Background As the number of COVID-19 cases continues to rise, public health efforts must focus on preventing avoidable fatalities. Understanding the demographic and clinical characteristics of deceased COVID-19 patients; and estimation of time-interval between symptom onset, hospital admission and death could inform public health interventions focusing on preventing mortality due to COVID-19. Methods We obtained COVID-19 death summaries from the official dashboard of the Government of Tamil Nadu, between 10th May and July 10, 2020. Of the 1783 deaths, we included 1761 cases for analysis. Results The mean age of the deceased was 62.5 years (SD: 13.7). The crude death rate was 2.44 per 100,000 population; the age-specific death rate was 22.72 among above 75 years and 0.02 among less than 14 years, and it was higher among men (3.5 vs 1.4 per 100,000 population). Around 85% reported having any one or more comorbidities; Diabetes (62%), hypertension (49.2%) and CAD (17.5%) were the commonly reported comorbidities. The median time interval between symptom onset and hospital admission was 4 days (IQR: 2, 7); admission and death was 4 days (IQR: 2, 7) with a significant difference between the type of admitting hospital. One-fourth of (24.2%) deaths occurred within a day of hospital admission. Conclusion Elderly, male, people living in densely populated areas and people with underlying comorbidities die disproportionately due to COVID-19. While shorter time-interval between symptom onset and admission is essential, the relatively short time interval between admission and death is a concern and the possible reasons must be evaluated and addressed to reduce avoidable mortality.

Introduction: At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. Methodology: The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. Results: As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p<0.001, p=0.03). The higher number of cases per million population (p=0.001), prevalence of diabetes and hypertension (p=0.012), cardiovascular diseases (p=0.05), and any cancer (p<0.001) were significantly associated with increased aCFR. The performance of state health systems and proportion of public health expenditure were not associated with aCFR. Conclusions: Socio-demographic factors and burden of non-communicable diseases (NCDs) were found to be the predictors of aCFR. Focused strategies that would ensure early identification, testing and effective targeting of non-literate, elderly, urban population and people with comorbidities are critical to control the pandemic and fatalities.

Background Estimating the clinical demand for blood and components arising in a health facility is crucial to ensure timely availability of blood. This study aims to estimate disease-specific clinical demand, supply and utilization of whole blood and components in India. Methods We conducted a national level cross-sectional study in five randomly selected states from five regions of the country. We included 251 public and private facilities representing primary, secondary and tertiary care facilities. We collected annual disease-specific demand, supply and utilization of blood and components using a structured tool. We estimated the national demand by extrapolating the study data (demand and beds) to the total number of estimated beds in the country. Findings According to the study, the total clinical demand of 251 health facilities with 51,562 beds was 474,627 whole blood units. Based on this, the clinical demand for India was estimated at 14·6 million whole blood units (95 CI: 14·59–14·62), an equivalent of 36·3 donations per 1,000 eligible populations, which will address whole blood and component requirement. The medicine specialty accounted for 6·0 million units (41·2%), followed by surgery 4·1 million (27·9%), obstetrics and gynecology 3·3 million (22·4%) and pediatrics 1·2 million (8·5%) units. The supply was 93% which is equivalent to 33·8 donations against the demand. Conclusion The study indicated a demand and supply gap of 2.5 donations per 1,000 eligible persons which is around one million units. The gap emphasises the need for sustained and concerted efforts from all stakeholders and for increasing the awareness about repeat voluntary non-remunerated blood donation (VNRBD); optimizing the availability of blood components through efficient blood component separation units; promoting modern principles of patient blood management and strengthening capacities of human resources in the blood transfusion system in India.

Background The population need for blood is the total volume required to transfuse all the individuals who need transfusion in a defined population over a defined period. The clinical demand will arise when people with a disease or condition who require transfusion, access healthcare services, and subsequently the clinicians request blood. Essentially, the conversion of need to demand must be maximum to avoid preventable mortality and morbidity. The study estimated the population need for blood in India. Methods The methodology included a comprehensive literature review to determine the diseases and conditions requiring transfusion, the population at risk, and prevalence or incidence; and Delphi method to estimate the percentage of people requiring transfusion, and the quantum. Results The estimated annual population need was 26.2 million units (95% CI; 17.9–38.0) of whole blood to address the need for red cells and other components after the separation process. The need for medical conditions was 11.0 million units (95% CI:8.7–14.7), followed by surgery 6.6 million (95% CI:3.8–10.0), pediatrics 5.0 million (95% CI:3.5–7.0), and obstetrics and gynecology 3.6 million units (95% CI:1.9–6.2). The gap between need and demand which depends upon the access and efficiency of healthcare service provision was estimated at 13 million units. Conclusion The study brings evidence to highlight the gap between need and demand and the importance of addressing it. It cannot be just the responsibility of blood transfusion or health systems, it requires a multi‐sectoral approach to address the barriers affecting the conversion of need to clinical demand for blood.

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