An analysis of NHS Stop Smoking advisors' smoking history, level of training and impact on self-reported advisor quit rate

• Main Author: Anastasi, Natasha Angela
• Published: London Metropolitan University 2015
• Subjects: 362.29; 610 Medicine & health
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681352

Smoking is a deep-rooted and complex psychological, behavioural, social and physiological practice. Smoking is reinforced by positive reinforcement outcomes and negative withdrawals symptoms experienced during abstinence (Marks et al., 2006). Furthermore smoking is a primary contributing factor in health inequality (Raw, McNeill, & West, 1998). Current data suggests that smoking rates in England have fallen to their lowest rate in over eighty years (Brown & West, 2014). Despite the downwards trend in smoking prevalence, smoking remains the UK’s biggest preventable cause of premature mortality (Twigg, Moon, & Walker, 2004). It has been acknowledged that the specialist Stop Smoking support programmes provided by the NHS have helped reduce smoking prevalence (Bauld, Bell, McCullough, Richardson, & Greaves, 2010). Whilst there has been a vast amount of research investigating the most effective behavioural and pharmacological support models (Lancaster, Stead, Silagy, & Sowden, 2000) there has been little research on the impact of smoking cessation advisor smoking status and clinical effectiveness (Lindson-Hawley, Begh, McDermott, McEwen, & Lycett, 2013). This study aimed to identify factors that contribute to NHS Stop Smoking advisor performance. Factors analysed included: advisor smoking status (historic and present); attitudes and beliefs towards smoking; level of training; proportion of time spent supporting patients; and number of patients supported. This study implemented a quantitative cross-sectional design. An online survey was used which consisted of three standardised questionnaires. The sample consisted of Stop Smoking advisors (n=159) from 24 London boroughs. The results were analysed using an ordinal logistic regression. The regression model showed no significant impact on the majority of the variables investigated. The non-contributing variables included: level of training; years practiced; level of advisor education; and number of patients supported in a given year. The model did however show that proportion of time spent delivering smoking cessation support significantly influenced quit rate. The results reinforce previous literature by Lindson-Hawley et al. (2013) which suggests smoking status does not significantly influence smoking practitioners’ outcomes or attitudes towards smoking. Proportion of time spent providing support was found to be a significant factor in predicting quit rate. These results suggest that these factors should be considered when recruiting, commissioning and training new smoking cessation advisors or provider organisations.