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1.
MMWR Morb Mortal Wkly Rep ; 69(34): 1173-1176, 2020 Aug 28.
Article in English | MEDLINE | ID: covidwho-732628

ABSTRACT

State and local health departments in the United States are using various indicators to identify differences in rates of reported coronavirus disease 2019 (COVID-19) and severe COVID-19 outcomes, including hospitalizations and deaths. To inform mitigation efforts, on May 19, 2020, the Kentucky Department for Public Health (KDPH) implemented a reporting system to monitor five indicators of state-level COVID-19 status to assess the ability to safely reopen: 1) composite syndromic surveillance data, 2) the number of new COVID-19 cases,* 3) the number of COVID-19-associated deaths,† 4) health care capacity data, and 5) public health capacity for contact tracing (contact tracing capacity). Using standardized methods, KDPH compiles an indicator monitoring report (IMR) to provide daily analysis of these five indicators, which are combined with publicly available data into a user-friendly composite status that KDPH and local policy makers use to assess state-level COVID-19 hazard status. During May 19-July 15, 2020, Kentucky reported 12,742 COVID-19 cases, and 299 COVID-19-related deaths (1). The mean composite state-level hazard status during May 19-July 15 was 2.5 (fair to moderate). IMR review led to county-level hotspot identification (identification of counties meeting criteria for temporal increases in number of cases and incidence) and facilitated collaboration among KDPH and local authorities on decisions regarding mitigation efforts. Kentucky's IMR might easily be adopted by state and local health departments in other jurisdictions to guide decision-making for COVID-19 mitigation, response, and reopening.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Epidemiological Monitoring , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Coronavirus Infections/mortality , Coronavirus Infections/therapy , Hospitalization/statistics & numerical data , Humans , Kentucky/epidemiology , Mortality/trends , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Public Health Practice
2.
BMC Public Health ; 20(1): 1238, 2020 Aug 14.
Article in English | MEDLINE | ID: covidwho-713857

ABSTRACT

BACKGROUND: Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months. METHODS: Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex. RESULTS: COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16% overlap was estimated in northern cities, with quotas decreasing by age, from 67% in the 15-64 years old to 1% only among subjects 85+ years old. CONCLUSIONS: An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.


Subject(s)
Coronavirus Infections/mortality , Mortality/trends , Pneumonia, Viral/mortality , Adolescent , Adult , Aged , Aged, 80 and over , Cities/epidemiology , Female , Humans , Italy/epidemiology , Male , Middle Aged , Pandemics , Spatio-Temporal Analysis , Young Adult
5.
Int J Environ Res Public Health ; 17(16)2020 08 05.
Article in English | MEDLINE | ID: covidwho-696422

ABSTRACT

In Italy, the COVID-19 epidemic curve started to flatten when the health system had already exceeded its capacity, raising concerns that the lockdown was indeed delayed. The aim of this study was to evaluate the health effects of late implementation of the lockdown in Italy. Using national data on the daily number of COVID-19 cases, we first estimated the effect of the lockdown, employing an interrupted time series analysis. Second, we evaluated the effect of an early lockdown on the trend of new cases, creating a counterfactual scenario where the intervention was implemented one week in advance. We then predicted the corresponding number of intensive care unit (ICU) admissions, non-ICU admissions, and deaths. Finally, we compared results under the actual and counterfactual scenarios. An early implementation of the lockdown would have avoided about 126,000 COVID-19 cases, 54,700 non-ICU admissions, 15,600 ICU admissions, and 12,800 deaths, corresponding to 60% (95%CI: 55% to 64%), 52% (95%CI: 46% to 57%), 48% (95%CI: 42% to 53%), and 44% (95%CI: 38% to 50%) reduction, respectively. We found that the late implementation of the lockdown in Italy was responsible for a substantial proportion of hospital admissions and deaths associated with the COVID-19 pandemic.


Subject(s)
Coronavirus Infections/epidemiology , Hospitalization/statistics & numerical data , Intensive Care Units/statistics & numerical data , Mortality/trends , Pneumonia, Viral/epidemiology , Quarantine/statistics & numerical data , Betacoronavirus , Humans , Interrupted Time Series Analysis , Italy/epidemiology , Pandemics
6.
PLoS One ; 15(7): e0236779, 2020.
Article in English | MEDLINE | ID: covidwho-691131

ABSTRACT

It is paramount to expand the knowledge base and minimize the consequences of the pandemic caused by the new Coronavirus (SARS-Cov2). Spain is among the most affected countries that declared a countrywide lockdown. An ecological study is presented herein, assessing the trends for incidence, mortality, hospitalizations, Intensive Care Unit admissions, and recoveries per autonomous community in Spain. Trends were evaluated by the Joinpoint software. The timeframe employed was when the lockdown was declared on March 14, 2020. Daily percentage changes were also calculated, with CI = 95% and p<0.05. An increase was detected, followed by reduction, for the evaluated indicators in most of the communities. Approximately 18.33 days were required for the mortality rates to decrease. The highest mortality rate was verified in Madrid (118.89 per 100,000 inhabitants) and the lowest in Melilla (2.31). The highest daily percentage increase in mortality occurred in Catalonia. Decreasing trends were identified after approximately two weeks of the institution of the lockdown by the government. Immediately the lockdown was declared, an increase of up to 33.96% deaths per day was verified in Catalonia. In contrast, Ceuta and Melilla presented significantly lower rates because they were still at the early stages of the pandemic at the moment of lockdown. The findings presented herein emphasize the importance of early and assertive decision-making to contain the pandemic.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Outcome Assessment, Health Care/methods , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Quarantine/methods , Coronavirus Infections/mortality , Coronavirus Infections/virology , Humans , Incidence , Intensive Care Units , Mortality/trends , Patient Admission/trends , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Spain/epidemiology
8.
Public Health ; 185: 261-263, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-658503

ABSTRACT

BACKGROUND: There is emerging evidence about characteristics that may increase the risk of coronavirus disease 2019 (COVID-19) mortality, but they are highly correlated. METHODS: An ecological analysis was used to estimate associations between these variables and age-standardised COVID-19 mortality rates at the local authority level. RESULTS: Ethnicity, population density and overweight/obesity were all found to have strong independent associations with COVID-19 mortality, at the local authority level. DISCUSSION: This analysis provides some preliminary evidence about which variables are independently associated with COVID-19 mortality and suggests that others (deprivation and pollution) are not directly linked. It highlights the importance of multivariate analyses to understand the factors that increase vulnerability to COVID-19.


Subject(s)
Coronavirus Infections/mortality , Health Status Disparities , Pneumonia, Viral/mortality , Air Pollution/adverse effects , Air Pollution/statistics & numerical data , England/epidemiology , Ethnic Groups/statistics & numerical data , Humans , Mortality/trends , Multivariate Analysis , Obesity/epidemiology , Pandemics , Population Density , Risk Factors , Socioeconomic Factors
9.
J Am Med Dir Assoc ; 21(7): 915-918, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-651906

ABSTRACT

OBJECTIVES: Initial data on COVID-19 infection has pointed out a special vulnerability of older adults. DESIGN: We performed a meta-analysis with available national reports on May 7, 2020 from China, Italy, Spain, United Kingdom, and New York State. Analyses were performed by a random effects model, and sensitivity analyses were performed for the identification of potential sources of heterogeneity. SETTING AND PARTICIPANTS: COVID-19-positive patients reported in literature and national reports. MEASURES: All-cause mortality by age. RESULTS: A total of 611,1583 subjects were analyzed and 141,745 (23.2%) were aged ≥80 years. The percentage of octogenarians was different in the 5 registries, the lowest being in China (3.2%) and the highest in the United Kingdom and New York State. The overall mortality rate was 12.10% and it varied widely between countries, the lowest being in China (3.1%) and the highest in the United Kingdom (20.8%) and New York State (20.99%). Mortality was <1.1% in patients aged <50 years and it increased exponentially after that age in the 5 national registries. As expected, the highest mortality rate was observed in patients aged ≥80 years. All age groups had significantly higher mortality compared with the immediately younger age group. The largest increase in mortality risk was observed in patients aged 60 to 69 years compared with those aged 50 to 59 years (odds ratio 3.13, 95% confidence interval 2.61-3.76). CONCLUSIONS AND IMPLICATIONS: This meta-analysis with more than half million of COVID-19 patients from different countries highlights the determinant effect of age on mortality with the relevant thresholds on age >50 years and, especially, >60 years. Older adult patients should be prioritized in the implementation of preventive measures.


Subject(s)
Coronavirus Infections/mortality , Mortality/trends , Pandemics/statistics & numerical data , Pneumonia, Viral/mortality , Age Distribution , Aged , Aged, 80 and over , China/epidemiology , Coronavirus Infections/epidemiology , Female , Humans , Italy/epidemiology , Male , Middle Aged , New York/epidemiology , Pneumonia, Viral/epidemiology , Spain/epidemiology , United Kingdom/epidemiology
10.
Euro Surveill ; 25(26)2020 07.
Article in English | MEDLINE | ID: covidwho-639161

ABSTRACT

A remarkable excess mortality has coincided with the COVID-19 pandemic in Europe. We present preliminary pooled estimates of all-cause mortality for 24 European countries/federal states participating in the European monitoring of excess mortality for public health action (EuroMOMO) network, for the period March-April 2020. Excess mortality particularly affected ≥ 65 year olds (91% of all excess deaths), but also 45-64 (8%) and 15-44 year olds (1%). No excess mortality was observed in 0-14 year olds.


Subject(s)
Cause of Death/trends , Coronavirus Infections/mortality , Coronavirus/isolation & purification , Influenza, Human/mortality , Pneumonia, Viral/mortality , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Betacoronavirus , Child , Child, Preschool , Coronavirus Infections/diagnosis , Disease Outbreaks , Europe/epidemiology , Female , Humans , Infant , Infant, Newborn , Influenza, Human/diagnosis , Male , Middle Aged , Mortality/trends , Pandemics , Pneumonia, Viral/diagnosis , Population Surveillance , Preliminary Data , Young Adult
11.
Neurol Sci ; 41(9): 2317-2324, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-635550

ABSTRACT

INTRODUCTION: In the current study, we evaluated factors that increase the coronavirus disease (COVID-19) patient death rate by analyzing the data from two cohort hospitals. In addition, we studied whether underlying neurological diseases are risk factors for death. METHODS: In this retrospective cohort study, we included 103 adult inpatients (aged ≥ 18 years). We evaluated differences in demographic data between surviving and non-surviving COVID-19 patients. RESULTS: In a multivariate logistic analysis, age and the presence of chronic lung disease and Alzheimer's dementia (AD) were the only significant parameters for predicting COVID-19 non-survival (p < 0.05). However, hypertension, coronary vascular disease, dyslipidemia, chronic kidney disease, diabetes, and history of taking angiotensin II receptor blockers (ARBs) or angiotensin-converting enzyme (ACE) inhibitors, as well as nonsteroidal anti-inflammatory drugs (NSAIDs), were not significantly associated with the death of COVID-19 patients. The optimal cutoff value obtained from the maximum Youden index was 70 (sensitivity, 80.77%; specificity, 61.04%), and the odds ratio of non-survival increased 1.055 fold for every year of age. CONCLUSIONS: Clinicians should closely monitor and manage the symptoms of COVID-19 patients who are over the age of 70 years or have chronic lung disease or AD.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnosis , Coronavirus Infections/mortality , Nervous System Diseases/diagnosis , Nervous System Diseases/mortality , Pneumonia, Viral/diagnosis , Pneumonia, Viral/mortality , Age Factors , Aged , Aged, 80 and over , Cohort Studies , Female , Humans , Lung Diseases/diagnosis , Lung Diseases/mortality , Male , Middle Aged , Mortality/trends , Pandemics , Predictive Value of Tests , Republic of Korea/epidemiology , Retrospective Studies , Risk Factors
13.
Chaos ; 30(6): 061108, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-622581

ABSTRACT

This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their cases and death counts. The total number of clusters and individual countries' cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices. With this offset in mind, we identify anomalous countries in the progression from COVID-19 cases to deaths. This analysis can aid in highlighting the most and least significant public policies in minimizing a country's COVID-19 mortality rate.


Subject(s)
Cluster Analysis , Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Pneumonia, Viral/epidemiology , Pneumonia, Viral/mortality , Time and Motion Studies , Betacoronavirus , Humans , Mortality/trends , Pandemics
16.
J Int Med Res ; 48(6): 300060520931298, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-611437

ABSTRACT

OBJECTIVE: To analyse mortality statistics in the United Kingdom during the initial phases of the severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic and to understand the impact of the pandemic on national mortality. METHODS: Retrospective review of weekly national mortality statistics in the United Kingdom over the past 5 years, including subgroup analysis of respiratory mortality rates. RESULTS: During the early phases of the SARS-CoV-2 pandemic in the first months of 2020, there were consistently fewer deaths per week compared with the preceding 5 years. This pattern was not observed at any other time within the past 5 years. We have termed this phenomenon the "SARS-CoV-2 paradox." We postulate potential explanations for this seeming paradox and explore the implications of these data. CONCLUSIONS: Paradoxically, but potentially importantly, lower rather than higher weekly mortality rates were observed during the early stages of the SARS-CoV-2 pandemic. This paradox may have implications for current and future healthcare utilisation. A rebound increase in non-SARS-CoV-2 mortality later this year might coincide with the peak of SARS-CoV-2 admissions and mortality.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Lung Diseases/mortality , Pneumonia, Viral/epidemiology , Pneumonia, Viral/mortality , Betacoronavirus , England/epidemiology , Humans , Lung Diseases/epidemiology , Mortality/trends , Pandemics , Retrospective Studies , Wales/epidemiology
17.
JAMA Netw Open ; 3(6): e2012403, 2020 06 01.
Article in English | MEDLINE | ID: covidwho-607323

ABSTRACT

Importance: Data from the coronavirus disease 2019 (COVID-19) pandemic in the US show large differences in hospitalizations and mortality across race and geography. However, there are limited data on health information, beliefs, and behaviors that might indicate different exposure to risk. Objective: To determine the association of sociodemographic characteristics with reported incidence, knowledge, and behavior regarding COVID-19 among US adults. Design, Setting, and Participants: A US national survey study was conducted from March 29 to April 13, 2020, to measure differences in knowledge, beliefs, and behavior about COVID-19. The survey oversampled COVID-19 hotspot areas. The survey was conducted electronically. The criteria for inclusion were age 18 years or older and residence in the US. Data analysis was performed in April 2020. Main Outcomes and Measures: The main outcomes were incidence, knowledge, and behaviors related to COVID-19 as measured by survey response. Results: The survey included 5198 individuals (mean [SD] age, 48 [18] years; 2336 men [45%]; 3759 white [72%], 830 [16%] African American, and 609 [12%] Hispanic). The largest differences in COVID-19-related knowledge and behaviors were associated with race/ethnicity, sex, and age, with African American participants, men, and people younger than 55 years showing less knowledge than other groups. African American respondents were 3.5 percentage points (95% CI, 1.5 to 5.5 percentage points; P = .001) more likely than white respondents to report being infected with COVID-19, as were men compared with women (3.2 percentage points; 95% CI, 2.0 to 4.4 percentage points; P < .001). Knowing someone who tested positive for COVID-19 was more common among African American respondents (7.2 percentage points; 95% CI, 3.4 to 10.9 percentage points; P < .001), people younger than 30 years (11.6 percentage points; 95% CI, 7.5 to 15.7 percentage points; P < .001), and people with higher incomes (coefficient on earning ≥$100 000, 12.3 percentage points; 95% CI, 8.7 to 15.8 percentage points; P < .001). Knowledge of potential fomite spread was lower among African American respondents (-9.4 percentage points; 95% CI, -13.1 to -5.7 percentage points; P < .001), Hispanic respondents (-4.8 percentage points; 95% CI, -8.9 to -0.77 percentage points; P = .02), and people younger than 30 years (-10.3 percentage points; 95% CI, -14.1 to -6.5 percentage points; P < .001). Similar gaps were found with respect to knowledge of COVID-19 symptoms and preventive behaviors. Conclusions and Relevance: In this survey study of US adults, there were gaps in reported incidence of COVID-19 and knowledge regarding its spread and symptoms and social distancing behavior. More effort is needed to increase accurate information and encourage appropriate behaviors among minority communities, men, and younger people.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/epidemiology , Coronavirus Infections/psychology , Health Risk Behaviors/physiology , Pneumonia, Viral/epidemiology , Pneumonia, Viral/psychology , Adult , African Americans/psychology , African Americans/statistics & numerical data , Aged , Case-Control Studies , Coronavirus Infections/mortality , Coronavirus Infections/prevention & control , Culture , European Continental Ancestry Group/psychology , European Continental Ancestry Group/statistics & numerical data , Female , Hispanic Americans/psychology , Hispanic Americans/statistics & numerical data , Hospitalization/trends , Humans , Incidence , Income/trends , Knowledge , Male , Middle Aged , Mortality/trends , Pandemics/prevention & control , Pneumonia, Viral/mortality , Pneumonia, Viral/prevention & control , Prevalence , Surveys and Questionnaires , United States/epidemiology , United States/ethnology
18.
Int J Environ Res Public Health ; 17(12)2020 06 18.
Article in English | MEDLINE | ID: covidwho-603701

ABSTRACT

Today, the world is facing the challenge of a major pandemic due to COVID-19, which has caused more than 6.1 million cases of infection and nearly 370,000 deaths so far. Most of the deaths from the disease are clustered in the older population, but the young and children are not spared. In this context, there is a critical need to revisit the formula for calculating potential years of life lost (PYLL). Data on age-specific deaths due to COVID-19 in three countries, including the United States (US), Italy, and Germany, were evaluated. New York State, as a significant outlier within the US, was also included. PYLLs in the US were five times as high as those of Italy. Compared with Germany, PYLLs in Italy were 4 times higher, and the rates in the US were 23, 25, and 18 times higher when using upper age limits of 70, 75, and 80, respectively. Standardized PYLLs in New York were 2 times as high as the rates in Italy, and 7 to 9 times as high as PYLLs in Germany. The revised formula of PYLL, using an upper limit of age 80, is recommended to accurately measure premature deaths due to a major disastrous disease such as COVID-19.


Subject(s)
Coronavirus Infections/mortality , Mortality, Premature , Mortality/trends , Pneumonia, Viral/mortality , Germany/epidemiology , Health Services Research , Humans , Italy/epidemiology , Life Expectancy , Pandemics , United States/epidemiology
19.
Leukemia ; 34(8): 2173-2183, 2020 08.
Article in English | MEDLINE | ID: covidwho-601049

ABSTRACT

We studied 1859 subjects with confirmed COVID-19 from seven centers in Wuhan 1651 of whom recovered and 208 died. We interrogated diverse covariates for correlations with risk of death from COVID-19. In multi-variable Cox regression analyses increased hazards of in-hospital death were associated with several admission covariates: (1) older age (HR = 1.04; 95% Confidence Interval [CI], 1.03, 1.06 per year increase; P < 0.001); (2) smoking (HR = 1.84 [1.17, 2.92]; P = 0.009); (3) admission temperature per °C increase (HR = 1.32 [1.07, 1.64]; P = 0.009); (4) Log10 neutrophil-to-lymphocyte ratio (NLR; HR = 3.30 [2.10, 5.19]; P < 0.001); (5) platelets per 10 E + 9/L decrease (HR = 0.996 [0.994, 0.998]; P = 0.001); (6) activated partial thromboplastin (aPTT) per second increase (HR = 1.04 [1.02, 1.05]; P < 0.001); (7) Log10 D-dimer per mg/l increase (HR = 3.00 [2.17, 4.16]; P < 0.001); and (8) Log10 serum creatinine per µmol/L increase (HR = 4.55 [2.72, 7.62]; P < 0.001). In piecewise linear regression analyses Log10NLR the interval from ≥0.4 to ≤1.0 was significantly associated with an increased risk of death. Our data identify covariates associated with risk of in hospital death in persons with COVID-19.


Subject(s)
Betacoronavirus/isolation & purification , Biomarkers/blood , Coronavirus Infections/mortality , Lymphocytes/pathology , Mortality/trends , Neutrophils/pathology , Pneumonia, Viral/mortality , Severity of Illness Index , Adult , Aged , Aged, 80 and over , Coronavirus Infections/blood , Coronavirus Infections/pathology , Female , Follow-Up Studies , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/blood , Pneumonia, Viral/pathology , Prognosis , ROC Curve , Risk Factors , Survival Rate
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