COMMENTARY

Bulk-billing of general practitioner services: the evidence

In the December edition of the Australian Journal of Public Health, Rosenman and Mackinnon present evi- dence that bulk-billing causes a significant increase in service use.’ They argue that their results support the view that ‘the effect of bulk-billing is independent of indices of medical need [and that] this implies that bulk-billing may increase service rates . . . not clearly directed to improving access to medically necessary services’ (p. 419). The study is important as it rep- resents the first systematic evidence to support these commonly made assertions. As with all studies which rely upon correlational analysis the quality of the evi- dence must be carefully assessed. This is particularly true as the conclusions are hard to reconcile with earlier evidence.

Bulk-billing and need This study does not contain any direct indices of need, as implied by the conclusions quoted above. The suggestion that bulk-billing promotes unnecess- ary service use is based upon the greater impact of bulk-billing in urban areas. The authors argue that in urban, but not in rural areas, supply may exceed demand; that service provision adapts to ‘need’ in rural areas and that bulk-billing is used in urban areas to provide services above the minimum medically necessary. The conclusion is, at best, speculative. Pre- vious studies have not found a close relationship between demand and need as implied here. In the RAND health insurance experiment-the bench- mark analysis of demand-a major conclusion was that there was no relationship between the extra ser- vices induced by lower prices and the need for these services as judged by clinicians.‘ More importantly, if all the variables relevant to the analysis were included and measured perfectly then the effect of an excess supply of doctors in urban areas would be detected by, and attributed to, the variable measuring doctor supply. However, as described by Rosenman and Mackinnon, the measure of doctor supply is very imperfect. For example, above a very low cutoff point, part-time doctors are defined as being full- time and so there is a systematic bias in the measure which correlates with the number of part-time doc- tors. Further, part of the variation in the doctor supply will be explained by need and demand vari- ables unobserved in this study. Consequently, the doctor supply, as defined here, is a very imperfect measure of excess supply. A more plausible expla- nation of the rural-urban difference is that as the doctor supply increases (in urban areas), a variety of competitive activities are undertaken, including bulk- billing. With the doctor supply measured very imper- fectly and with bulk-billing measured with complete precision (from Health Insurance Commission records) the latter variable is likely to represent a bet- ter proxy measure of these competitive activities than

the former. Medical need does not enter into the explanation.

Seruice use Since problems such as these characterise most stat- istical analyses it is most unfortunate that Rosenman and Mackinnon do not present the regression equations, diagnostic statistics or even a correlation matrix for the key time-series analysis. Rather it is simply reported that in the multiple regression equations a 10 percent increase in bulk-billing (say, from 50 to 60 per cent) implies a 5.4 per cent increase in per capita use (1.4 per cent ‘usage’, 4 per cent use per patient). If this were the result of the increased provision of services by bulk-billing doc- tors only then the extrapolation of these results would imply that doctors with 100 per cent bulk- billing would be providing at least 50 per cent more services than those with no bulk-billing-at least twice the effect expected from the reduced price (extrapolating from the RAND experiment and assuming average copayments) and far more than implied by my earlier work^.^.^

These earlier studies used Health Insurance Com- mission records of all Australian general prac- titioners in four one-week periods in each of the years 1984, 1985 and 1990 to analyse the relationships between bulk-billing and patients per general prac- titioners, gross fees received per patient and gross fees received per general practitioner. A subsidiary study also examined the practice characteristics of all general practitioners whose bulk-billing pattern had varied by more than 50 per cent (up or down) in each of two periods. These separate data sets resulted in a consistent pattern in which bulk-billing was associ- ated with no consistent change in practice (other than decreased fees) until at least 95 per cent of the practice was bulk-billed. Above this level, patient numbers increased between 12 and 23 per cent (depending on the year), total fees per patient were about average (implying more services to offset lower individual fees) and medical incomes were about 20 per cent above average. These results suggest that bulk-billing has exactly the same effect as price com- petition: it attracts services away from other pro- viders and increases demand per patient. But this competitive outcome occurs only when patients are assured of being bulk-billed. Before this point, gross medical incomes declined as bulk-billing increased, reflecting the lower fee per service. Even if the increased demand associated with the bulk-billing practices was not at the expense of other practices and represented a net increase in demand, their aggregate effect is less than implied by the results of Rosenman and Mackinnon.

Several factors could explain these discrepancies. First, as noted above, the bulk-billing variable in Rosenman and Mackinnon’s study is likely to rep- resent a better measure of competition than the doc- tor supply as measured. Second, the regression equations for demand are ‘misspecified’. Both price and income are omitted. Research Paper 1 of the

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COMMENTARY

National Health Strategy reports that ‘serious chronic conditions’ are 67 and 37 per cent higher for low-income men and women respectively after adjusting for age and 40 and 13 per cent higher respectively after additionally adjusting for the Aus- tralian Bureau of Statistics’ index of socioeconomic status. The omitted income variable therefore corre- lates negatively with need and also with bulk-billing. Similarly, price (the fees ,of other general prac- titioners in the electoral division) and bulk-billing would be expected to correlate, since, as noted above, increased bulk-billing represents effective competition. The omission of price from the analysis will result in the effects of lower fees elsewhere being attributed to bulk-billing.

These statistical uncertainties indicate that the effects of bulk-billing in the study must be inter- preted with great caution, as acknowledged by the authors. However, even if the effects were as great as implied by the study there is still the policy question of whether bulk-billing should be abolished, as advo- cated by the Australian Medical Association. There are several reasons for doubting the wisdom of such regulation. First, the evidence suggests that bulk- billing affects general practitioner services, services where patients may exercise some discretion. There is no evidence and less reason for expecting that it sig- nificantly affects the majority of specialist services. Second, if bulk-billing is associated with an increased number of doctor-induced short visits, as is often asserted, then this outcome is a function of the incen- tives embodied in the present fee schedule and it is the schedule which should be adjusted. Third, and more importantly, bulk-billing is the result of price competition and an excess doctor supply. It is very hard to suppress the effects of competition. If bulk- billing were prohibited, tho’se presently gaining an advantage would simply seek. other methods for sup- plementing income. The appropriate response to an excess doctor supply is to decrease the supply. Rais- ing prices and inconveniencing patients may reduce service use and output per doctor but a reduction in doctor productivity and a suppression of price com- petition does not represent sensible microeconomic reform.

Jeff Richardson National Centre fm Health Program Evaluation

Melbourne

References Rosenman SJ, Mackinnon A. General practitioner services under Medicare. Aurl J Public Healfh 1992: 16: 4 19-26. Manning WG. Newhouse JP, Duan N, Keller EB, et al. Health insurance and the demand for Medicare: evidence from a randomised experiment. Am fia Reu 1987; 77: 251-77. Richardson J . Does bulk billing cause the abuse of Medicare? Community Health Stud 1987; 11: 98-105. Richardson J. The effects of consumer co-payments in medical care. National Health Strategy background paper 5. Canberra: Department of Health, Housing and Community Services, 1991.

Bulk-billing of Medicare: more about the evidence

Research which appears to confirm politically motiv- ated assertions warrants careful scrutiny. Professor Richardson has examined our results and prefers his findings over ours. His objections are about tech- nique and interpretation. More fundamental how- ever, are differences between the hypotheses and the nature of the data whch cause the apprent discrep- ancy between our study and his previous evidence.

Richardson sought the relationship between bulk- billing and the medical income generated and ser- vices provided.’.’ He used provider-level data collected over a limited period. Even the use of a complex set of variables to reflect price did not allow Richardson to account for more than a small percent- age of the total variance.’ Thus, his analyses leave much of the observed variance to be explained. We sought the relationship of the distribution of pro- viders and bulk-billing with the resultant service usage, and we used data aggregated by electorate. At a higher level of aggregation, an effect may become apparent which is not visible within an individual doctor’s practice.

The technical issues he raises are the absence of direct ‘need’ and ‘demand’ indices, the imperfect measure of doctor supply, inadequate explication of regression results and ‘misspecified’ regression equations.

Direct indices of need and demand do not exist but there is clear evidence that age and relative poverty result in higher illness rates and demand for services. The inclusion of these in the regression models pro- vides the best indicators of need and demand avail- able in these data. The measure of general practitioner numbers is imperfect but it is biased rather than ‘noisy’, and the bias leads to a relative overestimate of general practitioner numbers in urban areas.J This bias overestimates the contri- bution of general practitioner numbers in the cities, increasing the share of variance explained by this at the expense of other factors. Thus, variance attributed to bulk-billing is likely to be an underesti- mate rather than an overestimate. The models were carefully examined, were reviewed in a more com- plete form by this journal and, we believe, reasonably represent the data. The data are publicly available; replication and further analysis is possible. Professor Richardson’s apparent reductio ad absurdum extrapolating 100 per cent bulk-billing doctors to 50 per cent greater service numbers is deceptive on two counts. First, our finding refers to variance between electorates and not between doctors and the two are not equivalent. This makes irrelevant the second count that linear regressions are approximations in which significant error may be seen at the extremes even when they provide excellent fitting in the range of proportion of bulk-billing seen in electorates (30 to 94 per cent). It is interesting but irrelevant that per-patient service rates range from 1.96 to 2.95, a range of 50 per cent.

If the equations are ‘misGecified’, it is not for the

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