Key findings: Potentially preventable hospitalisations by condition
Variation across similar local areas by condition
Rates of potentially preventable hospitalisations for each of the five conditions vary substantially across local areas, even after accounting for differences in the age of populations.
Remoteness and lower socioeconomic status are also factors that influence rates. This may be due to poorer health and, potentially, poorer access to health care services for people living in these communities.
However, substantial variation in age-standardised rates was also found across local areas that are similar in terms of remoteness and socioeconomic status. This variation may be due to differences in primary health and hospital care provided in some areas compared to others, particularly for acute conditions such as cellulitis and kidney and urinary tract infections. These types of acute conditions are less likely to be impacted by differences in patient’s health needs or the treatment preferences of patients.
Chronic obstructive pulmonary disease (COPD)
In 2013–14, across similar local areas in major cities with lower socioeconomic status, age-standardised rates for COPD were five times higher in some areas compared to others (Figure 5).
Diabetes complications
In 2013–14, across similar local areas in major cities with higher socioeconomic status, age-standardised rates for diabetes complications were four times higher in some areas compared to others (Figure 6).
Heart failure
In 2013–14, across similar local areas in major cities with lower socioeconomic status, age-standardised rates for heart failure were almost three times higher in some areas compared to others (Figure 7)
Cellulitis
In 2013–14, across similar local areas in major cities with higher socioeconomic status, age-standardised rates for cellulitis were almost five times higher in some areas compared to others. Similar differences in rates were evident across local areas in major cities with lower socioeconomic status (Figure 8).
Kidney and urinary tract infections (UTIs)
In 2013–14, across similar local areas in major cities with lower socioeconomic status, age-standardised rates for kidney and urinary tract infections were 2.5 times higher in some areas compared to others. Similar differences in rates were evident across local areas in major cities with higher socioeconomic status (Figure 9).
For these five conditions, variation in rates was also seen across other groups of similar areas, including areas of higher or lower socioeconomic status in major cities, inner and outer regional areas and remote areas (Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 and Chronic conditions, Acute and vaccine-preventable conditions, Chronic obstructive pulmonary disease, Diabetes complications, Heart failure, Cellulitis and Kidney and urinary tract infections sections).
Figure 5: Age-standardised rates of potentially preventable hospitalisations and bed days for COPD by local area (SA3), remoteness and socioeconomic status, 2013–14
Rate per 100,000 people
Each circle represents an SA3 and the size represents the number of bed days. The bigger the dot, the greater the number of bed days for that local area. The rate is shown by the relative position of the dot up and down the page, the higher the dot, the higher the rate.
Scale for this figure
| Remoteness/Socioeconomic status | Statistical Area Level 3 name | State | PPH per 100,000 people | Total PPH bed days |
|---|---|---|---|---|
| Major cities – Higher SES | Holland Park - Yeronga | QLD | 275 | 873 |
| Major cities – Higher SES | Boroondara | VIC | 63 | 968 |
| Major cities – Medium SES | Jimboomba | QLD | 455 | 532 |
| Major cities – Medium SES | Campbelltown (SA) | SA | 109 | 639 |
| Major cities – Lower SES | Mount Druitt | NSW | 778 | 2,301 |
| Major cities – Lower SES | Hurstville | NSW | 155 | 1,544 |
| Inner regional | Loddon - Elmore | VIC | 551 | 529 |
| Inner regional | Macedon Ranges | VIC | 93 | NP |
| Outer regional | Palmerston | NT | 621 | NP |
| Outer regional | Albany | WA | 167 | NP |
| Remote (incl. very remote) | Barkly | NT | 1,400 | 327 |
| Remote (incl. very remote) | Esperance | WA | 201 | NP |
Notes: There are 22 conditions for which a hospitalisation is considered to be potentially preventable. Hospitalisations from both public and private hospitals are included.
Bed days are the number of days an admitted patient is in hospital. A patient admitted and discharged on the same day is allocated one bed day.
Local area (SA3) results have been categorised by remoteness using ABS Remoteness Areas 2011. The Major cities category which contains two-thirds of SA3s has been further categorised by socioeconomic status using the ABS IRSD SEIFA Index 2011.
More information on the categories of remoteness and socioeconomic status can be found at http://www.myhealthycommunities.gov.au and in this report’s Technical Supplement and Glossary. The scale of the y-axis is non-linear above 4,000 to include areas that are outliers.
Sources: National Health Performance Authority analysis of Admitted Patient Care National Minimum Data Set 2013–14, data supplied March 2015; and Australian Bureau of Statistics Estimated Resident Population 30 June 2013.
Figure 6: Age-standardised rates of potentially preventable hospitalisations and bed days for diabetes complications by local area (SA3), remoteness and socioeconomic status, 2013–14
Rate per 100,000 people
Each circle represents an SA3 and the size represents the number of bed days. The bigger the dot, the greater the number of bed days for that local area. The rate is shown by the relative position of the dot up and down the page, the higher the dot, the higher the rate.
Scale for this figure
| Remoteness/Socioeconomic status | Statistical Area Level 3 name | State | PPH per 100,000 people | Total PPH bed days |
|---|---|---|---|---|
| Major cities – Higher SES | Centenary | QLD | 234 | 269 |
| Major cities – Higher SES | Pennant Hills - Epping | NSW | 57 | NP |
| Major cities – Medium SES | Southport | QLD | 304 | 738 |
| Major cities – Medium SES | Sunnybank | QLD | 86 | 203 |
| Major cities – Lower SES | Mount Druitt | NSW | 345 | 1,772 |
| Major cities – Lower SES | Hurstville | NSW | 118 | 825 |
| Inner regional | Sorell - Dodges Ferry | TAS | 464 | 206 |
| Inner regional | Hawkesburry | NSW | 91 | NP |
| Outer regional | Outback - North and East | SA | 538 | 827 |
| Outer regional | Burnie - Ulverstone | TAS | 106 | 251 |
| Remote (incl. very remote) | Barkly | NT | 956 | 315 |
| Remote (incl. very remote) | Esperance | WA | 147 | NP |
Notes: There are 22 conditions for which a hospitalisation is considered to be potentially preventable. Hospitalisations from both public and private hospitals are included.
Bed days are the number of days an admitted patient is in hospital. A patient admitted and discharged on the same day is allocated one bed day.
Local area (SA3) results have been categorised by remoteness using ABS Remoteness Areas 2011. The Major cities category which contains two-thirds of SA3s has been further categorised by socioeconomic status using the ABS IRSD SEIFA Index 2011.
More information on the categories of remoteness and socioeconomic status can be found at http://www.myhealthycommunities.gov.au and in this report’s Technical Supplement and Glossary. The scale of the y-axis is non-linear above 4,000 to include areas that are outliers.
Sources: National Health Performance Authority analysis of Admitted Patient Care National Minimum Data Set 2013–14, data supplied March 2015; and Australian Bureau of Statistics Estimated Resident Population 30 June 2013.
Figure 7: Age-standardised rates of potentially preventable hospitalisations and bed days for heart failure by local area (SA3), remoteness and socioeconomic status, 2013–14
Rate per 100,000 people
Each circle represents an SA3 and the size represents the number of bed days. The bigger the dot, the greater the number of bed days for that local area. The rate is shown by the relative position of the dot up and down the page, the higher the dot, the higher the rate.
Scale for this figure
| Remoteness/Socioeconomic status | Statistical Area Level 3 name | State | PPH per 100,000 people | Total PPH bed days |
|---|---|---|---|---|
| Major cities – Higher SES | Wanneroo | WA | 224 | 1,850 |
| Major cities – Higher SES | Sherwood - Indooroopilly | QLD | 88 | NP |
| Major cities – Medium SES | Chermside | QLD | 420 | 1,888 |
| Major cities – Medium SES | Noosa | QLD | 130 | NP |
| Major cities – Lower SES | Mount Druitt | NSW | 351 | 1,604 |
| Major cities – Lower SES | Hurstville | NSW | 123 | 1,435 |
| Inner regional | Wagga Wagga | NSW | 317 | 2,307 |
| Inner regional | Macedon Ranges | VIC | 94 | NP |
| Outer regional | Goldfields | WA | 416 | 446 |
| Outer regional | Burnie - Ulverstone | TAS | 140 | 458 |
| Remote (incl. very remote) | Barkly | NT | 770 | 219 |
| Remote (incl. very remote) | Esperance | WA | 128 | NP |
Notes: There are 22 conditions for which a hospitalisation is considered to be potentially preventable. Hospitalisations from both public and private hospitals are included.
Bed days are the number of days an admitted patient is in hospital. A patient admitted and discharged on the same day is allocated one bed day.
Local area (SA3) results have been categorised by remoteness using ABS Remoteness Areas 2011. The Major cities category which contains two-thirds of SA3s has been further categorised by socioeconomic status using the ABS IRSD SEIFA Index 2011.
More information on the categories of remoteness and socioeconomic status can be found at http://www.myhealthycommunities.gov.au and in this report’s Technical Supplement and Glossary. The scale of the y-axis is non-linear above 4,000 to include areas that are outliers.
Sources: National Health Performance Authority analysis of Admitted Patient Care National Minimum Data Set 2013–14, data supplied March 2015; and Australian Bureau of Statistics Estimated Resident Population 30 June 2013.
Figure 8: Age-standardised rates of potentially preventable hospitalisations and bed days for cellulitis by local area (SA3), remoteness and socioeconomic status, 2013–14
Rate per 100,000 people
Each circle represents an SA3 and the size represents the number of bed days. The bigger the dot, the greater the number of bed days for that local area. The rate is shown by the relative position of the dot up and down the page, the higher the dot, the higher the rate.
Scale for this figure
| Remoteness/Socioeconomic status | Statistical Area Level 3 name | State | PPH per 100,000 people | Total PPH bed days |
|---|---|---|---|---|
| Major cities – Higher SES | Manly | NSW | 376 | 794 |
| Major cities – Higher SES | Cottesloe - Claremont | WA | 78 | 316 |
| Major cities – Medium SES | Jimboomba | QLD | 374 | 387 |
| Major cities – Medium SES | Blue Mountains | NSW | 111 | 410 |
| Major cities – Lower SES | Mount Druitt | NSW | 454 | 1,210 |
| Major cities – Lower SES | Tullamrine - Broadmeadows | VIC | 133 | 660 |
| Inner regional | Devonport | TAS | 590 | 522 |
| Inner regional | Macedon Ranges | VIC | 119 | NP |
| Outer regional | Innisfail - Cassowary Coast | QLD | 878 | 714 |
| Outer regional | Meander Valley - West Tamar | TAS | 121 | 184 |
| Remote (incl. very remote) | Barkly | NT | 2,272 | 417 |
| Remote (incl. very remote) | Esperance | WA | 256 | NP |
Notes: There are 22 conditions for which a hospitalisation is considered to be potentially preventable. Hospitalisations from both public and private hospitals are included.
Bed days are the number of days an admitted patient is in hospital. A patient admitted and discharged on the same day is allocated one bed day.
Local area (SA3) results have been categorised by remoteness using ABS Remoteness Areas 2011. The Major cities category which contains two-thirds of SA3s has been further categorised by socioeconomic status using the ABS IRSD SEIFA Index 2011.
More information on the categories of remoteness and socioeconomic status can be found at http://www.myhealthycommunities.gov.au and in this report’s Technical Supplement and Glossary. The scale of the y-axis is non-linear above 4,000 to include areas that are outliers.
Sources: National Health Performance Authority analysis of Admitted Patient Care National Minimum Data Set 2013–14, data supplied March 2015; and Australian Bureau of Statistics Estimated Resident Population 30 June 2013.
Figure 9: Age-standardised rates of potentially preventable hospitalisations and bed days for kidney and urinary tract infections by local area (SA3), remoteness and socioeconomic status, 2013–14
Rate per 100,000 people
Each circle represents an SA3 and the size represents the number of bed days. The bigger the dot, the greater the number of bed days for that local area. The rate is shown by the relative position of the dot up and down the page, the higher the dot, the higher the rate.
Scale for this figure
| Remoteness/Socioeconomic status | Statistical Area Level 3 name | State | PPH per 100,000 people | Total PPH bed days |
|---|---|---|---|---|
| Major cities – Higher SES | Cleveland - Stradbroke | QLD | 202 | 780 |
| Major cities – Higher SES | Burnside | SA | 160 | 531 |
| Major cities – Medium SES | Mudgeeraba - Tallebudgera | QLD | 304 | 240 |
| Major cities – Medium SES | Keilor | VIC | 147 | 600 |
| Major cities – Lower SES | Mount Druitt | NSW | 454 | 1,210 |
| Major cities – Lower SES | Kiama - Shellharbour | NSW | 232 | 1,040 |
| Inner regional | Tumut - Tumbarumba | NSW | 305 | 188 |
| Inner regional | Hobart - South and West | TAS | 164 | 274 |
| Outer regional | Griffith - Murrumbidgee (West) | NSW | 516 | 872 |
| Outer regional | Huon - Bruny Island | TAS | 120 | NP |
| Remote (incl. very remote) | Barkly | NT | 2,272 | 417 |
| Remote (incl. very remote) | Esperance | WA | 256 | NP |
Notes: There are 22 conditions for which a hospitalisation is considered to be potentially preventable. Hospitalisations from both public and private hospitals are included.
Bed days are the number of days an admitted patient is in hospital. A patient admitted and discharged on the same day is allocated one bed day.
Local area (SA3) results have been categorised by remoteness using ABS Remoteness Areas 2011. The Major cities category which contains two-thirds of SA3s has been further categorised by socioeconomic status using the ABS IRSD SEIFA Index 2011.
More information on the categories of remoteness and socioeconomic status can be found at http://www.myhealthycommunities.gov.au and in this report’s Technical Supplement and Glossary. The scale of the y-axis is non-linear above 4,000 to include areas that are outliers.
Sources: National Health Performance Authority analysis of Admitted Patient Care National Minimum Data Set 2013–14, data supplied March 2015; and Australian Bureau of Statistics Estimated Resident Population 30 June 2013.