NSW Population Health Survey (SAPHaRI). Centre for Epidemiology and Evidence, NSW Ministry of Health.
The indicator shows self-reported data collected through Computer Assisted Telephone Interviewing (CATI). Estimates were weighted to adjust for differences in the probability of selection among respondents and were benchmarked to the estimated residential population using the latest available Australian Bureau of Statistics mid-year population estimates. Adults are defined as persons aged 16 years and over in the NSW Population Health Survey.
In order to address diminishing coverage of the population by landline telephone numbers (<85% since 2010), a mobile phone number sampling frame was introduced into the 2012 survey.
LL/UL 95%CI = lower and upper limits of the 95% confidence interval for the point estimate.
The Australian Bureau of Statistics (ABS) has produced measures of socioeconomic disadvantage since the 1971 Census. The Socio-Economic Indexes for Areas (SEIFA) were first produced in 1990 and consisted of five indexes formed from the 1986 Census data (ABS).
There are four SEIFA indexes currently produced. In each census year, the ABS assigns index SEIFA scores to non-overlapping geographical areas covering all Australia calculated from the various socioeconomic characteristics from the Census of the people living in areas.
Each index is a summary of a different subset of Census variables and focuses on a different aspect of socioeconomic advantage and disadvantage (ABS, 2018). The reference value for the whole of Australia is set to 1,000. Lower values indicate lower socioeconomic status.
The indexes are:
• Index of Relative Socio-Economic Disadvantage (IRSD)
• Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD)
• Index of Economic Resources (IER)
• Index of Education and Occupation (IEO).
In the IRSD, the constituent characteristics relate to occupation, education, non-English speaking background and the economic resources of the household. From 2011, the proportion of Aboriginal people is no longer a constituent variable of IRSD (ABS, 2011).
The score for each index is an ordinal measure with a mean of 1000 and standard deviation of 100 for Australia, and from 2011, based on the index scores of all Statistical Areas Level 1 (SA1) in Australia. Scores for larger geographic areas such as Local Government Areas (LGAs) and Postal Areas (POA) are population-weighted averages of scores in constituent SA1.
The overall scores for states are not available because as the size of an area increases, it becomes correspondingly more heterogeneous and the socioeconomic index becomes less and less meaningful. For very large areas, it is more useful to look at the distribution of SA1 scores within each area. The distributions of SA1 scores within each state and territory are available at the ABS web site (ABS).
The ABS has released SEIFA scores after the last five censuses. The methods used to calculate scores were similar in 1986, 1991 and 1996, but changed in 2001, 2006 and 2011. The major change in 2006 was that the census data used in the calculation of the indexes was based on people's usual area of residence rather than their location on census night (place of enumeration) and in 2011 a new geography standard was used and the proportion of Aboriginal people was no longer a constituent variable of IRSD (ABS 2013). SEIFA 2016 broadly uses the same method that was used for SEIFA 2011, though there were updates to SA1 boundaries in many areas (ABS 2018).
In the Index of Relative Socio-Economic Disadvantage (IRSD), the constituent characteristics relate to occupation, education, non-English speaking background and the economic resources of the household. There are currently 16 variables contributing to the index and the proportion of Aboriginal people is no longer a constituent variable of IRSD (ABS 2018). This is the most frequently used and quoted SEIFA index.
The Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) consists of 25 contributing variables. They summarise information about the economic and social conditions of people and households within an area, including both relative advantage and disadvantage measures.
A low score indicates relatively greater disadvantage and a lack of advantage in general. For example, an area could have a low score if there are (among other things) many households with low incomes, or many people in unskilled occupations. A high score indicates a relative lack of disadvantage and greater advantage in general. For example, an area may have a high score if there are (among other things) many households with high incomes, or many people in skilled occupations (ABS 2016)
The Index of Economic Resources (IER) focuses on the financial aspects of relative socio-economic advantage and disadvantage, by summarising variables related to income and wealth. Education and occupation variables are excluded from this index because they are not direct measures of economic resources. Some relevant data on assets such as savings or equities are also not included because this information was not collected in the Census. There are 14 contributing variables. (ABS 2018)
The Index of Education and Occupation (IEO) is designed to reflect the educational and occupational level of communities. The education variables in this index show either the level of qualification achieved or whether further education is being undertaken. The occupation variables classify the workforce into the major groups and skill levels of the Australian and New Zealand Standard Classification of Occupations (ANZSCO) and the unemployed. This index does not include any income variables. There are 10 variables contributing to the total score. (ABS 2018)
Socioeconomic disadvantage is associated with a higher prevalence of health risk factors and higher rates of hospitalisations, deaths and other adverse health outcomes. Maps of socioeconomic disadvantage by LGA viewed in conjunction with maps of health outcomes can assist in identifying factors which may be associated with poorer outcomes.
The NSW population was divided into five groups based on the IRSD scores of their SA2 of residence. This means that SA2s were sorted by IRSD score and assigned to population-weighted quintiles, each containing close to one-fifth of the total population. In some charts and data tables on HealthStats NSW, the quintiles were divided into three groups: the lowest SES population-weighted quintile, the highest SES population-weighted quintile, and the rest of the population, comprising the remaining three population-weighted quintiles.
Postal Areas (POAs) were grouped into quintiles of socioeconomic status based on the IRSD.
Adhikari P. Socio-economic indexes for areas: Introduction, use and future directions. ABS Catalogue no. 1351.0.55.015. Canberra: ABS, 2006.
Australian Bureau of Statistics. Socio-Economic Indexes for Areas (SEIFA) - Technical Paper, 2011. SEIFA Cat no 2033.0.55.001. Canberra: ABS, 2013.
Australian Bureau of Statistics. Socio-Economic Indexes for Areas (SEIFA) - Technical Paper, 2016. SEIFA Cat no 2033.0.55.001. Canberra: ABS, 2018.
Australian Bureau of Statistics. 1996 Census of population and housing. Socioeconomic indexes for areas. 2039.0. Canberra: ABS, 1998. Available at http://www.ausstats.abs.gov.au/ausstats/free.nsf/0/C17E9A880591BB45CA256AE9001BCD57/$File/2039.0_1996.pdf
Australian Bureau of Statistics. Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016. Catalogue no 2033.0.55.001. Canberra: ABS, 2013. Available at http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/2033.0.55.001~2016~Main%20Features~SOCIO-ECONOMIC%20INDEXES%20FOR%20AREAS%20(SEIFA)%202016~1
The NSW Ministry of Health has conducted the Adult Population Health Survey (since 1997) and the Child Population Health Survey (since 2001) through 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 surveys are to provide detailed information on the health of adults and children in NSW and to support planning, implementation and evaluation of health services and programs in NSW.
The survey instruments include question modules on health behaviours, health status, and other associated factors. The methods and all questions are approved for use by the NSW Population and Health Services Research Ethics Committee. While some questions are collected annually, other questions are collected less frequently. The instrument is translated into 5 languages: Arabic, Chinese, Greek, Italian and Vietnamese.
The target population for the survey is all state residents living in private households. The target sample was approximately 1,000 persons in each of the health administrative areas (total sample 8,000-16,000 depending on the number of administrative areas).
From 1997 to 2010 the random digit dialling (RDD) landline sampling frame was developed as follows. Records from the Australia on Disk electronic white pages (phone book) were geo-coded using MapInfo mapping software. 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 non-listed 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.
From 2011 onwards the RDD landline sampling frame was developed as follows: Australian Communications and Media Authority exchange district and charge zone prefixes were generated for each of the strata (that is Local Health Districts introduced in January 2011) using “best fit” postcode (ACMA 2011). All prefixes were expanded with suffixes ranging from 0000 to 9999. The sample was then randomly ordered within each stratum. The estimated numbers required for each stratum was then forwarded to Sampleworx, who used proprietary software to test each numbers current status (valid, invalid or unknown and business, non-business or unknown). The resulting valid non-business and valid unknown numbers were used for the survey.
From 2012 onwards mobile only phone users were included into the surveys using an overlapping dual-frame design, which incorporates three groups of respondents: landline only users, mobile only users and landline and mobile users.
The RDD mobile sampling frame was developed by Sampleworx and included using all known Australian mobile prefixes. Sampleworx used proprietary software to test each number to identify valid and invalid numbers. A random sample of valid mobile numbers was then provided for use for the survey.
The introduction of this design was prompted by the increasing numbers of mobile-only phone users in the general population. Because this design increases the representativeness of the survey sample the production of unbiased estimates over time is also improved. This improvement has been confirmed by an analysis of unweighted estimates, which indicated that a greater proportion of younger people, of males, and of people born overseas participated in the mobile sample compared with the landline sample. Further, comparison of the demographic characteristics of the survey sample for the first quarter of 2012 with the NSW population showed that the NSW Population Health Survey was more representative of the NSW population than the previous sample (Barr et al. 2012).
Due to this change in design, the 2012 NSW PHS estimates reflect both changes that have occurred in the population over time and changes due to the improved design of the survey.
When considering significant differences over time excluding the 2010 and 2011 data points ensures that all of the estimates are from sampling frames that had adequate coverage of the population, that is 85% or more.
When the Australia on Disk electronic white pages became available, reliable introductory letters were sent to the selected households (1997 to 2008). Households were contacted using random digit dialling. Depending on the frame either one person from the household was randomly selected or the mobile phone holder was selected for inclusion in the survey.
Interviews are carried out continuously between February and December each year. An 1800 freecall contact number and website details are provided to potential respondents, so they can 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 until the end of 2014. For 2015, the NSW Population Health Survey was outsourced to McNair Ingenuity Research Pty Ltd, which is a social and market research company. All protocols related to the collection of respondent data have been implemented by McNair.
Up to 7 calls are made to establish initial contact with a household, and up to 5 calls are made in order to contact a selected respondent. Respondents reached by a landline phone number undergo a within-household selection process, where each member of the household has an equal chance of selection for interview. Respondents reached via mobile phone do not undergo this household selection process. Where a child under the age of 16 has been chosen within the household, the parent or main carer for that child completes the interview on their behalf. When an adult respondent that lives in a household with a child or children is selected for interview, at the end of their interview, they are offered to opportunity to complete a secondary interview about one of their children. In 2015, approximately 41% of all primary adult respondents living in households with at least one child under the age of 16 took up this option. If a parent completing an interview about their children is unsure of their child’s height and/or weight, the respondent is offered the opportunity to be contacted at a later date for this information.
For analysis, the survey sample was weighted to adjust for differences in the probabilities of selection among respondents. 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. Population data based on Australian Bureau of Statistics estimates and population projections based on data from the NSW Department of Planning and Infrastructure have been used to calibrate weights to the population within each health administrative area. and the Australian Bureau of Statistics latest mid-year population estimates (excluding residents of institutions) for each health administrative area.
Call and interview data were manipulated and analysed using SAPHaRI and SAS version 9.4 (SAS). The Taylor series expansion method was used to estimate sampling errors of estimators based on the stratified random sample. The 95 per cent confidence interval provides a range of values that should contain the actual value 95 per cent of the time.
Estimates were smoothed using least-squares spline transformation (CEE, Adult survey methods: web page).
Further information on the methods and weighting process is provided elsewhere (CEE, Child survey methods: web page).
A proportional hazard regression model with time equal to one unit using PROC SURVEYREG in SAS software was fitted. PROC SURVEYREG produces relative risks while taking into account the complex survey design; the strata and weights. The strata are a combination of Local Health District and year. The weights are based on the probability of selection in the survey and the age and sex structure of the population each year. For trend analysis, the weights are recalibrated to te 2001 Australlian Standard Population by five year age groups to age standardise the analysis. Age-sex standardisation was implemented across the complete survey file (both adult and child records). Separate models were fitted for each sex, and each model had year as the independant variable and the binary indicator as the dependant variable.
Estimated annual rates of change for health indicators (and associated 95% confidence intervals) were calculated from these models as relative differences. If the confidence intervals for the relative difference did not overlap a value of 1, the change was considered statistically significant.
In the reporting of trend analysis results on the topic landing page data summary tables (such as http://www.healthstats.nsw.gov.au/IndicatorGroup/ChildObesityTopic ), up and down arrows are used to show statistically significant increasing and decreasing annual rates of change. If the rate of change is not statistically significant, the trend is considered stable as illustrated by horizontal arrows. For statistically significant trends, the percentage point difference between modelled prevalence for the most recent year of data and that for 5 (short term trend) or 10 years (long term trend) prior. The short term percentage point difference is based on the model incorporating the 5 most recent years of data. The long term trend analysis is based on the model incorporating yje 10 most recent years of data. For context, the raw prevalence (and associated 95% confidence interval) estimated from the survey for the most recent year is also reported in the data summry tables.
Australian Bureau of Statistics. Standard Population for Use in Age-Standardisation Table (Cat. no. 3101.0), 2013
Australian Communications and Media Authority (ACMA). Communications report 2010-11 series: Report 2 – Converging communications channels: Preferences and behaviours of Australian communications users. Commonwealth of Australia, 2011. Available at http://www.acma.gov.au/
Barr ML, Ritten JJ, Steel DG, Thackway SV. ‘Inclusion of mobile phone numbers into an ongoing population health survey in New South Wales, Australia: design, methods, call outcomes, costs and sample representativeness’. BioMed Central: Medical Research Methodology 2012, 12:177 (22 November 2012). Available at www.biomedcentral.com/1471-2288/12/177.
Centre for Epidemiology and Evidence. NSW Adult Population Health Survey Methods. CEE, NSW Ministry of Health. Available at http://www.health.nsw.gov.au/surveys/adult/Pages/default.aspx
Centre for Epidemiology and Evidence. NSW Child Population Health Survey Methods. CEE, NSW Ministry of Health. Available at http://www.health.nsw.gov.au/surveys/child/Pages/default.aspx
PitneyBowes Software. MapInfo (software). PBS as MapInfo Corporation: version 1997. Available at www.pbinsight.com.au
Sampleworx Pty Ltd. Available at http://www.sampleworx.com.au
SAS Institute. The SAS System Enterprise Guide version 7.15 (software). Cary, NC: SAS Institute Inc., 2017. Available at www.sas.com
United Directory Systems. Australia on Disk (software). UDS: version 2004. Available at www.uniteddirectorysystems.com
The indicator includes those who had a car.
The question used to define the indicator was: Are people allowed to smoke in your car: yes, no, and don't have a car?
Data from the NSW Population Health Survey is used to measure the NSW State Government targets on reducing smoking in the population and is comparable with other sources of information on smoking in NSW.
• 11.2% of adults aged 16 years and over (12.1% of men and 10.2% of women) smoked daily in NSW in 2019 and 15.5% (18.0% of men and 13.1% of women) were current (daily or occasional) smokers. Estimates were produced from the NSW Adult Population Health Survey (self-reported using Computer Assisted Telephone Interviewing or CATI).
• 14.8% of persons aged 15 years and over (18.3% of males and 11.5% of females) in NSW were current smokers (defined as daily, at least once a week or less than weekly), as estimated from the 2017-18 National Health Survey (interviewer-administered questionnaire).
• 9.1% of mothers smoked during pregnancy in 2018, as reported to the NSW Perinatal Data Collection.
• 6.4% of students aged 12-17 years (7.0% of boys and 5.7% of girls) were current smokers, as estimated from the 2017 NSW School Students Health Behaviours Survey (self-completed questionnaire).
• 26.4% of Aboriginal adults aged 16 years and over smoked daily in NSW in 2018-2019 and 31.5% were current (daily or occasional) smokers. Estimates were produced from the NSW Adult Population Health Survey (self-reported using CATI).
• 43.2% of Aboriginal mothers smoked during pregnancy in 2018, as reported to the NSW Perinatal Data Collection.
Self-reported data on current smoking have been collected for adults in NSW since 1997 through the NSW Population Health Survey, since 1977-78 through the National Health Survey (from 1995), since 1985 through the National Drug Strategy Household Survey, and since 2011 through the Australian Health Survey.
Self-reported data on current smoking have been collected for students in NSW since 1984 through the NSW School Students Health Behaviours Survey.
Prevalence estimates, although differing slightly between surveys because of different sampling frames, participation rates and modes of collection (telephone, self-completed questionnaires, face-to-face personal interview and drop-and-collect), have all been decreasing over time.
A total of 60,249 hospitalisations were attributed to smoking in NSW in 2017-18, which was approximately 2.0% of all hospitalisations.
The rate of hospitalisations attributable to smoking decreased in males by nearly 23%, compared to a 10% decrease among females in NSW between 2001-02 and 2017-18. Rates have stabilised in recent years.
The rate of hospitalisations attributable to smoking increased in both Aboriginal males and Aboriginal females in the period between 2001-02 and 2011-12. In recent years, the rates have remained stable.
A total of 6,631 deaths were attributed to smoking in NSW in 2017, which was 12.6% of all deaths in 2017. In 2017, the rate of deaths attributable to smoking in males and females was 86.7 and 50.0 deaths per 100,000 population, respectively.
Australian Institute of Health and Welfare. National Drug Strategy Household Survey report. Available at: https://www.aihw.gov.au/about-our-data/our-data-collections/national-drug-strategy-household-survey
Australian Bureau of Statistics. Australian Health Survey. Available at: http://www.abs.gov.au/australianhealthsurvey
Tobacco smoking is one of the biggest causes of premature death and is a leading preventable cause of chronic disease in New South Wales. It is a major risk factor for cardiovascular disease, a range of cancers, chronic obstructive pulmonary disease, coronary heart disease and a variety of other diseases and conditions. Approximately one in five of all cancer deaths are due to tobacco smoking.
There is a no safe level of exposure to second-hand tobacco smoke. In adults, breathing second-hand smoke can increase the risk of heart disease, lung cancer and other lung diseases. It can worsen the effects of existing illnesses such as asthma and bronchitis. For children, inhaling second-hand smoke is even more dangerous. Children are more likely to suffer health problems due to second-hand smoke such as bronchitis, pneumonia and asthma.
Australia has one of the most comprehensive tobacco control policies and programs in the world. The aim of the tobacco control programs in NSW is to contribute to a continuing reduction of smoking prevalence rates in the community.
Information on NSW Health tobacco and smoking control programs and policies is available at: http://www.health.nsw.gov.au/tobacco.
Cancer Institute at: https://www.cancerinstitute.org.au/
I Can Quit at http://www.icanquit.com.au
Information on NSW Health programs and policies is available at http://www.health.nsw.gov.au/tobacco.
Australian Bureau of Statistics at http://www.abs.gov.au
Australian Institute of Health and Welfare at http://www.aihw.gov.au
I Can Quit at http://www.icanquit.com.au