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Healthy Communities: Potentially preventable hospitalisations in 2013–14 - Report - Key findings: Potentially preventable hospitalisations by condition

Healthy Communities: Potentially preventable hospitalisations in 2013–14

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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
Major cities
Socioeconomic status
Higher
Medium
Lower
Inner regional
Outer regional
Remote

Is a chart of age-standardised rates of potentially preventable hosipitalisations and bed days for COPD by local area (SA3), remoteness and socioeconomic status, 2013-14.

The following link expands the table data. Show tabular data Hide tabular data

Table for Figure 5: Shows the highest and lowest age-standardised rates of potentially preventable hospitalisations and bed days for COPD by SA3 for each Remoteness/Socioeconomic status

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
Major cities
Socioeconomic status
Higher
Medium
Lower
Inner regional
Outer regional
Remote

Is a chart of age-standardised rates of potentially preventable hosipitalisations and bed days for diabetes complications by local area (SA3), remoteness and socioeconomic status, 2013-14.

The following link expands the table data. Show tabular data Hide tabular data

Table for Figure 6: Shows the highest and lowest age-standardised rates of potentially preventable hospitalisations and bed days for diabetes complications by SA3 for each Remoteness/Socioeconomic status

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
Major cities
Socioeconomic status
Higher
Medium
Lower
Inner regional
Outer regional
Remote

Is a chart of age-standardised rates of potentially preventable hosipitalisations and bed days for heart failure by local area (SA3), remoteness and socioeconomic status, 2013-14.

The following link expands the table data. Show tabular data Hide tabular data

Table for Figure 7: Shows the highest and lowest age-standardised rates of potentially preventable hospitalisations and bed days for heart failure by SA3 for each Remoteness/Socioeconomic status

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
Major cities
Socioeconomic status
Higher
Medium
Lower
Inner regional
Outer regional
Remote

Is a chart of age-standardised rates of potentially preventable hosipitalisations and bed days for cellulitis by local area (SA3), remoteness and socioeconomic status, 2013-14.

The following link expands the table data. Show tabular data Hide tabular data

Table for Figure 8: Shows the highest and lowest age-standardised rates of potentially preventable hospitalisations and bed days for cellulitis by SA3 for each Remoteness/Socioeconomic status

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
Major cities
Socioeconomic status
Higher
Medium
Lower
Inner regional
Outer regional
Remote

Is a chart of age-standardised rates of potentially preventable hosipitalisations and bed days for kidney and urinary tract infections by local area (SA3), remoteness and socioeconomic status, 2013-14.

The following link expands the table data. Show tabular data Hide tabular data

Table for Figure 9: Shows the highest and lowest age-standardised rates of potentially preventable hospitalisations and bed days for kidney and urinary tract infections by SA3 for each Remoteness/Socioeconomic status

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.

Download Report (PDF, 38.1 MB)