ABSTRACT
@#Introduction: The elderly population is susceptible to malnutrition due to the physiological and functional changes caused by ageing. Hence, to prevent the degenerative nutritional conditions of the elderly and improve their quality of life, this study aimed to determine the influencing factors of malnutrition among Filipino elderly. Methods: Anthropometric component data from the 8th National Nutrition Survey (NNS) were utilised along with the nutritional status of Filipino elderly, based on body mass index as the dependent variable and variables from the clinical and health, dietary, and socioeconomic components of NNS as explanatory factors. Multinomial logistic regression analysis was then performed in fitting models. Results: High magnitude and severity of malnutrition were observed among Filipino elderly – 20.2% were underweight and 24.8% were overweight. Significant influencing factors in estimating an elderly’s odds of being underweight were the elderly’s age group, presence of hypertension, presence of anaemia, and adequacy of vitamin A intake. Whereas, significant influencing factors in estimating an elderly’s odds of being overweight/obese included highest educational attainment, presence of anaemia, hypertension, diabetes, and dyslipidaemia. Conclusion: Knowing the influencing factors may help the elderly become more aware and conscious of their health, as well as to promote nutrient intakes to prevent malnutrition that can worsen their health conditions. Additionally, concerned institutions in the country could use the findings of this study as one of the bases in strengthening their approach and implementation or even provide relevant and timely policies and programmes that address malnutrition in the elderly of this country.
ABSTRACT
O objetivo deste estudo foi aplicar uma técnica automatizada de mapeamento de solos a partir de modelos preditivos ajustados em uma área de referência e, posteriormente, aplicados em áreas próximas de mesmas relações solo-paisagem. Modelos lineares generalizados foram desenvolvidos utilizando-se de nove atributos de terreno, derivados de um Modelo Digital de Elevação, como covariáveis preditoras e classes de solos, obtidas em um levantamento convencional, como variáveis dependentes. Os modelos foram capazes de distinguir as três principais formas da paisagem local. Classes de solos de pedogênese intimamente ligada às covariáveis preditoras obtiveram os melhores resultados. O mapa de solos gerado apresentou uma reprodutibilidade de 46,12 por cento e uma exatidão de 21,06 por cento.
The aim of this study was to apply an automated technique of soil mapping from predictive models developed at a reference area, into nearby areas of the same soil-landscape relationships. Generalized linear models were developed using nine terrain attributes derived from a digital elevation model as covariate predictors, with soil classes, obtained from a conventional soil survey, as dependent variables. The models were able to distinguish the three main forms of the local landscape. Soil classes with pedogenesis intimately tied to the predictive covariates obtained the best results. The soil maps generated, showed a reproducibility of 46.12 percent and an accuracy of 21.06 percent.
ABSTRACT
Objective:To explore the main factors influencing the serviceman's choice of medical-care units,so as to provide scientific ground for optimal health resource distribution and health policy reform.Methods: Multinomial logistic regression was used to analyze the main factors influencing the choice of medical-care units by servicemen.Results: The main factors influencing the choice were the services and sepecific jobs,followed by degrees of satisfaction with the drug supply and health status.Conclusion: The selection of medical units by servicemen to seek medical advice was influenced by many factors.Efforts should be made to strengthen construction of grassroot military health units and to improve the utilization of health resources.