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Journal of the Australian Healthcare & Hospitals Association
RESEARCH ARTICLE

Cost drivers of remote clinics: remoteness and population size

Yuejen Zhao A B and Rosalyn Malyon A
+ Author Affiliations
- Author Affiliations

A Health Gains Planning Branch, Department of Health and Families, 4th Floor, 81 Smith Street, Darwin, NT 0800, Australia.

B Corresponding author. Email: yuejen.zhao@nt.gov.au

Australian Health Review 34(1) 101-105 https://doi.org/10.1071/AH09685
Submitted: 27 August 2008  Accepted: 1 April 2009   Published: 25 March 2010

Abstract

This report examines the impact of remoteness and population size on the costs of providing primary health care services in remote Northern Territory Indigenous communities. For remote health clinics servicing a population of similar size, total expenditure increased as remoteness increased. Total expenditure in remote clinics increased with population size, but average per capita expenditure was highest in clinics servicing populations of less than 200 people and lowest for populations of between 600 and 999. Staffing costs comprised over 70% of expenses. The largest non-staffing cost was property management. The higher costs of clinics that are in more remote locations or servicing smaller populations need to be recognised in funding distribution methodologies.

What is known about the topic? People in rural and remote locations tend to have poorer health status and poorer access to primary care services than those in urban areas. There has, however, been a lack of information on the relative cost of providing primary care services in remote areas and the nature of those costs, particularly in remote Indigenous communities.

What does this paper add? This study analyses the costs of primary care services in Northern Territory remote Indigenous communities and their associations with two key cost drivers: remoteness and population size.

What are the implications for practitioners? This paper provides information on the importance of including remoteness and population size in resource allocation formulas for primary care services in remote areas.


Acknowledgements

We express our appreciation to Oliver Dimito and Paul Quinlan from Remote Health Branch, NT Department of Health and Families for provision and validation of the financial data.


References


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