Assessing the Inequality of Health Distribution among the Provinces of Iran during the Fifth Development Plan (2011-2015)

Mansour Mahinizadeh ¹* , Mohammad Reza Pourghorban ²

1. Department Economics, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran
2. Department Economics, Payame Noor University of Babol, Babol, Iran

ARTICLE INFO		ABSTRACT
Original Article Received: 20 December 2021 Accepted: 1 March 2022		Background: Health, both physically and mentally, raises the level of human capital. This study aims to challenge the performance of the Fifth Development Plan (2011-2015) regarding inequality in the distribution of health facilities among the provinces of the country.  Methods: This study is analytical cross sectional. In this research, using the TOPSIS method and using the Gini coefficient index and inequality ratios, was evaluated the distribution of health facilities among the provinces of the country. Also, to determine the degree of health development among the provinces of the country, from 13 indices including hospital per capita, hospital bed per capita, health house per capita, health center per capita, laboratory per capita, pharmacy per capita, general practitioner per capita, specialist physician per capita and other health care staff per capita were used. The software used in this research is SPSS 25. Results: The results show that in 2011, the provinces of Tehran, Khorasan Razavi and Isfahan were at the highest level and the provinces of South Khorasan, Ilam and Kohgiluyeh and Boyer-Ahmad were at the lowest level in this regard. While, in 2015, the provinces of Tehran, Khorasan Razavi maintained their previous position and Fars province was in the third place. The province of Ilam, was still at the lowest level. The Gini coefficient of distribution of health facilities among the provinces of the country in 2011 was 0.49, and increased to 0.52 in 2015. The share ratio of the top 20% to the bottom 20% among the provinces in terms of enjoying health facilities in 2011 and 2015 was equal to 32 and 37, respectively. Conclusion: The severity of inequality of health facilities among the provinces of the country has intensified during the Fifth Plan. The results show that the provinces have a significant difference in their position in access to health facilities and this indicates equal distribution of health facilities among the provinces of the country. Keywords: Provinces of the Country, TOPSIS Method, Gini Coefficient, Inequality, Health Distribution, Development Plan.
Corresponding Author: Mansour Mahinizadeh mahinizadeh@yazd.ac.ir

How to cite this paper:
Mahinizadeh M, Pourghorban MR. Assessing the inequality of health distribution among the provinces of Iran during the Fifth Development Plan (2011-2015). J Community Health Research 2022; 11(4): 262-276.

Introduction

To determine the degree of development of the provinces of the country in terms of health facilities, first the required data were taken from the statistical yearbook (10) of the country and then the TOPSIS method was used. This method was proposed by Hung and Yang in 1981 (11).This model is one of the most common multi-criteria decision making models (MCDM).Considering that in order to use this method, it is necessary to determine the importance of the desired criteria, Shannon entropy method has been used to weigh and determine their degree of importance. The main idea of Shannon's entropy method is based on the assumption that the higher the scatter in the values of one criterion, the more important that criterion is. According to the explanations given above, the degree of importance or weight of each of the criteria of this research for the years 2011 and 2015 has been calculated. In order to rank the provinces of the country and determine the degree of development of their health, the steps of TOPSIS method are described. In this method, m alternatives are evaluated and ranked according to n criteria. As mentioned before, in this study, m indicates the provinces of the country and n indicates the desired criteria. Problem solving by TOPSIS method consists of 6 steps as follows:
The first step: to convert the decision matrix to a standard matrix. This is done by the vector normalization method, Equation 1, as follows:

In this equation, i represents each alternative and j represents each criterion, a represents each member of the data matrix, and n _ij represents the standardized component of the decision matrix. The resulting matrix, Equation 2, is called the standardized matrix or ND and is represented as follows:

The second step: to form a standard weighted matrix. Therefore, the weight of each specified criterion should be formed and a new matrix, Equation 3, should be formed according to the weight of the criteria as follows:

In this matrix, V is a standard weighted matrix and W_n*n  is a diagonal matrix of the weights obtained for the criteria in the Shannon entropy method.
The third step: to determine the ideal positive and negative solution. Using Equation 2 and Equation 3, the positive and negative ideal alternatives are identified Equations 4, 5, 6 and 7. Considering that all the criteria introduced in Table 1 are directly related to the development of the health level of the provinces of the country, in these relations, the "best values" for these criteria are the largest values and the "worst values" for them are the smallest values.


The fourth step is to calculate the distance of the alternatives from the positive and negative ideal alternatives, which is done using Equations 8 and 9, respectively.

The fifth step: to calculate the relative proximity, Equation 10.

According to the statistical population of this study and data related to the beginning and end of the study period (2011 and 2015), the above relation was calculated for all provinces of the country.
The sixth step: related to the ranking of provinces in terms of health facilities. Accordingly, any province with a larger Cl_i  is more developed. In other words, in the ranking of the provinces in question, a higher value of Cl_i  will indicate a higher rank of the relevant province (20).
After calculating the degrees of health facilities in the provinces of the country, it is now possible to evaluate the degree of equality or inequality in the distribution of health facilities between them by using the degrees of development calculated for the provinces of the country. An issue that has been considered in few studies in the field of health in Iran. For this purpose, two methods have been used. In the first method, the distribution of degrees of health facilities is evaluated in terms of normalcy, so that using EVIEWS 9 software, the distribution of developmental degrees for the provinces is drawn and the Jarque-Bera test (normal distribution test), using the relevant statistics which asymptotically has a chi-square distribution, is calculated and normality of the distribution is statistically tested. In the second method, Lorenz curve and Gini coefficient have been used to determine the level of inequality in the distribution of health facilities in the provinces of the country. In this method, at first the cumulative values are calculated for the degrees of enjoyment and then the Lorenz curve is drawn for the studied years (2011 and 2015) and then the Gini coefficient is calculated. Although the Gini coefficient can be calculated in different ways, it is briefly discussed below. One of the methods for calculating the Gini coefficient is the method of using the absolute value criterion of the difference of relative means, Equation 11, in which the Gini coefficient value is calculated equal to half of the mentioned criterion.

Where G(S) represents the Gini coefficient for the sample under study, n is the number of provinces, y_i is the cumulative value of the degrees of health facilities. In the framework of theoretical foundations, it should be noted that Romer, Mankiw and Weil (6) and Mankiw (7), incorporated the human capitalH_t, and labor-augmenting technical progressA_t , into the neoclassical growth model of Solow astwo other influential factors in the production process. Therefore, Equation 1 shows the neoclassical growth model in which labor forceL_t , physical capitalK_t , human capital H_t  and the level of labor augmenting technology A_t,  introduce a set of factors affecting production. In the literature of growth models, this neoclassical model falls into the category of endogenous growth models, Equation 12.

Where Y_t  total production, K_t physical capital, H_t  human capital, L_t  labor force and A_t  the level of labor augmenting technology of and A_tLt  effective labor force are effective and T is the coefficient of production (8).
Theoretically, the position of health and its role in the production process lies in the factor A_t as the level oflabor augmenting technology. Accordingly, the increase in the level of society's health facilities and the possibility of individuals’(especially the labor force) benefiting from it and its more fair distribution across all the regions of the country will increase the level of effective labor and consequently economic growth. In order to theoretically analyze this issue, it is assumed that A_t  is the level of health facilities that grows at the rate of x. dividing the production function byA_tLt , the per capita product of each effective labor force is obtained, Equation 13:

Following the Solow-Swan model, it is assumed that individuals consume a fixed share (1-s) of their gross income. Therefore, the accumulation flow is done as follows, Equation 14:

In which both capital goods are assumed to be depreciated at the same fixed rate. Here, in order to answer the key question of how much of the savings should be allocated to physical and human capital in total, it is assumed that households invest in capital goods in the hope of receiving higher profits. With this assumption, both rates of return on physical and human capital — and consequently both the final output of physical and human capital — must be equal, provided that the two types of investment are substitutes. The result is the following condition, Equation 15:

Equality of the marginal product of physical and human capital means the one-to-one relationship between human capital and physical capital, Equation 16.

In this way, Equation 5 can be used to remove the expression h_t  from Equation 3. As a result we have Equation 17:

Where A≡(ηηα(1-η)α+η) and A  is constant. Note that the accumulation equation obtained is similar to the accumulation equation obtained in the Solow-Swan growth model, except that the powerof the variable per capita capital stock in equation 6 is equal to the sum of the share of human capital and physical capital(α+η) rather than a.Now by derivation we get the convergence coefficient in the steady state, Equation 18.

The growth rate y_t is also equal to the weighted average growth rates of the two production input, Equation 19, saying:

As observed, given the importance of the impact of human capital on economic growth and development and the position of this issue in growth models, most economic development theorists first focused on the impact of education as one of the aspects of human capital, but after that, health was considered as another dimension of human capital that can promote economic growth by affecting the productivity of human resources. Therefore, health as one of the dimensions of human capital has an important place in improving the physical and mental condition of individuals and provides the basis for increasing productivity and thus economic growth and development of each country. The introduction of health in the literature of economic growth and development, especially since the 1970s and the efforts of people like Gary Becker, is considered a turning point in the promotion of classical growth models. Accordingly, the present article, considering the position of health in the growth and development of different regions, has tried to evaluate the situation of the provinces of the country in terms of health facilities and degree of health development, and in addition to investigate that how the health facilities have been distributed among the provinces. For this purpose, the Lorenz curve and Gini coefficient can be used to measure the equality of health facilities between different regions of the country with respect to their degree of development. The value of Gini coefficient is between zero and one. The Gini coefficient can theoretically range from zero which shows complete quality, namely all people have an equal share of resources to one to show complete inequality. According to Hiroshi et al. (9), Gini index less than 0.2 represents perfect equality, between 0.2–0.3 relative equality, between 0.3–0.4 adequate equality, between 0.4–0.6 big inequality, and above 0.6 represents severe inequality. The position of the provinces of the country in terms of the availability of health facilities is examined. For this purpose, first, the degree of importance or weight of the criteria, has been evaluated using the Shannon entropy method for 2011, which coincides with the initial year of the Fifth Development Plan.

Statistical analysis

In this research, to answer the basic question of whether the degree of development of the country's provinces in terms of health criteria follows a normal distribution or not, Jarque-bera test was used (Table 4). The significance level for the hypothesis test is considered equal to 5%. Based on the obtained results, the probability level of the Jarque-Bera statistic is lower than 5%, as a result, the null hypothesis that the distribution of the degree of health development is normal among the provinces of the country is rejected. Jarque-Bera test is defined as following equation 20:

In which JB, S and E are indicators of the Jarque-Bera statistic, skewness and Kurtosis of the distribution, respectively. These test is measure symmetry indices of the skewness and kurtosis of the normal distribution.

Results

With the TOPSIS method, the 31 provinces of the country are ranked in terms of having health facilities. The results of this method and the ranking of the provinces of the country are summarized in Table 3.
As can be seen in Table 2, the weights of health criteria were calculated based on Shannon entropy method. The research findings show that in 2011, among the studied criteria, per capita specialist physicians, per capita rehabilitation centers and per capita health centers with the figures of 0.171, 0.128 and 0.122 are of the highest importance weight, respectively. And per capita pharmacy, per capita paramedic and hospital per capita with the figures of 0.029, 0.037 and 0.044 are of the lowest importance weight, respectively.

Table 2. Estimation of the degree of importance of research criteria using Shannon entropy method

Row	Criterion per 1000 people	Weight of importance
1	Hospital per capita	0.044
2	Hospital bed per capita	0.056
3	Health house per capita	0.058
4	Health center per capita	0.122
5	Laboratory per capita	0.065
6	Pharmacy per capita	0.029
7	Rehabilitation center per capita	0.128
8	Paramedic per capita	0.037
9	Dentist per capita	0.087
10	Pharmacist per capita	0.09
11	General Practitioner, per capita	0.066
12	Specialist Physician, per capita	0.172
13	Other health care staff, per capita	0.047

Table 3. Development rank of the health sector of the provinces of the country using TOPSIS method

Province	2011		2015
	Rank	CLi*	Rank	CLi*
Tehran	1	0.786	1	0.836
RazaviKhorasan	2	0.602	2	0.444
Fars	5	0.44	3	0.384
Isfahan	3	0.501	4	0.369
East Azerbaijan	7	0.307	5	0.313
Mazandaran	4	0.448	6	0.31
Gilan	8	0.293	7	0.248
Kerman	10	0.235	8	0.214
West Azerbaijan	9	0.242	9	0.212
Khuzestan	6	0.312	10	0.19
Sistan and Baluchestan	11	0.2	11	0.181
Golestan	15	0.16	12	0.143
Kermanshah	13	0.164	13	0.139
Hamadan	12	0.173	14	0.136
Lorestan	16	0.158	15	0.123
Markazi	18	0.129	16	0.122
Hormozgan	21	0.116	17	0.116
Alborz	14	0.161	18	0.112
Kurdistan	19	0.128	19	0.111
Zanjan	22	0.104	20	0.086
Ardabil	23	0.103	21	0.079
Yazd	17	0.154	22	0.077
Chaharmahal and Bakhtiari	24	0.096	23	0.069
North Khorasan	27	0.077	24	0.069
South Khorasan	29	0.069	25	0.062
Bushehr	28	0.073	26	0.061
Qazvin	25	0.086	27	0.052
Semnan	20	0.128	28	0.049
Qom	26	0.086	29	0.042
Ilam	30	0.036	30	0.031
Kohgiloyeh and Boyer-Ahmad	31	0.000	31	0.002
* CLi  is the relative proximity according to Equation 10.

Table 4.Level of development based on health facilities of the provinces of the country (2011 and 2015)

Development Level	2011		2015
	Province	Zi *	Province	Zi *
Fully Developed	Tehran	3.231	Tehran	3.978
	RazaviKhorasan	2.193	RazaviKhorasan	1.623
	Isfahan	1.627	Fars	1.262
	Mazandaran	1.329	Isfahan	1.175
	Fars	1.283	East Azerbaijan	0.838
	Khozestan	0.565	Mazandaran	0.822
	East Azerbaijan	0.533	Gilan	0.445
	Gilan	0.458
Higher Developed	------------	ــــــــــ	Kerman	0.242
			West Azerbaijan	0.231
Semi-developed	West Azerbaijan	0.167	Khuzestan	0.097
	Kerman	0.130	Sistan and Baluchestan	0.043
	Sistan and Baluchestan	-0.069
Less Developed	Hamadan	-0.221	Golestan	-0.185
	Kermanshah	-0.270	Kermanshah	-0.206
	Alborz	-0.285	Hamadan	-0.223
	Golestan	-0.294	Lorestan	-0.304
	Lorestan	-0.303	Markazi	-0.311
	Yazd	-0.324
Undeveloped	Markazi	-0.465	Hormozgan	-0.349
	Kurdistan	-0.470	Alborz	-0.370
	Semnan	-0.471	Kurdistan	-0.374
	Hormozgan	-0.542	Zanjan	-0.52806
	Zanjan	-0.605	Ardabil	-0.570
	Ardabil	-0.613	Yazd	-0.582
	ChaharmahalandBakhtiari	-0.654	ChaharmahalandBakhtiari	-0.626
	Qazvin	-0.707	NorthKhorasn	-0.629
	Gom	-0.707	SouthKhorasn	-0.669
	North Khorasan	-0.757	Bushehr	-0.674
	Bushehr	-0.779	Qazvin	-0.728
	South Khorasan	-0.802	Semnan	-0.748
	Ilam	-0.987	Gom	-0.789
	Kohgiloyeh and Boyer-Ahmad	-1.192	Ilam	-0.854
			Kohgiloyeh and Boyer-Ahmad	-1.043

*ZI=CLi-μσ

Figure 2.Distribution of Standardized Development Index in Terms of EnjoyingtheHealth Facilities of the Provinces

 

Figure3.Comparison of Lorenz curve for health facilities index (2011 and 2015)

Discussion

Issues related to inequality in the distribution of health facilities are among the constant concerns of researchers, planners and policy makers in the field of health. Access to health care and treatment is a fundamental human right. In this regard, inequality in the geographical distribution of health and treatment resources in each country can make it difficult for people to access health services. Accordingly, the present study has tried to evaluate the position of the provinces of the country in terms of health facilities by using the indicators of health facilities by TOPSIS method. Although in almost all studies in the field of management in determining the position of different regions, it is often enough to calculateCLi and rank regions based on this criterion, in this study, in addition, these results have been used to determine the distribution of health facilities. Gini coefficient index was used to determine the distribution of health facilities. A subject that has been less used methodologically in research studies in this field. It is noteworthy that although the Gini coefficient is known as a tool to determine inequality in the distribution of health facilities and is considered as a strength of this study compared to previous studies, however, the present study probably has some weaknesses. Therefore, although this index has been used according to its potentials, it is possible that two different distributions have the same Gini coefficient. Therefore, to solve this problem, for each year, the ratio of provinces with the highest level of enjoyment (top 20%) to the provinces with the lowest level of enjoyment (bottom 20%)is calculated and then compared for the first year and the last year of the study period. The results show that the severity of inequality of health facilities among the provinces of the country has intensified during the Fifth Development Plan, and the following strategic proposals are presented
Now, we review a number of studies that have examined the health situation in different regions of Iran, and some similar foreign studies in other countries:
Meshkini et al. (12) have analyzed the development indicators in the border provinces of the country. For this purpose, multi-criteria decision making methods have been used. In this research, indices such as the percentage of specialist physicians, the percentage of dentists and the percentage of pharmacists in each province to the whole country, etc. have been used. The results of this study show that in total, the two border provinces of North Khorasan and Mazandaran are in the optimal range. Also, out of 16 border provinces studied, seven other provinces, including Khuzestan, East Azerbaijan, Kermanshah, West Azerbaijan, Hormozgan, Golestan and Gilan, are in the semi- optimal range. Finally, the remaining seven provinces, which include the provinces of Sistan and Baluchestan, Bushehr, Kurdistan, South Khorasan, Ilam, Ardabil and North Khorasan, are in the non-optimalrange.
Rezaei and Nouri(13) have evaluated the situation and trend of inequality in the distribution of resources in the health sector in the cities of Kurdistan province in the period of 8 years (2006-2017) using Gini coefficient and Lorenz curve. The method of this research is descriptive-analytical and criteria such as general practitioner, specialist physician, dentist, etc. have been used. Based on the findings of this study, the level of inequality in the distribution of health sector resources in 2013 has decreased compared to 2006, but there is still inequality.
Kazemi et al. (14) have examined the level of development of the provinces in terms of access to health services. The statistical population of this study includes all provinces of the country in 2012 and criteria such as the number of pharmacies, general practitioners, dentists, etc. have been used. In this study, at first the desired indices have been determined using Shannon Entropy method and then the developmental degrees of the provinces of the country have been evaluated using taxonomic method. The results of this study show that out of 31 studied provinces, 12 are developed, 9 are semi-developed and 10 are underdeveloped.
Omrani-kho et al. (15) study the level of equality in the distribution of dialysis beds and nephrologists in Iran in 2010. This applied study has been done by descriptive method. The study data included the number of dialysis beds, number of patients and number of nephrologists in each province and data analysis was performed using Lorenz curve and Gini coefficient index. The findings of this study show that despite the equal distribution of dialysis beds according to the need criterion, the distribution of nephrologists is unequal. Therefore, due to the fact that human resources and equipment are complementary in providing services, a more equal distribution of nephrologists can lead to better resource efficiency.
GhaedRahmati et al. (16) have identified and determined the degree of development of the cities of Sistan and Baluchestan province using the latest data and information related to the year 2006.In this study, 10 cities of the province have been evaluated based on 29 welfare indices, including health sector indices such as pharmacies, physicians, etc. The results of this research using taxonomic method indicate the lack of coordinated distribution of facilities and services in the cities of this province. As out of the ten cities studied, Zahedan and Zabol cities in respect of the allocation of resources and facilities and services in the first place and Iranshahr, Saravan, Chabahar, Khash and Nikshahr cities in the second place and Sarbaz, Konarak and Zahak cities in the last place.
Tofighi et al. (17) have studied the level of equality in the distribution of CCU1, ICU2, POST CCU3, POST ICU4, NICU5 beds among the provinces of the country. This is an applied study with the descriptive-analytical method. According to the results of this study, although the geographical maps of intensive care beds show different densities in the provinces of the country, but this difference is negligible in terms of Gini coefficient. Finally, it can be concluded that there is almost complete equality in the distribution of intensive care beds, except for NICU beds, among the provinces of our country.
Agbenyo et al. (18) examined the access of people to health services in a village in Ghana. This article uses a combined access approach to services and health care in the West Wa region. The method used in this research is GIS. The results of this study show that only a small number of people have physical access to the hospital. Poor road conditions are a major barrier to access to hospitals in the area, and the findings emphasize the need for an integrated approach to improving access to health services.
Darlington-Pollack and Norman (19) examine the relationship between health and ethnic inequalities in the UK. This paper examines ethnic inequalities in terms of health using cross-sectional data from 1991, 2001 and 2011. The method used in this study is descriptive, which was obtained through sampling of households aged 16 to 74 years. The findings show that the relationship between health and ethnic inequalities in the UK is positive.
Zheng et al. (20) examine the impact of China's economic reforms on population health and mortality in the region between 1980 and 2000.The method used in this study is descriptive. The results of this study show that during the study period, life expectancy in China has increased, but the level of inequality has increased in different cities of China, so that life expectancy for people living in the eastern provinces of China is much larger than the coastal areas of western China. Differences in life expectancy will lead to differences in regional economic development and increase in health inequalities in the region. Therefore, health policies and the public health system need to be improved in less developed areas.
Sun et al. (21) examined regional differences in health situation in China, using the EQ-5D instrument. The data used in this study were obtained through interviews. The results of this study show that the health situation in rural areas is worse than urban areas, which with the development of the country's economic level, the health situation in rural areas has improved.
Theodorakis and Mantzavinis(22) have examined and compared the level of inequality in the services provided by the primary care physicians in Ioannina city in Greece and Griocaster city in Albania in 2001 and have used criteria such as general practitioners, internal medicine specialists and so on. Using the Lorenz curve and Gini coefficient, they found that primary care physicians were unequally distributed in both regions, and that the inequality in physician distribution in rural Greece was significantly higher than in rural Albania.
Wallace and Gutierrez (23) in a study determined the share of access to health care for the elderly in the four largest cities in Latin America. The statistical population was over 60- year- old people in Sao Paulo, Brazil (143 people), Santiago, Chile (301 people), Mexico City, Mexico (247 people) and Montevideo, Uruguay (450 people).In this study, real health services (visiting a doctor in the last 12 months), wealth quintile, type of health insurance, education, health and demographic characteristics were examined. All countries have different levels of access to services, and this access depends on per capita national wealth.
Hiroshi et al. (9) analyzed the regional characteristics and geographic distribution of the medical staffs (physicians and nurses) and the patient beds in relation to the population and average death rates in each of the provinces in Thailand, using the Lorenz curve and Gini coefficients. Those data were obtained from surveys conducted by the Ministry of Public Health and the Office of the National Education Commission. It was demonstrated that there are certain clear uneven distributions in medical personnel, especially physicians (Gini index = 0.433), by province. For physicians, nurses, and patient beds, approximately 39.6%, 25.8% and 20.6% are concentrated in the Bangkok Metropolis. Specific ideas to solve those problems are discussed in order to overcome this health care crisis by the year 2025.
The results of the present study confirm that the distribution of health facilities in the provinces of the country has been unequal and this inequality has intensified. These results were consistent with the results of side studies.

Conclusion

The results show that the severity of inequality of health facilities among the provinces of the country has intensified during the Fifth Development Plan, and the following strategic proposals are presented. According to the results of the present study, considering that the provinces of the country have a significant difference in terms of their position in access to health facilities and this indicates a lack of equal distribution of health facilities among the provinces of the country, therefore, it is recommended that the necessary measures be considered in this regard. The results of the present study show the severity of inequality in health facilities in favor of more enjoying groups. Therefore, designing an efficient system for optimal, equal and systematic allocation of health facilities seems inevitable.

Acknowledgements

This study is based on independent academic studies approved by Yazd University with ethics ID: IR.YAZD.REC.1400.014. Thereby, the authors would like to thank and appreciate the vice chancellor for research of Yazd University, who have supported us in conducting this study by issuing a code of ethics.

Conflict of Interest

The authors declare that there is no conflict of interest.

Authors' contribution

MR.P. and M.M. Contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

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