Publications by authors named "Edwin Sam Asirvatham"

8 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

Is BCG associated with reduced incidence of COVID-19? A meta-regression of global data from 160 countries.

Clin Epidemiol Glob Health 2021 Jan-Mar;9:202-203. Epub 2020 Sep 5.

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

Background: Global research is running towards to find a vaccine to stop the threat of the COVID-19. The Bacillus Calmette-Guérin (BCG) vaccine that prevents severe forms of tuberculosis is getting more attention in this scenario. The objective of our study was to determine the association between BCG vaccine coverage and incidence of COVID-19 at a national-level across the Globe.

Methods: The data of 160 countries were included in the study. Meta-regression was done to estimate the difference in the incidence of COVID-19 cases between countries with BCG vaccination coverage. BCG coverage was categorized as ≤70%, >70% and no vaccination. The analyses were carried out by adjusting for factors such as population density, income group, latitude, and percentage of the total population under age groups 15-64 and above 65 years of each country.

Results: The countries that had ≤70% coverage of BCG vaccine reported 6.5 (95% CI: -8.4 to -4.5) less COVID-19 infections per 10,000 population as compared to countries that reported no coverage. Those that had >70% coverage reported 10.1 (95% CI: -11.4 to -8.7) less infections per 10,000 population compared to those with no BCG countries.

Conclusion: Our analysis suggests that BCG is associated with reduced COVID-19 infections if the BCG vaccine coverage is over 70%. The region-wise analyses also suggested similar findings, except the Middle East and North African region.
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http://dx.doi.org/10.1016/j.cegh.2020.08.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597766PMC
September 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

Modelling of reproduction number for COVID-19 in India and high incidence states.

Clin Epidemiol Glob Health 2021 Jan-Mar;9:57-61. Epub 2020 Jun 30.

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

Background: Since the onset of the COVID-19 in China, forecasting and projections of the epidemic based on epidemiological models have been in the centre stage. Researchers have used various models to predict the maximum extent of the number of cases and the time of peak. This yielded varying numbers. This paper aims to estimate the effective reproduction number (R) for COVID-19 over time using incident number of cases that are reported by the government.

Methods: Exponential Growth method to estimate basic reproduction rate R, and Time dependent method to calculate the effective reproduction number (dynamic) were used. "R0" package in R software was used to estimate these statistics.

Results: The basic reproduction number (R) for India was estimated at 1.379 (95% CI: 1.375, 1.384). This was 1.450 (1.441, 1.460) for Maharashtra, 1.444 (1.430, 1.460) for Gujarat, 1.297 (1.284, 1.310) for Delhi and 1.405 (1.389, 1.421) for Tamil Nadu. In India, the R at the first week from March 2-8, 2020 was 3.2. It remained around 2 units for three weeks, from March 9-29, 2020. After March 2020, it started declining and reached around 1.3 in the following week suggesting a stabilisation of the transmissibility rate.

Conclusion: The study estimated a baseline R of 1.379 for India. It also showed that the R was getting stabilised from first week of April (with an average R of 1.29), despite the increase in March. This suggested that in due course there will be a reversal of epidemic. However, these analyses should be revised periodically.
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http://dx.doi.org/10.1016/j.cegh.2020.06.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324346PMC
June 2020

Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models.

Clin Epidemiol Glob Health 2021 Jan-Mar;9:26-33. Epub 2020 Jun 27.

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

Background: Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models.

Methods: We used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions.

Results: The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant.

Conclusion: The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.
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http://dx.doi.org/10.1016/j.cegh.2020.06.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319934PMC
June 2020

The demand and supply of blood in India.

Lancet Haematol 2020 02;7(2):e94

Christian Medical Association of India, New Delhi, India.

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http://dx.doi.org/10.1016/S2352-3026(19)30255-8DOI Listing
February 2020

Trends in risk behaviors among female sex workers in south India: priorities for sustaining the reversal of HIV epidemic.

AIDS Care 2013 15;25(9):1129-37. Epub 2013 Jan 15.

a AIDS Prevention and Control Project, Voluntary Health Services , Chennai , India.

HIV epidemic in India is predominantly concentrated in subgroups of population, such as female sex workers (FSWs) and their clients, whose behavior exposes them to a higher risk of acquiring HIV infection. This paper aims to present the changing patterns of socio-demographic characteristics, behaviors, reported sexually transmitted infections (STIs), and associated factors among FSWs over 11 years. Multistage cluster sampling with probability-proportional-to-size (PPS) method was used in the surveys. A sample of 400 FSWs was studied every year. The mean age and literacy at the baseline level increased significantly over the years. House-based sex increased by 40% from 43.3% in 1997 to 83% in 2008 (p<0.001). Condom use at last sex with one-time clients; consistent condom use (CCU) with one-time and regular clients indicated increasing trends. FSWs reported low levels of condom use at last sex (14.5% in 1997 to 5% in 2008; p<0.001) and CCU (12.6% in 2004 to 3.6% in 2008; p<0.01) with regular partners. FSWs who used condom with one-time clients at last sex reported significantly less STI symptoms. A two-third reduction in genital ulcers was found from 13.1% in 1997 to 4.5% in 2008 (p<0.001). Nonliterate and hotel-based sex workers were 1.6 (1.0-2.5; 95% CI) and 2.2(1.3-3.7; 95% CI) times more likely to have reported STI symptoms. The percentage of FSWs who underwent HIV testing increased (p<0.001); similarly, a 20% increase was found in FSWs who availed counseling services from 65.2% in 1997 to 85.4% in 2008 (p<0.001). Poor, illiterate, and marginalized were more likely to get involved in risky behaviors which suggest the need for structural interventions as part of HIV prevention strategy.
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http://dx.doi.org/10.1080/09540121.2012.752562DOI Listing
March 2014