Decoding India's Low Covid-19 Case Fatality rate

Philip, Minu; Ray, Debraj; Subramanian, S.

doi:10.3386/w27696

Cited by 19 publications

(18 citation statements)

References 11 publications

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“…With model (1), we can estimate CFR for individual countries. For example, the CFR for the USA, India, China and Korea are estimated as 3.13%, 1.87%, 3.02% and 3.19%, close to estimates at 2.85% given by [13], 2.20% by [6], 2.30% by [10], 2.36% by [9], respectively. The worldwide CFR is estimated to be 2.76%, close to the WHO published 3.40% as of Mar 2020; in contrast, a direct calculation from the reported cases and death toll would give 4.66%.…”

Section: Analysis On Data As Of July 6 2020supporting

confidence: 69%

Towards Understanding the COVID-19 Case Fatality Rate

Yan

Chen²,

Yang

2021

Preprint

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An important parameter for COVID-19 is the case fatality rate (CFR). It has been applied to wide applications, including the measure of the severity of the infection, the estimation of the number of infected cases, risk assessment etc. However, there remains a lack of understanding on several aspects of CFR, including population factors that are important to CFR, the apparent discrepancy of CFRs in different countries, and how the age effect comes into play. We analyze the CFRs at two different time snapshots, July 6 and Dec 28, 2020, with one during the first wave and the other a second wave of the COVID-19 pandemic. We consider two important population covariates, age and GDP as a proxy for the quality and abundance of public health. Extensive exploratory data analysis leads to some interesting findings. First, there is a clear exponential age effect among different age groups, and, more importantly, the exponential index is almost invariant across countries and time in the pandemic. Second, the roles played by the age and GDP are a little surprising: during the first wave, age is a more significant factor than GDP, while their roles have switched during the second wave of the pandemic, which may be partially explained by the delay in time for the quality and abundance of public health and medical research to factor in.

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Section: Analysis On Data As Of July 6 2020supporting

confidence: 69%

Towards Understanding the COVID-19 Case Fatality Rate

Yan

Chen²,

Yang

2021

Preprint

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“…Our study also revealed a weaker increase in IFR over age than seen in other countries. It has already been noted that the pattern of mortality in low-and middle-income countries skews younger than would be predicted from age distribution (33,45,46). Our study suggests that a flatter age profile in mortality could be a major factor driving this difference.…”

supporting

confidence: 45%

“…Other researchers observed low case fatality rates (CFR) in Sub-Saharan Africa and proposed that vaccination, past infection history, and effective mitigation strategies might have reduced mortality (28)(29)(30). The age pattern of deaths in lower-income countries has skewed younger than in high-income countries, more so than can be explained by age distribution alone, but the explanation for this fact is not known (31)(32)(33).…”

mentioning

confidence: 99%

Representative Estimates of COVID-19 Infection Fatality Rates from Three Locations in India

Cai

Novosad

Tandel³

et al. 2021

Preprint

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There are very few estimates of the age-specific infection fatality rate (IFR) of SARS-CoV-2 in low- and middle-income countries. India reports the second highest number of SARS-CoV-2 infections in the world. We estimate age-specific IFR using data from seroprevalence surveys in Mumbai (population 12 million) and Karnataka (population 61 million), and a random sample of economically distressed migrants in Bihar with mortality followup. Among men aged 50–89, IFR is 0.12% in Karnataka (95% C.I. 0.09%–0.15%), 0.53% in Mumbai (0.52%–0.54%), and 5.64% among migrants in Bihar (0–11.16%). IFR in India is approximately twice as high for men as for women, is heterogeneous across contexts, and rises much less at older ages than in comparable studies from high income countries.

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“…The assumption that older patients in India would have a similar risk of severe disease and unfavorable outcome has guided the Indian public health response. However, initial Indian data suggested that younger people died at a higher rate than was expected (37%) from the Italian studies (26,27). Health-related data during COVID times is sparse despite marked regional differences in public health statistics related to cases, recovery and death.…”

Section: Introductionmentioning

confidence: 97%

“…However, initial Indian data suggested that younger people died at a higher rate than was expected (37%) from the Italian studies. 25 , 26 Health-related data during COVID times are sparse, despite marked regional differences in public health statistics related to cases, recovery, and death. The reported positivity rate in cities such as Delhi, Mumbai, and Ahmedabad has been nearly 5-times higher than the national average.…”

mentioning

confidence: 99%

Correlates of In-Hospital COVID-19 Deaths: A Competing Risks Survival Time Analysis of Retrospective Mortality Data

Goel

Raizada

Agrawal

et al. 2021

Disaster med. public health prep.

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Introduction: Several aspects of the COVID-19 pandemic remain ambiguous, including its transmission, severity, geographic and racial differences in mortality. These variations merit elaboration of local patterns to inform wider national policies. Methods: In a retrospective analysis, data of patients treated at a dedicated COVID hospital with moderate and severe illness during eight weeks of the pandemic was reviewed with attention to mortality in a competing risks framework. Results: A total of 1147 patients were hospitalized, and 312 (27.2%) died in hospital. Those who died were older (56.5 vs 47.6 years; p, < 0.0001). Of these, 885 (77.2%) had tested positive on RT-PCR, with 219 (24.2%) deaths (incidence rate, 1.9 per 100 person-days). Median time from onset of symptoms to death was 11 days. A competing risks analysis for in-hospital death revealed an adjusted cause-specific hazard ratio of 1.4 for each decade increase in age. Conclusions: This retrospective analysis provides broad patterns of disease presentation and mortality. Even COVID test negative patients will receive treatment at dedicated facilities and 33% presenting cases may die within the first 72 hours, most with comorbid illness. This should be considered while planning distribution of services for effective healthcare delivery

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Decoding India's Low Covid-19 Case Fatality rate

Cited by 19 publications

References 11 publications

Towards Understanding the COVID-19 Case Fatality Rate

Towards Understanding the COVID-19 Case Fatality Rate

Representative Estimates of COVID-19 Infection Fatality Rates from Three Locations in India

Correlates of In-Hospital COVID-19 Deaths: A Competing Risks Survival Time Analysis of Retrospective Mortality Data

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