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Published: 2020-12-31

Evidence-based decision making and covid-19: what a posteriori probability distributions speak

Department of Medicine, SMS Medical College, and Hospitals, Jaipur, Rajasthan, India
Department of Medicine, SMS Medical College, and Hospitals, Jaipur, Rajasthan, India
National Centre for Disease Informatics and Research, Indian Council of Medical Research, Bangalore, Karnataka, India
Department of Physiology, SMS Medical College and Hospitals, Jaipur, Rajasthan, India
Department of Physiology, Government Medical College, Barmer, Rajasthan, India
Department of Physiology, SMS Medical College and Hospitals, Jaipur, Rajasthan, India
Department of Physiology, SMS Medical College and Hospitals, Jaipur, Rajasthan, India
Department of Pharmacology, SMS Medical College and Hospitals, Jaipur, Rajasthan, India
Department of Physiology, SMS Medical College and Hospitals, Jaipur, Rajasthan, India
Department of Rheumatology, Medstar Washington Hospital Center, Washington DC 20010, USA
Department of Horticultural Science, North Carolina State University, Raleigh, NC 27695-7609, USA
Department of Horticultural Science, North Carolina State University, Raleigh, NC 27695-7609, USA
COVID-19, a posteriori Probability Distributions, Epidemiology, Evidence-Based Decision Making, Public Health, SARS CoV-2, India

Abstract

Background: In the absence of any pharmaceutical interventions, the management of the COVID-19 pandemic is based on public health measures. The present study fosters evidence-based decision making by estimating various “a posteriori probability distributions" from COVID-19 patients. 

Methods: In this retrospective observational study, 987 RT-PCR positive COVID-19 patients from SMS Medical College, Jaipur, India, were enrolled after approval of the institutional ethics committee. The data regarding age, gender, and outcome were collected. The univariate and bivariate distributions of COVID-19 cases with respect to age, gender, and outcome were estimated. The age distribution of COVID-19 cases was compared with the general population's age distribution using the goodness of fit c2 test. The independence of attributes in bivariate distributions was evaluated using the chi-square test for independence.

Results: The age group ‘25-29’ has shown highest probability of COVID-19 cases (P [25-29] = 0.14, 95% CI: 0.12- 0.16). The men (P [Male] = 0.62, 95%CI: 0.59-0.65) were dominant sufferers. The most common outcome was recovery (P [Recovered] = 0.79, 95%CI: 0.76-0.81) followed by admitted cases (P [Active]= 0.13, 95%CI: 0.11-0.15) and death (P [Death] = 0.08, 95%CI: 0.06-0.10). The age distribution of COVID-19 cases differs significantly from the age distribution of the general population (c2  =399.04, P < 0.001). The bivariate distribution of COVID-19 across age and outcome was not independent (c2 =106.21, df = 32, P < 0.001).

Conclusion: The knowledge of disease frequency patterns helps in the optimum allocation of limited resources and manpower. The study provides information to various epidemiological models for further analysis.



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How to Cite

1.
Bhandari S, Shaktawat AS, Tak A, Shukla J, Patel B, Singhal S, Gupta J, Kakkar S, Dube A, Dia S, Dia M, Wehner TC. Evidence-based decision making and covid-19: what a posteriori probability distributions speak. jidhealth [Internet]. 2020 Dec. 31 [cited 2022 Nov. 27];3(Special2):286-92. Available from: https://www.jidhealth.com/index.php/jidhealth/article/view/88