Turning Australia into a ‘flat-land’: What are the implications for workforce supply of addressing the disparity in rural–city dentist distribution?

Abstract

Dentistry in Australia has faced, and continues to face, significant workforce issues, in particular, a grossly distorted workforce distribution. In this study, an analysis of the consequences for the workforce that would occur under a series of reduced maldistribution scenarios is examined and reported. Three different scenarios were tested based on existing dental practice to population data at a national level. This study clearly highlights the very significant maldistribution of practices in Australia. However, more importantly, it highlights that to address this maldistribution requires something in the order of a tenfold increase in dental practice numbers (and the commensurate increase in workforce), which is not possible (or reasonable). As a nation, Australia has to look to other methods of achieving equity in access to good oral health. The application of modes of care delivery including, but not limited to visiting services needs to be examined and extended. Clearly, these new methodologies are going to rely on non-dental health professionals taking a far more significant role in leading oral health-care models as well as the expanded application of technology to bring unique skill bases to areas where these skilled individuals do not (and will not) reside.

INTRODUCTION

Most Australians (over 90%) live clustered in major cities or inner regional areas located near the coast. The remaining 10% of the population live widely scattered over vast areas, with most living in outer regional areas and <2.5% living in remote or very remote areas1., 2.. Dentistry in Australia has faced, and continues to face, significant workforce issues, in particular a grossly distorted dental workforce distribution³. The workforce maldistribution applies to all members of the dental team, including specialists, dentists, prosthetists, oral health therapists, hygienists and therapists⁴. The core issue is that the distribution of workforce is slanted away from disease burden. Higher rates of dental disease are experienced in areas where there are lower densities of practitioners. Rates of dental disease are also strongly associated with socioeconomics⁵ and although Australia is a relatively wealthy country [ranked fifth in median household income by the Organisation for Economic Co-operation and Development (OECD)⁶], poverty still exists within the highly uneven population distribution.

The distribution of dental workforce is currently significantly influenced by the economic structure in which care is provided. The vast majority (85%) is provided through private sector fee-for-service arrangements, resting on a private health insurance base. It is this base that to some extent drives the maldistribution of services, but as private small businesses, this is understandable from an economic viewpoint. The small (about 15% of total service) government sector cannot (and does not) cover the remaining population as it is complicated by the significant burden of disease present in this subset of the population that is dependent on public services. In recent times there has been significant discussion on efforts to address the maldistribution⁷. The mechanisms to address such a distribution issue have not been elucidated and this is left to future debate. In this study, an analysis of the consequences for the workforce that would occur under a series of reduced maldistribution scenarios is examined and reported.

METHODS AND MATERIALS

All population data were obtained from the most recently available Australian Census of Population and Housing (2006) and divided by Collection Districts (CD). These data were freely available through the web and are released by the Australian Bureau of Statistics (ABS). A CD is the smallest unit for collection of the data⁸. Also collected from the ABS website were CD and suburb boundary files in shapefile format. The 2006 census data was used, as economic indicators for the most recent census are not yet released.

The physical address for each private general dental practice (specialist and prosthetist practices were removed, as were government clinics) in all States and Territories of Australia was collated from a number of open sources including the Government Gazette and the Registration Board websites. These addresses were cross-checked against the Yellow Pages, in keeping with previous studies by our team⁹. All addresses were entered into a database and the longitude and latitude of each practice address was obtained using a free access geocoding website (http://google.com). A randomly selected sample of 5% of all geocoded practices was tested against personal knowledge and phone calls to test the integrity of the data (the confirmatory sample was found to be 100% concordant with the data collected from electronic sources).

All data analysis including the calculation of practice-to-population (PtP) ratios was completed using Excel (Version 2003; Microsoft, Redmond, WA, USA). Geographic boundary data for each CD and suburb were integrated to the population, suburb boundaries and dental practice data using arcgis (version 10; ESRI, Redlands CA, USA). All data for analysis was then extracted from the integrated geographically aligned database.

Scenarios

Three different theoretical scenarios were tested. In each case the scenario tested had two different drivers applied. Driver A was the average dental practice to population ratio for the highest 10 suburbs (by ratio) of New South Wales (NSW) and Driver B was the average dental practice to population ratio for the highest 20 suburbs (by ratio). An additional driver was applied to one of the scenarios. This driver was a variant on driver B, with the two core suburbs where major transport links of NSW meet, removed from the analysis. In both cases only suburbs with a total population exceeding 250 were used to calculate the average ratios.

Scenario 1 (single band scenario)

Scenario 1 tested the hypothesis that all suburbs (<500 km²) required a dentist to population ratio equivalent to Driver A or B and all other suburbs remained with a practice to population ratio as it currently stood.

Scenario 2 (two band scenario)

Scenario 2 tested the hypothesis that all suburbs <500 km² had a dentist to population ratio equal to Driver A or B while the larger suburbs (>500 but <10,000 km²) had ratios equivalent to the Driver A or B calculated from these second size-band of suburbs.

Scenario 3 (all the same scenario)

Scenario 3 applied Drivers A and B to all Australian suburbs irrespective of size (but <10,000 km²). In this scenario a variant of driver B was also applied (where the two suburbs that are the transport hubs of inner Sydney were removed). This mitigated against the driver of population movement to key suburbs that has been proposed by others as a ratio driver.

RESULTS

The total population (21.5 million people) of Australia was distributed across 8,381 suburbs. Across these suburbs a total of 6,901 separate private general dental practices were distributed representing a little over 10,000 individual practitioners¹⁰. The Australia-wide practice-to-population ratio was 1:3,100 (i.e. there was one practice for every 3,100 people). The drivers were calculated from the top 10 and 20 suburbs (by practice-to-population ratio) calculated both on total population per suburb and adults (greater than 17 years old; Figure 1). Driver A (top 10 suburb average practice-to-population ratio) was 226 for the total population and 206 for the adult population. Driver B (top 20 suburb average practice-to-population ratio) was 289 for total population and 258 for adult population. Again, these numbers are the number of people per practice. By averaging over the cluster of suburbs (10 or 20) this mitigated the effect of travel between suburbs driving the high ratios in a few suburbs.

Figure 1.

A sample of suburbs in Sydney mapped showing some of the suburbs used to develop the drivers of the scenarios. The highlighted suburbs are those that were included in the development of Driver A (pink, red) and Driver B (pink, red and blue), as well as the variant to Driver B (red suburbs were removed from sample).

Scenario 1

In this scenario the total number of practices required based on Driver A was 92,000 and applying this to the adult population only the total number was 101,000. Using Driver B that total number of practices was 72,000 and using adults only was 81,000 (Table 1).

Table 1.

The estimated number of dental practices for each State or Territory of Australia under three different scenarios (with the variant on scenario 3). Each scenario is driven by one of two driver assumptions (Driver A, top; Driver B, bottom)

	Number of practices					Per cent growth
	Current	Scenario 1	Scenario 2	Scenario 3	Scenario 3v	Scenario 1(%)	Scenario 2(%)	Scenario 3(%)	Scenario 3v(%)
Driver A
NSW	2,348	29,419	29,512	30,364		1,253	1,257	1,293
VIC	1,565	23,398	23,424	23,598		1,495	1,497	1,508
QLD	1,396	18,418	18,469	18,935		1,319	1,323	1,356
SA	598	6,817	6,841	7,014		1,140	1,144	1,173
WA	724	9,329	9,364	9,680		1,289	1,293	1,337
TAS	127	2,137	2,142	2,181		1,683	1,687	1,718
NT	17	870	873	888		5,116	5,134	5,222
ACT	126	1,574	1,574	1,574		1,249	1,249	1,249
Total	6,901	91,961	92,198	94,233		1,333	1,336	1,366
Driver B
NSW	2,348	23,068	23,108	23,803	19,802	982	984	1,014	843
VIC	1,565	18,342	18,356	18,499	15,390	1,172	1,173	1,182	983
QLD	1,396	14,442	14,462	14,843	12,348	1,035	1,036	1,063	885
SA	598	5,345	5,357	5,498	4,574	894	896	919	765
WA	724	7,316	7,330	7,588	6,313	1,011	1,012	1,048	872
TAS	127	1,676	1,678	1,710	1,423	1,319	1,321	1,346	1,120
NT	17	682	684	696	579	4,010	4,021	4,094	3,406
ACT	126	1,234	1,234	1,234	1,026	979	979	979	814
Total	6,901	72,105	72,209	73,872	61,455	1,045	1,046	1,070	891

ACT, Australian Capital Territory; VIC, Victoria; QLD, Queensland; SA, South Australia; WA, Western Australia; TAS, Tasmania; NT, Northern Territory; NSW, New South Wales.

Scenario 2

In this scenario the total number of practices required based on Driver A was 92,000 and with adults only it was 101,000. Using Driver B the total number of practices was 72,000 and using adults only it was 81,000 (Table 1).

Scenario 3

In this scenario the total number of practices required based on Driver A was 94,000 and for the adult population only it was 104,000. Using Driver B the total number of practices was 74,000 and using adults only it was 83,000 (Table 1). Using the B variant driver the total number of practices was 60,000 and for the adult population alone it was 68,000 practices (Table 1).

State-by-State effect

The State-by-State breakdown in the required growth in dental practice numbers finds the Northern Territory needing to increase practice numbers by 4,000–5,000% (Driver B and A, respectively) while New South Wales would only need to increase by about 1,000–1,300% (Drivers B and A, respectively).

Summary

In all three scenarios the results find that there needs to be a nine- to ten-fold growth in the number of dental practices to allow Australians to achieve a relative practice-to-population density approximating to the top 10 or 20 suburbs on NSW.

DISCUSSION

The analysis presented in this study clearly articulates the level of general dental service maldistribution that exists in Australia. Theoretically, this would immediately require a near-15 times increase in practitioner numbers to man the increased number of practices at approximately the same manning levels as current practices. The study has not attempted to adjust for the increase in disease burden that is present in the areas where, at the present, there is a paucity of practices⁹. The gradient of disease burden in dental health is steeply inclined towards poverty and the increased poverty found outside the core of cities would only further increase the maldistribution effect.

An argument can be made that there is an excess of dental practices in the driver areas used in this study (NSW). However, this argument has significant ramifications. To drive practices (and thus workforce) out of regions would require significant government intervention and would probably have an impact on various legal protections provided to small business in an open economy such as that of Australia. It is noted that the series of scenarios tested (coupled with the various drivers) were designed to diminish the focus on a small number of suburbs and do distribute the calculation drivers more widely (i.e. 20 suburbs). In addition, this study averaged over two clusters of suburbs (10 and 20 in size) to form driving ratios. This averaging mitigates against the idea that large numbers of people come to a select set of suburb hubs to receive care. Although it is acknowledged that some of this effect may remain in the analysis and lead to some overestimation, the magnitude of the effect in the three scenarios (and in particular when the known transport hub suburbs were removed) would only be marginally diminished by these sorts of population movements.

Clearly, the results of this study also highlight the very significant position that individual States are required to take as ‘safety net’ providers of dental care. It is immediately evident that the States must look closely at their workforce/practice distribution and ensure that they match the current (and predicted) location of under-served populations¹¹. With a very small base of total workforce output (about 15% of Australians total) these services cannot be expected to completely ‘fill the gap’ left by the maldistribution of private practices. The State will never be able to flatten the distribution.

The study was not designed to discuss supply and demand of services or to predict the future effects that changes in population demographics (and social expectations) may have on these variables. This is complex future prediction modelling based on many factors (known and unknown). In summary, our study examined only the current situation and looked at taking current levels of access and applying those more generally in Australia.

Underlying the study is the assumption that the ratios in NSW are a basis of ‘appropriate access’. Arguments can be made that the ratio is too high in NSW and the true maldistribution is less, as there is an oversupply of dentists in NSW. Further research is needed to determine the question of ‘what ratio of practice to population is right?’ The answer to this question, however, is not simple, and probably impossible because of the multitude of factors, other than population numbers, influencing oral health needs, demand and ultimately utilisation. It is well know that these variables are not equally distributed across the population. However, some of these, such as disease burden, are currently higher in the underserved areas. The free-market driven situation in Australia complicates this question even further. However, looking at close neighbours with similar economies, for example New Zealand, it is found that their practice to population ratios are significantly higher than in Australia (one practice for every 3,700 people in Auckland)¹². However, even this is not a reasonable comparator as differences in transport, accessibility, community dental history, other demographic factors and societal expectations all play a role in determining the ‘right ratio’. Clearly, accepting that the current arrangement is ‘right’ is not an option.

The study identifies that any effort to address maldistribution in a manner that is consistent with the current legal frameworks of open competition/free market are going to require significant increases in workforce numbers to even out the distribution of practices even in the current demand situation. The implications for workforce training are significant. A simplistic position is that to flatten population-to-practice ratios across Australia there would need to be somewhere between 10 and 15 times more practitioners. A simple estimate would say that this would require decades to achieve and multiples of new dental schools. In short, it would be not achievable, even if an immediate goal were applied. Various governments have started to work in small ways at ameliorating these issues. Australia has a number of new dental schools located in regions where need is high and we have various schemes (student placement, optional internships) that are assisting in amelioration of the maldistribution13., 14..

CONCLUSION

This study highlights the very significant maldistribution of practices in Australia. However, more importantly it highlights that to address this maldistribution within the current free market model requires something in the order of a tenfold increase in dental practice numbers (and the commensurate increase in workforce), assuming that the current ratios in the country’s largest city are a reasonable free market level. In NSW alone the current number of practices would have to rise from 2,300 to somewhere between 20,000 and 23,000 to equalize the practice to population ratio’s across the State and the Northern Territory has to move from <20 practices to somewhere between 600 and 700 practices. Clearly, these findings raise three key questions. First, is the elasticity of demand for dental services more driven by economic power than purest health care? Second, what type of maldistribution in service is society willing to accept? Third, is the level of practice density in the top 20 suburbs of NSW an oversupply situation? To rapidly flatten the maldistribution would be outside the capacity of Australia under any imaginable circumstances. It is estimated that it would require the immediate increase in dental school numbers by eight- to ten-fold or, alternatively, a quantum leap in the importation of dentists from other countries, none of which is possible (or reasonable). As a nation, Australia has to look to other methods of achieving equity in access to good oral health. The application of modes of care delivery including, but not limited to, visiting services needs to examined and extended. Clearly, these new methodologies are going to depend on non-dental health professionals taking a far more significant role in leading oral health-care models as well as the expanded application of technology to bring unique skill bases to areas where these skilled individuals do not (and will not) reside. This research clearly raises many more questions than it answers.

Acknowledgement

The authors thank Prof. Laurie Walsh for his insights during the early development of this study and the Brocher Foundation for the opportunity of the author (MT) to be a residential Fellow during the study development.

CONFLICTS OF INTEREST

None declared.