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1.
Braz. j. biol ; 84: e257402, 2024. tab, graf
Article in English | LILACS, VETINDEX | ID: biblio-1355856

ABSTRACT

Abstract Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.


Resumo A leishmaniose visceral (LV) é uma doença de natureza infecciosa, predominante em países de zonas tropicais. A predição de ocorrência de doenças infecciosas através da modelagem epidemiológica tem se revelado uma importante ferramenta no entendimento de sua dinâmica de ocorrência. O objetivo deste estudo foi desenvolver um modelo de previsão da incidência da LV no Maranhão usando o modelo de Média Móvel Integrada Autocorrelacionada Sazonal (SARIMA). Foram coletados os dados mensais de casos de LV através do Sistema de Informação de Agravos de Notificação (SINAN) correspondentes ao período de 2001 a 2018. O método de Box-Jenkins foi aplicado para ajustar um modelo de predição SARIMA para incidência geral e por sexo (masculino e feminino) de LV para o período de janeiro de 2019 a dezembro de 2023. Durante o período de 216 meses dessa série temporal, foram registrados 10.431 casos de LV no Maranhão, com uma média de 579 casos por ano. Em relação à faixa etária, houve maior registro no público pediátrico (0 a 14 anos). Houve predominância do sexo masculino, com 6437 casos (61,71%). Os valores do teste de Box-Pierce para incidência geral, sexo masculino e feminino reforçados pelos resultados do teste Ljung-Box sugerem que as autocorrelações de resíduos apresentam um comportamento de ruído branco. Para incidência mensal geral e por sexo masculino e feminino, os modelos SARIMA (2,0,0) (2,0,0), (0,1,1) (0,1,1) e (0,1,1) (2, 0, 0) foram os que mais se ajustaram aos dados, respectivamente. O modelo SARIMA se mostrou uma ferramenta adequada de previsão e análise da tendência de incidência da LV no Maranhão. A determinação da variação temporal e sua predição são determinantes no norteamento de medidas de intervenção em saúde.


Subject(s)
Humans , Male , Female , Infant, Newborn , Infant , Child, Preschool , Child , Adolescent , Leishmaniasis, Visceral/diagnosis , Leishmaniasis, Visceral/epidemiology , Seasons , Brazil/epidemiology , Incidence , Models, Statistical
2.
J. health med. sci. (Print) ; 8(1): 53-56, ene.-mar. 2022.
Article in Spanish | LILACS | ID: biblio-1395768

ABSTRACT

En estadística existen dos enfoques básicos, la estadística frecuentista que es la corriente principal y la estadística bayesiana. La mayoría de los principales métodos estadísticos son frecuentistas siendo el enfoque bayesiano más desconocido entre los investigadores. En el presente artículo se exponen los fundamentos lógicos del enfoque bayesiano y su uso mediante un ejemplo de aplicación. En este contexto, más que presentar un debate entre la lógica clásica y la bayesiana, se pretende mostrar de manera introductoria las enormes posibilidades que el enfoque bayesiano puede aportar a la investigación en las Ciencias de la Salud.


In the stadistic field there are two basic approaches, the Frequentist Statistics which is the primary one, and the Bayesian Statistics. The most used statistical methods are the Frequentist methods, being the Bayesian approach the most popular among researchers. In this article, the logical basis of the Bayendian approach and its use are exposed through an application example. In this context, rather than presenting a debate between the classic and the Bayensian logic, it is intended to demonstrate in an introductory method the considerable possibilities how Bayesian approach can contribute to Health and Sciences research.


Subject(s)
Bayes Theorem , Health Sciences/education , Algorithms , Models, Statistical
3.
Rev. bras. epidemiol ; 25: e220001, 2022. graf
Article in English, Portuguese | LILACS | ID: biblio-1365648

ABSTRACT

RESUMEN Usando un modelo de regresión polinomial con retraso, que empleó datos de COVID-19 de 2020 con ausencia de vacunas, se realizó la predicción de COVID-19 en un escenario con administración de vacunas para Tucumán en 2021. La modelación incluyó la identificación de un punto de quiebre de contagios entre ambas series con la mejor correlación. Previamente, se indicó por medio de correlación cruzada el lag que sirvió para obtener el menor error entre los valores esperados y los observados. La validación del modelo fue realizada con datos reales. En 21 días fueron predichos 18.640 casos de COVID-19 de 20.400 casos informados. El pico máximo de COVID-19 fue estimado 21 días antes con la intensidad esperada.


ABSTRACT: Using a lagged polynomial regression model, which used COVID-19 data from 2020 with no vaccines, the prediction of COVID-19 was performed in a scenario with vaccine administration for Tucumán in 2021. The modeling included the identification of a contagion breaking point between both series with the best correlation. Previously, the lag that served to obtain the smallest error between the expected and observed values was indicated by means of cross correlation. The validation of the model was carried out with real data. In 21 days, 18,640 COVID-19 cases out of 20,400 reported cases were predicted. The maximum peak of COVID-19 was estimated 21 days in advance with the expected intensity.


Subject(s)
Humans , COVID-19/epidemiology , Argentina/epidemiology , Brazil , Models, Statistical
4.
Chinese Journal of Epidemiology ; (12): 784-788, 2022.
Article in Chinese | WPRIM | ID: wpr-935459

ABSTRACT

The existence of garbage codes in death cause surveillance data sets could influence the accuracy of the death cause statistics, and subsequently affect the precision and effectiveness of public health policy making. International and domestic researchers have studied the characteristics of garbage codes in various death cause data sets from different countries or regions in the world. They proposed several approaches for redistributing garbage codes, such as expert consultancy, fixed proportional reassignment, using the information about death cause chain, building statistical models, and so on. This paper summarizes and compares the principles, applications and limitation of application scenarios of currently common methods for garbage code redistribution in order to provide some references for improving the accuracy and usefulness of the death cause data in China.


Subject(s)
Causality , Cause of Death , Data Collection , Humans , Models, Statistical , Public Policy
5.
Chinese Journal of Epidemiology ; (12): 739-746, 2022.
Article in Chinese | WPRIM | ID: wpr-935453

ABSTRACT

Objective: To introduce and compare four analysis methods of multiple parallel mediation model, including pure regression method, method based on inverse probability weighting, extended natural effect model method and weight-based imputation strategies. Methods: For the multiple parallel mediation model, the simulation experiments of three scenarios were carried out to compare the performance of different methods in estimating direct and indirect effects in different situations. Dataset from UK Biobank was then analyzed by using the four methods. Results: The estimation biases of the regression method and the inverse probability weighting method were relatively small, followed by the extended natural effect model method, and the estimation results of the weight-based imputation strategies were quite different from the other three methods. Conclusions: Different multiple parallel mediation analysis methods have different application situations and their own advantages and disadvantages. The regression method is more suitable for continuous mediator, and the inverse probability weighting method is more suitable for binary mediator. The extended natural effect model method has better performances when the residuals of two parallel mediators are positively correlated and the correlation degree is small. The weight-based imputation strategies might not be appropriate for parallel mediation analysis. Therefore, appropriate methods should be selected according to the specific situation in practice.


Subject(s)
Bias , Computer Simulation , Humans , Mediation Analysis , Models, Statistical , Probability , Regression Analysis , Research Design
6.
Chinese Journal of Epidemiology ; (12): 403-408, 2022.
Article in Chinese | WPRIM | ID: wpr-935403

ABSTRACT

Reduced rank regression is an extended multivariate linear regression model with the function of dimension reduction. It has been more and more widely used in nutritional epidemiology research to understand people's dietary patterns in recent years. However, there has been no existing Stata package or command to implement reduced rank regression independently. Therefore, we developed a new user-written package named "rrr" for its implementation in Stata. This paper summarizes the methodology of reduced rank regression, the development and functions of the Stata rrr package and its application in the China Kadoorie Biobank dataset, with the aim of facilitating the future wide use of this statistical method in epidemiology and public health research.


Subject(s)
China , Humans , Models, Statistical , Public Health , Regression Analysis
7.
Chinese Journal of Epidemiology ; (12): 118-122, 2022.
Article in Chinese | WPRIM | ID: wpr-935359

ABSTRACT

Due to the latent characteristics of HIV infection, exceptionality of HIV high-risk population, social discrimination and insufficient awareness of AIDS prevention, timely testing and diagnosis of HIV infection is still a challenge worldwide. Until recently, it is difficult to exactly understand the overall HIV epidemic only using routine surveillance data. Therefore, epidemiological and statistical modeling is widely used to address this issue. Almost at the same time when AIDS was firstly discovered firstly, scientists also began to study the methods for the estimation and prediction of HIV infection epidemic. This article summarizes the development of global and domestic HIV epidemic estimation for the further understanding of its current performance and methods applied to provide reference for the future work.


Subject(s)
Acquired Immunodeficiency Syndrome/epidemiology , Epidemics , HIV Infections/epidemiology , Humans , Models, Statistical
8.
Rev. colomb. obstet. ginecol ; 72(4): 396-406, Oct.-Dec. 2021. tab
Article in Spanish | LILACS | ID: biblio-1360992

ABSTRACT

RESUMEN Objetivo: Hacer un ejercicio académico, con datos locales reales, sobre la aplicación del C-Model v1.0 en cuanto a la manera como se obtiene y utiliza la información para generar el modelo, su aplicación a fin de identificar el posible exceso de cesáreas en una institución y, si se identifica, cómo se aplica la distribución de los partos según los grupos de la Clasificación de Robson para explicar ese exceso. Metodología: A partir de las bases de datos reales de cinco instituciones hospitalarias de Colombia, seleccionadas a conveniencia para lograr el cumplimiento de los objetivos, se estimó la razón estandarizada y la diferencia absoluta entre la proporción observada y la probabilidad esperada de cesárea según el C-Model v1.0 en cada institución. Con base en los supuestos que subyacen a la distribución de los grupos según la Clasificación de Robson, se proponen explicaciones a los excesos y a las diferencias entre las instituciones. Resultados: La razón estandarizada de cesárea aplicando el C-Model identificó excesos del procedimiento diferentes en presencia de proporciones institucionales similares de cesárea. Se encontró variabilidad importante en la proporción de cesárea dentro de grupos de mujeres con características clínicas y obstétricas similares que podría ser la explicación para los excesos detectados. Conclusión: El C-Model permite estimar proporciones de cesárea esperadas según las condiciones específicas de las mujeres atendidas en cada institución; su distribución de acuerdo con la Clasificación de Robson permite explorar el origen y las particularidades de dichas diferencias.


ABSTRACT Objective: To carry out an academic exercise based on real local data regarding the application of the C-Model v1.0 to determine how data are gathered and used to generate the model, how the model is applied in order to identify potential excess numbers of cesarean sections in an institution, and when identified, how the model is applied to distribute deliveries according to the Robson Classification system and explain excess numbers. Methodology: The standardized ratio and absolute difference between the observed proportion and the expected probability of c-sections according to the C-Model v1.0 were estimated for each institution using real databases of five hospitals in Colombia. Convenience selection was used to meet the objectives. Based on the assumptions underpinning group distributions according to the Robson classification, proposed explanations for excess numbers and differences among institutions are presented. Results: Applying the C-Model, the c-section standardized ratio identified different excess numbers of the procedure in the presence of similar institutional c-section proportions. Important variability was found in the proportion of c-sections among women with similar clinical and obstetric characteristics, which might explain the excess numbers identified. Conclusion: The C-Model allows to estimate expected c-section proportions according to the specific characteristics of the women seen at each institution; their distribution according to the Robson Classification is a way to explore the origin and particulars of those differences.


Subject(s)
Female , Cesarean Section , Models, Statistical , Forecasting
9.
Rev. cuba. inform. méd ; 13(2): e462, 2021. tab, graf
Article in Spanish | LILACS, CUMED | ID: biblio-1357280

ABSTRACT

En el control de enfermedades infecciosas resulta esencial usar modelos epidemiológicos, sin embargo, existen herramientas que permiten el monitoreo y control estadístico de la transmisión de este tipo de enfermedad en el tiempo. El objetivo de este trabajo de investigación fue proporcionar un análisis de la dinámica diaria de transmisión de la COVID-19 en Cuba mediante dos cartas de control, basadas en un modelo probabilístico fundamentado en las distribuciones binomial y Poisson. Los dos métodos se aplicaron, utilizando los reportes diarios publicados por el Ministerio de Salud Pública, a un proceso cuya variable en estudio es de atributos y con poca información sobre su estabilidad. Las cartas aplicadas fueron clave para mejorar la estabilidad del proceso, en la medida en que se detectaron, identificaron y sugirió la eliminación de causas especiales para reducir la variación; y en el monitoreo para asegurar que las mejoras a generarse se puedan conservar(AU)


In the control of infectious diseases it is essential to use epidemiological models; however, there are tools that allow monitoring and statistical control of the transmission of this type of disease over time. The objective of this research work was to provide an analysis of the daily dynamics of COVID-19 transmission in Cuba through two control charts, based on a probabilistic model based on the binomial and Poisson distributions. The two methods were applied, using the daily reports published by the Ministry of Public Health, to a process whose variable under study is attributes type and with little information on its stability. The applied charts were key to improve the stability of the process, insofar as they were detected, identified and suggested the elimination of special causes to reduce the variation; and in monitoring to ensure that the improvements to be generated can be preserved(AU)


Subject(s)
Humans , Male , Female , Communicable Disease Control , Models, Statistical , COVID-19/transmission , Cuba
10.
Rev. bras. med. esporte ; 27(3): 303-306, July-Sept. 2021. tab
Article in English | LILACS | ID: biblio-1288585

ABSTRACT

ABSTRACT Introduction According to the 2015 National Physical Health Monitoring Report, most of the national physical health indicators have begun to rebound, but some people's physical health is still declining. Object The thesis studies the problems existing in people's physical exercise and guides the development of these people's habits. Methods Our mathematical statistics and other research methods investigate the current situation of people's physical exercise habits, and explore the factors that restrict habits from the factors that affect the formation of sports and fitness concepts. Result The proportion of people developing physical exercise habits is low. People invest less time and energy in physical exercise. Conclusion The less time and energy that people invest in physical exercise is the main reason that affects their belief in exercise and fitness and physical exercise habits. Level of evidence II; Therapeutic studies - investigation of treatment results.


RESUMO Introdução De acordo com o Relatório Nacional de Monitoramento de Saúde Física de 2015, a maioria dos indicadores nacionais de saúde física começou a se recuperar, mas a saúde física de algumas pessoas ainda está em declínio. Objetivo a tese estuda os problemas existentes no exercício físico das pessoas e orienta o desenvolvimento dos hábitos dessas pessoas. Métodos Nossas estatísticas matemáticas e outros métodos de pesquisa investigam a situação atual dos hábitos de exercício físico das pessoas e exploram os fatores que restringem os hábitos e os fatores que afetam a formação de conceitos de esportes e preparação física. Resultado a proporção de pessoas que desenvolvem hábitos de exercícios físicos é baixa. As pessoas investem menos tempo e energia em exercícios físicos. Conclusão O pouco tempo e energia que as pessoas investem na prática de exercícios físicos é o principal motivo que afeta sua crença na prática de exercícios e hábitos de exercício físico. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.


RESUMEN Introducción De acuerdo con el Informe Nacional de Monitoreo de la Salud Física de 2015, la mayoría de los indicadores nacionales de salud física han comenzado a recuperarse, pero la salud física de algunas personas todavía está en declive. Objetivo la tesis estudia los problemas existentes en el ejercicio físico de las personas y orienta el desarrollo de los hábitos de estas personas. Métodos Nuestras estadísticas matemáticas y otros métodos de investigación investigan la situación actual de los hábitos de ejercicio físico de las personas y exploran los factores que restringen los hábitos y los factores que afectan la formación de conceptos deportivos y la preparación física. Resultado la proporción de personas que desarrollan hábitos de ejercicio físico es baja. La gente invierte menos tiempo y energía en el ejercicio físico. Conclusión El poco tiempo y energía que las personas invierten en la práctica de ejercicio físico es el principal motivo que incide en su creencia en la práctica de ejercicio y hábitos de ejercicio físico. Nivel de evidencia II; Estudios terapéuticos: investigación de los resultados del tratamiento.


Subject(s)
Humans , Male , Female , Child , Adolescent , Adult , Middle Aged , Exercise/physiology , Healthy Lifestyle , Time Factors , Models, Statistical , Habits
12.
Arq. bras. med. vet. zootec. (Online) ; 73(4): 949-954, Jul.-Aug. 2021. graf, ilus
Article in English | LILACS, VETINDEX | ID: biblio-1285268

ABSTRACT

The purpose of this study was to model the factors affecting the 305-day milk yield of dairy cows by using Automatic Linear Modeling Technique (ALM). The data set of this study consisted of eight different cow breeds grown in eight province of Turkey. Results of ALM showed that the accuracy of the model was 64.2 % means that 64.2% of the variation in the 305-day milk yield could be explained by the constructed model. Created model was consisted of four factors namely the Breed, Lactation Length, Parity, and Province. Therefore, those selected factors were more efficient than the others in predicting the 305-day milk yield.(AU)


O objetivo deste estudo foi modelar os fatores que afetam a produção de leite das vacas leiteiras em 305 dias, utilizando a Técnica de Modelagem Linear Automática (ALM). O conjunto de dados deste estudo consistia em oito raças diferentes de vacas cultivadas em oito províncias da Turquia. Os resultados da ALM mostraram que a precisão do modelo era de 64,2% significa que 64,2% da variação na produção de leite de 305 dias poderia ser explicada pelo modelo construído. O modelo criado consistia de quatro fatores: Raça, Comprimento da Lactação, Paridade e Província. Portanto, esses fatores selecionados foram mais eficientes do que os outros na previsão da produção de leite de 305 dias.(AU)


Subject(s)
Animals , Female , Cattle , Lactation , Linear Models , Laboratory and Fieldwork Analytical Methods/methods , Milk , Turkey , Models, Statistical
13.
Electron. j. biotechnol ; 52: 85-92, July. 2021. graf, tab
Article in English | LILACS | ID: biblio-1283600

ABSTRACT

BACKGROUND: Nonribosomal peptide synthases (NRPS) can synthesize functionally diverse bioactive peptides by incorporating nonproteinogenic amino acids, offering a rich source of new drug leads. The bacterium Escherichia coli is a well-characterized production host and a promising candidate for the synthesis of nonribosomal peptides, but only limited bioprocess engineering has been reported for such molecules. We therefore developed a medium and optimized process parameters using the design of experiments (DoE) approach. RESULTS: We found that glycerol is not suitable as a carbon source for rhabdopeptide production, at least for the NRPS used for this study. Alternative carbon sources from the tricarboxylic acid cycle achieved much higher yields. DoE was used to optimize the pH and temperature in a stirred-tank reactor, revealing that optimal growth and optimal production required substantially different conditions. CONCLUSIONS: We developed a chemically defined adapted M9 medium matching the performance of complex medium (lysogeny broth) in terms of product concentration. The maximum yield in the reactor under optimized conditions was 126 mg L-1, representing a 31-fold increase compared to the first shaking-flask experiments with M9 medium and glycerol as the carbon source. Conditions that promoted cell growth tended to inhibit NRPS productivity. The challenge was therefore to find a compromise between these factors as the basis for further process development.


Subject(s)
Peptide Synthases/metabolism , Bioreactors/microbiology , Escherichia coli , Temperature , Biotechnology , Carbon/metabolism , Models, Statistical , Electrophoresis, Polyacrylamide Gel , Bioengineering , Hydrogen-Ion Concentration
14.
Poblac. salud mesoam ; 18(2)jun. 2021.
Article in Spanish | LILACS-Express | LILACS, SaludCR | ID: biblio-1386912

ABSTRACT

Resumen El estudio que da lugar al presente artículo surge a partir de los resultados obtenidos en el marco de un convenio de colaboración firmado por la Dirección General de Estadística de la Municipalidad de Rosario y la Escuela de Estadística de la Facultad de Ciencias Económicas y Estadística de la Universidad Nacional de Rosario. Entre sus objetivos, se plantea el de obtener pronósticos probabilísticos de la fecundidad para la Ciudad de Rosario. Para ello, con base en estadísticas vitales, estimaciones y proyecciones de población se construyen escenarios probables, pasados y futuros, tanto para la tasa global de fecundidad como para las tasas específicas de fecundidad. Los resultados de este estudio, basados en la aplicación de modelos probabilísticos de pronóstico, permiten conocer estructuras y tendencias, pasadas y futuras de la fecundidad, de modo que puedan generarse diagnósticos que sean de utilidad para la evaluación y gestión del sistema de salud o bien para el desarrollo de nuevas políticas públicas. Los resultados indican que Rosario tuvo, tiene y seguirá teniendo un cambio en los patrones de fecundidad más rápido y marcado que el promedio nacional. Si bien este hecho es esperable, en un contexto signado por los avances en la salud pública, que permiten acceder a más y mejor atención en salud reproductiva, la metodología aquí empleada se basa únicamente en la extrapolación de las tendencias, por ello la retroproyección debe ser analizada cuidadosamente. Con posterioridad, en la sección metodológica, se presentan los modelos probabilísticos de pronóstico que se emplean para la obtención de resultados.


Abstract The study that gives rise to this article arises from the results obtained in the framework of a collaboration agreement signed by the Statistical Office of Rosario City and the School of Statistics of the Faculty of Economic Sciences and Statistics (National University of Rosario). Among its objectives is to obtain probabilistic fertility forecasts for Rosario City. For this, based on vital statistics, estimates and population projections, probable scenarios, past and future, are constructed, both for the global fertility rate and the specific fertility rates. The results of this study, based on the application of probabilistic prognostic models, allow to know structures and trends, past and future, of fertility, so that diagnoses can be generated that are useful for the evaluation and management of the health system or good for the development of new public policies. The results indicate that Rosario had, has and will continue to have a change in fertility patterns faster and more marked than the national average. Although this fact is to be expected in a context marked by advances in public health (which allow access to more and better reproductive health care), the methodology used here is based solely on the extrapolation of trends, therefore, the backprojection must be carefully analyzed.


Subject(s)
Humans , Models, Statistical , Fecundity Rate , Fertility , Argentina
15.
Rev. cuba. salud pública ; 47(2): e2591, 2021. tab, graf
Article in Spanish | LILACS, CUMED | ID: biblio-1341482

ABSTRACT

Introducción: La influenza tiene elevado impacto en la mortalidad humana y en Cuba la categoría influenza y neumonía ocupa el cuarto lugar entre sus causales generales. En los países templados, con marcada estacionalidad, esto se capta con modelos estadísticos, tarea que se dificulta en el trópico y pendiente en Cuba por la ausencia de igual definición estacional. Objetivo: Estimar el impacto histórico de la influenza tipo A y B y los subtipos A(H3N2) y A(H1N1) sobre la mortalidad mediante el ajuste de un modelo de regresión a las condiciones estacionales específicas de Cuba. Métodos: Se ejecutó un estudio longitudinal y retrospectivo. En un primer paso se ajustaron dos modelos de Poisson con la mortalidad influenza y neumonía total y las personas ≥ 65 años de edad como variables respuestas en los cinco meses de mayor positividad en influenza, desde la temporada 1987-1988 hasta la 2004-2005 y los positivos en tipo A y en tipo B como explicatorias. En otro par de modelos se estimó el impacto del A(H3N2) y el A(H1N1), considerando como respuesta los fallecidos atribuidos previamente al tipo A. Resultados: Se atribuyeron a la influenza 7803 fallecidos entre todas las edades y 6152 entre las personas ≥ 65 años de edad, con un 56,3 por ciento asociados al A(H3N2), el 17,6 por ciento al A(H1N1) y el 26,1 por ciento al tipo B. Conclusiones. Se logró estimar el impacto de la influenza sobre la mortalidad mediante el ajuste para Cuba de un modelo estadístico que permitió demostrar la asociación de la circulación de estos virus con la mortalidad en el país, lo que ratifica la necesidad de reforzar la vigilancia, el control y la vacunación contra esta infección viral. Se demuestra la posibilidad de ajustar estos modelos de regresión a otros virus respiratorios y a la actual pandemia por la COVID-19, en las condiciones estacionales de Cuba(AU)


Introduction: Influenza has a high impact on human mortality and in Cuba influenza and pneumonia rank fourth among its general causes. In temperate climate countries, with marked seasonality, this is captured by statistical models, a task that is difficult in the tropics and pending in Cuba due to the absence of the same seasonal definition. Objective: Estimate the historical impact of influenza type A and B and subtypes A(H3N2) and A(H1N1) on mortality, by adjusting a regression model to the specific seasonal conditions of Cuba. Methods: A longitudinal and retrospective study was performed. In a first step, two Poisson models were adjusted with influenza and total pneumonia mortality and people ≥ 65 years old as response variables in the five months with the highest positivity to influenza in the period 1987-1988 to 2004-2005, and the positive ones to type A and type B as explanatory variables. In another pair of models was estimated the impact of A(H3N2) and A(H1N1), considering as a response the deaths previously attributed to type A. Results: 7 803 deaths among all ages and 6 152 among 65-year-olds were attributed to influenza, with 56.3 percent associated to A(H3N2), 17.6 percent to A(H1N1) and 26.1 percent to type B. Conclusions: It was possible to estimate the impact of influenza on mortality by adjusting for Cuba a statistical model that demonstrated the association of the circulation of these viruses with the mortality in the country, which confirms the need to strengthen surveillance, control and vaccination against this viral infection. The possibility of adjusting in the seasonal conditions of Cuba these regression models to other respiratory viruses and the current pandemic by COVID-19 is demonstrated(AU)


Subject(s)
Humans , Male , Female , Models, Statistical , Influenza, Human/mortality , Retrospective Studies , Longitudinal Studies , Cuba
16.
Gac. méd. Méx ; 157(3): 240-245, may.-jun. 2021. tab, graf
Article in Spanish | LILACS | ID: biblio-1346102

ABSTRACT

Resumen Introducción: La escasez de aplicaciones centradas en la persona y con vistas al desarrollo de la conciencia del riesgo que representa la pandemia de COVID-19 estimula la exploración y creación de herramientas de carácter preventivo accesibles a la población. Objetivo: Elaboración de un modelo predictivo que permita evaluar el riesgo de letalidad ante infección por el virus SARS-CoV-2. Métodos: Exploración de datos públicos de 16 000 pacientes positivos a COVID-19, para generar un modelo discriminante eficiente, valorado con una función score y que se expresa mediante un cuestionario autocalificado de interés preventivo. Resultados: Se obtuvo una función lineal útil con capacidad discriminante de 0.845; la validación interna con bootstrap y la externa, con 25 % de los pacientes de prueba, mostraron diferencias marginales. Conclusión: El modelo predictivo, basado en 15 preguntas accesibles puede convertirse en una herramienta de prevención estructurada.


Abstract Introduction: The scarcity of person-centered applications aimed at developing awareness on the risk posed by the COVID-19 pandemic, stimulates the exploration and creation of preventive tools that are accessible to the population. Objective: To develop a predictive model that allows evaluating the risk of mortality in the event of SARS-CoV-2 virus infection. Methods: Exploration of public data from 16,000 COVID-19-positive patients to generate an efficient discriminant model, evaluated with a score function and expressed by a self-rated preventive interest questionnaire. Results: A useful linear function was obtained with a discriminant capacity of 0.845; internal validation with bootstrap and external validation, with 25 % of tested patients showing marginal differences. Conclusion: The predictive model with statistical support, based on 15 accessible questions, can become a structured prevention tool.


Subject(s)
Humans , Male , Female , Infant , Child, Preschool , Child , Adolescent , Adult , Middle Aged , Aged , Young Adult , Models, Statistical , COVID-19/prevention & control , Discriminant Analysis , Linear Models , Risk , COVID-19/mortality
17.
Rev. cuba. med. mil ; 50(1): e970, 2021. tab, graf
Article in Spanish | LILACS, CUMED | ID: biblio-1289498

ABSTRACT

Introducción: El cáncer de tiroides es el tumor maligno más común originado en órganos endocrinos (más del 92 por ciento) y comprende un grupo de tumores que son diferentes clínicamente y epidemiológicamente. En los últimos años se ha incrementado el uso de los modelos predictivos en la práctica médica para determinar la mejor conducta en pacientes con tumores de la glándula tiroides. Objetivo: Desarrollar un modelo probabilístico de predicción de la recidiva en pacientes con cáncer de tiroides. Métodos: Se realizó un estudio prospectivo longitudinal, en el Hospital Militar Central Dr. Carlos J. Finlay, desde enero de 2015 hasta febrero del 2020. Se incluyeron 63 pacientes que ingresaron al estudio por muestreo aleatorio simple con remplazo, se confeccionó un modelo predictivo utilizando una regresión logística binaria en el programa R. Resultados: El grupo de edad más afectado estuvo entre los 40 y 59 años, predominó el sexo femenino y el carcinoma papilar, la vascularización y la irregularidad fueron los elementos ultrasonográficos más detectados. El estadístico de Wald fue significativo con una distribución normal en todas las variables analizadas lo cual indica que sus coeficientes son diferentes de 0 y deben ser incluidos en el modelo La variable con mayor influencia en el índice de recidiva resultó ser la diferenciación celular Conclusiones: Los factores con mayor influencia en la recidiva en la serie estudiada resultaron el grado de diferenciación, la presencia de vascularización e irregularidad en la ecografía y el tamaño tumoral con cifras similares a las reportadas nacional e internacionalmente(AU)


Introduction: Thyroid cancer is the most common malignant tumor originating in endocrine organs (more than 92%) and comprises a group of tumors that are clinically and epidemiologically different. In recent years, the use of predictive models has increased in medical practice to determine the best behavior in patients with tumors of the thyroid gland. Objective: To develop a probabilistic model for predicting recurrence in patients with thyroid cancer. Methods: A longitudinal prospective study was carried out at the Dr. Carlos J Finlay Central Military Hospital, from January 2015 to February 2020. 63 patients who entered the study by simple random sampling with replacement were included; a predictive model was made using a binary logistic regression in program R. Results: The most affected age group was between 40 and 59 years old, female sex predominated and papillary carcinoma, vascularization and irregularity were the most detected ultrasound elements. The Wald statistic was significant with a normal distribution in all variables analyzed, which indicates that their coefficients are different from 0 and should be included in the model. The variable with the greatest influence on the recurrence rate turned out to be cell differentiation. Conclusions: The final binary logistic regression model had an adequate goodness of fit and discrimination was very good, with an acceptable receiving operator area under the curve (AU)


Subject(s)
Humans , Thyroid Neoplasms , Carcinoma, Papillary , Simple Random Sampling , Logistic Models , Prospective Studies , Models, Statistical
18.
ABCS health sci ; 46: e021304, 09 fev. 2021. tab, ilus
Article in English | LILACS | ID: biblio-1343358

ABSTRACT

INTRODUCTION: The isotemporal substitution model (ISM) is a statistical approach that estimates the effects of replacing, in minutes, a block of physical activity or sedentary behavior by another block with different intensity. Previous studies have used the ISM to evaluate the effect of different isotemporal substitutions on body composition. Thus, the ISM can contribute to the understanding of changes in body composition related to distinct lifestyles and, hence, guiding future recommendations for maintaining and/or improving body composition. OBJECTIVE: To review the effect of replacing sedentary behavior by physical activity on body composition change analyzed through ISM. METHODS: Original articles in English were identified from searches in PubMed and Periódicos Capes databases. The search was carried out by two researchers. Last search was performed in October 2020. RESULTS: A total of 17 included articles, which evaluated different applications of ISM in relation to body composition change, mostly obtained by BMI and body fat. The physical activity was mainly assessed by using an accelerometer. Several methodological differences among the included studies limited comparisons between findings, including the sample profile and cut off points for physical activity. CONCLUSION: Among the studies that evaluate the effect of replacing sedentary behavior for different intensities of physical activity through ISM, replacing sedentary behavior by moderate-to-vigorous physical activity presented a more consistent effect in body composition change in comparison to replacement by other physical activity intensities, even for small blocks of time (five minutes).


Subject(s)
Humans , Body Composition , Exercise , Sedentary Behavior , Models, Statistical
19.
Rev. ANACEM (Impresa) ; 15(1): 42-48, 2021. tab
Article in Spanish | LILACS | ID: biblio-1282102

ABSTRACT

INTRODUCCIÓN: La pandemia por SARS-COV-2 ha generado mortalidad por exceso, aun así, se deben revisar la mortalidad atribuida a otras enfermedades. El siguiente trabajo pretende identificar la tendencia de mortalidad no relacionada con COVID-19 en la región del Bio-bio, periodo 2016-2020. Material y Método: Estudio descriptivo, ecológico, longitudinal. Se estudió la población de la región del Biobío, periodo 2016-2020. Los datos se obtuvieron del departamento de estadística e información en Salud. Se estudió: Distribución etaria, sexo, tasa de mortalidad general y específica, y promedio anual del número de muertos en el periodo 2016-2019; excluyendo la causa de muerte por enfermedad COVID-19 o sospechosa de COVID-19. Se realizó un análisis descriptivo. Se utilizó el programa Microsoft Excel 365® para análisis. Resultados: 2016-2019 fallecieron más hombres (n=19.110; 53,00%), siendo el principal grupo etario de 75-79 años (n=2.433; 12,73%), en el caso de las mujeres fue el grupo de 90-99 años (n=2.832; 16,71%). En 2020, fallecieron más hombres que mujeres, de los mismos grupos etarios respectivamente. Tasa de mortalidad general 2020 fue 544,39 x100.000 hbts., inferior a la de otros años, excepto en 2016. Sin embargo, el periodo Enero-abril 2020, la tasa de mortalidad es mayor comparado con los años anteriores. El promedio de muertes 2016-2019 fue 9.016,0 ±186,5, siendo el total en 2020 n=9.057. Discusión: La pandemia ha afectado a pacientes con patologías que han presentado una atención poco efectiva u inoportuna, falleciendo por el SARS-COV-2 o por sus comorbilidades, camuflándose sus registros. Lo cual dificultará interpretar dichos valores.


INTRODUCTION: The SARS-COV-2 pandemic has generated excess mortality, even so, the mortality attributed to other diseases should be reviewed. The study objective was to identify the mortality trend unrelated to COVID-19 in the Bio-bio Region between 2016-2020. Material and Method: Descriptive, ecological, longitudinal study. The population of the Biobío region was studied between the years 2016-2020. Data were obtained from the Department of Statistics and Health Information, DEIS. It was studied: Age distribution, sex, general and specific mortality rate, annual average of the number of deaths between 2016-2020, excluding mortality from (or suspected) COVID-19 disease. A descriptive analysis was performed. Microsoft Excel 365® software was used for the analysis. Results: 2016-2019 mortality rate was higher for men (n=19,110; 53.00%), with the highest rates in the 75-79 years group (n=2,433; 12.73%); women 90- 99 years (n=2,832; 16.71%) presented the highest mortality rates. In 2020 more men than women continued to die in the same age groups, respectively. The general mortality rate 2020 was 544,39 x 100,000 inhabitants, which is lower than that of any other year, except for 2016. However, from January to April 2020, the mortality rate was higher when compared to the previous years. The average of deaths 2016-2019 was 9,016.0 ± 186.5, meanwhile in the same period in 2020 was 9,057. Discussion: The pandemic has affected patients with pathologies who have presented ineffective or untimely care, dying from SARS-COV-2 or its comorbidities, then their records get camouflaged, which will make it difficult to interpret these values.


Subject(s)
Humans , Male , Female , Infant, Newborn , Infant , Child, Preschool , Child , Adolescent , Adult , Middle Aged , Aged , Aged, 80 and over , Disease , Mortality/trends , Cause of Death/trends , COVID-19 , Chile/epidemiology , Epidemiology, Descriptive , Survival Rate , Models, Statistical , Pandemics
20.
Epidemiol. serv. saúde ; 30(1): e2020080, 2021. tab, graf
Article in English, Portuguese | LILACS | ID: biblio-1154144

ABSTRACT

Objetivo: Avaliar a capacidade preditiva de diferentes modelos de série temporal de casos de malária no estado do Amapá, Brasil, no período 1997-2016. Métodos: Estudo ecológico de séries temporais com casos de malária registrados no Amapá. Foram utilizados dez modelos estatísticos determinísticos ou estocásticos para simulação e teste em horizontes de previsão de 3, 6 e 12 meses. Resultados: O teste inicial mostrou que a série é estacionária. Os modelos determinísticos apresentaram melhor desempenho do que os modelos estocásticos. O modelo ARIMA apresentou erros absolutos menores do que 2% na escala logarítmica e erros relativos 3,4-5,8 vezes menores em relação ao modelo nulo. A predição de casos futuros de malária nos horizontes de 6 e 12 meses de antecedência foi possível. Conclusão: Recomenda-se o uso de modelo ARIMA para a previsão de cenários futuros e para a antecipação do planejamento nos serviços de saúde dos estados da Região Amazônica.


Objetivo: Evaluar el poder predictivo de diferentes modelos de series de temporales de casos de malaria en el estado de Amapá, Brasil, en el periodo 1997-2016. Métodos: Se trata de un estudio ecológico de series de temporales con casos de malaria registrados en el estado de Amapá. Se utilizaron diez modelos estadísticos determinísticos o estocásticos para la simulación y la prueba en horizontes de predicción de 3, 6 y 12 meses. Resultados: La prueba inicial mostró que la serie es estacionaria. Los modelos determinísticos mostraron mejor desempeño que los modelos estocásticos. El modelo ARIMA mostró errores absolutos menores al 2% en la escala logarítmica y errores relativos 3,4-5,8 veces menores que el modelo nulo. La predicción de casos futuros en horizontes de 6 y 12 meses de antelación fue posible. Conclusión: Se recomienda utilizar el modelo ARIMA para predecir escenarios futuros y anticipar la planificación en los servicios de salud en los estados de la Región Amazónica.


Objective: To evaluate the predictive power of different malaria case time-series models in the state of Amapá, Brazil, for the period 1997-2016. Methods: This is an ecological time series study with malaria cases recorded in the state of Amapá. Ten deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. Results: The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. It was possible to predict future malaria cases 6 and 12 months in advance. Conclusion: The ARIMA model is recommended for predicting future scenarios and for earlier planning in state health services in the Amazon Region.


Subject(s)
Humans , Decision Support Techniques , Epidemiological Monitoring , Malaria/epidemiology , Brazil/epidemiology , Time Series Studies , Models, Statistical
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