In reply: I acknowledge that data from community controlled health services were not included in our study.1 The high mobility of this population is well recognised and is most common between related communities.2 The bulk of primary care services in remote Northern Territory communities are provided through the 54 government clinics, and we have captured the movement between those services in our dataset. The lesser degree of movement between government and community controlled clinics3 would not have substantively affected our results or our conclusions.
We used propensity score matching4 to improve comparability of the low, medium or high primary care use groups. As shown in the Box, we adjusted for key confounders (age, sex, number of chronic diseases) and found no statistically significant differences between groups. All communities in this study were geographically classified as remote or very remote5 and were similar in terms of their SEIFA (Socio-Economic Indexes for Areas) score.6 Other factors raised by Whyatt and colleagues, including social acceptability and the behaviour of health care providers, may well have significant influence on decisions to use primary care services and, in part, explain the poorer outcomes among the low primary care users.
We are confident that the evidence generated by this study is of use to policymakers and health planners in their efforts to strengthen primary care in remote areas of Australia.
Proportion of patients in each primary care use group before and after propensity score matching, by age, sex and number of chronic diseases
Low-use (n = 6987) | Medium-use (n = 5926) | High-use (n = 1271) | χ2 significance (P) | ||||||||||||
Before | After | Before | After | Before | After | Before | After | ||||||||
Age (years) | |||||||||||||||
15–29 | 48% | 20% | 47% | 19% | 20% | 20% | 523.3* | 2.04† | |||||||
30–39 | 24% | 23% | 25% | 25% | 23% | 23% | |||||||||
40–49 | 14% | 26% | 15% | 27% | 27% | 27% | |||||||||
50–59 | 7% | 18% | 8% | 17% | 17% | 17% | |||||||||
60–69 | 7% | 13% | 5% | 12% | 13% | 13% | |||||||||
Sex | |||||||||||||||
Male | 50% | 35% | 39% | 35% | 33% | 33% | 523.3* | 2.07† | |||||||
Female | 50% | 65% | 61% | 65% | 67% | 67% | |||||||||
Number of chronic diseases | |||||||||||||||
0 | 63% | 10% | 43% | 10% | 10% | 10% | 2004.8* | 11.12† | |||||||
1 | 17% | 16% | 22% | 16% | 16% | 16% | |||||||||
2 | 9% | 22% | 17% | 23% | 23% | 23% | |||||||||
3 | 7% | 28% | 13% | 30% | 31% | 31% | |||||||||
4 | 4% | 20% | 5% | 17% | 16% | 16% | |||||||||
5 | 1% | 4% | 1% | 5% | 5% | 5% |
|
| |||||||
* P < 0.01. † P > 0.05. | |||||||||||||||
- 1. Thomas SL, Zhao Y, Guthridge SL, Wakerman J. The cost-effectiveness of primary care for Indigenous Australians with diabetes living in remote Northern Territory communities. Med J Aust 2014; 200: 658-662. <MJA full text>
- 2. Zhao Y, Condon JR, Li SQ, et al. Indigenous patient migration patterns after hospitalisation and the potential impacts on mortality estimates. Australas J Reg Stud 2013; 19: 321-341.
- 3. Zhao Y, Paice J, Murtagh D, et al. Population estimates for Indigenous health zones in the Northern Territory. Darwin: Department of Health and Community Services, 2007. http://digitallibrary.health.nt.gov.au/dspace/bitstream/10137/64/1/Health_Zones_Internals.pdf (accessed Aug 2014).
- 4. Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med 1997; 127 (8 Pt 2): 757-763.
- 5. Australian Bureau of Statistics. Australian statistical geography standard: volume 5 – remoteness structure, July 2011. Canberra: ABS, 2013. (ABS Cat. No. 1270.0.55.005.) http://www.abs.gov.au/ausstats/abs@.nsf/mf/1270.0.55.005 (accessed Aug 2014).
- 6. Australian Bureau of Statistics. Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2011. Canberra: ABS, 2013. (ABS Cat. No. 2033.0.55.001.) http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001 (accessed Aug 2014).
No relevant disclosures.