Modelling continuous abstinence rates over time from clinical trials of pharmacological interventions for smoking cessation.

BACKGROUND AND AIM: It is useful, for theoretical and practical reasons, to be able to specify functions for continuous abstinence over time in smoking cessation attempts. This study aimed to find the best-fitting models of mean proportion abstinent with different smoking cessation pharmacotherapies up to 52 weeks from the quit date. METHODS: We searched the Cochrane Database of Systematic Reviews to identify randomized controlled trials (RCTs) of pharmacological treatments to aid smoking cessation. For comparability, we selected trials that provided 12 weeks of treatment. Continuous abstinence rates for each treatment at each follow-up point in trials were extracted along with methodological details of the trial. Data points for each pharmacotherapy at each follow-up point were aggregated where the total across contributing studies included at least 1000 participants per data point. Continuous abstinence curves were modelled using a range of different functions from the quit date to 52-week follow-up. Models were compared for fit using R2 and Bayesian information criterion (BIC). RESULTS: Studies meeting our selection criteria covered three pharmacotherapies [varenicline, nicotine replacement therapy (NRT) and bupropion] and placebo. Power functions provided the best fit (R2  > 0.99, BIC < 17.0) to continuous abstinence curves from the target quit date in all cases except for varenicline, where a logarithmic function described the curve best (R2  = 0.99, BIC = 21.2). At 52 weeks, abstinence rates were 22.5% (23.0% modelled) for varenicline, 16.7% (16.0% modelled) for bupropion, 13.0% (12.4% modelled) for NRT and 8.3% (8.9% modelled) for placebo. For varenicline, bupropion, NRT and placebo, respectively, 55.9, 65.0, 62.3 and 56.5% of participants who were abstinent at the end of treatment were still abstinent at 52 weeks. CONCLUSIONS: Mean continuous abstinence rates up to 52 weeks from initiation of smoking cessation attempts in clinical trials can be modelled using simple power functions for placebo, nicotine replacement therapy and bupropion and a logarithmic function for varenicline. This allows accurate prediction of abstinence rates from any time point to any other time point up to 52 weeks.

Development of a tool to assess beliefs about mythical causes of cancer: the Cancer Awareness Measure Mythical Causes Scale.

OBJECTIVES: We aimed to develop a reliable and valid measure to assess public beliefs in mythical causes of cancer: the Cancer Awareness Measure-MYthical Causes Scale (CAM-MYCS). DESIGN AND SETTING: Cancer myth items were generated from a literature review, social media and interviews (n=16). The CAM-MYCS was prepared by reducing items using (a) an online sample (n=527) with exploratory factor analysis and (b) cancer experts with Delhpi methodology (n=13). To assess test-retest reliability and sensitivity to change, students (n=91) completed the CAM-MYCS at baseline and 1 week after exposure to information on lifestyle-related cancer causes or control information. Construct validity was tested by comparing CAM-MYCS scores between cancer experts (n=25) and students (n=91). Factor structure and internal reliability were investigated in a national sample (n=1993). RESULTS: Out of 42 items generated, 12 were retained based on factor loadings, prevalence of endorsement and expert consensus. CAM-MYCS scores improved (fewer myths endorsed) among students exposed to information on cancer causes compared with the control group (p<0.001) and showed high test-retest reliability (r=0.90, p<0.001). Cancer experts reported higher CAM-MYCS scores (fewer myths endorsed) than students (p<0.001). The factor structure of the CAM-MYCS was confirmed in the national sample and internal reliability was high (α=0.86). Inclusion of the CAM-MYCS alongside items assessing knowledge of actual cancer causes did not affect responses. CONCLUSIONS: The CAM-MYCS tool is a reliable and valid tool assessing beliefs in mythical causes of cancer, and it can be used alongside items assessing known causes of cancer.

Prevalence of beliefs about actual and mythical causes of cancer and their association with socio-demographic and health-related characteristics: Findings from a cross-sectional survey in England.

BACKGROUND: Literature on population awareness about actual causes of cancer is growing but comparatively little is known about the prevalence of people's belief concerning mythical causes of cancer. This study aimed to estimate the prevalence of these beliefs and their association with socio-demographic characteristics and health behaviours. METHODS: A survey containing validated measures of beliefs about actual and mythical cancer causes and health behaviours (smoking, alcohol consumption, physical activity, fruit and vegetable consumption, overweight) was administered to a representative English population sample (N = 1330). RESULTS: Awareness of actual causes of cancer (52% accurately identified; 95% confidence interval [CI] 51-54) was greater than awareness of mythical cancer causes (36% accurately identified; 95% CI 34-37; P < 0.01). The most commonly endorsed mythical cancer causes were exposure to stress (43%; 95% CI 40-45), food additives (42%; 95% CI 39-44) and non-ionizing electromagnetic frequencies (35%; 95% CI 33-38). In adjusted analysis, greater awareness of actual and mythical cancer causes was independently associated with younger age, higher social grade, being white and having post-16 qualifications. Awareness of actual but not mythical cancer causes was associated with not smoking and eating sufficient fruit and vegetables. CONCLUSIONS: Awareness of actual and mythical cancer causes is poor in the general population. Only knowledge of established risk factors is associated with adherence to behavioural recommendations for reducing cancer risk.

Characterising the nicotine metabolite ratio and its association with treatment choice: A cross sectional analysis of Stop Smoking Services in England.

Pharmacotherapy provision based on Nicotine Metabolite Ratio (NMR) status (slow/normal metabolism) may improve smoking cessation rates. However, it is unclear whether NMR status is consistent across patient characteristics and current treatment choice. Data come from 1,826 participants attending Stop Smoking Services (SSS) across England in 2012/13. Sociodemographic, mental/physical health, smoking and treatment characteristics (nicotine replacement therapy vs. other pharmacotherapy; group vs. one-to-one behavioural support) were assessed. Salivary nicotine metabolites were measured and NMR (3-hydroxycotinine/cotinine) computed, characterising smokers as slow (NMR < 0.31) or normal (NMR ≥ 0.31) metabolisers. Normal metabolisers were older than slow metabolisers (Odds Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.32-1.69) but no other characteristics were associated with NMR status. Overall, predictors accounted for only 7.3% of NMR variance. In adjusted analysis, pharmacotherapy type was not associated with NMR status, but normal metabolisers were less likely to use group support (OR = 0.67, 95% CI = 0.51-0.89). NMR status does not vary substantially across sociodemographic characteristics. Given its impact on pharmacotherapy efficacy, the lack of an association with pharmacotherapy choice suggests there is scope to use NMR status to optimise the selection and efficacy of smoking cessation pharmacotherapy. The unexpected association of NMR status with behavioural support should be explored further.

Prescribing tamoxifen in primary care for the prevention of breast cancer: a national online survey of GPs' attitudes.

BACKGROUND: The cancer strategy for England (2015-2020) recommends GPs prescribe tamoxifen for breast cancer primary prevention among women at increased risk. AIM: To investigate GPs' attitudes towards prescribing tamoxifen. DESIGN AND SETTING: In an online survey, GPs in England, Northern Ireland, and Wales (n = 928) were randomised using a 2 × 2 between-subjects design to read one of four vignettes describing a healthy patient seeking a tamoxifen prescription. METHOD: In the vignette, the hypothetical patient's breast cancer risk (moderate versus high) and the clinician initiating the prescription (GP prescriber versus secondary care clinician [SCC] prescriber) were manipulated in a 1:1:1:1 ratio. Outcomes were willingness to prescribe, comfort discussing harms and benefits, comfort managing the patient, factors affecting the prescribing decision, and awareness of tamoxifen and the National Institute for Health and Care Excellence (NICE) guideline CG164. RESULTS: Half (51.7%) of the GPs knew tamoxifen can reduce breast cancer risk, and one-quarter (24.1%) were aware of NICE guideline CG164. Responders asked to initiate prescribing (GP prescriber) were less willing to prescribe tamoxifen than those continuing a prescription initiated in secondary care (SCC prescriber) (68.9% versus 84.6%, P<0.001). The GP prescribers reported less comfort discussing tamoxifen (53.4% versus 62.5%, P = 0.01). GPs willing to prescribe were more likely to be aware of the NICE guideline (P = 0.039) and to have acknowledged the benefits of tamoxifen (P<0.001), and were less likely to have considered its off-licence status (P<0.001). CONCLUSION: Initiating tamoxifen prescriptions for preventive therapy in secondary care before asking GPs to continue the patient's care may overcome some prescribing barriers.