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@#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.
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BACKGROUND: The success of an injury intervention program can be measured by the proportion of successful return to work (RTW). This study examined factors of successful return to employment among workers suffering from work-related injuries. METHODS: Data were obtained from the Social Security Organization, Malaysia database consisting of 10,049 RTW program participants in 2010–2014. The dependent variable was the RTW outcome which consisted of RTW with same employer, RTW with new employer or unsuccessful return. Multinomial logistic regression was performed to test the likelihood of successful return with same employer and new employer against unsuccessful return. RESULTS: Overall, 65.3% of injured workers were successfully returned to employment, 52.8% to the same employer and 12.5% to new employer. Employer interest; motivation; age 30–49 years; intervention less than 9 months; occupational disease; injuries in the lower limbs, upper limbs, and general injuries; and working in the manufacturing, services, and electrical/electronics were associated with returning to work with the same employer against unsuccessful return. Male, employer interest, motivation, age 49 years or younger, intervention less than 6 months, occupational disease, injuries in the upper limbs and services sector of employment were associated with returning to new employer against unsuccessful return. CONCLUSION: There is a need to strengthen employer commitment for early and intensified intervention that will lead to improvement in the RTW outcome.
Subject(s)
Humans , Male , Case Management , Employment , Logistic Models , Lower Extremity , Malaysia , Motivation , Occupational Diseases , Rehabilitation , Return to Work , Social Security , Upper ExtremityABSTRACT
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.
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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.