Abstract
Tobacco smoking is associated with adverse health effects and its relationship to pain is complex. The longitudinal effect of smoking on patients attending a tertiary pain management center is not well-established. Using the Collaborative Health Outcomes Information Registry (CHOIR) of patients attending the Stanford Pain Management Center from 2013-2017, we conducted a propensity-weighted analysis to determine independent effects of smoking on chronic pain patients. We adjusted for covariates including age, gender, body mass index, depression and anxiety history, ethnicity, alcohol use, marital status, disability, and education. We compared smokers and non-smokers on pain intensity, physical function, sleep, and psychological and mood variables using self-reported NIH PROMIS outcomes. We also conducted a linear mixed-model analysis to determine effect of smoking over time. 12,368 patients completed the CHOIR questionnaire of which 8,584 patients had complete data for propensity analysis. Smokers at time of pain consultation reported significantly worse pain intensities, pain interference, pain behaviors, physical functioning, fatigue, sleep-related impairment, sleep disturbance, anger, emotional support, depression, and anxiety symptoms than non-smokers (all p<0.001). In mixed model analysis, smokers tended to have worse pain interference, fatigue, sleep-related impairment, anger, emotional support, and depression over time compared to non-smokers. Patients with chronic pain who smoke have worse pain, functional, sleep, and psychological and mood outcomes compared to non-smokers. Smoking also has prognostic importance for poor recovery and improvement over time. Further research is needed on tailored therapies to assist people with chronic pain who smoke and to determine an optimal strategy to facilitate smoking cessation.
Introduction
Tobacco smoking remains a significant public health concern. While prevalence rates of smoking have declined over the past decade, approximately 16% of the US adult population continues to smoke cigarettes (approximately 10% in California where this study was conducted).[17,22] While there are a number of known adverse health effects related to tobacco smoking, studies in the last two decades have also documented an association with chronic pain.[28] A meta-analysis of cross-sectional data identified that smokers are approximately 30% more likely to suffer from low back pain.[29] Further, smokers with chronic pain suffer from higher pain intensities and use more opioids than non-smokers.[16]
The relationship between smoking and pain is complex, and appears to be at least in part bi-directional. Nicotine appears to have an acute anti-nociceptive effect in animals and humans, which is thought to be mediated through agonism of the nicotinic acetylcholine receptors (nAChR), modulation of the descending pain-inhibitory pathway, and activation of the endogenous opioid system and neuroendocrine system.[6,12,24,33,34] Despite a potential analgesic effect, epidemiological studies suggest that chronic tobacco use increases the risk for persistent pain. A study of patients with sub-acute low back pain found that smoking status predicted persistence of pain one-year after onset.[26]
While most, but not all studies, have documented a positive association between smoking and pain, many of these investigations were of low methodological quality and did not include an adjusted analysis. Smoking is highly associated with certain demographic and socioeconomic factors, and these same factors are also highly associated with chronic pain. Since it is not possible to randomize patients to smoke tobacco, statistical adjustments are needed to mitigate the effect of potential confounders.
Here we present a propensity-weighted analysis of smokers versus non-smokers referred for an evaluation at a tertiary academic pain management center. We sought to identify whether smokers at time of consult report significantly worse pain-related, physical function, mood, and psychological outcomes. Further, we conducted a longitudinal analysis to understand the effects of smoking on pain-related outcomes while receiving care at a tertiary care center.
Methods
This study was a retrospective review of data collected as part of routine clinical care. This study was approved by the Institutional Review Board at the Stanford University School of Medicine.
Procedures
The Stanford Pain Management Center is a tertiary pain management referral center located in Redwood City, California. The majority of patients referred to this center are sent from their primary care physician, specialty clinician, or community-based pain practitioner, and it is not uncommon to receive out-of-state referrals. Patients referred to this clinic typically have complex pain conditions that have been refractory to initial pain management strategies trialed by their primary care or referring physicians. The Stanford Pain Management Center offers multidisciplinary care that includes medication, interventional, psychological, complementary and alternative medicine, physical therapy, and self-management recommendations via an interdisciplinary team of pain medicine physicians, advanced practice providers, nurses, pain psychologists, physical therapists, and complex care case managers.
Prior to initial consultation, scheduled patients are sent an email with a secure, patient-specific link to complete the Collaborative Health Outcomes Information Registry (CHOIR) questionnaire. Patients either fill out the questionnaires at home or on a tablet provided to them prior to their arrival at the clinic. CHOIR is an open-source learning healthcare system platform (http://choir.stanford.edu).[31] CHOIR captures patient demographics and characteristics as well as self-reported measures of physical, mental, and social well-being using validated National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information Systems (PROMIS) assessments. NIH PROMIS measures are psychometrically-validated assessments tools endorsed by the NIH for people ages 5–90 years old. CHOIR also contains legacy instruments to assess domains not covered by PROMIS. CHOIR also employs a local computerized adaptive testing (CAT) approach for NIH PROMIS measures that identifies optimal items within a domain based on previous responses. CAT-based questionnaires can allow fewer items to be tested but yield greater efficiency in domain assessment and precision.[11]
After completion of the CHOIR questionnaire, clinicians involved in the care of patient are able to review responses. At the end of the first visit, patients receive a broad range of recommendations which include medications, diagnostic imaging, therapeutic nerve blocks, or referrals to see the clinic’s pain psychologist or physical therapist. Patients typically follow-up within 6–8 weeks of their initial consultation. At return visits, patients complete a CHOIR follow-up questionnaire which includes the PROMIS measures; it is worth noting that there are no current limitations on the minimum interval required before measuring change scores of the PROMIS measures. Patient’s will continue to be seen at the clinic if further investigations or treatments are needed, otherwise they will return to their primary care physician for ongoing pain care.
Patient Demographics and Characteristics
Data on patient’s age, gender, weight, height, and history of depression and anxiety was retrieved from their electronic medical records; a history of depression or anxiety was determined using the International Classification of Diseases (ICD) 10th Revision codes. Data on patients’ current tobacco smoking status was evaluated with by responding with a “Yes” or “No” question to the question “Do you smoke?” under the ‘Tobacco smoking section’ of CHOIR. Other data at initial consultation included pain intensity, ethnicity (Hispanic), race (Caucasian, African American, Native, Other), marital status, disability, education (no high school, high school, college, graduate level). Pain intensity was assessed on an 11-point numerical rating scale (NRS), ranging from 0 being no pain and 10 being worst possible pain. Patients were asked to rate their average, current, and worst pain on the NRS scale in the previous 7 days.
PROMIS Outcomes
PROMIS outcomes collected as part of the CHOIR questionnaire included Pain Interference, Pain Behavior, Physical Function, Fatigue, Depression, Anxiety, Sleep Disturbance, Sleep-related Impairment, Anger, and Emotional Support. A description of each PROMIS outcome can be found at http://www.nihpromis.org/measures/domainframework1.
PROMIS measures are scored on a T-score metric that utilize US population values. A T-score of 50 is the mean of the reference population and 10 is the standard deviation of that population. Therefore, a score of 40 is one standard deviation lower than the mean reference population and a score of 60 is one standard deviation higher than the mean reference population. A higher score denotes more of the concept being measured — for example, a higher score on fatigue means more fatigue, a lower score on emotional support means lower emotional support.
Statistical Analysis
Baseline characteristics at the time of initial pain consult were summarized as mean and standard deviations for continuous variables, and number and percentages for categorical variables. Wilcoxon rank-sum test was used to compare continuous variables and chi-square test for categorical variable.
We conducted a propensity score analysis to adjust for covariates to understand the independent effects of smoking status at initial pain consultation on the improvement of pain over time at a tertiary pain clinic. Since the decision to smoke was not randomized and thus susceptible to treatment-selection bias, propensity score analyses allow for balance of known covariates between the treatment (smokers) and non-treatment (non-smokers) groups; this is in contrast to randomized data that balance unknown covariates.
The propensity score for each patient was calculated based on known covariates that are known to influence the decision to smoke or not to smoke, and were available for inclusion. Covariates in the model included age, gender, body mass index (kg/m2), depression and anxiety history, ethnicity, alcohol use, marital status, receiving disability, and highest educational attainment. Only patients with complete data were included in the analysis. Ethnicity was divided into several self-reported categories (i.e., Caucasian, African American, Asian, Native/Pacific Islander, Hispanic/Latino, and Others), as well as highest education level (i.e., No high school education, High school graduate, College graduate, Graduate degree or higher). The inverse probability of treatment weighting (IPTW) approach was chosen to provide balanced weighting of the propensity score between smokers and non-smokers.[2] Balance was achieved by excluding patients that did not have a comparable propensity score in the other treatment group, satisfying the ‘common support’ condition in propensity score analyses.[25] Trimming of extreme values of the propensity weight was conducted (<0.5% and >99.5%) to improve model misspecification.[19] Balance of the propensity score weighting was evaluated using the standardized difference of each covariate (difference is means/percentages over the pooled standard deviation).[1] The standardized differences between the unweighted and propensity-weighted groups were evaluated (Table 1). A standardized difference ≥10% is considered a meaningful imbalance between groups.[1]
Table 1:
Baseline characteristics and propensity-weighting balance
| Variable | Unweighted | Weighted | ||||||
|---|---|---|---|---|---|---|---|---|
| Non-Smokers (N=7857) |
Smokers (N=727) |
P-value* | Standardized Difference |
Non-Smokers (N=6480) |
Smokers (N=566) |
P-value* | Standardized Difference |
|
| Age (years), mean (SD) | 49.4 (16.5) | 47.9 (12.9) | 0.0203 | −0.1 | 49.3 (16.4) | 48.9 (13.7) | 0.5358 | −0.025 |
| BMI (kg/m2), mean (SD) | 27.5 (6.7) | 28.2 (6.9) | 0.0012 | 0.1134 | 27.5 (6.7) | 27.2 (6.4) | 0.1866 | −0.0571 |
| Female gender, n (%) | 5254 (68.5) | 430 (60.2) | <.0001 | −0.1729 | 4414 (68.1) | 373 (65.9) | 0.2841 | −0.0466 |
| Ethnicity | ||||||||
| Hispanic/Latino, n (%) | 830 (11.3) | 83 (12.2) | 0.4413 | 0.0304 | 732 (11.3) | 62 (11.0) | 0.8068 | −0.0108 |
| Race | ||||||||
| White, n (%) | 4423 (60.2) | 422 (62.3) | 0.2687 | 0.0446 | 3928 (60.6) | 336 (59.4) | 0.5599 | −0.0255 |
| African American, n (%) | 214 (2.9) | 37 (5.5) | 0.0003 | 0.1278 | 208 (3.2) | 17 (2.9) | 0.7203 | −0.016 |
| Asian, n (%) | 643 (8.7) | 26 (3.8) | <.0001 | −0.203 | 553 (8.5) | 50 (8.8) | 0.8245 | 0.0097 |
| Native/Pacific Islander, n (%) | 72 (1.0) | 16 (2.4) | 0.0009 | 0.1081 | 73 (1.1) | 7 (1.3) | 0.7513 | 0.0135 |
| Others, n (%) | 2000 (27.2) | 176 (26.0) | 0.4992 | −0.0273 | 1717 (26.5) | 156 (27.6) | 0.5682 | 0.0249 |
| Education | ||||||||
| No high school, n (%) | 443 (5.7) | 88 (12.2) | <.0001 | 0.2306 | 375 (5.8) | 37 (6.6) | 0.4401 | 0.033 |
| High school, n (%) | 575 (7.4) | 89 (12.4) | <.0001 | 0.1678 | 507 (7.8) | 46 (8.1) | 0.8314 | 0.0093 |
| College, n (%) | 4732 (60.7) | 477 (66.3) | 0.0034 | 0.1155 | 3964 (61.2) | 339 (59.8) | 0.5338 | −0.0272 |
| Graduate degree, n (%) | 1988 (25.5) | 62 (8.6) | <.0001 | −0.4608 | 1580 (24.4) | 143 (25.2) | 0.6553 | 0.0195 |
| Married, n (%) | 4860 (62.6) | 354 (49.6) | <.0001 | −0.2635 | 3948 (60.9) | 339 (59.9) | 0.6371 | −0.0206 |
| Disabled, n (%) | 1771 (22.5) | 261 (36.0) | <.0001 | 0.298 | 1537 (23.7) | 141 (25.0) | 0.503 | 0.0291 |
| Alcohol use, n (%) | 3182 (40.5) | 255 (35.1) | 0.0046 | −0.1111 | 2601 (40.1) | 241 (42.6) | 0.2515 | 0.0501 |
| History of anxiety, n (%) | 202 (2.8%) | 25 (3.8%) | 0.1309 | 0.0574 | 188 (2.9%) | 17 (2.9%) | 0.9839 | 0.0009 |
| History of depression, n (%) | 763 (10.4%) | 81 (12.3%) | 0.1447 | 0.0575 | 703 (10.8%) | 60 (10.6%) | 0.8709 | −0.0071 |
After the two groups of equal weighting on the propensity scores were created, a mixed model analysis was performed to determine the effect of smoking status on pain intensity and PROMIS outcomes over time. Time points included in the mixed model analysis included the initial consultation (Time 1) and first follow-up (Time 2). The fixed effect in our model was smoking status and the random effects was individual patients. A type 3 test was used to evaluate for the interaction between treatment group and time.
All analyses were conducted using SAS version 7.15 (SAS Institute Inc, Cary, NC, USA). Statistical significance was set to p<0.05.
Results
From October 30, 2013 to September 26, 2017, a total of 12,368 patients completed the CHOIR questionnaire. A total of 8,584 patients had complete data available for smoking status at initial consultation of which 9.25% of patients reported that they were smokers. Table 1 reports the baseline characteristics of smokers and non-smokers. Majority of patients were female, Caucasian, married, and had a college or graduate degree. There were significant differences and large standardized differences in the baseline characteristics of the unweighted groups. After propensity score weighting, there were 566 patients in the smoking group and 6480 patients in the non-smoking group. There were no significant differences in baseline characteristics in the propensity weighted groups and all standardized differences were less than 10%.
Table 2 reports the differences in pain intensities between smokers and non-smokers. Smokers at baseline reported significantly greater pain intensity at time of consult (pain intensity now; 6.09 [0.11] versus 4.89 [0.03], p<0.0001). In the week prior to evaluation, smokers reported significantly greater average pain intensity (6.39 [0.10] versus 5.48 [0.03], p<0.0001) and greater worst-pain intensity (8.26 [0.10] versus 7.46 [0.03], p<0.0001). Pain intensities all remained significantly different at follow-up. There was no interaction between group and time.
Table 2:
Pain intensities between smokers and non-smokers
| Smokers | Non-smokers | |||
|---|---|---|---|---|
| Type III test |
mean (SD) | mean (SD) | p-value | |
| Pain intensity, worst | 0.8603 | |||
| Time 1 | 8.26 (0.1) | 7.46 (0.03) | <0.0001* | |
| Time 2 | 7.84 (0.13) | 7.02 (0.04) | <0.0001* | |
| Pain intensity, now | 0.3635 | |||
| Time 1 | 6.09 (0.11) | 4.89 (0.03) | <0.0001* | |
| Time 2 | 5.93 (0.15) | 4.61 (0.04) | <0.0001* | |
| Pain intensity, average | 0.1341 | |||
| Time 1 | 6.39 (0.1) | 5.48 (0.03) | <0.0001* | |
| Time 2 | 6.27 (0.13) | 5.18 (0.04) | <0.0001* |
Table 3 reports the results of function and sleep-related outcomes. PROMIS measures of pain interference and pain behavior were significantly worse for smokers than non-smokers at baseline and at follow-up (all outcomes: p<0.0001). Physical function was also significantly worse in smokers at baseline and follow-up (p<0.0001). Pain interference significantly worsened over time in smokers compared to non-smokers (interaction test: p=0.02). PROMIS pain fatigue, sleep-related impairment, and sleep disturbances were all significantly worse in smokers compared to non-smokers at baseline and follow-up (all outcomes: p<0.0001). Pain fatigue (interaction test: p=0.02) and sleep-related impairment (interaction test: p=0.02) significantly worsened over time in smokers compared to non-smokers.
Table 3:
Functional and sleep outcomes between smokers and non-smokers
| Smokers | Non-smokers | |||
|---|---|---|---|---|
| Type III test |
mean (SD) | mean (SD) | p-value | |
| PROMIS Pain Interference | 0.024* | |||
| Time 1 | 66.73 (0.33) | 63.74 (0.09) | <0.0001* | |
| Time 2 | 67.53 (0.42) | 63.62 (0.12) | <0.0001* | |
| PROMIS Pain Behavior | 0.578 | |||
| Time 1 | 60.35 (0.21) | 58.42 (0.06) | <0.0001* | |
| Time 2 | 60.57 (0.28) | 58.48 (0.08) | <0.0001* | |
| PROMIS Physical Function | 0.280 | |||
| Time 1 | 34.69 (0.47) | 37.79 (0.14) | <0.0001* | |
| Time 2 | 34.2 (0.57) | 37.78 (0.17) | <0.0001* | |
| PROMIS Pain Fatigue | 0.017* | |||
| Time 1 | 61.28 (0.44) | 58.39 (0.13) | <0.0001* | |
| Time 2 | 63.38 (0.54) | 59.33 (0.15) | <0.0001* | |
| PROMIS Sleep-related Impairment | 0.015* | |||
| Time 1 | 58.21 (0.43) | 55.75 (0.13) | <0.0001* | |
| Time 2 | 59.95 (0.55) | 56.29 (0.15) | <0.0001* | |
| PROMIS Sleep Disturbance | 0.906 | |||
| Time 1 | 59.06 (0.41) | 55.93 (0.12) | <0.0001* | |
| Time 2 | 59.06 (0.52) | 55.87 (0.15) | <0.0001* |
Table 4 reports the results of mood and psychological outcomes. Anger, depression, and anxiety were significantly worse in smokers compared to non-smokers at baseline and follow-up (all outcomes: p<0.0001). Smokers also reported significantly less emotional support than non-smokers at baseline (p=0.001) and at follow-up (p=0.0001). Interaction tests for anger, depression, and emotional support were all significant.
Table 4:
Mood and psychological outcomes between smokers and non-smokers
| Smokers | Non- smokers |
|||
|---|---|---|---|---|
| Type III test |
mean (SD) | mean (SD) | p-value | |
| PROMIS Anger | 0.007* | |||
| Time 1 | 52.97 (0.45) | 49.17 (0.13) | <0.0001* | |
| Time 2 | 54.65 (0.57) | 49.42 (0.16) | <0.0001* | |
| PROMIS Emotional Support | 0.007* | |||
| Time 1 | 50.41 (0.41) | 51.16 (0.12) | 0.0012* | |
| Time 2 | 49.63 (0.51) | 51.63 (0.15) | 0.0002* | |
| PROMIS Depression | 0.014* | |||
| Time 1 | 57.19 (0.43) | 53.64 (0.12) | <0.0001* | |
| Time 2 | 58.6 (0.53) | 53.92 (0.15) | <0.0001* | |
| PROMIS Anxiety | 0.054 | |||
| Time 1 | 57.83 (0.43) | 54.75 (0.12) | <0.0001* | |
| Time 2 | 59.6 (0.53) | 55.6 (0.15) | <0.0001* |
Discussion
We found that smokers have significantly worse outcomes compared to non-smokers at both initial consultation and the first follow-up visit in this analysis of a large chronic pain registry of patients attending a tertiary pain management center. Further, smokers tend to have worse pain interference, fatigue, sleep-related impairment, anger, emotional support, and depression over time compared to non-smokers. These results are particularly concerning as the patients who smoke report worse outcomes despite longitudinal care at a tertiary pain management center. Our findings highlight the need to treat patients with chronic pain, who smoke, as a distinct, higher-risk cohort. Research is needed to identify optimal interventions beyond interdisciplinary pain care that are likely to reverse perturbations in function, sleep, and mood unique to this cohort.
There are several strengths to this study. First, we included a large number of patients into this analysis. The number of patients included provided sufficient power to detect small differences in outcomes. Second, we adjusted for demographic, socioeconomic, and common mood disorders. Many of these factors are highly associated with both pain and smoking and may be potential confounders. Our adjusted analysis using propensity-weighting aimed to minimize the effect of confounding and provide potential evidence for independent effects.
Similar to results from our study, previous analyses have identified that current tobacco smoking is linked to greater pain severity and functional impairment.[15,16] These studies suggest that patients with chronic pain tend to smoke more, particularly if they are concomitantly using opioids.[6,9] Further, the relationship between smoking and pain appears to be bi-directional with pain increasing smoking behaviors and smoking increasing pain. The driver behind pain leading to more smoking may be related to an acute analgesic effect of nicotine. Meta-analytical data of human experimental studies suggest that nicotine administered through tobacco produces a small to medium pain inhibitory effect.[7] Further, nicotine has also been shown to increase pain thresholds and tolerance to applied painful stimuli.[14] These analgesic effects provide positive reinforcement and promote the conditioned use of nicotine-containing products. Nicotine’s other positive mood and cognitive effects such as mild euphoria, increased energy, and heightened arousal may also serve as a coping strategy for those in pain.[13] Smoking as a coping strategy for those with chronic pain is significantly and positively associated with increased pain intensity, pain interference, and fear of pain compared to both non-smokers.[23]
While behavioural mechanisms can help explain how pain increases tobacco use, it’s not entirely clear how chronic tobacco use increases pain. Nicotine’s acute analgesic effect appears to be primarily mediated through the nAChR.[28] These receptors are involved in the neuroendocrine system increasing sympathetic activation and leading to decreased pain perception. However, chronic nicotine exposure can lead to nAChR desensitization, excess nAChRs, and tolerance that can develop as quickly as smoking more than a few cigarettes in a row; the degree of desensitization, however, is incomplete and highly variable.[5,27] Further, the withdrawal of nicotine, even after a few hours of smoking abstinence, will allow the excess nAChRs to rapidly recover from desensitization leading to hyper-excitability of the nicotinic cholinergic system; this can result in symptoms of unrest and agitation which can drive the motivation for the next cigarette to desensitize the nAchRs.[27] Most notably, these changes in activation/sensitization, and tolerance/desensitization to nicotine occur in rapid cycles throughout the course of the day. Among daily tobacco smokers randomized to smoking abstinence versus continued ad lib smoking with no pain at baseline, those who abstained from smoking for 12 to 24 hours were almost 3.5 times more likely to endorse pain at follow-up.[18] Thus, acute nicotine abstinence precipitates increased pain, which further perpetuates tobacco use. Smoking also causes oxidative stress, inflammation, and impairs oxygen delivery, reducing recovery and healing after injury and accelerating degenerative changes.[4,20] It is possible that tobacco use, in and of itself, may contribute to the development of numerous chronic pain conditions — observational studies report the association between current smoking and the subsequent development of rheumatoid arthritis.[32] Thus, it appears that smoking initiates a detrimental positive feedback cycle as pain prompts smoking behaviors and smoking increases pain over time.
Our longitudinal analysis highlights the prognostic importance of smoking on pain recovery and improvement. Despite receiving similar care, smokers consistently reported worse pain interference, pain fatigue, sleep-related impairment, anger, emotional support, depression, and anxiety than non-smokers. It is not readily apparent whether smokers fare worse or whether non-smokers fare better in the context of chronic pain, and it may be that smoking reduces the benefit of pain treatment that non-smokers would otherwise experience. Nonetheless, this finding is likely stems from multifactorial influence of biopsychosocial factors related to hyper-excitability in nAchRs, pain-related behavioral cues for smoking, and environmental interactions. Interestingly, Hooten et al. conducted a prospective study of a 3-week multidisciplinary pain rehabilitation program and demonstrated that smokers achieved similar or significantly better outcomes than non-smokers.[16] Differences in the response of smokers seen in this study may relate to the focus and intensity of the management program administered — patients underwent a high-intensity program, 8-hours a day for 15 days, of cognitive behavioral therapy, daily physical reconditioning, occupational therapy, and educational sessions. While our center offers multidisciplinary care, since our analysis was limited to the first two patient visits, most patients did not receive prolonged and intensive psychological and physical therapy treatments. Nonetheless, results from the Hooten et al. study suggest that smokers can benefit from a comprehensive multidisciplinary pain management strategy.
While smoking cessation should be a general recommendation for all patients, it is not clear if smoking cessation among patients with chronic pain will necessarily lead to improved outcomes. Acute nicotine withdrawal in habituated smokers can lead to negative somatic and affective symptoms which could result in less coping and increased pain.[35] Further, smokers deprived of nicotine have shorter pain latency to heat and reduced tolerance to electrically-induced pain —this may help explain why few patients with chronic pain who smoke are able to successfully quit.[10,21,30] A randomized trial of veteran smokers with chronic illnesses (e.g., diabetes, hypertension, heart disease) found no difference in pain scores at 5-months in those who quit versus those that did not quit smoking.[3] A brief, integrated telephone-based intervention for smoking cessation and pain management is one such strategy to promote smoking cessation while attempting to reduce pain intensity and improve functional impairments.[8] Future studies will need to evaluate the effectiveness of smoking cessation on pain and functional outcomes, but also identify an appropriate cessation plan including the potential use of nicotine replacement therapies.
Several limitations of this investigation exist. Smoking is related to many demographic and socioeconomic variables and while a propensity-analysis was used to adjust for baseline covariates, the presence of confounding cannot be excluded. While covariates were chosen based on their potential influence in someone’s decision to smoke, other covariates may have not been accounted for such as concomitant substance abuse. Further, prevalence rates of covariates found in our cohort of patients differ from rates found in national datasets. The prevalence of current smoking among patients with chronic pain in our study was approximately 9%. The estimated prevalence of current tobacco smoking amongst U.S. adults is 15.5%, and this prevalence has remained stable since 2015.[17] Nonetheless, the prevalence rate of smoking in California is approximately 10%, where our study was conducted, which is similar to our finding.[22] The disparity in findings may also relate to local geographic variations or perhaps the sensitivity of our smoking assessment. Additionally, utilizing data that was collected as part of routine clinical care at Stanford Healthcare (i.e., electronic medical records), may account for discrepancies between rates of comorbidities (i.e., history of depression and anxiety) in our study cohort compared to those of national averages. Furthermore, our results are limited to the first two patient visits, which may not capture the benefits achieved from certain interventions requiring repeated visits (i.e., cognitive behavioral therapy, physical therapy). Our analysis is also based upon the assumption that both smokers and non-smokers received similar pain treatments. While we believe this is a reasonable assumption, in that clinicians at our clinic do not adjust their recommendations based on smoking status, this disparity could potentially lead to differential treatment plans. Also, the CHOIR questionnaires did not collect physician-reported information such as pain diagnoses, opioid use data, or details regarding during or amount of tobacco use. These data would have been informative to include in our analyses.
Smoking and pain share a complex, bi-directional relationship. Those in pain who smoke may do so for the immediate and temporary analgesic effect and for a behavioral coping mechanism. Unfortunately, smoking will contribute to more pain and worse outcomes over time through the development of tolerance and increased pain intensity during the acute stages of nicotine withdrawal. Clinicians should be aware that smokers with chronic pain will present at baseline with lower quality of life and have a slightly worse prognosis than non-smokers, for not only physical function, sleep, and mood outcomes, but also for pain-related outcomes. Further investigations are needed to understand the effect of tobacco smoking and smoking cessation on chronic pain as well as optimal management plans to mitigate the effects of smoking.
Acknowledgements
We would like to acknowledge the contributions of Dr. Juliette Hong for her statistical support and assistance. The authors of this study have no conflicts of interest to disclose.
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