Information component 
Pg 4 Health Summary – Indicator 12 
Subject category / domain(s) 
The way we live 
Indicator name (* Indicator title in health profile) 
Estimated prevalence of adult smoking (*Adults who smoke) 
PHO with lead responsibility 
SEPHO 
Date of PHO dataset creation 
15/12/2006 
Indicator definition 
Prevalence of smoking, percentage of resident population, adults, 20002002, persons 
Geography 
Local Authority: County Districts, Metropolitan County Districts, Unitary Authorities, London Boroughs. 
Timeliness 
Updated as adhoc; the next generation of synthetic estimates is due for release in Summer 2007. 
Rationale:What this indicator purports to measure

Expected prevalence of adult smoking. 
Rationale:Public Health Importance

Smoking is the most important cause of preventable ill health and premature mortality in the UK. It is linked to respiratory illness, cancer and coronary heart disease. Smoking not only affects the smoker; over 17,000 children under the age of five are admitted to hospital every year with illnesses resulting from passive smoking.A list of disease specific conditions attributable to smoking is published in The Smoking Epidemic in England, HDA, 2004 http://www.nice.org.uk/page.aspx?o=502811Smoking is a modifiable lifestyle risk factor; effective tobacco control measures can reduce the prevalence of smoking in the population. 
Rationale: Purpose behind the inclusion of the indicator 
To estimate the expected proportion of adult smokers in local authorities given the characteristics of local authority populations.Smoking prevalence is a direct measure of health care need i.e. the ability to benefit from tobacco control interventions, including smoking cessation services. 
Rationale:Policy relevance

Choosing Health: Making healthy choices easier.http://www.dh.gov.uk/en/Publicationsandstatistics/ Publications/PublicationsPolicyAndGuidance/DH_4094550. Smoking Kills. A White Paper on Tobacco http://www.dh.gov.uk/en/Publicationsandstatistics/ Publications/PublicationsPolicyAndGuidance/DH_4006684Tackling Health Inequalities: A Programme for Actionhttp://www.dh.gov.uk/en/Publicationsandstatistics/ Publications/PublicationsPolicyAndGuidance/DH_4008268 
Interpretation: What a high / low level of indicator value means 
Given the characteristics of the local population, a high indicator value (red circle in health summary chart) represents a statistically significant higher level of estimated adult smoking prevalence for that local authority when compared to the national value.Given the characteristics of the local population, a low indicator value (amber circle in health summary chart) represents a statistically significant lower level of estimated adult smoking prevalence for that local authority when compared to the national value. However smoking at any prevalence level greater than 0 is undesirable, and therefore a low indicator value should not m ean that PH action is not needed. 
Interpretation: Potential for error due to type of measurement method 
It is important that users note that as these synthetic estimates are modelled they do not take account of any additional local factors that may impact on the true smoking prevalence rate in an area (e.g. local initiatives designed to reduce smoking). The figures, therefore, cannot be used to monitor performance or change over time.The model is a nonaetiological model i.e. is not based on known aetiological risk factors. This may lead to estimated smoking drinking levels which are at odds with, for example, local lifestyle survey results or modelled estimates which use known covariates such as socioeconomic status, age, gender and ethnicity such as the smoking prevalence estimates modelled in the Health Poverty Index available at www.hpi.org.uk (see variables used in generation of model in calculation of indicator section below).There may also be a discrepancy between the modelled lower tier estimates (districts) and upper tier (County geographies and above) estimates which are based on actual Health Survey for England data. This has lead to inconsistencies between lower tier and county estimates for some areas as the datasets are derived using different methods. 
Interpretation: Potential for error due to bias and confounding 
The synthetic estimates are subject to both sampling error and modelling error. Sampling error arises from the fact that only one of a number of possible samples from the population has been selected. Generally, the smaller the sample size the larger the variability in the estimates that one would expect to obtain from all the possible samples. The use of statistical models for prediction involves making assumptions about relationships in the data. The suitability of the chosen models for the given data and the validity of the model in describing real world dynamics have a bearing on the nature and magnitude of the errors introduced. A key source of modelling error arises from omitting variables that would otherwise help improve the model predictions either by error or because there is no available or reliable data source for them.The synthetic estimate generated for a particular area is the expected measure for that area based on its population characteristics – and not an estimate of the actual prevalence. In statistical terms, the synthetic estimate is actually a biased estimate of the true value for the area and, as such, should be treated with caution. As mentioned above, the modelbased estimates are unable to take account of any additional local factors that may impact on the true prevalence rate (e.g. local initiatives designed to reduce smoking levels).Validation exercises were used to check the appropriateness of the chosen models. Confidence intervals are placed around the synthetic estimates to capture both sampling and modelling error. The confidence intervals provide a range within which we can be fairly sure the ‘true’ value for that area lies. We recommend that users need to look at the confidence interval for the estimates, not just the estimate. Estimates for two areas can only be described as significantly different if the confidence intervals for the estimates do not overlap.Users should also note that the potential sources of bias and error also apply to any ranking or banding of the smallarea estimates. NatCen do not encourage any ranking of small area estimates within larger areas such as Local Authorities, Primary Care Organisations and Strategic Health Authorities. 
Confidence Intervals: Definition and purpose 
A confidence interval is a range of values that is normally used to describe the uncertainty around a point estimate of a quantity, for example, a mortality rate. This uncertainty arises as factors influencing the indicator are subject to chance occurrences that are inherent in the world around us. These occurrences result in random fluctuations in the indicator value between different areas and time periods. In the case of indicators based on a sample of the population, uncertainty also arises from random differences between the sample and the population itself.The stated value should therefore be considered as only an estimate of the true or ‘underlying’ value. Confidence intervals quantify the uncertainty in this estimate and, generally speaking, describe how much different the point estimate could have been if the underlying conditions stayed the same, but chance had led to a different set of data. The wider is the confidence interval the greater is the uncertainty in the estimate.Confidence intervals are given with a stated probability level. In Health Profiles 2007 this is 95%, and so we say that there is a 95% probability that the interval covers the true value. The use of 95% is arbitrary but is conventional practice in medicine and public health. The confidence intervals have also been used to make comparisons against the national value. For this purpose the national value has been treated as an exact reference value rather than as an estimate and, under these conditions, the interval can be used to test whether the value is statistically significantly different to the national. If the interval includes the national value, the difference is not statistically significant and the value is shown on the health summary chart with a white symbol. If the interval does not include the national value, the difference is statistically significant and the value is shown on the health summary chart with a red or amber symbol depending on whether it is worse or better than the national value respectively. 