Number Of Coronavirus Disease 2019 Deaths Research Articles

COVID-19 Deaths and the Impact of Health Disparities, Hospital Characteristics, Community, Social Distancing, and Health System Competition

The United States has one of the highest cumulative mortalities of coronavirus disease 2019 (COVID-19) and has reached 1 million deaths as of May 19th, 2022. Understanding which community and hospital factors contributed to disparities in COVID-19 mortality is important to inform public health strategies. This study aimed to explore the potential relationship between hospital service area (1) community (ie, health professional shortage areas, market competition, and uninsured percentage) and (2) hospital (ie, teaching, system, and ownership status) characteristics (2013-2018) on publicly available COVD-19 (February to October 2020) mortality data. The study included 2514 health service areas and used multilevel mixed-effects linear model to account for the multilevel data structure. The outcome measure was the number of COVID-19 deaths. This study found that public health, as opposed to acute care provision, was associated with community health and, ultimately, COVID-19 mortality. The study found that population characteristics including more uninsured greater proportion of those over 65 years, more diverse populations, and larger populations were all associated with a higher rate of death. In addition, communities with fewer hospitals were associated with a lower rate of death. When considering region in the United States, the west region showed a higher rate of death than all other regions. The association between some community characteristics and higher COVID-19 deaths demonstrated that access to health care, either for COVID-19 infection or worse health from higher disease burden, is strongly associated with COVID-19 deaths. Thus, to be better prepared for potential future pandemics, a greater emphasis on public health infrastructure is needed.

Good and bad high-frequency volatility spillovers among developed and emerging stock markets

PurposeThis paper examines dynamic return spillovers and connectedness networks among international stock exchange markets. The authors account for asymmetry by distinguishing between positive and negative returns.Design/methodology/approachThis paper employs the spillover index of Diebold and Yilmaz (2012) to measure the volatility spillover index for total, positive and negative volatility.FindingsThe results show time-varying and asymmetric volatility spillovers among the stock markets under investigation. During the coronavirus disease 2019 (COVID-19) pandemic, bad volatility spillovers are more pronounced and dominated over good volatility spillovers, indicating contagion effects.Originality/valueThe presence of confirmed COVID-19 cases positively (negatively) affects the good and bad spillovers under low and intermediate (upper) quantiles. Both types of spillovers at various quantiles agree also influenced by the number of COVID-19 deaths.

Factors Associated With Racial Differences in Deaths Among Nursing Home Residents With COVID-19 Infection in the US

It is important to understand differences in coronavirus disease 2019 (COVID-19) deaths by nursing home racial composition and the potential reasons for these differences so that limited resources can be distributed equitably. To describe differences in the number of COVID-19 deaths by nursing home racial composition and examine the factors associated with these differences. This cross-sectional study of 13 312 nursing homes in the US used the Nursing Home COVID-19 Public File from the Centers for Medicare and Medicaid Services, which contains COVID-19 cases and deaths among nursing home residents as self-reported by nursing homes beginning between January 1, 2020, and May 24, 2020, and ending on September 13, 2020. Data were analyzed from July 28 to December 18, 2020. Confirmed or suspected COVID-19 infection. Confirmed cases were defined as COVID-19 infection confirmed by a diagnostic laboratory test. Suspected cases were defined as signs and/or symptoms of COVID-19 infection or patient-specific transmission-based precautions for COVID-19 infection. Deaths associated with COVID-19 among nursing home residents. Death counts were compared by nursing home racial composition, which was measured as the proportion of White residents. Among 13 312 nursing homes included in the study, the overall mean (SD) age of residents was 79.5 (6.7) years. A total of 51 606 COVID-19-associated deaths among residents were reported, with a mean (SD) of 3.9 (8.0) deaths per facility. The mean (SD) number of deaths in nursing homes with the lowest proportion of White residents (quintile 1) vs nursing homes with the highest proportions of White residents (quintile 5) were 5.6 (9.2) and 1.7 (4.8), respectively. Facilities in quintile 1 experienced a mean (SE) of 3.9 (0.2) more deaths than those in quintile 5, representing a 3.3-fold higher number of deaths in quintile 1 compared with quintile 5. Adjustment for the number of certified beds reduced the mean (SE) difference between these 2 nursing home groups to 2.2 (0.2) deaths. Controlling for case mix measures and other nursing home characteristics did not modify this association. Adjustment for county-level COVID-19 prevalence further reduced the mean (SE) difference to 1.0 (0.2) death. In this study, nursing homes with the highest proportions of non-White residents experienced COVID-19 death counts that were 3.3-fold higher than those of facilities with the highest proportions of White residents. These differences were associated with factors such as larger nursing home size and higher infection burden in counties in which nursing homes with high proportions of non-White residents were located. Focusing limited available resources on facilities with high proportions of non-White residents is needed to support nursing homes during potential future outbreaks.

Epidemiology of Coronavirus Disease 2019 in Japan: Descriptive Findings and Lessons Learned through Surveillance during the First Three Waves.

Introduction: Coronavirus disease 2019 (COVID-19) has caused unprecedented global morbidity and mortality. Japan has faced three epidemic “waves” of COVID-19 from early 2020 through early 2021. Here we narratively review the three waves in Japan, describe the key epidemiologic features of COVID-19, and discuss lessons learned. Methods: We assessed publicly available surveillance data, routine surveillance reports, and other relevant sources―multiple indicators were monitored to improve interpretation of surveillance data. Weekly trends for each wave were described based on the number of case notifications; number of tests performed; proportion of those tests that were positive for the novel coronavirus; the prevalent number of COVID-19 hospitalizations (total hospitalizations and those categorized as severe); and number of COVID-19 deaths. For each indicator and wave, we recorded the first calendar week to show an increase over two consecutive previous weeks, along with the peak week. Results: The spring wave was characterized by detection of cases imported from China, followed by notifications of sporadic cases without travel history, clusters, and mild/asymptomatic cases. The summer wave saw a large increase in notifications and a younger age distribution, but in the context of increased testing with lower test positivity. The winter wave brought considerable morbidity and mortality, surpassing the cumulative case counts and fatalities from the earlier waves, with high peak values. Overall, relative to the first wave, the burden of severe outcomes was lower in the second and higher in the third wave, but varied by prefecture. In all three waves, severe outcomes peaked after notification counts and test positivity peaked; severe outcomes were also consistently skewed toward the elderly. Conclusions: Important lessons were learned from each wave and across waves―some aspects remained constant, while others changed over time. In order to rapidly detect an increase in incidence, continuous, timely, and sensitive surveillance―using multiple information sources with careful interpretations―will be key in COVID-19 control.

Municipality-Level Predictors of COVID-19 Mortality in Mexico: A Cautionary Tale.

Local characteristics of populations have been associated with coronavirus disease 2019 (COVID-19) outcomes. We analyze the municipality-level factors associated with a high COVID-19 mortality rate (MR) of in Mexico. We retrieved information from cumulative confirmed symptomatic cases and deaths from COVID-19 as of June 20, 2020, and data from most recent census and surveys of Mexico. A negative binomial regression model was adjusted, the dependent variable was the number of COVID-19 deaths, and the independent variables were the quintiles of the distribution of sociodemographic and health characteristics among the 2457 municipalities of Mexico. Factors associated with high MRs from COVID-19, relative to quintile 1, were diabetes and obesity prevalence, diabetes mortality rate, indigenous population, economically active population, density of economic units that operate essential activities, and population density. Among factors inversely associated with lower MRs from COVID-19 were high hypertension prevalence and houses without sewage drainage. We identified 1351 municipalities without confirmed COVID-19 deaths, of which, 202 had high and 82 very high expected COVID-19 mortality (mean = 8 and 13.8 deaths per 100,000, respectively). This study identified municipalities of Mexico that could lead to a high mortality scenario later in the epidemic and warns against premature easing of mobility restrictions and to reinforce strategies of prevention and control of outbreaks in communities vulnerable to COVID-19.