The results of this analysis document that the growing problem of prescription opioid abuse places a substantial economic burden on society, specifically in the realms of health care, criminal justice, and lost workplace productivity.
These findings provide further evidence that prescription opioid abuse is an increasing and substantial burden on society that needs to be addressed in a comprehensive manner. As discussed above, the impact of opioid abuse not only affects the health outcomes and costs of the US population ($25.0 billion), but also imposes a large economic burden on the workplace ($25.6 billion), the justice system ($5.1 billion), and society as a whole ($55.7 billion). Although differences in data and methods preclude exact comparison with previous societal estimates , it is clear that the costs of opioid abuse have increased substantially due to changes in the prevalence of opioid abuse and associated costs. Key changes include, for example, increases from 2001 to 2007 (compared with Birnbaum et al. ) in the prevalence of opioid abuse (approximately 13%), the excess cost per opioid abuse patient (47% after adjusting for inflation), the cost of substance abuse treatment (up to 48%), the proportion of substance abuse admissions attributable to opioids (138%), and total police and legal expenditures (16% and 10%, respectively).
Reducing the substantial economic burden of prescription opioid abuse will require sustained efforts from academic researchers, industry, health care providers, and government to implement appropriate actions. A number of initiatives to reduce the prevalence of opioid abuse are already underway. For example, government initiatives such as the National All Schedules Prescription Electronic Reporting Act (NASPER) have allocated funding for the creation and enhancement of state PDMPs . The FDA now requires the preparation of a REMS for certain prescription opioid manufacturers. Industry has begun to manufacture abuse-deterrent opioid formulations, and clinical initiatives have included the publication and adoption of “universal precautions” for pain management as well as the development of models to better identify patients at risk of opioid abuse [35–37].
In particular, the difference between overall societal costs and the current level of spending on research and prevention is quite substantial, with such expenditures together accounting for less than 1% of total societal costs. Thus, increased funding of research and prevention programs may present an opportunity for new efforts to combat the escalating problem of prescription opioid abuse. The benefits of investing more resources into these two components could be considerable, since adequate prevention and research programs could later result in reductions in excess medical and drug costs (estimated at $23.7 billion), treatment costs ($1.1 billion), and other cost components previously discussed, such as criminal justice ($5.1 billion) and lost workplace productivity ($25.6 billion). The adoption of PDMPs to identify patients at risk of abuse in many states is a step in the right direction, but 16 states still have yet to implement PDMPs, including nine which have not even passed legislation mandating their creation .
Despite the evident growth in prevalence and treatment admissions associated with opioid abuse, quantifying the growth in societal costs is not straightforward; recent improvements in the analytic approaches and changes in data collection methodology make overall comparison with previous results difficult . For example, this study calculates cost categories not previously estimated, such as those of caregivers, prevention and research, property lost due to crime, excess medically related absenteeism, disability, and presenteeism. To further confound the comparison, the current study includes costs associated with opioid dependence as well as abuse (which was the only aspect of abuse considered previously), and criminal justice costs here include those associated with violent crime. Some secondary data sources, such as DAWN and NSDUH, also have updated their methodologies. To the extent that comparisons can be made, claims data estimates are consistent with prior research and suggest an increase in the costs of abuse, even after controlling for inflation.
There are several other limitations to this study. Florida Medicaid figures are not representative of the national Medicaid population, as Florida spends only $4,487 per Medicaid enrollee, ranking it 43rd and below the national average of $5,163 . However, as noted, these data were adjusted to the US Medicaid population based on a per enrollee spending ratio.
This study is also limited by the variability among the distinct secondary data sets. However, adjusting the opioid abuse-related allocation of a specific component allowed for increased compatibility among different sources. For example, when using the apportionment method to estimate substance abuse prevention costs associated with prescription opioids, total prevention costs (which include spending for illicit and prescription drug, alcohol, and tobacco abuse prevention) were apportioned based on the ratio of NSDUH-reported abuse of prescription opioids to abuse of illicit drugs, alcohol, or tobacco, whereas lost wages/employment costs (which include costs due to illicit drug abuse) were apportioned based on the ratio of NSDUH-reported abuse of prescription opioids to abuse of illicit drugs only. Notwithstanding this, the different definitions and concepts of opioid abuse make comparisons across data sets problematic. To the authors' knowledge, this is the first attempt to integrate the different data sets in one analysis.
In addition to the difficulty in addressing variability between data sets, the secondary sources used have their own limitations. Data from DAWN used to calculate the number of premature deaths associated with opioid abuse represent drug-related deaths in selected metropolitan areas and states only, and may not be representative of drug-related death patterns in areas not reported. To the authors' knowledge, DAWN data provide the best available estimate of drug-related deaths in the United States. Attribution factors from ONDCP used to apportion arrests and incarcerations due to drug abuse were developed prior to 2007, and therefore may not accurately represent the proportion of arrests and incarcerations attributable to drug abuse. However, criminal justice costs may be underestimated because the overall number of reported drug abusers has grown at a much higher rate than the overall number of arrests since the development of the attribution factors. Conversely, in this case, the apportionment method used may lead to overestimation because prescription opioid abuse may not be associated with the same likelihood of arrest as abuse of other drugs. Additionally, presenteeism costs were estimated using a ratio of average presenteeism costs relative to overall medical, drug, absenteeism, and disability costs reported in Goetzel et al. for the 10 most common conditions examined (e.g., allergies, cancer, depression/mental illness) . Because opioid abuse specific estimates were not available, it was assumed that presenteeism costs for employees with opioid abuse followed the same ratio.
As noted above, there are various issues in defining opioid abuse. For example, ICD-9-CM codes do not allow for differentiation between prescription opioid abuse and heroin abuse, and therefore, this study likely included both types of patients. Where possible, heroin abuse was separated from prescription opioid abuse in cost analyses using secondary data sources. This study estimates costs using two general definitions of abuse. First, per-patient excess medical and drug costs were calculated using a diagnosis-based definition. This approach captured patients with abuse, dependence, and misuse (e.g., poisoning). Opioid abuse may be underdiagnosed due, in part, to the associated stigma , and therefore the patient population used to calculate excess costs may not be representative of the cost profile of undiagnosed patients with opioid abuse. Second, the number of opioid abuse patients used to carry out the quantity and apportionment methods was limited to those reported by NSDUH as meeting the DSM-IV criteria for abuse or dependence (1.7 million), similar to an ICD-9-CM diagnostic approach to abuse. The absence of cost information for the approximately 12.5 million nonmedical users of prescription drugs  and their caregivers in the United States means that this study likely understates both the excess health care and total societal costs. It is also important to mention that this study does not attempt to address causality. While the societal costs refer to those costs associated with opioids abusers, they may not be directly attributable to the opioid abuse. For example, health care costs include costs of comorbidities that are unrelated to opioid abuse per se. While point estimates of the various measures of societal cost have not been presented here, the authors believe the estimates understate the true economic burden of prescription opioid abuse, dependence and misuse, and thus these estimates are conservative.
To better understand how various assumptions impacted the total societal cost estimate, sensitivity analyses were conducted. The primary driver of societal costs was the number of opioid abusers, which is used to multiply costs per patient in all cost components that use the quantity method. Changing the number of opioid abusers by ±25% impacts the total societal cost estimate by $10 billion ($45.7–65.7 billion). This finding suggests that the increasing prevalence of opioid abuse may be driving increases in societal costs. Another key assumption is the discount rate used in the calculation of the costs of premature death. For example, if the discount rate were decreased to 3%, the societal cost estimate would increase from $55.7 billion to $61.2 billion. While current market conditions may suggest that a lower rate could be more appropriate, 6% is consistent with past research  and results in a more conservative cost estimate. A systematic analysis that varied other major assumptions for each cost component by 25% (all else equal) found that no other individual assumption resulted in a change of more than $2 billion in the overall societal cost estimate.
Future research should attempt to assess other components of societal costs that are directly linked to or caused by prescription opioid abuse not considered here (e.g., automobile accidents, insurance fraud, workers compensation) as well as improve understanding of the relationship between opioid abuse and associated comorbidities (e.g., by studying whether mental illness predates a diagnosis of opioid abuse or vice versa). It would also be informative to analyze the costs of undertreating pain, which can result from practitioners' concerns about addiction and abuse .
Further efforts to separately categorize prescription and nonprescription opioid abuse would allow researchers to better understand illicit opioid use as well as identify possible sources of distribution. Lastly, it would be useful to improve the ability to identify patients at risk of opioid abuse using data similar to that available to PDMPs and third-party payers, similar to the prototype approach developed by White et al. but using national data . Such an approach could aid in the development of national initiatives and research studies toward the prevention of opioid abuse.
As this study has shown, prescription opioid abuse concerns far more than those individuals directly affected by the condition. It is associated with a myriad of societal problems related to productivity losses and increasing criminal and legal justice costs that are rapidly becoming a major public health and economic concern. A multifaceted, coordinated response involving physicians, health care professionals, researchers (including the pharmaceutical industry), and the government is likely required to make substantial progress on this serious issue.