Abstract
Background
Efficient blood transfusion planning requires knowledge of the distribution and accessibility of blood banking facilities (BBFs). We mapped population‐level, timely geographic access to BBFs across 735 districts across India.
Study Design and Methods
We conducted a pan‐India geospatial modelling study using 2024 data from e‐RaktKosh, a centralised blood management system. As outcomes, we estimated BBFs per million people, travel time to the nearest BBF, and access population coverage (APC), defined as the proportion of the population with access to the nearest BBF by motorised transport (60 min) and walking (30 min).
Results
In 2024, India had 5679 BBFs, averaging 4.13 facilities per million people. Median travel times to the nearest BBF were 35.25 min by motorised transport and 253.27 min by walking. For APCs, 93.86% of people were within 60 min of their nearest BBF by motorised transport, while 16.21% were within 30 min by walking. There were large differences in APCs across districts and states, with notable rural–urban disparities.
Discussion
Access to blood banking facilities in India exhibits geographic disparities, with the northeastern states and island Union Territories having prolonged median travel times and low APC values. Rural areas are disproportionately adversely affected. Future efforts should focus on using timely geographic access data to inform strategic infrastructure planning and resource allocation, enhancing healthcare equity.
Keywords: access, blood banks, geospatial, India, lower‐middle‐income countries
1. INTRODUCTION
The World Health Organisation (WHO) classifies blood and blood products as essential medicines and notes a significant gap in access to these critical resources across countries, adversely impacting access to blood in low‐ and middle‐income countries. 1 Timely geographic access to blood and blood products is essential across acute specialties, including emergency, intensive care, surgical, obstetric, trauma, and anaesthesia care. Beyond acute specialties, such access is vital for managing chronic hemato‐oncological conditions that require regular transfusions. 2 Hence, establishing efficient and equitable blood banking systems is arguably the most important way to meet the above‐mentioned healthcare needs. 3
Timely geographic access to blood relies, partly, on spatial proximity to blood banking facilities (BBFs). Prolonged travel times delay access to blood transfusions, potentially increasing the risk of adverse outcomes, especially for patients with critical or chronic conditions or who need emergent or urgent care. 4 Hence, access time to BBFs is vital for the equitable utilisation of blood banking services. 5
In low‐ and middle‐income countries (LMICs), the blood supply relies primarily on blood banks and storage facilities. LMICs are known to face barriers concerning availability, safety, and infrastructure. 5 , 6 While studies in LMICs have focused on these challenges, efforts to evaluate timely geographic access to BBFs are limited and localised to smaller geographies, regardless of the high priority. 7 , 8
India, the most populous country with over 1.4 billion residents, faces formidable challenges in ensuring access to a large and heterogeneous blood banking system regulated under the National Blood Transfusion Council (NBTC). 9 Previously, studies have evaluated the performance of Indian BBFs for blood volume availability, accreditation, safety, infrastructure, regulations, and so forth, using systematic composite indices. 10 , 11 However, they did not include timely geographic access, which is the primary focus of this study. Recent regional studies have focused on identifying 'Blood Deserts', and building on these findings, we aim to provide a pan‐Indian assessment with resolution at the district level and an urban–rural divide. 12
We aimed to assess the various measures of geographic access to BBFs in India. First, we mapped the BBF geolocations and estimated BBF density per million people. Second, we analysed the travel times to the nearest BBFs by different modes of transport. Third, we assessed the proportion of people who can access BBFs within 30 and 60‐min intervals, which we defined as access population coverage (APC). We estimated the above‐mentioned outcomes of geographic accessibility at national, state, and district levels with rural and urban disaggregations. Finally, we assessed state‐level correlations between APC and previously published BBF performance indices as a step towards concurrent validation.
2. METHODS
2.1. Data sources
We obtained geographic locations for 5679 blood banking facilities (BBFs) among Government, Red Cross, Charitable, and Private centers across 735 districts, within 28 states and eight Union Territories (UTs) from e‐RaktKosh, a centralised blood management system maintained by the Centre for Development of Advanced Computing (CDAC). 13
All available records across states and UTs were manually extracted into Microsoft Excel and reviewed for duplicate entries and omissions to ensure accuracy. Variables included state, facility name, address, contact number, email address, and ownership category. Facilities under e‐RaktKosh were classified as hospital‐based blood banks, standalone blood centers, blood storage units (BSUs) attached to secondary/rural health facilities, and institutional blood centers. The extracted centers were collectively labelled as “Blood Bank Facilities” for the purpose of the study.
Population counts were sourced from high‐resolution (1 km2) United Nations‐adjusted projections for 2020 from the WorldPop dataset. 14 We obtained friction surface 2019 rasters for motorised transport and walking from the Malaria Atlas Project (MAP), an international scientific effort, primarily concerned with mapping the global response to malaria. 15 We used these rasters to calculate travel times to BBF from each square kilometre (1 km2). 16 MAP and WorldPop are highly cited global sources that have been previously verified. 17 , 18
We obtained the administrative boundaries of India from publicly available and validated shapefiles. 19 A previously published global raster with ordinal catchment area (CA) categories based on population densities and proximity to high‐density urban areas provided standardised rural–urban regions. 20 We classified CA categories >7 as rural areas. This threshold corresponds to the URCA classification methodology, where categories 1–7 represent the seven urban agglomeration sizes based on population (ranging from large cities with >5 million inhabitants to towns of 20 000–50 000 people), while categories 8–30 represent rural catchment areas classified by travel time (<1 h, 1–2 h, 2–3 h) to these urban centers and hinterland regions beyond 3 h from any urban agglomeration of at least 20 000 people.
The data sources used in this study range over the period from 2019 to 2024 (e‐RaktKosh (2024), WorldPop (2020), and MAP (2019)). Although these datasets span over 5 years, the blood bank locations, road infrastructure, and population spatial distributions change gradually over time.
2.2. Outcomes
We studied three outcomes. First, BBF density was examined as the number of BBFs per million people to understand the geographic distribution of BBFs relative to the underlying population residing in a given geography. Second, the travel time to the nearest BBF was investigated. Travel time estimates for each 1 km2 raster were used, and the median and interquartile range (IQR) were reported at the aggregate group level. Finally, the access population coverage (APC) was defined as the percentage of the population with timely access to the nearest BBF. We used 60 min as the timely access threshold for travel by motorised transport and 30 min for walking. Travel time thresholds such as 30 min for walking and 60 min for motorised travel are commonly used in spatial accessibility research to reflect meaningful bounds on how far individuals will travel to seek health services. 18 For example, survey evidence shows that most adults report current travel to primary care is ≤30 min, and only a small proportion exceeds 60 min, with average acceptable travel times clustering around these values for both urban and rural residents. 21 This supports the use of 30 and 60 min as pragmatically relevant thresholds for walking and motorised accessibility measures in health access analyses. Hence, APC integrated both the population and timeliness aspects of access.
2.3. Analysis
We manually cleaned the BBF addresses to improve machine readability before geocoding, performed using the Google Maps application programming interface (API) and the ‘Awesome Table’ add‐on for Google Sheets. Any BBFs not captured were manually geocoded in Google Maps. The locations with the most relevant address strings were selected from those that returned multiple sets of coordinates. Geo‐coordinate similarity was used to identify duplicate facilities using exact coordinate matching (rounded to 4 decimal places, ~11 m precision) combined with fuzzy name matching (>90% string similarity) for facilities within 1 km proximity. Points beyond India's geographic extent (latitude 6° N–37° N, longitude 68° E–98° E) were excluded.
The WorldPop 2020 and rural–urban CA rasters were analysed to estimate population distribution at various geographic levels. The global multi‐category CA raster (1 km2 resolution at the equator, with each pixel representing an agglomeration category) was first cropped to fit within India's national boundaries. The raster was then divided into two layers: rural (CA agglomeration categories >7) and urban (CA agglomeration categories ≤7). We then overlaid the WorldPop 2020 population raster for India (also 1 km2 resolution) on these binarized rural–urban CA rasters to get rural and urban population rasters. To ensure compatibility, the CA raster was resampled to match the population raster's extent, resolution, and origin. In the rural population raster, rural area pixels were assigned a value of 1, while urban areas were marked as ‘NA’, and vice versa in the urban population raster. Residential population in each pixel was calculated by multiplying the population count by the corresponding category value (1 for rural areas in the rural raster). The total population was derived by summing the rural and urban populations. This was done at all geographic levels: subnational (districts and states) and national (all‐India). It is important to note that not all districts had both rural and urban areas, as some were entirely rural. In this analysis, 728 districts had rural areas, while 691 districts had urban areas.
We calculated the travel times based on the shortest time taken to traverse the MAP friction surface from each 1 km2 pixel to its nearest BBF location. 18 Two accessibility rasters were generated based on the modes of transport: one for walking and the other for motorised transport, providing travel times in minutes assuming optimal speeds. We estimated the median and interquartile range (IQR) for travel times across the district, state, and national levels.
For APC calculations, we used timely‐access thresholds (60 min for motorised transport and 30 min for walking) to create binary accessibility rasters, in which pixels below the threshold were coded 1 and those beyond were marked as ‘NA’. The accessibility raster was then aligned and overlaid onto the population raster to determine the population with timely access. We calculated the sum of pixels coded ‘1’ to get the population with timely access at different geographic levels, that is, district, state, and national levels. Finally, the proportion of the population with timely access (i.e., the APC) was calculated by dividing the population with access in a region by its residential population for rural and urban disaggregations.
We conducted the above geospatial analyses in R software (version 4.3.2). Overall, they are similar to our recent work on population‐level timely geographic access to medical college hospitals, COVID testing centers, primary and community healthcare centers, surgical care facilities, and palliative care centers in India. 22 , 23 , 24 , 25 , 26
Previously, we analysed blood banking performance at the state and district levels using 2016 NBTC data. 11 In that analysis, we constructed a composite index made up of 49 state‐level variables across seven themes, including accreditation, ownership, regulation, infrastructure, safety, volume, and workforce. As an initial concurrent validation (i.e., sanity check) for the timely access work presented here, we assessed the ecological relationships between the APC values for BBFs and the previously described BB composite performance index using Spearman correlations. We conducted the analysis using the SRPlot tool. 27 We classified the strength of correlation as follows: very strong (0.7 to 0.9), strong (0.4 to 0.7), moderate (0.3 to 0.4), weak (0.2 to 0.3), and negligible (0.1 to 0.2), and used the conventional 5% alpha threshold for determining statistical significance. 28 We used state‐level data rather than district‐level data for this analysis because district boundaries have undergone significant changes between 2016 and 2024.
3. RESULTS
3.1. Density of BBFs
In 2024, India had 5679 BBFs, that were included in this analysis. Among states/UTs, Tamil Nadu had the highest number of BBFs (642), while the UT of Lakshadweep had just one BBF (Figure 1A). The national BBF density was 4.13 facilities per million people (Table 1). The BBF density ranged from 1.34 in Bihar to 25.35 in Lakshadweep. Twelve (36%) states had BBF density below the national value. Among 735 districts, Bangalore in Karnataka had the highest number of BBFs (104), while 108 districts had only one BBF each. Regarding BBF density, Serchhip in Mizoram had the highest BBF density (40.54), while Suburban Mumbai (n = 56) in Maharashtra had the lowest (0.19) (Figure 1B). Three hundred and sixty‐four districts had BBF density lower than the national density value. Generally, districts in Tamil Nadu, Kerala, and South Karnataka had higher densities, while those in northern and eastern states of Bihar, Uttar Pradesh, West Bengal, and Jharkhand had lower BBF densities.
FIGURE 1.
(A) Geographic locations and (B) density per million people of blood banking facilities across 735 Indian districts.
TABLE 1.
Summary of national, urban, and rural values for median access times and access population coverage.
| Value | National | Urban | Rural |
|---|---|---|---|
| Median (Interquartile Range) motorised travel time in minutes | 35.25 (20.20–62.40) | 6.66 (2.37–20.22) | 35.91 (20.99–63.05) |
| Median (Interquartile Range) walking travel time in minutes | 253.27 (153.11–392.3) | 66.65 (25.83–188.33) | 256.59 (157.87–394.67) |
| Access population coverage (APC) within motorised transport by 60 min | 93.86% | 99.44% | 91.83% |
| Access population coverage (APC) within walk by 30 min | 16.21% | 53.02% | 2.63% |
3.2. Travel time to the nearest BBF
3.2.1. Motorised transport
By motorised transport, the national median travel time to the nearest BBF was 35.25 min (IQR: 20.20–62.40). For urban areas, the median travel time was 6.66 min (IQR: 2.37–20.22), about 5 times lower than that for rural areas: 35.91 min (IQR: 20.99–63.05) (Figure 2A). Among states/UTs, Lakshadweep had the shortest median travel time to the nearest BBF at 1.40 min (IQR: 0.70–1.77), which was over 25 times faster than the national median. Ladakh had the longest median travel time at 375.52 min (IQR: 193.31–668.20). Across districts, Mumbai in Maharashtra recorded the shortest median travel time of 0.92 min (IQR: 0.52–1.35), about 38 times faster than the national average. Anjaw in Arunachal Pradesh had the longest median travel time of 1434.51 min (IQR: 991.29–1913.09), more than 40 times the national value.
FIGURE 2.
Median travel time (in minutes) to the nearest blood banking facility by (A) motorised transport and (B) walking across 36 states/Union Territories. The dotted lines note the national values.
3.2.2. Walking
By walking, the national median travel time to the nearest BBF was 253.27 min (IQR: 153.11–392.38). For urban areas, the median travel time was 66.65 min (IQR: 25.83–188.33), about 4 times faster than that for rural areas: 256.59 min (IQR: 157.87–394.67) (Figure 2B). Among states/UTs, Lakshadweep had the shortest median travel time to a BBF by walking, at 9.63 min (IQR: 4.81–13.01), over 26 times faster than the national value. Across districts, Lakshadweep in the Lakshadweep Islands recorded the shortest median travel time at 9.63 min (IQR: 4.81–13.01), over 26 times faster than the national median time. The North & Middle Andaman region of the Andaman and Nicobar Islands had the longest median travel time of 2331.40 min (IQR: 1617.12–2995.20), more than 9 times the national value.
3.3. Access population coverage (APC)
3.3.1. Motorised transport
Nationally, 1 291 542 656 people (93.86% of the Indian population) had access to BBFs within 60 min by motorised transport. Among the states/UTs, New Delhi and Chandigarh had the highest APC of 100%, while the Andaman and Nicobar Islands had the lowest APC of 48.56% (Figure 3A). Forty‐nine districts had an APC value of 100%, while three districts had an APC of 0% (Figure 3B).
FIGURE 3.
Access population coverage within 60 min by motorised transport for (A) states/Union Territories and (B) districts.
Nationally, 99.44% of the population residing in urban areas and 91.83% of those in rural areas could access their nearest BBF within 60 min. Urban areas of five states/UTs had an APC of 100%, while only two rural areas achieved the same. At the district level, 396 urban and 45 rural areas had 100% APC, while three rural and two urban areas had an APC value of 0% (Figure 4).
FIGURE 4.
Access population coverage within 60 min by motorised transport in rural and urban areas across districts.
3.3.2. Walking
Nationally, 223 158 576 people (16.21% of the Indian population) had access to BBFs within 30 min by walking. Among the states/union territories, New Delhi led with an APC of 69.05%, while Ladakh had the lowest value at 2.76% (Figure 5A). Among districts, Shahdara, New Delhi, had an APC of 97.94%, while 39 districts had an APC value of 0% (Figure 5B).
FIGURE 5.
Access population coverage within 30 min by walking across (A) states/Union Territories and (B) districts.
Nationally, 53.02% of the population residing in urban areas and only 2.63% of rural areas had access to BBFs within 30 min by walking. Among states/UTs, rural Puducherry had the highest APC at 13.52%, while rural areas in two states/UTs had an APC of 0%. Urban areas in Mizoram recorded the highest APC of 91.76% while those in Bihar noted the lowest APC of 21.90%. At the district level, only the urban Lower Subansiri in Arunachal Pradesh had an APC of 100%, while urban areas in 30 districts had an APC of 0%. There were 48 rural areas across districts without access to BBFs within 30 min, while one area had 100% APC (Figure 6).
FIGURE 6.
Access population coverage within 30 min by walking in rural and urban areas across districts.
3.4. Correlations with blood banking performance index
In a sample of 28 states and eight UTs, we found a moderate positive correlation between the blood banking performance index and APC values for APC within 60 min by motorised transport (R = 0.39, 95% CI 0.06–0.64; p = 0.022) and a strong positive correlation for APC within 30 min by walking (R = 0.42, 95% CI 0.11–0.66; p = 0.011). Both these were statistically significant at the alpha threshold of 0.05.
4. DISCUSSION
4.1. Summary of findings
In 2024, India had 5679 blood banking facilities (BBFs) serving about 1.4 billion people. The national density was 4.13 BBFs per million people, ranging from 1.34 to 25.35 BBFs per million people across states/UTs. The median walking time to the nearest BBF was 253.27 min, while the median time for motorised transport was 35.25 min. Nationally, 93.86% of the population could reach their nearest BBF in 60 min by motorised transport, and 16.21% could reach it within 30 min by walking. This meant that 88 521 598 people did not have access to a BBF within 60 min by motorised transport and 1 208 079 465 did not have access to the nearest BBF within 30 min by walking. Urban areas had almost universal access, whereas rural areas had lower coverage. We also observed heterogeneities in access outcomes across states/UTs and districts. At the state level, the APCs were correlated with the blood banking performance index, providing preliminary evidence of concurrent validity.
4.2. Previous studies and relevance of findings
In this study, we measured travel times over distances, as the duration to reach a facility depends on various factors beyond distance. Further, APC integrates population with travel time to offer a more comprehensive view of geographical accessibility to care. Previously, another modelling study, which focused more on blood availability, also assessed timely access in specific regions of the northern and northeastern Indian states. 12 However, a pan‐India assessment providing district‐level values with rural–urban disaggregations was missing.
Our study noted disparities between rural and urban areas. Rural areas had lower BBF densities, longer travel times, and lower APC values than urban areas. These findings echo the observations of the Association of Rural Surgeons of India (ARSI) that highlighted the disparity in blood supply distribution between rural and urban areas, noting that while over two‐thirds of the population resided in rural areas, most blood banks were concentrated in urban centers. 5
4.3. Policy implications
Our findings help determine whether the lack of access to BBFs is due to a shortage of BBFs, challenges in reaching them, or both.
We noted disparities between rural and urban areas. Rural areas with low BBF density, such as Bihar, West Bengal, Uttar Pradesh, and Jharkhand, should prioritise establishing new BBFs to meet population demands. Several districts within these states have both low APC and prolonged travel times, indicating compounded geographical issues. Rural regions of Arunachal Pradesh, Assam, and parts of Uttar Pradesh face challenges in travel times mainly due to challenging terrain and poor road infrastructure. In these areas, deploying mobile blood banks and establishing satellite blood banks can help expand services and increase blood collection coverage. Furthermore, partnerships with non‐governmental organisations and local community health workers can bridge coverage gaps. 29
In urban areas, despite having a higher number of BBFs, access remains limited due to high population density and unequal distribution of blood banks, which are often concentrated in centers. 30 This makes it difficult for people in peripheral regions to access blood banks during emergencies, especially in metropolitan cities such as Mumbai, New Delhi, and Kolkata. 31 Districts, such as Suburban Mumbai, that have low BBF density due to high population denominators, highlight areas of priority where redistribution and decentralisation of BBFs are effective solutions. Moreover, the public‐private divide, the cost of blood and blood products, and emergency transport delays due to traffic congestion can further exacerbate the lack of timely access. 22 To address these challenges, decentralising blood bank functions, promoting real‐time inventory systems (e.g., e‐RaktKosh), establishing blood transport services, and educating the public about access points can help improve access population coverage. 13 , 32
In states with longer travel times and where building BBFs might be challenging, such as Ladakh, Arunachal Pradesh, and Himachal Pradesh, the focus should shift to alternative solutions like walking blood banks, immediate autologous transfusion, and donor blood drop strategies. 29 Rigorous mapping of ‘blood deserts’, that is, regions lacking access to blood and blood products and associated healthcare services, can help inform local policy changes and implementation strategies.31
4.4. Strengths and limitations
To evaluate timely geographic access to BBFs in India, we employed a novel method using multiple outcome measures. Our pan‐India study comprehensively analysed the densities of BBFs per million people, travel times to the nearest BBF by two modes of transport (walking and motorised), and access population coverage, which combines population and timeliness measures, for 5679 BBFs across 735 districts. A novel feature of the study is the assessment of access across rural and urban areas. We also contribute to the broader global health data inventory by adding geolocation data for the Indian BBFs. The study focuses on timely geographic access beyond the common blood donation index, which is critical from a health systems perspective. These study methods can be adopted for similar analyses in other low‐ and middle‐income countries.
Our study has several limitations. First, our analysis is subject to the assumptions and limitations of the original data sources, including e‐RaktKosh, Worldpop, and MAP. WorldPop estimates are derived from census redistribution models and may not fully capture local population heterogeneity, recent demographic change, or short‐term mobility. MAP travel‐time rasters are based on cost‐surface modelling with assumed travel speeds and do not reflect real‐world variability in transport availability, road conditions, or health‐system constraints. As a result, accessibility estimates should be interpreted as indicative and comparative rather than precise measures of realised access. Second, the APC estimates do not consider whether individuals have access to or own motor vehicles. Third, in regions of high population density, the low per‐capita BBF density primarily reflects the large population denominator. Median travel times in regions with limited ground transport infrastructure likely represent upper‐bound estimates due to the above limitations. Hence, these ratios should not be interpreted as inequity in isolation. Another factor to note is that of traffic congestion which can delay timely access. Fourth, our study calculates travel times and APCs based on walking and motorised land transport, without factoring in other modes of travel, such as waterways and airways, since travel time raster data to consider such complex travel scenarios remains unavailable. We interpret walking‐based travel times as upper bounds and motorised travel times as lower bounds on accessibility, with true travel times likely lying between these two estimates. Fifth, we did not examine differences in access based on demographic factors such as age, gender, or socioeconomic status. Sixth, the policy implications suggested in this study are presented as potential strategies particular to the mode of transport, based on previous similar studies. We have not evaluated the feasibility, cost‐effectiveness, regulatory compliance, and operational requirements of these suggested implications. Finally, the correlations between the blood banking performance index and APC values are exploratory since the data for these variables are collected at different time points. The analysis is ecological and can have aggregation bias. Even so, these findings are novel and pave the way for future studies that provide a more nuanced and comprehensive picture of geographical access to BBFs.
5. CONCLUSION
In this study, we analysed geographic access to blood bank facilities using multiple outcome measures, including population, travel times, and a combination of the two. Our study provides the first comprehensive pan‐India assessment of timely geographic access to blood banking facilities, highlighting significant rural–urban and regional disparities. By integrating population distribution with travel time, we identify ‘blood deserts’. Future policies must focus on the equitable distribution of facilities based on travel times. Future research should focus on utilising geospatial tools to optimise resource allocation and improve accessibility.
AUTHOR CONTRIBUTIONS
Study concept and design: Siddhesh Zadey. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Gaurav Urs, Siddhesh Zadey. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Aamir Miyajiwala, Ankit Raj, Siddhesh Zadey. Administrative, technical, or material support: Siddhesh Zadey. Study supervision: Siddhesh Zadey.
FUNDING INFORMATION
None.
CONSENT TO PARTICIPATE
Not applicable, as we did not use any human participant or animal subject data. All data used in this manuscript were taken from public sources.
CONSENT FOR PUBLICATION
Patient consent for publication statement is not applicable or needed for this study.
CONFLICT OF INTEREST STATEMENT
Siddhesh Zadey serves as the co‐founding director of the Association for Socially Applicable Research (ASAR), Chair of the Asia Working Group, the G4 Alliance, Fellow at the Lancet Commission on a Citizen‐Centred Health System for India, and the Drafting Committee Member for Maharashtra State Mental Health Policy. The other authors declare no conflicts of interest.
ACKNOWLEDGEMENTS
We thank the Health Informatics Group, which manages e‐Raktkosh, for sharing the list of blood banking facilities with us.
DATA AVAILABILITY STATEMENT
All data produced in the present study are available upon reasonable request to the authors.
REFERENCES
- 1. World Health Organization . Availability, safety and quality of blood products (WHA63.12). 2024. https://www.who.int/publications/i/item/WHA63
- 2. Cabalar I, Le TH, Silber A, et al. The role of blood testing in prevention, diagnosis, and management of chronic diseases: a review. Am J Med Sci. 2024;368(4):274‐286. doi: 10.1016/j.amjms.2024.04.009 [DOI] [PubMed] [Google Scholar]
- 3. World Health Organization . Blood safety and availability. World Health Organization; 2023. https://www.who.int/news-room/fact-sheets/detail/blood-safety-and-availability [Google Scholar]
- 4. Gammon RR, Rosenbaum L, Cooke R, et al. Maintaining adequate donations and a sustainable blood supply: lessons learned. Transfusion. 2021;61:294‐302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Jenny HE, Saluja S, Sood R, et al. Access to safe blood in low‐income and middle‐income countries: lessons from India. BMJ Glob Health. 2017;2:e000167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Kralievits KE, Raykar NP, Greenberg SLM, Meara JG. The global blood supply: a literature review. Lancet. 2015;385(Suppl 2):S28. [DOI] [PubMed] [Google Scholar]
- 7. Custer B, Zou S, Glynn SA, et al. Addressing gaps in international blood availability and transfusion safety in low‐ and middle‐income countries: a NHLBI workshop. Transfusion. 2018;58:1307‐1317. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Kundu S, Munoz‐Valencia A, Virk SK, et al. Defining blood deserts and access to blood products for 660 million people: A geospatial analysis of eight states in northern India. BMJ Glob Health. 2024;9. doi: 10.2139/ssrn.4724384 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Mammen JJ, Asirvatham ES, Lakshmanan J, et al. The clinical demand and supply of blood in India: a national level estimation study. PLoS One. 2022;17:e0265951. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Asirvatham ES, Mammen JJ, Lakshman J, et al. Assessment of performance of blood banks in India: a national level cross sectional study. Indian J Hematol Blood Transfus. 2021;37:640‐647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Urs GM, Krishna Kumar P, Kamath Y, Zadey S. Indexing blood banking performance in India: a retrospective cross‐sectional analysis of states and districts. Dialogues in Health. 2023;3:100160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Kundu S, Munoz Valencia A, Virk SK, et al. Defining blood deserts and access to blood products for 660 million people: a geospatial analysis of eight states in northern India. BMJ Glob Health. 2024:9. doi: 10.1136/bmjgh-2024-015637 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. G O I. e‐RaktKosh . Centralized Blood Bank Management System. eRaktKosh. 2016. https://www.eraktkosh.in/BLDAHIMS/bloodbank/transactions/bbpublicindex.html
- 14. School of Geography and Environmental Science . University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur, Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. doi: 10.5258/SOTON/WP00671 [DOI]
- 15. MAP. Malaria Atlas Project – MAP. Welcome to the Malaria Atlas Project – MAP. https://malariaatlas.org/
- 16. European Commission . Global Accessibility Map. Global Accessibility Map. 2024. https://forobs.jrc.ec.europa.eu/gam/description
- 17. Tatem AJ. WorldPop, open data for spatial demography. Sci Data. 2017;4:170004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Weiss DJ, Nelson A, Vargas‐Ruiz CA, et al. Global maps of travel time to healthcare facilities. Nat Med. 2020;26:1835‐1838. [DOI] [PubMed] [Google Scholar]
- 19. Sabde Y, Diwan V, De Costa A, Mahadik VK. Mapping the rapid expansion of India's medical education sector: planning for the future. BMC Med Educ. 2014;14:266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Cattaneo A, Nelson A, McMenomy T. Global mapping of urban–rural catchment areas reveals unequal access to services. Proceedings of the National Academy of Sciences. 2021;118(2). doi: 10.1073/pnas.2011990118 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Ozawa T, Liu Y, Mattke S. Older adults’ current and maximum acceptable travel times for primary care: results from a national survey. Innov Aging. 2025;9. doi: 10.1093/geroni/igaf122.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Thakkar H, Reddy C, Passi VR, et al. Geospatial modeling study assessing population level accessibility to medical college hospitals in India. Discov Public Health. 2026;23:157. doi: 10.1186/s12982-026-01463-7 [DOI] [Google Scholar]
- 23. Patil S, Miyajiwala A, Arora A, et al. Geospatial accessibility to primary and community health centres in India. medRxiv. 2025. doi: 10.1101/2025.05.13.25327502 [DOI] [Google Scholar]
- 24. Sharma P, Thakkar H, Patil A, et al. Access to palliative care in India: situational analysis and modeling of access from public healthcare centers. Ecancer medical science. 2025;19:2038. doi: 10.3332/ecancer.2025.2038 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Poddar S, Gorkar A, Passi VR, et al. Geographical Access to COVID‐19 Testing Centers in India. BMC Health Serv Res. 2025;25(1):1551. doi: 10.1186/s12913-025-13657-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Jadhav T, Vissoci JRN, Zadey S. Measuring timely geographical access to surgical care in India: a geospatial modelling study. Lancet Glob Health. 2022;10(1):S29. doi: 10.1016/S2214-109X(22)00158-9 [DOI] [Google Scholar]
- 27. Tang D, Chen M, Huang X, et al. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023;18(11):e0294236. doi: 10.1371/journal.pone.0294236 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Akoglu H. User's guide to correlation coefficients. Turk J Emerg Med. 2018;18:91‐93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Raykar NP, Raguveer V, Abdella YE, et al. Innovative blood transfusion strategies to address global blood deserts: a consensus statement from the blood delivery via emerging strategies for emergency remote transfusion (blood DESERT) coalition. Lancet Glob Health. 2024;12:e522‐e529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Mammen JJ, Asirvatham ES, Sarman CJ, Ranjan V, Charles B. A review of legal, regulatory, and policy aspects of blood transfusion services in India: issues, challenges, and opportunities. Asian J Transfus Sci. 2021;15:204‐211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. World Health Organization . Guidance on Ensuring A Sufficient Supply of Safe Blood and Blood Components during Emergencies. World Health Organization; 2023. [Google Scholar]
- 32. The Lancet Haematology . Blood deserts: a vision to tackle blood inaccessibility. Lancet Haematol. 2024;11:e241. [DOI] [PubMed] [Google Scholar]
- 33. Kundu S, Gerk A, Glatter R, Poenaru D. Innovative transfusion strategies for blood deserts in disaster settings. BMJ GlobHealth. 2024:9. doi: 10.1136/bmjgh-2024-016854 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data produced in the present study are available upon reasonable request to the authors.