Efecto de factores sociodemográficos en la mortalidad del COVID-19 en Costa Rica: un enfoque geográfico

Introducción: La enfermedad por coronavirus (COVID-19) se ha extendido entre la población de todo el país y ha tenido un gran impacto a nivel mundial. Sin embargo, existen diferencias geográficas importantes en la mortalidad de COVID-19 entre las diferentes regiones del mundo y en Costa Rica. Objetivo: Explorar el efecto de algunos de los factores sociodemográficos en la mortalidad de COVID-19 en pequeñas divisiones geográficas o cantones de Costa Rica. Métodos: Usamos registros oficiales y aplicamos un modelo de regresión clásica de Poisson y un modelo de regresión ponderada geográficamente. Resultados: Obtuvimos un criterio de información de Akaike (AIC) más bajo con la regresión ponderada (927.1 en la regresión de Poison versus 358.4 en la regresión ponderada). Los cantones con un mayor riesgo de mortalidad por COVID-19 tuvo una población más densa; bienestar material más alto; menor proporción de cobertura de salud y están ubicadas en el área del Pacífico de Costa Rica. Conclusiones: Una estrategia de intervención de COVID-19 específica debería concentrarse en áreas de la costa pacífica con poblaciones más densas, mayor bienestar material y menor población por unidad de salud.

Introduction: The coronavirus disease (COVID-19) has spread among the population of Costa Rica and has had a great global impact. However, there are important geographic differences in mortality from COVID-19 among world regions and within Costa Rica. Objective: To explore the effect of some sociodemographic factors on COVID-19 mortality in the small geographic divisions or cantons of Costa Rica. Methods: We used official records and applied a classical epidemiological Poisson regression model and a geographically weighted regression model. Results: We obtained a lower Akaike Information Criterion with the weighted regression (927.1 in Poisson regression versus 358.4 in weighted regression). The cantons with higher risk of mortality from COVID-19 had a denser population; higher material well-being; less population by health service units and are located near the Pacific coast. Conclusions: A specific COVID-19 intervention strategy should concentrate on Pacific coast areas with denser population, higher material well-being and less population by health service units.

Regional differences in health screening participation between before and during COVID-19 pandemic

BACKGROUND@#Health screening is a preventive and cost-effective public health strategy for early detection of diseases. However, the COVID-19 pandemic has decreased health screening participation. The aim of this study was to examine regional differences in health screening participation between before and during COVID-19 pandemic and vulnerabilities of health screening participation in the regional context.@*METHODS@#Administrative data from 229 districts consisting of 16 provinces in South Korea and health screening participation rate of each district collected in 2019 and 2020 were included in the study. Data were then analyzed via descriptive statistics and geographically weighted regression (GWR).@*RESULTS@#This study revealed that health screening participation rates decreased in all districts during COVID-19. Regional vulnerabilities contributing to a further reduction in health screening participation rate included COVID-19 concerns, the population of those aged 65+ years and the disabled, lower education level, lower access to healthcare, and the prevalence of chronic disease. GWR analysis showed that different vulnerable factors had different degrees of influence on differences in health screening participation rate.@*CONCLUSIONS@#These findings could enhance our understanding of decreased health screening participation due to COVID-19 and suggest that regional vulnerabilities should be considered stringent public health strategies after COVID-19.

Análisis de los hurtos en Colombia durante el año 2017 mediante los modelos de regresión lineal múltiple y la regresión ponderada geográficamente / Analysis of thefts in Colombia during 2017 using multiple linear regression models and geographically weighted regression / Análise dos furtos na Colômbia durante o ano 2017 mediante os modelos de regressão linear múltipla e a regressão ponderada geográficamente

Resumen Según información procedente del observatorio del delito de la Policía Nacional de Colombia, los hurtos a personas y de celulares han presentado una tendencia al alza desde el año 2003 (Norza, Peñalosa y Rodríguez, 2017). Esta tendencia motivó la realización del presente estudio para analizar la relación entre los factores socioeconómicos y el hurto en los diferentes municipios de Colombia durante el año 2017, mediante el uso de modelos de regresión lineal múltiple y regresión geográficamente ponderada utilizando fuentes de información secundaria segregada a nivel municipal. Se constató que las variables matriculados en instituciones de educación superior por cada mil personas, presupuesto per cápita asignado por el sistema general de participaciones y la categoría del municipio explican el 69,5% de la variabilidad del logaritmo del hurto a personas y de celulares en 532 municipios mediante un modelo de regresión lineal múltiple estimado globalmente y el 50,16% utilizando el modelo de regresión ponderada geográficamente omitiendo en este último la categoría del municipio. En este modelo hubo ligeras variaciones en los coeficientes a nivel municipal, lo que refleja que la heterogeneidad social y económica tiene efectos en los indicadores de hurto a nivel nacional.

Abstract According to information from the crime observatory of the National Police of Colombia, thefts from people and of cell phones have shown an upward trend since 2003 (Norza, Peñalosa and Rodríguez, 2017). This trend motivated the carrying out of the current study to analyze the relationship between socioeconomic factors and theft in the different municipalities of Colombia during 2017, through the use of multiple linear regression models and geographically weighted regression using secondary information sources segregated to municipal level. It was validated that variables enrolled in higher education institutions per thousand people, budget per capita allocated by the general system of participations and the category of the municipality account for 69,5% of the variability of the logarithm of theft from individuals and of cellphones in 532 municipalities using a globally estimated multiple linear regression model and 50,16% using the geographically weighted regression model omitting in the latter the category of the municipality. In this model there were slight variations in the coefficients at the municipal level, reflecting that the social and economic heterogeneity has effects on indicators of theft nationwide.

Resumo Segundo informação proveniente do observatório do delito da Polícia Nacional da Colômbia, os furtos a pessoas e de celulares têm apresentado uma tendência de aumento desde o ano 2003 (Norza, Peñalosa y Rodríguez, 2017). Esta tendência motivou a realização do presente estudo para analisar a relação entre os fatores socioeconómicos e o furto em os diferentes municípios da Colômbia durante o ano 2017, mediante o uso de modelos de regressão linear múltipla e regressão geograficamente ponderada utilizando fontes de informação secundária segregada a nível municipal. Constatou-se que as variáveis matriculadas em instituições de ensino superior por cada mil pessoas, orçamento per capita atribuído pelo sistema geral de participações e a categoria do município explicam o 69,5% da variabilidade do logaritmo do furto a pessoas e de celulares em 532 municípios mediante um modelo de regressão linear múltipla estimado globalmente e o 50,16% utilizando o modelo de regressão ponderada geograficamente omitindo neste último a categoria do município. Neste modelo houve ligeiras variações nos coeficientes a nível municipal, o que reflete que a heterogeneidade social e económica tem efeitos nos indicadores de furto a nível nacional.

Geographic weighted regression: applicability to epidemiological studies of leprosy

Abstract: INTRODUCTION: Geographic information systems (GIS) enable public health data to be analyzed in terms of geographical variability and the relationship between risk factors and diseases. This study discusses the application of the geographic weighted regression (GWR) model to health data to improve the understanding of spatially varying social and clinical factors that potentially impact leprosy prevalence. METHODS: This ecological study used data from leprosy case records from 1998-2006, aggregated by neighborhood in the Duque de Caxias municipality in the State of Rio de Janeiro, Brazil. In the GWR model, the associations between the log of the leprosy detection rate and social and clinical factors were analyzed. RESULTS: Maps of the estimated coefficients by neighborhood confirmed the heterogeneous spatial relationships between the leprosy detection rates and the predictors. The proportion of households with piped water was associated with higher detection rates, mainly in the northeast of the municipality. Indeterminate forms were strongly associated with higher detections rates in the south, where access to health services was more established. CONCLUSIONS: GWR proved a useful tool for epidemiological analysis of leprosy in a local area, such as Duque de Caxias. Epidemiological analysis using the maps of the GWR model offered the advantage of visualizing the problem in sub-regions and identifying any spatial dependence in the local study area.

Current and historical climate signatures to deconstructed tree species richness pattern in South America / Efeitos dos climas atual e histórico no padrão desconstruído de riqueza de espécies arbóreas na região Neotropical

The purpose of this study was to investigate the importance of present and historical climate as determinants of current species richness pattern of forestry trees in South America. The study predicted the distribution of 217 tree species using Maxent models, and calculated the potential species richness pattern, which was further deconstructed based on range sizes and modeled against current and historical climates predictors using Geographically Weighted Regressions (GWR) analyses. The current climate explains more of the wide-ranging species richness patterns than that of the narrow-ranging species, while the historical climate explained an equally small amount of variance for both narrow-and-wide ranging tree species richness patterns. The richness deconstruction based on range size revealed that the influences of current and historical climate hypotheses underlying patterns in South American tree species richness differ from those found in the Northern Hemisphere. Notably, the historical climate appears to be an important determinant of richness only in regions with marked climate changes and proved Pleistocenic refuges, while the current climate predicts the species richness across those Neotropical regions, with non-evident refuges in the Last Glacial Maximum. Thus, this study's analyses show that these climate hypotheses are complementary to explain the South American tree species richness.

O objetivo deste estudo foi testar qual dos climas, atual ou histórico, é o principal preditor do padrão atual de riqueza de espécies arbóreas de interesse comercial. Nós modelamos a distribuição de 217 espécies usando Maxent e usamos esses mapas preditivos para obter o padrão de riqueza de espécies. A riqueza foi desconstruída em relação ao tamanho da distribuição geográfica das espécies e modelada contra os climas atual e histórico utilizando Regressões Geograficamente Ponderadas. O clima atual explicou melhor o padrão de riqueza das espécies com ampla distribuição geográfica do que de espécies com distribuição restrita, enquanto o clima histórico explicou a mesma variância na riqueza dos dois grupos de espécies. Nossas análises com plantas sul americanas mostram diferentes relações da riqueza de espécies ampla e restritamente distribuídas com os climas atual e histórico, quando comparado aos resultados encontrados no hemisfério norte. O clima histórico se mostra como importante preditor da riqueza somente em regiões com mudanças climáticas acentuadas e onde ocorreram refúgios Pleistocênicos, enquanto o clima atual é o melhor da riqueza nas regiões Neotropicais sem evidências de refúgios durante o máximo da ultima glaciação. Dessa maneira, nossos resultados indicam que essas hipóteses são complementares para explicar a riqueza predita de espécies arbóreas da América do Sul.