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Why estimating population-based case fatality rates during epidemics may be misleading.

ABSTRACT: Different ways of calculating mortality ratios during epidemics can yield widely different results, particularly during the COVID-19 pandemic. We formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality ratios. The key parameters that affect the dynamics of the different mortality estimates are the incubation period and the length of time individuals were infected before confirmation of infection. We stress that none of these ratios are accurately represented by the often misinterpreted case fatality ratio (CFR), the number of deaths to date divided by the total number of infected cases to date. Using available data on the recent SARS-CoV-2 outbreaks and simple assumptions, we estimate and compare the different dynamic mortality ratios and highlight their differences. Informed by our modeling, we propose a more systematic method to determine mortality ratios during epidemic outbreaks and discuss sensitivity to confounding effects and errors in the data.

SUBMITTER: Bottcher L

PROVIDER: S-EPMC7276002 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Why estimating population-based case fatality rates during epidemics may be misleading.

Böttcher Lucas L Xia Mingtao M Chou Tom T

medRxiv : the preprint server for health sciences 20200330

Different ways of calculating mortality ratios during epidemics can yield widely different results, particularly during the COVID-19 pandemic. We formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality ratios. The key parameters that affect the dynamics of the different mortality estimates are the incubation period and the length of time individuals were infected before confirma ...[more]

PMID: 32511575

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Project description:BackgroundWe describe the clinical characteristics, treatments and in-hospital case-fatality rates in an unselected population of patients admitted for acute myocardial infarction.MethodsFrom January 2000 to June 2007, we tracked consecutive patients who were admitted to 7 tertiary referral and 21 county hospitals in Romania for medical treatment of ST-segment elevation acute myocardial infarction. These patients were enrolled in the Romanian Registry for ST-segment Elevation Myocardial Infarction. For this prospective study, we collected data on demographic characteristics, cardiovascular risk factors, various aspects of treatment for myocardial infarction, and in-hospital death.ResultsThe 9186 patients in the study group had a mean age of 63.8 years. The median time from onset of symptoms to thrombolysis was 230 (interquartile range 120-510) minutes. Of the 9186 patients, 4986 (54.3%) had hypertension, 1974 (21.5%) had diabetes mellitus, 3545 (38.6%) had lipid disorders and 4653 (50.7%) were smokers. The in-hospital mortality rate was 12.7% (1170 deaths). The study group consisted of 2893 women and 6293 men. The women were older than the men and had higher rates of hypertension and diabetes mellitus but were less likely to be smokers. A smaller proportion of women than men presented within 2 hours after onset of symptoms (23.1% v. 34.4%, p < 0.001). Smaller proportions of women received thrombolytics (40.8% v. 53.5%, p < 0.001), anticoagulants (93.4% v. 95.2%; p = 0.001), antiplatelet agents (88.3% v. 91.2%, p < 0.001) and primary percutaneous coronary interventions (1.5% v. 2.2%, p = 0.030). The risk of in-hospital death was greater for women, even after adjustment for confounders (odds ratio 1.33, 95% confidence interval 1.13-1.56; p < 0.001).InterpretationThe rates of reperfusion therapy for patients with acute myocardial infarction were low, and in-hospital case-fatality rates were high in this study. Excess in-hospital mortality was more pronounced among women.

Project description:Epidemics may pose a significant dilemma for governments and individuals. The personal or public health consequences of inaction may be catastrophic; but the economic consequences of drastic response may likewise be catastrophic. In the face of these trade-offs, governments and individuals must therefore strike a balance between the economic and personal health costs of reducing social contacts and the public health costs of neglecting to do so. As risk of infection increases, potentially infectious contact between people is deliberately reduced either individually or by decree. This must be balanced against the social and economic costs of having fewer people in contact, and therefore active in the labor force or enrolled in school. Although the importance of adaptive social contact on epidemic outcomes has become increasingly recognized, the most important properties of coupled human-natural epidemic systems are still not well understood. We develop a theoretical model for adaptive, optimal control of the effective social contact rate using traditional epidemic modeling tools and a utility function with delayed information. This utility function trades off the population-wide contact rate with the expected cost and risk of increasing infections. Our analytical and computational analysis of this simple discrete-time deterministic strategic model reveals the existence of an endemic equilibrium, oscillatory dynamics around this equilibrium under some parametric conditions, and complex dynamic regimes that shift under small parameter perturbations. These results support the supposition that infectious disease dynamics under adaptive behavior change may have an indifference point, may produce oscillatory dynamics without other forcing, and constitute complex adaptive systems with associated dynamics. Implications for any epidemic in which adaptive behavior influences infectious disease dynamics include an expectation of fluctuations, for a considerable time, around a quasi-equilibrium that balances public health and economic priorities, that shows multiple peaks and surges in some scenarios, and that implies a high degree of uncertainty in mathematical projections.

Project description:ObjectiveThere has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020-January 31, 2021) and part of the second wave (February 1, 2021-May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations.ResultsBoth wave 1 and wave 2 estimates qualitatively show that there is a large degree of "covert infections" in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71-11.47) and for deaths as 3.56 (95% CrI 3.48-3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26-28.81) and to 5.77 (95% CrI 5.34-6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates.

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Why estimating population-based case fatality rates during epidemics may be misleading.

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Why estimating population-based case fatality rates during epidemics may be misleading.

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