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Correlates of COVID-19 mortality: A descriptive study

1 Department of Community Medicine, Bankura Sammilani Medical College, Bankura, West Bengal, India
2 Department of Community Medicine, ID and BG Hospital, Kolkata, India
3 Department of Community Medicine, Murshidabad Medical College, Berhampore, West Bengal, India
4 Department of Community Medicine, Medical College, Kolkata, India
5 Department of Community Medicine, Calcutta National Medical College, Kolkata, India
6 General Practitioner, Kolkata, India

Correspondence Address:
Tanushree Mondal,
Bidyadhari Housing Cooperative Society, CC-7, Flat No. 503, Newtown, Narkelbagan More, Near Biswa Bangla Gate, Kolkata - 700 156, West Bengal
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/mjdrdypu.mjdrdypu_282_20

Background and Objectives: The enigma COVID-19 pandemic already involved major parts of the globe with a toll of 3,175,207 victims and 224,172 deaths from 215 countries/territories as on May 1, 2020. It cripples nations by the loss of human resources, economic decline, hunger, unemployment insecurities giving way to mental morbidities, and still many others to be discovered. A systematic search about correlates of its killing attribute is urgently warranted. Materials and Methods: A cross-sectional survey for 3 weeks (03/5/2020–23/5/2020) was conducted in a teaching institution at Kolkata aiming to describe the magnitude and correlates of COVID-19 mortality. Data pertaining to COVID-19 cases, deaths of affected countries, and their potential correlates were retrieved from various public domains, for example, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports, worldpopulationreview.com, data.worldbank.org. Results: Multiple linear regressions analysis through forward method revealed a maximum R2 of 57.7% (P = 0.03) and a significant model fit (P = 0.000) for COVID-19 mortality rate per million which was revealed to have a positive association with median age of the population of the country (β= 0.073), proportion of population sustaining obesity (β= 0.051) and %of population consumed alcohol over the past 12 months (β= 0.018). It meant for 1 year increase in median age COVID-19 mortality would be increased by 8.0%. Similarly, COVID-19 mortality would be increased by 2.0% and 5.0% by inclusion in the model of 1% alcoholic, and 1% obese individual, respectively, Conclusion: Notwithstanding variations in testing, reporting, and patients' management strategy the findings of this research have some implications to the scientific fraternity and policymakers.

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