Patient “Catastrophizing” Associated with Expectations of Opioid Prescriptions for Acute Pain Control ========================================================================================================= * Eriko Onishi * Jennifer Lucas * Tetsuro Maeno * Steffani R. Bailey ## Abstract *Background:* The prescription of opioids for acute pain may be a driving factor in chronic opioid abuse. We examined patients’ characteristics associated with the expectation of the receipt of opioid prescriptions for acute pain control. *Methods:* A 1-time survey was administered to adult patients at family medicine clinics in the Pacific Northwest between November 2018 and January 2019. Logistic regression modeled adjusted odds of expecting an opioid prescription in ≥ 3 of the 4 dispositional acute pain scenarios by patient demographics, opioid use, past-week pain intensity and duration, past-week anxiety, and pain catastrophizing. *Results:* The survey was completed by 108 patients (62% female, 48% between 30 and 49 years of age, 75% non-Hispanic Whites). Most patients (71%) expected an opioid prescription in ≥ 1 of the 4 scenarios; 26% expected a prescription in ≥ 3 scenarios. Patients with higher levels of pain catastrophizing had more than 3 times greater odds of expecting opioids than those with lower pain catastrophizing (OR, 3.73; *P* = .032; 95% CI, 1.12-12.46); no other characteristics were statistically significant. *Conclusion:* Higher pain catastrophizing was associated with increased odds of expecting opioids in dispositional acute pain scenarios in outpatient settings. Future studies can determine whether addressing pain catastrophizing reduces expectations of opioid prescribing for acute pain control. The finding that most patients expected opioid prescriptions in acute pain scenarios needs further exploration into other potential factors associated with these expectations. Evidence-based guidelines for condition-specific acute pain management are warranted for appropriate opioid prescribing and to guide treatment expectations. * Acute Pain * Catastrophization * Family Physicians * Logistic Models * Opioid-Related Disorders * Opioids * Outpatients * Pain Management * Physician's Practice Patterns * Surveys and Questionnaires ## Introduction The opioid epidemic in the United States has resulted in increased efforts to reduce chronic opioid use. Increasingly, studies suggest that opioid exposure for acute pain control (ie, sudden onset of pain that lasts no longer than 90 days1) could be a leading cause of chronic opioid abuse.2,3 Systematic reviews largely agree that opioids have greater short-term analgesic efficacy than placebos4; thus, opioids are routinely used for acute pain management in many US health care settings.1,3 In 2000, the Joint Commission introduced standards for organizations to improve care for patients with pain, which included emphasizing the need for organizations to perform systematic assessments using quantitative measure for pain (eg, place pain on a 10-point score).5 These standards may have resulted in increasing opioid prescriptions in the US health care system.5 Recently, with increasing knowledge of the highly addictive properties of opioids and significantly increased mobility and mortality from opioid use, prescribing patterns of opioids have been changing as general guidelines are being developed to encourage limiting opioid prescription for both chronic (ie, pain that persists or recurs for more than 3 months1,6) and acute pain.1,7 Studies have examined patient factors associated with receipt of opioids for chronic pain in outpatient settings and postsurgical opioid use; however, to our knowledge, none have examined the patient characteristics associated with the expectation of provision of opioids for acute pain control in outpatient settings.8⇓⇓–11 Several studies on postsurgical acute and chronic pain showed that increased anxiety and depression are associated with increased opioid requirement and patients who were anxious had significantly higher pain scores compared with less anxious patients.12⇓⇓⇓⇓–17 Pain catastrophizing, a cascade of negative thoughts and emotions in response to actual or anticipated pain,18 is shown to predict delayed opioid cessation after surgery19 as well as predict acute postoperative pain and chronic postsurgical pain.16,20⇓⇓–23 Pain catastrophizing has been associated with long-term opioid use,24 opioid craving,25 and opioid misuse26 in outpatient settings. One study among a chronic pain population found a significant relationship between pain intensity and opioid prescriptions that was much stronger in women, especially those with high levels of pain catastrophizing.18 Demographic characteristics have been found to be associated with acute and chronic postsurgical pain intensity and opioid use, as well as in chronic opioid use in primary care settings, including age,9,27,28 gender,15,29⇓–31 race,9 socioeconomic status,10,17,32 and education level.14 The current study examines patients’ expectations of receipt of an opioid prescription for acute pain control and patient characteristics associated with these expectations. Understanding the factors associated with expectations of opioids among patients, especially for acute pain relief in outpatient settings, could aid in providing patient-centered health care and education with the goal of reducing unnecessary opioid exposure. ## Methods ### Study Design and Settings This cross-sectional study utilized a 1-time survey with a convenience sample of patients in clinic waiting rooms at 2 family medicine clinics in Portland, Oregon: Clinic A serves a high proportion of patients with private insurance, and Clinic B, which is a federally qualified health center (FQHC), primarily serves Medicaid-insured or uninsured patients. ### Recruitment Methods and Inclusion Criteria Adult patients were recruited from the waiting rooms of 2 clinics. The principal investigator visited each clinic waiting room multiple times between November 2018 and January 2019. Visits to the clinics varied in length and occurred at varying times of the day depending on the investigator’s schedule. Patients in the waiting room were approached to ask if they would be willing to complete a survey on opioid use and pain. Patients were verbally assessed for study eligibility, which included being a patient at the clinic for a scheduled appointment or an accompanying family member who identified as being a patient of the clinic, at least 18 years of age, could read and write English, and who could provide verbal consent; there were no other inclusion or exclusion criteria for participation. ### Survey Creation Process The study team created the original survey. The survey was piloted with 10 randomly recruited individuals (patients or family members who were visiting the lobby of the health center in which one of the study clinics was located). Verbal feedback was solicited after completion of the survey and used to develop the final version. ### Survey Measures After providing verbal consent, participants completed a 21-question article survey in writing. (Appendix Table 1 includes survey items analyzed in this article). It took an average of 10 to 15 minutes to complete the survey; patients either completed the survey while in the waiting room or while in the examination room and returned the survey to the principal investigator before leaving the clinic. The following items are included in the current analysis. View this table: [Table 1.](http://www.jabfm.org/content/33/6/858/T1) Table 1. Patient Demographics (n = 108) #### Demographics Participant demographic information including: age (18 to 29 years, 30 to 39 years, 40 to 49 years, 50 to 59 years, 60 to 69 years, 70 to 79 years, and > 80 years old), gender (male, female, prefer not to describe, prefer not to answer), ethnicity (Hispanic, non-Hispanic, prefer not to answer), race (Black, White, Asian, Native American, Native Hawaiian or other Pacific Islander, other, prefer not to answer), and education (not completed high school, completed high school or earned General Education Diploma (GED), completed college degree, completed graduate degree or more, prefer not to answer). #### Anxiety We used the Patient-Reported Outcome Measurement Information System (PROMIS)-29 Profile v2.0 Anxiety 4a33 to assess levels of anxiety in the past 7 days. The response for each item was scored between 1 and 5 with a higher score indicating higher anxiety levels (max score, 20) ([www.healthmeasures.net/explore-measurement-systems/promis](http://www.healthmeasures.net/explore-measurement-systems/promis)). A T-score of 50 (raw score just below 6) is the norm for the US general population with a standard deviation of 10. Based on a previous psychometric study among patients with chronic pain, we used a cut-point of 8 and above to indicate anxiety symptoms that would likely result in an anxiety disorder diagnosis.34 #### Pain Intensity and Duration We used the PROMIS-29 Profile v2.0 to assess patients’ average pain intensity in the past 7 days and the duration of the pain. The average pain intensity item has 11 response options, ranging in value from 0 (no pain) to 10 (the worst imaginable pain). Based on previous literature, pain intensity was categorized as mild (1 to 4), moderate (5 to 6), or severe (7 to 10).35 Duration of pain was measured in number of weeks and months (0 to 1 week, 1 to 4 weeks, 1 to 3 months, over 3 months). #### Pain Catastrophizing The Pain Catastrophizing Scale consists of 13 questions and has been validated by a number of studies and in various populations.36 However, to ensure brevity of the survey, we used a 2-item measure to assess pain catastrophizing based on previous literature.37,38 Each item was measured using a Likert scale from 1 (Never feel this way) to 5 (Always feel this way) and we averaged the score by dividing by 2. Lutz et al37 reported that a cutoff of 3 and greater had a sensitivity of 81.4% and specificity of 46.5% for predicting risk for opioid medication misuse among chronic pain patients in primary care. #### History of Opioid Prescriptions The patient’s personal experiences with opioid prescriptions were assessed, including history of receiving opioid prescriptions and prescription frequency. #### Duration and/or Regular Use of Opioids We asked about the duration of use of the last opioid prescription, including regular use of opioids for chronic medical conditions, as a proxy for chronic opioid use. Chronic opioid use was defined as patient-reported use for longer than 3 months, or if the patient reported regular or as needed use of opioids for chronic medical conditions. #### Opioid Expectations in Dispositional Acute Pain Situations We created dispositional acute pain situations using 4 unique scenarios commonly seen in primary care settings or dental care: 1) a sprained ankle, 2) a root canal procedure, 3) a cast placement for a broken arm, and 4) acute back pain. Patients indicated their levels of agreement that they would expect their doctors or dentists to prescribe opioids for their acute pain control in each scenario. The levels of agreement were rated on a 5-point Likert Scale (“strongly disagree,” “disagree,” “not sure,” “agree,” and “strongly agree”). Expectations of opioid prescriptions for the 4 different scenarios were dichotomized into “strongly disagree/disagree/not sure” versus “agree/strongly agree.” This research was approved by the Institutional Review Board of the participating institution. ### Data Storage Article survey data were manually transferred and stored in the Oregon Clinical & Translational Research Institute’s REDCap installation, a secure Web-based research data collection and management system, which then was transferred to Stata/IC15.1 (StataCorp, LLC, College Station, TX) for analysis. #### Data Analysis We first examined patient characteristics overall and by each clinic. We tested for significant differences between clinics using the χ2 test. We then examined characteristics of patients by the number of dispositional acute pain situations in which the patient expected to be prescribed an opioid (n = 0 to 4 situations). Finally, we conducted multiple logistic regression analysis to compute the odds of expecting opioid prescriptions (“agree/strongly agree”) in at least 3 out of 4 acute pain scenarios by age, gender, race, education level, pain intensity in the past 7 days, duration of pain, anxiety level in the past 7 days, pain catastrophizing scores, and chronic opioid use. Given the small sample size, categories were collapsed for most variables. Model diagnostics were conducted to ensure that the regression model met assumptions and had acceptable discrimination and fit. All statistical tests were 2-sided, and significance was defined as *P* < .05. All statistical analyses were performed using Stata/IC15.1. ## Results ### Study Sample and Characteristics Table 1 shows our study sample and patient characteristics, overall and by study clinic. A total of 108 surveys were completed (n = 54 per clinic: it should be noted that not all respondents answered every question). Females accounted for 62% of participants and nearly half (48%) of the patient sample was between the ages of 30 and 49 years. Most of survey respondents were non-Hispanics (91%) and Whites (75%), and most either completed high school or GED (40%) or completed a college degree (40%). The mean past-week anxiety score was 7.74, with nearly half (48%) of the patients with a score of 8 and above, indicating a positive screen for anxiety. Our study sample had a mean past-week pain intensity of 3.49 out of 10. For duration of pain, the highest proportion of patients reported being “not in pain” (35%), followed by pain duration of more than 3 months (31%). The mean catastrophizing score was 2.17, out of a maximum score of 5. The majority (n = 88/106; 83%) of patients had ever had opioids prescribed and of those with at least 1 prescription, most (n = 64/86; 74%) reported having opioids prescribed more than once (data not shown). Of patients who reported having ever been prescribed opioids, nearly half (45%) reported use of less than 1 week each time (Table 2). View this table: [Table 2.](http://www.jabfm.org/content/33/6/858/T2) Table 2. Distribution of Anxiety Score, Pain Intensity, Pain Duration Pain Catastrophizing Scores and Opioid Use Duration (n = 108) Patient characteristics were similar between the 2 clinic sites, with a few exceptions. The FQHC clinic had more patients with lower education level, higher anxiety scores and higher pain intensity than the clinic with more privately insured patients (Table 1 and Table 2). ### Opioid Expectations in Dispositional Acute Pain Situations The majority (71%) of participants agreed or strongly agreed that they would expect an opioid to be prescribed in at least 1 or more of the 4 different scenarios and roughly a quarter (26%) of patients expected them in 3 or more scenarios. Table 3 describes patient demographics by the number of scenarios in which they would expect an opioid prescription. There were no statistically significant differences among opioid expectations by clinics, gender, age, or education. In unadjusted analysis, there was a difference by race, with whites more likely to expect opioids in the majority of the presented situations than nonwhites. View this table: [Table 3.](http://www.jabfm.org/content/33/6/858/T3) Table 3. Number of Scenarios Expecting Opioid Prescription by Clinic and Patient Demographics (n = 108 Patients) ### Patient Characteristics Associated with Opioid Expectations in Dispositional Acute Pain Situations Table 4 demonstrates the results of the multiple logistic regression analysis of the patient factors associated with the adjusted odds of expecting opioid prescriptions in at least 3 out of 4 acute pain scenarios. Patients who had a pain catastrophizing score of 3 and above had more than 3 times greater odds of expecting opioids than those with a pain catastrophizing score of 2 and below (OR, 3.73; *P* = .032; 95% CI, 1.12-12.46); no other characteristics were statistically significant. View this table: [Table 4.](http://www.jabfm.org/content/33/6/858/T4) Table 4. Adjusted Odds of Expecting Opioid Prescriptions in at Least 3 out of 4 Acute Pain Scenarios by Patient Characteristics (n = 93) ## Discussion Our main objective was to examine patients’ expectations of receipt of an opioid prescription for acute pain control in outpatient settings and the patient characteristics associated with these expectations. The only factor associated with increased expectations of opioids was higher pain catastrophizing. This finding was similar to other studies that found patients with increased pain catastrophizing had more opioid prescriptions for treatment of chronic pain.18,24 To our knowledge, this is the first study to examine the association between pain catastrophizing and these expectations for outpatient treatment of acute pain. Previous research also found that anxiety and pain catastrophizing predicted increased acute postsurgical pain.16,21⇓–23 However, we did not observe higher anxiety scores associated with increased expectations of opioids in our study. Some patients may have anxiety toward opioid side effects, which could lessen the expectation of opioid prescription. The concept of pain catastrophizing refers to an exaggerated irrationally negative cognitive and emotional reaction or response to actual or anticipated pain,36,37 which may include active rumination on pain, a feeling of helplessness, and magnification of the pain.37,39 Pain catastrophizing has been recognized as a predictor of a number of pain-related outcomes (eg, pain severity, pain-related activity interference, disability, depression, frequent use of health care system), both in pain-free healthy individuals as well as patients with different types of chronic pain conditions.36,40 Some studies suggested that cognitive therapy, behavioral therapy, or both can be effective in reducing pain catastrophizing thoughts and improving pain-related outcomes.26,36 Assessing and treating pain catastrophizing thoughts could avoid unnecessary opioid prescriptions for acute pain and provides holistic patient care. Most patients in our study indicated that they would expect an opioid prescription in at least 1 out of 4 of the dispositional acute pain scenarios described in the survey. This is of concern because outcomes of acute pain, especially in musculoskeletal pain, have been found to be influenced by expectation for a given intervention rather than the specific intervention itself.41 The National Academies of Sciences, Engineering, and Medicine formed the Clinical Practice Guideline for Prescribing Opioids for Acute Pain and in their report the committee considered main factors which influence clinicians in prescribing opioids. These include acute pain presentation (eg, trauma, nontraumatic, exacerbation of chronic conditions), care settings (eg, outpatient, inpatient, emergency department), availability to access to other resources (eg, insurance coverage, pharmacy access, follow-up plans), and influencing factors such as patient characteristics (eg, age, sex, comorbidities).1 Evaluation of self-reported factors influencing opioid prescribing for acute pain among almost 200 primary care physicians in 1 Pacific Northwest state found that about half admitted that patient expectations and satisfaction were important factors.3 Thus, patient opioid expectations can influence prescribers’ decision to prescribe opioids. Over the last 2 decades in the United States, opioids have been prescribed routinely for any acute pain conditions mainly governed by subjective pain level assessment5 and also influenced by patient expectations and satisfaction.3 Indeed, the fact that the majority of patients expected opioids may be the result of recent opioid overprescribing practice patterns in medical settings. Evidence-based guidelines for acute pain control for specific medical conditions, including specific opioid indications and nonopioid treatments, are needed to provide standards of care across health care settings.1 These guidelines could lead to reduced overall exposure of opioids among patients1,42⇓–44 and appropriate expectations of opioid prescriptions among patients with acute pain. Limitations of our study include a relatively small number of patients that were recruited using convenience sampling within a single academic institution. While both clinics are in the same academic institution, the patient demographics vary greatly by clinic (with 1 being an FQHC), and thus represent a somewhat diverse patient population. The survey design had limitations as well. For one, we were unable to conduct conventional psychometric testing due to limited resources. It is also possible that patient responses to the questions regarding history of prescription opioid use could be subject to recall bias. There are also limitations in our use of dispositional acute pain situation scenarios. Patient’s past experiences with specific scenarios, or lack thereof, could have influenced their expectations for opioids in the scenarios described. Finally, while most patient-level variables often associated with opioid prescribing expectations for chronic pain were not significantly associated with expectations for acute pain control, other unmeasured patient-level variables such as other medical or psychiatric comorbidities could be associated with these expectations; future studies should explore these potential relationships. In our study, the majority (83%) of patients ever had opioids prescribed, which was slightly higher than what was reported in larger nationwide survey (64%),45 and this may have biased our findings. Future studies with larger sample sizes are warranted to further our understanding of the association of pain catastrophizing and opioid expectations, including whether addressing and reducing pain catastrophizing thoughts among outpatients with acute pain can reduce opioid prescriptions. ## Conclusion Our study demonstrated that higher pain catastrophizing increases the odds of expecting opioids in dispositional acute pain scenarios in outpatient settings. Future studies are needed to determine whether addressing pain catastrophizing in the context of acute pain reduces expectations of opioid prescribing and unnecessary opioid use. We also found that most patients expected to be prescribed an opioid in at least 1 acute pain scenario. Developing and disseminating evidence-based guidelines for acute pain control for specific medical conditions could result in both appropriate opioid prescribing and patient expectations of opioids for acute pain management. ## Acknowledgments The authors thank Rick Deyo, MD, MPH and Patty Carney, PhD for assisting in survey development and acknowledge the patients who graciously completed surveys for the study. ## Appendix ## Notes * This article was externally peer reviewed. * *Conflict of interest: *None. * *Funding:* None. * *Author contributions:* EO, TM, and SRB developed the survey. EO conceived of the manuscript concept. EO conducted analysis of the outcome supervised by JL. EO, JL, TM, and SRB drafted the manuscript and contributed to the manuscript revisions. * To see this article online, please go to: [http://jabfm.org/content/33/6/000.full](http://jabfm.org/content/33/6/000.full). * Received for publication March 11, 2020. * Revision received June 17, 2020. * Accepted for publication June 18, 2020. ## References 1. 1.National Academies of Sciences, Engineering, and Medicine. Framing Opioid Prescribing Guidelines for Acute Pain: Developing the Evidence. Washington, DC: The National Academies Press; 2019. 2. 2.Alam A, Gomes T, Zheng H, Mamdani MM, Juurlink DN, Bell CM. Long-term analgesic use after low-risk surgery: a retrospective cohort study. Arch Intern Med 2012;172:425–30. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1001/archinternmed.2011.1827&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=22412106&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) [Web of Science](http://www.jabfm.org/lookup/external-ref?access_num=000301381300010&link_type=ISI) 3. 3.Onishi E, Kobayashi T, Dexter E, Marino M, Maeno T, Deyo RA. Comparison of opioid prescribing patterns in the United States and Japan: primary care physicians' attitudes and perceptions. J Am Board Fam Med 2017;30:248–54. [Abstract/FREE Full Text](http://www.jabfm.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NToiamFiZnAiO3M6NToicmVzaWQiO3M6ODoiMzAvMi8yNDgiO3M6NDoiYXRvbSI7czoyMDoiL2phYmZwLzMzLzYvODU4LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 4. 4.Chou R, Turner JA, Devine EB, et al. The effectiveness and risks of long-term opioid therapy for chronic pain: a systematic review for a National Institutes of Health Pathways to Prevention Workshop. Ann Intern Med 2015;162:276–86. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.7326/M14-2559&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=25581257&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 5. 5.Baker DW. History of The Joint Commission’s pain standards: lessons for today's prescription opioid epidemic. JAMA 2017;317:1117–8. 6. 6.Treede RD, Rief W, Barke A, et al. Chronic pain as a symptom or a disease: the IASP classification of chronic pain for the International Classification of Diseases (ICD-11). Pain 2019;160:19–27. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1097/j.pain.0000000000001384&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 7. 7.Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. MMWR Recomm Rep 2016;65:1–49. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.15585/mmwr.rr6501e1&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=26987082&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 8. 8.Rosenbloom JM, Burns SM, Kim E, August DA, Ortiz VE, Houle TT. Race/ethnicity and sex and opioid administration in the emergency room. Anesth Analg 2019;128:1005–12. 9. 9.Tong ST, Hochheimer CJ, Brooks EM, et al. Chronic opioid prescribing in primary care: factors and perspectives. Ann Fam Med 2019;17:200–6. [Abstract/FREE Full Text](http://www.jabfm.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6ODoiYW5uYWxzZm0iO3M6NToicmVzaWQiO3M6ODoiMTcvMy8yMDAiO3M6NDoiYXRvbSI7czoyMDoiL2phYmZwLzMzLzYvODU4LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 10. 10.Hah JM, Bateman BT, Ratliff J, Curtin C, Sun E. Chronic opioid use after surgery: implications for perioperative management in the face of the opioid epidemic. Anesth Analg 2017;125:1733–40. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1213/ANE.0000000000002458&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 11. 11.Halbert BT, Davis RB, Wee CC. Disproportionate longer-term opioid use among U.S. adults with mood disorders. Pain 2016;157:2452–7. 12. 12.Carr EC, Thomas NV, Wilson-Barnet J. Patient experiences of anxiety, depression and acute pain after surgery: a longitudinal perspective. Int J Nurs Stud 2005;42:521–30. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1016/j.ijnurstu.2004.09.014&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=15921983&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 13. 13.Aceto P, Lai C, Perilli V, et al. Factors affecting acute pain perception and analgesics consumption in patients undergoing bariatric surgery. Physiol Behav 2016;163:1–6. 14. 14.Taenzer P, Melzack R, Jeans ME. Influence of psychological factors on postoperative pain, mood and analgesic requirements. Pain 1986;24:331–42. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1016/0304-3959(86)90119-3&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=3960574&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) [Web of Science](http://www.jabfm.org/lookup/external-ref?access_num=A1986A709700005&link_type=ISI) 15. 15.Erdogan E, Ozenc E. Factors associated with acute and chronic pain after inguinal herniorraphy. Rom J Anaesth Intensive Care 2018;25:31–5. 16. 16.Theunissen M, Peters ML, Bruce J, Gramke HF, Marcus MA. Preoperative anxiety and catastrophizing: a systematic review and meta-analysis of the association with chronic postsurgical pain. Clin J Pain 2012;28:819–41. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1097/AJP.0b013e31824549d6&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=22760489&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) [Web of Science](http://www.jabfm.org/lookup/external-ref?access_num=000309658500010&link_type=ISI) 17. 17.Rhon DI, Snodgrass SJ, Cleland JA, Sissel CD, Cook CE. Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule. Perioper Med 2018;7:25. 18. 18.Sharifzadeh Y, Kao MC, Sturgeon JA, Rico TJ, Mackey S, Darnall BD. Pain catastrophizing moderates relationships between pain intensity and opioid prescription: nonlinear sex differences revealed using a learning health system. Anesthesiology 2017;127:136–46. 19. 19.Helmerhorst GT, Vranceanu AM, Vrahas M, Smith M, Ring D. Risk factors for continued opioid use one to two months after surgery for musculoskeletal trauma. J Bone Joint Surg Am 2014;96:495–9. [Abstract/FREE Full Text](http://www.jabfm.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NjoiamJqc2FtIjtzOjU6InJlc2lkIjtzOjg6Ijk2LzYvNDk1IjtzOjQ6ImF0b20iO3M6MjA6Ii9qYWJmcC8zMy82Lzg1OC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 20. 20.Darnall BD, Colloca L. Optimizing placebo and minimizing nocebo to reduce pain, catastrophizing, and opioid use: a review of the science and an evidence-informed clinical toolkit. Int Rev Neurobiol 2018;139:129–57. 21. 21.Pavlin DJ, Sullivan MJ, Freund PR, Roesen K. Catastrophizing: a risk factor for postsurgical pain. Clin J Pain 2005;21:83–90. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1097/00002508-200501000-00010&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=15599135&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) [Web of Science](http://www.jabfm.org/lookup/external-ref?access_num=000225958300010&link_type=ISI) 22. 22.Papaioannou M, Skapinakis P, Damigos D, Mavreas V, Broumas G, Palgimesi A. The role of catastrophizing in the prediction of postoperative pain. Pain Med 2009;10:1452–9. 23. 23.Granot M, Ferber SG. The roles of pain catastrophizing and anxiety in the prediction of postoperative pain intensity: a prospective study. Clin J Pain 2005;21:439–45. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1097/01.ajp.0000135236.12705.2d&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=16093750&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) [Web of Science](http://www.jabfm.org/lookup/external-ref?access_num=000231534600010&link_type=ISI) 24. 24.Lovejoy TI, Dobscha SK, Turk DC, Weimer MB, Morasco BJ. Correlates of prescription opioid therapy in veterans with chronic pain and history of substance use disorder. J Rehabil Res Dev 2016;53:25–36. 25. 25.Martel MO, Jamison RN, Wasan AD, Edwards RR. The association between catastrophizing and craving in patients with chronic pain prescribed opioid therapy: a preliminary analysis. Pain Med 2014;15:1757–64. 26. 26.Arteta J, Cobos B, Hu Y, Jordan K, Howard K. Evaluation of how depression and anxiety mediate the relationship between pain catastrophizing and prescription opioid misuse in a chronic pain population. Pain Med 2016;17:295–303. 27. 27.El-Aqoul A, Obaid A, Yacoub E, Al-Najar M, Ramadan M, Darawad M. Factors associated with inadequate pain control among postoperative patients with cancer. Pain Manag Nurs 2018;19:130–8. 28. 28.Stafford C, Francone T, Roberts PL, Ricciardi R. What factors are associated with increased risk for prolonged postoperative opioid usage after colorectal surgery? Surg Endosc 2018;32:3557–61. 29. 29.Shresha R, Shrestha D, Kayastha R. Post-operative pain and associated factors in patients undergoing single visit root canal treatment on teeth with vital pulp. Kathmandu Univ Med J 2018;16:220–3. 30. 30.Alí A, Olivieri JG, Duran-Sindreu F, Abella F, Roig M, García-Font M. Influence of preoperative pain intensity on postoperative pain after root canal treatment: a prospective clinical study. J Dent 2016;45:39–42. 31. 31.Arias A, de la Macorra JC, Hidalgo JJ, Azabal M. Predictive models of pain following root canal treatment: a prospective clinical study. Int Endod J 2013;46:784–93. 32. 32.Clarke H, Soneji N, Ko DT, Yun L, Wijeysundera DN. Rates and risk factors for prolonged opioid use after major surgery: population based cohort study. BMJ 2014;348:g1251. [Abstract/FREE Full Text](http://www.jabfm.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiYm1qIjtzOjU6InJlc2lkIjtzOjE3OiIzNDgvZmViMTFfMy9nMTI1MSI7czo0OiJhdG9tIjtzOjIwOiIvamFiZnAvMzMvNi84NTguYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 33. 33.Kroenke K, Baye F, Lourens SG. Comparative validity and responsiveness of PHQ-ADS and other composite anxiety-depression measures. J Affect Disord 2019;246:437–43. 34. 34.Kroenke K, Yu Z, Wu J, Kean J, Monahan PO. Operating characteristics of PROMIS four-item depression and anxiety scales in primary care patients with chronic pain. Pain Med 2014;15:1892–901. 35. 35.Woo A, Lechner B, Fu T, et al. Cut points for mild, moderate, and severe pain among cancer and non-cancer patients: a literature review. Ann Palliat Med 2015;4:176–83. [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=26541396&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 36. 36.Quartana PJ, Campbell CM, Edwards RR. Pain catastrophizing: a critical review. Expert Rev Neurother 2009;9:745–58. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1586/ern.09.34&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=19402782&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 37. 37.Lutz J, Gross R, Long D, Cox S. Predicting risk for opioid misuse in chronic pain with a single-item measure of catastrophic thinking. J Am Board Fam Med 2017;30:828–31. [Abstract/FREE Full Text](http://www.jabfm.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NToiamFiZnAiO3M6NToicmVzaWQiO3M6ODoiMzAvNi84MjgiO3M6NDoiYXRvbSI7czoyMDoiL2phYmZwLzMzLzYvODU4LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 38. 38.Jensen MP, Keefe FJ, Lefebvre JC, Romano JM, Turner JA. One- and two-item measures of pain beliefs and coping strategies. Pain 2003;104:453–69. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1016/S0304-3959(03)00076-9&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=12927618&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) [Web of Science](http://www.jabfm.org/lookup/external-ref?access_num=000185101600005&link_type=ISI) 39. 39.Leung L. Pain catastrophizing: an updated review. Indian J Psychol Med 2012;34:204–17. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.4103/0253-7176.106012&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=23441031&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) 40. 40.Swinkels-Meewisse IE, Roelofs J, Oostendorp RA, Verbeek AL, Vlaeyen JW. Acute low back pain: pain-related fear and pain catastrophizing influence physical performance and perceived disability. Pain 2006;120:36–43. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1016/j.pain.2005.10.005&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=16359797&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom) [Web of Science](http://www.jabfm.org/lookup/external-ref?access_num=000235111100006&link_type=ISI) 41. 41.Bialosky JE, Bishop MD, Cleland JA. Individual expectation: an overlooked, but pertinent, factor in the treatment of individuals experiencing musculoskeletal pain. Phys Ther 2010;90:1345–55. [Abstract/FREE Full Text](http://www.jabfm.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6OToicHRqb3VybmFsIjtzOjU6InJlc2lkIjtzOjk6IjkwLzkvMTM0NSI7czo0OiJhdG9tIjtzOjIwOiIvamFiZnAvMzMvNi84NTguYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 42. 42.Meisenberg BR, Grover J, Campbell C, Korpon D. Assessment of opioid prescribing practices before and after implementation of a health system intervention to reduce opioid overprescribing. JAMA Netw Open 2018;1:e182908. 43. 43.Mundkur ML, Franklin JM, Abdia Y, et al. Days’ supply of initial opioid analgesic prescriptions and additional fills for acute pain conditions treated in the primary care setting—United States, 2014. MMWR Morb Mortal Wkly Rep 2019;68:140–3. 44. 44.Mark J, Argentieri DM, Gutierrez CA, et al. Ultrarestrictive opioid prescription protocol for pain management after gynecologic and abdominal surgery. JAMA Netw Open 2018;1:e185452. 45. 45.Blendon RJ, Benson JM. The public and the opioid-abuse epidemic. N Engl J Med 2018;378:407–11. [CrossRef](http://www.jabfm.org/lookup/external-ref?access_num=10.1056/NEJMp1714529&link_type=DOI) [PubMed](http://www.jabfm.org/lookup/external-ref?access_num=29298128&link_type=MED&atom=%2Fjabfp%2F33%2F6%2F858.atom)