Publications by authors named "Charishma Jones Sarman"

3 Publications

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A national level estimation of population need for blood in India.

Transfusion 2021 May 15. Epub 2021 May 15.

National AIDS Control Organization (NACO), New Delhi, 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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http://dx.doi.org/10.1111/trf.16369DOI Listing
May 2021

Demystifying the varying case fatality rates (CFR) of COVID-19 in India: Lessons learned and future directions.

J Infect Dev Ctries 2020 Oct 31;14(10):1128-1135. Epub 2020 Oct 31.

Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India.

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.
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http://dx.doi.org/10.3855/jidc.13340DOI Listing
October 2020

Who is dying from COVID-19 and when? An Analysis of fatalities in Tamil Nadu, India.

Clin Epidemiol Glob Health 2021 Jan-Mar;9:275-279. Epub 2020 Oct 3.

Professor, Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, 632 002, India.

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.
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http://dx.doi.org/10.1016/j.cegh.2020.09.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532809PMC
October 2020