ABSTRACT
Este trabalho tem como objetivo determinar uma relação linear entre a Taxa de Mortalidade Infantil (TMI) e um conjunto de variáveis socioeconômicas observadas por unidades federativas no período de 2005 à 2010 utilizando o modelo de dados em painel de efeitos fixo e aleatório. Metodologia: trata-se de um estudo descritivo com abordagem quantitativa, com utilização dos Sistema de Informação sobre Mortalidade (SIM) e o Sistema de Informações sobre Nascidos Vivos (SINASC) e em seguida utilizou-se o software R para realizar esta análise de dados com a função plm. Resultados: os estudos mostram que o modelo mais adequado é o de efeito fixo com transformação logarítmica nas variáveis independentes e na variável dependente que foram as seguintes: TMI, taxa de analfabetismo, PIB per capita, proporção pessoas com baixa renda, percentual da população servida por rede de abastecimento de água e a proporção da população servida por coleta de lixo. Conclusão: As variáveis independentes que causam impacto significativo na TMI são taxa de analfabetismo, PIB per capita e proporção de pessoas com baixa renda.
This work aims to determine a linear relationship between the Infant Mortality Rate (IMR) and a set of socioeconomic variables observed by federative units in the period from 2005 to 2010 using the fixed and random effects panel data model. Methodology: this is a descriptive study with a quantitative approach, using the Mortality Information System (SIM) and the Live Birth Information System (SINASC) and then using the R software to perform this data analysis with the plm function. Results: studies show that the most appropriate model is the fixed effect model with logarithmic transformation in the independent variables and the dependent variable, which were as follows: IMR, illiteracy rate, GDP per capita, proportion of people with low income, percentage of the population served by water supply network and the proportion of the population served by garbage collection. Conclusion: The independent variables that have a significant impact on IMR are the illiteracy rate, GDP per capita and the proportion of people with low income.
Este trabajo tiene como objetivo determinar una relación lineal entre la Tasa de Mortalidad Infantil (TMI) y un conjunto de variables socioeconómicas observadas por las unidades federativas en el período 2005 a 2010 utilizando el modelo de datos de panel de efectos fijos y aleatorios. Metodología: se trata de un estudio descriptivo con enfoque cuantitativo, utilizando el Sistema de Información de Mortalidad (SIM) y el Sistema de Información de Nacidos Vivos (SINASC) y luego utilizando el software R para realizar este análisis de datos con la función plm. Resultados: los estudios muestran que el modelo más adecuado es el modelo de efectos fijos con transformación logarítmica en las variables independientes y la variable dependiente, las cuales fueron las siguientes: TMI, tasa de analfabetismo, PIB per cápita, proporción de personas con bajos ingresos, porcentaje de la población atendida por red de suministro de agua y la proporción de la población atendida por recolección de basura. Conclusión: Las variables independientes que tienen un impacto significativo en la TMI son la tasa de analfabetismo, el PIB per cápita y la proporción de personas con bajos ingresos.
ABSTRACT
Background: Stunting in children under 5 years of age is a condition where they have a length or height that is less than ?2 standard deviations of the growth standard of Indonesian children. Stunting is caused by chronic malnutrition in the first 1000 days of life. The spatial panel data method was developed to solve problems related to spatial objects that are measured periodically by involving elements of area and time. Objectives: The purpose of this study was to determine the best model and factors that influence stunting in children under 5 years of age in Indonesia using spatial panel data. Methods: The data used were from the website of the Central Statistics Agency and the publications of the Ministry of Health of the Republic of Indonesia in 2015–2019. Determination of the selected model is done by comparing the random effect spatial autoregressive model and spatial error model (SEM) random effect based on the value and Akaike information criterion (AIC). SEM random effect produces the largest value and the smallest AIC. Results: The selected spatial panel data model in determining the factors that influence stunting in children under 5 years of age in Indonesia is the SEM random effect based on the largest and AIC compared to other models. Conclusion: Based on the selected model, children under five with malnutrition and poor nutrition, receiving Vitamin A, and the average monthly per capita expenditure on food have a significant effect on the percentage of stunting in children under five in Indonesia.
ABSTRACT
This study analyzed WHO-standardized nutritional indicators of children from selected households within communities that were sampled from all districts of Botswana. Data from the 2007 Botswana Family Health Survey were fitted into multilevel models that seek to account for variability due to the macro- and micro-units that have been hierarchically selected. This allowed for estimation of different levels of intraclass correlations while simultaneously assessing the model-fit by accounting for the influence on the nutritional indicators due to the fixed variables attributable to these macro- and micro-units. The results show that variation in nutritional status of under-five children in Botswana is a function of characteristics of the households and communities within which they live. As much as 17% of variation is due to differences in the communities and households. Economic status of households holds an important key in predicting the nutritional status of children.
ABSTRACT
Objective To illustrate the evaluation effect of bivariate analysis of sensitivity and specificity meta-analysis model in diagnosis test to provide basis for selecting better evaluation method of diagnostic test.Methods Bivariate model was presented by reanalyzing the data from a published meta-analysis of two diagnostic techniques in diagnosis of schistosomiasis japonica.Results The bivariate model could directly provide summary estimates of(logit)sensitivity,specificity and DOR with corresponding 95% CI for two diagnostic tests(IHA and ELISA).Also,it could elicit any significant difference that existed among sensitivity,specificity and DOR between the two diagnostic methods,and incorporate any correlation that existed between sensitivity;specificity.Conclusion The bivariate model preserves the two dimensional nature of the original data,and separates effects of sensitivity and specificity,which is more rational than a net effect on diagnostic odds ratio scale as in SROC approach.The bivariate model is appropriate and agile,and can be used as an extension and improvement of the traditional SROC method.