NSW Population Health Survey (SAPHaRI). Centre for Epidemiology and Evidence, NSW Ministry of Health.
Kessler 10 (K10) is a 10-item questionnaire that measures anxiety, depression, agitation, and psychological fatigue in the most recent 4-week period. Refer to Methods tab for more information.
The K10 questions were included in the survey every year between 2002 and 2011. From 2011, the questions were included every second year.
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 New South Wales Population Health Survey uses Kessler 10 Plus (K10 ) to measure psychological distress in people aged 16 years and over. K10 is a 10-item questionnaire that measures anxiety, depression, agitation, and psychological fatigue in the most recent 4-week period, with additional questions to establish the effect of the distress. For each item in the questionnaire there is a 5-level response scale based on the amount of time (from none of the time to all of the time) the person experienced the particular symptom. When scoring responses, 1-5 points were assigned to each symptom, with 1 indicating none of the time and 5 indicating all of the time. The total score ranges from 10 points (all responses none of the time) to 50 points (all responses all of the time).
Responses are classified into 4 categories: low when the score is 10-15, moderate when the score is 16-21, high when the score is 22-29, and very high when the score is 30 or higher.
The indicator showing all categories of response, that is all scores, includes all participants of the survey. The categories shown are: Low (K10 between 10 and 15.9), Moderate (K10 between 16 and 21.9), High (K10 between 22 and 29.9), and Very high (K10 of 30 and over).
The questions used to define the indicator were all the questions in the K-10 Questionaire. The K10 questions were included in the survey every year between 2002 and 2011. From 2011, the questions were included every second year.
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
• 17.7% of adults aged 16 years and over (15.7% of men and 19.5% of women) experienced high or very high levels of psychological distress, as estimated from the 2019 NSW Adult Population Health Survey (self-reported using Computer Assisted Telephone Interviewing or CATI).
• 12.8% of adults aged 18 years and over (11.8% of males and 13.8% of females) in NSW experienced high or very high levels of psychological distress, as estimated from the 2017-18 Australian Health Survey (interviewer-administered questionnaire).
• Overall suicide rates dropped in NSW between 1997 and 2007 but have increased since this time. In 2017, 868 people died by suicide and males accounted for around 77.6% of these deaths.
• In 2018-19, there were 7,018 hospitalisations of NSW residents for intentional self-harm. Females accounted for 62% of these hospitalisations.
• In 2017, 14.0% of secondary school students reported high levels of psychological distress in the previous six months (9.7% of males and 18.2% of females). The proportion of students reporting high levels of psychological distress has remained stable over the last 3 years (2014 to 2017).
• Generally, a lower proportion of elderly adults have high levels of psychological distress than the overall adult population in NSW.
• The least socioeconomically disadvantaged adults had lower levels of psychological distress than the overall adult population in NSW.
• The proportion of adults reporting high and very high levels of psychological distress has remained fairly stable over the last decade.
Mental health disorders relate to behaviours and conditions which interfere with social functioning and capacity to negotiate daily life. Mental problems are also associated with higher rates of health risk factors, poorer physical health, and higher rates of deaths from many causes including suicide.
The classification of mental and behavioural disorders is difficult and warrants close attention to the types of disorders and syndromes which are included and excluded when comparing results from different sources. Further discussion of this issue is contained in the Methods tab.
Mental ill health is one of the leading causes of non-fatal burden of disease and injury in Australia. Mental ill health was estimated to account for 12% of the disease burden in Australia in 2015, with anxiety and depression, alcohol abuse and personality disorders accounting for almost three-quarters of this burden. Only 2.5% of the burden from mental disorders is due to mortality, most of which is accounted for by fatal outcomes associated with substance abuse (AIHW 2019).
Australian Institute of Health and Welfare 2019. Australian Burden of Disease Study: Impact and causes of illness and death in Australia 2015. Australian Burden of Disease Study series no. 19. BOD 22. Canberra: AIHW. Available at: https://www.aihw.gov.au/getmedia/c076f42f-61ea-4348-9c0a-d996353e838f/aihw-bod-22.pdf.aspx?inline=true
NSW has a range of mental health programs covering early intervention, prevention and promotion initiatives in place across the age spectrum. See http://www.health.nsw.gov.au/mentalhealth/Pages/default.aspx
Beyondblue at http://www.beyondblue.org.au
Black Dog Institute at http://www.blackdoginstitute.org.au
WayAhead: Mental Health Association NSW at https://wayahead.org.au
Australian Bureau of Statistics at http://www.abs.gov.au
Australian Institute of Health and Welfare at http://www.aihw.gov.au
healthdirect at http://www.healthdirect.gov.au