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      Individual behavioural counselling for smoking cessation

      1 , 2
      Cochrane Tobacco Addiction Group
      Cochrane Database of Systematic Reviews
      Wiley

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          Abstract

          Individual counselling from a smoking cessation specialist may help smokers to make a successful attempt to stop smoking.

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          Most cited references153

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          Measuring inconsistency in meta-analyses.

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            Physician advice for smoking cessation.

            Healthcare professionals frequently advise people to improve their health by stopping smoking. Such advice may be brief, or part of more intensive interventions. The aims of this review were to assess the effectiveness of advice from physicians in promoting smoking cessation; to compare minimal interventions by physicians with more intensive interventions; to assess the effectiveness of various aids to advice in promoting smoking cessation, and to determine the effect of anti-smoking advice on disease-specific and all-cause mortality. We searched the Cochrane Tobacco Addiction Group trials register in January 2013 for trials of interventions involving physicians. We also searched Latin American databases through BVS (Virtual Library in Health) in February 2013. Randomised trials of smoking cessation advice from a medical practitioner in which abstinence was assessed at least six months after advice was first provided. We extracted data in duplicate on the setting in which advice was given, type of advice given (minimal or intensive), and whether aids to advice were used, the outcome measures, method of randomisation and completeness of follow-up.The main outcome measure was abstinence from smoking after at least six months follow-up. We also considered the effect of advice on mortality where long-term follow-up data were available. We used the most rigorous definition of abstinence in each trial, and biochemically validated rates where available. People lost to follow-up were counted as smokers. Effects were expressed as relative risks. Where possible, we performed meta-analysis using a Mantel-Haenszel fixed-effect model. We identified 42 trials, conducted between 1972 and 2012, including over 31,000 smokers. In some trials, participants were at risk of specified diseases (chest disease, diabetes, ischaemic heart disease), but most were from unselected populations. The most common setting for delivery of advice was primary care. Other settings included hospital wards and outpatient clinics, and industrial clinics.Pooled data from 17 trials of brief advice versus no advice (or usual care) detected a significant increase in the rate of quitting (relative risk (RR) 1.66, 95% confidence interval (CI) 1.42 to 1.94). Amongst 11 trials where the intervention was judged to be more intensive the estimated effect was higher (RR 1.84, 95% CI 1.60 to 2.13) but there was no statistical difference between the intensive and minimal subgroups. Direct comparison of intensive versus minimal advice showed a small advantage of intensive advice (RR 1.37, 95% CI 1.20 to 1.56). Direct comparison also suggested a small benefit of follow-up visits. Only one study determined the effect of smoking advice on mortality. This study found no statistically significant differences in death rates at 20 years follow-up. Simple advice has a small effect on cessation rates. Assuming an unassisted quit rate of 2 to 3%, a brief advice intervention can increase quitting by a further 1 to 3%. Additional components appear to have only a small effect, though there is a small additional benefit of more intensive interventions compared to very brief interventions.
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              Estimation of a common effect parameter from sparse follow-up data.

              Breslow (1981, Biometrika 68, 73-84) has shown that the Mantel-Haenszel odds ratio is a consistent estimator of a common odds ratio in sparse stratifications. For cohort studies, however, estimation of a common risk ratio or risk difference can be of greater interest. Under a binomial sparse-data model, the Mantel-Haenszel risk ratio and risk difference estimators are consistent in sparse stratifications, while the maximum likelihood and weighted least squares estimators are biased. Under Poisson sparse-data models, the Mantel-Haenszel and maximum likelihood rate ratio estimators have equal asymptotic variances under the null hypothesis and are consistent, while the weighted least squares estimators are again biased; similarly, of the common rate difference estimators the weighted least squares estimators are biased, while the estimator employing "Mantel-Haenszel" weights is consistent in sparse data. Variance estimators that are consistent in both sparse data and large strata can be derived for all the Mantel-Haenszel estimators.
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                Author and article information

                Journal
                146518
                Cochrane Database of Systematic Reviews
                Wiley
                14651858
                March 31 2017
                Affiliations
                [1 ]King’s College London; GKT School of Medical Education; London UK
                [2 ]University of Oxford; Nuffield Department of Primary Care Health Sciences; Radcliffe Observatory Quarter Woodstock Road Oxford UK OX2 6GG
                Article
                10.1002/14651858.CD001292.pub3
                6464359
                28361496
                7b4c4808-8a0b-4450-bfeb-97075ba26b33
                © 2017
                History

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