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Paucity and Disparity of Publicly Available Sex-Disaggregated Data for the COVID-19 Epidemic Hamper Evidence-Based Decision-Making.

ABSTRACT: COVID-19 has joined the long list of sexually dimorphic human disorders. Higher lethality in men, evident in the first reports from China, was confirmed in the subsequent Italian outbreak. Newspapers and scientific journals commented on this finding and the preexisting conditions, biological processes, and behavioral differences that may underlie it. However, little appeared to be released about sex differences in severity of disease, comorbidities, rate of recovery, length of hospital stay, or number of tests performed. Systematic analysis of official websites for 20 countries and 6 US states revealed a wide disparity in sex-disaggregated data made available to the public and scholars. Only a handful reported cases by sex. None of the other characteristics, including deaths, were stratified by sex at the time. Beyond suboptimal sex disaggregation, we found a paucity of usable raw data sets and a generalized lack of standardization of captured data, making comparisons difficult. A second round of data capture in April found more complete, but even more disparate, information. Our analysis revealed a wide range of sex ratios among confirmed cases. In countries where a male bias was initially reported, the proportion of women dramatically increased in 3 weeks. Analysis also revealed a complex pattern of sex ratio variation with age. Accurate, peer-reviewed, analysis of harmonized, sex-disaggregated data for characteristics of epidemics, such as availability of testing, suspected source of infection, or comorbidities, will be critical to understand where the observed disparities come from and to generate evidence-based recommendations for decision-making by governments.

SUBMITTER: Kocher K

PROVIDER: S-EPMC7781558 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Paucity and Disparity of Publicly Available Sex-Disaggregated Data for the COVID-19 Epidemic Hamper Evidence-Based Decision-Making.

Kocher Kristen K Delot-Vilain Arthur A Spencer D'Andre D LoTempio Jonathan J Délot Emmanuèle C EC

Archives of sexual behavior 20210104 2

COVID-19 has joined the long list of sexually dimorphic human disorders. Higher lethality in men, evident in the first reports from China, was confirmed in the subsequent Italian outbreak. Newspapers and scientific journals commented on this finding and the preexisting conditions, biological processes, and behavioral differences that may underlie it. However, little appeared to be released about sex differences in severity of disease, comorbidities, rate of recovery, length of hospital stay, or ...[more]

PMID: 33398705

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Project description:BACKGROUND:Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions. RESULTS:We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side. CONCLUSIONS:The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.

Dataset Information

Paucity and Disparity of Publicly Available Sex-Disaggregated Data for the COVID-19 Epidemic Hamper Evidence-Based Decision-Making.

Publications

Paucity and Disparity of Publicly Available Sex-Disaggregated Data for the COVID-19 Epidemic Hamper Evidence-Based Decision-Making.

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