties with regression analyses of age-adjusted rates. Biometrics. 1984;40(2):437-443. doi: 10.2307/2531396.

33. Heckman JJ, Hotz JV. Choosing among alternative nonexperimental methods for estimating the impact of social programs: the case of manpower training. J Am Stat Assoc. 1989;84(408):862-874. doi: 10.2307/2290059.

34. Unick GJ, Rosenblum D, Mars S, Ciccarone D. Intertwined epidemics: national demographic trends in hospitalizations for heroin- and opioid-related overdoses, 1993-2009. PLoS One. 2013;8(2):e54496. doi: 10.1371/journal.pone.0054496.

35. Dasgupta N, Creppage K, Austin A, Ringwalt C, Sanford C, Proescholdbell SK. Observed transition from opioid analgesic deaths toward heroin. Drug Alcohol Depend. 2014;145:238-241. doi: 10.1016/j.drugalcdep.2014.10.005.

36. Internet pharmacies: federal agencies and states face challenges combating rogue sites, particularly those abroad. Government Accountability Office website. http://www.gao.gov/assets/660/655751.pdf. Published July 2013. Accessed May 20, 2014. 

37. Opioid overdose. CDC website. http://www.cdc.gov/drugoverdose/states/state_prevention.html. Updated August 30, 2016. Accessed September 5, 2016.

38. Medicare Part D opioid prescriber summary file 2014. CMS website. https://data.cms.gov/Public-Use-Files/Medicare-Part-D-Opioid-Prescriber-Summary-File-201/e4ka-3ncx. Accessed May 1, 2016.

39. Ashburn MA. The evolution of prescription drug monitoring programs. Pharmacoepidemiol Drug Saf. 2016;25(7):852-853. doi: 10.1002/pds.4036.

40. Manasco AT, Griggs C, Leeds R, et al. Characteristics of state prescription drug monitoring programs: a state-by-state survey. Pharmacoepidemiol Drug Saf. 2016;25(7):847-851. doi: 10.1002/pds.4003.
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