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1.
PLoS One ; 15(10): e0240153, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33007054

RESUMEN

The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Modelos Estadísticos , Neumonía Viral/epidemiología , Australia/epidemiología , Betacoronavirus , Humanos , Pandemias
2.
BMC Infect Dis ; 20(1): 735, 2020 Oct 07.
Artículo en Inglés | MEDLINE | ID: mdl-33028283

RESUMEN

BACKGROUND: The pandemic of COVID-19 has occurred close on the heels of a global resurgence of measles. In 2019, an unprecedented epidemic of measles affected Samoa, requiring a state of emergency to be declared. Measles causes an immune amnesia which can persist for over 2 years after acute infection and increases the risk of a range of other infections. METHODS: We modelled the potential impact of measles-induced immune amnesia on a COVID-19 epidemic in Samoa using data on measles incidence in 2018-2019, population data and a hypothetical COVID-19 epidemic. RESULTS: The young population structure and contact matrix in Samoa results in the most transmission occurring in young people < 20 years old. The highest rate of death is the 60+ years old, but a smaller peak in death may occur in younger people, with more than 15% of total deaths in the age group under 20 years old. Measles induced immune amnesia could increase the total number of cases by 8% and deaths by more than 2%. CONCLUSIONS: Samoa, which had large measles epidemics in 2019-2020 should focus on rapidly achieving high rates of measles vaccination and enhanced surveillance for COVID-19, as the impact may be more severe due to measles-induced immune paresis. This applies to other severely measles-affected countries in the Pacific, Europe and elsewhere.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/mortalidad , Sarampión/epidemiología , Sarampión/mortalidad , Neumonía Viral/epidemiología , Neumonía Viral/mortalidad , Adolescente , Adulto , Distribución por Edad , Anciano , Niño , Preescolar , Comorbilidad , Infecciones por Coronavirus/virología , Femenino , Humanos , Incidencia , Lactante , Recién Nacido , Masculino , Sarampión/inmunología , Sarampión/prevención & control , Persona de Mediana Edad , Modelos Estadísticos , Pandemias , Neumonía Viral/virología , Samoa/epidemiología , Vacunación , Adulto Joven
4.
J Korean Med Sci ; 35(34): e317, 2020 Aug 31.
Artículo en Inglés | MEDLINE | ID: mdl-32864913

RESUMEN

BACKGROUND: The novel coronavirus (coronavirus disease 2019 [COVID-19]) outbreak began in China in December last year, and confirmed cases began occurring in Korea in mid-February 2020. Since the end of February, the rate of infection has increased greatly due to mass (herd) infection within religious groups and nursing homes in the Daegu and Gyeongbuk regions. This mass infection has increased the number of infected people more rapidly than was initially expected; the epidemic model based on existing studies had predicted a much lower infection rate and faster recovery. METHODS: The present study evaluated rapid infection spread by mass infection in Korea and the high mortality rate for the elderly and those with underlying diseases through the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model. RESULTS: The present study demonstrated early infection peak occurrence (-6.3 days for Daegu and -5.3 days for Gyeongbuk) and slow recovery trend (= -1,486.6 persons for Daegu and -223.7 persons for Gyeongbuk) between the actual and the epidemic model for a mass infection region compared to a normal infection region. CONCLUSION: The analysis of the time difference between infection and recovery can help predict the epidemic peak due to mass (or normal) infection and can also be used as a time index to prepare medical resources.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/transmisión , Modelos Estadísticos , Neumonía Viral/epidemiología , Neumonía Viral/transmisión , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Betacoronavirus , Niño , Preescolar , Infecciones por Coronavirus/patología , Femenino , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Casas de Salud/estadística & datos numéricos , Pandemias , Neumonía Viral/patología , República de Corea/epidemiología , Factores de Tiempo , Adulto Joven
5.
Rev Soc Bras Med Trop ; 53: e20200481, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32876321

RESUMEN

INTRODUCTION: Mathematical models have been used to obtain long-term forecasts of the COVID-19 epidemic. METHODS: The daily COVID-19 case count in two Brazilian states was used to show the potential limitations of long-term forecasting through the application of a mathematical model to the data. RESULTS: The predicted number of cases at the end of the epidemic and at the moment that the peak occurs, is highly dependent on the length of the time series used in the predictive model. CONCLUSIONS: Predictions obtained during the course of the COVID-19 pandemic need to be viewed with caution.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Coronavirus , Pandemias , Neumonía Viral/epidemiología , Betacoronavirus , Predicción , Humanos , Modelos Estadísticos
6.
BMC Infect Dis ; 20(1): 649, 2020 Sep 03.
Artículo en Inglés | MEDLINE | ID: mdl-32883213

RESUMEN

BACKGROUND: More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. METHODS: We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution. RESULTS: Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. CONCLUSIONS: Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.


Asunto(s)
Dengue/epidemiología , Clima , Brotes de Enfermedades , Humanos , Modelos Estadísticos , Factores de Riesgo , Sri Lanka/epidemiología , Temperatura , Viaje
7.
Medicine (Baltimore) ; 99(35): e21897, 2020 Aug 28.
Artículo en Inglés | MEDLINE | ID: mdl-32871921

RESUMEN

Allogeneic red blood cell transfusion (ABT) is 1 of the poor prognostic factors for morbidity and mortality in patients with hip fracture, particularly among elderly patients. This study aimed to investigate the risk factors for ABT and 1-year mortality in elderly patients undergoing surgery for femoral neck fracture.A total of 225 elderly patients who underwent femoral neck fracture surgery between May 2013 and November 2015 at a tertiary medical center were retrospectively recruited. Medical records were analyzed.The median patient age was 80 years and 28.4% were men. A total of 113 patients received ABT (50.2%). Multivariate logistic regression analysis showed that female sex (odds ratio [OR] 2.606, 95% confidence interval [CI] 1.283-5.295, P = .008), malignancy (OR 5.098, 95% CI 1.725-15.061, P = .003), chronic kidney disease stage ≥ 3 (OR 3.258, 95% CI 1.603-6.622, P = .001), and anemia (hemoglobin < 12 g/dL) (OR 4.684, 95% CI 2.230-9.837, P < .001) were significantly associated with ABT. The 1-year mortality rate after surgery was 15.1%. Male sex (OR 2.477, 95% CI 1.101-5.575, P = .028), ABT (OR 2.367, 95% CI 1.036-5.410, P = .041), and intensive care unit admission (OR 5.564, 95% CI 1.457-21.249, P = .012) were significantly associated with 1-year mortality.In this study, underlying comorbidities such as chronic kidney disease and malignancy were associated with ABT. Furthermore, ABT was a significant independent risk factor for 1-year mortality. These findings suggest that underlying comorbidities and the need for ABT should be considered in the risk assessment of elderly patients with femoral neck fracture to improve the outcomes after surgery.


Asunto(s)
Causas de Muerte , Transfusión de Eritrocitos , Fracturas del Cuello Femoral/cirugía , Factores de Edad , Anciano , Anciano de 80 o más Años , Anemia/complicaciones , Femenino , Fracturas del Cuello Femoral/complicaciones , Humanos , Masculino , Modelos Estadísticos , Neoplasias/complicaciones , Insuficiencia Renal Crónica/complicaciones , Estudios Retrospectivos , Medición de Riesgo , Factores de Riesgo , Factores Sexuales
8.
Eur J Epidemiol ; 35(8): 749-761, 2020 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-32888169

RESUMEN

The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions and measurement errors in the data. We use the model to conduct inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Predicción , Pandemias , Neumonía Viral/epidemiología , Informática en Salud Pública/métodos , Teorema de Bayes , Betacoronavirus , Simulación por Computador , Brotes de Enfermedades , Humanos , Italia/epidemiología , Modelos Biológicos , Modelos Estadísticos , Cuarentena , Reino Unido/epidemiología , Estados Unidos/epidemiología
9.
Medicine (Baltimore) ; 99(33): e21085, 2020 Aug 14.
Artículo en Inglés | MEDLINE | ID: mdl-32871979

RESUMEN

The lymph nodal invasion diagnosis is critical for therapeutic-decision and follows up in gastric cancer. However, the number of nodes to be examined for nodal invasion diagnosis is still under controversy, and the model for quantifying risk of missing positive node is currently not reported yet. We analyzed the nodal invasion status of 13,857 gastric cancer samples with records of primary tumor stage, the number of examined and positive lymph nodes in the surveillance, epidemiology, and end results (SEER) database, fitting a beta-binomial model. The nodes need to be examined with different primary tumor stage were determined based on the model. Overall, examining 11 lymph nodes reduces the probability of missing positive nodes to <10%, and the currently median nodes dissected is adequate (12 nodes). While the number of nodes demands to be dissected for T1, T2, T3, and T4 subgroups are 6, 19, 40, and 66, respectively. The currently implemented median value for these samples was 12, 12, 13, and 16, separately. It implies that the number of nodes to be examined is sufficient for early gastric cancer (T1), but it is inadequate for middle and advanced gastric cancer (T2-T3). The clinical significance of nodal staging score was validated with survival information. In summary, we first quantified the lymph nodes to be examined during surgery using a beta-binomial model, and validated with survival information.


Asunto(s)
Ganglios Linfáticos/patología , Metástasis Linfática/diagnóstico , Metástasis Linfática/patología , Estadificación de Neoplasias/métodos , Neoplasias Gástricas/diagnóstico , Neoplasias Gástricas/patología , Reacciones Falso Negativas , Femenino , Humanos , Ganglios Linfáticos/cirugía , Masculino , Persona de Mediana Edad , Modelos Estadísticos , Probabilidad , Estudios Retrospectivos , Programa de VERF , Sensibilidad y Especificidad , Neoplasias Gástricas/epidemiología , Neoplasias Gástricas/cirugía , Análisis de Supervivencia
10.
BMC Pediatr ; 20(1): 410, 2020 09 01.
Artículo en Inglés | MEDLINE | ID: mdl-32873269

RESUMEN

BACKGROUND: The emerging virus is rampaging globally. A growing number of pediatric infected cases have been reported. Great efforts are needed to cut down the transmission. METHODS: A single-arm meta-analysis was conducted. We searched PubMed, Google Scholar, Web of Science, and several Chinese databases for studies presenting characteristics of children confirmed with Coronavirus Disease 2019 (COVID-19) from December 12, 2019 to May 10, 2020. Quality Appraisal of Case Series Studies Checklist was used to assess quality and publication bias was analyzed by Egger's test. Random-effect model was used to calculate the pooled incidence rate (IR) or mean difference (MD) with 95% confidence intervals (CI), or a fixed model instead when I2 < 50%. We conducted subgroup analysis according to geographic region. Additionally, we searched United Nations Educational Scientific and Cultural Organization to see how different countries act to the education disruption in COVID-19. RESULTS: 29 studies with 4300 pediatric patients were included. The mean age was 7.04 (95% CI: 5.06-9.08) years old. 18.9% of children were asymptomatic (95% CI: 0.121-0.266), 37.4% (95% CI: 0.280-0.474) had no radiographic abnormalities. Besides, a proportion of 0.1% patients were admitted to intensive care units (0, 95% CI: 0.000-0.013) and four deaths were reported (0, 95% CI: 0.000-0.000). Up to 159 countries have implemented nationwide school closures, affecting over 70% of the world's students. CONCLUSION: Children were also susceptible to SARS-CoV-2, while critical cases or deaths were rare. Characterized by mild presentation, the dilemma that children may become a potential spreader in the pandemic, while strict managements like prolonged school closures, may undermine their well-beings. Thus, the public policies are facing challenge.


Asunto(s)
Betacoronavirus , Técnicas de Laboratorio Clínico , Infecciones por Coronavirus/diagnóstico , Infecciones por Coronavirus/prevención & control , Política de Salud , Pandemias/prevención & control , Neumonía Viral/diagnóstico , Neumonía Viral/prevención & control , Índice de Severidad de la Enfermedad , Adolescente , Niño , Preescolar , Infecciones por Coronavirus/epidemiología , Salud Global , Humanos , Modelos Estadísticos , Neumonía Viral/epidemiología , Instituciones Académicas
11.
Artículo en Inglés | MEDLINE | ID: mdl-32911738

RESUMEN

COVID-19 has posed an unprecedented global public health threat and caused a significant number of severe cases that necessitated long hospitalization and overwhelmed health services in the most affected countries. In response, governments initiated a series of non-pharmaceutical interventions (NPIs) that led to severe economic and social impacts. The effect of these intervention measures on the spread of the COVID-19 pandemic are not well investigated within developing country settings. This study simulated the trajectories of the COVID-19 pandemic curve in Jordan between February and May and assessed the effect of Jordan's strict NPI measures on the spread of COVID-19. A modified susceptible, exposed, infected, and recovered (SEIR) epidemic model was utilized. The compartments in the proposed model categorized the Jordanian population into six deterministic compartments: suspected, exposed, infectious pre-symptomatic, infectious with mild symptoms, infectious with moderate to severe symptoms, and recovered. The GLEAMviz client simulator was used to run the simulation model. Epidemic curves were plotted for estimated COVID-19 cases in the simulation model, and compared against the reported cases. The simulation model estimated the highest number of total daily new COVID-19 cases, in the pre-symptomatic compartmental state, to be 65 cases, with an epidemic curve growing to its peak in 49 days and terminating in a duration of 83 days, and a total simulated cumulative case count of 1048 cases. The curve representing the number of actual reported cases in Jordan showed a good pattern compatibility to that in the mild and moderate to severe compartmental states. The reproduction number under the NPIs was reduced from 5.6 to less than one. NPIs in Jordan seem to be effective in controlling the COVID-19 epidemic and reducing the reproduction rate. Early strict intervention measures showed evidence of containing and suppressing the disease.


Asunto(s)
Control de Enfermedades Transmisibles/métodos , Infecciones por Coronavirus/epidemiología , Pandemias , Neumonía Viral/epidemiología , Betacoronavirus , Simulación por Computador , Humanos , Jordania/epidemiología , Modelos Estadísticos , Índice de Severidad de la Enfermedad
12.
J Med Internet Res ; 22(9): e20924, 2020 09 22.
Artículo en Inglés | MEDLINE | ID: mdl-32915762

RESUMEN

BACKGROUND: SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. OBJECTIVE: The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. METHODS: Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. RESULTS: A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. CONCLUSIONS: DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19-free only when there is an effective vaccine, and the "social" end of the pandemic will occur before the "medical" end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/transmisión , Política de Salud , Modelos Biológicos , Modelos Estadísticos , Neumonía Viral/epidemiología , Neumonía Viral/transmisión , Vigilancia en Salud Pública/métodos , Betacoronavirus , Infecciones por Coronavirus/prevención & control , Humanos , Pandemias/prevención & control , Pandemias/estadística & datos numéricos , Neumonía Viral/prevención & control , Reproducibilidad de los Resultados , Estados Unidos/epidemiología
13.
Rev Esp Salud Publica ; 942020 Sep 23.
Artículo en Español | MEDLINE | ID: mdl-32963218

RESUMEN

In December 2019, an acute respiratory disease outbreak from zoonotic origin was detected in the city of Wuhan, China. The outbreak's infectious agent was a type of coronavirus never seen. Thenceforth, the Covid-19 disease has rapidly spread to more than 200 countries around the world. To minimize the devastating effects of the virus, the States have adopted epidemiological measures of various kinds that involved enormous economic expenses and the massive use of the media to explain the measures to the entire population. For the prediction and mitigation of infectious events, various epidemiological models, such as SIR, SEIR, MSIR and MSEIR, are used. Among them, the most widely used is the SIR model, which is based on the analysis of the transition of individuals susceptible to infection (S) to the state of infected individuals that infect (I) and, finally, to that of recovered (cured or deceased) (R), by using differential equations. The objective of this article was the mathematical development of the SIR model and its application to predict the course of the Covid-19 pandemic in the city of Santa Marta (Colombia), in order to understand the reason behind several of the measures of containment adopted by the States of the world in the fight against the pandemic.


Asunto(s)
Control de Enfermedades Transmisibles , Infecciones por Coronavirus/epidemiología , Modelos Estadísticos , Neumonía Viral/epidemiología , Betacoronavirus , Ciudades , Colombia/epidemiología , Infecciones por Coronavirus/prevención & control , Brotes de Enfermedades , Humanos , Pandemias/prevención & control , Neumonía Viral/prevención & control
14.
PLoS One ; 15(9): e0239800, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32970786

RESUMEN

The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled Ro = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of 'super-spreaders' on the inference of Ro are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.


Asunto(s)
Número Básico de Reproducción , Infecciones por Coronavirus/epidemiología , Neumonía Viral/epidemiología , Betacoronavirus , Control de Enfermedades Transmisibles , Predicción , Humanos , Modelos Estadísticos , Pandemias
15.
Lancet Public Health ; 5(10): e543-e550, 2020 10.
Artículo en Inglés | MEDLINE | ID: mdl-32979305

RESUMEN

BACKGROUND: To date, research on the indirect impact of the COVID-19 pandemic on the health of the population and the health-care system is scarce. We aimed to investigate the indirect effect of the COVID-19 pandemic on general practice health-care usage, and the subsequent diagnoses of common physical and mental health conditions in a deprived UK population. METHODS: We did a retrospective cohort study using routinely collected primary care data that was recorded in the Salford Integrated Record between Jan 1, 2010, and May 31, 2020. We extracted the weekly number of clinical codes entered into patient records overall, and for six high-level categories: symptoms and observations, diagnoses, prescriptions, operations and procedures, laboratory tests, and other diagnostic procedures. Negative binomial regression models were applied to monthly counts of first diagnoses of common conditions (common mental health problems, cardiovascular and cerebrovascular disease, type 2 diabetes, and cancer), and corresponding first prescriptions of medications indicative of these conditions. We used these models to predict the expected numbers of first diagnoses and first prescriptions between March 1 and May 31, 2020, which were then compared with the observed numbers for the same time period. FINDINGS: Between March 1 and May 31, 2020, 1073 first diagnoses of common mental health problems were reported compared with 2147 expected cases (95% CI 1821 to 2489) based on preceding years, representing a 50·0% reduction (95% CI 41·1 to 56·9). Compared with expected numbers, 456 fewer diagnoses of circulatory system diseases (43·3% reduction, 95% CI 29·6 to 53·5), and 135 fewer type 2 diabetes diagnoses (49·0% reduction, 23·8 to 63·1) were observed. The number of first prescriptions of associated medications was also lower than expected for the same time period. However, the gap between observed and expected cancer diagnoses (31 fewer; 16·0% reduction, -18·1 to 36·6) during this time period was not statistically significant. INTERPRETATION: In this deprived urban population, diagnoses of common conditions decreased substantially between March and May 2020, suggesting a large number of patients have undiagnosed conditions. A rebound in future workload could be imminent as COVID-19 restrictions ease and patients with undiagnosed conditions or delayed diagnosis present to primary and secondary health-care services. Such services should prioritise the diagnosis and treatment of these patients to mitigate potential indirect harms to protect public health. FUNDING: National Institute of Health Research.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Diagnóstico , Pandemias , Neumonía Viral/epidemiología , Atención Primaria de Salud/estadística & datos numéricos , Adulto , Enfermedades Cardiovasculares/diagnóstico , Trastornos Cerebrovasculares/diagnóstico , Diabetes Mellitus Tipo 2/diagnóstico , Femenino , Medicina General/estadística & datos numéricos , Humanos , Masculino , Trastornos Mentales/diagnóstico , Persona de Mediana Edad , Modelos Estadísticos , Neoplasias/diagnóstico , Estudios Retrospectivos , Reino Unido/epidemiología , Adulto Joven
16.
Bull Math Biol ; 82(9): 118, 2020 09 04.
Artículo en Inglés | MEDLINE | ID: mdl-32888118

RESUMEN

The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/prevención & control , Modelos Biológicos , Pandemias/prevención & control , Neumonía Viral/prevención & control , Número Básico de Reproducción/estadística & datos numéricos , Bioestadística , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/transmisión , Humanos , Conceptos Matemáticos , Modelos Estadísticos , Pandemias/estadística & datos numéricos , Neumonía Viral/epidemiología , Neumonía Viral/transmisión , Factores de Tiempo , Estados Unidos/epidemiología
18.
Nat Commun ; 11(1): 4392, 2020 09 01.
Artículo en Inglés | MEDLINE | ID: mdl-32873810

RESUMEN

The successful mitigation of emerging wildlife diseases may involve controversial host culling. For livestock, 'preemptive host culling' is an accepted practice involving the removal of herds with known contact to infected populations. When applied to wildlife, this proactive approach comes in conflict with biodiversity conservation goals. Here, we present an alternative approach of 'proactive hunting surveillance' with the aim of early disease detection that simultaneously avoids undesirable population decline by targeting demographic groups with (1) a higher likelihood of being infected and (2) a lower reproductive value. We applied this harvesting principle to populations of reindeer to substantiate freedom of chronic wasting disease (CWD) infection. Proactive hunting surveillance reached 99% probability of freedom from infection (<4 reindeer infected) within 3-5 years, in comparison to ~10 years using ordinary harvest surveillance. However, implementation uncertainties linked to social issues appear challenging also with this kind of host culling.


Asunto(s)
Sacrificio de Animales/métodos , Animales Salvajes , Conservación de los Recursos Naturales/métodos , Monitoreo Epidemiológico/veterinaria , Reno , Enfermedad Debilitante Crónica/diagnóstico , Factores de Edad , Animales , Simulación por Computador , Femenino , Masculino , Modelos Estadísticos , Dinámica Poblacional , Factores Sexuales , Enfermedad Debilitante Crónica/prevención & control , Enfermedad Debilitante Crónica/transmisión
19.
Int J Med Sci ; 17(15): 2257-2263, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32922189

RESUMEN

Background: Corona Virus Disease 2019 (COVID-19) has become a global pandemic. This study established prognostic scoring models based on comorbidities and other clinical information for severe and critical patients with COVID-19. Material and Methods: We retrospectively collected data from 51 patients diagnosed as severe or critical COVID-19 who were admitted between January 29, 2020, and February 18, 2020. The Charlson (CCI), Elixhauser (ECI), and age- and smoking-adjusted Charlson (ASCCI) and Elixhauser (ASECI) comorbidity indices were used to evaluate the patient outcomes. Results: The mean hospital length of stay (LOS) of the COVID-19 patients was 22.82 ± 12.32 days; 19 patients (37.3%) were hospitalized for more than 24 days. Multivariate analysis identified older age (OR 1.064, P = 0.018, 95%CI 1.011-1.121) and smoking (OR 3.696, P = 0.080, 95%CI 0.856-15.955) as positive predictors of a long LOS. There were significant trends for increasing hospital LOS with increasing CCI, ASCCI, and ASECI scores (OR 57.500, P = 0.001, 95%CI 5.687-581.399; OR 71.500, P = 0.001, 95%CI 5.689-898.642; and OR 19.556, P = 0.001, 95%CI 3.315-115.372, respectively). The result was similar for the outcome of critical illness (OR 21.333, P = 0.001, 95%CI 3.565-127.672; OR 13.000, P = 0.009, 95%CI 1.921-87.990; OR 11.333, P = 0.008, 95%CI 1.859-69.080, respectively). Conclusions: This study established prognostic scoring models based on comorbidities and clinical information, which may help with the graded management of patients according to prognosis score and remind physicians to pay more attention to patients with high scores.


Asunto(s)
Comorbilidad , Infecciones por Coronavirus/mortalidad , Enfermedad Crítica/mortalidad , Modelos Estadísticos , Neumonía Viral/mortalidad , Índice de Severidad de la Enfermedad , Adulto , Anciano , Anciano de 80 o más Años , Betacoronavirus/aislamiento & purificación , Betacoronavirus/patogenicidad , Toma de Decisiones Clínicas , Infecciones por Coronavirus/diagnóstico , Infecciones por Coronavirus/terapia , Infecciones por Coronavirus/virología , Femenino , Mortalidad Hospitalaria , Humanos , Tiempo de Internación/estadística & datos numéricos , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/diagnóstico , Neumonía Viral/terapia , Neumonía Viral/virología , Pronóstico , Estudios Retrospectivos , Medición de Riesgo/métodos
20.
Environ Health ; 19(1): 94, 2020 08 31.
Artículo en Inglés | MEDLINE | ID: mdl-32867766

RESUMEN

BACKGROUND: Various risk factors influence obesity differently, and environmental endocrine disruption may increase the occurrence of obesity. However, most of the previous studies have considered only a unitary exposure or a set of similar exposures instead of mixed exposures, which entail complicated interactions. We utilized three statistical models to evaluate the correlations between mixed chemicals to analyze the association between 9 different chemical exposures and obesity in children and adolescents. METHODS: We fitted the generalized linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) to analyze the association between the mixed exposures and obesity in the participants aged 6-19 in the National Health and Nutrition Examination Survey (NHANES) 2005-2010. RESULTS: In the multivariable logistic regression model, 2,5-dichlorophenol (2,5-DCP) (OR (95% CI): 1.25 (1.11, 1.40)), monoethyl phthalate (MEP) (OR (95% CI): 1.28 (1.04, 1.58)), and mono-isobutyl phthalate (MiBP) (OR (95% CI): 1.42 (1.07, 1.89)) were found to be positively associated with obesity, while methylparaben (MeP) (OR (95% CI): 0.80 (0.68, 0.94)) was negatively associated with obesity. In the multivariable linear regression, MEP was found to be positively associated with the body mass index (BMI) z-score (ß (95% CI): 0.12 (0.02, 0.21)). In the WQS regression model, the WQS index had a significant association (OR (95% CI): 1.48 (1.16, 1.89)) with the outcome in the obesity model, in which 2,5-DCP (weighted 0.41), bisphenol A (BPA) (weighted 0.17) and MEP (weighted 0.14) all had relatively high weights. In the BKMR model, despite no statistically significant difference in the overall association between the chemical mixtures and the outcome (obesity or BMI z-score), there was nonetheless an increasing trend. 2,5-DCP and MEP were found to be positively associated with the outcome (obesity or BMI z-score), while fixing other chemicals at their median concentrations. CONCLUSION: Comparing the three statistical models, we found that 2,5-DCP and MEP may play an important role in obesity. Considering the advantages and disadvantages of the three statistical models, our study confirms the necessity to combine different statistical models on obesity when dealing with mixed exposures.


Asunto(s)
Exposición a Riesgos Ambientales/efectos adversos , Monitoreo del Ambiente/métodos , Contaminantes Ambientales/efectos adversos , Obesidad Pediátrica/epidemiología , Adolescente , Teorema de Bayes , Niño , Femenino , Humanos , Modelos Lineales , Masculino , Modelos Estadísticos , Encuestas Nutricionales , Obesidad Pediátrica/inducido químicamente , Prevalencia , Estados Unidos/epidemiología
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