Notes on the data: Chronic diseases and conditions

Estimated population with osteoporosis, 2017–18

 

Policy context:  Osteoporosis is a condition which occurs when/ in which bones lose minerals such as calcium faster than the body is able to replenish which leads to a decrease in bone mineral density. This causes bones to become thin, weak and brittle where broken bones are more likely to result from minor bumps or accidents [1, 2]. Osteoporosis is generally under-diagnosed as there are no obvious symptoms. As it is difficult to determine the true prevalence of this condition, cases are often undiagnosed until a fracture occurs [1].

According to self-reported data from the 2017-18 National Health Survey (NHS), around 924,000 Australian have osteoporosis with one in five Australians (20%) aged 75 and over experiencing this condition. Osteoporosis is more prevalent in women than men and prevalence increases with age. In 2017-18, 29% of women aged 75 and over had osteoporosis compared to 10% of men [2]. In addition, people with osteoporosis were more likely than those without this condition to describe their health as poor (15% to 5.4%); to experience severe or very severe bodily pain in the last 4 weeks; and to experience very high levels of psychological distress [2].

References

  1. Australian Institute of Health and Welfare (AIHW). Estimating the prevalence of osteoporosis in Australia. Cat. no. PHE 178. 2014. Canberra: AIHW. Available from: https://www.aihw.gov.au/reports/chronic-musculoskeletal-conditions/estimating-the-prevalence-of-osteoporosis-in-austr/contents/summary; last accessed 11 December 2019
  2. Australian Institute of Health and Welfare (AIHW). Osteoporosis. Cat. no. PHE 233. 2019. Canberra: AIHW. Available from: https://www.aihw.gov.au/reports/chronic-musculoskeletal-conditions/osteoporosis; last accessed 11 December 2019
 

Notes:

Differences from data published by the Australian Bureau of Statistics (ABS):

Data by quintile of socioeconomic disadvantage and Remoteness will differ to the extent that data extracted from Survey TableBuilder have been randomised, whereas those published by the ABS are not. In addition, rates published by the ABS for modelled estimates are generally crude rates; rates published by PHIDU are age-standardised.

Modelled Estimates:

In the absence of data from administrative data sets, estimates are provided at the Population Health Area (PHA) level for selected chronic diseases and conditions from the 2017–18 National Health Survey (NHS), conducted by the Australian Bureau of Statistics (ABS). The estimates at the Population Health Area (PHA) are modelled estimates produced by the ABS (estimates at the Local Government Area (LGA) and Primary Health Network (PHN) level were derived from the PHA estimates).

Details of the method used and accuracy of results are available from the ABS paper Explanatory Notes: Modelled estimates for small areas based on the 2017-18 National Health Survey, available here.

For the Primary Health Network (PHN) data, differences between the PHN totals and the sum of LGAs within PHNs result from the use of different geographic correspondence files.

Estimates for quintile of socioeconomic disadvantage of area and Remoteness Area are direct estimates, extracted using the ABS Survey TableBuilder.

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.

The numbers are estimates for an area, not measured events as are, for example, death statistics. As such, they should be viewed as a tool that, when used in conjunction with local area knowledge and taking into consideration the prediction reliability, can provide useful information that can assist with decision making for small geographic regions. Of particular note is that the true value of the published estimates is also likely to vary within a range of values as shown by the upper and lower limits published in the data (xlsx) and viewable in the column chart in the single map atlases.

What the modelled 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 level of each health indicator for an area with those characteristics. In the absence of accurate, localised information about the health indicator, 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 health indicator.

The relatively high survey response rate in the NHS provides a high level of coverage across the population; however, the response rate among some groups is lower than among other groups, e.g., those living in the most disadvantaged areas have a lower response rate than those living in less disadvantaged areas. Although the sample includes the majority of people living in households in private dwellings, it excludes those living in the very remote areas of Australia and discrete Aboriginal and Torres Strait Islander communities; whereas these areas comprise less than 3% of the total population, Aboriginal people comprise up to one third of the population. The survey does not include persons usually resident in non-private dwellings (hospitals, gaols, nursing homes - and also excludes members of the armed forces).

This and other limitations of the method mean that estimates have not been published for PHAs with populations under 1,000.

The ABS used a number of methods to measure the quality of the estimates, one of which is the relative root mean squared error (RRMSE) of the modelled estimates. The RRMSEs are included with the data. Users are advised that:

  • estimates with RRMSEs less than 25% are considered reliable for most purposes;
  • 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.

Indicator detail

These data refer to persons ever been told diagnosed by a doctor or nurse as having osteoporosis or osteopenia (current and long term). A long-term condition is defined as a condition that is current and has lasted, or is expected to last, for 6 months or more.

 

Geography: Data available by Population Health Area, Local Government Area, Primary Health Network, quintile of socioeconomic disadvantage of area and Remoteness Area

 

Numerator:  Estimated number of people with osteoporosis as a current, long-term condition

 

Denominator:  Total population

 

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

 

Source:  

PHA, LGA & PHN: Age-standardised rates are based on Australian Bureau of Statistics data, produced as a consultancy for PHIDU, from the 2017-18 National Health Survey

Quintiles & Remoteness: Compiled by PHIDU based on direct estimates from the 2017–18 National Health Survey, ABS Survey TableBuilder.

 

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