key: cord-0822286-lvzs3kg6
authors: Takagi, Hisato; Kuno, Toshiki; Yokoyama, Yujiro; Ueyama, Hiroki; Matsushiro, Takuya; Hari, Yosuke; Ando, Tomo
title: Meta‐regression of COVID‐19 prevalence/fatality on socioeconomic characteristics of data from top 50 U.S. large cities
date: 2020-08-02
journal: J Med Virol
DOI: 10.1002/jmv.26335
sha: 9ecec0ea208da4e0badc42a2bbf07f98fed7038f
doc_id: 822286
cord_uid: lvzs3kg6

Reuters reported "The coronavirus mortality rate among some of the poorest Catalans is five times higher than among the wealthiest residents of the Spanish region, a study showed, in the latest evidence of how COVID-19 hits the needy hardest" on 22 May, 2020 (https://www.reuters.com/article/us-health-coronavirus-spain-study/virus-deaths-f ive-times-higher-among-poor-in-spanish-region-idUSKBN22Y23M). This article is protected by copyright. All rights reserved.

Meta-regression of COVID-19 prevalence/fatality on socioeconomic characteristics of data from top 50 U.S. large cities

To the Editor, Reuters reported "The coronavirus mortality rate among some of the poorest Catalans is five times higher than among the wealthiest residents of the Spanish region, a study showed, in the latest evidence of how COVID-19 hits the needy hardest" on 22 May 2020 (https://www. reuters.com/article/us-health-coronavirus-spain-study/virus-deaths-fivetimes-higher-among-poor-in-spanish-region-idUSKBN22Y23M). It has been suggested that outcomes of pandemic influenza are associated with socioeconomic status. 1 Socioeconomic characteristics also may affect prevalence and case fatality of coronavirus disease 2019 (COVID-19). To screen potential risk and protective socioeconomic factors for COVID-19 prevalence and fatality, meta-regression of data from the top 50 U.S. large cities was performed. (Table S1 ). Random-effects meta-regression was performed using OpenMetaAnalyst (http://www.cebm.brown.edu/openmeta/index.html). We defined COVID-19 prevalence and case-fatality, respectively, as confirmed cases divided by population and deaths by confirmed cases. A meta-regression graph depicted the COVID-19 prevalence or fatality (plotted as the logarithm-transformed prevalence or fatality on the y-axis) as a function of a given factor (plotted as a socioeconomic characteristic on the x-axis).

Covariates with a significantly (P < .05) positive or negative coefficient in the univariable model were together entered into the multivariable model.

Results of the meta-regression were summarized in Table 1 .

A coefficient (slope of the meta-regression line) for COVID-19 prevalence was significantly negative for male sex (P < .001; Figure 1A ), education attainment (P = .011), computer (P < .001; Figure 1B ) and Internet (P < .001) use, and private health insurance (P = .029), which suggests that COVID-19 prevalence may decrease significantly as male sex, education attainment, computer and Internet use, and private health insurance increases. Whereas the coefficient was significantly positive for Black race (P < .001), never matrimony (P < .001; Figure 1C ), unemployment (P = .003), and poverty (P < .001), which suggests that COVID-19 prevalence may increase significantly as Black race, never matrimony, and poverty increases. In the multivariable model entering all these nine covariates, the coefficient was significantly negative for male sex (P = .036) and computer use (P = .024), and significantly positive for never matrimony (P < .001), which suggests that male sex and computer use may be independently and negatively associated with COVID-19 prevalence and never matrimony may be independently and positively associated with COVID-19 prevalence.

A coefficient for COVID-19 fatality was significantly negative for no health insurance (P = .007), and significantly positive for elderly (P < .001; Figure 1D ), unemployment (P = .011), and public coverage (P = .011). In the multivariable model, the coefficient was significantly positive only for the elderly (P = .004), which suggests that the elderly may be independently and positively associated with COVID-19 fatality.

The present results suggest an independent and negative association of male sex and computer use with COVID-19 prevalence, an independent and positive association of never matrimony with COVID-19 prevalence, and an independent and positive association of elderly with COVID-19 fatality. Our findings never denote, for instance, that a subject having a computer is at a low risk of COVID-19 prevalence, which should be noted. Because of the community-level epidemiological study, F I G U R E 1 Meta-regression graph depicting the coronavirus disease 2019 (COVID-19) (A-C) prevalence or (D) fatality (plotted as the logarithm-transformed prevalence or fatality on the y-axis) as a function of a given factor (plotted as a socioeconomic characteristic on the x-axis)

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