Public Health Expenditure in Brazil: an Analysis of Efficiency
Autoria: Lucas Maia dos Santos, Marco Aurélio Marques Ferreira, Márcio Augusto Gonçalves, Evandro Rodrigues de Faria
Abstract An univocal understanding of the relationship between public administration and performance is an important and elusive goal for researchers in public administration. After decentralization of both actions and services of healthcare system, the financial resources passed to be transferred to the municipal health funds, as being the administration of the resources under the responsibility of the municipal manager. Taking into account the limited existence of resources it is opportune to question the way the application of the resources in health has been managed. So, this study was carried out to investigate the performance of public expenditures in the public healthcare, as taking 160 administrative microregions on southeastern Brazil as reference. With limitation of the internal availability of governmental resources, mainly aggravated by the Brazilian government's impossibility to republish the provisory contribution on monetary transactions called Contribuição Provisória Sobre Movimentação Financeira (CPMF), in which most resources were consigned to healthcare, the current discussion leans over the optimization capacity of those limited sources against the need for enlargement of the attendance to population. Initially, the relevant variables were explored after the descriptive analysis with quantitative and qualitative efforts. The efficiency scores were generated, by applying the data envelopment analysis (DEA) and using the bootstrap procedure on the sample’s efficiency mean, in order to create the mean’s confidence intervals. According to the results, higher efficiency was observed for the microregions with more than 500 thousand inhabitants as well as in the capitals to detriment of the interior. High amplitude of the efficiency scores from 0.26 to 1 were observed, that means disparity in resource application or no standardizations of output creation. It is distinguished that 16.8% microregions presented the highest scores, as most being in the State of São Paulo. The efficiency means is between 0.68 and 0.75, at 95% confidence. Thus, the results showed that the performance of the Southern microregions can be considered as intermediary and 18,8% of microregions have a weak performance. Finally, it assures the importance of the promotion of policies for improvement of the health efficiency as a function of the interregional particularities. The mainly limitation of this study is that was impossible to measure the quality of the services. Microregions that make more ambulatory procedures and taking care of more families don’t mean owning the better health system. But in a country where many people have no accesses to some healthcare services is important to consider the quantity despite of quality.
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1. Introduction
This study was carried out to investigate the performance of the public expenditure in healthcare, as taking the administrative microregions on southern Brazil as reference for analysis from the IBGE (Brazilian Institute of Geography and Statistics) classification. The study is based on the factual presupposition of optimization of the resources in the public sector, because the idea that performance understood as the production of good results must be the main objective of all governor, as seeking the maximization of the social welfare. An univocal understanding of the relationship between public administration and performance is an important and elusive goal for researchers in public administration. The health, education, feeding and freedom constitute a fundamental right of the human being, since they situate as essential dimension of the quality of life (QOL). It is a result from the combination among the social, economical, political and cultural factors that are particularly presented in each society. Moreover, understand the performance of public expenditure is a way to manage accountability and proceed the idea of the the best value for money. Since the 19-ies, in Brazil there is a concern on the part of the health researchers and governmental heads in verifying the quality and effectiveness in the delivery of the health services in the public sector. See Serapioni (1999) and Castiel (2008). Thus, it becomes more and more necessary the use of techniques and methods that make possible an evaluation of the sector performance, as taking the regional public units responsible for execution of the health services as reference. After decentralization of the actions and services of health, the financial resources were transferred from the Ministério da Saúde (Brazilian Healthcare Ministry) to the municipal health funds, as being the administration of resources and the warranty of quality services for the population under the responsibility of the municipal governors. Due to the complexity of some health services and to the partition of the regional infrastructure through displacement of assistance, municipal consortia and concentration of attendance services on microregional plan, it becomes appropriate to use the registrations and accountancy of the information in this territorial unit, on such a way that several counties are represented by pole-cities. Thus, taking into account the limited existence of resources as well as the characteristics and specificities of each Brazilian microregion, it is opportune to question how application of the resources has been managed, as well as to investigate the products from those applications at interregional level, as a form to creating public intervention policies in favor of quantitative and qualitative improvements in the health. So, the analysis of the performance in public expenditures of the health sector will be accomplished by applying the Data Envelopment Analysis (DEA) that seeks to quantify and compare the efficiency in using the resources for the provision of goods and services. For complementation of this stage, the bootstrap procedures will be used to measure the effect of the errors on the efficiency estimators. This study takes as reference either the works by either O´Toole (1999) and Bretschneider et al. (2007) who measured the performance in education by applying DEA, and those by Marinho (2001; 2003), Faria et al. (2008) and Gonçalves et al. (2008) who discourse on the performance in the health area. The researches and scientific productions mentioned on health sector have been contributing for the comparative evaluation of efficiency in the area, as establishing internal reference units in studies addressed to hospitals, clinics and health ambulatory services. So, the present work will show the inter comparison as methodological progress, as being an unpublished application in the Brazilian scenery.
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2. Theoretical Framework
2.1. Understanding the healthcare system in Brazil
The debate about the reality of the healthcare in Brazil was supported on studies and researches that pointed out the fragility of the development model and also characterized the healthcare system adopted during the military dictatorship as centralizer, hierarchized and mainly inefficient and irrational. In the 1970s, the sanitary movement appeared from the population’s dissatisfaction to discuss how would should be the health management. In 1987, the federal government created the unified health decentralized system named Sistema Único Descentralizado de Saúde - SUDS, that implemented some proposals of the sanitary movement, as mainly looking for decentralization of the system. Then, the sanitary reform project named Projeto de Reforma Sanitária was taken to the National Constituent Assembly and approved in almost its totality in the Federal Constitution of 1988, that adopted the proposal of a unified health system called Sistema Único de Saúde – SUS. The Federal Constitution of 1988 considers the health, from art. 196 to art. 200, and the most important point is the conception of the health as right of all and the duty of the State, as well as its universal and equalitarian access, besides its inclusion into tripod of the social security together with the social welfare (BRAZIL, 1988). For several economical and political matters due to the privatizing character of the State, the SUS still was not implemented in its plenitude; however its guidelines are the pathway for the improvement of the health system in Brazil, as the recent researches point out. With the creation of regulating agencies in Brazil, there was a decrease in the role of the State as concerning to the supply of public services as well as a regulation process that stimulates both competition and innovation. Thus, “the control and evaluation of the processes that are interesting for all have been transferred “to no-state entities, as dislocating the focus of the government's collection to society" (RIZZOTTO, 2000:210). In Brazil, the problems involving the health sector are many and they stand out when analyzing the medical establishments, the sectored policies and the health model that has been adopted in the last decades. Then, there is a need for rationalization of the actions in this extent and always looking for optimization of the resource applications. Thus, this study will approach a multidimensional focus of the performance according to Forbes and Lynn (2007), to the detriment of other approaches only under the monetary viewpoint. Nowadays, it is possible to notice the effort of the counties towards the attendance of expectations and the accomplishment of goals in the management of the local policies. The great challenge appearing with the regionalization of the social policies, such as health, is to manage a heterogeneous service network that is not institutionally integrated, since the attendance to health was historically linked to federal sphere. It can be said that the decentralization process radically imposed the sub regional management, where the counties start to assume the functions of coordination of the local health policies, and should accomplish the goals of the national programs, by using the resources consigned by the federal government. With limitation of the internal availability of governmental resources, mainly aggravated by the Brazilian government's impossibility to republish the provisory contribution on monetary transactions called Contribuição Provisória Sobre Movimentação Financeira (CPMF), in which most resources were consigned to health, the current discussion leans over the optimization capacity of those limited sources against the need for enlargement of the attendance to population.
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The performance and organizational effectiveness notion are related, being popular among researchers in public administration (SCOTT; DAVIS, 2007; SELDEN; SOWA, 2004). Performance maybe the narrowest of both concepts, as typically focusing on the outputs and results of the programs or policies, whereas the organizational effectiveness is wideer as taking into account the agents within and among the organizational system. According to Forbes and Lynn (2007, p3)
(…) performance refers to output results and their outcomes obtained from processes, products, and services that permit evaluation and comparison relative to goals, standards, past results, and other organizations. Performance can be expressed in non-financial and financial terms.
Here, the performance will be proximately linked to the efficiency term that is seen, in administration, as a global return measure in a system. Then, from it is derived the fact of the technical efficiency linked to the optimization of resources to be called as productive efficiency or global productivity measure. This occurs because, in administration the organizations with and without lucrative objectives, for instance the hospitals and/or units that render health services are seen as an open system because they influence and are influenced by the environment, as well as for admitting the same components of the other systems, that are: inputs, transformation process and outputs. In context of the public policies aiming at the social welfare, the efficiency should be seen as the combination of the economical rationality with the values of freedom, equality, justice and defense of the welfare. Sustaining such a proposition, it is opportune to mention the work by Faria's et al. (2008) among others who broach the efficiency in health.
3. Research Methodology
3.1. Data collection and sampling
The study will have the administrative microregions on Brazilian Southeastern as reference. The Brazilian Southeast region is one of the regions defined by the Instituto Brasileiro de Geografia e Estatística - IBGE, that is composed by the states of São Paulo, Minas Gerais, Rio de Janeiro and Espírito Santo. This region is outstanding because it is a transition land between the Northeast area and South area.
Figuure 1. Southeastern Brazil Source: IBGE (2009)
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Southeast is the most populous and rich region in Brazil. It occupies 10.85% Brazilian territory. Highly urbanized (90.5%), it shelters the three more important metropolises of the country, that are São Paulo, Rio de Janeiro and Belo Horizonte. The Southeastern region presents high social indexes: it has the second highest IDH in Brazil (0.824) as losing for Southern region only, and its per capita PIB is the highest of the country, R$ 15.468,00 (IBGE 2009). Table 1 shows a summary of the demographic statistics in Southeastern region.
Table 1 Demographic statistics of the Southeastern Brazil States States Minas Gerais São Paulo Rio de Janeiro Espírito Santo Estimated population 20.033.665 41.384.039 16.010.429 3.487.199 Number of counties 853 645 92 78 Number of microregions 67 63 18 12 Capital Belo Horizonte São Paulo Rio de Janeiro Vitória Source: IBGE 2009
3.2. Analytical procedures
The development of this work will be concatenated by the following investigation procedures, as contextualized at the methodological scientific clippings: 1. Exploration of the relevant variables, after descriptive analysis with quantitative and qualitative efforts of investigation. 2. Generation of efficiency scores of expenditures in health in each microregion on Southeastern Brazil. At this stage, it is intended to use the version of the software EMS - Efficiency Measurement System 1.3, in order to precede the measure of the technical efficiency. 3. Accomplishment of the bootstrap procedure in order to create confidence intervals on the efficiency means generated by the microregion extracts.
3.3. Data collection
The main tool used in the collection of data was the secondary analyses and documental research, through documents, reports and statistics, electronically accessed in official sites that divulge the indicators and results of researches in the health area (IBGE, DATASUS, Ministério da Saúde, Tesouro Nacional). In order to evaluate the efficiency of the health sector in the different microregions under research, the selection of the variables that will represent the resources (inputs) as well as other ones that will represent the services or products offered to society (outputs) will be performed.
3.4. Efficiency measurement and analysis variables
In the present study, the measure of the efficiency will be accomplished through the Data Envelopment Analysis (DEA) by using the classic model BCC with orientation to product. In DEA, the mathematical programming is used to measure the efficiency in terms of the distance from each Decision Making Units (DMU) of its respective efficiency frontier determined from the data of the production of the unit group. The DEA model with product-orientation tries to maximize the proportional increase in the levels of product, as maintaining fixed the amount of inputs and, according to Charnes 5
et al. (1994) and Estelita Lins and Meza (2000), it can be algebraically represented by the following Linear Programming Problem (PPL):
maxφ,λ φ, s.a. φyi - Yλ ≤ 0, (1) - xi + Xλ ≤ 0, -λ ≤ 0, where, yi is a vector (m x 1) of quantities of product of the i-eth DMU; x1 is a vector (k x 1) of amounts of input of the i-eth DMU; Y is a matrix (n x m) of products of the n DMUs; X is a matrix (n x k) of inputs of the n DMUs; λ is a vector (n x 1) of weights; and φ is a scalar from which the values are equal or higher than 1 and it indicates the efficiency score of DMUs, in which a value equal to 1 indicates relative technical efficiency of the i-eth DMU, relative to the other ones, and a value higher than 1 evidences the presence of relative technical inefficiency. The φ indicates the proportional increase in the products that the i-eth DMU can reach, as keeping constant the amount of input. The problem presented in equation 1 is solved n times, as being once for each DMU and, as a result, it presents the values of φ and λ, as being φ the efficiency score of the DMU under analysis and λ supplies the efficient DMUs that serve as reference or benchmark for the inefficient i-eth DMU. In order to incorporate the possibility for variable returns to scale, Banker et al. (1984) proposed the BCC model of the data envelopment analysis, as introducing a convexity restriction into CCR model CCR, presented in equation 1. Whereas the CCR model considers constant returns to scale, the BCC considers variable returns to scale. So, the focus of the model allows for capturing the effects along the production function and resulting from alterations in the production scale. The BCC model, presented in PPL (2), is less restrictive than the CCR model and, according to Banker and Thrall (1992) it allows to decompose the technical efficiency into scale efficiency and single technical efficiency. In this study, only the results referring to BCC model will be analyzed. The BCC model, that presupposes variable returns to scale and product-orientation, can be represented by the following algebraic notation:
maxφ,λ φ, s.a. φyi - Yλ ≤ 0, (2) - xi + Xλ ≤ 0, N1’λ = 1, - λ ≤ 0, where N1 is a vector (nx1) of numbers ones. The other variables were previously described. As emphasized by Belloni (2000), the models CCR and BCC present areas with different viabilities. The viable area of the BCC model is restricted to the convex combinations of the production plans observed, which is characterized by the variable returns to the scale. As consequence, as considering the orientation to product, the indicator for efficiency of the BCC model is lower or equal to the indicator for efficiency of the CCR model. In the composition of the matrix of inputs and outputs, the items already validated by other studies such as those by Marinho (2001; 2003) and Faria et al. (2008) will be used. In this optics and having both physical and human resources used in the health sector as
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reference, the following variables will be chosen to compose the analysis of the efficiency. They are:
Resources /Inputs - inputs (X): Health establishments: regulation of health services, center of the hemotherapic and/or hematological attention, psychosocial attention center, family health supporting center, normal childbirth center, basic health unit, specialized clinic, isolated clinic, cooperative, exceptional drugstore and popular drugstore programs, hospitals, central laboratory of public health, polyclinic, health center, specialized emergency room, general emergency room, secretary of health, among others. Equipments: represented by equipments including, X ray, mammograph, electrocardiograph, ultrasound, tomography, among others; Professionals: represented by professionals including, social assistant, surgeon dentist, nurse, doctors, administrative technician, therapists, among others.
Products/Services - outputs (Y): Ambulatory production: approved amount represented by services accomplished in ambulatory, as including: consultations, prostheses, exams, ambulatory surgeries, chemotherapy and others. Procedure group: ortheses, prostheses and special materials, complementary actions of the attention to health, actions for promotion and prevention in health, procedures with diagnosis purpose, clinical procedures, surgical procedures, transplants of organs, tissues and cells, medicines, among others. Families accompanied: number of families attended by the basic attention programs, PSF (Family Health Program) and PACS (Community Agents of Health Program).
3.5. Confidence intervals of the efficiency by bootstrap procedure
Although the DEA methods are widely applied on efficiency analysis, as already mentioned in several works, most researchers have been ignoring the effect of the error on the efficiency estimators resulting from this approach. Corroborating this observation, Dong and Featherstone (2004) assured that DEA traditional applications have been ignoring or just superficially discussed the matter of the uncertainty associated to the estimates of efficiency of the Data Envelopment Analysis. Probably, this occurs because DEA is a deterministic approach, therefore any result differing from the absolute efficiency production on the frontier is interpreted as inefficiency by the authors. In this way, the works aiming to compare efficiency means under doubtful estimates can lead to deceiving conclusions, therefore committing all their results. In this optics, several works have been indicating the need for investigating the estimates accomplished under the results of the DEA approach, such as Pires and Branco (1996), Efron (1987) and Souza and Tabak (2002). A way to work around this problem has been the use of the statistical technique of bootstrap. The bootstrap idea is to use a single data group available to proceed an experiment type in which the proper data are used to obtain artificial samples, by using the random resample procedure. Thus, it is a substitution principle, such as the principle of the relative frequency substitution (Souza and Tabak, 2002). The central purpose is to verify the reliability of the accomplished estimate. The focus of the experiments has been the bootstrapping on the mean or the median of the efficiency scores through successive samplings, in which the derived results have been compared with the results from estimates of the pure nonparametric approach.
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Taking the bootstrap approach associated to the data envelopment analysis, the works by Xue and Harker (1999), Souza and Tabak (2002) and Dong and Featherstone (2004) and others can be mentioned. In those works, the bootstrap approach was used in order to compare the reliability of the estimates accomplished on statistics derived from the efficiency scores, as ascribing confidence intervals to them for their validation. According to Dong and Featherstone (2004), due to limitations of the DEA nonparametric approach under discussion, nowadays the bootstrap is the main tool to investigate the reliability of the estimators of the efficiency scores, because it attributes confidence intervals to them. The algorithm of the bootstrap procedure allows countless resamplings from multiple iterations, as accomplished by computer procedures that act on the scores (φ ) of the DEA efficiency, which allows to validate or to refute a priori the calculated mean under confidence intervals constructed. So, suppose that a statistics φ (x) of one group of data X n, n= 1, ..., N, denoted by N- dimensional vector x. A way to approximate the distribution of φ (x) is to accomplish the bootstrap procedure with this data group. For doing this, a number of bootstrap samples should be raffled (for instance, A) as being each one of size N. This resample is accomplished with replacement; so, each bootstrap sample will contain some of the N original observations at once, and no other original observations any time, under completely random way. With the aid of computer procedures, it is possible to calculate (x(i)) as maintaining the result. Then, the total operation is repeated for i=1,.., A bootstrap samples, and at the end there are A statistics φ (x*(i)). Then, those statistics are used to estimate any aspect of the distribution of φ (x) that can be interesting. A theoretical, empirical and more deepened approach of the bootstrap technique can be found in Tibshirani (1996), Pires and Branco (1996) and Papadopoulos et al. (2001). In this study, the bootstrap procedure will be adopted in order to establish confidence intervals that would allow for the accomplishment of reliable inferences on the efficiency differences among the microregions. It is intended to use 1.000 samples (random iterations) by bootstrap and applying the available technologies.
4 – General results and analysis
Table 2 shows the descriptive statistics of the variables used in the efficiency analysis. As discoursed in the methodology, the following variables were included: the health institutions, the health equipments, the number of professionals, ambulatory production and accompanied families. In all variables are noticed a high standard deviation, that is superior to the values of the means. For that fact is expected, because the allocation of the public resources in Brazil occurs as a function of the number of inhabitants by county and there is a large dispersion among the number of inhabitants in Southeast. It is noticed that there was an average of 446 health establishments by microregion on southeastern, approximately 2.257 health equipments, 4.234 professionals, an ambulatory production around 4.234 and 54.325 families accompanied. Moreover the maximum and minimum shows the high amplitude of the data due to the population differences among microregions.
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Table 2 Variables used in the efficiency model - data relative to 2007 Minimum Maximum Mean Standard deviation Health establishments 19,00 10.075,00 446,15 952,87 Health equipments 33,00 82.502,00 2.257,57 8.010,77 Health professionals 256,60 140.109,40 4.234,878 13.571,97 Ambulatory production 34.080,00 22.056.268,00 670.114,77 2.037.375,70 Accompanied families 3.118,00 1.115.116,00 54.325,48 113.058,24 Source: research findings
In order to identify the effect of the population on the variables under use, Table 3 shows a division among counties with microregions with more than 500 thousand inhabitants and other ones with less than 500 thousand inhabitants. The reduction in the standard deviation was evident in both groups. So, higher homogeneity of the variables can be noticed with the reduction of the standard deviation, taking into account that the difference of population among the microregions is relevant. The means of the variables of the class with less than 500 thousand inhabitants were lower for all variables, as expected. Approximately, 85% of the sample are classified in microregions from which the population is inferior to 500 thousand inhabitants, that is exactly 134 microregions. The ANOVA test for those two classes showed to be significant at 1% of significance for all variables, therefore making possible to infer that there are differences between both groups.
Table 3 Variables used in the efficiency model - data relative to 2007 Minimum Maximum Mean Standard deviation Health establishments 19,00 785,00 236,56 158,92 Lower than Health equipments 33,00 2256,00 740,98 535,02 500 Health professionals 256,60 22890,60 2.223,09 2.727,81 thousands Ambulatory production 34080,00 810.659,00 255.150,81 165.850,96 inhabitants Accompanied families 3118,00 94.147,00 33.253,42 18.101,89 Health establishments 229,00 10.075,00 1.526,37 2.047,22 More than Health equipments 1895,00 82.502,00 10.073,87 18.186,46 500 Health professionals 1079,20 140.109,40 14.603,31 31.597,77 thousands Ambulatory production 480345,00 22.056.27 2.808.775,16 4.535.812,93 inhabitants Accompanied families 31054,00 1.115.17 162.927,62 254.764,83 Source: result of the research
By its time, and accomplishing a comparison among states, Table 4 shows that São Paulo has the highest amount of inputs, as well as the highest ambulatory production and number of accompanied families. This is evident, because it is the most populous state in Brazil, since its population corresponds to 50% the southeast region. On the other hand, in spite of its population to be lower than Minas Gerais, the State of Rio de Janeiro has higher means for the variables. Although, superior values for the standard deviation are observed, which shows the presence of some microregions with higher resources than the most ones, as tending to elevate the mean. On the other hand, Minas Gerais possesses many microregions relatively with population relatively small, which tends to reduce the mean of those variables. On the other hand, Rio de Janeiro State have less cities, with more inhabitants in that though. The mean test (ANOVA) showed that no significant differences occurs among the states. This provides an analytical advantage to the study, since it is statistically possible to consider the homogeneity of the allocation of resources among states.
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Table 4 Variables used in the efficiency model by states on Southern region States N Minimum Maximum Mean Standard deviation Health establishments 66 28,00 4.282,00 329,15 541,21 Health equipments 66 33,00 22.933,00 1.123,83 2.831,76 Minas Health professionals 66 322,20 46.078,00 2.524,17 5.645,54 Gerais Ambulatory production 66 44.113,00 6.980.250,00 375.475,05 852.428,46 Accompanied families 66 9.976,00 701.510,00 50.791,98 84.146,59 Health establishments 63 19,00 10.075,00 580,63 1.292,89 Health equipments 63 88,00 82.502,00 3.108,58 10.390,11 São Health professionals 63 256,60 140.109,40 5.630,133 17.631,03 Paulo Ambulatory production 63 34.080,00 22.056.268,00 980.525,48 2.805.034,38 Accompanied families 63 3.118,00 1.115.116,00 55.991,29 139.194,92 Health establishments 18 50,00 4.501,00 503,67 1.019,72 Health equipments 18 205,00 54.228,00 3.949,60 12.573,54 Rio de Health professionals 18 279,80 89.644,60 6.989,91 20.677,09 Janeiro Ambulatory production 18 40.658,00 10.953.256,00 903.103,24 2.517.502,16 Accompanied families 18 6.236,00 636.439,00 68.614,69 143.833,01 Health establishments 13 63,00 1.468,00 308,78 371,98 Health equipments 13 220,00 11.556,00 1.546,62 3.066,78 Espírito Health professionals 13 413,20 14.812,20 2.343,708 3.828,01 Santo Ambulatory production 13 52.937,00 1.919.129,00 339.080,44 498.978,57 Accompanied families 13 11.332,00 135.582,00 44.406,87 33.179,03 Source: results of research
Another important comparison for the variables components of the efficiency analysis is the relationship between metropolitan regions and the interior. In Table 5, it is possible to notice that the microregions of the capitals, that is, the metropolitan regions have very superior mean for all variables. It is highlighted that the most important capitals of the country are in Southeastern region: São Paulo, Rio de Janeiro, Belo Horizonte and Vitória. The mean test significantly confirmed at 5% there are differences among the means of those two groups.
Table 5 Variables used in the efficiency model by interior and metropolitan areas N Minimum Maximum Mean Standard deviation Health establishments 156 19,00 2.284,00 327,29 333,48 Health equipments 156 33,00 12.786,00 1.217,90 1.567,21 Interior Health professionals 156 256,60 140.109,40 3.339,36 11.359,64
Ambulatory production 156 34.080,00 4.189.521,00 418.650,39 542.634,65 Accompanied families 156 3.118,00 184.874,00 39.124,55 26.241,48 Health establishments 4 1.468,00 10.075,00 5.081,63 3.604,15 Health equipments 4 11.556,00 82.502,00 42.804,85 32.029,99 Capitals Health professionals 4 6.105,00 89.644,60 39.159,95 37.780,07 Ambulatory production 4 1.919.129,00 22.056.268,00 10.477.225,65 8.559.024,33 Accompanied families 4 135.582,00 1.115.116,00 647.161,73 401.564,91 Source: results of the research
After all those comparisons, the descriptive statistics of the efficiency scores produced by DEA are presented in Table 6. It is noticed an ample amplitude variation of the efficiency scores that varied from 0.26 to 1, approximately. Thus, a more appropriate investigation of the data distribution and concentration conditions is required. Such analytical perspective is materialized in Table 6, where the descriptive statistics for the technical efficiency scores in the Health microregions on Southeastern are presented. As distinguished in methodology, the
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analysis will be based only on BCC model, because it is less restrictive and makes possible the same analytical effect of the model CCR.
Table 6 Efficiency scores N Minimum Maximum Mean Standard deviation Skewness Kurtosis Eficiency scores 160,00 0,26 1,00 0,72 0,21 -0,15 -1,04 Source: result of the research
Figure 2 presents the distribution of the efficiency scores determined with the BCC model for the microregions. The coefficient of kurtosis shows that the flattening of the distribution, while platicurtic, presents a higher slope in distance around the mean. That provides a evidence that there are more differences among efficiency scores of the microregions. The negative asymmetry shows the impact of the influence from lower scores on the efficiency levels. The occurrence of those lower scores denotes an accentuated elongation at left tail, as indicating that a practical interpretation leads to the observation of considerable inefficiency scores in using the resources destined to health. Figure 2 illustrates those observations.
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30
20 Frequency
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0 0,200 0,400 0,600 0,800 1,000 Modelo BCC Figure 2 - Histogram of the efficiency scores determined by the BBC model Source: results of research
For the qualitative evaluation of the results, a criterion for categorization of the DMUs was built on the grounds of the results, by taking the mean and standard deviation as reference, while main refined descriptive statistics (Table 7).
Table 7 Classification of the microregion performance Criterion Scores Performance % das DMUs Under the mean less 1 standard deviation E < 51,07 Weak 18,8% The mean more and less 1 standard deviation 51,07 < E < 92,12 Good 58,1% Above the mean plus 1 standard deviation E >92,12 Excellent 23,1% Source: results of the research
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It is worth to highlight that 27 DMUs, that is 16.8%, presented the maximum scores, therefore representing benchmarks for public policies in the sector, mainly considering their relative capacity for using the human resources and materials. Table 8 shows DMUs that represent the benchmarks. Those were the microregions on Southeastern that show better use of the health inputs showed in the metodology. Those microregions are benchmark because they can supply more outputs which the same level of input as the other microregions.
Table 8 Efficient microregions State Microregion State Microregion Espírito Santo Itapemirim São Paulo Presidente Prudente Minas Gerais Januária São Paulo Marília Minas Gerais Janaúba São Paulo Capão Bonito Minas Gerais Salinas São Paulo Guaratinguetá Minas Gerais Montes Claros São Paulo Bananal Minas Gerais Grão Mogol São Paulo Paraitinga Minas Gerais Bocaiúva São Paulo Osasco Minas Gerais Peçanha São Paulo Franco da Rocha Minas Gerais Manhuaçu São Paulo Guarulhos Minas Gerais Muriaé São Paulo Itapecerica da Serra Minas Gerais B Horizonte São Paulo Mogi das Cruzes Rio de Janeiro S.M.Madalena São Paulo Santos Rio de Janeiro Rio de Janeiro São Paulo São Paulo São Paulo Campinas Source: Result of the research
Other 58.1%, that is 93 microregions present an intermediary efficiency mean for the sector, therefore demonstrating moderate power for using the resources addressed to health, when the microregional perspective is taken into account. At an inferior dimension are 18.8%, which represents 30 microregions of the sample. Those delineated the scores lower than 51.07 and could be considered as inefficient in optimization of the services in the health sector. Because DEA is a deterministic approach, any result differing from the full efficiency can be interpreted as inefficiency, which provides the opportunity for emergency of the pseudo-efficient and pseudo-inefficient DMUs resulting from errors of data collection or randomly attributed factors, therefore endangering the estimates accomplished on the efficiency scores (XUE and HARKER, 1999; DONG and FEATHERSTONE, 2004). In order to correct this limitation, several works such as those carried out by Efron (1987), Xue and Harker (1999), Löthgren and Tambour (1999), Souza and Tabak (2002) have been suggesting the use of the bootstrap for correction of this limitation, since this procedure is more refined than the mean test and mainly because thousands of possible interactions in the resampling approach. After 1,000 interactions, some confidence intervals were built at 95% confidence for the means of the health efficiency scores, as presented in Table 9.
Table 9 Confidence interval under the bootstrap approach Observed mean Minimum Maximum Efficiency scores 0,72 0,68 0,75 Source: Results of the research
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Figure 3 shows the density probability of the distribution of the efficiency scores for southeastern region. As obtained by the boostrap model, it is observed that the average of efficiency scores are between 0.68 and 0.75. Considering the classification in Table 6, it can be checked that the microregions on southeastern have an intermediary use of the resources applied in health. In other words, it can be affirmed that the microregions could increase their production of services (outputs) as considering the current levels of inputs under use.
Density 0 5 10 15 20 25
0.66 0.68 0.70 0.72 0.74 0.76 Value Figure 3 – Probability distribution of the efficiency scores Source: Result of the research
Table 10 presents the efficiency scores of each State. When accomplishing the ANOVA test, it was not possible to identify significant differences among States means. However, when accomplishing an independent t-test 2X2, it was verified that at 1% significant level, the mean efficiency in Minas Gerais differs from São Paulo. However, at 1% significance, São Paulo has an efficiency mean that differs from Espírito Santo. It was difficult to make a conclusion about which State has better performance. By logical reasoning, the State of Espírito Santo has higher efficiency mean than the States of Rio de Janeiro and São Paulo. The State of Espírito Santo was the state with the least number of efficient microregions, but on average its microregions can produce more with their inputs nevertheless.
Table 10 Efficiency by southern States States N Minimum Maximum Mean Standard deviation Minas Gerais 66 0,31 1,00 0,75 0,18 São Paulo 63 0,26 1,00 0,70 0,24 Rio de Janeiro 18 0,39 1,00 0,66 0,21 Espírito Santo 13 0,54 1,00 0,71 0,16 Source: Result of the research
In Table 11, the means of efficiency among the groups of capitals and interior were demonstrated. Because the small number of observations in the group of capitals, it was chosen not to accomplish the mean test. However, both the means of the variables composing the efficiency model and the mean of the efficiency scores were higher for the group of capitals.
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Table 1 Efficiency between interior and metropolitan regions N Minimum Maximum Mean Standard deviation Interior 156 0,26 1,00 0,71 0,20 Metropolitan regions 4 0,58 1,00 0,90 0,21 Source: Result of the research
Table 12 shows the counties with more than 500 thousands inhabitants to be more efficient than the other ones, with 1% significance according to the mean test ANOVA.
Table 2 Efficiency by microregion with more and less than 500 thousand inhabitants N Minimum Maximum Mean Standard deviation Less than 500 thousand inhabitants 134 0,27 1,00 0,70 0,20 More than 500 thousand inhabitants 26 0,37 1,00 0,81 0,20 Source: Result of the research
In their works on efficiency in health, some authors point out that one of the most important aspects of the DEA approach is the comparison of the efficiency by taking into account the real operational conditions of the services of health. Sometimes, this leads the data to demonstrate a reality that is different from that one the development indexes present. When evaluating the performance of some public hospitals in the Brazilian capitals, in terms of the internments in their medical clinics, Gonçalves et al. (2008:432) verified that four cities identified as ‘100%’ efficient (Palmas, Macapá, Teresina and Goiânia) are not among the states with higher gross domestic product (GDP) per capita or in which the great technological and educational centers of the Country are located." On the other hand, the capitals presenting the worst performances "have more complex characterization whereas group, as including either cities that have tradition in formation of human health resources as other ones that, similarly to those with better performance, are away from the main technological and educational centers of the Country." The authors suggest those data to indicate the occurrence of independence among the classification scores measuring the sectorial efficiency and the variables per capita expense with the basic health programs and IDH of the capitals. In other study on the efficiency in the Brazilian health sector, Marinho (2003,530) proposes the evaluation of the hospital and ambulatory services in the counties of the State of Rio de Janeiro. The study presents the evidence that "although the size of the municipal PIB favors the counties, as providing them with higher capacity to respond to the problems, the per capita income has “null effect". According to the author…“the resident population can be reasonably rich and healthy, but the assisted population can be very poor and sick’’. The author also verifies that the richest counties serve as rampart for the poorest counties. This reality transcends the possibilities for actuation by the local health managers. However, it is possible to emphasize that the present work corroborates with the other ones, concerning to disparities in the efficiency levels found in several counties, which testifies in favor of the exchange measures and experiences exchanges in order to minimize such discrepancies by means of the best use of productive resources and mainly by optimization of the human efforts.
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6 - Final considerations
The results demonstrated that, in the aggregate, the performance of the southeastern microregions in optimization of the resources can be considered as intermediary, because most microregions are concentrated in scores close to mean. However, the fact of the high intra-regional disparity is preoccupying, since the high standard deviation point out the disparities in management of the health resources. This subject becomes worse when a few groups of microrregions with maximum relative performance are observed and counterbalanced by an expressive group of counties with reduced performance at levels below 1/3 of the maximum potential use of resources. That can be a hypothesis of the diversity in public health outputs or in other words, small level of standardization of the processes. The waste and the operation of the health system below the efficiency conditions evidence, in practice, that the health services rendered to society is below the relative potential capacity. On the other hand, the results indicate managerial gaps able of be supplied through public policies with qualitative and quantitative interventions, that can be executed through an investigation in loco, by taking as reference the results presented in this work together with the other studies discoursed here. The results also indicate the areas that serve as benchmark for the other ones, what testifies in favor of the largest exchange of experiences among the inter-regional managers, a fact that can be stimulated through the institutional relationship nets that are partly fomented by the regional general offices and intermunicipal consortia of health or through a state policy from which the objective is to improve the efficiency of the services in the health area. It was verified that the metropolitan areas can be more efficient than the interior. This fact is corroborated by the reality, as considering that the capitals possess the largest attendance infrastructure and the most specialized centers. This centers can gain in scale as well because the technology despite of the interior counties. In this study, it was also possible to verify that the most populous microregions are more efficient. Concerning to states, some differences in efficiency were verified between Minas Gerais and São Paulo, Espírito Santo and São Paulo, Espírito Santo and Rio de Janeiro. However, São Paulo has the highest number of Benchmarks. Finally, the work asserts the importance to promote policies for the improvement of the health efficiency, by considering the diversities of the counties and microregions of the country as a function of the inter-regional particularities. The mainly limitation of this study is that was impossible to measure the quality of the services. Microregions that make more ambulatory procedures and taking care of more families don’t mean owning the better health system. But in a country where many people have no accesses to some healthcare services is important to consider the quantity despite of quality.
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