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1.
Lancet Respir Med ; 9(4): 397-406, 2021 04.
Artículo en Inglés | MEDLINE | ID: covidwho-1180129

RESUMEN

BACKGROUND: Analysis of the effect of COVID-19 on the complete hospital population in England has been lacking. Our aim was to provide a comprehensive account of all hospitalised patients with COVID-19 in England during the early phase of the pandemic and to identify the factors that influenced mortality as the pandemic evolved. METHODS: This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. All patients aged 18 years or older in England who completed a hospital stay (were discharged alive or died) between March 1 and May 31, 2020, and had a diagnosis of COVID-19 on admission or during their stay were included. In-hospital death was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between death and several covariates: age, sex, deprivation (Index of Multiple Deprivation), ethnicity, frailty (Hospital Frailty Risk Score), presence of comorbidities (Charlson Comorbidity Index items), and date of discharge (whether alive or deceased). FINDINGS: 91 541 adult patients with COVID-19 were discharged during the study period, among which 28 200 (30·8%) in-hospital deaths occurred. The final multilevel logistic regression model accounted for age, deprivation score, and date of discharge as continuous variables, and sex, ethnicity, and Charlson Comorbidity Index items as categorical variables. In this model, significant predictors of in-hospital death included older age (modelled using restricted cubic splines), male sex (1·457 [1·408-1·509]), greater deprivation (1·002 [1·001-1·003]), Asian (1·211 [1·128-1·299]) or mixed ethnicity (1·317 [1·080-1·605]; vs White ethnicity), and most of the assessed comorbidities, including moderate or severe liver disease (5·433 [4·618-6·392]). Later date of discharge was associated with a lower odds of death (0·977 [0·976-0·978]); adjusted in-hospital mortality improved significantly in a broadly linear fashion, from 52·2% in the first week of March to 16·8% in the last week of May. INTERPRETATION: Reductions in the adjusted probability of in-hospital mortality for COVID-19 patients over time might reflect the impact of changes in hospital strategy and clinical processes. The reasons for the observed improvements in mortality should be thoroughly investigated to inform the response to future outbreaks. The higher mortality rate reported for certain ethnic minority groups in community-based studies compared with our hospital-based analysis might partly reflect differential infection rates in those at greatest risk, propensity to become severely ill once infected, and health-seeking behaviours. FUNDING: None.


Asunto(s)
/mortalidad , Mortalidad Hospitalaria/tendencias , Grupos Minoritarios/estadística & datos numéricos , Pandemias/estadística & datos numéricos , Aceptación de la Atención de Salud/estadística & datos numéricos , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Comorbilidad , Conjuntos de Datos como Asunto , Registros Electrónicos de Salud/estadística & datos numéricos , Inglaterra/epidemiología , Femenino , Humanos , Tiempo de Internación/estadística & datos numéricos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Factores de Riesgo , Índice de Severidad de la Enfermedad , Factores Sexuales , Adulto Joven
2.
Lancet Respir Med ; 9(4): 407-418, 2021 04.
Artículo en Inglés | MEDLINE | ID: covidwho-1180128

RESUMEN

BACKGROUND: Most low-income and middle-income countries (LMICs) have little or no data integrated into a national surveillance system to identify characteristics or outcomes of COVID-19 hospital admissions and the impact of the COVID-19 pandemic on their national health systems. We aimed to analyse characteristics of patients admitted to hospital with COVID-19 in Brazil, and to examine the impact of COVID-19 on health-care resources and in-hospital mortality. METHODS: We did a retrospective analysis of all patients aged 20 years or older with quantitative RT-PCR (RT-qPCR)-confirmed COVID-19 who were admitted to hospital and registered in SIVEP-Gripe, a nationwide surveillance database in Brazil, between Feb 16 and Aug 15, 2020 (epidemiological weeks 8-33). We also examined the progression of the COVID-19 pandemic across three 4-week periods within this timeframe (epidemiological weeks 8-12, 19-22, and 27-30). The primary outcome was in-hospital mortality. We compared the regional burden of hospital admissions stratified by age, intensive care unit (ICU) admission, and respiratory support. We analysed data from the whole country and its five regions: North, Northeast, Central-West, Southeast, and South. FINDINGS: Between Feb 16 and Aug 15, 2020, 254 288 patients with RT-qPCR-confirmed COVID-19 were admitted to hospital and registered in SIVEP-Gripe. The mean age of patients was 60 (SD 17) years, 119 657 (47%) of 254 288 were aged younger than 60 years, 143 521 (56%) of 254 243 were male, and 14 979 (16%) of 90 829 had no comorbidities. Case numbers increased across the three 4-week periods studied: by epidemiological weeks 19-22, cases were concentrated in the North, Northeast, and Southeast; by weeks 27-30, cases had spread to the Central-West and South regions. 232 036 (91%) of 254 288 patients had a defined hospital outcome when the data were exported; in-hospital mortality was 38% (87 515 of 232 036 patients) overall, 59% (47 002 of 79 687) among patients admitted to the ICU, and 80% (36 046 of 45 205) among those who were mechanically ventilated. The overall burden of ICU admissions per ICU beds was more pronounced in the North, Southeast, and Northeast, than in the Central-West and South. In the Northeast, 1545 (16%) of 9960 patients received invasive mechanical ventilation outside the ICU compared with 431 (8%) of 5388 in the South. In-hospital mortality among patients younger than 60 years was 31% (4204 of 13 468) in the Northeast versus 15% (1694 of 11 196) in the South. INTERPRETATION: We observed a widespread distribution of COVID-19 across all regions in Brazil, resulting in a high overall disease burden. In-hospital mortality was high, even in patients younger than 60 years, and worsened by existing regional disparities within the health system. The COVID-19 pandemic highlights the need to improve access to high-quality care for critically ill patients admitted to hospital with COVID-19, particularly in LMICs. FUNDING: National Council for Scientific and Technological Development (CNPq), Coordinating Agency for Advanced Training of Graduate Personnel (CAPES), Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ), and Instituto de Salud Carlos III.


Asunto(s)
/epidemiología , Monitoreo Epidemiológico , Disparidades en Atención de Salud/estadística & datos numéricos , Mortalidad Hospitalaria/tendencias , Pandemias/estadística & datos numéricos , Adulto , Anciano , Anciano de 80 o más Años , Brasil/epidemiología , /terapia , Comorbilidad , Femenino , Geografía , Accesibilidad a los Servicios de Salud/organización & administración , Accesibilidad a los Servicios de Salud/estadística & datos numéricos , Necesidades y Demandas de Servicios de Salud/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Masculino , Persona de Mediana Edad , Admisión del Paciente/estadística & datos numéricos , Respiración Artificial/estadística & datos numéricos , Estudios Retrospectivos , Adulto Joven
3.
Health Aff (Millwood) ; 39(10): 1792-1798, 2020 10.
Artículo en Inglés | MEDLINE | ID: covidwho-1177766

RESUMEN

Motor vehicle crashes remain the leading cause of adolescent mortality and injury in the United States. For young drivers, crash risk peaks immediately after licensure and declines during the next two years, making the point of licensure an important safety intervention opportunity. Legislation in Ohio established a unique health-transportation partnership among the State of Ohio, Children's Hospital of Philadelphia, and Diagnostic Driving, Inc., to identify underprepared driver license applicants through a virtual driving assessment system. The system, a computer-based virtual driving test, exposes drivers to common serious crash scenarios to identify critical skill deficits and is delivered in testing centers immediately before the on-road examination. A pilot study of license applicants who completed it showed that the virtual driving assessment system accurately predicted which drivers would fail the on-road examination and provided automated feedback that informed drivers on their skill deficits. At this time, the partnership's work is informing policy changes around integrating the virtual driving assessment system into licensing and driver training with the aim of reducing crashes in the first months of independent driving. The system can be developed to identify deficits in safety-critical skills that lead to crashes in new drivers and to address challenges that the coronavirus disease 2019 pandemic has introduced to driver testing and training.


Asunto(s)
Conducción de Automóvil/legislación & jurisprudencia , Infecciones por Coronavirus/prevención & control , Concesión de Licencias/legislación & jurisprudencia , Pandemias/prevención & control , Neumonía Viral/prevención & control , Administración de la Seguridad/organización & administración , Interfaz Usuario-Computador , Adolescente , Infecciones por Coronavirus/epidemiología , Estudios de Factibilidad , Femenino , Humanos , Masculino , Vehículos a Motor/estadística & datos numéricos , Ohio , Pandemias/estadística & datos numéricos , Philadelphia , Proyectos Piloto , Neumonía Viral/epidemiología , Transportes/métodos , Adulto Joven
6.
PLoS One ; 16(4): e0249285, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1167111

RESUMEN

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. OBJECTIVES: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. METHODS: Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. RESULTS: Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. CONCLUSION: Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.


Asunto(s)
/mortalidad , Aprendizaje Automático , Pandemias/estadística & datos numéricos , Respiración Artificial/estadística & datos numéricos , Ventiladores Mecánicos/estadística & datos numéricos , Anciano , Servicio de Urgencia en Hospital/tendencias , Femenino , Mortalidad Hospitalaria/tendencias , Humanos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos
7.
PLoS One ; 16(4): e0249280, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1167110

RESUMEN

International scientific collaborations have always been regarded as critical actions to address global pandemics, however, there was an obvious uncertainty between international collaboration and the COVID-19 control. We aim to combine digital data-based strategies to produce meaningful and advanced insights into the imbalance between COVID-19 and international collaboration, as well as reveal possible influencing factors, and ultimately enhance global collaboration. We conducted three retrospective cohort studies using respectively COVID-19 data from WHO, a complete dataset of scientific publications on coronavirus-related research from WoS, and daily data from Google Trends (GT). The results of geovisualization and spatiotemporal analysis revealed that the global COVID19 pandemic still remains serious. The global issue of imbalance between international collaborations and pandemic does exit, and the nations with good pandemic control had their own characteristics in above-mentioned correlation. Digital epidemiology provides, at least in part, evidence-based assessment and scientific advice to understand the imbalance between international collaborations and COVID-19. Our investigation demonstrates that transdisciplinary conversation through digital data-based strategies can help us fully understand the complex factors influencing the effectiveness of international scientific collaboration, thus facilitating the global response to COVID-19.


Asunto(s)
Investigación Biomédica , Manejo de Datos , Cooperación Internacional , Pandemias/estadística & datos numéricos , Investigación Biomédica/organización & administración , Investigación Biomédica/estadística & datos numéricos , Humanos , Colaboración Intersectorial , Estudios Retrospectivos
8.
PLoS One ; 16(4): e0249133, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1167108

RESUMEN

BACKGROUND: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. METHODS AND FINDINGS: We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases. CONCLUSIONS: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available).


Asunto(s)
Prestación de Atención de Salud , Demografía/estadística & datos numéricos , Pandemias/estadística & datos numéricos , Factores Socioeconómicos , /mortalidad , Prestación de Atención de Salud/organización & administración , Prestación de Atención de Salud/estadística & datos numéricos , Instituciones de Salud/estadística & datos numéricos , Política de Salud , Humanos , Factores de Riesgo , Estados Unidos
9.
PLoS One ; 16(4): e0248828, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1167100

RESUMEN

BACKGROUND: On January 30th 2020, the World Health Organization (WHO) declared a international health emergency due to the unprecedented phenomenon of COVID-19. After this declaration countries swiftly implemented a variety of health policies. In this work we examine how rapid countries responded to this pandemic using two events: the day in which the first case of COVID-19 was reported, and first day in which countries used school closure as one of the measures to avoid outbreaks. We also assessed how countries' health systems, globalization, economic development, political systems, and economic integration to China, Republic of Korea and Italy increased the speed of adoption. METHODS: We compiled information from multiple sources, from December 31st 2019 to June 1st 2020, to trace when 172 countries reported their first COVID-19 case and implemented school closure to contain outbreaks. We applied cross-national Weibull survival analysis to evaluate the global speed of detection of first COVID-19 reported cases and school closure. RESULTS: Ten days after WHO declared COVID-19 to be an international emergency, relative to seven days from this declaration, countries were 28 (95% CI: 12-77) times more likely to report first COVID-19 cases and 42 (95% CI: 22-90) times more likely to close schools. One standard deviation increase in the epidemic security index rises the rate of report first cases by 37% (Hazard Ratio (HR) 1.37 (95% CI: 1.09-1.72) and delays the adoption for school closures by 36% (HR 0.64 (95% CI:0.50-0.82). One standard deviation increase in the globalization index augments the adoption for school closures by 74% (HR 1.74 (95% CI:1.34-2.24). CONCLUSION: After the WHO declared a global emergency, countries were unprecedently acting very rapidly. While countries more globally integrated were swifter in closing schools, countries with better designed health systems to tackle epidemics were slower in adopting it. More studies are needed to assess how the speed of school closures and other policies will affect the development of the pandemic.


Asunto(s)
Salud Global/estadística & datos numéricos , Reglamento Sanitario Internacional/estadística & datos numéricos , Pandemias , Cuarentena/estadística & datos numéricos , Instituciones Académicas , /epidemiología , China , Humanos , Internacionalidad , Italia , Pandemias/prevención & control , Pandemias/estadística & datos numéricos , República de Corea , Instituciones Académicas/organización & administración , Instituciones Académicas/estadística & datos numéricos
10.
J R Soc Interface ; 18(176): 20200916, 2021 03.
Artículo en Inglés | MEDLINE | ID: covidwho-1161023

RESUMEN

Epidemiological data about SARS-CoV-2 spread indicate that the virus is not transmitted uniformly in the population. The transmission tends to be more effective in select settings that involve exposure to relatively high viral dose, such as in crowded indoor settings, assisted living facilities, prisons or food processing plants. To explore the effect on infection dynamics, we describe a new mathematical model where transmission can occur (i) in the community at large, characterized by low-dose exposure and mostly mild disease, and (ii) in so-called transmission hot zones, characterized by high-dose exposure that can be associated with more severe disease. The model yields different types of epidemiological dynamics, depending on the relative importance of hot zone and community transmission. Interesting dynamics occur if the rate of virus release/deposition from severely infected people is larger than that of mildly infected individuals. Under this assumption, we find that successful infection spread can hinge upon high-dose hot zone transmission, yet the majority of infections are predicted to occur in the community at large with mild disease. In this regime, residual hot zone transmission can account for continued virus spread during community lockdowns, and the suppression of hot zones after community interventions are relaxed can cause a prolonged lack of infection resurgence following the reopening of society. This gives rise to the notion that targeted interventions specifically reducing virus transmission in the hot zones have the potential to suppress overall infection spread, including in the community at large. Epidemiological trends in the USA and Europe are interpreted in light of this model.


Asunto(s)
/epidemiología , Modelos Biológicos , Pandemias , Número Básico de Reproducción/estadística & datos numéricos , Simulación por Computador , Humanos , Conceptos Matemáticos , Pandemias/prevención & control , Pandemias/estadística & datos numéricos , Cuarentena , Carga Viral/estadística & datos numéricos
11.
Workplace Health Saf ; 69(4): 154-160, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: covidwho-1159537

RESUMEN

BACKGROUND: Planning occupational health and wellness services and support directed toward low-wage, essential workers in the COVID-19 pandemic has posed a number of challenges across work settings. This article explores the concerns and needs of low-wage essential workers as understood by experts in the field. METHODS: Leading experts in the areas of occupational health and safety, risk management, insurance, and professional education/training were identified and invited to participate in a Round Table discussion. Questions posed to experts were based on literature that addressed COVID-19, essential workers, low-wage workers, infection transmission, education/training, and social justice. FINDINGS: Experts agreed that special considerations must be in place to address the concerns and needs of the low-wage essential worker. These special considerations should address not only the worker's occupational experience but their family and home environment, fears and anxieties, and the economic impact of the COVID-19 restrictions and requirements. CONCLUSION/APPLICATION TO PRACTICE: The occupational health professional is a key resource to employers charged with addressing the concerns and needs of low-wage, essential workers during the COVID-19 pandemic.


Asunto(s)
Renta/estadística & datos numéricos , Exposición Profesional/efectos adversos , Recursos Humanos/estadística & datos numéricos , /etiología , Humanos , Exposición Profesional/estadística & datos numéricos , Pandemias/prevención & control , Pandemias/estadística & datos numéricos
13.
Front Public Health ; 9: 661482, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1155198

RESUMEN

This paper examines the effects of pandemic uncertainty on socially responsible investments. We use the overall corporate sustainability performance index in the Global-100 Most Sustainable Corporations in the World dataset to measure socially responsible investments. The global pandemic uncertainty is also measured by the World Pandemic Uncertainty Index. We focus on the panel dataset from 2012 to 2020, and the results show that the World Pandemic Uncertainty Index is positively related to socially responsible investments. The main findings remain significant when we utilize various panel estimation techniques.


Asunto(s)
/economía , Inversiones en Salud/economía , Inversiones en Salud/estadística & datos numéricos , Modelos Económicos , Pandemias/estadística & datos numéricos , Responsabilidad Social , Incertidumbre , Humanos
14.
Front Public Health ; 9: 632043, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1155197

RESUMEN

The coronavirus pandemic has highlighted the capitalist dysfunction showing that considering profit over people can be deadly. The study reveals the LME economies were more responsive toward the impact of the disease outbreaks as compared to the CME economies wherein the impact of the disease was moderated by the government involvement. This allows us to draw that the impact of the disease outbreaks can be moderated by increasing the involvement of the government authorities.


Asunto(s)
/economía , Comercio/economía , Comercio/estadística & datos numéricos , Brotes de Enfermedades/economía , Brotes de Enfermedades/estadística & datos numéricos , Pandemias/economía , Humanos , Modelos Teóricos , Pandemias/estadística & datos numéricos
15.
Front Public Health ; 9: 628479, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1154263

RESUMEN

Background: COVID-19 has caused a global public health emergency. Government mitigation strategies included a series of behavior-based prevention policies that had a likely impact on the spread of other contagious respiratory illnesses, such as seasonal influenza. Our aim was to explore how 2019-2020 influenza tracked onto COVID-19 pandemic and its mitigation methods. Materials and Methods: We linked the WHO FluNet database and COVID-19 confirmed cases (Johns Hopkins University) for four countries across the northern (Canada, the United States) and southern hemispheres (Australia, Brazil) for the period 2016-2020. Graphical presentations of longitudinal data were provided. Results: There was a notable reduction in influenza cases for the 2019-2020 season. Northern hemisphere countries experienced a quicker ending to the 2019-2020 seasonal influenza cases (shortened by 4-7 weeks) and virtually no 2020 fall influenza season. Countries from the southern hemisphere experienced drastically low levels of seasonal influenza, with consistent trends that were approaching zero cases after the introduction of COVID-19 measures. Conclusions: It is likely that the COVID-19 mitigation measures played a notable role in the marked decrease in influenza, with little to no influenza activity in both the northern and southern hemispheres. In spite of this reduction in influenza cases, there was still community spread of COVID-19, highlighting the contagiousness of SARS-CoV-2 compared to influenza. These results, together with the higher mortality rate from SARS-CoV-2 compared to influenza, highlight that COVID-19 is a far greater health threat than influenza.


Asunto(s)
/epidemiología , Gripe Humana/epidemiología , Gripe Humana/fisiopatología , Internacionalidad , Pandemias/estadística & datos numéricos , Evaluación de Síntomas/estadística & datos numéricos , Australia/epidemiología , Brasil/epidemiología , Canadá/epidemiología , Femenino , Humanos , Masculino , Salud Pública/estadística & datos numéricos , Estados Unidos/epidemiología
16.
PLoS Comput Biol ; 17(2): e1008728, 2021 02.
Artículo en Inglés | MEDLINE | ID: covidwho-1154072

RESUMEN

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method-referred to as mixture-model approach-for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys.


Asunto(s)
/métodos , /epidemiología , Modelos Estadísticos , Pandemias , Anticuerpos Antivirales/sangre , Infecciones Asintomáticas/epidemiología , /estadística & datos numéricos , Biología Computacional , Simulación por Computador , Intervalos de Confianza , Reacciones Falso Negativas , Reacciones Falso Positivas , Humanos , Incidencia , Funciones de Verosimilitud , Pandemias/estadística & datos numéricos , Curva ROC , Reproducibilidad de los Resultados , Sensibilidad y Especificidad
17.
J Public Health Manag Pract ; 27(3): 305-309, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1150048

RESUMEN

To understand county-level variation in case fatality rates of COVID-19, a statewide analysis of COVID-19 incidence and fatality data was performed, using publicly available incidence and case fatality rate data of COVID-19 for all 67 Alabama counties and mapped with health disparities at the county level. A specific adaptation of the Shewhart p-chart, called a funnel chart, was used to compare case fatality rates. Important differences in case fatality rates across the counties did not appear to be reflective of differences in testing or incidence rates. Instead, a higher prevalence of comorbidities and vulnerabilities was observed in high fatality rate counties, while showing no differences in access to acute care. Funnel charts reliably identify counties with unexpected high and low COVID-19 case fatality rates. Social determinants of health are strongly associated with such differences. These data may assist in public health decisions including vaccination strategies, especially in southern states with similar demographics.


Asunto(s)
/epidemiología , /prevención & control , Causas de Muerte/tendencias , Pandemias/estadística & datos numéricos , Vacunación/estadística & datos numéricos , Vacunación/normas , Adulto , Anciano , Anciano de 80 o más Años , Alabama , Femenino , Predicción , Disparidades en el Estado de Salud , Humanos , Incidencia , Masculino , Persona de Mediana Edad , Prevalencia
18.
J Public Health Manag Pract ; 27(3): 295-298, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1150047

RESUMEN

OBJECTIVE: To assess whether county age distribution is associated with age-specific COVID-19 infection, emergency department, hospitalization, and mortality rates. DESIGN: Florida's 2020 COVID-19 cases are summarized into age-specific county rates and supplemented with socioeconomic and demographic characteristics and 2020 presidential voting results to assess the association of population age structure and political choices with age-specific COVID-19 infection, emergency, hospitalization, and mortality rates. RESULTS: Younger counties experienced higher under-25 infection rates, as well as higher over-64 infection, emergency, and hospitalization rates. Older counties experienced reduced infection rates for all ages and decreased over-64 emergency and hospitalization rates. Trump's vote share was associated with higher infection rates for all and higher over-64 emergency, hospitalization, and mortality rates. CONCLUSIONS: Younger counties experience higher COVID-19 infection rates for all residents, with elevated morbidity risks among seniors. Older counties had lower COVID-19 infection, emergency, and hospitalization rates. Age-specific messaging may help slow pandemic spread.


Asunto(s)
/mortalidad , Causas de Muerte , Hospitalización/estadística & datos numéricos , Pandemias/estadística & datos numéricos , Política , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Femenino , Florida/epidemiología , Humanos , Masculino , Persona de Mediana Edad , Factores Socioeconómicos , Adulto Joven
19.
J Public Health Manag Pract ; 27(3): 268-277, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1150045

RESUMEN

CONTEXT: There is a need to understand population race and ethnicity disparities in the context of sociodemographic risk factors in the US experience of the COVID-19 pandemic. OBJECTIVE: Determine the association between county-level proportion of non-Hispanic Black (NHB) on county COVID-19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. DESIGN AND SETTING: This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. MAIN OUTCOME MEASURES: Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. RESULTS: In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp ß = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp ß = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). CONCLUSIONS: The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19.


Asunto(s)
Afroamericanos/estadística & datos numéricos , /mortalidad , Grupos Étnicos/estadística & datos numéricos , Disparidades en el Estado de Salud , Grupos Minoritarios/estadística & datos numéricos , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Gobierno Local , Masculino , Persona de Mediana Edad , Pandemias/estadística & datos numéricos , Vigilancia de la Población , Factores de Riesgo , Factores Socioeconómicos , Estados Unidos/epidemiología
20.
J Public Health Manag Pract ; 27(3): 233-239, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1150041

RESUMEN

OBJECTIVE: To more comprehensively estimate COVID-19-related mortality in Los Angeles County by determining excess all-cause mortality and pneumonia, influenza, or COVID (PIC) mortality. DESIGN: We reviewed vital statistics data to identify deaths registered in Los Angeles County between March 15, 2020, and August 15, 2020. Deaths with an ICD-10 (International Classification of Diseases, Tenth Revision) code for pneumonia, influenza, or COVID-19 listed as an immediate or underlying cause of death were classified as PIC deaths. Expected deaths were calculated using negative binomial regression. Excess mortality was determined by subtracting the expected from the observed number of weekly deaths. The Department of Public Health conducts surveillance for COVID-19-associated deaths: persons who died of nontraumatic/nonaccidental causes within 60 days of a positive COVID-19 test result were classified as confirmed COVID-19 deaths. Deaths without a reported positive SARS-Cov-2 polymerase chain reaction result were classified as probable COVID-19 deaths if COVID-19 was listed on their death certificate or the death occurred 60 to 90 days of a positive test. We compared excess PIC deaths with the number of confirmed and probable COVID-19 deaths ascertained by surveillance. SETTING: Los Angeles County. PARTICIPANTS: Residents of Los Angeles County who died. MAIN OUTCOME MEASURE: Excess mortality. RESULTS: There were 7208 excess all-cause and 5128 excess PIC deaths during the study period. The Department of Public Health also reported 5160 confirmed and 323 probable COVID-19-associated deaths. CONCLUSIONS: The number of excess PIC deaths estimated by our model was approximately equal to the number of confirmed and probable COVID-19 deaths identified by surveillance. This suggests our surveillance definition for confirmed and probable COVID-19 deaths might be sufficiently sensitive for capturing the true burden of deaths caused directly or indirectly by COVID-19.


Asunto(s)
/mortalidad , Causas de Muerte , Gripe Humana/mortalidad , Pandemias/estadística & datos numéricos , Neumonía/mortalidad , Vigilancia de la Población , Salud Pública/estadística & datos numéricos , Adulto , Anciano , Anciano de 80 o más Años , Ciudades/epidemiología , Ciudades/estadística & datos numéricos , Femenino , Humanos , Gripe Humana/epidemiología , Los Angeles/epidemiología , Masculino , Persona de Mediana Edad , Neumonía/epidemiología
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