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01.12.2011 | Research article | Ausgabe 1/2011 Open Access

BMC Public Health 1/2011

Aboriginal premature mortality within South Australia 1999-2006: a cross-sectional analysis of small area results

Zeitschrift:
BMC Public Health > Ausgabe 1/2011
Autoren:
David Banham, Heather Jury, Tony Woollacott, Robyn McDermott, Fran Baum
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1471-2458-11-286) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

DB maintains the South Australian burden of disease series, operationalised the study, performed data analysis, drafted and revised the manuscript. HJ contributed to data analysis and revising the manuscript. TW participated in the study design and manuscript revision. RM assisted with manuscript drafting and revision. FB conceived of the original research question and helped draft and revise the manuscript. All authors read and approved the final manuscript.

Background

Indigenous people around the world experience poorer mortality outcomes than their non-Indigenous contemporaries [1]. The average life expectancy of Aboriginal Australians at birth is almost 11 years less than that of the general population [2] and this difference is larger than others in similarly developed settings [1]. In response, the Council of Australian Governments (COAG) have formally committed to 'Closing the gap', a strategy to reduce areas of Aboriginal disadvantage [3] including life expectancy.
Aboriginal mortality outcomes are not homogenous though and regularly reported data shows variations in outcomes between jurisdictions. For example, in South Australia the difference between Aboriginal and non-Aboriginal life expectancy is about 13 years [2, 4]. Less frequent reporting also describes important variations by area remoteness [5] and within jurisdictions [6, 7]. Of course, within group variations in population health outcomes disaggregated by socio-economic status are also frequently reported [8, 9]. However, a recent examination of Australian population health data showed how the use of a single area measure fails to adequately inform planning on issues such as health workforce training and incentives [10]. A more fruitful approach is to combine analysis of area socio-economic disadvantage and remoteness [9, 11].
There is an increasing need to inform: health priority setting [12]; appropriate resourcing [10]; and, tailored preventive and service responses that are relevant to population need, by providing comprehensive information about small area variations in health status. The South Australian Aboriginal Health Care Plan [13] responded to such need by profiling each of 11 smaller, intrastate regions on a range of health related indicators.
The Plan used premature mortality to assess the mortality burden using Years of Life Lost (YLL) whereby all deaths contribute YLL, but the younger the age at death, the greater the YLL number. A separate brief report outlines results by region and details the amount and leading causes of premature mortality for South Australia and each of the Aboriginal Health Care Plan regions [14]. These regional results illustrate the considerable diversity in the health and wellbeing of Aboriginal people. For example, within the Adelaide metropolitan area loss from premature mortality in the western suburbs is almost double that of southern suburbs. In other parts of the state, regional outcomes are even more disparate ranging from better outcomes among Aboriginal people in the South-East and Peri-Urban ring surrounding Adelaide to areas such as the West Coast with four-fold higher rates of loss.
This paper aggregates mortality data for the period 1999 to 2006 to enable description of premature mortality outcomes at the smallest geographic level currently possible. After initially describing premature mortality outcomes for Aboriginal and non-Aboriginal South Australians, the analysis then examines how these outcomes vary for each sub-population across area level socio-economic disadvantage and geographic remoteness.

Methods

Estimated resident population (ERP) for Aboriginal South Australians in the 2006 Census year [15] came from the Australian Bureau of Statistics (ABS) as did the most closely aligning 'low series' population estimates for the preceding years, 1999-2005 [16]. The 2006 estimates also included age and sex profiles by rurality, and total population for Statistical Local Areas (SLAs), the relatively small geographic areas used in intrastate analysis [17]. These profiles formed the basis for interpolating 1999-2005 Aboriginal ERP by age, sex and SLA. The difference between Aboriginal population estimates and the yearly total ERP from annual ABS releases [18] were used for enumerating non-Aboriginal people. The mean annual total population for each SLA was 12 216 (SD = 9 672) and ranged from 0 to 34 797.
Outcomes data were drawn from unit level mortality records processed within South Australia's summary population health measurement series for the years 1999 to 2006 [19]. Premature mortality for each death is expressed using years of life lost (YLL), which is the remaining expected, standard life expectancy [20] at age of death, time discounted at an annual rate of 3 percent. For example, if a death occurs at age 50 and the average life expectancy at 50 is 34 years, then this death contributes 34 YLL, or approximately 20 YLL after time discounting.
Deaths where Aboriginality, defined as Aboriginal and/or Torres Strait Islander, was not stated or unknown were categorised as non-Aboriginal. The deceased's place of usual residence is a mandatory field using SLAs. Changes in statistical geography during the observation period saw several new SLAs created. For example, Anangu Pitjantjatjara and Maralinga Tjarutja SLAs were partitioned from the rest of Unincorporated Far North SLA in 2005. Deaths occurring in those areas from 1999 to 2004 show Unincorporated Far North as place of usual residence, so for the purpose of this analysis the three SLAs were collapsed into one.
The ABS rank Australia's populated SLAs by their socio-economic characteristics [21]. Of the four composite index scores available for statistical tests, and despite including Aboriginality in its construction, the Index of Relative Socio-economic Disadvantage (IRSD) [21] is most frequently used in South Australian analyses [8, 9] because of its predictive validity. SLAs also have a measure of geographic remoteness, ARIA+, ranging from 0 (high accessibility) to 15 (high remoteness) as determined by road distance to service centres [17, 22]. Additional file 1 Appendix 1 details SLA remoteness level and disadvantage ranking. SLAs with nominal population, no recorded deaths and no relative IRSD rank would not contribute to the analysis and were omitted.
As SLA age and sex profiles vary and Aboriginal case and population numbers are often small, population and YLL figures were collapsed into age categories of 0-4 then 10 year age groups to 55+ [23] before indirectly age and sex adjusting results [24] using Stata version 11.1 [25] to enable comparison across areas. SLA outcomes represent the ratio of observed versus expected premature mortality as based on the South Australian total for 1999 to 2006. For example, an outcome of 150 indicates the SLA's ratio of observed versus expected premature mortality was one and a half times, or 50% higher, than the South Australian average after adjusting for sex and age differences.
Cross tabulations describe population and YLL distribution across demographic variables, then standardised YLL ratios by area level disadvantage and remoteness. Relationships between area level outcomes of IRSD ranking (where 1 = least disadvantage upwards) and remoteness categories [11] (where 0 = Major cities with ARIA+ < = 0.2; 1 = Regional, ARIA+ > 0.2 & < = 5.92; 2 = Remote, ARIA+ > 5.92) and the possible interaction between area disadvantage and remoteness were examined graphically. Models were then derived for Aboriginal and non-Aboriginal populations using least squares regression with each SLA's contribution weighted according to their population size. The final model was selected on the basis of the best fit to Aboriginal outcomes and ease of explanation. Outlying SLAs were tested for removal at p = 0.05 [26] if detected. Given the relatively small numbers underlying the analysis and a small positive skew in residuals, standard errors robust to heteroskedasticity were used [27]. Variance inflation factors were also examined using an upper value of 10 to indicate multicollinearity among predictor variables and undue influence on the least square estimates [28].

Results

Population

Aboriginal people comprise 1.7% of South Australia's population (Table 1) and their demographic profile varies markedly from the rest of the population in several ways. The Aboriginal community's age profile is much younger. Almost twice the proportion of Aboriginal people are aged under 15 years (36.6% compared to 18.7%) while less than 8% are aged 55 years or more (compared to over 25% in the rest of the community). When the total population is divided into disadvantage quintiles, half of Aboriginal South Australians live in the most disadvantaged areas and three-quarters in the two most disadvantaged quintiles. Additionally, Aboriginal people are also more likely to live in Regional areas than non-Aboriginal people (33.2% compared to 25.0%) and far more likely to live in Remote areas (18.8% and 3.5% respectively).
Table 1
Mean annual population distribution by area disadvantage, remoteness and Aboriginality in South Australia 1999-2006
   
Area Remoteness (ARIA+)
  
Aboriginal
  
Major cities
Regional
Remote
Total
   
N
Percent
N
Percent
N
Percent
N
Percent
 
Age
         
  
0-4
1555
12.2%
1073
12.2%
516
10.4%
3144
11.9%
  
5-14
3240
25.5%
2164
24.6%
1139
22.9%
6543
24.7%
  
15-24
2674
21.0%
1697
19.3%
973
19.6%
5344
20.2%
  
25-34
1763
13.9%
1204
13.7%
778
15.6%
3745
14.1%
  
35-44
1553
12.2%
1104
12.6%
706
14.2%
3363
12.7%
  
45-54
1072
8.4%
770
8.8%
467
9.4%
2309
8.7%
  
55+
872
6.9%
776
8.8%
396
8.0%
2042
7.7%
 
Area Disadvantage (2006 IRSD)
         
  
Q5 Least Disadvantage
1077
4.1%
181
0.7%
69
0.3%
1328
5.0%
  
Q4
1612
6.1%
467
1.8%
123
0.5%
2202
8.3%
  
Q3
2159
8.1%
945
3.6%
146
0.6%
3250
12.3%
  
Q2
2988
11.3%
2136
8.1%
1169
4.4%
6292
23.8%
  
Q1 Most Disadvantage
4893
18.5%
5058
19.1%
3467
13.1%
13418
50.7%
  
Total
12728
48.0%
8788
33.2%
4974
18.8%
26490
100.0%
Non-Aboriginal
         
   
Major cities
Regional
Remote
Total
 
Age
         
  
0-4
60971
5.7%
23292
6.2%
3601
6.8%
87864
5.9%
  
5-14
130165
12.1%
54464
14.5%
7721
14.6%
192350
12.8%
  
15-24
150100
14.0%
43148
11.5%
5781
10.9%
199030
13.3%
  
25-34
150983
14.1%
43859
11.7%
7170
13.5%
202012
13.5%
  
35-44
158910
14.8%
57251
15.2%
8196
15.5%
224358
15.0%
  
45-54
149555
14.0%
54636
14.5%
7610
14.3%
211801
14.1%
  
55+
271048
25.3%
99060
26.4%
12964
24.4%
383072
25.5%
 
Area Disadvantage (2006 IRSD)
         
  
Q5 Least Disadvantage
265311
17.7%
39752
2.6%
3936
0.3%
308999
20.6%
  
Q4
227407
15.2%
57809
3.9%
7304
0.5%
292519
19.5%
  
Q3
204166
13.6%
83724
5.6%
15335
1.0%
303225
20.2%
  
Q2
185390
12.4%
104032
6.9%
20317
1.4%
309739
20.6%
  
Q1 Most Disadvantage
189458
12.6%
90394
6.0%
6152
0.4%
286004
19.1%
  
Total
1071732
71.4%
375711
25.0%
53044
3.5%
1500487
100.0%

Deaths

Of 94 785 deaths among people who usually resided in South Australia in the years 1999 to 2006, 1041 (1.1%) were identified as Aboriginal. All but 176 of total death records referenced a known SLA within SA (117 people were living overseas at the time of death; 42 were of no fixed place; and 17 did not reside in a defined SLA). While Aboriginal people were over represented among these with 13 deaths (7.4%), the records were omitted from subsequent analyses because of the focus on results for small areas.

Premature mortality

The 94 609 deaths featuring in this analysis were responsible for a total of 878 251 YLLs. Aboriginal deaths accounted for 19 665 (2.2%) of the total YLL accrued (Table 2).
Table 2
Association of death and premature mortality outcomes with demographic variables and Aboriginality in South Australia 1999-2006
  
Aboriginality
  
  
Aboriginal
non-Aboriginal
Total
  
N
Percent
N
Percent
N
Percent
Deaths
Total
1028
1.1%
93581
98.9%
94609
100.0%
YLL
Total
19665
2.2%
858586
97.8%
878251
100.0%
Gender
Male
11316
54.7%
470460
49.4%
481776
49.5%
 
Female
8349
40.3%
388126
40.8%
396475
40.8%
Age
0-4
1609
8.2%
20284
2.4%
21893
2.5%
 
5-14
351
1.8%
4728
0.6%
5079
0.6%
 
15-24
1710
8.7%
23634
2.8%
25344
2.9%
 
25-34
2690
13.7%
34054
4.0%
36745
4.2%
 
35-44
3921
19.9%
51884
6.0%
55805
6.4%
 
45-54
4179
21.2%
86244
10.0%
90422
10.3%
 
55+
5205
26.5%
637757
74.3%
642963
73.2%
Area Disadvantage
Q5 Least Disadvantage
408
2.1%
144135
16.8%
144544
16.5%
(2006 IRSD)
Q4
1034
5.3%
148964
17.3%
149997
17.1%
Quintile
Q3
1404
7.1%
176592
20.6%
177996
20.3%
 
Q2
6836
34.8%
200686
23.4%
207522
23.6%
 
Q1 Most Disadvantage
9984
50.8%
188209
21.9%
198193
22.6%
Area Remoteness
Major cities
7334
37.3%
610100
71.1%
617434
70.3%
(ARIA+)
Regional
5855
29.8%
219115
25.5%
224970
25.6%
 
Remote
6476
32.9%
29371
3.4%
35847
4.1%
While the sex distribution of YLL by Aboriginality did not notably differ, the age distribution of YLL did vary markedly. The proportions of YLL from deaths at ages 0 to 54 years are up to 3 times higher among Aboriginal compared to non-Aboriginal people. The upper age category of 55 and over accounted for almost three quarters of non-Aboriginal YLL compared to around one-quarter for Aboriginal people.
YLL distribution also varied widely across socioeconomic disadvantage quintiles whereby YLL numbers increased with disadvantage, particularly among Aboriginal people. Aboriginal YLL was distributed fairly evenly across remoteness level but was highly concentrated in Major cities among non-Aboriginals.

Standardised premature mortality ratios

Significant variations in standardised YLL ratios (SYLLR) exist between Aboriginal/non-Aboriginal groups and within Aboriginal results. Overall, Aboriginal YLL outcomes were 2.65 times that of the South Australian average (Table 3).
Table 3
Association of standardised YLL ratio (SYLLR) and area attributes by Aboriginality in South Australia 1999-2006
 
Area Remoteness (ARIA+)
   
Aboriginal
Major cities
Regional
Remote
Total
   Area Disadvantage (2006 IRSD)
 
95% Confidence intervals
 
95% Confidence intervals
 
95% Confidence intervals
 
95% Confidence intervals
 
SYLLR
Lower
Upper
SYLLR
Lower
Upper
SYLLR
Lower
Upper
SYLLR
Lower
Upper
   Q5 Least Disadvantage
134
15
253
61
0
238
0
n.a
n.a
117
22
212
   Q4
200
141
258
100
47
153
155
72
238
176
134
242
   Q3
185
143
227
111
52
171
124
0
284
161
126
219
   Q2
201
155
247
225
159
291
418
283
552
290
231
259
   Q1 Most Disadvantage
270
195
346
257
214
300
580
445
716
305
245
330
Total
219
186
252
221
191
251
446
370
521
262
235
247
Non-Aboriginal
Major cities
Regional
Remote
Total
  
95% Confidence intervals
 
95% Confidence intervals
 
95% Confidence intervals
 
95% Confidence intervals
 
SYLLR
Lower
Upper
SYLLR
Lower
Upper
SYLLR
Lower
Upper
SYLLR
Lower
Upper
   Q5 Least Disadvantage
85
71
100
66
56
75
70
n.a
n.a
83
71
94
   Q4
89
79
99
87
76
99
90
76
99
89
82
95
   Q3
95
87
102
95
90
95
102
86
104
95
91
99
   Q2
110
97
110
103
97
109
96
88
118
106
100
112
   Q1 Most Disadvantage
118
107
129
113
108
118
97
96
130
116
110
122
Total
98
92
104
97
92
102
95
89
102
98
94
102
Outcomes for Aboriginal people within areas of least disadvantage remained higher than the SA average. Premature mortality loss then increased as disadvantage increased with rates observed in areas of most disadvantage being three times those of South Australia overall. To a lesser extent, relative YLL rates increased as areas became more remote and this is particularly so when contrasting the two-fold difference between Remote and the other two categories.
The results also indicate an interaction between level of remoteness and socio-economic disadvantage because the range of Aboriginal SYLLRs increased markedly as remoteness category increased. For example, within Major Cities, ratios ranged from 133 for least disadvantage up to 271 for most disadvantage, a difference of 138. In Remote areas, no Aboriginal deaths were recorded in the least disadvantaged SLA while the most disadvantaged areas had a ratio of 580.
Non-Aboriginal outcomes also show a persistent, albeit smaller increase in premature mortality as disadvantage increased and nominal differences between remoteness levels.
The final multivariate models (Table 4) were significant for Aboriginal, F(3, 117) = 26.7, p < 0.001, and non-Aboriginal people alike, F(3, 117) = 15.14, p < 0.001. Using the predictor variables of area disadvantage ranking, remoteness category and an interaction term of remoteness category (squared) by disadvantage rank, the models accounted for 53.0% of the variance in Aboriginal SYLLRs and 34.6% of variance in non-Aboriginal outcomes.
Table 4
Modelled relationship of SLA attributes and standardised premature mortality rates by Aboriginality in South Australia 1999-2006
 
Aboriginal
non-Aboriginal
 
  
95% Confidence Intervals
  
95% Confidence Intervals
  
 
β
Lower
Upper
p value
β
Lower
Upper
p value
Variance Inflation Factor
Constant
122.46
61.06
183.85
< 0.001
79.05
69.37
88.73
< 0.001
 
Area Disadvantage (2006 IRSD) rank
1.27
0.41
2.13
< 0.01
0.35
0.23
0.47
< 0.001
1.230
   (1 = least disadvantage; 121 = most disadvantage)
         
Area Remoteness
-109.15
-171.70
-46.60
< 0.001
-3.97
-12.78
4.85
0.37
5.64
   (0 = Major cities; 1 = Regional; 2 = Remote)
         
Remoteness by Remoteness by Disadvantage
0.99
0.64
1.35
< 0.001
-0.01
-0.08
0.06
0.82
6.08
The models indicate that increased disadvantage rank was associated with increased loss from premature mortality, regardless of Aboriginality. Among Aboriginal people though, a significant main effect for area remoteness was also observed. Aboriginal results also show a significant interaction between area disadvantage and remoteness. This interaction is synergistic, or reinforcing, and indicates that as area remoteness changed so too did the effect of increased disadvantage with a particularly strong effect in Remote areas. Figures 1 and 2 illustrate the results for Aboriginal and non-Aboriginal populations respectively. The figures display each SLA's outcome, disadvantage rank and remoteness category weighted by the relevant Aboriginal/non-Aboriginal population, then overlay the predicted results for each combination of disadvantage, remoteness and remoteness category (squared) by disadvantage rank.

Discussion

The years of life lost because of premature mortality among Aboriginal South Australians occurred at more than two and a half times the rate of non-Aboriginal people after allowing for sex and age differences between the populations. This is consistent with national data [5] and clearly illustrates the magnitude of the Gap in mortality outcomes within South Australia.
The analysis also clearly demonstrates the heterogeneity of results for Aboriginal people in South Australia. The results highlight a range of premature mortality outcomes existing among Aboriginal South Australians and underlying the gap in outcomes between Aboriginal and non-Aboriginals. Nevertheless, while significant differences in outcomes among Aboriginal South Australians exist, wherever Aboriginal deaths occurred they were generally associated with higher rates of loss compared to South Australia as a whole.
The findings are consistent with a recent national study [5] of Aboriginal premature mortality in showing incremental increases in mortality related loss as remoteness (measured by the road distance to service centres) increased. However, South Australian Aboriginals experienced greater per capita loss in remote areas. While equivalent proportions of premature mortality were observed in remote areas (35% nationally and 33% in South Australia), a smaller proportion of Aboriginal South Australians live in remote areas (19% in South Australia and 25% in Australia).
The South Australian results also show a clear association between increased socio-economic disadvantage at an area level and increasing premature mortality. The gradient observed is consistent with national death rates among Aboriginals younger than age 65 [9]. For example, the ratio of loss in high to low disadvantage quintiles was 2.6 in South Australia and 2.3 in Australia.
The current results also indicate the relationship between premature mortality, area level disadvantage and remoteness category is not just additive. A synergy between the two ecological variables of disadvantage and remoteness was found whereby the socio-economic gradient of premature mortality loss became steeper as remoteness increased. Road traffic accidents, the third largest contributor to premature mortality among Aboriginal people in South Australia [14], illustrate how these factors can adversely effect mortality outcomes. Greater socio-economic disadvantage reduces the likelihood of safe, reliable transport and increases the risk of accident. Increased remoteness may be associated with poorer road conditions while adversely affecting access to emergency medical services in the event of an accident. Any, or all, of these factors can exacerbate mortality from road traffic accidents which contributes proportionately more loss in areas of greater disadvantage and/or remoteness [14].
The results suggest comparatively better outcomes for Aboriginal people in less disadvantaged and Regional areas. This is congruent with the Aboriginal Health Care Planning Regional profiles for Peri-Urban Adelaide and the South-East [14] where relatively lower rates of premature mortality were observed. However, such outcomes are experienced by less than 10% of Aboriginal people living in Regional/Remote areas of average or less than average disadvantage. Fully half of the Aboriginal population live in the most disadvantaged of areas; and, a quarter of these in the Remote areas of the state.
The findings have important implications. Wide ranging, complex mortality gaps raise questions about how best to respond: including, questions of how to equitably distribute resources according to need. Some health economists argue for vertical equity [29]. In this particular case, this means a response that is suitably weighted to overcome the influence of factors such as disadvantage and remoteness. Indeed, vertical equity is often advocated for use within government policy as a means of promoting equitable resource allocation [30] but has less history within health per se [31]. If it is preferable to implement a response of that kind, then what weights should apply? Recent reports demonstrate that per capita health expenditure for Aboriginal people is positively weighted and increases in remote, rural areas [32]. However, other analyses indicate a single factor model is limited. For example, in the case of adequately matching primary health care related workforce and training supply with population health need, it is more appropriate to include both remoteness and disadvantage [10]. Our findings suggest a vertically equitable response would take account of area level remoteness, socio-economic disadvantage and the synergy between these two factors. In turn the findings could help inform discussion about appropriate resourcing levels and distribution necessary to make outcomes more equitable and contribute to closing the gap in outcomes.
Significant amounts of premature mortality loss among Aboriginal South Australians can be categorised as potentially avoidable (70% compared to 41% among non-Aboriginal people) [14]. Many of the conditions are amenable to health interventions (such as prevention of chronic disease, suicide and trauma related injury), while other opportunities for preventing early loss of life lie in other sectors (such as housing improvement, road traffic mortality and education). This highlights the importance of South Australian Government initiatives like Health in All Policies [33] in encouraging all agencies to work to alleviate the social determinants of health disadvantage.
This current analysis adds to our understanding of the nature and distribution of premature mortality outcomes within the Aboriginal community in South Australia. However, several factors suggest caution in interpreting the findings. Firstly, it is important to guard against the ecological fallacy whereby area characteristics are attributed to individuals. For example, it does not necessarily follow that a person residing in a disadvantaged area is a disadvantaged individual. Also, Aboriginal data is statistically volatile because of small population numbers and death counts. As mentioned earlier, Aboriginal population estimates are 'experimental' in nature [15]. Revised enumeration from Census 2011 can potentially improve certainty of these estimates within each Australian jurisdiction. Under-identification of Aboriginal people in service and death records is also a longstanding difficulty and leads to underestimating premature mortality in the Aboriginal community. A recent ABS data linkage study applied new statistical techniques and indicated about 86% of South Australian Aboriginal deaths are correctly identified (85% nationally) [34]. Routinely applying these methods to South Australia's death records will eventuate in better identification and coverage. In turn, this will improve precision in describing the relationship between area characteristics and population outcomes.
Notwithstanding these issues, the models derived in this analysis used two available area level indicators and accounted for half the variance in Aboriginal outcomes. This highlights the need to improve our understanding and accounting of influences on Aboriginal health outcomes. Ideally this will incorporate information of greater relevance not just at an individual level, for example access to fresh water, but also at social and cultural levels, such as access to homeland living.

Conclusions

This small area analysis of premature mortality outcomes draws attention to equity gaps requiring a policy response and "helps make the invisible visible" [35]. The results clearly describe the relationship between area level socio-economic deprivation, remoteness and early loss of life in the midst of an affluent society. Further enumeration making better use of linked administrative data can help us monitor the outcomes of initiatives to improve equity and reduce the Aboriginal mortality gap.

Acknowledgements

The thoughts and opinions expressed herein do not necessarily reflect those of the South Australian Government or SA Health.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

DB maintains the South Australian burden of disease series, operationalised the study, performed data analysis, drafted and revised the manuscript. HJ contributed to data analysis and revising the manuscript. TW participated in the study design and manuscript revision. RM assisted with manuscript drafting and revision. FB conceived of the original research question and helped draft and revise the manuscript. All authors read and approved the final manuscript.
Zusatzmaterial
Additional file 1: Aboriginal premature mortality outcomes and SLA attributes in South Australia 1999-2006. Details each Statistical Local Area's: ARIA+ remoteness level; 2006 relative socioeconomic disadvantage ranking within South Australia; and standardised premature mortality ratio for the years 1999-2006. (XLS 216 KB)
12889_2010_3081_MOESM1_ESM.XLS
Authors’ original file for figure 1
12889_2010_3081_MOESM2_ESM.pdf
Authors’ original file for figure 2
12889_2010_3081_MOESM3_ESM.pdf
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