Notes on the data: Community strength and wellbeing indicators

Community strengths; Personal and financial stressors; Health status and disability; and Access to services: financial and transport barriers - modelled estimates, 2014


Policy context:   A strong community is one that is sustainable over generations and resilient in times of crisis; and has assets in the resources, skills and commitment of its members, not only material ones [1]. Social participation and involvement in local governance are the hallmarks of strong communities. Forms of social participation, such as volunteering or being a member of a community group, can benefit individuals in areas such as improved health and wellbeing, social inclusion and reduced crime, improved local services and facilities, and better educational outcomes [2].

Community strength indicators measure how people feel about aspects of the community in which they live, and their participation in opportunities to shape their community. Healthy communities need a balance between three types of social connection: close personal networks, broader community networks (made through work, school, interest groups, volunteering activities etc.), and governance networks involved in decision-making [3].

Examples of having positive personal networks include the ability to access emotional or financial support in times of crisis, as well as being prepared to offer such support to others beyond immediate household members [4]. Those who do not have such supports experience poorer health and wellbeing, greater stress in their lives and a higher risk of poverty and social exclusion [2]. Community members who report fair or poor health or a disability, and who are also financially stressed may delay in seeking medical care, or in purchasing prescribed medication because of the cost. Other barriers which can adversely affect people’s health and wellbeing are lack of transport, other difficulties accessing needed services, and feeling unsafe in their local environment [5].

Details regarding responses to the individual indicators listed below (e.g., proportion of the population able to get support in times of crisis, or who experienced a barrier to accessing healthcare when needed it in the last 12 months, with main reason being cost of service) are at


  1. The identification and analysis of indicators of community strength and outcomes. (Occasional Paper 3). Canberra, ACT: Department of Family and Community Services; 2001.
  2. Indicators of community strength: a framework and evidence. Melbourne: Department for Victorian Communities; 2006.
  3. The state of social capital: bringing back in power, politics and history. Theory and Society. 2002;31(5):573-621.
  4. Arashiro Z. Money matters in times of change: Financial vulnerability through the life course. Melbourne, Victoria: Brotherhood of St Laurence; 2011.
  5. AISB (Australian Social Inclusion Board). Social inclusion in Australia: how Australia is faring. Canberra: Office of the Prime Minister; 2010.

Notes:  The Australian Bureau of Statistics (ABS) 2014 General Social Survey (GSS) includes a range of questions which aim to assess community strength, both in terms of its positive aspects (such as volunteering, tolerance of other cultures and availability of personal supports) and the negative effects on people when community strength is less apparent (such as feeling unsafe in the community, social isolation and the consequences of financial stress and disadvantage). The GSS collected data on the range of social dimensions from the same individual to enable analysis of the interrelationships in social circumstances and outcomes, including the exploration of multiple advantage and disadvantage experienced by that individual. The ABS survey was conducted by personal interview and included people aged 18 years and over resident in private dwellings, throughout the Australia, other than those living in very remote areas and people living in discrete Aboriginal and Torres Strait Islander communities.

The modelled estimates presented have been synthetically predicted at the Population Health Area (PHA) level. The following is the list of indicators for which modelled estimates were produced:

Indicators of community strength and wellbeing

Community strengths (modelled estimates)

  • Persons aged 18 years and over who did unpaid voluntary work in the last 12 months through an organisation
  • Persons aged 18 years and over who are able to get support in times of crisis from persons outside the household
  • Persons aged 18 years and over (or their partner) who provide support to other relatives living outside the household
  • Persons aged 18 years and over who feel very safe/safe walking alone in local area after dark
  • Persons aged 18 years and over who disagree/strongly disagree with acceptance of other cultures
  • In the past 12 months, felt that they had experienced discrimination or have been treated unfairly by others

Personal and financial stressors (modelled estimates)

  • Persons aged 18 years and over whose household could raise $2,000 within a week
  • Persons aged 18 years and over who had government support as their main source of income in the last 2 years
  • Persons aged 18 years and over who had government support as their main source of income, for 13 months or more, within the past 24 months

Access to services: financial and transport barriers (modelled estimates)

  • Persons aged 18 years and over who often has a difficulty or can't get to places needed with transport, including housebound
  • Persons aged 18 years and over who experienced a barrier to accessing healthcare when needed it in the last 12 months, with main reason being cost of service

For further information on these indicators, please refer to the Glossary and associated information at

Notes on modelled estimates

Through the use of synthetic estimation techniques it is possible to produce PHA-level statistics. Synthetic estimation predicts a value for an area with a small population based on modelled survey data and known characteristics of the area. A modelled estimate can be interpreted as the likely value for a 'typical' area with those characteristics. The model used for predicting small area data is determined by analysing data at a higher geographic level, in this case Australia. The relationship observed at the higher geographic level between the characteristic of interest and known characteristics is assumed to also hold at the small area level. The estimates are made by applying the model to data on the known characteristics that can be reliably estimated at the small area level. This modelling technique can be considered as a sophisticated prorating of Australian estimates to the small area level.

The ABS has used various methods to produce small area predictions from a number of surveys. The methods are described in the Small Area Estimates Manual version 1.0 which was released in May 2006.

Users of these modelled estimates should note that they do not represent data collected in administrative or other data sets. As such, they should be used with caution, and treated as indicative of the likely social dimensions present in an area with these demographic and socioeconomic characteristics.

What the estimates do achieve, however, is to summarise the various demographic, socioeconomic and administrative information available for an area in a way that indicates the expected social dimensions for a typical area in Australia with the same characteristics. In the absence of accurate, localised information about these indicators, such predictions can usefully contribute to policy and program development, service planning and other decision-making processes that require an indication of the geographic distribution of the social indicator.

Although the GSS sample includes the majority of people living in households in private dwellings, it excludes those living in the most remote areas of Australia and in discrete Aboriginal communities; whereas these areas comprise less than 3% of the total population, Aboriginal people comprise up to one third of the population in these areas. This and other limitations of the method mean that estimates have not been published for PHAs with populations under 1,000, or with a high proportion of their population in:

  1. non-private dwellings (hospitals, gaols, nursing homes - and also excludes members of the armed forces);
  2. in very remote or in discrete Aboriginal communities, as determined by the ABS; and
  3. where the relative root mean square errors (RRMSEs) on the estimates was 1 or more (estimate replaced with ≠)


  1. Estimates with RRMSEs from 0.25 and to 0.50 have been marked (~) to indicate that they should be used with caution; and those greater than 0.50 but less than 1 are marked (~~) to indicate that the estimate is considered too unreliable for general use.

Numerator:  Estimated persons aged 18 years and over for the relevant indicator


Denominator:  Total population aged 18 years and over


Detail of analysis:  Indirectly age-standardised rate per 100 population; or indirectly age-standardised ratio, based on the Australian standard


Source:  Compiled by PHIDU based on modelled estimates from the 2014 General Social Survey, ABS (unpublished).


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