Journal of Pharmacoeconomics and Pharmaceutical Management 2015; 1(2): 61-64

Journal of Pharmacoeconomics and Pharmaceutical Management Journal homepage: http://jppm.tums.ac.ir

Determining the entitlement to structural indicators of health by means of fuzzy AHP and TOPSIS: a case study in Sistan and Baluchestan, Iran

Hadi Hayati1, Saeid Karimi2, Jamil Sadeghifar3, Javad Ebrahimzadeh4*, Somaye Afshari5, Bahman Khosravi5, Ensieh Ashrafi5 1 Department of Pharmacoeconomy and Pharmaceutical management, School of pharmacy, Tehran University of Medical Sciences, Tehran, Iran. 2 Health Management and Economics Research center, School of Management and Medical Information, Isfahan University of Medical Sciences, Isfahan, Iran. 3 Health Management and Economics Sciences Research Center, Iran University of Medical Sciences, Tehran, Iran 4 Hospital Management Research Center, Iran University of Medical Sciences, Tehran, Iran 5 Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

ABSTRACT Background: Health care section as an important social part of a community plays a determining role in people’s health and therefore all people must equally and purposefully enjoy health services and facilities; failure in this regard may lead to unpleasant consequences. Methods: The research is both applied and analytical which ranks the counties of Sistan and Baluchestan province in terms of entitlement to health structural indicators in 2011. Thus, 15 health structural indicators were selected and data were collected from Statistical Center of Iran. Experts’ opinions and FAHP technique were used to weight the indicators. Finally the counties ranked by TOPSIS technique. Data were analyzed using Excel, Expert Choice, and LINGO. Findings: FAHP’s results showed that indicator of health house workers (Behvarz) in health centers was more important and dentist indicator less important than other indicators. Besides, TOPSIS technique showed that Zahedan, Zabol, and Iranshahr were the highest and Delgan, Zaboli, and Sib and Suran were the least entitled counties, respectively. Conclusion: Based on our findings, there seems to be a huge gap between the counties of the province in terms of their entitlement to health structural indicators. It is therefore suggested that authorities of the health section set the priorities according to the counties’ entitlement to the indicators to remove inequalities in the province.

Keywords: Structural indicators of health, Case study, Sistan and Baluchestan, FAHP, TOPSIS.

1. Introduction study on the degree of development in Eastern Azerbaijan’s Counties by Health is one of the most fundamental rights of individuals which must means of Analytic Hierarchy Process (AHP) and Technique for Order of be equally accessible for all people. Equality in health sector has Preference by Similarity to Ideal Solution (TOPSIS) and selection of 8 different dimensions including distribution of healthcare resources, healthcare indicators, the counties were ranked and inequalities were financing health services, and accessing health services and facilities identified [4]. Other studies in Golestan and Qazvin provinces revealed that have posed various issues for policymaking in the health section [1, huge gaps among the counties in term of structural health indicators [11, 2]. In developing countries like Iran, health inequality and imbalance 12]. A research in Belgium examined development in different parts of especially at regional levels is a major issue with unpleasant the country; it ranked various regions with 33 indicators of economy, consequences at all levels that may be considered as weaknesses of health, education, culture etc. by means of multivariate statistical governments leading to vast dissatisfaction in the society [3, 4]. Equality technique of factor analysis. Results lead to the "identification of nine and justice will lead to more social integrity, unity and social axes of socio-economic characterization, and the division of the contribution and less social stress [5]. Portuguese territory into four regions with differing degrees of Since health needs are mandatory, involuntary, and inevitable, failure development, reflecting the well-known asymmetry between coastal and to access health services will lead to impairment, premature mortality, inland zones"[13]. and backbreaking conditions in society and continuity of poverty-illness Fair entitlement to healthcare services must be considered as an and impairment cycle [6]. Establishing systems of health service inseparable part of development. In each social-political system and provision and providing appropriate care, both sufficient and on time geographical location, there are many differences in the health status and are among the necessities which secure individuals’ health and are accessibility of services for various groups and this is also true for mostly determined by governments; yet providing the best healthcare different geographical regions of a country [1, 14]. The goal of research in services is not enough and appropriate distribution and accessibility of the health section is to increase people’s health and fairness and equality the services are very important. Today, the rate of development of each in health services; thus, top planners and policymakers should be aware society is judged according to its people’s health quality, degree of fair of indicators and the quality of entitlement to facilities and services in distribution of health services in different parts of the society and also different parts of their country. Accordingly the present study was protection of disadvantaged people against factors harmful to the performed in order to rank the counties of Sistan and Baluchestan society’s health [1, 7-9]. province in respect to their entitlement to structural health indicators. Justice and equality especially in healthcare industry through access to Results could help authorities in equal appropriation of healthcare such services will reduce health and consequently total costs and develop facilities in these provinces. the country [10]. There is a large body of research on the rate of accessibility and entitlement to health indicators in various regions. In a

*Corresponding author. Tel/Fax: +982166482606, Email: [email protected], Javad Ebrahimzadeh Article information: Received date: 17/09/2014 Accepted date: 27/11/2014 Available online: 14/05/2015

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2. Methods necessary to calculate the consistency ratio for experts’ responses. The The research is both applied and analytical performed as a field study. It ratio shows consistency among comparisons and the acceptable number examined the counties of Sistan and Baluchestan province in 2011. Data when criteria are more than four equals 0.1. After ensuring the were collected by means of a researcher made form. Researchers acceptability of data’s inconsistency ratio, indicators’ weights had to be obtained necessary permits and gathered data from Statistical Center of calculated. The present research employed Chang’s Extended Analysis Iran. method (EA) to calculate the weights. The numbers used in this method In order to rank the counties, 15 structural health indicators were are rectangular fuzzy numbers. The steps of FAHP according to EA selected. The choices depended on data availability, having structural Method are as follows: nature, and experts’ opinions. The proportion of the following indicators Step 1: Calculation of Si for each rows of the paired-comparison matrix is is per 10 thousand people: as follows; i and j show row and column indicators respectively. Number of general practitioner, specialist practitioner, dentist, mmm SU=⊗[] U−1 pharmacist, paramedic, active treatment center, fixed bed in active kijij jij===111 treatment center, urban health center (per 10 thousand of urban people), Step 2: After the calculation of S for each row in EA method, their rural health center (per 10 thousand of rural people), rural active health k magnitude in relation to each other should be determined. In general if house (per 10 thousand of rural people), health house Behvarz (Behvarz is M and M are two rectangular fuzzy numbers, the magnitude of M to a heath house worker), laboratory, pharmacy, radiology center, 1 2 1 M shown as V(M ≥M ) is defined as: rehabilitation center. 2 1 2

Firstly, the weight of indicators affecting the entitlement to healthcare V (M ≥M ) =1 if m ≥ m services was determined; then experts’ views on the importance of the 1 2 1 2 V (M ≥M ) =hgt (M ∩M ) Otherwise indicators were received and combined through a paired-comparison 1 2 1 2 hgt (M ∩M ) = u – u / (u – l )+ (m – m ) questionnaire and each indicator’s degree of importance was determined 1 2 1 2 1 2 2 1 by means of Fuzzy Analytical Hierarchy Process (FAHP). The reliability of Step 3: Calculation of the magnitude of a k rectangular fuzzy number is the paired-comparison questionnaire was tested regarding consistency another rectangular fuzzy number calculated by: ratio by Expert Choice software. In general, if inconsistency ratio is lower V (M ≥M ,…,M )=V(M ≥M ), …., V(M ≥M ) than 0.1 then the matrix group is consistent. Weighting values were 1 2 k 1 2 1 k The indicators’ weights in paired-comparison matrix is calculated in EA determined based on opinions of five experts. This number of purposive method as follows: samples was chosen according to their experience, knowledge and W ' (x ) = Min {V (S ≥S )}, k=1,2,….m k≠i awareness of the health system. Then by employing the weights obtained i i k Therefore weight vectors of the indicators will be like the following which from FAHP, the counties were ranked by means of TOPSIS technique in is the abnormal coefficient vector of fuzzy AHP: terms of their entitlement to structural health indicators. Other analyses W ' (x ) = [W ' (c ), W ' (c ),…, W ' (c )]T were performed by Excel, Expert Choice, and LINGO. i 1 2 m The abnormal coefficient vector of fuzzy AHP is made normal by means of AHP is one of the most well-known techniques for multi-criteria decision this equation: making and indicator weighting. AHP is based on paired-comparison or w ' = i binary indicators or decision making choices. This technique helps W i decision makers to focus on comparison of two criteria regardless of w 'i external factors [15, 16]. However in AHP, the decision maker cannot express final preferences, instead, he/she judges based on his or her After calculation of indicators’ weights, ranking of the counties was impression. In other words this approach cannot properly reflect the achieved through TOPSIS technique. TOPSIS is one of the best multi uncertainty in human thought. This uncertainty in preferences can be criteria decision making models which is widely used by researchers. In patterned by means of fuzzy theory. In terms of fuzzy sets, what is this method, m items (here counties) are assessed by n indicators in a attributed to a decision maker is a fuzzy number which is defined by a decision making matrix. The idea behind this technique is that the membership set. Here the membership function defines the degree to selected item must have the minimum interval from the positive ideal which elements are members of a priority set [17]. Therefore fuzzy AHP solution (the best possible state) and have the maximum interval from was employed in this study to determine indicators’ weights. Experts’ the negative ideal solution (the worst possible state) [19]. To solve the responses to paired-comparisons were gathered according to linguistic problem with TOPSIS, first the decision making matrix is formed without scales and a 9-point rating system. It was necessary to make responses any scales and then by applying the weights, the unscaled weighted analyzable. Therefore, the linguistic scales used in the questionnaire were matrix is obtained; both positive and negative ideal solutions are turned into triangular fuzzy numbers. Table 1 shows linguistic variables determined and the intervals between items and the ideal solutions, + and their counterpart fuzzy numbers. positive and negative, are calculated. Finally C i which is the relative The next step after turning responses into fuzzy numbers was making proximity of each item to ideals is determined. This indicator is then responses integrated which was done by the method suggested by − S i th Buckley [18]. Before calculating the weight of criteria by fuzzy AHP, it was defined to combine (the interval between i item and negative

Table 1. Triangular fuzzy numbers of Linguistic variables used in this study. Fuzzy numbers Linguistic variables Triangular fuzzy numbers Triangular fuzzy reciprocal numbers (Equal strong (1،1،1) (1،1،1 1 (Intermediate (1،2،3) (1/3 ،1/2 ،1 2 (Marginally Strong (2،3،4) (1/4 ،1/3 ،1/2 3 (Intermediate (3،4،5) (1/5 ،1/4 ،1/3 4 (Strong (4،5،6) (1/6 ،1/5 ،1/4 5 (Intermediate (5،6،7) (1/7 ،1/6 ،1/5 6 (Very Strong (6،7،8) (1/8 ،1/7 ،1/6 7 (Intermediate (7،8،9) (1/9 ،1/8 ،1/7 8 (Extremely Strong (9،9،9) (1/9 ،1/9 ،1/9 9

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Table 2. Calculation of the degree of feasibility of 15 indicators Fuzzy weight of dimension S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 Min.

S1 1 1 1 1 1 1 1 0.997 0.953 0.95 0.946 1 1 1 0.946 S2 0.908 1 1 0.986 0.926 0.972 0.905 0.85 0.846 0.841 0.962 0.943 1 1 0.841 S3 0.838 0.975 0.993 0.953 0.864 0.932 0.835 0.759 0.754 0.747 0.918 0.89 0.997 1 0.747 S4 0.851 0.983 1 0.962 0.876 0.942 0.847 0.773 0.769 0.762 0.928 0.901 1 1 0.762 S5 0.93 1 1 1 0.947 0.988 0.928 0.878 0.874 0.87 0.979 0.962 1 1 0.87 S6 0.985 1 1 1 1 1 0.982 0.934 0.931 0.927 1 1 1 1 0.927 S7 0.945 1 1 1 1 0.96 0.945 0.894 0.89 0.886 0.992 0.975 1 1 0.886 S8 1 1 1 1 1 1 1 0.951 0.947 0.943 1 1 1 1 0.943 S9 1 1 1 1 1 1 1 1 0.996 0.992 1 1 1 1 0.992 S10 1 1 1 1 1 1 1 1 1 0.996 1 1 1 1 0.966 S11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 S12 0.937 1 1 1 1 0.958 1 0.933 0.871 0.866 0.86 0.978 1 1 0.86 S13 0.961 1 1 1 1 0.981 1 0.958 0.898 0.894 0.888 1 1 1 0.888 S14 0.853 0.979 1 0.995 0.959 0.877 0.94 0.849 0.777 0.773 0.766 0.927 0.9 1 0.766 S15 0.837 0.967 0.99 0.983 0.946 0.862 0.927 0.834 0. 762 0.757 0.75 0.913 0.886 0.988 0.75

+ − S i th + S ideal) and (the interval between i item and positive ideal) and C = i therefore to compare items in relation to each other: i SS+−+ ii C + Item ranking is according to descending values of i in a way that the item with the highest value is at the top and the item with the lowest value is at the bottom of the ranking.

Table 3. Degree of significance (weights) of structural health 3. Results indicators. The fuzzy AHP was employed in relation to Chang’s EA method in order to Indicator Importance level Rank of determine the relative significance of indicators. After combination of (W) indicator experts’ opinions and calculation of consistency rate at 0.09, the Sk of General practitioner 0.072 4 each indicator was determined and the magnitudes of all Sks were Specialist 0.064 11 compared. Results are presented in Table 2. practitioner The lowest value for each indicator’s degree of magnitude in respect to Dentist 0.056 15 other indicators forms the W ' vector. Finally when this weight vector is Pharmacologist 0.0578 13 non-scaled, the weight vector W is obtained which shows the weights of Paramedic 0.066 9 indicators. The weight of each indicator is therefore as shown in Table 3. Active treatment 0.07 6 Based on the indicators’ weights and applying them to the decision center making matrix, the counties of Sistan and Baluchestan province were Fixed bed inactive 0.0672 8 ranked by means of TOPSIS technique. Each county with higher C + enjoys treatment center i more from the indicators. Ranking of the counties is presented in Table 4. Urban Health center 0.071 5

Rural health center 0.075 3 4. Discussion Active health house 0.0756 2 Weighting results showed that the Behvarz indicator with the weight of Behvarz 0.076 1 0.076 and active health house indicator with the weight of 0.0756 are the Laboratory 0.065 10 most significant indicators and also the main indicators of entitlement. Pharmacy 0.0674 7 These two indicators were also the first priorities in the study of Lotfi et Radiology center 0.058 12 al. On the other hand, dentist and rehabilitation center indicators with Rehabilitation center 0.057 14 the weights of 0.056 and 0.057 respectively are less significant indicators. Lotfi et al study finding supports this ranking [20]. It is noteworthy that the difference between the weights of the indicators Table 4. Ranking of Sistan and Baluchestan’s counties in terms of and their priorities and those of other studies like those of Zangiabadi et entitlement to structural health indicators. al and Tahari Mehrjerdi et al is due to different number and kind of Positive ideal Negative ideal Province C + Rank indicators and weighting methods [3, 4]. separation (S +) separation (S -) i i i Results of TOPSIS technique placed Zahedan in the first rank that was due Zahedan 0.034 0.107 0.758 1 to being the capital of the province and the existing Zahedan Medical Zabol 0.056 0.085 0.605 2 University. Zabol, Iranshahr, and Khash are the next counties which enjoy Iranshahr 0.056 0.076 0.577 3 structural health indicators almost similarly. In the research done by Khash 0.066 0.074 0.53 4 Ghaed Rahmati et al, Zahedan and Zabol were ranked first too [21]. Saravan 0.074 0.071 0.489 5 Counties of Zaboli and Sib and Suran are at the bottom of the ranking Zehak 0.08 0.063 0.439 6 with a great distance from other counties and as a result enjoy health Chabahar 0.089 0.064 0. 339 7 services poorly. These findings indicate that northern counties near to Nik shahr 0.091 0.042 0.315 8 Zahedan enjoy structural health indicators more than southern counties Hirmand 0.107 0.043 0.284 9 which are far from Zahedan, the capital of the province. In a research on Konarak 0.108 0.033 0.237 10 ranking the regional development of health section in Mazandaran Sarbaz 0.11 0.029 0.211 11 province, Lotfi et al argued that those counties which are geographically Dalgan 0.12 0.017 0.125 12 distant from the capital are deprived from healthcare services [20]; this Zaboli 0.121 0.014 0.101 13 supports the findings of the present study. Moreover, Delgan, Zaboli, and Sib and 0.122 0.011 0.08 14 Sib and Suran which are positioned at the bottom of the ranking are Suran among the counties recently added to Sistan and Baluchestan province 63

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