Impacto das variáveis socioeconômicas no desempenho do Enem: uma análise espacial e sociológica / Impacto de las variables socioeconómicas en el desempeño del Enem: un análisis espacial y sociológico / Impact of socioeconomic variables on Enem performance: a spatial and sociologic analysis

Resumo O objetivo deste trabalho é identificar variáveis com maior impacto no desempenho dos municípios no Exame Nacional do Ensino Médio (Enem), tanto para a prova objetiva quanto para a redação, com base em técnicas de estatística espacial, que permite analisar a dinâmica da influência territorial, e da perspectiva da sociologia da educação. Pretende-se mensurar o grau de importância de cada categoria de variáveis para a média e a variância das notas do Enem. Para isso, utilizam-se modelos estatísticos e geoespaciais, como regressão linear múltipla e regressão espacial, a partir do Enem de 2018. Para a prova objetiva, variáveis como o percentual de estudantes com bolsa, renda, raça, escolaridade e nível instrucional da mãe são fatores relevantes para o desempenho e a dispersão das notas dos estudantes de cada município. Para a redação, as variáveis são similares às da prova objetiva, mas com menor impacto na média e na dispersão das notas. Esse fator explicativo aumenta quando introduzimos um componente espacial no modelo para as notas de redação, indicando que outros fatores regionais, diferentes dos socioeconômicos, impactam o desempenho e a dispersão dos municípios. Os resultados reforçam os estudos da sociologia da educação, ao destacarem as disparidades socioeconômicas refletidas no desempenho estudantil, fundamentos da reprodução escolar das desigualdades mencionadas por Bourdieu (1998, 2008, 2009). Estudos com essa perspectiva integrada são relevantes para o entendimento da influência no nível da localidade dos municípios, podendo evidenciar lacunas específicas, direcionando e influenciando ações públicas visando à superação das desigualdades.

Resumen El objetivo de este trabajo es identificar variables con mayor impacto en el desempeño de los municipios en el Examen Nacional de Enseñanza Media (Enem), tanto para la prueba objetiva como para la prueba de redacción, a partir de técnicas de estadística espacial que permiten analizar la dinámica de la influencia territorial, y de la perspectiva de la sociología educacional. Pretendemos medir el grado de importancia de cada categoría de variables para la media y varianza de la puntuación del Enem. A tal fin, utilizamos modelos estadísticos y análisis geoespaciales, como regresión lineal y espacial, a partir del Enem 2018. Los resultados muestran que, para la prueba objetiva del Enem, variables como el porcentaje de alumnos becarios, renta, raza, escolaridad y nivel de instrucción de las madres son factores relevantes para el desempeño y dispersión de las notas de los estudiantes en cada municipio. Para el desempeño en redacción, las variables son similares a las de la prueba objetiva, no obstante, con menor impacto en la media y dispersión de la puntuación. Cuando se introduce un componente espacial en el modelo de puntuación de redacción, el factor explicativo aumenta, señalando que existen otros criterios regionales, distintos a los socioeconómicos, que impactan sobre el desempeño y dispersión de los municipios. Los resultados refuerzan los estudios de la sociología de la educación, ya que subrayan las disparidades socioeconómicas reflejadas en el desempeño de los estudiantes, que son fundamentos de la reproducción escolar de las desigualdades analizadas por Bourdieu (1998, 2008, 2009). Las investigaciones con esta perspectiva integrada son relevantes para la comprensión de la influencia en la regionalidad de los municipios, pudiendo revelar brechas específicas y, en consecuencia, dirigir e incidir en acciones públicas orientadas a la superación de desigualdades.

Abstract This study aims to identify variables with the highest impact on students' performance in the National Exam of High School (Enem), per municipality. The research adopted the educational sociology perspective and used spatial statistics to analyze the dynamics of territorial influence. We analyzed Enem 2018, measuring the degree of importance of each category of variables for the average and variance of students' grades in the exam - which is separated in an essay and an objective test - and used statistical modeling and geospatial analysis, such as linear and spatial regressions. The results indicate that, for the objective test, variables like percentage of students with scholarship, income, race, schooling, and education level of students' mothers are relevant to students' performance and dispersion of grades in each municipality. For the essay, variables were similar to the objective test but with less impact on the average and variance of the grades. This explicative factor increases when a spatial component is introduced in the model for the essay grades, indicating that there are other regional factors, besides socioeconomics, impacting the performance and dispersion per municipality. The results reinforce the sociological studies on education since the socioeconomic disparities reflected in the students' performance stand out, which are also pointed out by Bourdieu (1998, 2008, 2009) in his studies of the fundamentals of school productivity of inequalities. Studies with this integrated perspective are relevant to understand the influence of municipalities' location, evidencing specific gaps and, consequently, directing and influencing public actions to overcome inequalities.

Transmissão da hanseníase na Bahia, 2001-2015: modelagem a partir de regressão por pontos de inflexão e estatística de varredura espacial / Transmisión de la lepra en Bahia, 2001-2015: modelado a partir de regresión por Joinpoint y estadística de barrido espacial / Leprosy transmission in Bahia, 2001-2015: modeling based on Jointpoint regression and spatial scan statistics

Objetivo: descrever a tendência e a distribuição espacial da hanseníase no estado da Bahia, Brasil, em 2001-2015. Métodos: estudo ecológico misto dos indicadores epidemiológicos da hanseníase; na análise temporal, utilizou-se a regressão Joinpoint, e a estatística de varredura espacial na identificação de clusters da doença; a tendência foi classificada como estacionária, crescente ou decrescente; calculou-se a variação percentual anual (APC: annual percent change) e a variação percentual anual média (AAPC: average annual percent change). Resultados: houve redução da prevalência (AAPC = -5,6; p<0,001), do abandono (AAPC = -13,7; p<0,001) e de mulheres doentes (AAPC = -0,6; p<0,001); o coeficiente de casos novos de grau II (AAPC = 2,7; p<0,001) e a proporção de casos multibacilares (AAPC = 2,2; p<0,001) apresentaram tendência crescente; revelou-se distribuição espacial heterogênea, concentrada em três regiões destacadas (norte, oeste e sul do estado), e variação entre indicadores. Conclusão: sugere-se persistência da transmissão da hanseníase no estado, diagnóstico tardio e elevada prevalência oculta.

Objetivo: describir la tendencia y distribución espacial de la lepra en el estado de Bahia, Brasil, en 2001-2015. Métodos: estudio ecológico mixto de los indicadores epidemiológicos de la lepra; se utilizó la regresión Joinpoint para el análisis temporal y la estadística espacial para la identificación de clusters de la enfermedad; la tendencia se clasificó en estacionaria, creciente o decreciente; se calculó el cambio porcentual anual (APC: annual percent change) y la variación porcentual anual promedio (AAPC: average annual percent change). Resultados: se ha reducido la prevalencia (AAPC = -5,6; p<0,001), el abandono (AAPC = -13,7; p<0,001) y las mujeres enfermas (AAPC = -0,6; p<0,001); la tasa de nuevos casos de grado II (AAPC = 2,7; p<0,001) y la proporción de casos multibacilares (AAPC = 2,2; p<0,001) presentaron una tendencia de crecimiento; la distribución espacial fue heterogénea, con concentración en tres regiones de destaque (norte, oeste y sur del estado) y variación entre indicadores. Conclusión: sugiere persistencia de la transmisión de la lepra en el estado, diagnóstico tardío y elevada prevalencia oculta.

Objective: to describe the trend and the spatial distribution of leprosy in the state of Bahia, Brazil, 2001-2015. Methods: this was a mixed ecological study of epidemiological indicators of leprosy; Jointpoint regression was used for the temporal analysis, while spatial scan statistics were used to identify clusters of the disease; the trend was classified as stationary, increasing or decreasing; we calculated the annual percent change (APC) and average annual percent change (AAPC). Results: there was a reduction in prevalence (AAPC = -5.6; p<0,001), treatment dropout (AAPC = -13.7; p<0.001), and females with leprosy (AAPC = -0.6; p<0.001); the new grade II case coefficient (AAPC = 2.7; p<0.001) and the proportion of multibacillary cases (AAPC = 2,2; p<0.001) showed a growing trend; spatial distribution was heterogeneous and concentrated in three regions in particular (north, west and south of the state), with variation between the indicators. Conclusion: persisting leprosy transmission in the state, late diagnosis and high hidden prevalence is suggested.

Geospatial analysis and epidemiological aspects of human infections with Blastocystis hominis in Mazandaran Province, northern Iran / 한국역학회지

OBJECTIVES: Blastocystis hominis is a very common large intestinal protozoan with global prevalence in humans and non-human hosts. No precise statistics exist regarding the geographical distribution of Blastocystis that would enable the identification of high-risk communities. Therefore, the current research aimed to characterize the spatial patterns and demographic factors associated with B. hominis occurrence in northern Iran.METHODS: The current study was performed among 4,788 individuals referred to health centers in Mazandaran Province, from whom stool samples were obtained. Socio-demographic data were gathered using a questionnaire. Samples were examined by a direct wet mount, the formalin-ethyl acetate concentration technique, and trichrome staining. Moran local indicators of spatial association and a geographically weighted regression model were utilized to analyze the results.RESULTS: Generally, the infection rate of Blastocystis parasites was 5.2%, and was considerably higher in the age group of 10-14 years (10.6%) than in other age groups (p=0.005). Our data showed important associations between the occurrence of B. hominis and age, residence, job, contact with domestic animals, anti-parasitic drug consumption, and elevation above sea level (p<0.001).CONCLUSIONS: The current study characterized for the first time the infection rate and risk of B. hominis in the north of Iran, and produced a prediction map. It is expected that this map will help policymakers to plan and implement preventive measures in high-risk areas and to manage already-infected patients.

Geospatial analysis and epidemiological aspects of human infections with Blastocystis hominis in Mazandaran Province, northern Iran / 한국역학회지

OBJECTIVES: Blastocystis hominis is a very common large intestinal protozoan with global prevalence in humans and non-human hosts. No precise statistics exist regarding the geographical distribution of Blastocystis that would enable the identification of high-risk communities. Therefore, the current research aimed to characterize the spatial patterns and demographic factors associated with B. hominis occurrence in northern Iran. METHODS: The current study was performed among 4,788 individuals referred to health centers in Mazandaran Province, from whom stool samples were obtained. Socio-demographic data were gathered using a questionnaire. Samples were examined by a direct wet mount, the formalin-ethyl acetate concentration technique, and trichrome staining. Moran local indicators of spatial association and a geographically weighted regression model were utilized to analyze the results. RESULTS: Generally, the infection rate of Blastocystis parasites was 5.2%, and was considerably higher in the age group of 10-14 years (10.6%) than in other age groups (p=0.005). Our data showed important associations between the occurrence of B. hominis and age, residence, job, contact with domestic animals, anti-parasitic drug consumption, and elevation above sea level (p<0.001). CONCLUSIONS: The current study characterized for the first time the infection rate and risk of B. hominis in the north of Iran, and produced a prediction map. It is expected that this map will help policymakers to plan and implement preventive measures in high-risk areas and to manage already-infected patients.

Regional Variation in the Incidence of Diabetes-Related Lower Limb Amputations and Its Relationship with the Regional Factors / 대한족부족관절학회지

PURPOSE: To investigate the spatial distribution of diabetes-related lower limb amputations and analyze the relationship between the spatial distribution of diabetes-related lower limb amputations and regional factors. MATERIALS AND METHODS: This study was performed based on the data from the Korean Health Insurance Review and Assessment Service, in 2016. The unit of analysis was the administrative districts of city·gun·gu. The dependent variable was the age- and sex-adjusted incidence of diabetes-related lower limb amputations and the regional variables were selected to represent two aspects: socioeconomic factors, and health and medical factors. Along with traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for spatial analysis. RESULTS: The age- and sex-adjusted incidence of diabetes-related lower limb amputation varied according to region. OLS regression showed that the incidence of diabetes-related lower limb amputation had significant relationships with the health and medical factors (number of healthcare institution and doctors per 100,000 population). In GWR, the effects of regional factors were not consistent. CONCLUSION: The spatial distribution of the incidence of diabetes-related lower limb amputations and the effects of regional factors varied according to the regions. The regional characteristics should be considered when establishing health policy related to diabetic foot care.

Análise espacial da Tuberculose no Rio de Janeiro no período de 2005 a 2008 e fatores socioeconômicos associados utilizando microdado e modelos de regressão espaciais globais / Spatial analysis of Tuberculosis in Rio de Janeiro in the period from 2005 to 2008 and associated socioeconomic factors using micro data and global spatial regression models

Resumo Este trabalho analisa o padrão espacial da tuberculose no período de 2005 a 2008 identificando variáveis socioeconômicas relevantes para a ocorrência da doença através de modelos estatísticos espaciais. É um estudo ecológico realizado no Rio de Janeiro com casos novos. Utilizou-se o setor censitário como unidade de análise. Foram calculadas as taxas de incidência e usado o método Bayesiano Empírico Local. Foi constatada a autocorrelação espacial com Índice de Moran e LISA. Usando teste de Spearman, as variáveis com correlação estatisticamente significativas a 5% foram utilizadas nos modelos. No modelo de regressão multivariado clássico as variáveis Proporção de responsável com renda entre 1 e 2 salários-mínimos, Proporção de analfabetos, Proporção de domicílios com pessoas que moram sozinhas e Renda média do responsável se ajustaram melhor. Essas variáveis foram inseridas nos modelos Spatial Lag e Spatial Error e os resultados comparados. O primeiro apresentou os melhores parâmetros: R2 = 0,3215, Log da Verossimilhança = -9228, AIC = 18468 e SBC = 18512. Os métodos estatísticos apresentaram-se eficientes na identificação de padrões espaciais e definição de determinantes da doença dando uma visão da heterogeneidade no espaço, possibilitando uma atuação mais direcionada a populações específicas.

Abstract The present study analyses the spatial pattern of tuberculosis (TB) from 2005 to 2008 by identifying relevant socioeconomic variables for the occurrence of the disease through spatial statistical models. This ecological study was performed in Rio de Janeiro using new cases. The census sector was used as the unit of analysis. Incidence rates were calculated, and the Local Empirical Bayesian method was used. The spatial autocorrelation was verified with Moran's Index and local indicators of spatial association (LISA). Using Spearman's test, variables with significant correlation at 5% were used in the models. In the classic multivariate regression model, the variables that fitted better to the model were proportion of head of family with an income between 1 and 2 minimum wages, proportion of illiterate people, proportion of households with people living alone and mean income of the head of family. These variables were inserted in the Spatial Lag and Spatial Error models, and the results were compared. The former exhibited the best parameters: R2 = 0.3215, Log-Likelihood = -9228, Akaike Information Criterion (AIC) = 18,468 and Schwarz Bayesian Criterion (SBC) = 18,512. The statistical methods were effective in the identification of spatial patterns and in the definition of determinants of the disease providing a view of the heterogeneity in space, allowing actions aimed more at specific populations.

Analysis of the relationship between community characteristics and depression using geographically weighted regression / 한국역학회지

OBJECTIVES: Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions. METHODS: Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran's I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models. RESULTS: In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms. CONCLUSIONS: The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.

Analysis of the relationship between community characteristics and depression using geographically weighted regression / 한국역학회지

OBJECTIVES: Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions.METHODS: Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran's I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models.RESULTS: In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms.CONCLUSIONS: The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.

Geographic information system analysis on the distribution of patients visiting the periodontology department at a dental college hospital

PURPOSE: The aim of this study is to analyze and visualize the distribution of patients visiting the periodontology department at a dental college hospital, using a geographic information system (GIS) to utilize these data in patient care and treatment planning, which may help to assess the risk and prevent periodontal diseases. METHODS: Basic patient information data were obtained from Dankook University Dental Hospital, including the unit number, gender, date of birth, and address, down to the dong (neighborhood) administrative district unit, of 306,656 patients who visited the hospital between 2007 and 2014. The data of only 26,457 patients who visited the periodontology department were included in this analysis. The patient distribution was visualized using GIS. Statistical analyses including multiple regression, logistic regression, and geographically weighted regression were performed using SAS 9.3 and ArcGIS 10.1. Five factors, namely proximity, accessibility, age, gender, and socioeconomic status, were investigated as the explanatory variables of the patient distribution. RESULTS: The visualized patient data showed a nationwide scale of the patient distribution. The mean distance from each patient's regional center to the hospital was 30.94±29.62 km and was inversely proportional to the number of patients from the respective regions. The distance from a regional center to the adjacent toll gate had various effects depending on the local distance from the hospital. The average age of the patients was 52.41±12.97 years. Further, a majority of regions showed a male dominance. Personal income had inconsistent results between analyses. CONCLUSIONS: The distribution of patients is significantly affected by the proximity, accessibility, age, gender and socioeconomic status of patients, and the patients visiting the periodontology department travelled farther distances than those visiting the other departments. The underlying reason for this needs to be analyzed further.