holistic planning for urban & rural health care
TRANSCRIPT
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Holistic Planning for Urban & Rural Health Care Infrastructure:
A Case Study for a District in India
Sanjay Sinha 1, Priyanka Sharma2 1 Knowledge Expert (Geo Analytics), Boston Consulting Group
2 Senior Analyst (Geo Analytics), Boston Consulting Group The Boston Consulting Group,
10th Floor, Building no. 9A, DLF Cyber City, Gurgaon, Haryana, 122002, India
Word Limit of the Paper should not be more than 3000 Words = 7/8 Pages)
Abstract:
The health and welfare of the citizens of a district are managed in a three tier system comprising of the (1) Community Health Centre (CHC)-A 30 bed hospital/referral unit (2) The Primary Heath care center (PHC). A referral unit for 6 sub centers and (3) Sub Centre- The most peripheral point of contact between the primary health care system and the community. These facilities and in turn linked to District hospitals, which are generally located at large urban centers.
The locations of these facilities have traditionally been carried out on the basis of the population of the location of the village/town i.e. as a location’s population increases, the facilities get upgraded. However, by not taking into account the spatial relationship between the population centers, leads to a sub optimal allocation of resources.
While conducting this case study in Hisar district, it was found that ~40% of the rural population in this district cannot access hospitals within 30mins of travel
About the Author:
Sanjay Sinha
Sanjay leads the geo-analytics practice of BCG in APAC region. Previously he was an infrastructure specialist at World Bank in Washington DC, where he set up the first GIS unit for solutions in the infrastructure, health and education sectors. E mail ID: [email protected]
Contact: +918130821929
Priyanka Sharma
Priyanka is a senior geospatial analyst in BCG and a strong believer in location based data driven decision making. With nearly 7 years of experience in addressing the geospatial and data driven questions, she is trying to combine her love for GIS and socioeconomic data with an analytical yet creative approach. E mail ID: [email protected]
Contact: +919811947327
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time. With a detailed street network dataset, we have generated travel time polygons around existing facilities using service area polygon tool in network analyst extension in ArcGIS.
This analysis played a vital role in finding villages falling inside these areas. In addition to this, we could extract two additional optimal locations which can be further used to set up new hospitals/ambulance etc. Interesting fact was improving the infrastructure of existing city hospitals and adding new hospitals can lead to ~95% of the population coverage. We have used location allocation tool in network analyst extension of ArcGIS. These numbers indicate its overall ability to attend emergencies.
A spatial approach takes into account the locations of the all population centers. The most accurate source of population statistics is the census, which was incorporated in this analysis. As people have to travel to health centers and the time taken is a critical life/death issue, travel time polygons were generated. The ESRI Street Map Premium which contains a detailed network data set for entire India was also used.
Using the existing facilities as the origins a catchment analysis was carried out. The option ‘non-overlapping’ was selected to prevent double counting of villages that fall in multiple catchment areas.
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After determining the current coverage, we developed recommendations for siting of new facilities. The objective of this exercise was to find locations that serve a higher proportion of the population within ‘X’ minutes. A spatial approach to select health facility locations provide for higher levels of coverage. This approach is superior to the current method of selecting villages/towns on the population.
This type of analysis promotes the efficient utilization of funds and man power, form the supply side. The general population also sees a reduction in the time taken to reach a medical facility. Once the Geo approach is in place, continuous decision making support can be provided to the management of health facilities. The census contains demographic information such as children by age groups, male/female population, additional health sector programs can be planned.
Examples include – selection of locations of angadwadis, pre-natal care centers, HIV/AID clinics etc. ESRI software and tools can be leveraged to provide inputs in other activities such as ambulance locations, optimal paths for inspectors and similar functions.
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Introduction
Access to primary health care played an essential role in maintaining health standards for a developing country like India. While there are enormous amount of inequities in health care across wide sprawl of urban and rural areas, there is a strong requirement to increase the performance of the accessibility to basic health care centers from a spatial perspective. Understanding the performance of overall health system from a location perspective could be much more important for stronger planning and evidence based health care policy development. This case study is based on analysing spatial accessibility to these nearest health care facilities/hospitals in Hisar district of Haryana and finding the optimal no. of facilities required to cover maximum population in a given interval of travel time. Despite the fact that spatial accessibility to health care facilities is a strong area to focus but this seems to be less studied at a rural district level. There could be several reasons to it like lack of high resolution data, awareness about geographic perspective, improper planning etc. This study emphasized on free available data from Census of India so that district centers can easily avail this information to incorporate this study. Overall this study talk about optimal utilization of available health care resources in rural areas. It is possible to provide medical care even in the most untouched rural areas while utilizing simple procedures at a nominal cost. Using all the layers together (Census, streets, locations), we can build a geospatial framework which can help us understand population coverage, visualize the inefficiencies and come up with alternatives
Study Area and Datasets Study Area This study uses health facility locations, ESRI’s street data, and population data from Census of India for Hisar district in Haryana, India According of Census of India, Hisar is one of the 22 districts in Haryana with a population of 1,743,931 of which male and female are 931,562 and 812,369 respectively. Most of the population in Hisar is rural with depending on farming for its livelihood. The health and welfare of citizens of Hisar district in managed in a three tier system –
(i) Community Health Centre (CHC) (ii) Primary Health Centre (PHC) (iii) Sub Centre
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CHC’s are the ones with 30 bed hospital/referral unit, PHC’s are a kind of referral unit/dispensaries for 6 sub centers and sub centers are considered to be the most peripheral contact between the primary health care system and the community. These facilities and in turn linked to District hospitals, which are generally located at large urban centers. The locations of these facilities have traditionally been carried out on the basis of the population of the location of the village/town i.e. as a location’s population increases, the facilities get upgraded. However, by not taking into account the spatial relationship between the population centers, leads to a sub optimal allocation of resources.
Datasets In this study, we have used data from different source. We tried to leverage census of India for both spatial and non-spatial information –
(i) Health Care Facilities Locations – Primary health care facility database was downloaded from Census of India (http://censusindia.gov.in/) database. There were total of four city hospitals used in this study : Hisar, Hansi, Adampur and Narnaund
(ii) Population Data & Village Locations: Coordinates (Latitude & Longitude) of these villages along with their population data was also downloaded from village town directory of Census of India (http://www.censusindia.gov.in/(S(a5yfmt55stqidf454rahowjx))/2011census/censusdata2k11.aspx). In total, we have used 280 village locations with their population data for this analysis. Population varies from 50 people till maximum of 301,383
(iii) Street Level Data – High resolutions street map data was used from ESRI’s street map premium (iv) Administrative Boundaries : The administrative boundaries at a district level was downloaded from
GADM (http://www.gadm.org/)
We have used excel and ESRI’s ArcGIS to perform data acquisition and preprocessing. Village locations with population data were manually geocoded using Google Maps (https://www.google.co.in/maps/)
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Fig: 1 – City Hospitals & Primary Health Facilities in Hisar, Haryana
Travel Time Analysis: Spatial accessibility to health care facilities In order to analyze the spatial accessibility and the geographical coverage of the existing facilities, we have generated travel time polygons around each facility. Figure depicts the most of the population in Hisar district is underserved. Using high resolution street level data from ESRI, travel time polygons were generated for different scenarios – a) 10 min b) 20 mins c) 30 mins d) 45 mins travel time
Adampur
Narnaund
Hisar
Hansi
City Hospitals PHC
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This travel time reflects the amount of time taken by the patient to reach health care facilities along the detailed network on rural roads. For each scenario, there is a significant variation in total population covered. The existing health facility network covers only 30% of the total population.
Table 1 Travel Time Scenarios to the health center
Facility Name Population Covered across different travel time (%)
10 min 20 mins 30 mins
Adampur 2% 6% 13%
Hansi 7% 10% 18%
Hisar 19% 26% 36%
Narnaund 2% 4% 10%
Total Coverage 30% 46% 77%
Fig: 2 – Variation in Population Coverage across different travel time to the facilities
We also tried to investigate spatial accessibility and population coverage up to 60 mins of travel time. However, travelling time to the nearest health facility does not taken account of components like type of facility available, access to advanced machine etc. Somehow travel time for each health care facility provides a numeric measure to access the performance.
0%
5%
10%
15%
20%
25%
30%
35%
40%
Adampur Hansi Hissar Narnaund
Spatial Variation & Population Coverage
10 min 20 mins 30 mins
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We can study these measures further to understand the poor causes of the health system. This simple yet powerful study provides a more comprehensive, detailed and realistic methods/tools to understand the overall performance and the spatial extent of the health care systems.
Fig: 3 – Existing Scenario: Spatial accessibility to hospital facilities based on various travel time
Underserved Population
30 mins
20 mins
10 mins
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Analysis & Results Spatial accessibility to Health Facilities This analysis was performed using ESRI’s ArcGIS software using ESRI street map premium. Network Analyst extension takes speed and travel time and other constraints into account. We carried out different travel time scenarios (10 min, 20 min, 30 min and 60 mins) to analyze the spatial accessibility and variation in population coverage. For all the health care facilities, 60 mins was considered as a maximum travel time among all the scenarios. We have also added a visual representation (figure 1) where we can see the overall population coverage for each of the health care facilities. For 10 mins scenarios, health care facilities in Adampur and Narnaund showed minimum population coverage. On the other hand, highest amount of population coverage was observed around health care facility located in Hisar city. Since, this Hisar facility is located at a city center, it can be easily approachable by most of the villagers in a given period of time. Patient prefer to walk or use bicycle and then use public transportation to reach the city center (Hisar) For all the travel time scenarios, Narnaund represents lowest degree of spatial accessibility. There could be several reasons tagged to it like poor accessibility, transportation services, and poor infrastructure. This leads to further recommendation like spending funds for better infrastructure in Narnaund facility. The highest level of geographical accessibility was observed in scenario with Hisar facility where only 36% population can reach the hospital facility in 30 mins. Current Scenario: Spatial Distribution & Population Coverage for existing Health Care Network In order to analyze the spatial distribution of current health care network, we have generated non overlapping travel time polygons in ArcGIS. Using network analyst extension and high level Street map premium data, travel time polygons were generated. Using a catchment area analysis approach, for each facility we have used village level population data to summarize at a polygon level. In other words, village locations are used as a demand locations and population covered played an important role in studying the overall spatial distribution. Figure2 indicates the extent of the catchment areas calculated based on each travel time. Current distribution indicates Narnaund and Adampur facilities have lowest degree of spatial accessibility. The existing network covers only 46% of the total population in 20 mins of travel time. Finding Potential Locations to set up new facilities: Maximize Population Coverage
Using a location allocation tool in ESRI’s ArcGIS, we came up with two new potential locations (Fig. 4) to set up new facilities. Setting up facilities at these two locations can lead to ~95% population coverage in 30 mins of travel time. To summarize, in case of emergency, 95% of the population can reach hospital facilities within 30 mins of travel time. We can also setup, new emergency ambulance to provide first aid to the patients in critical situation.
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Fig: 4 – Potential Locations to maximize population coverage
Potential locations for
setting up new facilities
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Conclusion Running different travel time scenarios leads to significant geographical variations in spatial accessibility. This study clearly reveals that majority of the population in rural parts of Hisar district is underserved and most of the population do not have access to existing facilities within 30 – 40 mins travel time. It is also demonstrated that leveraging census data and village level data in a geospatial framework can play a vital role in designing evidence based planning and allocating facilities in an efficient manner. This study explored available data options from Census of India which is freely available at a very high resolution. In addition to this, we have explored important aspects of locating health care facilities in rural districts like Hisar. This study seems to be simple yet very powerful once implemented while deciding potential sites for setting up new hospitals. It clearly demonstrated that regardless of number of facilities and dispensaries, majority of the population is still uncovered and they cannot reach hospitals in 30 mins. Half of the faculties do not have required emergency facilities to treat the patients on an urgent basis. Although, ministry of health is trying to make substantial efforts in improving the health care infrastructure in Haryana by addressing number of issues. Access to health care centers, shortage of health staff and poor infrastructure is still needs to be attended. Tragedies like Gorakhpur hospital’s remind us to pay attention to health care framework as early as possible and as efficiently as possible. Spatial access to these health care facilities and the overall variation in population coverage in this case study provided simple but powerful platform that can be clearly used to help health research and decision making planning at a district level. Strong investment to strengthen the health care infrastructure in highly needed to expand the overall coverage. In addition to this, one of the result of this study was we could able to find 2 optimal locations to set up primary health care centers which will lead to 95% of the population coverage in 30 mins. This will further result in providing valuable support to the authorities in supporting the overall development of infrastructure at such a low cost. At the same time, if we have limited resources and running costs to set up a new infrastructure, we can focus on implementing economically feasible solutions to reduce the inequities. This could be spending more on existing facilities and setting up new dispensaries with an emergency facilities at the optimal locations or we can set up emergency ambulance with basic facilities at one of these optimal sites. This could be more generic and comprehensive approach to address issues like these at a district/sub district level. To conclude, we can easily design good quality and cost effective geospatial framework for health care infrastructure in a developing country like India. Simple geospatial methods can be easily incorporated which can play a vital role in evidence based planning and also help in finding potential sites which can reduce the inequity and maximize population coverage. Lastly, this also represents the important of leveraging census level information at such a granular level.
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References 1. http://www.census2011.co.in/census/district/219-hisar.html 2. http://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/location-allocation.htm 3. Blandford JI, Kumar S, Luo W, MacEachren AM. It’s a long, long walk: accessibility to hospitals,
maternity and integrated health centers in Niger. Int J Health Geogr. 2012; 11:24. 4. Clark RA, Coffee N, Turner D, Eckert KA, van Gaans D, Wilkinson D, et al. Application of geographic
modeling techniques to quantify spatial access to health services before and after an acute cardiac event. Health Serv Outcomes Res. 2012;125:2006–14.
5. http://www.ijsrp.org/research-paper-0812/ijsrp-p0891.pdf 6. http://www.livemint.com/Opinion/qXD81719wXXDQVpGyyARrO/Seven-charts-that-show-why-Indias-
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