Edwin Sam Asirvatham1, Jeyaseelan Lakshmanan2, Charishma Jones Sarman3, Melvin Joy4. 1. Health Systems Research India Initiative (HSRII), Trivandrum, India. aedwinsam@yahoo.com. 2. Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India. ljey@hotmail.com. 3. Independent Public Health Consultant, New Delhi, India. charishma.jones@gmail.com. 4. Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India. melvinmj94@gmail.com.
Abstract
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. Copyright (c) 2020 Edwin Sam Asirvatham, Jeyaseelan Lakshmanan, Charishma Jones Sarman, Melvin Joy.
INTRODUCTION: At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19infected 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. Copyright (c) 2020 Edwin Sam Asirvatham, Jeyaseelan Lakshmanan, Charishma Jones Sarman, Melvin Joy.
Entities:
Keywords:
CFR; COVID-19; Case Fatality Rate; fractional regression; predictors
Authors: Dante S Harbuwono; Dwi O T L Handayani; Endang S Wahyuningsih; Novita Supraptowati; Farid Kurniawan; Syahidatul Wafa; Melly Kristanti; Nico I Pantoro; Robert Sinto; Heri Kurniawan; Dicky L Tahapary Journal: Prim Care Diabetes Date: 2021-11-12 Impact factor: 2.459