Smoking-cessation advice from dental care professionals and its association with smoking status

AimsAudit carried out to assess whether or not patients had been asked about their smoking status during admission onto an acute adult mental health ward, as well as if they had received any smoking cessation advice or offered nicotine replacement therapy.Background•Physical health outcomes in patients with serious mental illness (SMI) are consisitently worse than the general public This is due to multiple factors; adverse effects of medication (including metabolic syndromes with psychotropics) as well as poor lifestyle factors such as smoking status•Patients with an SMI are 3–6 times more likely to die due to coronary artery disease. 70% of patients in inpatient psychiatric units are smokers, a strong independent risk factor for cardiovascular disease.•Smoking cessation is a potent modifiable risk factor that can prevent mortality and reduce morbidity.MethodA cross-sectional review of all 34 inpatients across four general adult acute psychiatric wards.Patient records were explored using the Aneuran Bevan Health Board admission proformas to identify evidence of smoking status and whether advice was offered.ResultSmoker but not given cessation advice n = 13 (38%)Not asked about smoking n = 11 (32%)Smoker and given cessation advice n = 4 (12%)Non-smoker n = 6 (18%)ConclusionPatients were asked about their smoking status the majority of the time (68%) but provision of advice or nicotine replacement therapy was only done in 14% of potential smokers (identified smokers and patients not asked about smoking status).A consideration to be taken into account is that on admission, a patient's physical health status may be unknown, with the additional difficulty of a patient's acute distress complicating the physical examination, smoking status and modification of patient's smoking status may not be the highest priory in that context.Data regarding asking about smoking were different amongst wards, potentially signifying differences between assessors willingness to ask about smoking status.There is a lack of smoking cessation literature available on the wards and patients are often unaware of what options are available to quit smoking.The audit simply determined whether or not assessors were documenting smoking status, it does not measure the quantity or quality of smoking cessation advice provided.Further quality improvement projects should be launched, with focus groups as the intial step at further investigating inpatient smoking rates, as well as attempting to reduce them in a more systemic way.

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Summary of: What determines the provision of smoking cessation advice and counselling by dental care teams?

BDJ ◽

10.1038/sj.bdj.2009.269 ◽

2009 ◽

Vol 206 (7) ◽

pp. 376-377

Author(s):

C. A. Ramseier

Keyword(s):

Smoking Cessation ◽

Dental Care ◽

Smoking Cessation Advice

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What determines the provision of smoking cessation advice and counselling by dental care teams?

BDJ ◽

10.1038/sj.bdj.2009.272 ◽

2009 ◽

Vol 206 (7) ◽

pp. E13-E13 ◽

Cited By ~ 17

Author(s):

J. P. Rosseel ◽

J. E. Jacobs ◽

S. R. Hilberink ◽

I. M. Maassen ◽

R. H. B. Allard ◽

...

Keyword(s):

Smoking Cessation ◽

Dental Care ◽

Smoking Cessation Advice

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Time trends in access to smoking cessation support for people with depression or severe mental illness: a cohort study in English primary care

BMJ Open ◽

10.1136/bmjopen-2020-048341 ◽

2021 ◽

Vol 11 (12) ◽

pp. e048341

Author(s):

Milena Falcaro ◽

David Osborn ◽

Joseph Hayes ◽

Gary Coyle ◽

Lisa Couperthwaite ◽

...

Keyword(s):

Mental Health ◽

Primary Care ◽

Mental Illness ◽

Smoking Cessation ◽

Cohort Study ◽

Severe Mental Illness ◽

Smoking Status ◽

Smoking Cessation Advice ◽

Quit Attempts ◽

Over Time

ObjectivesTo investigate delivery of smoking cessation interventions, recorded quit attempts and successful quitting rates within primary care in smokers with depression or severe mental illness (SMI) compared with those without.DesignLongitudinal cohort study using primary healthcare records.SettingEnglish primary care.Participants882 849 patients registered with participating practices recorded as current smokers during 2007–2014, including three groups: (1) 13 078 with SMI, (2) 55 630 with no SMI but recent depression and (3) 814 141 with no SMI nor recent depression.OutcomesRecorded advice to quit smoking, referrals to smoking cessation services, prescriptions for smoking cessation medication, recorded quit attempts and changes of smoking status.ResultsThe majority (>70%) of smokers had recorded smoking cessation advice. This was consistently higher in those with SMI than the other cohorts of patients, although the gap greatly reduced in more recent years. Increases in smoking cessation advice over time were not accompanied by increases in recorded attempts to quit or changes of smoking status. Overall nicotine replacement therapy prescribing by general practitioners (GPs) was higher in those with SMI (10.1%) and depression (8.7%) than those without (5.9%), but a downward time trend was observed in all groups. Bupropion and varenicline prescribing was very low and lower for those with SMI. Few smokers (<5%) had referrals to stop smoking services, though this increased over time, but no significant differences were observed between those with and without mental health problems.ConclusionsThere was no evidence of consistent inequalities in access to GP-delivered smoking cessation interventions for people with mental health conditions. Smoking cessation advice was widely reported as taking place in all groups. In order to address the widening gap in smoking prevalence in those with poor mental health compared with those without, the emphasis should be on addressing the quality of advice and support given.

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Smoking cessation interventions for patients with coronary heart disease and comorbidities: an observational cross-sectional study in primary care

British Journal of General Practice ◽

10.3399/bjgp16x688405 ◽

2016 ◽

Vol 67 (655) ◽

pp. e118-e129 ◽

Cited By ~ 3

Author(s):

David N Blane ◽

Daniel Mackay ◽

Bruce Guthrie ◽

Stewart W Mercer

Keyword(s):

Mental Health ◽

Primary Care ◽

Coronary Heart Disease ◽

Smoking Cessation ◽

Smoking Status ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Smoking Cessation Advice ◽

Health Comorbidities

BackgroundLittle is known about how smoking cessation practices in primary care differ for patients with coronary heart disease (CHD) who have different comorbidities.AimTo determine the association between different patterns of comorbidity and smoking rates and smoking cessation interventions in primary care for patients with CHD.Design and settingCross-sectional study of 81 456 adults with CHD in primary care in Scotland.MethodDetails of eight concordant physical comorbidities, 23 discordant physical comorbidities, and eight mental health comorbidities were extracted from electronic health records between April 2006 and March 2007. Multilevel binary logistic regression models were constructed to determine the association between these patterns of comorbidity and smoking status, smoking cessation advice, and smoking cessation medication (nicotine replacement therapy) prescribed.ResultsThe most deprived quintile had nearly three times higher odds of being current smokers than the least deprived (odds ratio [OR] 2.76; 95% confidence interval [CI] = 2.49 to 3.05). People with CHD and two or more mental health comorbidities had more than twice the odds of being current smokers than those with no mental health conditions (OR 2.11; 95% CI = 1.99 to 2.24). Despite this, those with two or more mental health comorbidities (OR 0.77; 95% CI = 0.61 to 0.98) were less likely to receive smoking cessation advice, but absolute differences were small.ConclusionPatterns of comorbidity are associated with variation in smoking status and the delivery of smoking cessation advice among people with CHD in primary care. Those from the most deprived areas and those with mental health problems are considerably more likely to be current smokers and require additional smoking cessation support.

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Relation between Smoking Status of Physicians and Their Enthusiasm to Offer Smoking Cessation Advice.

Internal Medicine ◽

10.2169/internalmedicine.36.162 ◽

1997 ◽

Vol 36 (3) ◽

pp. 162-165 ◽

Cited By ~ 50

Author(s):

Masahiko KAWAKAMI ◽

Seiichi NAKAMURA ◽

Hiyori FUMIMOTO ◽

Jun TAKIZAWA ◽

Michiko BABA

Keyword(s):

Smoking Cessation ◽

Smoking Status ◽

Smoking Cessation Advice

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Improving smoking cessation in first episode psychosis: a quality improvement project by the City & Hackney Early and Quick Intervention Psychosis (EQUIP)

BMJ Open Quality ◽

10.1136/bmjoq-2020-001002 ◽

2020 ◽

Vol 9 (4) ◽

pp. e001002

Author(s):

Orestis Kanter Bax ◽

Nadim Hakim ◽

Michael Jeggo ◽

Declan Phelan ◽

Timothy Stevens ◽

...

Keyword(s):

Smoking Cessation ◽

Quality Improvement ◽

Patient Participation ◽

Smoking Status ◽

First Episode Psychosis ◽

Public Health Issue ◽

First Episode ◽

Improvement Project ◽

Episode Psychosis ◽

The City

Smoking tobacco is a major public health issue and a significant cause of increased mortality. People with a first episode of psychosis are more likely to smoke and the subgroup that goes on to have schizophrenia will have a significantly reduced life expectancy to the general population. The City & Hackney Early and Quick Intervention in Psychosis Team is a community mental health team at East London NHS Foundation Trust, providing outpatient care for adults presenting with first episode psychosis. This project aimed to increase the number of smoking cessation referrals from EQUIP to national smoking cessation services to 15% of the total team caseload over 6 months initially. A secondary measure was to complete an assessment of the smoking status for 90% of the caseload at all times. Change ideas were tested using plan-do-study-act cycles. A smoking cessation referral pathway was created and disseminated to the outpatient and inpatient services. The project was discussed at least monthly at the clinical team meeting. An education and skills building session was organised and took place at the team away day and an education drop-in session for patients was organised. The project was slow to take-off and patient participation was essential in driving progress. The aim was achieved at 23 months. A collateral benefit indicated that 25.7% of the total number of smokers had been recorded as having stopped smoking during the course of this project. This project demonstrates the effectiveness of quality improvement methodology facilitated by efficient leadership, collaborative teamwork, patient participation and persistence to address a complex problem that has significant consequences to patient health.

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Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052584 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2584

Author(s):

Cheng-Chien Lai ◽

Wei-Hsin Huang ◽

Betty Chia-Chen Chang ◽

Lee-Ching Hwang

Keyword(s):

Machine Learning ◽

Smoking Cessation ◽

Success Rate ◽

Prediction Models ◽

Smoking Status ◽

Medical Center ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Support Vector ◽

Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

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