Rethinking case fatality ratios for covid-19 from a data-driven viewpoint.
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ABSTRACT:
SUBMITTER: Rosakis P
PROVIDER: S-EPMC7286834 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Rethinking case fatality ratios for covid-19 from a data-driven viewpoint.
Rosakis Phoebus P Marketou Maria E ME
The Journal of infection 20200611 2
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Project description:Knowing which populations are most at risk for severe outcomes from an emerging infectious disease is crucial in deciding the optimal allocation of resources during an outbreak response. The case fatality ratio (CFR) is the fraction of cases that die after contracting a disease. The relative CFR is the factor by which the case fatality in one group is greater or less than that in a second group. Incomplete reporting of the number of infected individuals, both recovered and dead, can lead to biased estimates of the CFR. We define conditions under which the CFR and the relative CFR are identifiable. Furthermore, we propose an estimator for the relative CFR that controls for time-varying reporting rates. We generalize our methods to account for elapsed time between infection and death. To demonstrate the new methodology, we use data from the 1918 influenza pandemic to estimate relative CFRs between counties in Maryland. A simulation study evaluates the performance of the methods in outbreak scenarios. An R software package makes the methods and data presented here freely available. Our work highlights the limitations and challenges associated with estimating absolute and relative CFRs in practice. However, in certain situations, the methods presented here can help identify vulnerable subpopulations early in an outbreak of an emerging pathogen such as pandemic influenza.
| S-EPMC4540071 | biostudies-literature
Project description:While surveillance can identify changes in COVID-19 transmission patterns over time and space, sections of the population at risk, and the efficacy of public health measures, reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases. Our primary objective was to estimate the percentage of cases reported in the general community, considered as those that occurred outside of long-term care facilities (LTCFs), in specific provinces and Canada as a whole. We applied a methodology using the delay-adjusted case fatality ratio (CFR) to all cases and deaths, as well as those representing the general community. Our second objective was to assess whether the assumed CFR (mean = 1.38%) was appropriate for calculating underestimation of cases in Canada. Estimates were developed for the period from March 11th, 2020 to September 16th, 2020. Estimates of the percentage of cases reported (PrCR) and CFR varied spatially and temporally across Canada. For the majority of provinces, and for Canada as a whole, the PrCR increased through the early stages of the pandemic. The estimated PrCR in general community settings for all of Canada increased from 18.1% to 69.0% throughout the entire study period. Estimates were greater when considering only those data from outside of LTCFs. The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07% on March 11th, 2020 to 2.00% on September 16th, 2020. Therefore, the true CFR in the general community in Canada was likely less than 2% on September 16th. According to our analysis, some provinces, such as Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatchewan reported a greater percentage of cases as of September 16th, compared to British Columbia, Ontario, and Québec. This could be due to differences in testing rates and criteria, demographics, socioeconomic factors, race, and access to healthcare among the provinces. Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study. The estimates provide context to the summative state of the pandemic in Canada, and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.
| S-EPMC7718109 | biostudies-literature
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| S-EPMC7270730 | biostudies-literature
Project description:Previous studies have identified dementia as a risk factor for death from coronavirus disease 2019 (COVID-19). However, it is unclear whether Alzheimer's disease (AD) is an independent risk factor for COVID-19 case fatality rate. In a retrospective cohort study, we identified 387,841 COVID-19 patients through TriNetX. After adjusting for demographics and comorbidities, we found that AD patients had higher odds of dying from COVID-19 compared to patients without AD (Odds Ratio: 1.20, 95%confidence interval: 1.09-1.32, p < 0.001). Interestingly, we did not observe increased mortality from COVID-19 among patients with vascular dementia. These data are relevant to the evolving COVID-19 pandemic.
| S-EPMC8841099 | biostudies-literature
Project description:This article contains data on country-specific variability in Covid-19 prevalence, incidence, and case fatality rate among the 238 countries globally. We used the World Health Organization worldwide Covid-19 tracking site to determine the number of confirmed Covid-19 cases, the number of fatalities attributed to Covid-19, and the case fatality rate for each of 238 countries. Using data from the United Nations Department of Economic and Social Affairs, we extracted key country-specific metrics with potential associations with Covid-19 including total population, land area, population density, percentage of residents living in urban areas, and median age. We extracted country-specific economic indicators from The World Bank Group Open Data database. All data were extracted on August 15, 2020. We developed consolidated data sets and calculated the country-specific point prevalence and incidence of Covid-19 and associated deaths. These data are associated with the article "Spatial Analysis of Global Variability in Covid-19 Burden". Data are stored in a comma separated value format and can be downloaded from the Data in Brief website.
| S-EPMC7495013 | biostudies-literature
Project description:ObjectivesThe case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) varies significantly between countries. We aimed to describe the associations between health indicators and the national CFRs of COVID-19.MethodsWe identified for each country health indicators potentially associated with the national CFRs of COVID-19. We extracted data for 18 variables from international administrative data sources for 34 member countries of the Organization for Economic Cooperation and Development (OECD). We excluded the collinear variables and examined the 16 variables in multivariable analysis. A dynamic web-based model was developed to analyse and display the associations for the CFRs of COVID-19. We followed the Guideline for Accurate and Transparent Health Estimates Reporting (GATHER).ResultsIn multivariable analysis, the variables significantly associated with the increased CFRs were percentage of obesity in ages >18 years (β = 3.26; 95%CI = 1.20, 5.33; p 0.003), tuberculosis incidence (β = 3.15; 95%CI = 1.09, 5.22; p 0.004), duration (days) since first death due to COVID-19 (β = 2.89; 95%CI = 0.83, 4.96; p 0.008), and median age (β = 2.83; 95%CI = 0.76, 4.89; p 0.009). The COVID-19 test rate (β = -3.54; 95%CI = -5.60, -1.47; p 0.002), hospital bed density (β = -2.47; 95%CI = -4.54, -0.41; p 0.021), and rural population ratio (β = -2.19; 95%CI = -4.25, -0.13; p 0.039) decreased the CFR.ConclusionsThe pandemic hits population-dense cities. Available hospital beds should be increased. Test capacity should be increased to enable more effective diagnostic tests. Older patients and patients with obesity and their caregivers should be warned about a potentially increased risk.
| S-EPMC7510430 | biostudies-literature
Project description:A health data economy has begun to form, but its rise has been tempered by the profound lack of sharing of both data and data products such as models, intermediate results, and annotated training corpora, and this severely limits the potential for triggering economic cluster effects. Economic cluster effects represent a means to elicit benefit from economies of scale from internal data innovations and are beneficial because they may mitigate challenges from external sources. Within institutions, data product sharing is needed to spark data entrepreneurship and data innovation, and cross-institutional sharing is also critical, especially for rare conditions.
| S-EPMC6284141 | biostudies-other
Project description:We estimated the case-fatality risk for coronavirus disease cases in China (3.5%); China, excluding Hubei Province (0.8%); 82 countries, territories, and areas (4.2%); and on a cruise ship (0.6%). Lower estimates might be closest to the true value, but a broad range of 0.25%-3.0% probably should be considered.
| S-EPMC7258483 | biostudies-literature