Screening, Brief Intervention, and Referral to Treatment in Oklahoma (SBIRT-OK)
is an evidence-based, integrated approach to identify and intervene with patients whose
patterns of tobacco, alcohol, and/or drug use, or depression, put their health at risk.
There were an estimated 28 million current cigarette smokers in the US, and approximately twice as many former smokers, in 2021.1 Smoking cessation is associated with large reductions in excess mortality compared with continued smoking,2 but the timescale over which cause-specific mortality benefits of cessation may develop is unclear.3-6 Quantifying excess cause-specific mortality among former smokers by years since quitting may inform clinical decision-making and screening programs.
Smoking cessation is known to have numerous health benefits, but it can also induce adverse physiological effects, including those affecting the gastrointestinal tract (GIT). Understanding the adverse physiological effects of smoking cessation on the GIT is critical for healthcare professionals and smokers attempting to quit, as it enables them to anticipate and manage potential challenges during the smoking cessation process. Although the detrimental effects of smoking on the GIT have been well established, there is a gap in the literature regarding the specific physiological reactions that may occur upon smoking cessation. This mini-review summarizes the current literature on the predisposing factors, pathophysiology, clinical presentation, and treatment options for adverse physiological effects of smoking cessation on the GIT. We aimed to raise awareness among busy clinical professionals about these adverse effects, empowering them to effectively support individuals striving to quit smoking and maintain their cessation. By consolidating the existing knowledge in this field, this review offers practical implications for smokers, healthcare providers, and policymakers to optimize smoking cessation interventions and support strategies to improve health outcomes.
Importance: Lung cancer is the second most common cancer and the leading cause of cancer death in the US. In 2020, an estimated 228 820 persons were diagnosed with lung cancer, and 135 720 persons died of the disease. The most important risk factor for lung cancer is smoking. Increasing age is also a risk factor for lung cancer. Lung cancer has a generally poor prognosis, with an overall 5-year survival rate of 20.5%. However, early-stage lung cancer has a better prognosis and is more amenable to treatment.
Objective: To update its 2013 recommendation, the US Preventive Services Task Force (USPSTF) commissioned a systematic review on the accuracy of screening for lung cancer with low-dose computed tomography (LDCT) and on the benefits and harms of screening for lung cancer and commissioned a collaborative modeling study to provide information about the optimum age at which to begin and end screening, the optimal screening interval, and the relative benefits and harms of different screening strategies compared with modified versions of multivariate risk prediction models.
Population: This recommendation statement applies to adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.
Evidence assessment: The USPSTF concludes with moderate certainty that annual screening for lung cancer with LDCT has a moderate net benefit in persons at high risk of lung cancer based on age, total cumulative exposure to tobacco smoke, and years since quitting smoking.
Recommendation: The USPSTF recommends annual screening for lung cancer with LDCT in adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years. Screening should be discontinued once a person has not smoked for 15 years or develops a health problem that substantially limits life expectancy or the ability or willingness to have curative lung surgery. (B recommendation) This recommendation replaces the 2013 USPSTF statement that recommended annual screening for lung cancer with LDCT in adults aged 55 to 80 years who have a 30 pack-year smoking history and currently smoke or have quit within the past 15 years.
Background: Mobile phone-based smoking cessation support (mCessation) offers the opportunity to provide behavioural support to those who cannot or do not want face-to-face support. In addition, mCessation can be automated and therefore provided affordably even in resource-poor settings. This is an update of a Cochrane Review first published in 2006, and previously updated in 2009 and 2012.
Objectives: To determine whether mobile phone-based smoking cessation interventions increase smoking cessation rates in people who smoke.
Search methods: For this update, we searched the Cochrane Tobacco Addiction Group's Specialised Register, along with clinicaltrials.gov and the ICTRP. The date of the most recent searches was 29 October 2018.
Selection criteria: Participants were smokers of any age. Eligible interventions were those testing any type of predominantly mobile phone-based programme (such as text messages (or smartphone app) for smoking cessation. We included randomised controlled trials with smoking cessation outcomes reported at at least six-month follow-up.
Data collection and analysis: We used standard methodological procedures described in the Cochrane Handbook for Systematic Reviews of Interventions. We performed both study eligibility checks and data extraction in duplicate. We performed meta-analyses of the most stringent measures of abstinence at six months' follow-up or longer, using a Mantel-Haenszel random-effects method, pooling studies with similar interventions and similar comparators to calculate risk ratios (RR) and their corresponding 95% confidence intervals (CI). We conducted analyses including all randomised (with dropouts counted as still smoking) and complete cases only.
Main results: This review includes 26 studies (33,849 participants). Overall, we judged 13 studies to be at low risk of bias, three at high risk, and the remainder at unclear risk. Settings and recruitment procedures varied across studies, but most studies were conducted in high-income countries. There was moderate-certainty evidence, limited by inconsistency, that automated text messaging interventions were more effective than minimal smoking cessation support (RR 1.54, 95% CI 1.19 to 2.00; I2 = 71%; 13 studies, 14,133 participants). There was also moderate-certainty evidence, limited by imprecision, that text messaging added to other smoking cessation interventions was more effective than the other smoking cessation interventions alone (RR 1.59, 95% CI 1.09 to 2.33; I2 = 0%, 4 studies, 997 participants). Two studies comparing text messaging with other smoking cessation interventions, and three studies comparing high- and low-intensity messaging, did not show significant differences between groups (RR 0.92 95% CI 0.61 to 1.40; I2 = 27%; 2 studies, 2238 participants; and RR 1.00, 95% CI 0.95 to 1.06; I2 = 0%, 3 studies, 12,985 participants, respectively) but confidence intervals were wide in the former comparison. Five studies compared a smoking cessation smartphone app with lower-intensity smoking cessation support (either a lower-intensity app or non-app minimal support). We pooled the evidence and deemed it to be of very low certainty due to inconsistency and serious imprecision. It provided no evidence that smartphone apps improved the likelihood of smoking cessation (RR 1.00, 95% CI 0.66 to 1.52; I2 = 59%; 5 studies, 3079 participants). Other smartphone apps tested differed from the apps included in the analysis, as two used contingency management and one combined text messaging with an app, and so we did not pool them. Using complete case data as opposed to using data from all participants randomised did not substantially alter the findings.
Authors' conclusions: There is moderate-certainty evidence that automated text message-based smoking cessation interventions result in greater quit rates than minimal smoking cessation support. There is moderate-certainty evidence of the benefit of text messaging interventions in addition to other smoking cessation support in comparison with that smoking cessation support alone. The evidence comparing smartphone apps with less intensive support was of very low certainty, and more randomised controlled trials are needed to test these interventions.
Tobacco use is the leading preventable cause of disease, disability, and death in the US. In 2014, it was estimated that 480 000 deaths annually are attributed to cigarette smoking, including second hand smoke exposure. Smoking during pregnancy can increase the risk of numerous adverse pregnancy outcomes (eg, miscarriage and congenital anomalies) and complications in the offspring (including sudden infant death syndrome and impaired lung function in childhood). In 2019, an estimated 50.6 million US adults (20.8% of the adult population) used tobacco; 14.0% of the US adult population currently smoked cigarettes and 4.5% of the adult population used electronic cigarettes (e-cigarettes). Among pregnant US women who gave birth in 2016, 7.2% reported smoking cigarettes while pregnant
No abstract available.
INTRODUCTION:
E-cigarettes deliver an aerosol of nicotine by heating a liquid and are promoted as an alternative to combustible tobacco. This study determines the longitudinal associations between e-cigarette use and respiratory disease controlling for combustible tobacco use.
METHODS:
This was a longitudinal analysis of the adult Population Assessment of Tobacco and Health Waves 1, 2, and 3. Multivariable logistic regression was performed to determine the associations between e-cigarette use and respiratory disease, controlling for combustible tobacco smoking, demographic, and clinical variables. Data were collected in 2013-2016 and analyzed in 2018-2019.
RESULTS:
Among people who did not report respiratory disease (chronic obstructive pulmonary disease, chronic bronchitis, emphysema, or asthma) at Wave 1, the longitudinal analysis revealed statistically significant associations between former e-cigarette use (AOR=1.31, 95% CI=1.07, 1.60) and current e-cigarette use (AOR=1.29, 95% CI=1.03, 1.61) at Wave 1 and having incident respiratory disease at Waves 2 or 3, controlling for combustible tobacco smoking, demographic, and clinical variables. Current combustible tobacco smoking (AOR=2.56, 95% CI=1.92, 3.41) was also significantly associated with having respiratory disease at Waves 2 or 3. Odds of developing respiratory disease for a current dual user (e-cigarette and all combustible tobacco) were 3.30 compared with a never smoker who never used e-cigarettes. Analysis controlling for cigarette smoking alone yielded similar results.
CONCLUSIONS:
Use of e-cigarettes is an independent risk factor for respiratory disease in addition to combustible tobacco smoking. Dual use, the most common use pattern, is riskier than using either product alone.
Abstract
Background
Mobile phone‐based smoking cessation support (mCessation) offers the opportunity to provide behavioural support to those who cannot or do not want face‐to‐face support. In addition, mCessation can be automated and therefore provided affordably even in resource‐poor settings. This is an update of a Cochrane Review first published in 2006, and previously updated in 2009 and 2012.
Objectives
To determine whether mobile phone‐based smoking cessation interventions increase smoking cessation rates in people who smoke.
Search methods
For this update, we searched the Cochrane Tobacco Addiction Group's Specialised Register, along with clinicaltrials.gov and the ICTRP. The date of the most recent searches was 29 October 2018.
Selection criteria
Participants were smokers of any age. Eligible interventions were those testing any type of predominantly mobile phone‐based programme (such as text messages (or smartphone app) for smoking cessation. We included randomised controlled trials with smoking cessation outcomes reported at at least six‐month follow‐up.
Data collection and analysis
We used standard methodological procedures described in the Cochrane Handbook for Systematic Reviews of Interventions. We performed both study eligibility checks and data extraction in duplicate. We performed meta‐analyses of the most stringent measures of abstinence at six months' follow‐up or longer, using a Mantel‐Haenszel random‐effects method, pooling studies with similar interventions and similar comparators to calculate risk ratios (RR) and their corresponding 95% confidence intervals (CI). We conducted analyses including all randomised (with dropouts counted as still smoking) and complete cases only.
Main results
This review includes 26 studies (33,849 participants). Overall, we judged 13 studies to be at low risk of bias, three at high risk, and the remainder at unclear risk. Settings and recruitment procedures varied across studies, but most studies were conducted in high‐income countries. There was moderate‐certainty evidence, limited by inconsistency, that automated text messaging interventions were more effective than minimal smoking cessation support (RR 1.54, 95% CI 1.19 to 2.00; I2 = 71%; 13 studies, 14,133 participants). There was also moderate‐certainty evidence, limited by imprecision, that text messaging added to other smoking cessation interventions was more effective than the other smoking cessation interventions alone (RR 1.59, 95% CI 1.09 to 2.33; I2 = 0%, 4 studies, 997 participants). Two studies comparing text messaging with other smoking cessation interventions, and three studies comparing high‐ and low‐intensity messaging, did not show significant differences between groups (RR 0.92 95% CI 0.61 to 1.40; I2 = 27%; 2 studies, 2238 participants; and RR 1.00, 95% CI 0.95 to 1.06; I2 = 0%, 3 studies, 12,985 participants, respectively) but confidence intervals were wide in the former comparison. Five studies compared a smoking cessation smartphone app with lower‐intensity smoking cessation support (either a lower‐intensity app or non‐app minimal support). We pooled the evidence and deemed it to be of very low certainty due to inconsistency and serious imprecision. It provided no evidence that smartphone apps improved the likelihood of smoking cessation (RR 1.00, 95% CI 0.66 to 1.52; I2 = 59%; 5 studies, 3079 participants). Other smartphone apps tested differed from the apps included in the analysis, as two used contingency management and one combined text messaging with an app, and so we did not pool them. Using complete case data as opposed to using data from all participants randomised did not substantially alter the findings.
Authors' conclusions
There is moderate‐certainty evidence that automated text message‐based smoking cessation interventions result in greater quit rates than minimal smoking cessation support. There is moderate‐certainty evidence of the benefit of text messaging interventions in addition to other smoking cessation support in comparison with that smoking cessation support alone. The evidence comparing smartphone apps with less intensive support was of very low certainty, and more randomised controlled trials are needed to test these interventions.
Plain Language Summary
Can programmes delivered by mobile phones help people to stop smoking?
Background
Tobacco smoking is a leading cause of preventable death. Mobile phones can be used to support people who want to quit smoking. In this review, we have focused on programmes that use text messages or smartphone apps to do so.
Search date
We searched for published and unpublished studies in October 2018.
Study characteristics
We included 26 randomised controlled studies (involving over 33,000 people) that compared smoking quit rates in people who received text messages or smartphone apps to help them quit, with people who did not receive these programmes. We were interested in studies that measured smoking for six months or longer.
Key results
We found that text messaging programmes may be effective in supporting people to quit, increasing quit rates by 50% to 60%. This was the case when they were compared to minimal support or were tested as an addition to other forms of stop‐smoking support. There was not enough evidence to determine the effect of smartphone apps.
Quality and completeness of the evidence
Most of the studies were of high quality, although three studies had high drop out rates. We are moderately confident in the results of the text messaging interventions, but there were some issues with unexplained differences between study findings and for some comparisons there was not much data. We have low confidence in the results concerning smartphone apps, and more studies are needed in this field.
Draft: Recommendation Summary
Population
Recommendation
Grade
(What's This?)
Men ages 65 to 74 years who have ever smoked
The USPSTF recommends one-time screening for abdominal aortic aneurysm (AAA) with ultrasonography in men ages 65 to 75 years who have ever smoked.
Men ages 65 to 75 years who have never smoked
The USPSTF recommends that clinicians selectively offer screening for AAA with ultrasonography in men ages 65 to 75 years who have never smoked rather than routinely screening all men in this group. Evidence indicates that the net benefit of screening all men in this group is small. In determining whether this service is appropriate in individual cases, patients and clinicians should consider the balance of benefits and harms on the basis of evidence relevant to the patient’s medical history, family history, other risk factors, and personal values.
Women who have never smoked with no family history
The USPSTF recommends against routine screening for AAA with ultrasonography in women who have never smoked and have no family history.
Women ages 65 to 75 years who have ever smoked or have a family history
The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening for AAA with ultrasonography in women ages 65 to 75 years who have ever smoked or have a family history.
