Spanish public hospital waiting lists: A theoretical and empirical approach

Rodríguez-Álvarez, Ana; Rosete-Rivero, Mayte Working Paper Spanish public hospital waiting lists: A theoretical and empirical approach Economics Discussion Papers, No. 2017-25 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Rodríguez-Álvarez, Ana; Rosete-Rivero, Mayte (2017) : Spanish public hospital waiting lists: A theoretical and empirical approach, Economics Discussion Papers, No. 2017-25, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/158567 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0) Discussion Paper No. 2017-25 | May 10, 2017 | http://www.economics-ejournal.org/economics/discussionpapers/2017-25 Spanish public hospital waiting lists: a theoretical and empirical approach Ana Rodríguez-Álvarez and Mayte Rosete-Rivero Abstract The main objective of this work is to study the effects that supply and demand factors have on waiting lists. With this aim, the authors discuss a model which explains the factors that can affect the production of healthcare, demand for healthcare, and finally, the inequalities between health supply and demand. This analysis proves that, due to imbalances between supply and demand, there is an excess of demand that is equal to the waiting lists. This demand excess is called the waiting list function. Hence, the second part of this paper develops an empirical analysis which estimates the function for the waiting lists of Spanish public hospitals for the period 1996 to 2009. As a result of the estimation, the supply and demand factors influencing waiting lists, as well as their evolution, are determined and studied. An imbalance between supply and demand reduces the supply and increases demand resulting in the amount traded by the market being less than potential demand. JEL H4 I3 Keywords Hospital waiting lists; Spanish public healthcare; NHS; excess demand; demand and supply factors Authors Ana Rodríguez-Álvarez, Department of Economics, University of Oviedo, Spain, [email protected] Mayte Rosete-Rivero, Oviedo Efficiency Group, Oviedo, Spain The authors acknowledge financial support from the project “Oviedo Efficiency Group” FC-15- GRUPIN14-048. (European Regional Development Fund (FEDER) and Principality of Asturias, Science, Technology and Innovation Plan (2013-2017). Citation Ana Rodríguez-Álvarez and Mayte Rosete-Rivero (2017). Spanish public hospital waiting lists: a theoretical and empirical approach. Economics Discussion Papers, No 2017-25, Kiel Institute for the World Economy. http://www.economicsejournal.org/economics/discussionpapers/2017-25 2 1. INTRODUCTION The National Health System (NHS) constitutes an important pillar in the Welfare State. In the case of Spain, there have been significant achievements since its implementation such as the equity of and accessibility to healthcare, the supply of a wide-ranging, highquality and controlled healthcare, a wider system of primary healthcare and the integration and coordination of the different public structures and health services managed by the NHS. The Health Care General Act (LGS 1986) established the National Health System with many features which still exist today. It was designed to give universal coverage characterised by being a free service at the point of delivery. Additionally, it was agreed to be financed by general taxation and to integrate the service network under its structure. Furthermore, it established an agreement to transfer political competences to the Autonomous Communities with the commitment to provide services in basic health areas with a new model of primary health care. The process through which the competences at the sub-national level were reallocated culminated in 2002. However, despite these gains and strengths, Spanish NHS suffers weaknesses in the form of bureaucratic inefficiencies, a high number of doctor per person appointments (above the average of the OECD countries), an ageing population, and long waiting lists/times for some specializations (Peiró and Ridau 2004; Rodríguez and Lovell, 2004; Prior and Solà, 2007). The concern with waiting lists/times has spread to all healthcare units regardless of the region, and as such, healthcare status is subject to improvement by either increasing inputs to produce the service or a better allocation of said inputs. In fact, waiting lists are considered one of the major challenges faced by the NHS in Spain and in many other OECD countries. This problem presents a social cost to patients, hospital 3 managers and politicians. Moreover, waiting lists/time may affect health outcomes and result in an unequal access to the health care system. However, from an economic viewpoint, these waiting lists/times do not necessarily need to be zero. Namely, given that Spanish healthcare faces uncertainty in terms of demand (Lovell et al., 2009; Rodríguez et al., 2012), if for example a hospital has sufficient capacity to meet patient needs in periods of high demand, this can produce excess capacity in periods of low demand (Worthington, 1987; Propper, 1990; Street and Duckett, 1995 or Iversen and Luras, 2002). This in turn translates itself, from the point of view of productivity analysis, into a certain degree of technical inefficiency. Hence, determining the optimum size of waiting lists requires a comparison of the social marginal cost related to the prolongation of a precarious health status with the marginal cost associated with a reduction in waiting lists/times (see Globerman, 1991; Cullis et al. 2000 or Siciliani et al., 2013 for a review). Waiting lists/times can be altered essentially by patient demand and by the capacity of supply. Policies implemented by health managers to reduce waiting lists and times include, for example, guaranteeing a maximum waiting time and sub-contracting private services or increasing hospital capacity. All the policies require both additional funds and a change in operational behaviour. However, over recent years and due to the budget cuts suffered by the healthcare systems, reducing the waiting lists/times remains one of the biggest challenges of the Spanish Health System. The problem of how to address the subject of waiting lists/times has already been considered in the literature. In this context, many authors define waiting lists as a system of rationing healthcare generated from an excess of demand (Lindsay and Feigenbaum, 1984; Worthington, 1987; Street and Duckett, 1996 or Cullis et al., 2000 among others). It is in this regard that the present paper undertakes both a theoretical 4 and empirical analysis of the characteristics for waiting lists generated by the Spanish National Health Care System, and more specifically the public healthcare system. Within the framework of a healthcare market, and taking into account the supply and demand for healthcare, Martin and Smith (1999) develop an elegant theoretical model with empirical implementation, where both demand and supply are a function of waiting times. This assumption is also considered in posterior models such as Gravelle et al. (2003); Windmeijer et al. (2005) and Martin et al. (2007). In our paper, we tackle the problem from a different perspective. Specifically, we start with traditional microeconomic theory in order to present both a potential demand for healthcare as well as a potential supply. Given that we assume that said healthcare occurs within a NHS, prices are subsidized meaning that the market price of healthcare will be below the equilibrium level occasioning an excess of demand (Worthington, 1987; Street and Duckett, 1996). Based on this idea, we develop a theoretical excess demand function (that is, a function of waiting lists) and we propose the estimation of an empirical example. In this sense, we present a waiting lists versus waiting times perspective. Several papers have revealed that although both concepts might be correlated, this correlation is imperfect. However, for the Spanish case, an advantage of using waiting lists versus waiting times could be that the waiting times are regulated in Spain, which means that, with the objective of not surpassing timelines, various autonomous communities have detected and reported several practices aimed at either concealing and/ or manipulating the real statistics of waiting times. This implies that the data for waiting times, when existent, should be treated with considerable caution. After performing the theoretical analysis, an empirical model is developed using panel data where we estimate a function for waiting lists. From an empirical standpoint, our model shares with previous ones the simultaneity problems associated with a joint 5 estimation of supply and demand. Nevertheless, given the use of panel data techniques, this problem is tackled in the empirical model. Finally, the main contribution of the paper is, to the best of our knowledge, that it constitutes the first study in the literature which analyzes both from a theoretical and empirical standpoint a waiting list function in the context of a National Health System (NHS). 2. MODELING HEALTHCARE WAITING LIST The model proposed in this section is developed using a microeconomic framework and ultimately serves to evaluate the excess demand for healthcare services. Considering the complexities of measuring health, from a conceptual point of view as well as an instrumental one, we approach it using healthcare provision as a proxy.1 Healthcare provision will include all those services performed by healthcare professionals aimed towards an individual or a group of individuals with the main objective of facilitating the recovery of the biological, psychological and social functions. The demand for healthcare services is not a goal in itself but instead the demand for a sought-after good: health. Therefore, the demand for health services can be understood as a derived demand from the demand for health. What follows is an analysis of the healthcare market, its particular conditions as well as the fundamental components of supply and demand. 2.1. HEALTHCARE DEMAND To obtain healthcare demand we apply the principles of classical consumer theory, which studies the preferences of a utility-maximizing consumer restricted by 1 It is important to differentiate between two types of goods: healthcare and health. In this paper we focus on the demand for healthcare instead of investment in health (Grossman, 1972). 6 their budget. Concretely, we assume that a consumer has to choose between two goods (healthcare and a second item). For this specific consumer, the ordinary utility function can be constructed as follows: (1) Where we define; Healthcare consumption; Other goods; individual state of health (assumed to be shortterm). The utility maximization process has to be restricted through monetary limitations. The real budgetary constraint is viewed as the combination of consumption possibilities given a set of market prices and initial wealth. These constraints were developed by Acton (1975) who incorporated the cost of time into the price vector for the model, expressed as follows: (2) (3) Defining: Total income; Unit Price for healthcare services; Hourly wage; Time spent on the consumption of healthcare goods; Unit Price for the alternative goods; Time spent on the consumption of other goods; Total price of healthcare; Total price for other goods. The consumer is granted an amount of income Y which they have to distribute between the consumption of both goods. The chosen combination will depend on the price of the goods but in the case of healthcare we have to add to the unit price (p) the indirect costs (wt) defined as the opportunity costs of time spent on acquiring healthcare. In the same way, the price of other goods (besides the direct unit price q) carries an indirect opportunity cost materialized in the time spent on acquiring other goods. The budgetary constraint will be subject to variations based on changes in prices, wages, income or the time allocated by each individual to the consumption of both goods. 7 The consumer’s equilibrium is the combination of goods that solves the following optimization process: (4) (5) Once the optimization problem defined in the equations (4) and (5) is solved, we can deduce the individual’s demand function (Md) defined as follows: (6) The quantity demanded is positively related to the price of the alternative goods and personal income (if we assume that healthcare is a normal good) and negatively related (if healthcare is an inferior good). Nonetheless, healthcare demand is negatively related to its own price and the individual’s level of health leading to a diminished demand when either of the latter variables increases. Finally, we define the market demand (in this case, society) as the sum of all individual demands at every price level. With this definition, the form adopted by the aggregated curve will depend on the form of the individual curves for each consumer. Consequently individual health status will be the main factor for defining the aggregate demand function. We have to assume that all individuals in this particular society enjoy the same level of health. The slope for the aggregated demand curve in a society that endures worse levels of health will be steeper in contrast to healthier societies where it will be much flatter. The aggregate demand function ( ) for the entire society depends on a large number of factors, the following being the most influential: (7) 8 Where we define: Quantity of the demanded Healthcare; Total Healhcare Price; Total Price of other goods; aggregate income; Health Status; Total population. Variations in the previous factors will result in changes in aggregate demand. Under the aforementioned assumptions, the price of other goods, aggregate income (in the case of a normal good), mean age and population will positively affect the quantity demanded. Meanwhile health status and healthcare price will affect it negatively. 2.2. HEALTHCARE SUPPLY State intervention in the healthcare system is justified by the numerous market failures. The healthcare system currently enacted in Spain is NHS regulated on a national scale. This service is granted by way of public financing and provision, staffed by State employees and offers free and universal access to all its services. The NHS leaves the private sector as an alternative for those citizens whose utility is not maximized by the public provision of healthcare and who are able to affront the cost of a private healthcare scheme in monetary terms One of the main reasons why some individuals opt for private healthcare services is precisely to avoid the long waiting lists of the public service which are due in part to the latter’s free and universal nature. The production agent in our model are hospitals. As explained by Cullis and West (1979) hospitals are suitable for comparison as a firm since both entities are faced with the task of choosing between a series of factors to produce a number of healthcare services (i.e. a multi-output firm). Also, it is important to mention that, judging by the total healthcare expenditure incurred, hospitals are the dominant institutions in the production of health. From the point of view of the Spanish healthcare sector we can 15 same occurs with the price of other goods, if we consider an average price for each year and autonomous community. The GDP variable has been taken from the Spanish Statistics Institute (INE, its acronym in Spanish) and is expressed as the gross domestic product per year in millions of euros. The variable of hospital outpatient visits (VISIT) is a proxy of morbidity and has been collected from the EESRI survey. We define it as the number of outpatient visits from patients to healthcare services, involving a medical diagnosis on an outpatient basis. Life expectancy (LIFEXP) has been estimated using data from the INE and we define it as the number of years people are expected to live per autonomous community. The population variable (POP) comes from the population data generated by the INE, which includes all residents in each Autonomous Community per year. Finally, the unemployment variable (UNEMPLOY) has been obtained from the Spanish Labour Force Survey and it is defined as the yearly unemployment rate: the number of people that want to find work and are actively searching for it. Table 1 briefly describes the variables used in the study. (INSERT TABLE 1 AROUND HERE) 4. EMPIRICAL MODEL We propose a panel model where the error term is not assumed to be independent and identically distributed (iid). Instead, it is assumed that the disturbance could be heteroskedastic and contemporaneously correlated across panels. We have also tested for a first-order autocorrelation issue (in our example it is likely that the waiting lists in t are associated with those of t-1). That is to say, we control for heterocedasticity; crosspanel correlation and first order autocorrelation. 16 (15) where i = 1; …;m is the number of regions (or panels); and t = 1;…; T; is the number of periods in panel i. The parameters of equation (15) are defined as follows: α, β are parameter vectors to be estimated; and Dt are vectors of firm and time-specific dummies respectively; and , is a disturbance that may not be iid. In fact, we have already tested the existence of heteroskedasticity, contemporaneous correlation of the errors; and serial-correlated errors. 5 Because of this, we use a panel corrected standard errors model (PCSE). 5. RESULTS The results obtained from the estimation of equation (15) are in Table 2. Before discussing our results, we wish to point out that the variables used in the estimation are defined in logarithms and these variables have been divided by the geometric mean. Therefore, the coefficients in Table 2 can be interpreted as elasticities. The budget variable (BUDGET) shows a negative and significant elasticity meaning that larger budgets would generate a reduction of waiting lists, specifically; keeping constant the rest of the variables, if the budget were increased by 1%, the waiting list 5 To test the existence of correlation we have applied the Wooldridge test for autocorrelation in panel data. The value of this test was 5.179, higher than the critical value of the F distribution at the 5% level of significance, which means that we can reject the null hypothesis of absence of first-order autocorrelation. To test the existence of heterocedasticity, we have applied a Wald test. The value of this test was 787.57, higher than the critical value of the chi-square distribution for 17 degrees of freedom at the usual levels of significance, which indicates that we can also reject the null hypothesis of absence of heterocedasticity. Finally, we have used the Pesaran test of cross sectional independence with the null hypothesis being rejected at the 1% level. 17 would decrease by aproximately 0.04%. This result logically indicates that an increase in budget translates itself into more resources and, therefore, in a greater probability of attending more patients, reducing in turn, waiting lists. Similarly, the capital variable (CAP) shows a negative and significant coefficient indicating that increases in the capital of hospitals also reduce waiting lists. More specifically, a potential increase in the fixed capital of hospitals would imply and improvement in the form of a decline in waiting lists of 0.08%. Therefore, both results seem to indicate as the theoretical model predicted, that increases in supply –either via an increased budget in current costs or capitalcould reduce hospital waiting lists. With respect to the demand variables, these present higher coefficients than those for supply variables, which would appear to indicate that demand variables are the ones which most influence variations in waiting lists. Hence, for example, the GDP variable, which is our income proxy, presents a negative elasticity indicating an inverse relationship between income and waiting lists. Concretely, if the GDP were increased by 1%, the waiting list would decrease by aproximately 0.91%, ceteris paribus. Although several authors have found a positive relationship between income and waiting times (Laudicella et al,, 2010 or Siciliani and Verzulli, 2009), these results may indicate a positive relationship between more income and better health which has been amply discussed in the literature. For example, Marmot et al (1978), Lantz et al. (2007,2010), Moss and Krieger (1995), Rose (2001), Case and Deaton (2005), Galama and Kipperluis (2010) amongst others, find that income and social class in general are a good predictors of morbidity and mortality given that lower social classes tend to lead less healthy lifestyles and behaviours than superior social classes. Moreover, this result could indicate that public hospital healthcare is an inferior good compared to its private counterpart. If patients experience an increase in income, they cease to wait for services 18 from the NHS and switch to the private hospital system where waiting lists are nonexistant or if they exist they are minimal in terms of time. Both reasons, may explain why GDP gains generate a reduction in the NHS patient waiting list On the other hand, the value of the coefficient of population (POP) is positive and signifcant indicating that more populated regions are those with the longer waiting lists. Concretely, the results indicate that if the population increases by 1%, the waiting list would rise by 0.65%. As regards morbidity, the variable for healthcare visits (VISIT) shows a positive and significant elasticity. A larger number of outpatient visits are indicative of a slightly more deteriorated population health, leading to an increase in the length of the waiting list. With these results we can say that if hospital visits increase by 1%, waiting lists would rise by aproximately 0.17%. In a similar way, an increase of a year in life expectancy (LIFEXP) significantly reduces waiting lists by 0.005%. The unemployment variable (UNEMPLOY) presents a negative but not significant elasticity.6 Numerous studies have documented a positive association between employment and health (Moser et al. (1987); Mathers and Schofiels (1998) or Roelfs et al. (2011)), whilst others find an inverse relationship (Boone and van Ours (2006); Ruhm (2000) or Stuckler et al. (2009)). We have therefore included this variable in the study in order to contrast the relationship. However, the result is not significant and thus we are unable to determine whether a clear relationship exists between unemployment and health and in consequence between unemployment and waiting lists. In order to control for the differences between the various CCAA, a regional dummy variable (DCCAA) has been included. The results seem to indicate, that except for a few exceptions, no significant differences exist between CCAA. With respect to time, the 6 The coefficient is significant by 10.2% which is at the limit of standard significance levels. 19 dichotomic time variables captured events common to all the autonomous communities during a period, such as for example the effects of the crisis or political changes which could have affected health policies. Whilst the results for the initial years (from 1996 to2005) are not significant, from 2005 onwards the relationship is significant and positive. This result can have two readings, one with its origin in supply and the other in demand. With respect to supply, this negative impact is visible at the commencement of the economic crisis as a reflection of the start of the budgetary problems and restrictive policies. On the demand side, the effects of determined factors on population health similar to those for all the CCAA and which vary in time (for example, the ageing population or the chronification of several illnesses previously terminal thanks to the technical advances in medical research) may have influenced an increase in the number of waiting lists. Figure 4 shows the evolution of waiting lists, assigning 1996 as the baseline year.7 (INSERT FIGURE 4 AROUND HERE) 6. CONCLUSIONS AND DISCUSSION This paper has performed both a theoretical as well as an empirical analysis of Spanish NHS waiting lists. First, we have applied a theoretical model that explains both the supply and demand of Spanish NHS hospitals. In Spain, public healthcare is subsidized so that patients do not have to pay for the service received. The theoretical framework justifies this waiting list increasing as a result of changes in demand (for example, 7Applying a Wald tests, we have checked whether the time effects, taken as a whole, are significant by testing whether the coefficients on Dt are simultaneously zero. The value of the test (χ2 (13) =37.87) allowing us to strongly reject the hypothesis that the joint effect of time effect has not affected waiting lists. In the same way, for the case of the regional individual dummies (CCAA) we find that the significance level of the test is very close to 0 (the value obtained with the Wald test was χ2(16)=114.34). 20 population or morbidity) as well as supply (budgetary cuts and capital) factors. As a consequence of these possible increases in demand and reductions in supply, excess demand has broadened, in other words, waiting lists have increased. Once the waiting list function was defined in the theoretical model, the next stage involved developing an empirical panel data model to estimate the former function. Thus, the main objective of the second part was to contrast the effects that both supply and demand factors have on waiting lists. We have used a data panel for the 17 Spanish Autonomous Communities observed over the 1996-2009 time-frame. The main contribution of this paper is that it combines a dual approach represented by a theoretical and empirical model in order to analyse the waiting list function in a rigorous way, shedding some light on what is, without doubt, a social and economic problem of enormous magnitude. The results obtained confirm the model presented: the waiting lists of Spanish public hospitals are affected by supply and demand variables. Concretely, an increase in the budget for the current costs of hospitals as well as capital diminishes waiting lists significantly. With respect to demand variables, results seem to indicate that these have a bigger impact than supply variables. Specifically, variables which approximate income, morbility (number of outpatient visits or life expectancy) and population, influence waiting lists significatively. In contrast, unemployment does not appear to have a significant effect on said waiting lists. Finally, we have observed that time has influenced an increase in waiting lists (the effect proving statistically significant from 2005 onwards). This result may be due either to restrictive supply budgets or demand factors such as population ageing or the chronification of certain illnesses thanks to technological advancements. Nowadays, the Spanish National Health System suffers from the austerity measures resulting from the impacts of the economic crisis. These measures include cutbacks in 21 the budget dedicated to public health. The model proposed predicts that this, together with an increase in citizen demand for health services, will result in a reduction in supply and an increase in demand and with it an excess of demand that translates itself into longer waiting times. During the period of study, we gain a glimpse of the future evolution of waiting lists.These results motivate a greater reflection on the barriers currently being generated by the NHS in terms of more restrictive services as a consequence of the cutbacks in production factors. Since one of the objectives of an NHS is equality, it is important to consider how austerity-fueled cutbacks will compromise service redistribution based on the most basic principles of social justice. Moreover as revealed by the study, the most affected citizens will be those with fewer resources, who have to resort to public services because of their inability to afford private treatment. Namely, those citizens who, already suffering from a reduction in their economic power because of the crisis, have to endure rising indirect costs (in the form of longer waiting lists) for public healthcare. 22 FIGURE 1. The Waiting List Model 23 FIGURE 2. Waiting List & Changes in Supply 24 FIGURE 3. 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