Danielle C Butler1, Stephen Petterson, Andrew Bazemore, Kirsty A Douglas. 1. Australian Primary Health Care Research Institute-ANU College of Medicine, Biology and the Environment, The Australian National University, Canberra, Australian Capital Territory, Australia. dbutler@med.usyd.edu.au
Abstract
OBJECTIVE: To examine whether measures of remoteness areas adequately reveal high need populations, measured against socioeconomic disadvantage and physician to population ratios. DESIGN: Exploratory spatial analysis of relationships between remoteness areas, medical workforce supply and the index of relative socioeconomic disadvantage (IRSD). Bivariate analyses examined associations between remoteness areas and IRSD. From this analysis, a composite score of deprivation was constructed combining measures of remoteness areas, physician to population ratios and IRSD, and validated against health outcome measures. These measures included avoidable mortality per 100,000, risk behaviour rate per 1000, diabetes rate per 1000. All analyses were conducted at the statistical local area level and weighted to be population representative. RESULTS: The percentage of small areas and populations within the most socioeconomically disadvantaged quintile rose with increasing remoteness. However, 12.8% of small areas within major cities and 40.7% of outer regional areas were also within the lowest socioeconomic quintile. There was a strong relationship between our composite score of deprivation and avoidable mortality, risk rate, diabetes rate and per cent Indigenous. Regression analysis examined the relationship between each element of the composite score and health outcomes. This revealed that the association between avoidable mortality and remoteness was lost after controlling for per cent Indigenous. CONCLUSIONS: Using remoteness areas alone to prioritize workforce incentive programs and training requirements has significant limitations. Including measures of socioeconomic disadvantage and workforce supply would better target health inequities and improve resource allocation in Australia.
OBJECTIVE: To examine whether measures of remoteness areas adequately reveal high need populations, measured against socioeconomic disadvantage and physician to population ratios. DESIGN: Exploratory spatial analysis of relationships between remoteness areas, medical workforce supply and the index of relative socioeconomic disadvantage (IRSD). Bivariate analyses examined associations between remoteness areas and IRSD. From this analysis, a composite score of deprivation was constructed combining measures of remoteness areas, physician to population ratios and IRSD, and validated against health outcome measures. These measures included avoidable mortality per 100,000, risk behaviour rate per 1000, diabetes rate per 1000. All analyses were conducted at the statistical local area level and weighted to be population representative. RESULTS: The percentage of small areas and populations within the most socioeconomically disadvantaged quintile rose with increasing remoteness. However, 12.8% of small areas within major cities and 40.7% of outer regional areas were also within the lowest socioeconomic quintile. There was a strong relationship between our composite score of deprivation and avoidable mortality, risk rate, diabetes rate and per cent Indigenous. Regression analysis examined the relationship between each element of the composite score and health outcomes. This revealed that the association between avoidable mortality and remoteness was lost after controlling for per cent Indigenous. CONCLUSIONS: Using remoteness areas alone to prioritize workforce incentive programs and training requirements has significant limitations. Including measures of socioeconomic disadvantage and workforce supply would better target health inequities and improve resource allocation in Australia.
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