Longitudinal associations between smoking and affect among cancer patients using varenicline to quit smoking

Carroll, Allison J.; Kim, Kristine; Miele, Andrew; Olonoff, Matthew; Leone, Frank T.; Schnoll, Robert A.; Hitsman, Brian

doi:10.1016/j.addbeh.2019.04.003

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…This represented a substantial increase in the number of completed appointments compared with the 6 weeks before the pilot implementation period (25 completions vs. four completions), but represents an opportunity for improvement. Other studies also suggest that one of the major challenges to improving tobacco treatment among cancer patients has been the low rate of participation by the patients [6,[51][52][53][54].…”

Section: Discussionmentioning

confidence: 99%

Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment

Jose

Ohde

Hays

et al. 2020

IJERPH

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Continued tobacco use after cancer diagnosis is detrimental to treatment and survivorship. The current reach of evidence-based tobacco treatments in cancer patients is low. As a part of the National Cancer Institute Cancer Center Cessation Initiative, the Mayo Clinic Cancer Center designed an electronic health record (EHR, Epic©)-based process to automatically refer ambulatory oncology patients to tobacco use treatment, regardless of intent to cease tobacco use(“opt out”). The referral and patient scheduling, accomplished through a best practice advisory (BPA) directed to staff who room patients, does not require a co-signature from clinicians. This process was piloted for a six-week period starting in July of 2019 at the Division of Medical Oncology, Mayo Clinic, Rochester, MN. All oncology patients who were tobacco users were referred for tobacco treatment by the rooming staff (n = 210). Of these, 150 (71%) had a tobacco treatment appointment scheduled, and 25 (17%) completed their appointment. We conclude that an EHR-based “opt-out” approach to refer patients to tobacco dependence treatment that does not require active involvement by clinicians is feasible within the oncology clinical practice. Further work is needed to increase the proportion of scheduled patients who attend their appointments.

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Section: Discussionmentioning

confidence: 99%

Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment

Jose

Ohde

Hays

et al. 2020

IJERPH

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“…Multiple papers are reported for two studies, and data from Abdelmutti [ 24 ] are reported in Guiliani [ 25 ]. Concurrent and additional reporting for outcomes in Crawford [ 26 ] are in four papers [ 27 , 28 , 29 , 30 ].…”

Section: Resultsmentioning

confidence: 99%

“…Increased referrals linked to electronic referral systems were reported in four studies [ 24 , 25 , 36 , 41 ]. Several studies reported the impact on quit rates [ 24 , 27 , 32 , 33 , 38 , 42 , 43 , 45 ] with Charlot et al [ 32 ] identifying a quit rate at 8 weeks of 50.1% and 44.3% at 3 months. Conlon et al [ 47 ] reported quit rates of 14.8% at 3 to 6 months and 12 months.…”

Section: Resultsmentioning

confidence: 99%

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Systematic Review of Smoking Cessation Interventions for Smokers Diagnosed with Cancer

Frazer

Bhardwaj

Fox

et al. 2022

IJERPH

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The detrimental impact of smoking on health and wellbeing are irrefutable. Additionally, smoking is associated with the development of cancer, a reduction treatment outcomes and poorer health outcomes. Nevertheless, a significant number of people continue to smoke following a cancer diagnosis. Little is understood of the smoking cessation services provided to smokers with cancer or their engagement with them. This systematic review aimed to identify existing smoking cessation interventions for this cohort diagnosed with breast, head and neck, lung and cervical cancers (linked to risk). Systematic searches of Pubmed, Embase, Psych Info and CINAHL from 1st January 2015 to 15th December 2020 were conducted. Included studies examined the characteristics of smoking cessation interventions and impact on referrals and quit attempts. The impact on healthcare professionals was included if reported. Included studies were restricted to adults with a cancer diagnosis and published in English. No restriction was placed on study designs, and narrative data synthesis was conducted due to heterogeneity. A review protocol was registered on PROSPERO CRD 42020214204, and reporting adheres to PRISMA reporting guidelines. Data were screened, extracted in duplicate and an assessment of the quality of evidence undertaken using Mixed Methods Assessment Tool. 23 studies met the inclusion criteria, representing USA, Canada, England, Lebanon, Australia and including randomized controlled trials (9), observational studies (10), quality improvement (3), and one qualitative study. Hospital and cancer clinics [including a dental clinic] were the settings for all studies. 43% (10/23) of studies reported interventions for smokers diagnosed with head and neck cancer, 13% (3/23) for smokers diagnosed with lung cancer, one study provides evidence for breast cancer, and the remaining nine studies (39%) report on multiple cancers including the ones specified in this review. Methodological quality was variable. There were limited data to identify one optimal intervention for this cohort. Key elements included the timing and frequency of quit conversations, use of electronic records, pharmacotherapy including extended use of varenicline, increased counselling sessions and a service embedded in oncology departments. More studies are required to ensure tailored smoking cessation pathways are co-developed for smokers with a diagnosis of cancer to support this population.

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“…The study participants were 119 cancer patients at 4 different time waves; week 0 (pre-quit), week 1 (target quit week), week 4, and week 12. Patients were smokers with a diagnosis of cancer and were recruited for a 24-week trial of extended duration varenicline plus behavioral counseling (Schnoll, 2018). Smoking was assessed via self-reported number of cigarettes in the past 24 hours.…”

Section: Example With Real Datamentioning

confidence: 99%

Bayesian information criterion accounting for the number of covariance parameters in mixed effects models

Heo

Lee

Kim

2020

CSAM

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Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.

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Longitudinal associations between smoking and affect among cancer patients using varenicline to quit smoking

Cited by 7 publications

References 20 publications

Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment

Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment

Systematic Review of Smoking Cessation Interventions for Smokers Diagnosed with Cancer

Bayesian information criterion accounting for the number of covariance parameters in mixed effects models

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