Quantification of accessibility to health facilities in rural areas

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Case Studies on Transport Policy journal homepage: www.elsevier.com/locate/cstp

Quantification of accessibility to health facilities in rural areas Shalini Kanuganti a, Ashoke Kumar Sarkar a, Ajit Pratap Singh a, Shriniwas S. Arkatkar b,* a b

Department of Civil Engineering, Birla Institute of Technology and Science, Pilani 333031, India Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat Ichchanath, Surat 395007, India

A R T I C L E I N F O

Article history: Received 30 November 2013 Received in revised form 1 July 2014 Accepted 23 August 2014 Available online xxx Keywords: Level of accessibility PMGSY roads Health care center Fuzzy logic

A B S T R A C T

The accessibility to medical facilities plays an important role in the overall health system of a country. Accessibility often refers to spatial or physical accessibility and is concerned with the complex relationship between the spatial separation of the population and the supply of health care facilities. There is a need to understand the current health care needs and also the existing practices. The traditional planning approach for rural transportation believes that building roads would ensure access to various infrastructure and services by motorized vehicles. However the impacts of such investments on rural development and also on health care have been found to be extremely mixed. Therefore an attempt has been made to quantify the impact of such investments on health sector. Prime Minister Gram Sadak Yojana (PMGSY) program, an Indian Government initiative is an example of one such investment. The accessibility to medical facilities of the villages connected by PMGSY roads is compared to villages which are not connected by any means, so that the effect on accessibility in the presence of a well constructed road can be determined. Quantifying accessibility in terms of health care contributes to a wider understanding of the performance of the health systems which in turn helps the policy decision maker(s) in identifying the deficiencies of the system so that remedial measures could be taken. However, it would be incomplete if the distance (or travel time), quality of service provided at the health care center and affordability of the users are not considered while quantifying accessibility. Therefore by integrating all the important factors influencing accessibility along with multi criteria decision making tools, a methodology is developed. This methodology includes three different multi-criteria decision making analysis tools: Simple Additive Weightage (SAW), Fuzzy aggregation method and Fuzzy preference decision analysis. Further these three methods are critically analyzed for their suitability to quantify accessibility to medical facilities. ß 2014 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.

1. Introduction Access to health is one of the basic needs for a country and it might be defined in terms of the ability of a population to receive appropriate, affordable and quality medical facilities when needed. In the rural areas, especially in a vast country such as India the medical facilities are sparsely distributed and construction of roads does not always ensure better accessibility. Thus while the road development programs are taken up in a region it is necessary also to determine the impact on the accessibility to basic facilities. Till a few years ago, India did not have a nation-wide program on the construction of rural roads and mostly earth or gravel roads were

* Corresponding author. Tel.: +91 8140252777. E-mail addresses: [email protected], [email protected] (S.S. Arkatkar).

constructed at the lowest government level with meager funding and thus there was not much scope to study the impact of such roads on accessibility. In 2000, for the first time, the Government of India had initiated a program solely for rural road development, popularly known as Prime Minister Gram Sadak Yojana (PMGSY), the objective of which was to connect all the villages having population over 500 in plain areas and over 250 in hilly and desert regions by the end of 2007. The target could not be achieved in time and thus the program is still continuing. It is widely accepted that these roads have improved social, physical, financial and human capital of the population of the connected villages (Sarkar, 2007). However, no study has yet been reported on the determination of the impact of these roads on the accessibility to health care facilities. To evaluate the impact of PMGSY roads on accessibility to health facilities, studies were conducted in two set of villages, one connected by PMGSY roads and the other one unconnected by any all-weather road. Also to include the effect of region and

http://dx.doi.org/10.1016/j.cstp.2014.08.004 2213-624X/ß 2014 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.

Please cite this article in press as: Kanuganti, S., et al., Quantification of accessibility to health facilities in rural areas. Case Stud. Transp. Policy (2014), http://dx.doi.org/10.1016/j.cstp.2014.08.004

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geography, two separate studies were conducted, one in plain agricultural area in Alwar district and the other in a desert region in Churu district. Both the districts are in the state of Rajasthan. The objective of the study was to quantify the level of accessibility to health facilities and three multi-criteria decision making tools have been used, namely simple additive weightage (SAW), fuzzy aggregation method and fuzzy preference decision analysis and the results have been compared.

quantification of effectiveness of travel characteristics and quality of services with respect to each individual may be better studied using multi-criteria decision analysis. Hence, an attempt has been made in the present study to quantify the level of accessibility to health facilities and three multi-criteria decision making tools have been used, namely simple additive weightage (SAW), fuzzy aggregation method and fuzzy preference decision analysis. 3. Objectives

2. Review of literature Physical isolation is one of the important factors for increase in mortality rate. Joseph and Phillips (1984) has stated that among the many factors that influence access to health care services, two of them are critical: physician supply and population demand. Several applications of physical accessibility to different kinds of health care facilities both in developed and developing countries have been carried out by Parker and Campbell (1998), Guagliardo (2004), Luo (2004) and Noor et al. (2004). A number of studies have been carried out on the use of GIS for measuring physical accessibility to basic needs such as health care by Lee (1995), Wilkinson et al. (1998), Albert et al. (2000) and Cromley and McLafferty (2002). Luo (2004) have stated that good primary care can prevent or reduce unnecessary expensive specialty care. US Federal register (2000), administrated by the Department of Health and Human Services (2000), Lee (1999) have developed certain criteria’s for identifying shortage areas, which mainly depends on two systems. One designates health professional shortage areas (HPSAs), the other medically underserved areas or populations (MUAs/MUPs). A summary of the historical development of the two systems can be found in Ricketts et al. (2007). In India while applying Integrated Rural Accessibility Planning (IRAP), attempts have been made by ILO ASIST AP (2003), Donnges (1998), Donnges et al. (2004), Sarkar (2005), Sarkar and Ghosh (2008) to develop simple techniques to measure accessibility to various sectors in rural areas of many developing countries that are being used for helping the local government representatives to prioritize the needs and taking decisions accordingly. Predominantly of all the sectors, lots of efforts have been made to improve accessibility to health care sector. In developing countries like India usually availability of public transport in rural areas is inadequate which severely affects the mobility of the residents. In the absence of facilities they need to depend mostly on personal and para-transit modes for getting access to basic facilities. Accessibility depends on travel time, travel cost, comfort, convince and the road condition and these parameters are difficult to quantify as they are fuzzy in nature. The concept of fuzzy sets was first introduced by Zadeh (1965). Since this introduction, it has been used in many areas related to human perception, such as the evaluation of service quality and the analysis of workload and risk in the workplace. Fuzzy sets, where a more flexible sense of membership is possible, are classes with unsharp and vague boundaries. In decision making processes, several categorical criteria with differing levels of importance are used to evaluate alternatives. To evaluate the alternatives according to the stated criteria requires procedures that aggregate the result for each criterion across each subject. The information from those several categorical criteria should be aggregated. One of common aggregation method used is concept of the weighted average based on fuzzy set theory, which is called a fuzzy weighted average method. The fuzzy weighted average method was successfully used to quantify level of service of buses in India by Kanuganti et al. (2013). It is clear from the above literature that even though enough work has been done on determining physical accessibility to health care by various researchers, no attempt has been made to study the effectiveness of both travel characteristics and quality of services with respect to each individual in rural environment. The

It is well acknowledged that the impact of providing better accessibility through the construction of roads, have both longterm and short-term effects on the health of the community. The study has helped to understand the health care needs and the existing practices of meeting them in the study area with special reference to maternal health. Very often there are suppressed demands for attending health centers in the absence of quality service and/or lack of mobility. Also, the mobility, particularly of women, might be affected due to cultural restrictions. The study has brought out all such issues including the role of transport and transport-related barriers in accessing health facilities. This helped in understating how the constraints on mobility affect the health of poor men and women establishing the relationship between mobility and health. The objectives of the paper are:  To identify the parameters to be considered to determine the level of accessibility to medical facilities in rural areas.  To arrive alternative indices using multi-criteria analysis for quantifying the level of accessibility individual villages.  To compare the results of all the alternative approaches to determine their strengths and weaknesses and to identify the most suitable one. The alternative approaches are to be tested in a few case studies in Rajasthan. Cluster of villages, both connected by PMGSY roads and unconnected would be considered for detailed analysis. 4. Methodology The accessibility to a facility or service depends on many parameters such as distance, travel time, travel cost, ownership of vehicles, availability and quality of public transport services, kind and quality of roads and many others. The parameters to be considered for assessing accessibility may vary with the purpose of the trip. Thus it is difficult to comprehend and compare the accessibility levels among villages by using subjective descriptions. At first a simple technique has been suggested to quantify accessibility to health so that the existing situations could be compared among villages. A simple additive method (SAW) is a conventional weighted average method used to calculate final scores. But in many research studies, user perceptions of certain subjects are evaluated using linguistic scales with a various numbers of descriptors. Accordingly two other methods, namely fuzzy aggregation method and fuzzy preference decision analysis (Singh and Vidyarthi, 2008; Singh and Dubey, 2012) have also been used for determining the level of accessibility. 4.1. Simple additive weightage method A simple technique for the quantification of accessibility has been suggested in this study by which the accessibility to different levels of health care facilities of each village could be quantified based on selected parameters and existing conditions. This would help to compare the levels of accessibility between connected and unconnected villages. It might be noted that the levels of accessibility calculated using this technique are not fuzzy values

Please cite this article in press as: Kanuganti, S., et al., Quantification of accessibility to health facilities in rural areas. Case Stud. Transp. Policy (2014), http://dx.doi.org/10.1016/j.cstp.2014.08.004

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Percentage of respondents 4%

Distance of facilities high

32% 32%

Fare of transportation high Poor overall transportation facilities Absence of public transport facilities

32% Fig. 1. Responses on the accessibility-related problems.

and would only help in comparing the levels among villages. The levels of accessibility (LOA) were then calculated using Eq. (1) for each village separately.

LOA ¼

N X wi  V i

(1)

i¼1

where N is number of parameters considered for quantifying level of accessibility, and wi and Vi are weight and score associated with ith parameter respectively. The weights associated with the selected parameters could be P normalized so that wi ¼ 1 and scores might be assigned on the selected parameters. Theoretically, the minimum and maximum possible value of LOA could be 1 and 5, representing highly dissatisfactory accessibility for 1 and extremely satisfactory for 5. 4.2. Fuzzy aggregation method Fuzzy logic is a dynamic problem-solving methodology with a lot of applications in various fields of applied science. The concept behind fuzzy interpretation provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. The decision making process in fuzzy logic resembles human decision making where precise solutions are found from approximate data. Fuzzy set theory has been applied in many

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studies using different types of fuzzy techniques, such as fuzzy inference system, fuzzy aggregation method, fuzzy regression, and fuzzy clustering. Among these techniques, the fuzzy aggregation method is the most appropriate fuzzy techniques for use in the quantification of villager’s perception. 4.2.1. Construction of membership functions Two membership functions were constructed including five scales of linguistic statement and the importance of the weight of five criteria. According to the opinions of experts in the field of fuzzy logic and initial review of the data, triangular membership function has found to be appropriate for fuzzy analysis. Also the triangular membership function is applied because of its simple formulations and computational efficiency, especially in real-time implementations. In fact limited data is used to derive triangular fuzzy membership functions for incorporating uncertain effects of the decision making process based on the opinion of the experts. To find the membership functions for five scales of linguistic statements, the universe interval (0–1.0) have been used. Review of the data indicated that triangular fuzzy membership functions were the most suitable type of membership function for representing the weights and ratings of accessibility. A triangular membership function is specified by three parameters {a, b, c}, and the precise appearance of the function is determined by the choice of parameters. The membership functions are represented graphically in Figs. 2 and 3 and also have been shown in Table 10. 4.2.2. Evaluation and aggregation of individual perception of service quality Fuzzy membership functions representing the response’s perception level for each linguistic scale, and fuzzy weights for criteria are used to calculate a fuzzy weighted average that represents the evaluated level of accessibility based on criteria. The level of accessibility (LOA) to health care facilities of a village could be expressed in Eq. (2). LOA ¼

N X wi V i

(2)

i¼1

where N is number of parameters considered for quantifying level of accessibility, and wi and Vi are weight and score associated with ith parameter respectively. And  is fuzzy operation.

Fig. 2. Descriptors for weightage.

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Fig. 3. Descriptors for ratings.

4.2.3. Defuzzification To transform the final fuzzy set that represents the group’s overall opinion into crisp numbers, a defuzzification procedure was conducted. In this study maximum membership technique of defuzzification process was used due to its simplicity and ease of computation. This method gives the output with the highest membership function. This technique is given by algebraic expression as Eq. (3).

mA ðxÞ  mA ! ðxÞ for all x 2 X

(3)

where x* is the defuzzified value. The results obtained are presented in Table 11. 4.3. Fuzzy preference decision analysis Using the different importance levels of each criterion for given alternative and the elements of fuzzy global decision matrix, fuzzy evaluation value of each alternative is calculated for all criteria j = 1,2, . . . n. It can be expressed in Eq. (4). Ei ¼

n X wi jfuzzy  V i jfuzzy

Similarly, E12 = 1.825 and E13 = 3.25 and therefore, final fuzzy evaluation value for alternative 1, E1 = (0.725, 1.825, 3.25). In the same way, final fuzzy evaluation value for all the alternatives can be calculated respectively as E2 E3 E4 E5 E6 E7

¼ ð0:75; 1:875; 3:4Þ ¼ ð0:925; 2:05; 3:3Þ ¼ ð0:85; 1:975; 3:6Þ ¼ ð0:825; 1:85; 2:925Þ ¼ ð0:95; 2:175; 3:675Þ ¼ ð0:8; 1:825; 3:15Þ

The fuzzy differences between upper and lower values for all possibly occurring combinations of Ea1 to determine the preference of alternative Ai over alternative Aj have been calculated and presented below: Z 12l ¼ E1l  E2u ¼ ð0:725  3:4Þ ¼ 2:675 Z 12u ¼ E1u  E2l ¼ ð3:25  0:75Þ ¼ 2:5 ; Z 12 ¼ ½Z 12l ; Z 12u  ¼ ½2:675; 2:5 Similarly, Z 13 ¼ ½Z 13l ; Z 13u  ¼ ½2:575; 2:325 and

(4)

Z 23 ¼ ½Z 23l ; Z 23u  ¼ ½2:55; 2:475

j¼1

For example, final fuzzy evaluation value in terms of triangular membership function for Adind village (alternative 1) in Alwar district, E1 = (E11, E12, E13) where E11 is the value of final fuzzy evaluation for alternative 1 with respect to first value of triangular fuzzy number (i.e. value corresponding to membership function value of zero), E12 is the value of final fuzzy evaluation for alternative 1 with respect to second value of triangular fuzzy number (i.e. value corresponding to membership function value of 1), E13 is the value of final fuzzy evaluation for alternative 1 with respect to third value of triangular fuzzy number (i.e. value corresponding to membership function value of zero). The value of this fuzzy evaluation can be calculated as follows:

The fuzzy preference relation matrix is calculated by following ways: The lower and upper values corresponding to zero membership function (i.e. Zijl and Ziju) are already calculated in above step. Assuming triangular membership function with equilateral triangle, the value of Zij corresponding to membership z þz value of 1 can be calculated as z1i j ¼ i jl 2 i ju : 1 2:675þ2:5 For example, z12 ¼ ¼ 0:0875: 2 Therefore the coordinates of vertices of triangular membership function curve corresponding z12 will be ðz12l ; mðz12l ÞÞat vertix 1 ¼ ð2:675; 0Þ,  1  z12 ; mðz112 Þ at vertix 2 ¼ ð0:0875; 1Þ ðz12u ; mðz12u ÞÞat vertix 3 ¼ ð2:5; 0Þ

E11 ¼

n X wi jfuzzy  V i jfuzzy j¼1

¼ ð0:7  0:5Þ þ ð0:3  0:5Þ þ ð0:1  0:5Þ þ ð0:7  0:25Þ þ ð0  0:75Þ ¼ 0:725

Now taking the region for S1 for z12 > 0, value of membership function m(z12) can be calculated corresponding to zero value of z12. Using the linear interpolation, we get m(z12) = 0.9662 for z12 = 0. If zijl > 0, then alternative Ai is absolutely preferred to Aj. If ziju < 0, then alternative Ai is not absolutely preferred to Aj. If zijl < 0 and ziju > 0, the degree of preference of alternative Ai over alternative Aj can be obtained by introducing a term prij. This term

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prij may be expressed as membership function mzi j ðxÞ and the fuzzy preference relation matrix (PR) may be expressed as 2

pr 11 PR ¼ 4 pr 21 pr 31

pr 12 pr 22 pr 32

3 pr 13 pr 23 5 pr 33

where pr11 = pr22 = pr33 = 0.5 and other entries of the matrix when i 6¼ j, are calculated as follows: Area covered under the tringular membership function curve varying from 0 to zi ju pi j ¼ Total area covered under the tringular membership function curve varying from zi jl to zi ju The final value fuzzy preference relation matrix is given as 2 3 0:500 0:467 0:450 0:414 0:547 0:384 0:505 6 0:533 0:500 0:485 0:446 0:581 0:416 0:539 7 6 7 6 0:550 0:515 0:500 0:457 0:601 0:425 0:557 7 6 7 7 PR ¼ 6 6 0:586 0:554 0:543 0:500 0:634 0:469 0:593 7 6 0:453 0:419 0:399 0:366 0:500 0:335 0:456 7 6 7 4 0:616 0:584 0:575 0:531 0:665 0:500 0:624 5 0:495 0:461 0:443 0:407 0:544 0:376 0:500 The fuzzy strict preference relation matrix can be calculated as 2

0:00 6 0:07 6 6 0:10 6 PRS ¼ 6 6 0:17 6 0:00 6 4 0:23 0:00

0:00 0:00 0:03 0:11 0:00 0:17 0:00

0:00 0:00 0:00 0:09 0:00 0:15 0:00

0:00 0:00 0:00 0:00 0:00 0:06 0:00

0:09 0:16 0:20 0:27 0:00 0:33 0:09

0:00 0:00 0:00 0:00 0:00 0:00 0:00

3 0:01 0:08 7 7 0:11 7 7 0:19 7 7 0:00 7 7 0:25 5 0:00

By computing the non-dominated degree of each alternative Ai (for i = 1, 2,3,4,5,6,7)

mND ðA1 Þ ¼ 1  maxð0; 0:07; 0:10; 0:17:0; 0:23; 0Þ ¼ 0:77 mND ðA6 Þ ¼ 1  0 ¼ 1 As per the above calculation, the alternative A6 has the highest non-dominated degree than other two alternative sites. Therefore alternative A6 has highest rank as r (A6) = 1. Deleting the alternative A6 from the fuzzy strict preference relation matrix. The resulting fuzzy strict preference relation matrix will be taken as

2

0:00 6 0:07 6 6 0:10 PRS ¼ 6 6 0:00 6 4 0:00 0:00

0:00 0:00 0:03 0:11 0:00 0:00

0:00 0:00 0:00 0:09 0:00 0:00

5

0:00 0:00 0:00 0:00 0:00 0:00

0:09 0:16 0:20 0:27 0:00 0:09

3 0:01 0:08 7 7 0:11 7 7 0:19 7 7 0:00 5 0:00

Which further gives non-dominated degree of alternative A4 as 1.0 and the same step is followed and thus fA6 g > fA4 g > fA3 g > fA2 g > fA5 g > fA1 g > fA7 g and alternative 6 is best compared to others. Similarly, all the above steps are used to calculate the ranks for connected and unconnected villages in both the districts. This methodology presented herein gives an insight to prioritize roads for safety mitigation measures which is expected to be useful to various decision makers to prioritize the villages. The least ranked village will have least access to medical care. 5. Case study In order to achieve the objectives of the study initially after carrying out the detailed review of literature, the attributes were chosen. Preliminary questionnaires were developed for household and village level surveys and pilot studies were conducted in a few villages in the study areas. In the household level interview survey data was collected to understand the socio-economic background of the family and the travel details of all the family members for visiting health facilities. Other parameters such as availability of public transport and type of road etc. were collected from village level survey. Necessary changes were incorporated in the questionnaire after collecting the feedback from the respondents of the pilot surveys and then the full-scale surveys were carried out. To understand the impact of construction of PMGSY roads on access to health care, survey has been conducted in few villages which are connected by PMGSY roads and few those are unconnected by any all-weather roads. In fact, the impact would be different depending on the location, geography, socio-economic conditions of the population, culture, location and quality of the existing health facilities in the area and many other factors. Keeping the above facts in view it was decided to take up two case studies: one in plain agricultural area and the other in an isolated desert region. Accordingly, studies were carried out in Neemrana block in Alwar district and Sardarshar block in Churu district. The

Table 1 Population of the villages and sample surveyed in the study area. Name of the district

Name of the block

Connectivity status

Name of village

Total population

Sample surveyed

Alwar

Neemrana

Connected by PMGSY

Adind Bhimsingpura Chawandi Daulatsignpura DaniChandaswali Kutina Mehatawas Batana DaniBeedlawali Kundansignpura Partapur SaperaBasti Umraogarh Amarsar Bhairumsar Lunasar Petamdesar Ashusar Dandusar Gomtia HariasarJatau Somansar

1400 1200 1400 375 250 3300 4000 404 150 700 260 100 300 992 872 1823 993 823 588 701 806 656

30 25 28 10 10 55 62 15 10 20 10 10 12 20 20 32 18 15 15 16 18 15

Unconnected

Churu

Sardarsahar

Connected by PMGSY

Unconnected

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Table 2 Population of the unconnected villages and their distances to the nearest all-weather roads in the study area in Alwar district. Name of village

Distance in km from nearest all-weather road

Batana DaniBeedwali Kundansingpura Partapur SaperaBasti Umraogarh

3 4 2 2 3 4

in Table 2 are at least 2 km away from any all-weather road and have been referred to as unconnected villages in this report. The distances to the villages range between 2 and 4 km to the nearest all-weather roads are also shown in the Table 2. The hierarchical structure of health care facilities provided by the state government in rural areas has are Sub-center (SC), Primary Health Center (PHC), Community Health Center (CHC) and Referral Center (RC). Their distances from the connected and unconnected villages are being shown in Tables 3 and 4 respectively. Out of the seven connected villages, three, namely Daulatsignpura, DaniChandaswali and Mehtawas have sub-centers and Kutina has both a sub-center and a Primary Health Center. For the other three villages, the residents need to travel some distances to receive minimum health care facilities. In case of unconnected villages (Table 2) the residents need to travel distances between 2 and 4 km for reaching the sub-center. However, the presence of the facilities does not ensure the quality of service provided by them. During the meeting with the villagers, they complained that the medicines were not available and the doctors or the nurses were only available for three to four hours in day. In such situations the villagers are forced to visit some private doctor or hospital. Also there is an assumption that the quality of service offered by private hospitals is better than that of government hospitals. The travel patterns of the villagers in connected and unconnected villages for normal health care needs and for emergency are being shown in Tables 6 and 7. It may be observed that during emergency, in the absence of facilities nearby, the villagers need to travel long distances.

selection of the villages was based on consultation with the local government officials and a few NGOs in the area. A total of 466 household interviews were conducted in both the districts and village level surveys were also conducted using participatory approach. In order to quantify accessibility and to avoid ambiguity and biasness, the samples were carefully selected from households with varying socio-economic backgrounds. The sample details of the village surveys are shown in Table 1. The response for accessibility related problems are shown in Fig. 1. The respondents were also requested to provide preferences to the given parameters. The preferences given were based on the role and importance of the parameter in defining the accessibility to health care facilities. A scale, between 1 and 5, used for representing the importance of the parameter, where 1 represented not at all important and 5 as highly important. 5.1. Accessibility status in selected villages in Alwar district

5.2. Accessibility status in selected villages in Churu district Alwar is one of the corner most districts of Rajasthan bordering Haryana and Delhi. The study was conducted in Neemrana Block. In all seven villages connected by PMGSY road (Table 1) and six villages not connected by any all-weather road (Table 1) were considered for the study. The villages were chosen in consultation with the local government officials and a few NGOs in the area. It may be observed from both the tables that the population ranges of the villages vary widely. On the one hand, Mehtawas has a population of as high as 4000; on the other hand Sapera Basti is a very small village with a population as low as 100. All the villages are either directly connected by PMGSY road or within 500 m from the alignment of any such road. All the unconnected villages shown

In Churu district the study was conducted in Sardarsahar block in four villages connected by PMGSY roads and five unconnected villages. The village names along with their population are tabulated in Table 1. It may be observed that the village population ranges between 872 and 1823 for the connected villages and between 588 and 823 for the unconnected ones. This block is in desert region with villages located at moderately high distances from one another. None of the villages, either connected or unconnected, has any kind of health facility and the distances to the nearest sub-centers (SC) are more than 5 km for all the villages and in some cases such as Patamdesar, Amarsar and HariasarJatan

Table 3 Health care facilities in the villages connected by PMGSY roads in the study area in Alwar district. Village

Adind Bhimsingpura Chawandi DaulatSingpura DaniChandaswali Kutina Mehtawas

Availability of SC

Availability of PHC

Availability of CHC

Availability of RC

Place

Dist. In km

Place

Dist. In km

Place

Dist. In km

Place

Dist. In km

Nanakwas Majhra Nanakwas Daulatsignpura DaniChandaswali Kutina Mehtawas

3 2 2 0 0 0 0

Mandhan Majhra Mandhan Neemrana Mandhan Kutina Mandhan

5 2 7 2 7 0 5

Majhri Majhri Majhri Majhri Majhri Shahjahapur Majhri

15 3

Behror Behror Behror Behror Behror Behror Behror

30 15 33 14 33 34 32

5 20 10 17

Table 4 Health care facilities in the villages unconnected by any all-weather road in the study area in Alwar district. Village

Batana DaniBeedwali Kundansingpura Partapur SaperaBasti Umraogarh

Availability of PHC

Availability of CHC

Place

Availability of SC Dist. In km

Place

Dist. In km

Place

Dist. In km

Availability of RC Place

Dist. In km

Siriani Giglana Daulatsignpura Gehlot Undroad Daulatsignpura

3 2 2 4 4 3

Kutina Mandhan Neemrana Kutina Mandhan Neemrana

3 10 4 7 2 4

Shahjahapur Shahjahapur Shahjahapur Shahjahapur Shahjahapur Shahjahapur

8 35 12 13 27 14

Behror Behror Behror Behror Behror Behror

23 35 14 20 30 13

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Distance in km from the nearest all-weather road

Ashusar Dandusar Gomtia HariasarJatau Somansar

5 6 6 3.5 5

they are about 10 km. The distances to the villages range between 2 and 4 km to the nearest all-weather roads are also shown in the Table 5. The distance to primary health centers (PHC) is more than 10 km in all the cases and for Lunasar it is about 40 km. SardarSahar, the block headquarters, is the only place in the block which has good facilities for health care with governmental hospital and a number of private nursing homes. The presence of a sub-center does not necessarily ensure quality of service. For example, the nearest SC from Somansar is in Khiwansar about 9 km away, but for normal health care needs the villagers travel to Pulasar (18 km) and Sardarsahar (26 km). For emergency, they sometimes travel 100 km to go to Bikaner where all modern medical facilities are available. The travel patterns of the villagers in connected and unconnected villages for normal health care needs and for emergency are being shown in Tables 8 and 9. 5.3. Quantification of accessibility to health care facilities It was observed from the village-level questionnaire survey that public transport services were not available in any of the selected villages. Since the villagers usually travel for their medical needs

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by walking or by using family owned or rented private vehicles, the parameters to be considered for quantifying the level of accessibility were chosen accordingly. In fact they were selected after conducting detailed discussions with the villagers in the study area. The parameters chosen were: distance of travel, type of road (earth, gravel, black top or concrete), condition of road, travel time and travel cost. Detailed questionnaires were developed to collect data separately from the households of the villages connected by PMGSY road and unconnected villages. The respondents were asked to assign weights on the selected parameters based on their importance in defining accessibility to health care facilities in a scale between 1 and 5, where 1 represented not at all important and 5 as highly important. The initial raw data pertaining to the assigned weights for all the parameters has been sensitively analyzed by removing the extreme outliers, before aggregation. The data points having values more than or less than three times standard deviation (m 3s: i.e. beyond 95% percentage interval) were considered as outliers. After removing these values the average values of weights were used for further analysis. Then the values were normalized so that the summation of all the weights was unity. The levels of satisfaction for the parameters under the existing conditions were also collected from each village on a scale between 1 and 5, where 1 represented highly dissatisfied and 5 highly satisfied. Some of the parameters were quantitative such as travel distance to medical facilities (in meters), travel time (in minutes) and the travel cost (in rupees). The absolute values for the above mentioned criteria’s, which were provided by the respondents, for different parameters were grouped into five range groups. The classification was based on the mean and standard deviation of the values obtained and accordingly scores are given. For type of roads, scores were given according to these specifications: earth road-1, gravel road-2, Black top-3, composite roads-4, Concrete-5. The

Table 6 Level of accessibility to health care facilities in the villages connected by PMGSY roads in the study area in Alwar district. Village Adind Bhimsingpura Chawandi Daulatsignpura DaniChandaswali Kutina Mehatawas

Weight Score Weight Score Weight Score Weight Score Weight Score Weight Score Weight Score

Distance

Road type

Road condition

Travel time

Travel cost

0.27 3.36 0.16 2.08 0.16 2.0 0.17 2.8 0.19 3.0 0.16 3.5 0.14 2.0

0.20 3.36 0.27 3.15 0.23 3.86 0.22 3.7 0.25 5.0 0.23 4.0 0.26 4.0

0.15 3.36 0.14 3.15 0.27 4.5 0.23 3.8 0.25 5.0 0.15 3.8 0.29 3.83

0.30 3.0 0.25 3.0 0.16 1.86 0.15 2.2 0.19 2.0 0.23 3.0 0.17 3.0

0.08 4.79 0.18 3.69 0.18 3.14 0.23 3.4 0.12 2.0 0.23 4.1 0.14 2.43

Table 7 Level of accessibility to health care facilities in the unconnected villages in the study area in Alwar district. Village Batana DaniBeedlawali Kundansignpura Partapur SaperaBasti Umraogarh

Weight Score Weight Score Weight Score Weight Score Weight Score Weight Score

Distance

Road type

Road condition

Travel time

Travel cost

0.15 1.6 0.21 2.0 0.20 2.0 0.20 2.0 0.21 1.0 0.29 2.0

0.21 2.0 0.22 1.0 0.25 1.0 0.20 1.56 0.21 1.0 0.23 3.6

0.26 1.2 0.22 1.0 0.24 2.0 0.19 2.0 0.21 1.0 0.23 3.6

0.21 1.2 0.21 2.0 0.19 2.0 00.21 2.33 0.21 1.0 0.15 1.2

0.17 1.2 0.14 3.6 0.12 3.8 .20 2.78 0.16 3.0 0.10 1.2

Please cite this article in press as: Kanuganti, S., et al., Quantification of accessibility to health facilities in rural areas. Case Stud. Transp. Policy (2014), http://dx.doi.org/10.1016/j.cstp.2014.08.004

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Table 8 Level of accessibility to health care facilities in the villages connected by PMGSY roads in the study area in Churu district. Distance

Village Amarsar Bhairumsar Lunasar Petamdesar

Weight Score Weight Score Weight Score Weight Score

0.16 3.8 0.20 4.0 0.14 2.0 0.18 3.6

Road type

Road condition

Travel time

Travel cost

0.19 3.8 0.20 4.0 0.23 3.4 0.21 3.8

0.21 3.8 0.20 4.0 0.22 3.2 0.22 3.6

0.21 3.05 0.15 3.0 0.16 2.4 0.15 3.1

0.24 4.25 0.25 5.0 0.25 3.8 0.24 4.6

respondents were asked to give a perceived level of satisfaction on condition of roads on a scale of 1 and 5, where 1 represented highly dissatisfied and 5 as highly satisfied. The averages of the scores are taken as the final score for analysis. Similar to the weight, scores were also sensitively analyzed using mean and standard deviation before aggregation. During data collection it has been observed that in both the areas where case studies were carried out, the health facilities were inadequate. The villagers need to travel long distances to get medical help and in the absence of public transport services they mainly walk, or use animal-drawn vehicles and two-wheelers for traveling shorter distances. However, for emergency medical needs, they hire jeeps. Sometimes, when jeeps are not available in the village, they get them from the nearest town and in the process vital time gets wasted and often the patient dies. 5.4. Results and discussion The accessibility Index for the villages connected by PMGSY on an average is 70% (i.e., 3.5 on a scale of 5) whereas for the unconnected villages is 40% (i.e., 1.5 on a scale of 5). Therefore the impact of constructing roads on the accessibility of medical facilities is 30%. The results from three different methods are represented in Table 12 and also in the form of a bar chart in Fig. 4. In Alwar district for connected villages SAW method and Fuzzy aggregation method have same order of ranking for alternatives but in fuzzy preference decision analysis where strict preference concept is used the ranking is varying. The Kutina village has ranked first as the villagers were highly satisfied by travel cost and road type. The village Methatawas has least access to health care due to long distance to medical facilities and consecutively higher travel cost. In alwar district for unconnected villages, the results from fuzzy aggregation and preference decision analysis are same. Village Kundansignpura has highest access as the villagers were highly contended with the travel costs and road condition. The Batana village has low access as the Table 9 Level of accessibility to health care facilities in the unconnected villages in the study area in Churu district. Village Ashusar Dandusar Gomtia HariasarJatau Somansar

Weight Score Weight Score Weight Score Weight Score Weight Score

Distance

Road type

Road condition

Travel time

Travel cost

0.15 1.60 0.14 3.0 0.11 3.0 0.18 2.1 0.11 2.0

0.24 1.4 0.20 2.10 0.22 2.9 0.20 2.4 0.23 2.0

0.26 2.0 0.23 2.00 0.22 2.4 0.20 2.2 0.23 2.1

0.18 2.4 0.16 2.67 0.22 1.84 0.23 2.0 0.18 2.5

0.17 2.4 0.27 2.00 0.23 2.0 0.19 2.3 0.25 2.4

Table 10 Membership function of fuzzy sets that represents letter grades for weights and ratings. Triangular function

Descriptors for weights Extremely important Very important Important Important to some extent Not at all important Descriptors for ratings Very good Good Fair Satisfactory Poor

a

b

c

0.7 0.5 0.3 0.1 0

1 0.7 0.5 0.3 0

1 0.9 0.7 0.5 0.3

0.75 0.5 0.25 0 0

1 0.75 0.5 0.25 0

1 1 0.75 0.5 0.25

villagers were highly unhappy with the huge travel cost, travel time and also with very poor road condition. In Churu district for connected villages using SAW method, Lunasar has lower accessibility index as the villagers were unsatisfied with the distance to medical facilities and travel time. The village Petamdesar is well accessed to health care as the facilities were not too far and the road type and condition were good. In unconnected villages, Gomtia village has highest accessibility index as the travel distance was less and the villagers were highly satisfied with the travel time. The village Somansar has lower access to health facilities due to longer distance to health center and no proper road condition. The deficiencies observed helps the planners to take policy decisions such as improving quality of the road, providing public transport and providing the health services to a cluster of villages having more population so that the travel time is minimized and quality of service is maximized. However the policy maker should study the deficit areas in detail and take an appropriate decision to improve accessibility. The variability in the results can be attributed to the usage of same individual values and fuzzified values in between SAW and fuzzy aggregated approach. Whereas the variability in the results of fuzzy aggregated approach and fuzzy preference decision analysis is due to the strict preference concept used in fuzzy preference decision analysis. The simple additive method is a simple and quick technique. However, it might be noted that the levels of accessibility calculated using this technique are discrete values whereas actually they are expected to be fuzzy in nature. In order to get more precise and high degree of accuracy, fuzzy aggregation method and fuzzy preference decision analysis have been used to quantify accessibility. However by closely observing initial data and results it has been noted that fuzzy preference decision analysis gives more realistic and accurate results.

Table 11 Level of accessibility using fuzzy aggregation method in Alwar district. Village name (connected by PMGSY)

a

b

c

Defuzzified values using maximum defuzzification technique

Adind Bhimsinghpura Chawandi Daulatsingpura DaniChandswali Kutina Mehtawas

0.725 0.75 0.925 0.85 0.825 0.95 0.8

1.825 1.875 2.05 1.975 1.85 2.175 1.825

3.25 3.4 3.3 3.6 2.925 3.675 3.15

1.825 1.875 2.05 1.975 1.85 2.175 1.825

Please cite this article in press as: Kanuganti, S., et al., Quantification of accessibility to health facilities in rural areas. Case Stud. Transp. Policy (2014), http://dx.doi.org/10.1016/j.cstp.2014.08.004

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Table 12 Comparison of ranks obtained from three different methods. S. No

Village

SAW method

Fuzzy aggregation method

Fuzzy preference decision analysis

6 3 4 2 5 1 7

6 3 4 2 5 1 7

6 4 3 2 5 1 7

6 4 3 2 1 5

6 4 1 3 2 5

6 4 1 3 2 5

2 4 1 3

1 3 2 4

1 3 4 2

3 2 5 4 1

3 5 4 2 1

3 4 5 2 1

Alwar district 1 2 3 4 5 6 7 1 2 3 4 5 6

Connected by PMGSY Adind Bhimsingpura Chawandi Daulatsignpura DaniChandaswali Kutina Mehatawas Unconnected villages Batana DaniBeedlawali Kundansignpura Partapur SaperaBasti Umraogarh

Churu district 1 2 3 4 1 2 3 4 5

Connected by PMGSY Amarsar Bhairumsar Lunasar Petamdesar Unconnected villages Ashusar Dandusar Gomtia HariasarJatau Somansar

Comparision of methods Rank with respect to other alternatives

8 7 6 5 4

SAW method

3

Fuzzy aggregation method

2 1

fuzzy preference decision analysis

0

Fig. 4. Comparison of results in the form of a bar chart.

6. Conclusion Based on the present study the following conclusions have been drawn:  The construction of PMGSY roads has certainly eased the approachability to medical facilities. However, the villagers were of the opinion that the true impact of the PMGSY roads would be achieved when public transport services become available in the villages.  All the three methods applied in this study namely simple additive weightage (SAW), fuzzy aggregation method and fuzzy preference decision analysis. Almost similar ranking in terms of accessibility with some minor variations. However the methods based on fuzzy set theory were more consistent. Further the methods will help the policy makers at the local government

level to identify the villages having low accessibility and take appropriate actions.  In quantifying accessibility to health facilities at present the study was restricted only to five parameters which only included travel characteristics and quality of road. Other important parameters such as quality of health facilities such as availability of physician, facilities, waiting time, availability of medicine, etc. can also be included in quantifying accessibility. Acknowledgements The findings of this paper are based on a study on Impact of Prime Minister Gram Sadak Yojana (PMGSY) roads on health in rural Rajasthan (India) supported by International Forum for Rural Transport and Development (IFRTD), UK.

Please cite this article in press as: Kanuganti, S., et al., Quantification of accessibility to health facilities in rural areas. Case Stud. Transp. Policy (2014), http://dx.doi.org/10.1016/j.cstp.2014.08.004

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Please cite this article in press as: Kanuganti, S., et al., Quantification of accessibility to health facilities in rural areas. Case Stud. Transp. Policy (2014), http://dx.doi.org/10.1016/j.cstp.2014.08.004