Abstract
The 2021 Australian Census asked about long-term illness for the first time. This new data provides a clearer picture of disease burden, including differentiating between urban and rural disease rates. Understanding disease burden by location should allow comparison with health workforce locations to identify disparities to allow better workforce planning for rural places. However, Australian census and workforce data are reported against different remoteness categorisation systems, the Australian Statistical Geography Standard – Remoteness Area (ASGS-RA) and the Modified Monash Model (MMM) respectively, making comparison difficult. This project aimed to allow comparison by mapping disease burden across MMM levels.
We examined publicly available Australian 2021 census data spatially, using a computer assisted geographic information system (ArcGIS). Data were mapped across Australia and spatially joined to existing MMM maps to determine MMM counts for total population, and for each diagnosed long-term health condition collected in the census.
Significant differences in reported health condition prevalence were noted between MMM levels. People in living in metropolitan areas (MMM1) typically lived longer and with lower levels of disease than people in regional and rural areas (MMM2-5), and disease prevalence tended to increase with remoteness in these levels. However, people in remote and very remote areas (MMM6-7) had the shortest length of life but reported less long-term illness. This lower disease rate may result from poor access to diagnostic services or decreased lifespan limiting impact of illnesses related to aging.
Comparison of disease rates to workforce numbers for all registered health professionals demonstrated small rural towns (MMM5) had the highest levels of long-term illness but the lowest number of registered health professionals, for all but paramedics. Paramedics are the only profession likely to be allocated work location based on community needs, which suggests factors other than demand influence health professional’s decisions about work locations.
We examined publicly available Australian 2021 census data spatially, using a computer assisted geographic information system (ArcGIS). Data were mapped across Australia and spatially joined to existing MMM maps to determine MMM counts for total population, and for each diagnosed long-term health condition collected in the census.
Significant differences in reported health condition prevalence were noted between MMM levels. People in living in metropolitan areas (MMM1) typically lived longer and with lower levels of disease than people in regional and rural areas (MMM2-5), and disease prevalence tended to increase with remoteness in these levels. However, people in remote and very remote areas (MMM6-7) had the shortest length of life but reported less long-term illness. This lower disease rate may result from poor access to diagnostic services or decreased lifespan limiting impact of illnesses related to aging.
Comparison of disease rates to workforce numbers for all registered health professionals demonstrated small rural towns (MMM5) had the highest levels of long-term illness but the lowest number of registered health professionals, for all but paramedics. Paramedics are the only profession likely to be allocated work location based on community needs, which suggests factors other than demand influence health professional’s decisions about work locations.
| Original language | English |
|---|---|
| Publication status | Published - 15 Mar 2025 |
| Event | University of Wollongong Rural Health Research Conference 2025 - Shoalhaven Campus , Nowra, Australia Duration: 14 Mar 2025 → 15 Mar 2025 https://www.uow.edu.au/science-medicine-health/rural-health-conference/ |
Conference
| Conference | University of Wollongong Rural Health Research Conference 2025 |
|---|---|
| Country/Territory | Australia |
| City | Nowra |
| Period | 14/03/25 → 15/03/25 |
| Internet address |
