Background

Globally, abundant opportunities exist for policymakers to improve the accessibility of rural and remote populations to primary health care through improving workforce retention. This paper aims to identify and quantify the most important factors associated with rural and remote Australian family physician turnover, and to demonstrate how evidence generated by survival analysis of health workforce data can inform rural workforce policy making.

Methods

A secondary analysis of longitudinal data collected by the New South Wales (NSW) Rural Doctors Network for all family physicians working in rural or remote NSW between January 1^st 2003 and December 31^st 2012 was performed. The Prentice, Williams and Peterson statistical model for survival analysis was used to identify and quantify risk factors for rural NSW family physician turnover.

Results

Multivariate modelling revealed a higher (2.65-fold) risk of family physician turnover in small, remote locations compared to that in small closely settled locations. Family physicians who graduated from countries other than Australia, United Kingdom, United States of America, New Zealand, Ireland, and Canada also had a higher (1.45-fold) risk of turnover compared to Australian trained family physicians. This was after adjusting for the effects of conditional registration. Procedural skills and public hospital admitting rights were associated with a lower risk of turnover. These risks translate to a predicted median survival of 11 years for Australian-trained family physician non-proceduralists with hospital admitting rights working in small coastal closely settled locations compared to 3 years for family physicians in remote locations.

Conclusions

This study provides rigorous empirical evidence of the strong association between population size and geographical location and the retention of family physicians in rural and remote NSW. This has important policy ramifications since retention grants for rural and remote family physicians in Australia are currently based on a geographical ‘remoteness’ classification rather than population size. In addition, this study demonstrates how survival analysis assists health workforce planning, such as through generating evidence to assist in benchmarking ‘reasonable’ lengths of practice in different geographic settings that might guide service obligation requirements.