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 Table of Contents  
Year : 2013  |  Volume : 57  |  Issue : 1  |  Page : 47-48  

Optimizing the routes and locations for primary healthcare clinics using network analysis: A geographic information systems application

1 Department of Community Health, Christian Medical College, Vellore, Tamil Nadu, India
2 Department of Health, Harrisburg, Pennsylvania, PA 17120, USA

Date of Web Publication4-May-2013

Correspondence Address:
Mark Rohit Francis
Department of Community Health, CMC Bagayam, Vellore - 632 002, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0019-557X.111378

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How to cite this article:
Francis MR, Devasundaram J, Balraj V. Optimizing the routes and locations for primary healthcare clinics using network analysis: A geographic information systems application. Indian J Public Health 2013;57:47-8

How to cite this URL:
Francis MR, Devasundaram J, Balraj V. Optimizing the routes and locations for primary healthcare clinics using network analysis: A geographic information systems application. Indian J Public Health [serial online] 2013 [cited 2022 Sep 26];57:47-8. Available from:


Routine primary care and some public health activities require repeated travel to provide preventive or curative services through mobile or fixed health clinics that are visited by one or more teams. Routes are usually planned using traditional paper maps and are assigned based on geographic contiguity or convenience. Of all the costs incurred in managing a mobile health clinic service, costs for transportation are known to be the most pressing, hence highlighting the need for more effective routing and clinic stop-ordering. [1] We used geographic information systems (GIS) software and network analysis tools to evaluate one such system run by the Community Health and Development Hospital (CHAD Hospital, Vellore, India) to determine if its traditional routes for delivery of health-care in an administrative block in Southern India, were optimized for time and expenses incurred for travel.

Digital road and street maps of the Kaniyambadi Administrative Block (82 villages) were made with recreational global positioning system (GPS) receivers and ArcGIS software (ESRI, Redlands, CA, USA) using standard methods. [2] The attribute table of the shape files was populated with fields such as the name, type, and make of road to provide attributes needed for analysis. The network analyst extension in ArcGIS 9.1 (ESRI, Redlands, CA, USA) was used for the network analysis portion of the exercise. Thirteen individual route maps (one for each of the 13 different clinic days) were created and printed from the clinic's monthly schedule. Using the same order of visits, clinic stops were fed into the network analyst "best-route finder" with distance as the impedance for all 13 routes. A comparison was made of the existing travel routes for the clinics with that of the best route finder in collaboration with the driver of the clinic vehicle on a test-run.

Of the 13 routes used to service 43 clinics, nine routes matched the network analysis routes. Site visits revealed that 3 of the 4 routes were unsuitable for reasons such as over-congestion, impediments that limited usability of a stretch of road, and a narrow stretch that the clinic vehicle could not negotiate. One suggested alternative route was found to be implementable [Figure 1]. The average distance traversed for an actual clinic run was 25.90 km as opposed to 25.62 km for the network analyst predicted clinic runs. The average cost per clinic run was Rs. 129.47 as opposed to Rs. 128.1 for the network analyst suggested routes (P value = 0.9554). With minimal likely gains from the software-predicted routes for this mobile clinic service, it may be established that the Doctor-managed mobile clinics by CHAD are managed and run optimally. The establishment of a GIS-enabled facility requires considerable initial investment on equipment such as computers, GIS software, dedicated data-collection equipment - GPS instruments and personnel. Once a GIS system is put in place, it is cost-efficient to run and maintain.
Figure 1: An example of the Sapthalipuram/Puthur/K. Puthur/Thuthipet clinic route showing dissonance by both the network analyst predictions and the route followed in actual practice

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This procedure has greatest potential for emerging situations such as occurred during the Pakistan earthquake of 2005 and the Haiti quake in 2010, where medical emergency response teams had to be dispatched amidst debris and potentially hazardous situations. Where access is limited or target areas situated in remote areas this methodology in conjunction with other GIS procedures, will serve as an efficacious planning and management tool. Relief and humanitarian mapping is fast gaining importance for better disaster management, especially in developing countries like India, thus, highlighting the potential of GIS based mapping and tools like the network analyst to aid effective public health action. [3],[4]

   Acknowledgment Top

The authors are grateful to Dr. Jayaprakash Muliyil (Professor), Dr. Venkat (Assistant Professor), Dr. Rajiv Sarkar (Project Manager), Mr. Sam (Co-ordinator) from the Community Health Department, CMC, Vellore for their help with the study and Mr. J. Senthil Kumar also from the same department for his help with GIS mapping and help with its editing.

   References Top

1.Vos J, Borgdorff MW, Kachidza EG. Cost and output of mobile clinics in a commercial farming area in Zimbabwe. Soc Sci Med 1990;31:1207-11.  Back to cited text no. 1
2.Sarkar R, Prabhakar AT, Manickam S, Selvapandian D, Raghava MV, Kang G, et al. Epidemiological investigation of an outbreak of acute diarrhoeal disease using geographic information systems. Trans R Soc Trop Med Hyg 2007;101:587-93.  Back to cited text no. 2
3.Global Map California, USA (2011). Available from: [cited 2012 Jul 17].  Back to cited text no. 3
4.Rao DP. Disaster Management. GIS Development, Jan 2012. Available from: [cited 2012 Feb 13].  Back to cited text no. 4


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