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Estimates of the numbers and rates of deaths and hospitalisations attributable to the use of tobacco, alcohol, to high body mass and other risk factors used age and sex-specific aetiologic fractions developed by the School of Population Health, University of Queensland and the Australian Institute of Health and Welfare and published in 2007 (Begg et al. 2007).
The contribution of 14 health risks to the total burden of disease was assessed by the School of Population Health, using methods developed by the WHO Comparative Risk Assessment project (Ezzati et al. 2004). Earlier work by English and colleagues (English et al. 1995) was also used with reference to risks from the use of drugs and alcohol by the researchers from the School of Population Health. The main elements of the methodology are the prevalence of exposure to a health risk in a population and information on the risk of disease, injury or death from this exposure, which is derived from meta-analysis of published scientific literature. Calculations result in estimates of the proportions of cases of specific diseases and injuries that could be attributed to each risk factor.
For this report, electronic files of the aetiologic fractions developed by Begg and colleagues were obtained directly from the School of Population Health, University of Queensland by the Centre for Epidemiology and Research. The disease and injury groupings used in these files were defined using coding developed for the Burden of disease study (BOD), but a mapping to ICD-10-AM codes was also provided.
There are two steps in applying the aetiologic fractions to a death or hospitalisation dataset:
(a) ill-defined categories (e.g. ICD-10-AM: heart failure, unspecified diabetes mellitus and injuries with unspecified intent) are redistributed into specific BOD categories based on other information in the record and/or on a pro rata basis;
(b) the aetiologic fractions are applied to categorised records.
References
Begg S, Vos T, Barker B. The burden of disease and injury in Australia, 2003. Cat. no. PHE 82 edition. Canberra: AIHW, 2007. Available at http://www.aihw.gov.au/publications/index.cfm/title/10317
English DR, Holman CDJ, Milne MG. The quantification of drug caused morbidity and mortality in Australia. Canberra: Commonwealth Department of Human Services and Health, 1995.
Ezzati M, Lopez AD, Rodgers A, Murray CJL (eds). Comparative quantification of health risks: Global and regional burden of disease attributable to selected major risk factors. Geneva: World Health Organization, 2004. Available at http://www.who.int/healthinfo/global_burden_disease/cra/en/
Local Government Areas (LGAs) are the spatial units which represent the geographical areas of incorporated local government councils. There were 153 LGAs in NSW in 2008, using the 2006 Australian Bureau of Statistics (ABS) Australian Standard Geographic Classification (ASGC) boundaries.
The ABS publishes preliminary estimates of the residential population of LGAs in an annual March report including estimates concerning the previous year (ABS 3218.0 2010).
Population estimates used in this report
Due to the spatial isolation of Lord Howe Island, both the health data and population from Lord Howe Island SLA (population 364 persons) were omitted from the analyses in this report and only Unincorporated Far West SLA (population 1,116 persons) was included as Unincorporated NSW LGA. The 153 LGAs ranged in population from 1,116 to over 280,000 (based on population estimates as at June 2006). The detailed distribution of the population across the local government areas is shown in the Population topic in the report.
There were 13 LGAs with total populations less than 3000, and of these four had populations less than 2000 (based on population estimates as at June 2006). The areas with the smallest populations are particularly vulnerable to variation in their numbers and statstics due to chance.
Smoothing of estimates for small areas using Bayesian smoothing methods in this report
The term ‘small area’ refers to a small geographical area and a small population. Data from a small area are characterised by considerable variability. Smoothing is a general term for methods of minimising variability in data. Examples include rounding, moving averages, extending the period of time in which cases are counted or increasing the size of areas considered. In addition, Bayesian statistical smoothing can be used to adjust raw estimates in small areas by taking into account information from adjacent areas (local or spatial variability) and from the whole state (global or non-spatial variability).
The local government areas (LGA) are the smallest level at which data are analysed in this report. 'Statistical smoothing' using Bayesian smoothing methods are used to control for random variability in the small area estimates and result in more conservative estimates for small areas. These methods are described in general in Smoothing of estimates for small areas using Bayesian smoothing methods and in detail in two sections: Smoothing of estimates for population-based indicators and Smoothing of estimates for binary-type indicators. These Methods sections are available with all LGA based indicators.
References
Australian Bureau of Statistics. Regional population growth, Australia, 2008-09. 3218.0. Canberra: ABS, 2010. Available at http://www.abs.gov.au/AUSSTATS/abs@.nsf/mf/3218.0
The smoothing used for the population-based indicators is obtained by applying the convolution or Besag, York and Mollie (BYM) model (Lawson et al, 2003). This model incorporates both spatially correlated and uncorrelated variation. It accounts for variability across the entire state (uncorrelated or global variation) as well as variability amongst the local government areas immediately adjacent to the area in question (spatially correlated or local variation). It is a fully Bayesian model which has been used substantially for disease mapping since it was introduced by Besag, York and Mollie in 1991.
Under the BYM model, the smoothed SIR/SMR (or relative risk) is implemented using Gibbs sampling within WinBUGS. The sample values for the parameter of interest ( i) obtained by running this model in WinBUGS range in value within each LGA. This range of values form what is known as the posterior distribution of i, which represents the expected distribution of the SIR/SMR for each LGA when adjusted (smoothed) for the two types of variability mentioned above. For each LGA the mean of this posterior distribution was used as the best estimate of the smoothed SIR/SMR, and the proportion of the probability distribution above or below unity (one) was used to estimate the statistical significance of the small area estimate relative to the state average. It should to be noted that the posterior distribution is dependent upon the expected number of cases: the higher the expected number of cases the smaller will be the standard error of the distribution, and hence the posterior distribution of i will be 'tighter' around its mean.
A Bayesian 95% credible interval, which is obtained by selecting the 2.5th and 97.5th percentiles of the posterior distribution, is analogous to the more common 95% confidence interval used in frequentist-based analyses. It can be interpreted as the range in which 95% of the SIR/SMR estimates are located.
As noted previously, the proportion of the posterior probability distribution that lie above or below unity (one) was used to estimate the statistical significance of the small area estimate relative to the state average. A two-sided test was used, so that if the proportion of the posterior distribution above or below unity was less than 0.025, then that area was considered to have significantly decreased or increased risk at the 5% level of significance respectively.
All tabular output is in alphabetical order by LGA. The columns in the output present the smoothed number of hospitalisations or deaths per year (suppressed if this was less than five); the smoothed SIR/SMR; the upper and lower 95% credible interval endpoints for the smoothed estimates of SSR/SMR; and the level of significance in relation to the state average indicated as follows:
| † | means more than 99.5% of the posterior distribution is above one. This indicates that the estimated LGA SMR/SIR is significantly higher than the state average at the 1% level of significance. |
| † † | means more than 97.5%, but less than 99.5% of the posterior distribution is above one. This indicates that the estimated LGA SMR/SIR is significantly higher than the state average at the 5% level of significance. |
| 0 | means that between 2.5 and 97.5% of the distribution is above one. This indicates that the LGA SMR/SIR is not significantly different to the state average. |
| - | means less than 2.5% of the posterior distribution is above one. This indicates that the LGA SMR/SIR is significantly lower than the state average at 5% level of significance. |
| -- | means less than 0.5% of the posterior distribution is above one. This indicates that the LGA SMR/SIR is significantly lower than the state average at the 1% level of significance. |
Smoothing of estimates for small areas using Bayesian smoothing methods in this report
The Bayesian smoothing methods are described in general in Smoothing of estimates for small areas using Bayesian smoothing methods and in greater detail in two other Methods sections: Smoothing of estimates for population-based indicators (this document) and Smoothing of estimates for binary-type indicators. These Methods sections are available with all LGA based indicators, general description with every map indicator and one of the detailed descriptions depending on the type of analysis in an LGA map indicator.
References
Lawson AB, Browne WJ, Vidal Rodeiro CL. Disease mapping with WinBUGS and MLwiN (statistics in practice). Chichester: Wiley and Sons, Ltd, 2003.
Smoothing of estimates in this report
Smoothing is a general term for methods used to minimise random variability in data. Examples include rounding, moving averages, extending the period of time in which cases are counted or increasing the size of areas considered. In addition, Bayesian statistical smoothing can be used to adjust raw estimates in small areas by taking into account information from adjacent areas (local or spatial variability) and from the whole state (global or non-spatial variability).
In this report, Bayesian statistical smoothing was used for estimates in Local Government Areas.
These methods are described in general in Smoothing of estimates for small areas using Bayesian smoothing methods (this document) and in greater detail in two other Methods sections: Smoothing of estimates for population-based indicators and Smoothing of estimates for binary-type indicators. These Methods sections are available with all LGA based indicators, general description with every map indicator and one of the detailed descriptions depending on the type of analysis in an LGA map indicator.
Smoothing of estimates for small areas using Bayesian smoothing methods
Mapping cases or rates of events of interest, such as rates of deaths, cases of a disease, or rates of smoking, can be very informative in understanding the geographical distribution of the events. However, low numbers and rates can occur if the event is rare or if the areas studied have small populations (‘small areas’). If numbers or rates are low, then they will also be very variable, since chance events will have an undue effect on the total number. Consequently, estimates of numbers or rates may be too changeable to be reliable for most purposes. Occasionally, there may not be any cases of interest at all in an area, and the estimated rate for that area would be zero.
More reliable estimates of numbers and rates can be obtained by extending the length of time within which the cases are counted, or by increasing the size of the areas considered, but each of these methods may contradict the purpose of the study and undermine the usefulness of the data.
Another option is to apply statistical smoothing methods to calculate more reliable estimates with data collected in a shorter, more up-to-date period. Rates can be estimated even when there were no cases in an area in the relevant period of time.
Bayesian statistical smoothing methods are used to improve the estimates for individual areas by including information on events in adjacent areas which are expected to be similar, and overall variability between all areas. Smoothing has the greatest effect for areas where the number of cases is the lowest.
In this report, Bayesian smoothing was used to adjust raw estimates in Local Government Areas by taking into account information from adjacent areas (local or spatial variability) and from the whole state (global or non-spatial variability).
For population-based indicators such as rates of hospitalisation and rates of death, Bayesian smoothing was performed using the convolution or Besag, York and Mollie (BYM) model (Lawson et al, 2003). This model is widely used for disease mapping. The smoothed estimates calculated are the age-standardised relative risk for each area compared to NSW. That is, the standardised incidence ratio (SIR) for hospitalisations and the standardised mortality ratio (SMR) for deaths. All models were fit using WinBUGS 1.4.3 (WinBUGS, 2007). The details are further discussed in Methods section Smoothing of estimates for population-based indicators.
For indicators such as smoking in pregnancy and attendance at antenatal care, which are based on binary outcomes, smoothing was obtained by modelling the data using a binomial distribution with a logit link function. Smoothed proportions incorporate both local and global information, but are not age standardised. The details are further discussed in Methods section Smoothing of estimates for binary-type indicators.
The results of the Bayesian smoothing were used to determine whether the results obtained from individual areas are significantly different from the estimate of the average for all areas. Smoothed estimates are displayed on choropleth maps for all indicators. The intensity of the colour of a LGA increases as the ratio increases, and the same scale is used for all maps. The level of significance and the direction of difference from the state average is shown using plus and minus signs. One plus sign means that the smoothed estimate for a LGA is significantly greater than the state average at the 5% level of significance and two plus signs mean that the estimate is significantly greater at the 1% level of significance. Conversely, one minus sign means that the smoothed estimate for a LGA is significantly lower than the state average at the 5% level of significance and two minus signs refer to the 1% level of significance. If an area does not differ from the state, then no symbol is shown.
The success of the Bayesian smoothing method depends largely on the degree of similarity between areas that are used in the calculations. In the case of Local Government Areas in NSW, similarity is very high and the method works well.
References
Lawson AB, Browne WJ, Vidal Rodeiro CL. Disease mapping with WinBUGS and MLwiN (statistics in practice). Chichester: Wiley and Sons, Ltd, 2003.
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The disease and injury groupings used in the analysis of aetiologic fractions were defined using coding developed for the Burden of disease study (BOD) with a mapping to ICD-10-AM codes (Begg et al. 2007). These resources were provided by the School of Population Health, University of Queensland directly to the Centre for Epidemiology and Research.
Refer to the Methods tab for more information on aetiologic fractions methodology.
References
Begg S, Vos T, Barker B. The burden of disease and injury in Australia, 2003. Cat. no. PHE 82 edition. Canberra: AIHW, 2007. Available at http://www.aihw.gov.au/publications/index.cfm/title/10317
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Related Indicators
• Smoking causes more than 5,200 deaths and just over 44,000 hospitalisations in NSW per year.
• In 2010, just under 16% of adults in NSW smoked (daily or occasionally), 18% of males and 13.5% of females.
• Rates of current (daily or occasional) smoking were highest amongst those aged 25-34 years. The oldest age group reported the lowest rates.
Smoking as a health risk factor
Tobacco smoking is the leading preventable cause of illness and premature death, particularly from cardiovascular disease; cancers of the lung, larynx, and mouth; and chronic obstructive pulmonary disease. It is a major risk factor for coronary heart disease, stroke, peripheral vascular disease, cancer and a variety of other diseases and conditions . Smoking also contributes to the risk of sudden infant death syndrome (SIDS) and low birthweight (U.S. Department of Health and Human Services 2004). Tobacco smoking contributes more drug-related hospitalisations and deaths than alcohol and illicit drug use combined (AIHW Cat. no. AUS 122 2010) and is estimated to kill approximately half (Peto et al. 2004) to two-thirds (Doll et al. 2004 ) of all its long-term users.
The currently reviewed evidence on the mechanisms by which smoking causes disease indicates that there is no risk-free level of exposure to tobacco smoke, which causes adverse health outcomes, particularly cancer and cardiovascular and pulmonary diseases, through mechanisms that include DNA damage, inflammation, and oxidative stress (U.S. Department of Health and Human Services 2010).
Exposure to environmental tobacco smoke (ETS), particularly indoors, carries well documented health risks.
Burden of disease due to smoking and prevalence in Australia
Tobacco smoking was responsible for 7.8% of the total burden of disease in Australia in 2003 (Begg et al. 2007). In 2004-05, the total social costs of tobacco use in Australia were estimated to be $31.5 billion with tangible costs of $12.0 billion (Collins DJ et al. 2008).
In 2007, around 2.9 million Australians aged 14 years and over smoked daily. Males were more likely to be daily smokers (18.0%) than females (15.2%) (AHIW Cat No. PHE 98 2008).
References
Australian Institute of Health and Welfare. Australia’s health 2010. Australia’s health series no. 12. Cat. no. AUS 122. Canberra: AIHW, 2010. Available at http://www.aihw.gov.au/publication-detail/?id=6442468376
Australian Institute of Health and Welfare. 2007 National Drug Strategy Household Survey: first results. Drug Statistics Series No 20. Cat No. PHE 98. Canberra: AHIW, 2008. Available at http://www.aihw.gov.au/publications/phe/ndshs07-fr/ndshs07-fr-no-questionnaire.pdf
Begg S, Vos T, Barker B. The burden of disease and injury in Australia, 2003. Cat. no. PHE 82 edition. Canberra: AIHW, 2007. Available at http://www.aihw.gov.au/publications/index.cfm/title/10317
Collins DJ, Lapsley HM. The cost of tobacco, alcohol and illicit drug abuse to Australian society in 2004-05. National Drug Strategy Monograph Series no. 64. Canberra: Department of Health and Ageing, 2008. Available at http://www.nationaldrugstrategy.gov.au/internet/drugstrategy/publishing.nsf/Content/mono64
Doll R, Peto R, Boreham J and Sutherland I. "Mortality in relation to their smoking: 50 years' observations on male British doctors ". British Medical Journal 2004. Vol328 1519-28.
Peto R, Lopez AD, Boreham J, Thun M, Heath JC. Mortality from smoking in developed countries 1950-2000. Oxford: Oxford University Press, 2004. Available at http://rum.ctsu.ox.ac.uk/~tobacco
U.S. Department of Health and Human Services. How tobacco smoke causes disease: the biology and behavioral basis for smoking-attributable disease: a report of the Surgeon General. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. Atlanta, GA: 2010. Available at http://www.surgeongeneral.gov/library/tobaccosmoke/index.html
U.S. Department of Health and Human Services. The health consequences of smoking: a report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centres for Diseases Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2004. Available at http://www.cdc.gov/tobacco/data_statistics/sgr/2004/index.htm
Plain packaging
Australia has one of the most comprehensive tobacco control policies and programs in the world and a new National Tobacco Strategy is expected to be developed in 2011. The National Preventative Health Strategy recommends a range of actions aimed at reducing chronic disease burden associated with three lifestyle risk factors: obesity, tobacco and excessive alcohol consumption. The Strategy recommends ending all remaining forms of advertising and promotion of tobacco products, including eliminating promotion of tobacco products through the design of a package, by amending the Tobacco Advertising Prohibition Act 1992 and the Trade Practices CPIS (Tobacco) Regulations 2004 to reduce tobacco consumption and prevalence of smoking within Australian communities. Public consultation on a draft Tobacco Plain Packaging Bill 2010 is underway.
Social marketing and other measures
Other proposed national strategies focus on revenue measures that would reduce the affordability of tobacco products, legislative reforms to address current deficiencies in tobacco regulation, funding for social marketing campaigns, Indigenous tobacco control, other initiatives to reduce social disparities in smoking such as subsidising nicotine replacement therapy for highly disadvantaged people, and health system interventions (Tobacco Working Group 2009).
Smoke-free Environment Act
The Smoke-free Environments Act 2000 protects the community from second-hand smoke by prohibiting smoking in all enclosed public places in NSW (with the exception of the private gaming areas in Star City Casino).
Public Health (Tobacco) Act
The Public Health (Tobacco) Act 2008 strengthen restricts the sale, advertising and display of tobacco products, non-tobacco smoking products and smoking accessories in NSW. Key provisions of the Act include the introduction of a tobacco retailer notification scheme, restricting tobacco sales to a single point of sale in any retail outlet, a ban on smoking in cars with children present, the removal of tobacco products removed from shopper loyalty programs and the introduction of a total display ban for retailers (with the exception of approved specialist tobacconists).
Targeting young people
Recent amendments to the Public Health Act 1991 further strengthen measures already in place to prevent young people from taking up smoking. These include banning the sale of sweet, fruit or confectionery flavoured tobacco products that may encourage young people to smoke, and banning the sale of tobacco products from mobile or temporary premises at events targeted at young people, such as music festivals. The NSW Government introduced a number of reforms to further reduce children and young people's exposure and access to tobacco in amendments to the Public Health (Tobacco) Act 2008.
Support for smoking cessation
Smoking cessation, or quitting, has immediate and important health benefits for individuals of all ages. Ex-smokers have improved life expectancy and reduced risk of smoking-related disease, compared to continuing smokers (Fiore et al. 2000). Dependence on tobacco-delivered nicotine can be characterised as a chronic relapsing disorder. Without assistance, around 95% of quitters will fail on any single attempt and most people make several attempts before they are successful. At least 70% of Australian smokers are believed to be dependent on tobacco-delivered nicotine (Ministerial Council on Drug Strategy 2005).
The Quitline
The correct use of nicotine replacement therapies, such as gum, lozenge, patch, sublingual tablet or inhaler, doubles the chance of successfully quitting smoking (Stead et al. 2008). The Quitline (13 7848) provides expert smoking cessation advice and quitting smokers can enrol in the free callback service, where an advisor will provide ongoing support throughout the quit attempt. The Quitline is accessible for the cost of a local call throughout NSW. A fax referral system is in place for all health services in NSW to refer clients who want to quit smoking to the NSW Quitline.
NSW Health has published a guide to brief intervention for health professionals, titled 'Let's take a moment'. The document outlines clear and practical advice in the provision of smoking cessation interventions for health professionals, based on evidence for best practice (NSW Department of Health Let's take a moment 2005).
References
Fiore MC, Baily WC, Cohen SJ, Dorfman SF, Goldstein MG. Treating tobacco use and dependence. Clinical Practice Guideline. Rockville, MD: U.S. Department of Health and Human Services, Public Health Service, U.S. Surgeon General, 2000. Available at http://www.surgeongeneral.gov/tobacco/treating_tobacco_use.pdf
Ministerial Council on Drug Strategy. National Tobacco Strategy 2004-2009. Canberra: Department of Health and Ageing, 2005. Available at http://www.health.gov.au/internet/main/publishing.nsf/Content/phd-pub-tobacco-tobccstrat2-cnt.htm
NSW Department of Health . Let's take a moment. Quit smoking brief intervention - a guide for all health professionals. Sydney: NSW Department of Health, 2005. Available at http://www.health.nsw.gov.au/pubs/2005/lets_take_a_moment.pdf
Stead LF, Perera R, Bullen C, Mant D, Lancaster T. Nicotine replacement therapy for smoking cessation. Second edition. Cochrane Database of Systematic Reviews, 2008. Available at http://www.ncbi.nlm.nih.gov/pubmed/18253970
Tobacco Working Group. Australia: the healthiest country by 2020. Technical Report No 2. Tobacco control in Australia: making smoking history. Including addendum for October 2008 to June 2009. Canberra: National Preventative Health Taskforce, Commonwealth of Australia, 2009.
Useful websites include:
Australian Bureau of Statistics at http://www.abs.gov.au
Australian Institute of Health and Welfare at http://www.aihw.gov.au
HealthInsite at http://www.healthinsite.gov.au