Defining blood deserts and access to blood products for 660 million people: a geospatial analysis of eight states in Northern India
Bibliographic record
Abstract
INTRODUCTION: Blood transfusion is crucial, but low-income and middle-income countries like India face a severe shortage of banked blood. This study focuses on the Empowered Action Group (EAG) states in India, where healthcare is limited, and health outcomes are poor. Our objective was to assess the blood banking infrastructure and access to blood products in these states. METHODS: We used e-Rakht Khosh, an online platform for blood availability data. We collected data on blood bank locations and stocks from 18 January to 9 February 2022 and used ArcGIS to determine the population residing within 30-60-90 min of a blood bank. Availability ratios were calculated by dividing available blood products by population in these catchment areas. Descriptive analysis characterised availability, and statistical tests evaluated differences across states and over the 4-week period. RESULTS: 806 of 824 blood banks reported data on blood stocks. Our analysis showed that 25.72% of the EAG states' population live within 30 min of a blood bank, while 61.45% and 92.46% live within 60 and 90 min, respectively. CONCLUSION: Blood availability rates were low in the EAG states, with only 0.6 units per 1000 people. Additionally, only 61% of the population had access to blood-equipped facilities within an hour. These rates fell below the standards of the Lancet Commission on Global Surgery (15 units per 1000 population) and the WHO (10 donations per 1000 population). The study highlights the challenges in meeting demand for blood in emergencies due to inadequate blood banking infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".