Inter-relacionamento de dados ambientais e de saúde: análise de risco à saúde aplicada ao abastecimento de água no Rio de Janeiro utilizando Sistemas de Informações Geográficas

O inter-relacionamento de dados ambientais e sanitários, necessário na avaliação da exposição de grupos populacionais a fatores de risco, é dificultado pela defasagem no tempo e espaço destes conjuntos de dados. Neste trabalho utilizaram-se como bases de dados georreferenciadas: os setores censitários, que contêm informações sobre a forma com que são abastecidos os domicílios; a rede de abastecimento de água, seus principais mananciais e reservatórios; a qualidade da água, segundo programa de monitoramento. Mediante operações espaciais entre estas camadas, foram localizados e quantificados grupos populacionais submetidos a risco, de acordo com diferentes critérios. Grande parte dos riscos associados ao abastecimento de água encontram-se localizados na encosta norte do Maciço da Tijuca, e na Zona Oeste, onde a população procura formas alternativas de abastecimento. Em razão das diferentes origens, objetivos e estruturas dos dados, os Sistemas de Informações Geográficas (SIG) podem ser utilizados como instrumento de organização, de validação destes dados e de verificação de possíveis inconsistências

Linkage of environmental and health data: health risk analysis of the Rio de Janeiro water supply using Geographical Information Systems

Made available in DSpace on 2010-08-23T16:58:45Z (GMT). No. of bitstreams: 3 license.txt: 1848 bytes, checksum: 91e3e8675022d3b5bafaf2bca961e4cd (MD5) BARCELLOS_Inter-relacionamento de dados ambientais e de saude_1998.pdf: 1072358 bytes, checksum: 555f6abe17b2e582e37d76bfe20511d1 (MD5) BARCELLOS_Inter-relacionamento de dados ambientais e de saude_1998.pdf.txt: 34386 bytes, checksum: bbbec9bb4ab08d69bcb0f4f3363fee82 (MD5) Previous issue date: 1998-07Made available in DSpace on 2010-11-04T14:19:56Z (GMT). No. of bitstreams: 3 BARCELLOS_Inter-relacionamento de dados ambientais e de saude_1998.pdf.txt: 34386 bytes, checksum: bbbec9bb4ab08d69bcb0f4f3363fee82 (MD5) BARCELLOS_Inter-relacionamento de dados ambientais e de saude_1998.pdf: 1072358 bytes, checksum: 555f6abe17b2e582e37d76bfe20511d1 (MD5) license.txt: 1848 bytes, checksum: 91e3e8675022d3b5bafaf2bca961e4cd (MD5) Previous issue date: 1998-07Fundação Oswaldo Cruz. Centro de Informação em Ciência e Tecnologia. Departamento de Informações em Saúde. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Centro de Informação em Ciência e Tecnologia. Departamento de Informações em Saúde. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Centro de Informação em Ciência e Tecnologia. Departamento de Informações em Saúde. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Centro de Informação em Ciência e Tecnologia. Departamento de Informações em Saúde. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Centro de Informação em Ciência e Tecnologia. Departamento de Informações em Saúde. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Centro de Informação em Ciência e Tecnologia. Departamento de Informações em Saúde. Rio de Janeiro, RJ, Brasil.O inter-relacionamento de dados ambientais e sanitários, necessário na avaliação da exposição de grupos populacionais a fatores de risco, é dificultado pela defasagem no tempo e espaço destes conjuntos de dados. Neste trabalho utilizaram-se como bases de dados georreferenciadas: os setores censitários, que contêm informações sobre a forma com que são abastecidos os domicílios; a rede de abastecimento de água, seus principais mananciais e reservatórios; a qualidade da água, segundo programa de monitoramento. Mediante operações espaciais entre estas camadas, foram localizados e quantificados grupos populacionais submetidos a risco, de acordo com diferentes critérios. Grande parte dos riscos associados ao abastecimento de água encontram-se localizados na encosta norte do Maciço da Tijuca, e na Zona Oeste, onde a população procura formas alternativas de abastecimento. Em razão das diferentes origens, objetivos e estruturas dos dados, os Sistemas de Informações Geográficas (SIG) podem ser utilizados como instrumento de organização, de validação destes dados e de verificação de possíveis inconsistências.Exposure assessment of population groups is based on linkage of environmental and health data. This relationship can be hard to establish due to spatial and temporal lags in data sets. GIS can be used as a basis for organizing health-related and environmental data sets. We examined potential health risk in the Rio de Janeiro city water supply based on the overlay of information layers containing data on the presence and quality of water supply services. We used census tracts as the primary georeferenced data, since they contain information on how households are supplied, water supply pipes, sources, and reservoirs, and water quality according to the monitoring program. Population groups exposed to risks were located and quantified using spatial operations among these layers and adopting different risk criteria. The main problems related to water supply are located on the northern slope of the Tijuca Mountain Range and in the western area of the city of Rio, where the population relies on alternative water supply sources. The different origins, objectives, and structures of data have to be analyzed critically, and GIS can be used as a data validation tool as well as an instrument for detailed identification of inconsistencies

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background: Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods: We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5-19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For school-aged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings: From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation: The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesity. Funding: UK Medical Research Council, UK Research and Innovation (Research England), UK Research and Innovation (Innovate UK), and European Union

Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology

The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5–15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction