Andrew D. Wiese, PhD; Marie R. Griffin, MD, MPH; William Schaffner, MD; C. Michael Stein, MB, ChB; Robert A. Greevy, PhD; Edward F. Mitchel Jr., MS; Carlos G. Grijalva, MD, MPH
Note: The corresponding author affirms that he has listed everyone who contributed significantly to the work.
Acknowledgment: The authors thank the Tennessee Bureau of TennCare of the Department of Finance and Administration, as well as the Tennessee Department of Health for providing data for the study.
Grant Support: By NIH, National Institute on Aging, through grants R03 AG042981, R01 AG043471, and TL1TR000447.
Disclosures: Dr. Schaffner reports personal fees from Merck, Pfizer, Dynavax, Seqirus, Sutrovax, and Shionogi during the conduct of the study. Dr. Stein reports grants from NIH during the conduct of the study. Dr. Grijalva reports grants from NIH during the conduct of the study and consulting fees from Pfizer and Merck and grants from Sanofi-Pasteur, the Campbell Alliance, Centers for Disease Control and Prevention, U.S. Food and Drug Administration and Agency for Healthcare Research and Quality outside the submitted work. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-1907.
Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer and Johnson & Johnson.
Reproducible Research Statement:Study protocol and statistical code: Available from Dr. Grijalva (email@example.com). Data set: Not available.
Requests for Single Reprints: Andrew D. Wiese, PhD Department of Health Policy, Vanderbilt University Medical Center, Suite 2600, Village at Vanderbilt, 1500 21st Avenue South, Nashville, TN 37212; e-mail, firstname.lastname@example.org.
Current Author Addresses: Drs. Wiese, Griffin, Schaffner, and Grijalva and Mr. Mitchel: Department of Health Policy, Vanderbilt University Medical Center, Suite 2600, Village at Vanderbilt, 1500 21st Avenue South, Nashville, TN 37212.
Dr. Stein: Department of Medicine, Vanderbilt University Medical Center, 2222 Pierce Avenue, Robinson Research Building, Suite 542, Nashville, TN 37232.
Dr. Greevy: Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End, Suite 11000, Nashville, TN 37203.
Author Contributions: Conception and design: A.D. Wiese, M.R. Griffin, W. Schaffner, C.M. Stein, R.A. Greevy, E.F. Mitchel, C.G. Grijalva.
Analysis and interpretation of the data: A.D. Wiese, M.R. Griffin, W. Schaffner, C.M. Stein, R.A. Greevy, E.F. Mitchel, C.G. Grijalva.
Drafting of the article: A.D. Wiese.
Critical revision for important intellectual content: A.D. Wiese, M.R. Griffin, W. Schaffner, C.M. Stein, R.A. Greevy, E.F. Mitchel, C.G. Grijalva.
Final approval of the article: A.D. Wiese, M.R. Griffin, W. Schaffner, C.M. Stein, R.A. Greevy, E.F. Mitchel, C.G. Grijalva.
Statistical expertise: A.D. Wiese, R.A. Greevy, C.G. Grijalva.
Obtaining of funding: W. Schaffner, C.G. Grijalva.
Administrative, technical, or logistic support: E.F. Mitchel.
Collection and assembly of data: A.D. Wiese, W. Schaffner, E.F. Mitchel, C.G. Grijalva.
Although certain opioid analgesics have immunosuppressive properties and increase the risk for infections in animals, the clinical effects of prescription opioid use on infection risk among humans are unknown.
To test the hypothesis that prescription opioid use is an independent risk factor for invasive pneumococcal disease (IPD).
Nested case–control study.
Tennessee Medicaid database linked to Medicare and Active Bacterial Core surveillance system databases (1995 to 2014).
1233 case patients with IPD aged 5 years and older matched to 24 399 control participants by diagnosis date, age, and county of residence.
Opioid use was measured on the basis of pharmacy prescription fills. Invasive pneumococcal disease was defined by the isolation of Streptococcus pneumoniae from a normally sterile site. The odds of current opioid use were compared between the case and control groups, accounting for known IPD risk factors. Secondary analyses categorized opioid use by opioid characteristics, applied an IPD risk score to assure comparability between exposure groups, and analyzed pneumonia and nonpneumonia IPD cases separately.
Persons in the case group had greater odds than control participants of being current opioid users (adjusted odds ratio [aOR], 1.62 [95% CI, 1.36 to 1.92]). Associations were strongest for opioids that were long acting (aOR, 1.87 [CI, 1.24 to 2.82]), of high potency (aOR, 1.72 [CI, 1.32 to 2.25]), or were used at high dosages (50 to 90 morphine milligram equivalents [MME]/d: aOR, 1.71 [CI, 1.22 to 2.39]; ≥90 MME/d: aOR, 1.75 [CI, 1.33 to 2.29]). Results were consistent when the IPD risk score was taken into account and pneumonia and nonpneumonia IPD were analyzed separately.
Unmeasured confounding and measurement error, although sensitivity analyses suggested that neither was likely to affect results. Actual opioid use and other nonprescription use (such as illicit opioid use) were not measured.
Opioid use is associated with an increased risk for IPD and represents a novel risk factor for these diseases.
National Institutes of Health.
Table 1. Study Opioid Classifications*
Appendix Table 1. Covariates Assessed in the 365 Days Preceding the Index Date
Appendix Table 2. IPD Risk Score Model: aORs for Laboratory-Confirmed IPD in a Logistic Regression Model Excluding All Well-Recognized Risk Factors for IPD Among Noncurrent Opioid Users Enrolled in Tennessee Medicaid, 1995–2014 (n = 21 800)
Flow diagram of patients meeting study eligibility criteria from the retrospective cohort of Tennessee Medicaid enrollees, 1995–2014.
ABCs = Active Bacterial Core surveillance; IPD = invasive pneumococcal disease.
Table 2. Characteristics of Case Patients and Matched Control Participants Enrolled in Tennessee Medicaid, 1995–2014*
Table 3. Distribution of Opioid Characteristics in Current Opioid Users Among Case Patients and Matched Control Participants Enrolled in Tennessee Medicaid, 1995–2014*
Table 4. Characteristics of Current Versus Remote Opioid Users Among Tennessee Medicaid Enrollees, 1995–2014*
Table 5. Crude and Adjusted Odds Ratios for Laboratory-Confirmed IPD, by Opioid Use Type, Among Tennessee Medicaid Enrollees, 1995–2014 (n = 25 362)
Appendix Table 3. aORs for Laboratory-Confirmed IPD in the Primary Analysis Among Tennessee Medicaid Enrollees, 1995–2014 (n = 25 632)
Appendix Table 4. aORs for Laboratory-Confirmed IPD, by Characteristics of Opioid, Among Tennessee Medicaid Enrollees, 1995–2014 (n = 25 632 in Each Stratified Analysis)*
Appendix Table 5. aORs for Laboratory-Confirmed IPD in Logistic Regression Model With Opioid Exposure Variable, Known Risk Factors for IPD, and IPD Risk Score Among Tennessee Medicaid Enrollees, 1995–2014 (n = 25 632)
Residual confounding scenarios between opioid use and IPD, with the prevalence of the confounder among remote opioid users at 10%.
The figure represents different scenarios in which a hypothetical unmeasured confounder could have influenced the observed aOR in the primary analysis. We used the lower bound of the CI of the aOR in the primary analysis for a conservative assessment of these scenarios: aOR, 1.36. For all scenarios in the figure, we assume that the prevalence of the unmeasured confounder is 10% among remote opioid users, our reference. The y-axis represents the absolute difference in prevalence of the unmeasured confounder between current and remote opioid users, whereas the x-axis represents different values of the OR representing the association between the unmeasured confounder and IPD. The colored bar represents the true OR we would expect to see under different unmeasured confounder scenarios. Thus, the true aOR would be <1 in all scenarios represented by the dark blue and light blue areas at the upper right of the figure. The red dots represent the unmeasured confounder scenarios at which a true aOR of 1.0 would be observed. For example, the red dot at the connecting point of 5.5 on the x-axis and 10 on the y-axis represents the following scenario: The true OR would be 1.0 if the observed aOR were 1.36 in the presence of unmeasured confounding from a variable that has a very strong association with IPD (OR, 5.5) and with a 10% absolute difference in prevalence between current and remote opioid users. For a confounder with a much weaker association with IPD (red dot at OR of 2.0), the absolute difference in prevalence of the confounder would need to be >35%. aOR = adjusted odds ratio; IPD = invasive pneumococcal disease; OR = odds ratio.
OR for deciles of risk score in the conditional logistic regression model with IPD as the outcome among the unexposed and the full study population.
Circles represent the OR point estimates, and brackets represent the 95% CI for each OR estimate. The horizontal line represents the null value for the OR [ln(1.0) = 0]. IPD = invasive pneumococcal disease; ln(OR) = natural log of the observed odds ratio; OR = odds ratio. Top. ln(OR) within each decile of the infection risk score among only noncurrent opioid users. Bottom. ln(OR) within each decile of the infection risk score in the entire study population.
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In this video, Andrew D. Wiese, PhD, MPH, offers additional insight into the article, "Opioid Analgesic Use and Risk for Invasive Pneumococcal Diseases. A Nested Case-Control Study."
Toshihiko Takada, Yuki Kataoka
Fukushima Medical University, Hyogo Prefectural Amagasaki General Medical Center
February 21, 2018
To the editor: We read the article by Dr. Wiese and his colleagues with great interest, especially their sensitivity analysis to deal with residual confounding (1). However, we would like to point out the following two issues.First, although the sensitivity analysis suggested that there were only weak unmeasured confounders that affected the result, the possibility of residual confounding by indication could not be excluded. As authors described in their previous study (2), pain might lead to both opioid use and the susceptibility to infection. Nonsignificant association between use of nonsteroidal anti-inflammatory drugs and invasive pneumococcal disease (IPD) made this hypothesis less likely. However, more severe pain that requires the use of opioids may impair the immune system. Further investigation including the assessment of severity of pain is expected. Second, the outcome of the study was IPD. We agree with the authors that the condition is a high priority issue of public health and could be confirmed by very objective methods. However, we wonder if the outcome of the overall infection could be more relevant for both patients and clinicians (3). References1. Wiese AD, Griffin MR, Schaffner W, Stein CM, Greevy RA, Mitchel EF, Jr., et al. Opioid Analgesic Use and Risk for Invasive Pneumococcal Diseases: A Nested Case-Control Study. Ann Intern Med. 2018.2. Wiese AD, Griffin MR, Stein CM, Mitchel EF, Jr., Grijalva CG. Opioid Analgesics and the Risk of Serious Infections Among Patients With Rheumatoid Arthritis: A Self-Controlled Case Series Study. Arthritis Rheumatol. 2016;68:323-31.3. Guyatt G, Montori V, Devereaux PJ, Schunemann H, Bhandari M. Patients at the center: in our practice, and in our use of language. ACP J Club. 2004;140:A11-2.
Andrew D. Wiese, Marie Griffin, William Schaffner, C. Michael Stein, Carlos Grijalva
Vanderbilt University Medical Center
May 11, 2018
We appreciate the comments from Takada and Kataoka in response to our recent article describing the association between the use of prescribed opioid analgesics and the risk of invasive pneumococcal disease (IPD).(1) The possibility of confounding by indication is a concern in the conduct of pharmacoepidemiological studies, as discussed in our article. Although evidence from animal studies suggests that acute pain is associated with immune modulation, whether this relationship also exists among humans is not well understood.(2,3) Acute pain has been linked to immunosuppression among patients undergoing surgical procedures in some studies but whether pain or another factor related to surgery (i.e., surgical trauma, hyperglycemia, hypothermia) was the primary driver of immunosuppression remains to be determined.(3,4) Furthermore, it is unclear whether pain is associated with a clinically important increased risk of infection among healthier or non-surgical populations.(4) Nevertheless, the results of our sensitivity analysis to assess if unmeasured confounding might impact the observed results indicated that a potential unmeasured confounder (i.e., pain intensity) would need to be a strong, independent risk factor for IPD and also have a large absolute difference in prevalence between opioid users and non-users to account for the observed association.We focused on laboratory-confirmed IPD as a highly specific outcome that helped minimize misclassification and allowed us to examine a prototypical community-acquired infection unlikely to be associated with other factors related to prescription opioid use (i.e., recent hospitalization or intravenous drug use). As discussed in our article, and further discussed in the companion editorial, our results complement existing studies that have reported that opioid analgesic use is associated with an increased risk of all-cause pneumonia and hospitalizations for other serious infections.(1,5) REFERENCES1. Wiese AD, Griffin MR, Schaffner W, et al. Opioid Analgesic Use and Risk for Invasive Pneumococcal Diseases: A Nested Case-Control Study. Annals of internal medicine. 2018.2. Page G. The Immune-Suppressive Effects of Pain. Austin, TX: Landes Bioscience; 2000-2013.3. Kurosawa S, Kato M. Anesthetics, immune cells, and immune responses. Journal of anesthesia. 2008;22(3):263-277.4. Homburger JA, Meiler SE. Anesthesia drugs, immunity, and long-term outcome. Current opinion in anaesthesiology. 2006;19(4):423-428.5. Dublin S, Walker RL, Jackson ML, et al. Use of opioids or benzodiazepines and risk of pneumonia in older adults: a population-based case-control study. Journal of the American Geriatrics Society. 2011;59(10):1899-1907.
Wiese AD, Griffin MR, Schaffner W, Stein CM, Greevy RA, Mitchel EF, et al. Opioid Analgesic Use and Risk for Invasive Pneumococcal Diseases: A Nested Case–Control Study. Ann Intern Med. ;168:396–404. doi: 10.7326/M17-1907
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Published: Ann Intern Med. 2018;168(6):396-404.
Published at www.annals.org on 13 February 2018
Infectious Disease, Streptococcal Infections.
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