The major Non-Communicable Chronic Diseases (NCCDs) have been responsible to a large proportion of deaths occurring before the age of 70, resulting in disability and leading to a high degree of limitation of sick people in their work and leisure activities, increasing the pressure on public health services (SCHMIDT et al., 2011).

Heart Diseases or Cardiovascular Diseases (CDs), occupy the first position as the cause of death among the NCCDs. In 20th century, less than 10% of deaths worldwide were attributed to CDs, however, in the beginning of the 21st century CDs caused nearly 50% of deaths in developed countries, and 25% in developing countries (FONSECA, 2013).

According to the data of the National Health Survey (NHS, 2013), 6.1 million people over the age of 18 reported having had a medical diagnosis indicating some heart disease in Brazil, the percentage of the urban area being equivalent to 4.4% of the total population, 3.3% higher than the rural area. Heart Diseases have, historically, the main risk factors are hypertension, smoking, high cholesterol, obesity, harmful eating habits and sedentary lifestyle (MACHADO, 2006). IHDs rank first in causes of death, especially premature deaths, being responsible for 17.33% of the world's total deaths (IHME, 2018).

Ischemic heart disease affects the arteries of the heart through the formation of fatty plaques in the coronary artery, compromising the circulation and causing ischemia, which is the decrease of nutrients and oxygen to the muscles of the heart, leading to pain or discomfort in the chest, heart palpitations, shortness of breath and tiredness because a part of the heart does not receive enough blood to pump properly (NCI, 2004).

Ischemic Heart Diseases occupied, according to information of PAHO (2017), the first position as the cause of death among nations with high income, among which Brazil is, killing 140 per 100,000 people only in 2016. Diseases in Brazil are divided according to the International Classification of Diseases and Related Health Problems, often called ICD. They provide codes relating to the classification of diseases and a wide variety of signs, symptoms, abnormalities, complaints and other circumstances relating to injury or illness.

Between 2005 and 2016 there was a 22.6% increase in the total number of deaths due to Ischemic Cardiopathy in Brazil. Data from the Institute of Health Metrics and Evaluation (IHME, 2017) demonstrated that there was a 15.7% increase in the causes of premature deaths due to Ischemic Heart Diseases, making these diseases the first place in the ranking of the main causes of death, followed by interpersonal violence, traffic accidents and stroke.

The morbidity caused by Cardiovascular Diseases represents a burden to the public health system in countries around the world. Townsend et al. (2015), showed that the rate of hospitalization to these diseases has been increasing in Europe since the turn of the century, with more countries with records of increased hospital admissions (34 countries) than countries with decreasing admission records (15 countries) by CDs.

The numbers of hospitalization authorizations for IHD in Brazil, considering people of both sexes and of all ages, also denote the increase in morbidity due to this group of pathologies in the country. According to the data extracted by the information system DATASUS (2008), the number of hospital admissions to IHD was of the order of 208,176, representing a cost of just over US$ 197 million and totaling 1,340,793 days of stay in public and private hospitals that had ties with the Health Unic System (HUS), also known in Brazil under the acronym SUS. When compared to the data for 2014, it is possible to observe an increase of approximately 22% in the number of authorizations for hospital admissions (253,834 in 2014), of which US$ 437,551,501.27 is the hospitalization cost for that year, with an increase of 22.3 % in the number of days spent in hospitals.

Most countries suffer from the accelerated growth of public debt, with health spending being one of the most important. One of the major developments faced by the Brazilian state is to ensure the long-term sustainable functioning of a free, universal public health system, without raising public spending. In order to reduce public debt, it is important that in addition to reducing spending on patient care, minimize lost work days. The fall in income affects GDP and consequently the denominator of the debt ratio that is determined by the expenditure/revenue ratio.

According to the date of Health Satellite Account, an extension of the System of National Accounts (SNC) published by the Brazilian Institute of Geography and Statistics (BIGS), also known as IBGE in Brazil, in the year 2015 the final consumption of health goods and services in Brazil was US$ 140 billion, which represented 9.1% of the Gross Domestic Product (GDP) of that year. Of this amount, the Government's consumption expenditure was equal to US$ 59 billion (3.1% of GDP) and the others US$ 81 billion was charged to consumption by households and non-profit institutions serving households (IBGE, 2017). Cardiovascular Diseases in Brazil, according to data from the System Information Health of SUS (SIH/SUS, 2018), were responsible for 1,130,962 hospitalizations in public and private hospitals, costing approximately US$ 616 million only in 2017.

Studies carried out over the last decade have shown how GDP has been impacted by some chronic diseases. Rasmussen, Sweeny, and Sheehan (2015) observed that chronic non-communicable diseases have been responsible for a negative impact on the Brazilian’s economy, projecting an estimated of 5.8% from the Gross Domestic Product loss for 2030, which would be equivalent to US$ 184 billion of losses in the public coffers. The same study showed that the number of people over 45 in the Brazilian’s workforce doubled from 15% in 1980 to 30% in 2015, with a forecast of 38% for the year 2030, which importance of assessing the impact of IHD on the country's productivity.

Tura, Silva and Pereira (2006), analysis of the time series of GDP per capita between 1947 and 2004 and IHD’s mortality between 1980 and 2002, found a correlation of -0.68 for males and -0.55 for females, 20-year lag between the series, it means’, IHD mortality in 2004 is associated with per capita GDP in 1984. The same study concluded that per capita GDP would correlate over the same 20-year period with 30.3% of the number of deaths in women and 46.2% for men, which creates strong evidence that GDP per capita can be considered one of the main risk factors for ischemic heart disease, with even greater participation than classic factors: gender, age, family history, diabetes mellitus, hypertension, smoking, sedentary lifestyle, among others.

Cost-benefit evaluation in public health is an important tool for decision-making in health-related policies. The economic impacts of population health can be evaluated using indicators of mortality and morbidity, as these reflect the costs that are directly linked to a specific condition, such as lost productivity measured by days of work lost, premature death and medical expenses, such as treatments, hospitalizations, exams and medicines (DRUMMOND, SCULPHER & TORRANCE, 2006).

The analysis of health conditions through the Global Disease Burden (GDB), provides comprehensive information on some of the major diseases. The GDB is measured by the indicator DALY, which in turn comprises the mortality indicator Years of Life Lost (YLL), plus the disability indicator Years Lost due to Disability (YLD).

DALY = YLL + YDL

The indicator DALY, which combines mortality and morbidity, has proved to be an efficient tool to analyzing the effect generated in the economy, caused by diseases and other conditions that lead to poor health.The causes that generate DALY can be understood according to their importance in the economy of a country, as shown Costa and Guilhoto (2014), where they use DALYs as a method to measure the economic impact of the installation of sewage digesters in Brazilian’s rural households, showing that a government budget savings of US$ 55 million would be realized, considering only the number of children who would no longer use public health services for treatment of diseases due to sanitary conditions related to the ideal treatment of sewage.

The analysis of data through the use of DALYs provides an understanding not only of the number of deaths in each country or continent, but also the impact of premature deaths and the disability or disability of the population, which together provide the GDB, where the influence of each pathological condition in the total number of DALYs of the study region (MURRAY; LOPEZ, 1996). This indicator also makes it possible to identify the causes that imply the greatest lack of health for the population, making it possible to classify the diseases or diseases that impact on the health condition of the population of each country, and also have flexible mechanisms that allow the updating of annual data. The DALY combines information on mortality and morbidity, constituting an essential tool for measuring and reducing the incidence of the disease (LEITE et al., 2015). One unit of DALY it is equal one year of lost healthy life (IHME, 2013). According to Marinho, Passos and França (2016, p. 714), the importance of the GDB has grown every year, in order to create frameworks for health actions:

Analyzes of disease burden are important to the elaboration of indicators that subsidize the planning, execution and evaluation of health actions. Traditional analyzes, made for each disease separately, may present inconsistencies, such as the sum of causes deaths exceeding total mortality.

Studies of Ischemic Heart Diseases, based on analyzes related to the burden of Global Illness, allow more precise assessments of the consequences of these diseases in Brazil, which contributes to the understanding of how they can impact economically in the country, exerting influence in the level of government spending, wages and consumption of economic agents.

Disability Adjusted Life of Years by Incapacity are introduced in the input-output tables as a vector of reduction of the number of working hours, thus generating a reduction in the sectoral and global wage mass, which in turn impacts on consumption. The disturbance of the demand causes the increase in the difference between planned and degraded production.

The Input-Output Matrix is a model constructed from data observed in a given economy, which may be a region or other delimited area that allows the observation of the interrelationships between sectors and industries (MILLER; BLAIR, 2009).

To Leontief (1951), the Input-Output Matrix allows a clear description of how sectors of the economy interact with each other considering a given time period and region. According to Guilhoto (2001, p. 11), Leontief developed a model capable of equating demand and supply within a network of multiple activities, where it showed the interactions between them:

What Leontief was able to accomplish was the construction of an "economical photograph" of the economy itself; in this photograph he showed how sectors are related to each other - that is, which sectors supply the others with services and products and which sectors buy from whom. The result was a unique and comprehensive view of how the economy works - how each sector becomes more or less dependent on others.

The input-output model enables the understanding of the product flows between the segments of an economy to a given period of time, evidencing the relationship between producers and consumers, as well as providing the interrelationship between the sectors that demand and offer inputs (TAKASAGO et al., 2017). It is an adaptation of the neoclassical theory of general equilibrium and it is described by a set of linear equations in which the ultimate goal is to estimate the economic impacts of a given system (LEONTIEF, 1951).

The approach used in the present study was based on Leontief (1951), where, the total gross output throughout the economy (x) is taken to be the sum of the production by Matrix that is destined for the various sectors for intermediate consumption (Z), which is demanded by the final consumers, denoted by the variable (y) as described by the Equation (1):

Equation (1)

The Equation (1) can be represented in a matrix form of technical coefficients (A), also known as a matrix of direct coefficients, as described below in the Equation (2):

Equation (2)

Where the is vector denotes a diagonal matrix with the elements of the vector along the main diagonal. In the Input-Output model, the transactions in the economy are made considering two sectors, a vendor or supplier, which can be indicated by the subscript i and a buyer sector, represented by the subscript j. Thus, relating each sale from the supplier sector to the buyer sector of matrix (Z), with the gross value of the production of the buyer sector (x), we will get what is known as technical coefficient (a), obtained from the ratio of the variables presented. This technical coefficient can best be visualized by the following Equation (3):

Equation (3)

Where each coefficient (a) represents the required quantity of sector inputs . to produce a unity of good (output) .. The model also assumes that there is direct proportionality between the gross production of the sector . and the total amount of inputs it acquires from other suppliers. From this assumption, it is understood that the technical coefficients (a) are constant, which creates a framework to obtain theEquation (4):

Equation (4)

Equation (4) shows that the purchases that a sector . effect of another particular sector . is calculated by multiplying the gross value of the output of this buyer sector by a constant coefficient (a). The matrix of technical coefficients is better visualized in Equation (5):

Equation (5)

The Equation (5) the main diagonal denotes the intrasectorial technical coefficients, while the secondary diagonal presents the intersectoral direct coefficients. Equation (6):

Equation (6)

Through the Equation (6) it becomes possible to obtain the total output that is needed to satisfy the final demand. Solving Equation (6), we arrive at the Equation (7):

Equation (7)

The matrix (I – A) is called the Leontief Matrix, and is called the Leontief Inverse Matrix. Through the Inverse of Leontief would be possible to calculate the production values (x) just by multiplying the inverse by the final demand, which creates the possibility of estimating the product in a simple way.

The tool developed by Leontief allows the understanding of how the sectors of the economy interact directly and indirectly in order to produce their final product. From this model, various analyzes can be carried out, such as the measurement of the impacts of the variation of the demand on the product, quantity of jobs generated, added value, among others (MARCONI; ROCHA; MAGACHO, 2016).

Searching the understanding of how a sector or group of sectors can be negatively or positively affected by external shocks, the concept of Inoperability of the Input-Output model assumes an important role. Based on the model improved by Leontief (1951), the IIM is defined as the inability of a system to perform its intended functions, denotes the level of system dysfunction, expressed as a percentage of the level of production desired by such a system. Intended to predict, through this concept, the result of economic losses suffered by individual sectors, which in turn is the result of a direct rupture with a particular sector or a group of sectors (SANTOS, 2012). Developed by Haimes e Jing (2001), from work of Leontief (1951), the inoperability formulation of the input-output model can be represented in Equation (8):

q = A* q + c* (8)

Where (q) is constituted as the vector of Inoperability given in terms of normalized economic losses, (A*) is the matrix of interdependence and (c*) is the demand disturbance, measured as a consequence of the difference in projected demand and degraded demand, which in this case will be one that was disturbed due to absenteeism in the work generated by the IHD.

Details of derivation of Inoperability of the Input-Product model can be found in the work of Santos (2012), in which an analysis of interdependence is performed valuing the reduction of demand through the concept of Inoperability. Santos e Haimes (2004), where demand reduction modeling is carried out through the IIM caused by terrorism and in Leung, Haimes e Santos (2007) which aims to present two perspectives: one on the supply side and the other on the product side, in order to complement and supplement the demand directed in the Inoperability Matrix of the Input-Output Matrix.

The equilibrium presented by Equation (8) can also be evidenced by the understanding that Inoperability is defined as the degraded and normalized lost production of the sector i, where ( ) is demonstrated in Equation (9):

Equation (9)

Where is the planned production level and i) is the value of the degraded production after a shock suffered. The value to be found by Equation (10):

0 <= qi <=1 (10)

The interval in Equation (10) has the meaning that when the (q.) is equal to 0, there is no disturbance in the economy and when it is equal to 1, the production was completely degraded due to the labor shortages by Coronary Artery Disease. Still further from Equation (8), two other auxiliary values can be obtained:

c* = * (c - d) (11)

A* = (A* ) (12)

Where (c*) is the variable that represents the demand disturbance and (c) is the final demand planned, being (d) given by demand degraded by the impact of the IHD on the workers.

Another auxiliary value obtained through the inoperability model equation of the product input in Equation (8) is the reference matrix of interdependence (A*), expressed as a function of the planned demand level and the direct coefficient matrix.

The economic impact of IHD can be given by how much public sector expends to cope with the morbidity generated by this group of pathologies. According to Table 1, in 2006, Ischemic Cardiopathy led to a total of 208,176 hospitalizations in public and private hospitals linked to SUS. It totaled 1,340,793 hospitalization days, costing US$ 213,505,120.41.

Table 1 - Number of hospital admissions, total values and days of stay per year, caused by IHD

Table 1
Number of hospital admissions total values and days of stay per year caused by IHD

Source: Prepared by authors from DATASUS data.

In addition to public expenditure on hospitalizations, the effects of these diseases can be estimated according to their impact on the absenteeism of workers in the productive sectors of the economy. Based on the assumption that absence from work leads to a loss of wages, it is possible to estimate the loss in terms of workers' compensation. Look to Figures 1and 2.

Figure 1
Salaries lost in 2006 from Ischemic Heart Diseases
Prepared by authors.

The sector that presented the highest salary loss due to the disease in 2006 was sector 51, which refers to public administration, defense and social security, registering a total of US$ 43,795,988.89, referring to the sum of lost wages of workers allocated in this sector. Then, sector 53 appears, which includes human health care activities; human health care integrated with social assistance provided in collective and private residences and social assistance services without accommodation, with wage loss accounted in US$ 13,536,556.17. In the third position, as most affected in terms of wages, there is sector 30, referring to retail trade, with losses of US$ 11,109,293.80. The sector with the lowest wage loss was sector 55, which concerns domestic services, with losses added up in US$ 17,765.80.

The direct impact on GDP, measured in terms of salary losses to the year 2006, was US$ 165,903,042.64. In addition to the impact on society and people's lifestyle, the estimation of the direct impact on the economy reaching the level of more than US$ 165 million, at the time, indicates the economic detrimental power of the group of pathologies that make up the IHD.

Figure 2
Economic loss, in US$, by sector in 2006
Prepared by authors

Regarding the total impact, considering now the direct and indirect effects of the disease in Input-Output Matrix of Brazil, given the influence of each sector in the economy, it was possible to measure the loss by productive segment to the year 2006, with the respective indicator of the Inoperability vector (.), which reflects how much the demand of each sector has been degraded, varying from 0 to 1, where 0 indicates that there is no sectoral loss at all, and 1 that the loss is 100%, that is, the economy was totally compromised by the shock.

The sector that presented the greatest loss was the chemical and chemical products segment, indicated by sector 11 of the WIOD table, with a loss of US$ 147,467,555.86, with an impact of 0.229% in this sector, as measured by the vector . of Inoperability. Sector 51, public administration, defense and social security, appears to have suffered the second largest impact, presenting a loss of US$ 65,205,766.28, with a 0.046%. Sector 10, corresponding to the manufacture of coke, petroleum products and biofuels, was the third most affected sector, with losses of US$ 52,445,243.59, and with a 0.065%. The sector that registered the lowest loss among those evaluated was the fishing and aquaculture segment, with a recorded loss of US$ 316,023.04, with a 0.020% Inoperability vector.

The total impact on the Brazilian economic matrix, calculated from the Leontief Inverse concept, is given by the sum of the impacts in each sector analyzed, totaling, to 2006, the value of US$ 807,108,399.94.

In 2014, taking into account the total expenditure by the government with the morbidity generated by Ischemic Heart Diseases, it can also be seen from Table 1 that these diseases were responsible for 253,834, hospital admissions, with a stay in these hospitals quantified at 1,640,937 days, leading to a total cost of US$ 437,551,501.27 to the public coffers. Look to Figures 3and 4.

Figure 3
Salaries lost in 2014 from Ischemic Heart Diseases
Prepared by authors.

Considering now the direct effects of Ischemic Cardiopathy on the Brazilian GDP, the impacts are measured based on the workers' wage loss. Sector 51, public administration, defense and social security, had the highest wage loss in 2014, with sums that reached US$ 103,707,024.16. The second sector with the highest salary loss among the 56 evaluated was sector 27, which concerns building construction activities, infrastructure works and specialized services for construction, with a loss of US$ 22,232,882.99. Another segment that had a large salary loss from Ischemic Heart Disease, appearing as the third most affected sector, was sector 52 (education activities), with salary suppression estimated at US$ 20,248,789.50. Among the sectors evaluated, the segment that showed the least direct impact was the one related to domestic services, illustrated by the number 55 and with an impact of US$ 7,945.96.

To the year 2014, the direct impact on GDP, measured in terms of wage losses, was estimated at US$ 361,984,144.89. This value reflects the direct effect of the ICDs on workers in the 56 sectors analyzed.

Figure 4
Economic loss, in US$, by sector in 2014
Prepared by authors.

The economic impact by sector, which considers the economic loss or shock caused by IHD in each segment, was also measured for 2014, with the corresponding Inoperability vector of each area. The sphere most affected in 2014, as well as the one with the highest wage loss, was that of public administration, defense and social security (sector 51), with a record of US$ 147,502,379.06, lost due to Ischemic Heart Diseases. The vector of inoperability of this sector was 0.050%, this being its impact in percentage in the referred sector. Sector 41, of financial services activities, is the second most impacted by the disease, with its loss valued at US$ 79,132,284.74 and its vector q at 0.025%. The construction segment, which covers building construction activities, infrastructure works and specialized services for construction, showed a loss of US$ 77,112,855.16, representing this value 0.022% of the total produced by this sector in the economy. The sector with the lowest loss among those evaluated was the sector 3, which deals with fishing and aquaculture activities, with losses of US$ 1,334,694.94 and a vector of normalized economic loss of 0.010%.

The total impact in 2014, generated from DALYs caused by Coronary Artery Disease, was US$ 1,297,819,879.47. This amount contemplates the total economic loss in the Input-Output Matrix of Brazil to the year under analysis, not considering the governmental expenditure coming from hospitalizations for these diseases. Comparison and sum of public expenditure with the total impact on the Brazilian Input-Output Matrix are carried out in the next section.

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