Consumes more than 2 standard drinks by age and sex, latest year

Since 2002, the NSW Ministry of Health, in conjunction with the administration of local health services in NSW, has conducted the New South Wales Population Health Survey, an ongoing survey of the health of people in NSW using computer-assisted telephone interviewing (CATI). The main aims of the NSW Population Health Survey are to provide detailed information on the health of the people of NSW and to support the planning, implementation and evaluation of health services and programs in NSW. Prior to the introduction of the continuous survey in 2002, the Centre for Epidemiology and Research conducted adult health surveys in 1997 and 1998, an older people's health survey in 1999, and a child health survey in 2001.

Survey instrument

The survey instrument for 2010 included question modules on demographics, health behaviours, health status, and health services. Most of the survey questions have been used in previous surveys. All questions not previously used were submitted to the NSW Population and Health Services Research Ethics Committee for approval prior to use. New questions were also field tested before inclusion. The instrument was translated into 5 languages: Arabic, Chinese, Greek, Italian and Vietnamese.

Survey sample

The target population for the continuous survey is all state residents living in households with private telephones. In 2010, the target sample was approximately 1,500 people in each of the 8 area health services which existed at that time (a total sample of 12,000). The survey results were analysed and reported for the 15 geographical local health districts which came into existence in January 2011.

The sampling frame was developed as follows. Records from the Australia on Disk electronic white pages (phone book) were geo-coded using MapInfo mapping software.[1,2] The geo-coded telephone numbers were assigned to statistical local areas and area health services. The proportion of numbers for each telephone prefix was calculated by area health service. All prefixes were expanded with suffixes ranging from 0000 to 9999. The resulting list was then matched back to the electronic phone book. All numbers that matched numbers in the electronic phone book were flagged and the number was assigned to the relevant geo-coded area health service. Unlisted numbers were assigned to the area health service containing the greatest proportion of numbers with that prefix. Numbers were then filtered to eliminate continuous nonlisted blocks of greater than 10 numbers. The remaining numbers were then checked against the business numbers in the electronic phone book to eliminate business numbers. Finally, numbers were stratified by area health service and randomly selected by area health service. Households were contacted using random digit dialling. One person from the household was randomly selected for inclusion in the survey.

Interviews

In 2010, interviews were carried out continuously between February and December. An 1800 freecall contact number was provided to potential respondents, so they could verify the authenticity of the survey and ask any questions regarding the survey. Trained interviewers at the Health Survey Program CATI facility carried out interviews. Up to 7 calls were made to establish initial contact with a household, and up to 5 calls were made in order to contact a selected respondent.

Definition of local health district, metropolitan, and rural-regional

Respondents were allocated to a local health district (LHD) by postcode. Where a respondent's postcode crossed LHD boundaries, an LHD concordance was used to randomly allocate the respondent to 1 of the LHDs crossing the postcode. The concordance was constructed using the 2010 G-NAF (Geocoded National Address File) and QuickLocate Geocoding SDK.[3] For the small number of respondents did not provide their postcode, other information on suburb, local government area, and area health service were used to allocate the respondent to an LHD. Respondents have all been allocated to 1 of the 15 geographical LHDs, or to the Albury Local Government Area.

In this report, the term metropolitan means the respondent lived in 1 of the 8 geographical LHDs designated greater metropolitan: Central Coast, Illawarra Shoalhaven, Nepean Blue Mountains, Northern Sydney, South Eastern Sydney, South Western Sydney, Sydney, and Western Sydney. The term rural-regional means the respondent lived in 1 of the 7 geographical LHDs designated rural or regional: Far West, Hunter New England, Mid North Coast, Murrumbidgee, Northern NSW, Southern NSW, and Western NSW.

Indices of remoteness and disadvantage

The Accessibility-Remoteness Index of Australia Plus (ARIA ) is the standard Australian Bureau of Statistics (ABS) endorsed measure of remoteness.[4] It is derived using the road distances from populated localities to the nearest service centres across Australia. For each locality, the accessibility to services is expressed as a continuous measure from 0 (high accessibility) to 15 (high remoteness) and grouped into 5 categories: major cities, inner regional, outer regional, remote, and very remote.

The Socio-Economic Indexes for Areas (SEIFA) describe the socioeconomic aspects of geographical areas in Australia, using a number of underlying variables such as family and household characteristics, personal educational qualifications, and occupation.[5] The SEIFA index used to provide breakdowns of the New South Wales Population Health Survey data in 2010 is the Index of Relative Socio-Economic Disadvantage. This index is calculated on attributes such as low income and educational attainment, high unemployment, and people working in unskilled occupations. The SEIFA index values are grouped into 5 quintiles, with quintile 1 being the least disadvantaged and quintile 5 being the most disadvantaged.

Both the ARIA and SEIFA indexes were assigned on respondents' postcode of residence. To enable socioeconomic copmparisons, prevalence estimates for each SEIFA quintile were calculated for most health indicators in this report.

Data analysis

For analysis, the survey sample was weighted to adjust for differences in the probabilities of selection among respondents. These differences were due to the varying number of people living in each household, the number of residential telephone connections for the household, and the varying sampling fraction in each health area.

Post-stratification weights were used to reduce the effect of differing non-response rates among males and females and different age groups on the survey estimates. These weights were adjusted for differences between the age and sex structure of the survey sample and the Australian Bureau of Statistics 2010 mid-year population estimates (excluding residents of institutions) for each area health service. This enables calculation of prevalence estimates for the state population rather than for the respondents selected. Further information on the methods and weighting process is provided elsewhere.[6-7]

Call and interview data were manipulated and analysed using SAS version 9.2.[8] The Taylor expansion method was used to estimate sampling errors of estimators based on the stratified random sample.[8]

The 95 per cent confidence interval provides a range of values that should contain the actual value 95 per cent of the time. The width of the confidence interval relates to the differing sample size for each indicator. In general, a wider confidence interval reflects less certainty in the estimate for that indicator. If confidence intervals do not overlap then the observed estimates are significantly different. If confidence intervals overlap slightly the observed estimates may be significantly different but further testing needs to be done to establish that significance. For a pairwise comparison of subgroup estimates, the P value for a two-tailed test was calculated using the t-test for differences in means from independent samples and a modified form of t-test, which accounts for the dependence of the estimates, to test for differences between sub-group estimates and total estimates.[8]

The indicators in this report are presented in graphical form (in the PDF and HTML versions) and in graphical and tabular form (in the HTML version). For each indicator, where data are available, the report includes bar charts of the indicator by age group, socioeconomic status, and local health district, and a line chart of trend by sex. In most cases, trend data are presented from the base year; that is, from the first year data were collected for that indicator. In the HTML version, the table below the chart presents further information, including a link to a downloadable CSV file, which contains an estimate of the number of people in the population corresponding to the prevalence estimates for the indicator. Both the PDF and HTML versions can be obtained from the New South Wales Population Health Survey website at www.health.nsw.gov.au/publichealth/surveys/index.asp.

In this report, separate statistics for Albury are not presented because of their small sample size. Respondents in Albury have been included in the analysis for the total population of NSW. Caution needs to be taken when interpreting estimates for Far West LHD, due to the small sample size. Results for any group with less than 30 respondents are treated as less reliable and have been suppressed from this report with the label 'n/a' displayed in related graphs.

In the online HTML version of the report, the bottom of each table contains links to downloadable CSV files which contain the population estimates and trends for that indicator.

References

1. Australia on Disk [software]. Sydney: Australia on Disk, 2004.
2. MapInfo [software]. Troy, NY: MapInfo Corporation, 1997.
3. QuickLocate 3 Geocoding SDK [software]. Greenwich, NSW: MapData Services, 2011. Further information available from www.mapds.com.au (accessed 28 April 2011).
4. Australian Bureau of Statistics. ASGC Remoteness Classification: Purpose and Use. Census Paper No. 03/01. Commonwealth of Australia, 2003.
5. Australian Bureau of Statistics. 1996 Census of Population and Housing: Socio-Economic Indexes for Areas, Information Paper, Catalogue no. 2039.0. Canberra: ABS, 1998.
6. Barr M, Baker D, Gorringe M, and Fritsche L. NSW Population Health Survey: Description of Methods. Available online at www.health.nsw.gov.au/resources/publichealth/surveys/health_survey_method.asp (accessed 2 March 2011).
7. Steel D. NSW Population Health Survey: Review of the Weighting Procedures. Available online at www.health.nsw.gov.au/pubs/2006/review_weighting.html (accessed 2 March 2011).
8. SAS Institute. The SAS System for Windows version 9.2. Cary, NC: SAS Institute Inc., 2009. Further information available from www.sas.com (accessed 2 March 2011).

In February 2009, the 2001 Australian Alcohol Guidelines were replaced with the Australian Guidelines to Reduce Health Risks from Drinking Alcohol, which are based on modelling of the lifetime risk of harm from drinking.

To assist monitoring lifetime risk of harm, as defined by Guideline 1 of the 2009 Guidelines, this indicator provides information on the proportion of adults who consume more than 2 standard drinks on a day when they consume alcohol.

The questions used to define the indicator were: How often do you usually drink alcohol? On a day when you drink alcohol, how many standard drinks do you usually have? A standard drink is equal to 1 middy of full-strength beer, 1 schooner of light beer, 1 small glass of wine, or 1 pub-sized nip of spirits.