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Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities

Management of coronavirus disease 2019 (COVID-19) in India is a top government priority. However, there is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. Data on COVID-19 cases and deaths in the first pandemic wave and 17 state-specific geod...

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Autores principales: Singh, Balbir B., Ward, Michael P., Lowerison, Mark, Lewinson, Ryan T., Vallerand, Isabelle A., Deardon, Rob, Gill, Jatinder P.S., Singh, Baljit, Barkema, Herman W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230847/
https://www.ncbi.nlm.nih.gov/pubmed/34222606
http://dx.doi.org/10.1016/j.onehlt.2021.100283
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author Singh, Balbir B.
Ward, Michael P.
Lowerison, Mark
Lewinson, Ryan T.
Vallerand, Isabelle A.
Deardon, Rob
Gill, Jatinder P.S.
Singh, Baljit
Barkema, Herman W.
author_facet Singh, Balbir B.
Ward, Michael P.
Lowerison, Mark
Lewinson, Ryan T.
Vallerand, Isabelle A.
Deardon, Rob
Gill, Jatinder P.S.
Singh, Baljit
Barkema, Herman W.
author_sort Singh, Balbir B.
collection PubMed
description Management of coronavirus disease 2019 (COVID-19) in India is a top government priority. However, there is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. Data on COVID-19 cases and deaths in the first pandemic wave and 17 state-specific geodemographic, socio-economic, health and comorbidity-related factors were collected. State-specific aCFRs were estimated, using a 13-day lag for fatality. To estimate country-level aCFR in the first wave, state estimates were meta-analysed based on inverse-variance weighting and aCFR as either a fixed- or random-effect. Multiple correspondence analyses, followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators. Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, cumulative aCFRs in the first pandemic wave on 27 July 2020 in India were 1.42% (95% CI 1.19%–1.70%) and 2.97% (95% CI 2.94%–3.00%), respectively. At the end of the first wave, as of 15 February 2021, a cumulative aCFR of 1.18% (95% CI 0.99%–1.41%) using random and 1.64% (95% CI 1.64%–1.65%) using fixed-effects models was estimated. Based on high heterogeneity among states, we inferred that the random-effects model likely provided more accurate estimates of the aCFR for India. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of multiple correspondence analyses. Univariable logistic regression confirmed associations between the aCFR and the proportion of urban population, and between aCFR and the number of persons diagnosed with diabetes, hypertension, cardiovascular diseases and stroke per 10,000 population that had visited NCD (Non-communicable disease) clinics. Incidence of pneumonia was also associated with COVID-19 aCFR. Based on predictor variables, we categorised 10, 17 and one Indian state(s) expected to have a high, medium and low aCFR risk, respectively. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities.
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spelling pubmed-82308472021-06-28 Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities Singh, Balbir B. Ward, Michael P. Lowerison, Mark Lewinson, Ryan T. Vallerand, Isabelle A. Deardon, Rob Gill, Jatinder P.S. Singh, Baljit Barkema, Herman W. One Health Research Paper Management of coronavirus disease 2019 (COVID-19) in India is a top government priority. However, there is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. Data on COVID-19 cases and deaths in the first pandemic wave and 17 state-specific geodemographic, socio-economic, health and comorbidity-related factors were collected. State-specific aCFRs were estimated, using a 13-day lag for fatality. To estimate country-level aCFR in the first wave, state estimates were meta-analysed based on inverse-variance weighting and aCFR as either a fixed- or random-effect. Multiple correspondence analyses, followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators. Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, cumulative aCFRs in the first pandemic wave on 27 July 2020 in India were 1.42% (95% CI 1.19%–1.70%) and 2.97% (95% CI 2.94%–3.00%), respectively. At the end of the first wave, as of 15 February 2021, a cumulative aCFR of 1.18% (95% CI 0.99%–1.41%) using random and 1.64% (95% CI 1.64%–1.65%) using fixed-effects models was estimated. Based on high heterogeneity among states, we inferred that the random-effects model likely provided more accurate estimates of the aCFR for India. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of multiple correspondence analyses. Univariable logistic regression confirmed associations between the aCFR and the proportion of urban population, and between aCFR and the number of persons diagnosed with diabetes, hypertension, cardiovascular diseases and stroke per 10,000 population that had visited NCD (Non-communicable disease) clinics. Incidence of pneumonia was also associated with COVID-19 aCFR. Based on predictor variables, we categorised 10, 17 and one Indian state(s) expected to have a high, medium and low aCFR risk, respectively. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities. Elsevier 2021-06-25 /pmc/articles/PMC8230847/ /pubmed/34222606 http://dx.doi.org/10.1016/j.onehlt.2021.100283 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
Singh, Balbir B.
Ward, Michael P.
Lowerison, Mark
Lewinson, Ryan T.
Vallerand, Isabelle A.
Deardon, Rob
Gill, Jatinder P.S.
Singh, Baljit
Barkema, Herman W.
Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities
title Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities
title_full Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities
title_fullStr Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities
title_full_unstemmed Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities
title_short Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities
title_sort meta-analysis and adjusted estimation of covid-19 case fatality risk in india and its association with the underlying comorbidities
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230847/
https://www.ncbi.nlm.nih.gov/pubmed/34222606
http://dx.doi.org/10.1016/j.onehlt.2021.100283
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