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.

Variabilidade espacial da composição do leite cru refrigerado no estado de Alagoas e na mesorregião do Agreste Pernambucano / Spatial variability of refrigerated raw milk composition in the Alagoas State and Agreste Pernambucano Mesorregion

O objetivo da realização deste trabalho foi analisar a variabilidade espacial da composição do leite cru refrigerado e elaborar mapas com interpolação de dados sobre os teores de gordura, proteína, lactose, sólidos totais e extrato seco desengordurado, no estado de Alagoas e na mesorregião do Agreste Pernambucano, em 2014 e 2015. Foram analisados 3.863 laudos oficiais de amostras de leite cru refrigerado, coletados de 432 tanques de expansão direta da região estudada. O grau de dependência espacial e a regressão geograficamente ponderada das variáveis foram analisados pelo software ArcGIS 10.3. A análise espacial mostrou predominância de áreas com teor de gordura de 3,1 a 3,6g/100g e áreas com teor de gordura de 3,6 a 4,2g/100g. Para o teor de lactose, foi observada área predominante com 4,32 a 4,45g/100g e algumas áreas com 4,46 a 4,54g/100g. Foi observada baixa influência da altitude, precipitação pluviométrica e interação precipitação x altitude sobre o teor de gordura, proteína, lactose, sólidos totais e extrato seco desengordurado na área estudada. Por fim, conclui-se que há variabilidade espacial para gordura, lactose, proteína, sólidos totais e extrato seco desengordurado do leite cru refrigerado produzido no estado de Alagoas e na mesorregião do Agreste Pernambucano.(AU)

The aim of this work was to analyze the spatial variability and draw maps with data interpolation on the fat, protein, lactose, total solids, and nonfat dry extract of refrigerated raw milk in the state of Alagoas and Mesoregion the Pernambuco Agreste in 2014 and 2015. A total of 3,863 fficial reports of samples of raw milk collected from 432 refrigerated tanks direct expansion of the studied region were analyzed. The degree of spatial dependence and geographically weighted regression of variables was analyzed using ArcGIS 10.3 software. The spatial analysis showed predominance of areas with a fat content of 3.1 to 3.6g/100g and areas with a fat content of 3.6 to 4.2g/100g. For lactose content predominant area of 4.32 to 4,45g/100g and some areas with 4.46 to 4,54g/100g were observed. Altitude, rainfall, and precipitation interaction x altitude of the fat, protein, lactose, total solids and nonfat dry extract in the study area showed little influence. Finally, there is spatial variability in fat, lactose, protein, total solids, and nonfat dry extract of refrigerated raw milk produced in the state of Alagoas and Pernambuco Mesoregion of Agreste.

Analysis of influencing factors on water iodine in Shandong Province based on geographically weighted regression / 中华地方病学杂志

Objective To analyze the influencing factors of water iodine in Shandong Province.Methods The county-based study set Shandong Province as a research site.The water iodine data of county (city) from 2008 to 2010 were obtained from Shandong Institute for Prevention and Control of Endemic Disease.Water iodine content was used as a dependent variable,and soil type,hydrogeological type,topography and distance to the Yellow River were analyzed as independent variables.Normality test and general linear regression analysis of the dependent variables were performed using SAS 9.3 software;geographically weighted regression (GWR) analysis was performed using SAM V4.0 software;related electronic maps were drawn using ArcGIS 9.3 software.Results A total of 108 164 water iodine content data were collected.General linear regression analysis showed that the constructed regression model was statistically significant (F =16.29,P ＜ 0.01),and the soil type was included in the model with a determination coefficient (R2) =0.51.GWR analysis showed that R2 =0.63 and the adjustive determination coefficient (R2adj) =0.59.Considering the autocorrelation of the variable space,the model's goodness of fit was better than that of the traditional general linear regression model.GWR analysis showed that soil type and distance to the Yellow River were major factors related to water iodine in Shandong Province.There was a negative correlation between soil type and spatial variability of water iodine in most areas of Shandong Province,the correlation coefficients weakened gradually from west to east,indicating a geographic gradient variability.The correlation coefficients of distance to the Yellow River and spatial variability of water iodine were negative in some areas,while they were positive in other areas,indicating a clear geographical variability from southwest to northwest.Conclusion The soil type and the distance to Yellow River are important factors affecting the spatial distribution of water iodine in Shandong Province.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data / Avaliação de técnicas geoestatísticas na estimativa da distribuição espacial da biomassa acima do solo na Amazônia utilizando sensoriamento remoto de alta resolução espacial

The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

A distribuição espacial da biomassa na Amazônia é heterogênea, variando temporalmente e espacialmente em relação aos diferentes tipos de formações vegetais abrangidas por este bioma. Estimativas de biomassa nesta região variam significativamente dependendo da abordagem aplicada e do conjunto de dados utilizados para sua modelagem. Assim, este estudo teve como objetivo avaliar três diferentes técnicas geoestatísticas na estimativa da distribuição espacial da biomassa acima do solo (BAS). As técnicas escolhidas foram: 1) regressão por mínimos quadrados ordinários (OLS), 2) regressão geograficamente ponderada (RGP) e, 3) regressão geograficamente ponderada - krigagem (RGP-K). Estas técnicas foram aplicadas sobre um mesmo conjunto de dados de campo, utilizando as mesmas variáveis ambientais decorrentes de dados cartográficos e de sensoriamento remoto de alta resolução espacial (RapidEye). Este trabalho foi desenvolvido na floresta amazônica da província de Sucumbíos no Equador. Os resultados deste estudo mostraram que a RGP-K, sendo uma técnica híbrida, forneceu estimativas estatisticamente satisfatórias com menor erro de predição em comparação com as outras duas técnicas. Além disso, observou-se que 75% da BAS foi explicada pela combinação de dados de sensoriamento remoto e variáveis ambientais, sendo os tipos de formações vegetais a variável de maior importância para estimar BAS. Cabe ressaltar que, embora o uso de imagens de alta resolução espacial melhora significativamente a estimativa da distribuição espacial da BAS, o processamento desta informação requer alta demanda computacional.

Spatial Distribution of Diabetes Prevalence Rates and Its Relationship with the Regional Characteristics / 보건행정학회지

This study purposed to analyze the relationship between spatial distribution of Diabetes prevalence rates and regional variables. The unit of analysis was administrative districts of city·gun·gu. Dependent variable was the age- and sex- adjusted diabetes prevalence rates and regional variables were selected to represent three aspects: demographic and socioeconomic factor, health and medical factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for the spatial analysis. Analysis results showed that age- and sex-adjusted diabetes prevalence rates were varied depending on regions. OLS regression showed that diabetes prevalence rates had significant relationships with percent of population over age 65 and financial independence rate. In GWR, the effects of regional variables were not consistent. These results provide information to health policy makers. Regional characteristics should be considered in allocating health resources and developing health related programs for the regional disease management.

Analysing the Effects of Regional Factors on the Regional Variation of Obesity Rates Using the Geographically Weighted Regression / 보건행정학회지

BACKGROUND: This study purposed to analyze the relationship between regional obesity rates and regional variables. METHODS: Data was collected from the Korean Statistical Information Service (KOSIS) and Community Health Survey in 2012. The units of analysis were administrative districts such as city, county, and district. The dependent variable was the age-sex adjusted regional obesity rates. The independent variables were selected to represent four aspects of regions: health behaviour factor, psychological factor, socio-economic factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis model, this study applied geographically weighted regression (GWR) analysis to calculate the regression coefficients for each region. RESULTS: The OLS results showed that there were significant differences in regional obesity rates in high-risk drinking, walking, depression, and financial independence. The GWR results showed that the size of regression coefficients in independent variables was differed by regions. CONCLUSION: Our results can help in providing useful information for health policy makers. Regional characteristics should be considered when allocating health resources and developing health-related programs.

Spatial analysis of PM10 and cardiovascular mortality in the Seoul metropolitan area

OBJECTIVES: Numerous studies have revealed the adverse health effects of acute and chronic exposure to particulate matter less than 10 mum in aerodynamic diameter (PM10). The aim of the present study was to examine the spatial distribution of PM10 concentrations and cardiovascular mortality and to investigate the spatial correlation between PM10 and cardiovascular mortality using spatial scan statistic (SaTScan) and a regression model. METHODS: From 2008 to 2010, the spatial distribution of PM10 in the Seoul metropolitan area was examined via kriging. In addition, a group of cardiovascular mortality cases was analyzed using SaTScan-based cluster exploration. Geographically weighted regression (GWR) was applied to investigate the correlation between PM10 concentrations and cardiovascular mortality. RESULTS: An examination of the regional distribution of the cardiovascular mortality was higher in provincial districts (gu) belonging to Incheon and the northern part of Gyeonggido than in other regions. In a comparison of PM10 concentrations and mortality cluster (MC) regions, all those belonging to MC 1 and MC 2 were found to belong to particulate matter (PM) 1 and PM 2 with high concentrations of air pollutants. In addition, the GWR showed that PM10 has a statistically significant relation to cardiovascular mortality. CONCLUSIONS: To investigate the relation between air pollution and health impact, spatial analyses can be utilized based on kriging, cluster exploration, and GWR for a more systematic and quantitative analysis. It has been proven that cardiovascular mortality is spatially related to the concentration of PM10.