Abstract
Background
Opioid safety initiatives may secondarily impact opioid prescribing and pain outcomes for cancer care.
Methods
We reviewed electronic health record data at a tertiary Veterans Affairs system (VA Palo Alto) for all patients from 2015 to 2021. We collected outpatient Schedule II opioid prescriptions data and calculated morphine milligram equivalents (MMEs) using Centers for Disease Control and Prevention conversion formulas. To determine the clinical impact of changes in opioid prescription, we used the highest level of pain reported by each patient on the 0-to-10 Numeric Rating Scale in each year, categorized into mild (0-3), moderate (4-6), and severe (7 and above).
Results
Among 89 569 patients, 9073 had a cancer diagnosis. Cancer patients were almost twice as likely to have an opioid prescription compared with noncancer patients (69.0% vs 36.7%, respectively). The proportion of patients who received an opioid prescription decreased from 27.1% to 18.1% (trend P < .01) in cancer patients and from 17.0% to 10.2% in noncancer patients (trend P < .01). Cancer and noncancer patients had similar declines of MMEs per year between 2015 and 2019, but the decline was more rapid for cancer patients (1462.5 to 946.4, 35.3%) compared with noncancer patients (1315.6 to 927.7, 29.5%) from 2019 to 2021. During the study period, the proportion of noncancer patients who experienced severe pain was almost unchanged, whereas it increased among cancer patients, reaching a significantly higher rate than among noncancer patients in 2021 (31.9% vs 27.4%, P < .01).
Conclusions
Our findings suggest potential unintended consequences for cancer care because of efforts to manage opioid-related risks.
Opioid management in clinical settings has gained significant attention, particularly in the context of the ongoing opioid crisis (1). This issue is even more complex when addressing the needs of patients with cancer, who often require pain management that may involve opioids (2,3). Veterans, as a unique population, may present specific patterns in opioid usage because of their distinct health-care system and potential comorbidities, including a higher prevalence of chronic pain and mental health conditions (4-6). The primary research question of this study is to understand how opioid use in cancer patients within a veteran population has changed over the past half-decade and the impact on pain outcomes, considering the evolving landscape of opioid prescription practices.
The existing literature on opioid use in cancer and noncancer populations, particularly within a veteran setting, reveals significant gaps (7). Many studies have explored opioid use in general or have focused on cancer pain management (8-10), and one study showed that the rising trend of opioid use among veteran cancer patients, which had been ongoing until the implementation of the Veterans Health Administration’s (VHA’s) Opioid Safety Initiative (OSI) in 2014, began to reverse between 2014 and 2016 (9). However, there is a lack of subsequent data on whether this trend continued or a direct comparison with opioid use trends for noncancer pain. This lack of comparison, especially in the context of veterans’ health care and initiatives such as the VHA’s OSI, leaves a critical void in understanding the distinct pain management needs and opioid risks faced by veteran cancer patient groups (11,12).
This study aims to bridge the gap in existing literature by providing a detailed analysis of opioid prescription trends and pain scores among cancer and noncancer patients within a veteran population. By examining electronic health record data from a tertiary Veterans Affairs (VA) system over an extended period (2015-2021), this study offers insights into the real-world implications of opioid management policies in a health-care system that serves a large number of individuals with unique health-care needs (13,14). The findings are expected to inform opioid prescribing, policy-making, and clinical practices, specifically tailored for veterans with cancer diagnoses (2,12,15).
Methods
We reviewed electronic health record data for all patients with 1 year of care or more within a tertiary-care VHA facility (VA Palo Alto) from 2015 to 2021. The study protocol was approved by the Stanford University–affiliated institutional review board as well as our VHA facility’s Research & Development Committee.
The VA Corporate Data Warehouse contains data on demographics, including age, gender (male or female), marital status, and race (White, Hispanic, Black, Asian, or Indian American). We used the information on comorbidities identified through the International Classification of Disease (ICD) Version 9 and 10 Clinical Modification (16) to determine the presence of bipolar disorder, generalized anxiety disorder, major depression, nicotine dependence disorder, opioid use disorder, posttraumatic stress disorder, substance use disorder, and chronic pain. We also extracted the history of smoking and alcohol use in the study population. Cancer incidence during the follow-up period was determined by links to VA databases that draw from VHA diagnoses tables, Beneficiary Identification Records Locator Subsystem, and the National Death Index (17). All individuals with a record of a malignant cancer code (ICD-9 codes 140-239 and ICD-10 codes C00-C96 excluding benign lesions and nonmelanoma skin cancers) during the study period were included in the study as cancer patients. We collected outpatient opioid prescriptions data (all Schedule II opioids) from VHA Pharmacy Tables, regardless of the timing of cancer diagnosis. We identified opioid prescriptions of 30, 60, and 90 consecutive days and calculated average morphine milligram equivalents (MMEs) per year using outpatient prescription data (quantity and day supply) and standard Centers for Disease Control and Prevention conversion formulas (18). To determine the clinical impact of changes in opioid prescription, we used the pain reported on the 0-to-10 Numeric Rating Scale (NRS). As the VHA considers pain to be the fifth vital sign, NRS data were available for all the outpatient visits during the study period (19). We used the highest pain score for each patient during a given year, and categorized it as mild (0-3), moderate (4-6), and severe (7 and above).
Demographic and personal characteristics between cancer and noncancer patients were compared using χ2 tests. We used joinpoint regression analyses with t tests to estimate the annual percentage change (APC) in the average annual MMEs for cancer and noncancer patients, separately (20,21). The trend of the line segment was used to quantify the APC. We used the grid search method for fitting the segmented line regression and obtained the best fit for each model using the Monte Carlo permutation method to test statistical significance. We used R software, version 3.0.2, and Joinpoint Trend Analysis Software (version 5.0.2) (20) for the statistical analyses. Two-sided P values less than .05 were considered significant.
Results
The study population was predominantly (92.9%) male; about half were 70 years or older (Table 1). Among 86 919 patients, 9073 (10.1%) were diagnosed with cancer from 2015 to 2021, with the 10 most common cancers listed in Table 2. Cancer patients were almost twice as likely to have an opioid prescription compared with noncancer patients (69.0% vs 36.7%, respectively). As Table 2 shows, patients with prostate and hematopoietic cancers were least likely to have used opioids, whereas patients with cancers of the liver and pancreas were most likely.
Table 1.
Numbers and percentages within categories defined by demographic variables
| Cancer patients, No. (%) |
Noncancer patients, No. (%) |
||||||
|---|---|---|---|---|---|---|---|
| Prescribed opioidsa (n = 6284) | No prescribed opioids (n = 2789) | Subtotal (n = 9073) | Prescribed opioids (n = 28 552) | No prescribed opioids (n = 49 294) | Subtotal (n = 77 846) | Total, No. (%) (N = 86 919) | |
| Age | |||||||
| 18-50 | 120 (1.9)c | 68 (2.4) | 188 (2.1)e | 5438 (19.0)c | 13 485 (27.4) | 18 923 (24.3) | 19 111 (22) |
| 51-70 | 1417 (22.6) | 489 (17.5) | 1906 (21) | 9881 (34.6) | 13 823 (28.0) | 23 704 (30.4) | 25 610 (29.5) |
| 71-100 | 4704 (74.9) | 2208 (79.2) | 6912 (76.2) | 13 079 (45.8) | 21 570 (43.8) | 34 649 (44.5) | 41 561 (47.8) |
| >100/missing | 43 (0.7) | 24 (0.9) | 67 (0.7) | 154 (0.5) | 416 (0.8) | 570 (0.7) | 637 (0.7) |
| Gender | |||||||
| Male | 6076 (96.7)d | 2719 (97.5) | 8795 (96.9)e | 26 465 (92.69)d | 45 497 (92.3) | 71 962 (92.4) | 80 757 (92.9) |
| Female | 208 (3.3) | 70 (2.5) | 278 (3.1) | 2087 (7.31) | 3797 (7.7) | 5884 (7.6) | 6162 (7.1) |
| Marital status | |||||||
| Married | 3121 (49.7)c | 1707 (61.2) | 4828 (53.2)e | 12 768 (44.7)e | 23 873 (48.4) | 36 641 (47.1) | 41 469 (47.7) |
| Divorced | 1886 (30.0) | 556 (19.9) | 2442 (26.9) | 8141 (28.5) | 10 812 (21.9) | 18 953 (24.3) | 21 395 (24.6) |
| Single | 686 (10.9) | 221 (7.9) | 907 (10.0) | 5032 (17.6) | 9326 (18.9) | 14 358 (18.4) | 15 265 (17.6) |
| Other | 591 (9.4) | 305 (10.9) | 896 (9.9) | 2611 (9.2) | 5283 (10.7) | 7894 (10.1) | 8790 (10.1) |
| Race/ethnicity | |||||||
| Asian patients | 169 (2.7) | 107 (3.8) | 276 (3) | 1215 (4.3) | 2778 (5.6) | 3993 (5.1) | 4269 (4.9) |
| Black patients | 524 (8.3) | 188 (6.7) | 712 (7.8) | 2759 (9.7) | 4455 (9.0) | 7214 (9.3) | 7926 (9.1) |
| Hispanic patients | 506 (8.6) | 229 (8.2) | 735 (8.1) | 2308 (8.1) | 4783 (9.7) | 7091 (9.1) | 7826 (9) |
| Native American patients | 32 (0.5) | 18 (0.7) | 50 (0.6) | 224 (0.8) | 343 (0.7) | 567 (0.7) | 617 (0.7) |
| Other patients | 216 (3.4) | 110 (3.9) | 326 (3.6) | 1205 (4.2) | 2491 (5.1) | 3696 (4.7) | 4022 (4.6) |
| Multirace patients | 667 (10.6) | 276 (9.9) | 943 (10.4) | 3944 (13.8) | 5841 (11.9) | 9875 (12.7) | 10 818 (12.4) |
| White patients | 4170 (66.4)c | 1861 (66.7) | 6031 (66.5)e | 16 897 (59.2)c | 28 603 (58.0) | 45 500 (58.4) | 51 531 (59.3) |
| Comorbidity | |||||||
| Bipolar disorder | 130 (2.1)c | 24 (0.9) | 154 (1.7)f | 822 (2.9)c | 756 (1.5) | 1578 (2.0) | 1732 (2) |
| Generalized anxiety disorder | 197 (3.13)c | 36 (1.3) | 233 (2.6)f | 1163 (4.1)c | 1189 (2.41) | 2352 (3) | 2585 (3) |
| Major depression | 2079 (33.1)c | 535 (19.2) | 2614 (28.8)e | 10 295 (36.1)e | 10 057 (20.4) | 20 352 (26.1) | 22 966 (26.4) |
| Nicotine dependence disorder | 908 (14.5)c | 165 (5.9) | 1073 (11.8)e | 3495 (12.2)c | 3053 (6.2) | 6548 (8.4) | 7621 (8.8) |
| Opioid use disorder | 145 (2.3)c | 0 (0) | 145 (1.6) | 1039 (3.6)c | 0 (0) | 1039 (1.3) | 1184 (1.4) |
| Posttraumatic stress disorder | 923 (14.7)c | 161 (5.8) | 1084 (11.9)e | 4374 (15.3)c | 2695 (5.47) | 7069 (9.1) | 8153 (9.4) |
| Substance use disorder | 785 (12.5)c | 157 (5.6) | 942 (10.4) | 4435 (15.5)c | 3882 (7.88) | 8317 (10.7) | 9259 (10.7) |
| Chronic pain | 4095 (65.2)c | 988 (35.4) | 5083 (56.0)e | 17 840 (62.5)c | 13 790 (28.0) | 31 630 (40.6) | 36 713 (42.2) |
| Smokingb | 6206 (98.8)c | 2516 (90.2) | 8722 (96.1)e | 27 460 (96.2)c | 33 724 (68.4) | 61 184 (78.6) | 69 906 (80.4) |
| Alcohol | 6277 (99.9)c | 2658 (95.3) | 8935 (98.5)e | 28 288 (99.1)c | 38 833 (78.8) | 67 121 (86.2) | 76 056 (87.5) |
Counts (column percent).
Prescribed opioids is defined as the presence of outpatient opioid prescriptions (Schedule II opioids).
Smoking is defined as having a history of smoking ever.
P < .001.
P < .05 opioid users vs opioid never-users (cancer and noncancer patients separately).
P < .001.
P < .05 cancer vs noncancer patients.
Table 2.
Number of patients with the 10 most common cancers (and their outpatient opioid use) within the Palo Alto VA system, 2015-2021
| Cancer type (ICD-10 and ICD-9 codes) | Total | Opioids prescribed a | No opioids prescribed a |
|---|---|---|---|
| Prostate (C61, 185) | 2791 | 1826 (65.4) | 965 (34.6) |
| Colorectal (C18, C19, C20, 153, 154) | 1566 | 1200 (76.6) | 366 (23.4) |
| Bladder (C67, 188) | 1474 | 1212 (82.2) | 262 (17.8) |
| Malignant melanoma of skin (C43, 172) | 1245 | 1016 (81.6) | 229 (18.4) |
| Lung (C33, C34, 162) | 1070 | 905 (84.6) | 165 (15.4) |
| Leukemia (C91-C95, 204-208) | 785 | 533 (67.9) | 252 (32.1) |
| Lymphoma (C81-C86, 200, 201) | 739 | 623 (84.3) | 116 (15.7) |
| Liver (C22, 155) | 715 | 648 (90.6) | 67 (9.4) |
| Pancreas (C25, 157) | 505 | 434 (85.9) | 71 (14.1) |
| Esophagus (C15, 150) | 381 | 304 (79.8) | 77 (20.2) |
| Multiple myeloma and malignant plasma cell neoplasms (C90, 203) | 277 | 194 (70.0) | 83 (30.0) |
Numbers within parentheses indicate the proportions for each cancer type. TBL = tracheal, bronchus, and lung.
Patients diagnosed with cancer were older and were more likely to be male, be married, and identify as White than noncancer patients. All studied comorbidities were significantly more common in cancer patients except substance use disorders (Table 1). Among cancer patients, those who used opioids were slightly younger, on average, and were more likely to be male, be married, and identify as Black. All comorbidities were more common in cancer patients who used opioids compared with those who did not. This difference was similarly seen in noncancer patients who received opioids compared with those who did not. The average MME in cancer patients who received opioids was 1714.3 mg/year compared with 1593.7 mg/year in noncancer patients (P < .05). Cancer patients were also significantly more likely to have received at least 1 prescription of more than 90 consecutive days (10.0%) compared with noncancer patients (8.0%) (Supplementary Table 1, available online). In both groups, hydrocodone acetaminophen combination was the most commonly prescribed opioid, followed by oxycodone. In cancer patients, codeine guaifenesin was the third most common prescription, whereas in noncancer patients, tramadol was more commonly prescribed than codeine guaifenesin.
The proportion of cancer patients who received at least 1 opioid prescription decreased from 27.1% in 2015 to 17.3% in 2020 and 18.1% in 2021 (Figure 1; trend P < .01). In noncancer patients, this proportion was 17.0% in 2015 and dropped to 9.1% in 2020 and 10.2% in 2021 (trend P < .01). Between 2015 and 2021, there was a 60.0% decrease in average MME per year in both cancer and noncancer patients (Figure 2). Joinpoint regression showed a significant APC of −12.2 (95% confidence interval [CI] = −13.3 to −9.8) for cancer patients between 2015 and 2019, which accelerated to −20.2 (95% CI = −23.0 to −16.3) during the past 2 years. In noncancer patients, the APC was constant at −14.4 (95% CI = −16.0 to −12.3); as a result, both groups had almost equal mean MME in 2021 (946.4 mg/year vs 927.7 mg/year in cancer and noncancer patients, respectively, P = .8).
Figure 1.
The proportion of cancer and noncancer patients within the Palo Alto VA system who received at least 1 outpatient opioid prescription between 2015 and 2021.
Figure 2.
Average morphine milligram equivalents (MMEs) among cancer and noncancer patients per year. Change in rates during 2015-2021 was calculated using joinpoint regression. The annual percent change (APC) and 95% confidence interval (CI) were as follows. For cancer patients for cancer patients Segment 1: -12.16 (95% CI = -13.34 to -9.76), and Segment 2: -20.22 (95% CI = -20.31 to -16.3); for noncancer patients: -14.19 (95% CI = -16.03 to -12.28). Two-sided t tests showed that the APC for cancer and noncancer patients were significantly different (P < .05).
As shown in Figure 3, in 2015, 29.5% of all cancer patients (57.9% of those using prescription opioids) and 27.4% of all noncancer patients (57.7% of those using prescription opioids) experienced severe pain. Although this figure was almost unchanged among the noncancer patients, it increased to 31.9% of all cancer patients (62.8% of those using prescription opioids) in 2021, which was significantly higher than among noncancer patients in the same year (27.4% and 57.9%, respectively; P < .01).
Figure 3.
Relative frequency of the highest level of pain reported by cancer and noncancer patients in each year between 2015 and 2021 within the Palo Alto VA system. Each patient’s highest Numeric Rating Scale record in a given year was categorized as mild (0-3), moderate (4-6), and severe (7-10).
Discussion
Our study’s results highlight an evolving landscape in opioid and pain management for cancer patients at a major VA medical center in the recent half-decade. Despite a comparable decrease in MMEs annually across both groups, cancer patients showed a notably sharper reduction from 2019 to 2021. At the same time, although severe pain reports in noncancer patients remained relatively stable, they intensified among cancer patients, reaching a higher rate in 2021 compared with noncancer patients. These results imply that efforts to regulate opioid prescriptions, such as the OSI, although successful in moderating opioid use among veterans, may also be unintentionally compromising the management of pain in cancer patients.
Decreasing opioid prescribing among veterans is consistent with nationwide opioid stewardship initiatives (15,22-25). However, these trends may not capture the full picture of cancer pain management needs and risks. Our study showed a deviation from the gradual decline reported by studies such as Chen et al. (26). We identified a markedly more precipitous decrease in opioid prescriptions among cancer patients, with a notable acceleration occurring from 2019 to 2021, underscoring a potentially sustained and differentiated impact of the OSI on cancer-related opioid use within the VHA. This timeframe, coinciding with the COVID-19 pandemic, likely played a role in exacerbating the decline because of the added complexities of health-care delivery during the crisis (27,28).
Our results indicate a potentially significant impact of opioid regulation policies on cancer patients (29,30). Although initiatives such as the OSI have targeted opioid use in noncancer patients, the consequences for cancer patients, who often rely on these medications for severe pain management, may be unintentional and profound (11,31). Recent studies have revealed that cancer patients and survivors have faced difficulties in accessing prescribed opioid medications, with such barriers becoming more pronounced since 2016 (32,33). The heightened scrutiny from regulatory agencies and the fear of legal action have led many health-care providers, including primary care doctors and oncologists, to reduce or even cease prescribing opioids (34-36). Insurance companies have also implemented more stringent measures for obtaining these medications, contributing to the reluctance among health-care providers to incorporate opioids into patient care regimens (7,37,38). It is also essential to acknowledge the complexities inherent in attributing the observed shifts in opioid prescribing solely to the OSI or other contemporaneous changes within the VA system. These changes encompass a broad spectrum of policy and cultural shifts, both internal and external to the VA, over the same period (39). However, a strength of our study is that we evaluated opioid prescribing (and pain scores) in noncancer patients to create a comparison group. Comparing opioid prescribing trends between cancer and noncancer patients allows us to isolate the effects of the OSI within the context of cancer pain management. By observing the prescribing patterns in noncancer patients—who are also subject to the same OSI policies—we could discern whether changes in opioid use among cancer patients are consistent with broader trends or are unique to the cancer patient population.
Given the possibility that the reductions in opioid prescriptions could reflect appropriate adjustments in clinical practice, rather than inherently problematic trends, we also evaluated pain outcomes, particularly pain scores among cancer and noncancer patients. We examined pain outcomes alongside prescribing patterns in noncancer patients to provide a baseline against which the impact of opioid stewardship on pain control in cancer patients can be assessed. This is particularly crucial given the different pain management needs between these two groups (40). We found that severe pain among noncancer patients remained relatively consistent, whereas a concerning rise in severe pain reports among cancer patients was observed, peaking in 2021. This discrepancy in pain trends, especially against the backdrop of declining opioid prescriptions, could be indicative of the complex interplay between regulatory influences and clinical pain management (39). We chose the maximum pain score reported within a year as a measure of pain control, acknowledging that population-level measures of pain are not clearly defined. The NRS is widely accepted in many health-care systems for its simplicity and directness in capturing patient-reported pain intensity (41). The use of NRS scores, which range from 0 (no pain) to 10 (worst pain imaginable), although straightforward, also presents analytical challenges because of the potential for zero inflation and the ordinal nature of the data (42). Regardless, the data indicate unmanaged pain in cancer survivors, potentially stemming from limited education on pain management, fears of opioid addiction, negative experiences with opioids, and regulatory policies that limit access to these medications (11,43,44). This underscores the need for improved access to multimodal pain management options and nonopioid alternatives, as well as support for policies that promote opioid education and responsible legislation (12,45).
Our findings for cancer patients suggest a reevaluation of current policies to ensure that cancer patients receive the medications they need without undue delay or difficulty (13). In addressing potential concerns around opioid misuse, it is also essential to recognize that cancer patients have similar risks of addiction compared with the general population, especially given shared risk factors such as cigarette smoking and alcohol overuse (46,47). Best practices for using opioid medications to treat cancer pain involve careful assessment of the pain, its impact on function, and the potential risks associated with opioid use (12). When prescribed, strategies to reduce the risk of misuse should be considered, such as using patient–provider agreements, checking prescription drug monitoring programs, and urine testing to minimize risks of diversion or inappropriate use (48). The National Comprehensive Cancer Network Guidelines for Adult Cancer Pain advocate for integrative and interventional strategies, emphasizing the shift from a goal of complete pain elimination to improving patient functionality and quality of life (3). This patient-centric approach is supported by recommendations for rigorous assessment and careful consideration of the risks and benefits of opioid use (49).
Our study contributes to the existing literature by providing a granular analysis of opioid prescribing patterns within a VA system, specifically postimplementation of the OSI (9,23). However, it was based on a single VA facility and may not reflect the diverse practices and patient populations across the broader VA health-care system. We plan to expand to a broader range of VA centers to validate these findings and explore their implications for pain management policies and practices in cancer care. Also, although the study’s observational design is strengthened by a detailed compilation of demographic and health-related variables, it is not designed to establish cause-and-effect relationships. Future studies should, therefore, broaden their scope to include multiple VA centers to enhance the generalizability and seek to elucidate causative factors behind opioid use patterns among cancer patients.
In conclusion, this study evaluated the changing landscape of opioid prescriptions in a veteran population, with a marked decrease in opioid use among cancer patients and an increase in severe pain reports. This opioid prescribing trend, although aligning with broader opioid safety efforts, raises concerns about potential undertreatment of pain. The findings call for a balanced approach to opioid prescribing, especially in cancer care, and underscore the need for ongoing research to monitor the impacts of policy changes on patient outcomes (3). Additional research should also focus on the development of comprehensive pain management protocols that effectively address the needs of cancer patients while mitigating the risks associated with opioid use.
Supplementary Material
Acknowledgments
The study sponsor had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication.
Contributor Information
Seshadri C Mudumbai, Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Han He, Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Ji-Qing Chen, Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Aditi Kapoor, Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Samantha Regala, Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Edward R Mariano, Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Randall S Stafford, Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Christian C Abnet, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Ruth M Pfeiffer, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Neal D Freedman, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Arash Etemadi, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Data availability
The data underlying this article cannot be shared due to restrictions of patient health information in accordance with the Veterans Health Administration.
Author contributions
Seshadri C. Mudumbai, MD, MS (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing—original draft; Writing—review & editing), Han He, PhD (Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Writing—original draft; Writing—review & editing), Ji-Qing Chen, PhD (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Visualization; Writing—review & editing), Aditi Kapoor, MS (Data curation; Formal analysis; Methodology; Resources; Software; Validation; Visualization; Writing—original draft; Writing—review & editing), Samantha Regala, BS (Funding acquisition; Investigation; Methodology; Project administration; Writing—review & editing), Edward R. Mariano, MD, MAS (Conceptualization; Investigation; Resources; Writing—review & editing), Randall S. Stafford, MD, PhD (Conceptualization; Investigation; Methodology; Writing—review & editing), Christian C. Abnet, PhD, MPH (Conceptualization; Funding acquisition; Methodology; Writing—review & editing), Ruth M. Pfeiffer, PhD (Conceptualization; Funding acquisition; Methodology; Writing—review & editing), Neal Freedman, PhD (Conceptualization; Investigation; Methodology; Writing—review & editing), Arash Etemadi, PhD (Conceptualization; Formal analysis; Investigation; Methodology; Project administration; Resources; Supervision; Visualization; Writing—original draft; Writing—review & editing)
Funding
This study was funded by the National Cancer Institute Intramural Program through an Inter-Agency Agreement (A2211-075-036-033419.0).
Conflicts of interest
The authors have no conflicts of interest to declare.
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Data Availability Statement
The data underlying this article cannot be shared due to restrictions of patient health information in accordance with the Veterans Health Administration.