Overall, obese individuals had 1.45 (CI, 1.27-1.66) times the odds of prescription opioid use compared with nonobese individuals (see Table 5, Supplemental Digital Content 1, which shows the underlying regression model for the PAF calculations; available at http://links.lww.com/PAIN/A821). At the population level, our analysis indicated that the fraction of prescription opioid use attributable to obesity was 14% (CI, 9%-19%). Extrapolating estimates from our sample to the U.S. population, 10.8 million adults aged 35 to 79 years were prescription opioid users, on average for each year of the study period, suggesting there might have been 1.5 million fewer opioid users under the hypothetical scenario where obese individuals were instead nonobese (CI, 0.9-2.0 million users). The sensitivity analysis using corrected risk ratios in PAF calculations yielded comparable estimates (PAF, 14%).
Recent CDC guidelines recommend several other drug classes for first-line treatment of chronic pain and encourage nonpharmacologic approaches, including cognitive behavioral therapy (CBT).12 Suggested first-line pharmacologic treatments include acetaminophen and NSAIDs for osteoarthritis and back pain and gabapentinoids for neuropathic pain. Reductions in pain with acetaminophen, however, are limited, and NSAIDs are contraindicated for several common obesity-related conditions, including cardiovascular disease, as well as other common medical issues such as renal insufficiency and gastrointestinal ulceration.12 Gabapentinoids, which include gabapentin and pregabalin, pose their own unique risks,18 especially in combination with certain other classes of drugs commonly prescribed among individuals with obesity. With respect to nonpharmacologic approaches, CBT and other approaches have shown promise but are generally underused or used in combination with, rather than as a substitute for, pharmacotherapy.
The limitations of our study merit acknowledgement. First, due to the cross-sectional nature of the study, it was not possible to determine the directionality of the associations or whether the associations between obesity and prescription opioid use were causal in nature. Conditions associated with opioid use may lead to weight gain or weight loss, possibly leading to confounding by illness.22,27 For example, serum vitamin D levels, which have an inverse association with opioid use, obesity, inflammation, and pain,11,24,51 may serve as a confounder of associations we investigate in this study. In the light of the potential for confounding, future research should confirm the findings of this study using incident study designs in which it is possible to establish the temporal precedence of weight status with respect to prescription opioid use. Second, our PAF calculations are based on an assumption of causality between exposure (BMI) and outcome (prescription opioid use) despite the aforementioned limitation of using observational data. Thus, our population-level estimates of the opioid use attributable to obesity are provisional and should be interpreted with caution. Third, the NHANES did not assess prescription opioid dosage, and we were therefore limited to evaluating broad categories of strength based on morphine equivalency criteria established by the CDC. Fourth, although the ability to directly link prescription opioid use to underlying reasons for use represented a strength of the analysis, the reasons were based on respondent self-report, potentially reducing their accuracy and clinical specificity. Nonetheless, these were the reasons for which the individuals thought they were using those opioids.
A. Stokes and S. Lee have received research funding from Ethicon, Inc. (a subsidiary of Johnson & Johnson, Inc.). C. Hsiao, J.R. Waggoner, and R.F. Scamuffa are employees of Ethicon, Inc. S.S. Johnston and E.M. Ammann are employees in Epidemiology, Medical Devices, Johnson & Johnson, Inc. The remaining authors have no conflicts of interest to declare.
Author contributions: A. Stokes designed the study, interpreted the data, and wrote the manuscript. K.M. Berry and J.M. Collins performed the analyses and contributed to interpreting the data and writing the manuscript. S.S. Johnston, C. Hsiao, J.R. Waggoner, E.M. Ammann, R.F. Scamuffa, D.J. Lundberg, and S. Lee contributed to the study design and interpretation of the data, and reviewed and edited the manuscript. D.H. Solomon, D.T. Felson, T. Neogi, and J.E. Manson reviewed the manuscript and contributed to the introduction and discussion. A. Stokes and K.M. Berry had full access to all the data in the study and take full responsibility for the integrity of the data and the accuracy of the data analysis.
Supplemental digital content
Supplemental digital content associated with this article can be found online at http://links.lww.com/PAIN/A821.
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