The demand for dental care: An assessment

Sot. Sci. Med. Vol. 21. No. 7. pp. 753-760. Printed in Great Bntain

THE DEMAND Health Economics

0277-9536/M $3.00 + 0.00 Pergamon Press Ltd

1985

FOR DENTAL

CARE: AN ASSESSMENT

BRIAN YULE and DAVID PARKIN Research Unit. University Medical Buildings, Foresterhill, Scotland

Aberdeen

AB9 2ZD,

Abstract-This paper reviews the existing literature on the demand for dental care and assesses its implications for the U.K., where such analysis has been lacking to date. The characteristics of the U.K. dental system are briefly outlined. and some observations on the theory of the demand for dental care are made. The main part of the paper consists of a review of empirical work, most of which is North American or Scandinavian. It is concluded that there is as yet no consensus on several empirical matters of major importance. Areas for future research, both in the U.K. and more widely, are presented.

INTRODUCTION The amount the demand research that limited; and

of research that has been conducted on for health care is now substantial, but specifically concerns dental care is more that on dental care in the U.K. is very

limited indeed. This paper has two aims. Firstly, we mean to critically review the literature on the demand for dental care and reach some conclusions about the value of existing research in this area. Secondly, since we are writing from a U.K. perspective, we aim to assess the implications of this literature for future research in the U.K., where economic analysis of the demand for dental care is long overdue. Although the studies surveyed are predominantly North American, the characteristics of dental care provision in the U.K. ensure that they also have relevance within the U.K. National Health Service (NHS). We begin by setting the scene for a study of demand in the U.K., with a short description of the dental care system. We then consider briefly some aspects of the theory of demand for dental care. The main part of the paper follows, which is a review of empirical studies; this should be of general interest, not just to those concerned with the NHS. We conclude with a summary of the empirical issues, and their implications for the study of demand in the U.K.

THE DEMAND FOR DENTAL CARE IN THE U.K.

The demand for dental care in the U.K has received very little attention from economists. This neglect is difficult to explain given the extent and vehemence of public debate in the U.K. on issues such as the effect of user charges on the uptake and use of dental services. To our knowledge only one econometric study of demand in the U.K. system has been undertaken [I]. However, this was not a study of the demand for dentistry per se, but of causal factors in the epidemiology of caries. In consequence the review of demand of the present paper

studies which forms the core contains no U.K. references.

Nevertheless, some aspects of the demand for dental care are the same irrespective of the method of provision. Thus we hope that by reviewing the

non-U.K. literature we will shed some light on the pertinent issues within the NHS. This hope is reinforced by the nature of the dental care system in the U.K. The vast majority of treatment is supplied within the General Dental Service (GDS) by independent dental practitioners operating under contract to the NHS. Each course of NHS treatment involves a contract to make the patient ‘dentally fit’ without resorting to private care. Dentists are free to accept as few or as many NHS patients as they wish, and are also permitted to practice privately (although such evidence as exists on the scale of private practice in the U.K. suggests it is fairly small). The GDS has two key features which are likely to affect the use of services and which are also typical of other dental care systems. First, it is unique in the NHS in remunerating its practitioners entirely on the basis of a fee per item of service. The fees are set according to the time taken to complete each item and are designed to ensure that dentists, on average, earn the income target set by the government. Secondly, and most importantly from the point of view of conventional demand analysis, dental care is one of very few NHS services which carry a money price to patients. Some patients (young people, expectant and nursing mothers and low income groups) are exempt from all charges, and some items (e.g. check-ups) are free of charge for the whole population. Apart from this, patients pay the full cost of routine treatment such as fillings and extractions up to a given level, and a fixed proportion of cost above this level. For more expensive items such as crowns, bridges and dentures patients pay less than the full cost, subject to an overall maximum charge per course of treatment. The balance of costs is covered by the government, mainly from general taxation. These arrangements mean that dentistry is unusual in the NHS. However they also mean that NHS dentistry has more in common with the systems featured in the demand literature than has virtually any other area of NHS provision. There are still differences, of course. For instance the issue of dental insurance which arises in the literature is of little current importance in the U.K. and is likely to remain so in the forseeable future. Also, in the NHS 753

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the money price of dental care is exogenously set by the government, rather than market determined. As a result the classic type of identification problem which has plagued much empirical research on demand is avoided. ECONOMIC

THEOh OF THE DEMAND FOR DENTAL CARE

Before proceeding to the empirical literature, some observations on theory would seem appropriate. There is, however, no distinct theory of the demand for dental care or dental health. Economists who have written on the subject have treated them as special, although not particularly unusual cases of the demand for health and health care generally. In the earlier literature this entailed treating dental care within the framework of traditional neoclassical consumer theory [2-4]. More recently the consumer theories proposed by Becker [5] and extended in the context of health by Grossman [6] have been applied to dental health and dental care [7-91. Under this approach dental health enters the consumer’s utility function as a ‘basic commodity’ which is produced in the household by combining market goods (dental services, dental floss, toothpaste etc.) together with the individual’s time. The framework provides a justification for incorporating time prices in the demand function, and also for a separation of income into wage and non-wage components. By comparison the ‘investment’ aspects of Grossman’s theory, stressing the concept of health as a stock which depreciates over time and in which health care is an investment, have received less attention in applications to dentistry (although see Pedersen and Petersen [8]). Moreover in some dental studies of the ‘household production’ type the relationship between the theoretical model developed and the empirical model tested is tenuous. Few authors are as self-effacing as Maljanen and Sintonen who explicitly set out to test a ‘more or less ad hoc’ model [lo]. There has been much debate in the health care literature following Arrow [ 1l] about whether health care is ‘different’ in important respects from other goods and services. Given this it is perhaps surprising that there has been little discussion of how dental care differs as a commodity, either from medical care or from other goods, or of the implications of such differences, if any, for the study of demand. For example, does the prevalence and nature of dental disease, together with the relative frequency of dental contacts, mean that informational asymmetries between consumer and supplier are less severe in dentistry than in medical care? If so, what are the implications of this for the role of the supplier in the demand process? Is ‘supplier-induced’ demand more, or less, likely? These questions have been glossed over in the literature, yet they are central to the appropriate specification of demand functions in dentistry [12]. We return to them in the empirical section. EMPIRICAL STUDIES OF THE DEMAND FOR DENTAL CARE

Table 1 gives some details of studies which have

presented empirical estimates of the demand for dental care. The estimation methods used have ranged from simple single equation models, in which a dependent variable (i.e. demand or utilization) is regressed on a number of explanatory variables such as price, income and fluoride coverage [2, 7, 10, 13, 15, 181 to simultaneous equation models in which dental utilization is determined by the interaction of supply and demand factors [3.4. 16. 171 and to simultaneous equation models in which the demand for dental care, and dental health status are jointly determined [8,9]. Measuring demand and utilization

The question of what is being demanded, and by whom, is crucial here. If patients demand particular types of care, largely independently of professional advice, utilization can be viewed as the product of distinct supply and demand relationships. making conventional modelling techniques appropriate. Alternatively, if patients simply demand care, leaving the choice of treatment and number of visits to be largely determined by their dentists, it becomes difficult to distinguish clearly the forces of demand and supply in the usual way. Recent evidence from Norway, at least, tends to support the latter viewpoint. Between 80 and 9O”/dof patients surveyed by Hoist [ 191stated that it was their dentist who decided the choice of treatment; usually, though by no means invariably, after some discussion with patients themselves. If this characterization is correct, utilization depends upon a complex process consisting of three interrelated stages: (a) the patient’s decision to seek care; (b) the dentist’s decision on the appropriate treatment and number of follow-up visits required; (c) the degree of patient compliance with the prescribed course of treatment. The measurement of demand is then difficult. One approach to this problem has been developed by Stoddart and Barer for medical care generally [20]. They break the utilization process up into patientinitiated and provider-generated phases, using ‘patient-initiated visits’ or ‘courses of treatment’ as measures of ‘demand’. Under such an approach, ‘demand’ equations are estimated incorporating prices and patient-reIated characteristics, whilst ‘utilization’ equations are estimated incorporating both Table I. Dental demand studies Author(s)

Year

Areas studied

Andersen and Benham [2] Upton and Silverman 113) Fcldstein [3] Phelps and Newhouse [I41 Maurizi [4] Holtmann and Olsen [7] Manning and Phelps [I51 Pcdcrsen and Petersen [S] Feldstein and Roehrig [I61 Mocniak [ 171 Hu [I81 Hay er al. [9] Maljanen and Sintonen [IO]

1970 1972 1973 1974 1975 1976 1978 1980 I980 198i 1981 1982 1983

U.S.A. U.S.A. U.S.A. U.S.A. U.S.A. U.S.A. U.S.A. Denmark U.S.A. U.S.A. U.S.A. U.S.A. Finland

The demand for dental care: an assessment demand and supply variables. Some special problems may arise in the application of this to dental care. Dental care is unusual in that preventive attendances are very common. Hence there is likely to be more scope for suppliers to influence initial contacts (via rebookings and recalls) than is typically the case in medical care. The most commonly used ‘demand’ measure in the empirical literature has been the number of dental visits [3,4, 7, 8,9, 15, 16, 181. There are, however, problems with total visits as a dependent variable: firstly (as a ‘demand’ measure) because no distinction is made between patient and dentist-initiated visits; and secondly (as a ‘utilization’ measure) because no account is taken of the volume or type of treatment provided. If concern is primarily with the patient’s decision to seek care, then the ‘episodic’ approach of Stoddart and Barer, using ‘courses of dental treatment’ as the dependent variable, would seem more appropriate. If, on the other hand, concern is primarily with overall utilization, we would ideally like to distinguish the type and/or volume of treatment involved. There are two possible approaches to this. First, the model may be specified at the level of specific dental treatments, such as cleanings, fillings, extractions and orthodontics [ 13, 151. Alternatively, some method can be found of aggregating diverse treatments into a standard measure of output, for example by using expenditures on dental care as the dependent variable [2, 10, 171. Unfortunately this measure may not always be an adequate indicator of the actual quantity/quality combination consumed, particularly if individuals face different prices. Another possible solution to this ‘aggregation problem’ would be to construct an output index which reflects the time a dentist takes to perform different dental procedures. Although such timeweighted indices have’ been used in dental productivity studies [21,22] they have not, to our knowledge, been used in utilization studies. Price effects The way in which price has been specified in dental ‘demand’ studies reflects the extent to which the data are aggregated across services. Manning and Phelps disaggregate to the level of individual services, using treatment-specific prices where available. (In fact explicit prices were obtained for only three of the seven services studied.) Alternatively, where specific services are not distinguished some ‘aggregate’ price variable is required. Holtmann and Olsen and also Pedersen and Petersen construct such a price variable by dividing dental expenditures (E) by total number of visits (V). However errors in the reporting of Vwill result in inconsistent estimates of the true ‘price’ coefficient. If E is measured with negligible error it is possible to use it rather than E/V as an explanatory variable, applying a simple transformation to obtain the required price coefficient [23]. If expenditure is also measured with error, further complications arise and inconsistency again results. Indeed, in view of the fairly small geographical areas covered by both the cross-sectional studies cited, it is possible that the observed price variation reflects differences in the

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quality of care provided, rather than ‘true’ price differences within each region. Money price invariably emerges as a significant factor in influencing dental demand/utilization, although the price elasticities reported in the literature vary considerably. Holtmann and Olsen estimate price elasticities in the range -0.03 to -0.19 in different specifications, using U.S. survey data and specifying ‘demand’ at the household rather than the individual level. However, such aggregation may not be appropriate if, as Manning and Phelps suggest, the true price coefficients vary significantly across family members. (This problem appears to be especially serious when the data for adults and children are aggregated.) Pedersen and Petersen report a price elasticity of -0.002 for their simple single equation model, using data from a survey of Danish industrial workers. (No elasticities are presented for their simultaneous equations model of dental visits and dental health status.) Hay et al. [9] find an out-of-pocket price elasticity of -0.2 using U.S. survey data, but their failure to justify the apparent use of annual dental expenses as an explanatory variable in their visits equation lowers the credibility of their results. Moreover, the attempt, in all three studies, to explain utilization (as proxied by total number of dental visits) solely with reference to demand factors (prices and patient-related characteristics) amounts to a misspecification. Both Feldstein and Maurizi attempt to disentangle supply and demand influences by using simultaneous equation techniques. They estimate highly significant price elasticities of - 1.4 and - 1.76 respectively (Feldstein using pooled cross-section/time series data for seven regions of the U.S., and Maurizi using cross-sectional survey data). However, in both cases the use of total dental visits as a dependent variable is of doubtful relevance because patients do not really ‘demand’ follow-up visits. Thus the estimated negative relationship between price and visits confounds two possible effects: the effect of price (expectations) on the number of initial visits requested and the effect of price on the uptake of subsequent visits, after diagnosis. Since the influence of price at each stage is likely to be different (not least because the latter decision will be taken in the light of information provided by the dentist), it is necessary to distinguish between these effects, and to assess their relative importance. Furthermore, as pointed out by Manning and Phelps, both the above studies suffer from identification problems. Feldstein’s supply curve includes only price, and a ‘dentists per capita’ variable which is also likely to be endogenous, given the possibility of inter-regional migration of dentists in response to ‘market forces’. Whilst evidence of such migration by dentists has been found in the United States [24], none has been found in the case of the NHS in Scotland [25]. Maurizi’s three factor input variables (number of dental chairs, number of dental auxiliaries, and dentists’ hours) are also likely to be endogenous since the dentist will choose their values in accordance with the amount of output to be produced, and their relative input prices. In this case neither demand curve is identified. The Division of Dentistry forecasting model out-

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YULEand DAVID PARKIN

lined by Mocniak [17] and Hixson [26] also consists of a simultaneous supply/demand system. Using aggregate time series data for the U.S., Mocniak reports an extremely high price elasticity of demand of -4.18. However, the model treats the aggregate supply of dentists as exogenous. If, as seems plausible, its value reacts to past or present values of price the demand curve may not be identified [27]. Indeed Musgrave [28] suggests that decisions are likely to be based on lagged rather than current prices and that the model’s use of the latter may have given rise to its autocorrelation problems. In addition the exclusion of income as a determinant of demand for the insured is not justified, even if they face zero money prices. Firstly, (wage) income will reflect the value of time used in consuming dental care; and secondly, income may be standing as a proxy for other variables (such as education, place of residence, or social class) which are excluded from the model. Manning and Phelps present separate price elasticity estimates for seven different types of dental treatment, using U.S. cross-sectional data, and stratifying by age and sex. Given the level of disaggregation and the inclusion of a supply-side variable in the form of ‘number of dentists per capita’, their results are best interpreted as utilization elasticities. They are likely seriously to underestimate the true response, however, since data were available only on whether or not specific services were consumed, effectively excluding the whole dimension of adjustment associated with the quantity consumed. Nevertheless, their results are important, indicating that utilization elasticities may vary considerably among services and by type of person, calling into question the appropriateness of aggregation across services and within families. Beyond such general statements it is difficult to draw any clear implications from the service-specific estimates, particularly in view of the absence of quantity data, and of explicit prices for four of the seven treatments studied. Like Manning and Phelps, both Maljanen and Sintonen and Hu include variables reflecting the supply of dentists per capita. Their results should therefore be interpreted as utilization elasticities. The study by Maljanen and Sintonen is unique among the studies listed in recognizing this important distinction. Using cross-sectional data from a survey of sick fund members in Finland they estimate an out-of-pocket price elasticity of utilization of - 0.16. Hu presents a number of elasticity estimates for above/below poverty and with/without insurance groupings. Price elasticities range from -0.10 (for above poverty/with or without insurance) to -0.28 (for below poverty/without insurance), the implication being that price elasticities are higher for low income groups and for those without insurance. Dental insurance

Phelps and Newhouse use data from various dental insurance plans to estimate the effects of price on utilization. Firstly they estimate the responsiveness of utilization implied by the premia charged by insurance companies at different coinsurance rates. Their findings indicate a 30% increase in utilization at full coverage as opposed to 20% coinsurance. It is not clear whether these results reflect the actual experi-

ence of insurance companies or their expectations of likely demand responses. Secondly, using data from before and after the introduction of an insurance scheme among Teamsters they calculate a 96”, increase in utilisation as a result of full coverage. Finally, Phelps and Newhouse compare the use of dental services by groups covered by a New York prepaid group practice plan with data for the U.S. population as a whole. They find that the utilization of individuals automatically enrolled through employment arrangements is 80”, higher than in the population as a whole. and that of voluntarily enrolled individuals is 180”, higher. However, no account is taken of factors such as income or fluoride coverage which may have contributed to this. They also find that children’s utilization is considerably more responsive to price than adults’. Although Manning and Phelps’ results refer to uninsured individuals they attempt to extrapolate their findings to conditions of 259: coinsurance and zero money prices (full coverage). They predict that a shift from no insurance to full coverage would result in a doubling of utilization among adults and a tripling of utilization among children. As the authors recognize, these results may not be very meaningful since they use prices well outside the range of the original data set. Hu uses three insurance categories: individuals with insurance, individuals without insurance and individuals receiving free dental care. Price and income elasticities are found to be lower for individuals with insurance than for those without it. Moreover the effect of dental insurance in increasing the number of dental visits appears to be much stronger for lower income groups. These results are important but their policy implications are unclear because they do not take into account the degree of coverage (e.g. the rate of co-insurance). Furthermore, the extent of coverage has probably changed significantly since Hu’s data were collected in 1971. Income effects

Most studies of the demand for dental care have used family rather than individual income in empirical estimations. Hixsdn [26] goes further, arguing that consumption should also be specified at family level. However, as Newhouse [27] points out, such aggregation may result in significant bias due to variation in the true income coefficients among family members. Thus in the absence of information as to how family income is allocated among individuals, it may be difficult to improve on the family income measure. Following Friedman [29] it has been suggested that dental care consumption will be related to ‘permanent’ or ‘expected regular’ income rather than measured income. If this is so, use of reported income data will tend to understate the ‘true’ income elasticity. Not surprisingly, given the measurement problems involved, few authors have addressed this question. Nevertheless, Andersen and Benham have attempted to estimate permanent income elasticities using two-stage instrumental variable techniques. Their results for dental care (though not for medical care) are consistent with the permanent income hypothesis (indicating elasticities with respect to permanent and measured income of 0.61 and 0.99

The demand for dental care: an assessment respectively), but are likely to be biased since they omit dental prices and fail to allow for inter-regional cost-of-living variations in measuring income [ 151. Grossman [6] has argued for a separation of wage and non-wage income on the basis that wage rates will affect the value of time used in the consumption of health care. The degree to which patients actually forgo earnings in order to visit the dentist is unclear from the literature. Maljanen and Sintonen tested the responsiveness of utilization to ‘the possibhty of seeing a dentist during working hours without losing wage(s)‘, but failed to find a statistically significant effect. Also, Manning and Phelps attempted to test for differences between income coefficients for various types of income, but failed to find any significant differences. Income elasticities ranging widely from 0.12 [7] to 2.14 [ 181have been found in the literature. Upton and Silverman reported separate estimates for different services, using cross-sectional data on the number of dental treatments of a particular kind carried out in 15 U.S. cities. Although their results indicate substantial differences in income elasticity across services, limited confidence can be placed in them due to the omission of dental prices and cost-of-living variations between cities. In addition, although their dependent variables are clearly utilization measures, they do not include supplier-related variables in the regression equation. Manning and Phelps also found differences in income elasticity across services and their finding that extractions may be an inferior good is intuitively plausible. Despite the shortcomings of these results, together with those on price elasticity, they indicate that the use of data aggregated across services may give a misleading impression of the impact of price and income changes on the utilization of dental services. Time prices

Holtmann and Olsen include both waiting time per visit and travel time from home to the dentist’s surgery in their model. Travel time is statistically insignificant whereas waiting time is highly significant, and waiting time elasticity exceeds money price elasticity in all equations. No account appears, however, to have been taken of the time spent undergoing treatment. In contrast, Pedersen and Petersen find insignificant coefficients on both waiting time and travel time from home. However, since patients may often travel from work to the dentist’s surgery, time from home may not be a good measure of travel time. In addition, as Holtmann and Olsen observe, travel time from home may be highly associated with suburban living, and hence with income. Maljanen and Sintonen found a highly significant elasticity of utilization of -0.66 for the total time involved in a dental visit. Such a result could have important policy implications. One of the special characteristics of dentistry is that most care can be directly provided by a general practitioner. Also, as Davis observes “the equipment and personnel required in the average practice permits considerable flexibility” [30]. Thus, there is potential for reducing time prices and hence increasing utilization by increasing the mobility of the dental practice. None of the above studies takes any account of the

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time lag between a patient’s request for treatment and his obtaining an appointment. Such ‘waiting-list time’ may not be a ‘time price’ in the usual sense (since the patient’s activities need not be affected in the interim) but it may nonetheless be a barrier to the uptake of care. Scarrott [31] for example, in a study of attitudes to dentists in the U.K. found that 3540% of those interviewed considered ‘readily available appointments’ an ‘important aspect’ of dental treatment. Whether this aspect affected the respondent’s decision to seek care, or merely their choice of dentist, is uncertain. Although neither Manning and Phelps nor Hu include time costs directly, it is possible that their ‘dentists per capita’ variables pick up some of the effect of access time on dental utilization. [The greater the number of dentists, ceteris paribus, the higher we might expect the demand for dental services to be, reflecting reduced ‘waiting-list time’, waiting time, travel time (and costs), or some combination of these.] Unfortunately, it is difficult to disentangle such effects from those of ‘supplier-induced demand’ or from the effect of increased supply under excess demand conditions (in the absence of equilibrating price movements.) Dental health indicators

The fact that dental disease is confined to a distinct area of the anatomy, and is fairly readily observable, has facilitated the development of indices of dental health to a much greater degree than has been possible for health more generally (although conceptual problems still remain-for example, do extractions raise dental health by removing disease, or lower it by removing teeth?). Such indices include the much-used decayed-missing-and-filled (DMF) index, as well as indices of periodontal (gum) disease, oral hygiene, oral health and ‘dental need’ 1321. Professionally assessed measures of dental health such as these could be very useful in explaining the type and quantity of treatment provided. Their usefulness in explaining the patient’s consultation decision might, however, be limited because patients and dentists assess dental health differently [33]. Manning and Phelps include dummy variables indicating whether or not respondents had toothache or bleeding gums during the survey year. Not surprisingly they find that the incidence of toothache increases substantially the probability that an individual will have a filling or extraction. Maljanen and Sintonen include a similar ‘acute need’ variable and find correspondingly strong effects on dental expenditures. However, since dental visits and expenditures are likely to affect dental health, as well as vice versa, single equation estimation techniques may be inappropriate (although Manning and Pheips in fact find no evidence of simultaneous equations bias). This problem leads Pedersen and Petersen, and Hay, Bailit and Chiriboga to estimate simultaneous equation systems in which dental visits and dental health status are jointly determined. Pedersen and Petersen find a positive relationship between the number of dental visits and good dental health (as measured by number of teeth remaining) which they explain in terms of a failure to distinguish between preventive and non-preventive visits. Good dental

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health, they suggest, results in a fairly large number of preventive visits in order to maintain it. Whilst this may be true, an additional explanation is that dental health is being measured by ‘number of teeth remaining’ and so their result may simply reflect the fact that people with few teeth require few dental treatments. This point highlights the major inadequacy of the index ‘number of teeth remaining’; no account is taken of the condition of those teeth which remain. Thus it is significant that the positive relationship between dental health and number of visits disappears when a DMF index is used. In addition, as the authors suggest, a distinction between preventive and non-preventive (restorative and extractive) visits is desirable on the grounds that, whilst current re,storative and extractive visits may influence current dental health status, such a relationship is unlikely to hold for current preventive visits. Other variables

A number of variables have been included in an attempt to capture the effect of patient tastes and attitudes on the demand for dental care. Pedersen and Petersen use an ‘expectations as to the value of care’ variable (incorporating the effects of education and previous dental care activity) in this context, and Holtmann and Olsen use variables reflecting ‘education’, ‘fear of the dentist’ and ‘lack of time, money and perceived need for dental visits’. Their findings indicate that the omission of such variables may significantly bias the estimated coefficients. In particular, it is likely that ‘fear of the dentist’ may play a major role in influencing dental attendance. Van Groenestijn et al. [34], for example, in a survey conducted in Amsterdam, found that 35% of irregular attenders had had ‘bad experiences of the dentist in the past’, or were ‘frightened of going’, whilst a further 20% did not consider regular attendance to be ‘necessary’. There is therefore a potential for programmes of dental health education on these aspects to affect demand. In both of the empirical studies above, the level of genera1 education was positively related to the number of dental visits, suggesting that the effect of education in increasing awareness of the potential benefits from treatment tends to outweigh its effect both of increasing efficiency in the production of dental health, and also of causing a substitution of other methods of dental care for dental visits. However, the use of education as a purely attitudinal variable is not wholly satisfactory, since it is likely to be highly correlated with other factors such as income, race and urbanization [3]. Fluoride variables have often been incorporated in dental demand/utilization analyses, although other obvious epidemiological variables, such as sugar consumption, have not, with one exception [I]. Feldstein and Maurizi find significant reductions in dental expenditures and dental visits, respectively, owing to fluoride coverage. Upton and Silverman find similar effects for specific services (particularly fillings), but the results of Manning and Phelps and of Hu are much less clear-cut. Mixed results are not necessarily surprising since the expected effects of fluoridation on utilisation are ambiguous [26]. If, for example, fluoridation results in more teeth being retained into

DAVID PARKIN

later life, the long term effect on dental utilization might well be positive. Age is clearly an important factor in determining both demand for, and utilization of. dental care facilities. Feldstein suggests that there may be an ‘inverted U’ relationship between visits and age. The number of visits per person increases up to 15-24 years of age, and declines slowly thereafter. This relationship is broadly supported by the visit estimates of Manning and Phelps. In addition. the type of dental service consumed appears to vary with age. Cleanings and fillings are more common among younger age groups, whilst gum treatment and denture work are more common for older patients. This evidence is generally borne out by Manning and Phelps’ service-specific estimates. Price/income

interactions

The extent of any variation in time and money price elasticities across income groups is of particular relevance for policy purposes. If, for example, it was established that time price elasticity exceeded money price elasticity for low income groups, policies aimed at the reduction of time prices (for example by ensuring a more even regional distribution of dentists or by increasing the mobility of the dental practice), would, ceteris paribus, be more effective in increasing utilization than reductions in out-of-pocket patient charges. The limited empirical evidence on this question is contradictory. Holtmann and Olsen split their data into four income classes and find, with one exception, that price elasticity falls as income rises, although differences between higher income classes are not statistically significant. Furthermore, they find that sensitivity to waiting time falls as income rises, although again differences are not statistically significant. Conversely, Manning and Phelps find substantially larger price elasticities as income rises. Hu argues that this may have arisen because Manning and Phelps attempt to estimate the relevant elasticities from the same demand function. If the effect of rising income is simply to cause a parallel outward shift of the demand curve we would indeed expect higher price elasticities with higher income. However, such an assumption does not appear to be inherent in Manning and Phelps’ specification; their interaction terms should account for changes in the slope of the demand curve as income varies. Hu estimates separate demand functions for above and below poverty income groups, finding that price elasticity falls as income rises. Maljanen and Sintonen adopt a similar approach and find likewise (although their results are tentative given the small number of observations in each income group). SUMMARY OF EMPIRICAL ISSUES AND IMPLICATIONS FOR THE STUDY OF DEMAND IN THE U.K.

Most of the studies reviewed have been North American or Scandinavian, and most have been cross-sectional analyses of survey data. Demand, or utilization, has been measured in various ways, the most common of which has been the number of visits. However, this measure is not entirely appropriate since it does not distinguish between patient- and

The demand for dental care: an assessment dentist-initiated visits, and takes no account of the amount or type of treatment performed. An improvement on this in the context of the NHS would be to follow the approach of Stoddart and Barer, using the number of initial consultations (predominantly, though perhaps not entirely, patientinitiated) as an indicator of demand. A time-based measure of utilization has special attractions in the NHS. Since, as mentioned earlier, the fee paid to dentists per item of service is directly related to the time taken to complete that item, dental fees provide a potentially reliable output measure [35]. This is preferable to the number of visits as a utilization measure since it is more sensitive to the volume of treatment provided. In addition the demand/ utilization distinction allows the influence of factors such as price, or supply, on initial contacts and subsequent use to be identified separately. Studies in the literature have found money price to be a significant influence on demand, measured both as the price of individual treatments, and as an average. However, the latter can lead to inconsistent estimation if derived from imprecise estimates of quantity and expenditure. This is unlikely to be a problem in the NHS where detailed breakdowns of quantity and expenditure are collected for administrative purposes. There are, however, problems of deciding on the appropriate price variable. For some units of routine treatment patients are charged the full cost and for other (subsequent) units they are charged a given proportion of this. Individual items of specialist treatment carry a separate charge, but consumers may not have to pay the full amount of these because of the maximum charge which applies to each course of treatment. In the case of routine treatment the difficulty is similar to that posed by deductibles plus coinsurance under health insurance; for specialist treatment the system is akin to one of . copayment; and dental care as a whole is subject to a maximum charge with ‘full coverage’ above that level. It is not clear, n priori, whether per-item charges, maximum charges, or intermediate ‘average’ charges come closer to the ‘prices’ perceived by patients or dentists. Ultimately the matter is an empirical one. Furthermore the incentives which a system of this type gives to patients and dentists require to be investigated [36]. Many of the studies reviewed here have used cross-sectional surveys over limited areas, which has left little scope for price variations except through quality differences. In the NHS the problem is even more serious than this: there is no price variation on a cross-sectional basis, except that which results from cost-of-living differences between regions. This means that it is really only feasible to investigate price effects using time-series data. As remarked earlier, the reviewed evidence on dental insurance is not relevant to the NHS. Indeed it is of limited value for policy purposes in insurance systems because the studies reported are rather dated and at times highly speculative in nature. Income effects have generally been found to be important in the literature, but conventional measures of income may not be very useful in empirical work. Nonetheless it is interesting to note that Manning and Phelps’ finding that extractions may be an

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inferior good is consistent with U.K. evidence that working class families have a higher preference for extraction, as opposed to restoration, than higher income groups [31]. Time prices have been studied in the literature, and have. not always proved a significant factor. Dental health status has also been studied, but with inconclusive results as to its relationship with demand. Attitude variables have been found to be important, as has fluoridation. In the U.K., two major population surveys of dental health and attitudes have been conducted [37,38]. However, these are not useful for the purposes of the time series analysis necessary to examine money price effects. An analysis of the effects of dental health, attitudes or time prices on demand or utilization in the U.K. would probably require specially generated data. Finally, price and income interactions have been found in the literature, but with contradictory results-price elasticities have been found in some cases to rise with income and in others to fall. Clearly this is an important issue for study in the U.K. and also more widely.

CONCLUSIONS

This paper has identified and discussed the factors which are most important to an economic analysis of the demand for dental care, with a particular view to promoting such research in the U.K. It is difficult to generalize from the results of the studies reviewed; indeed no consensus has emerged on some matters of major importance. However, together with our discussion of the characteristics of dental care provision in the U.K., the empirical literature serves to highlight a number of areas for future research. What, for example, are the relative roles of charges and time prices in influencing patient consultations and the use of services, and how do time and price elasticities vary across income groups? Does supplier inducement exist, and if so, does it take the form of changes in the volume of initial consultations, or changes in the volume of treatment for patients who are already in contact with the dental care system? How do dentists’ and patients’ perceptions of ‘need’ differ, and what are the implications of this for the uptake of services? These questions have a direct bearing on policies designed to influence efficiency in the provision of dental care services, and equity in their distribution, both in the NHS and elsewhere. Acknowledgement-We wish to thank our colleagues in HERU for their comments on earlier drafts of this paper.

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