Background
Indian medical education system is on the brink of a massive reform. The government of India has recently passed the National Medical Commission Bill (NMC Bill) to create a world class medical education system that ensures adequate supply of high quality of medical professionals in the country while being flexible enough to adapt to the changing needs of a transforming nation [
1]. This move is not surprising considering that India has always adhered to the tenet which asserts that the health of the country’s population influences its overall development and its economic prosperity [
2]. The country has taken positive steps in strengthening its commitment towards Universal Health Coverage (UHC) proposed by World Health Organization (WHO) [
3] by launching the ambitious Ayushman Bharat Health Scheme (Pradhan Mantri Jan Arogya Yojana) in 2018 [
4]. However according to the 2017 Global Monitoring Report published by WHO and World Bank, a mal-distributed health workforce prevents achievement of UHC [
5]. Health workers who constitute the human resources for health (HRH) are essential for efficient functioning of the health system [
6]. WHO thus recommends a minimum of 44.5 health professionals per 10,000 population [
7]. Published literature reveals that in India availability of health workers is well below the WHO recommendation [
8,
9]. India ranks 52nd among the 57 countries identified by The Global Health Workforce Alliance and WHO which are most severely affected by health workforce crisis [
10]. Inadequacies in the size and composition of HRH in India hence has led to inequities in health outcomes [
11]. Substantial disparity exists in the health performance of different provinces of the country which closely correlates with the density of health workers available [
11].
HRH in India is composed of a varied range of formal and informal healthcare providers of which allopathic doctors form an integral part [
12]. According to WHO, at least 10 doctors should cater to 10,000 population [
7]. In India the national density of allopathic doctors is 5.9 per 10,000 population of which 24% do not possess adequate training [
12]. Scarcity of trained doctors has persisted in the country despite the rapid increase in the number of medical schools making Indian medical education system the largest in the world [
13]. National shortage of doctors is juxtaposed with gross mal-distribution resulting in a wide divide in the availability of doctors in urban and rural areas [
14]
.
Boulet et al.(2007) have shown that density of practicing doctors in a region is closely associated with the density of medical schools located within its geographic limits [
13]
. To address the acute deficiency of trained doctors, Government of India has initiated several efforts to expand the medical education sector. Under the Pradhan Mantri Swasthya Suraksha Yojana, 6 new AIIMS (All India Institute of Medical Sciences) have been established in the country, which are now fully functional and 16 more projects have been announced. Seventy five government medical schools have been planned under the same national scheme for development in six phases [
15]
. Five hundred eighty million dollars have been allocated to this scheme for expansion of the Indian medical education system in the 2019–20 financial budget [
16]
.
However as Ananthkrishnan rightly pointed out, merely increasing the number of medical schools in the country and their total annual intake will not solve the health workforce crisis as long as the skewed country-wide distribution of medical schools is not corrected [
17]. Previous studies have revealed the uneven distribution of medical schools in India. Mahal et al. in 2006 showed that economically better off provinces of India have higher medical school density [
18]. Two subsequent studies have documented the substantial difference in medical school density between southern versus northern and eastern regions of India [
17,
19]. Shortage of medical schools in rural districts was documented by Brahmapurkar et al. [
20]
.
The blame for this disparity in medical school distribution in India can be partly attributed to the one-dimensional regulations guiding establishment of new medical schools in India. According to the prevalent MCI (Medical Council of India) norms, the criteria needed to be fulfilled to obtain a no-objection certificate (NOC) from the provincial government for opening a new medical school focused mostly on availability of adequate land and the potential demand rather than on the health needs of the area [
21]
. Hence a comprehensive reform of Indian medical education is the need of the hour. Acknowledging this need, the government of India is readying itself to modernize medical education in the country through promulgation of the National Medical Commission Bill (NMC Bill) [
1]. It is proposed in the bill that while permitting establishment of new medical schools the new regulating authority shall have due attention to financial resources, availability of adequate faculty and hospital facilities while also allowing the said criteria to be relaxed for those medical schools which are to be set up in an underserved area. However the bill has not clarified the definition of an underserved area. Therefore it is necessary to clearly demarcate areas which have the lowest medical school density and which would benefit the most by the establishment of a medical school. A search through the literature published on Indian medical education yielded very few studies that have attempted to spatially orient the distribution of medical schools in India [
19]. Hence this study explores the geographic distribution of medical schools in India. Special emphasis has been given to the mapping of new medical schools opened in the last decade to identify the ongoing pattern of expansion of medical education sector in India.
Although medical school density has been found to affect regional doctor density, the correlation between the number and distribution of medical schools and population health indicators is not well established especially in the Indian context. As India is poised at the brink of a new era of medical education, it is relevant to explore the association of medical school distribution with the background indicators of the area they serve.
This study was undertaken with the following objectives in mind-
1.To describe the spatio-temporal distribution of all public and private owned medical schools according to their distribution among Indian provinces and their year of inception.
2.To compare the geographic distribution of new medical schools (opened in the last decade i.e. 2009–2019) with that of old medical schools (established before 2009).
3.To compare the socioeconomic and health care profile of districts according to the presence or absence of new and old medical schools.
Methods
Study setting
India is a conglomerate of 37 administrative divisions (28 provinces and 9 Union Territories or UTs) [
22] with a total population of approximately 1211 million [
23]
. Administrative unit of each of those provinces/UTs is called district. As per to official statistics, India has a total of 640 districts [
24]. Each district is composed of varying proportion of urban and rural areas. According to Urban and Regional Development Plans Formulation and Implementation (URDFPI) Guidelines urban areas are further classified into towns and various classes of cities [
25]
.
Data sources
1.
Data on medical schools in India-
This study relied on the online database maintained by the Medical Council of India (MCI) for information regarding the location, year of inception and annual intake of medical schools. Till August 2019 MCI was the regulatory authority that guides medical education in India. Its accreditation was mandatory for establishment and functioning of medical schools [
26]
. The present study included all medical schools in India which provide medical education leading up to the MBBS (medical graduate) qualification, as listed in the official website of MCI as of 22
ndApril 2019 [
27].
2.Data on socioeconomic and health profile Indicators-
Provincial level health indicators: Province wise values for Infant mortality rate (IMR per 1000 live births) for the year 2016, Institutional deliveries (ID expressed as a percentage of total deliveries) for year 2015–16 and maternal mortality ratio (MMR per 100,000 live births) for the year 2014–16 were retrieved from the official website of National Institution for Transforming India (NITI), Government of India [
28‐
30]. These 3 indicators were specifically chosen keeping in mind the importance of maternal and child health in over all public health of a country and since developing countries like India are concentrating on improving MMR, IMR and ID to help reduce the overall health burden [
31].
District level indicators: District Fact Sheets of National Family Health Survey - 4 (2015–16, [
32]) provide district wise data on many key indicators of reproductive health and family planning, infant and child mortality, maternal and child health, nutrition, anaemia, utilization and quality of health and family planning services. Of all the available indicators we have chosen four representative ones that indicate population and household profile including infrastructure, sanitation, cooking fuel and literacy status of the districts to reflect the socio-economic status of the respective districts. Similarly, data on seven representative health-care indicators was retrieved for use to reflect the public health status of the respective districts. The indicators most likely to be influenced by the access to health care services were used as the effect of presence/absence of medical school will be most evident in these cases. The key indicators chosen have highlighted in Table
1.
Table 1Key socioeconomic and public health indicators chosen for analysis
Population and household profile | •Households with electricity, •Households using improved sanitation facility, •Households using clean fuel for cooking, •Women who are literate. | Socio-economic Status of districts |
Maternal and child health | •Mothers who had full antenatal care, •Mothers who received postnatal care from a doctor/ nurse/ LHV/ ANM/ midwife/ other health personnel within 2 days of delivery, •Children who received a health check after birth from a doctor/nurse/LHV/ANM/ midwife/other health personnel within 2 days of birth, •Births assisted by a doctor/ nurse/LHV/ANM/other health personnel, •Children with fever or symptoms of ARI in the last 2 weeks preceding the survey taken to a health facility, •Women Age 15–49 Years Who Have Ever Undergone Examinations of Cervix, •Women Age 15–49 Years Who Have Ever Undergone Examinations of Breast. | Public health status of districts |
Mapping
Locations of district headquarters (HQs) and boundaries were available in the digital map of India which was purchased from the office of Survey of India (SOI) in Dehra Dun, India (License No. “BP11CDLA183”). The provincial boundaries of Telangana province were updated from the GADM database (
www.gadm.org), version 3.6 (released on 6 May 2018). (Jammu and Kashmir and Ladakh were treated as one administrative division as the boundary maps separating the two and other relevant data were not available in retrieved databases). For analysis, the digital map that we used contained 29 provinces and 7 UTs. Locations of all the medical schools in MCI list were manually digitized by finding their coordinates on base maps of ArcInfo and open access databases like Google Maps and Bing Maps. These data were presented in maps using appropriate symbology.
Analysis
Using the year of inception as per MCI data, medical schools established before 2009 were termed as ‘old medical schools’ while medical schools opened during 2009 to 2019 were referred to as ‘new medical schools’. Country level distribution of old and new medical schools in public and private sector was plotted as a main map (scale 0 to 500Km). Detailed distribution of geographic clusters of medical schools at major cities and provincial capitals was visualized using insets (scale of 0 to 50 Km) in the same graphic. The ‘count features in a polygone’ algorithm of the QGIS Desktop (version 2.18.24) was used to count the number of old and new medical schools in a geographic territory like Province, UT or district. Near neighbourhood analysis which calculates the Nearest Neighbor ratio (NNR) and its Z score was done to check for statistical significance of the clustering of old as well as new medical schools in India. If the NNR is less than 1 it can be inferred that the pattern exhibits clustering (
https://docs.qgis.org/testing/en/docs/user_manual/processing_algs/qgis/vectoranalysis.html#qgisnearestneighbouranalysis). Distance matrix algorithm was used to determine the median distance of a ‘new medical school’ from its nearest ‘old medical school’. (
https://docs.qgis.org/2.18/en/docs/training_manual/vector_analysis/spatial_statistics.html?highlight=distance%20matrix).
Medical school density
Density of medical schools was calculated as the total number of medical schools per 10 million population (as per census 2011). To begin with the national medical school density was calculated. Subsequently, the density of medical schools for each province/UT was also calculated. Choropleth map was used to visualize the province/UT level quintiles of medical school density. Spearman’s rho was used to detect correlation between province/UT level medical school density (Old, New, and Total) and their annual intake with IMR, ID and MMR. Scatter plots were drawn to analyse the correlation between the medical school density of each province (log transformed) and its health care indicators- IMR, ID and MMR. The density of both ‘old medical schools’ as well as that of ‘new medical schools’) was used in creating scatter plots to separately observe the effect of both on provincial health profile.
District level analysis
Distribution of district level indicators was compared between districts with and without medical school till date. To study the pattern of expansion of medical schools in last decade districts were classified as group ‘A’ if they had any medical school in 2009 (or ‘old medical school’) and group ‘B’ if they did not have any medical school in 2009 (or ‘old medical school’). Proportion of districts which got a ‘new medical school’ during last decade (2009 to 2019) was compared between group A and B districts using Pearson Chi-square test.
To see the effect of new medical schools on district level indicators, distribution of district level population and household profile as well as health care indicators was compared between districts with and without medical school in 2019. To see the effect of new medical school distribution of district level indicators was compared separately for group ‘B’ districts with and without a new medical school in last decade (2009 to 2019). Non parametric approach (with application of Mann-Whitney U test) was used for comparison of district level indicators.
Software used: QGIS Desktop version 2.18.24, IBM SPSS Statistics version 25, Google Earth and Arc-Map version 10.
Ethical approval
The present study uses information obtained from online open access databases available on the websites of the following: the Medical Council of India, National Institution for Transforming India (NITI), the 2011 Census, the National Family Health Survey - 4 (2015–16) and GADM database. The study was approved by the Institutional Ethics Committee of R.D.Gardi Medical College, Ujjain Madhya Pradesh, India.
Conclusion
In this study we have mapped the locations of medical schools using geographic information system (GIS) to elaborate the spatial distribution of Indian medical education system and its influence on public health. Findings reveal that wide geographic areas in the Northern and North Eastern part of the country either have low medical school density or are completely devoid of medical schools. In contrast, medical schools are clustered in the southern provinces of the country. Within each province medical schools are again concentrated in and around major cities and capital regions. Districts with poorer population and household indicators have fewer medical schools. The shortage of trained health workforce in smaller cities, semi-urban and rural areas can be partly attributed to this pattern of aggregation. This mal-distribution is further compounded by selective opening of new medical schools within the catchment area of already established medical schools.
Keeping in mind the strengths and limitations of this study, it can be concluded that medical schools might have a positive influence on public health. Equitable regulation of grant of permission to open future medical schools might therefore be important to solve the health manpower crisis in India and thus lead to achievement of universal health coverage and finally better health. It is therefore recommended that further research be conducted to comprehensively understand the entire scenario. Based on insights thus generated the rules guiding medical school establishment in India can be finalised and included in the future strategies.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (
http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.