|
|
|
Select the rows below to view more detail on a method
The New South Wales Perinatal Data Collection (PDC), formerly called Midwives Data Collection (MDC) is a population-based collection covering all births in NSW public and private hospitals, as well as home births. It does not receive notifications of interstate births where the mother is resident in NSW.
The data collection has operated continuously since 1990 and covers: up to 2005, all births in NSW of at least 400 grams birth weight or 20 weeks gestation; and for 2006, stillbirths of at least 400 grams birthweight or 20 weeks gestation and all live births. The information is recorded by either the midwife or medical practitioner and includes demographic, medical and obstetric information on the mother and information on the labour, delivery and condition of the infant.
The collection has undergone three revisions over the years. Data on antenatal visits have been collected since 1994. The PDC database is compiled in the Information Management and Support Branch of the NSW Ministry of Health.
For this report, the PDC was accessed via HOIST. Data are presented for calendar years.
Perinatal deaths data for all population in NSW are obtained from the Australian Bureau of Statistics, but perinatal death data by baby's Aboriginal status are available only from the NSW PDC.
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
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.
Binary-type indicators do not use a population-based denominator. Usually the cases or events of interest are a subset of all cases, so the denominator is obtained from the same source as the cases of interest. Most of these indicators are binary in nature, for example smoking in pregnancy, where the mother either answers yes or no to the question 'did you smoke at all during pregnancy?', or else are made into a binary variable, for instance whether antenatal care was commenced before 20 weeks or not. Because of the binary nature of these indicators, the most appropriate model is the logistic model.
The smoothing used for these indicators is obtained by modelling the data using a binomial distribution with a logit link function (Lawson et al, 2003). The model still incorporates both spatially correlated (local variation) and uncorrelated variation (global variation), however results are not age-standardised. This model was also implemented using Gibbs sampling within WinBUGS. The sample values for the parameter of interest (smoothed proportion) range in value within each LGA. This range of values forms a distribution known as the posterior distribution of the smoothed proportion. For each LGA, the mean of the posterior distribution is used as the best estimate of the smoothed proportion.
In order to facilitate comparisons between areas and make mapping consistent with the population-based indicators, a posterior distribution for the prevalence ratio was calculated for each LGA by dividing sampled values of the smoothed proportion by the overall proportion for the state. The proportion of the posterior distribution of the smoothed prevalence ratio above unity was used as an estimate of the significance of the small area estimate relative to the state average. It should again be noted that the posterior distribution are dependent upon the total number of cases: the higher the total number of cases in an LGA the smaller the standard error of the distribution, and hence the posterior distribution of the smoothed prevalence ratio will be 'tighter' around its mean.
Ninety-five percent credible intervals for smoothed prevalence ratios and estimates of significance were obtained using methods similar to those discussed for continuous data elsewhere. All tabular output included the smoothed number of cases per year (suppressed if this was less than five); the smoothed proportion; the smoothed estimate of the prevalence ratio; the lower and upper 95% credible interval endpoints for the smoothed prevalence ratio; and the level of significance in relation to the state average.
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 and Smoothing of estimates for binary-type indicators (this document). 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.
Select the rows below to view more detail on a code
These data come from the NSW Perinatal Data Collection (PDC) where they are coded according to a completed NSW Perinatal Data Collection Form, which can be accessed via the intranet by NSW Health staff at http://internal.health.nsw.gov.au/data/collections/mdc/NSWH%20Perinatal%20Data.pdf.
Download the indicator content
|
Add indicator content to My Report
|
||||||||||||||||||||
Download the data
|
Download the associated information
Commentary on topics
linked to this indicator |
||||||||||||||||||||
Download the graph image
|
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