523 resultados para life tables

em Queensland University of Technology - ePrints Archive


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Neonate Lepidoptera are confronted with the daunting task of establishing themselves on a food plant. The factors relevant to this process need to be considered at spatial and temporal scales relevant to the larva and not the investigator. Neonates have to cope with an array of plant surface characters as well as internal characters once the integument is ruptured. These characters, as well as microclimatic conditions, vary within and between plant modules and interact with larval feeding requirements, strongly affecting movement behavior, which may be extensive even for such small organisms. In addition to these factors, there is an array of predators, pathogens, and parasitoids with which first instars must contend. Not surprisingly, mortality in neonates is high but can vary widely. Experimental and manipulative studies, as well as detailed observations of the animal, are vital if the subtle interaction of factors responsible for this high and variable mortality are to be understood. These studies are essential for an understanding of theories linking female oviposition behavior with larval survival, plant defense theory, and population dynamics, as well as modern crop resistance breeding programs.

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In many product categories of durable goods such as TV, PC, and DVD player, the largest component of sales is generated by consumers replacing existing units. Aggregate sales models proposed by diffusion of innovation researchers for the replacement component of sales have incorporated several different replacement distributions such as Rayleigh, Weibull, Truncated Normal and Gamma. Although these alternative replacement distributions have been tested using both time series sales data and individual-level actuarial “life-tables” of replacement ages, there is no census on which distributions are more appropriate to model replacement behaviour. In the current study we are motivated to develop a new “modified gamma” distribution by two reasons. First we recognise that replacements have two fundamentally different drivers – those forced by failure and early, discretionary replacements. The replacement distribution for each of these drivers is expected to be quite different. Second, we observed a poor fit of other distributions to out empirical data. We conducted a survey of 8,077 households to empirically examine models of replacement sales for six electronic consumer durables – TVs, VCRs, DVD players, digital cameras, personal and notebook computers. This data allows us to construct individual-level “life-tables” for replacement ages. We demonstrate the new modified gamma model fits the empirical data better than existing models for all six products using both a primary and a hold-out sample.

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Introduction Economic evaluations of interventions in the hospital setting often rely on the estimated long-term impact on patient survival. Estimates of mortality rates and long-term outcomes among patients discharged alive from the intensive care unit (ICU) are lacking from lower- and middle-income countries. This study aimed to assess the long-term survival and life expectancy (LE) amongst post-ICU patients in Thailand, a middle-income country. Methods In this retrospective cohort study, data from a regional tertiary hospital in northeast Thailand and the regional death registry were linked and used to assess patient survival time after ICU discharge. Adult ICU patients aged at least 15 years who had been discharged alive from an ICU between 1 January 2004 and 31 December 2005 were included in the study, and the death registry was used to determine deaths occurring in this cohort up to 31st December 2010. These data were used in conjunction with standard mortality life tables to estimate annual mortality and life expectancy. Results This analysis included 10,321 ICU patients. During ICU admission, 3,251 patients (31.5%) died. Of 7,070 patients discharged alive, 2,527 (35.7%) were known to have died within the five-year follow-up period, a mortality rate 2.5 times higher than that in the Thai general population (age and sex matched). The mean LE was estimated as 18.3 years compared with 25.2 years in the general population. Conclusions Post-ICU patients experienced much higher rates of mortality than members of the general population over the five-year follow-up period, particularly in the first year after discharge. Further work assessing Health Related Quality of Life (HRQOL) in both post-ICU patients and in the general population in developing countries is needed.

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Background The incidence of malignant mesothelioma is increasing. There is the perception that survival is worse in the UK than in other countries. However, it is important to compare survival in different series based on accurate prognostic data. The European Organisation for Research and Treatment of Cancer (EORTC) and the Cancer and Leukaemia Group B (CALGB) have recently published prognostic scoring systems. We have assessed the prognostic variables, validated the EORTC and CALGB prognostic groups, and evaluated survival in a series of 142 patients. Methods Case notes of 142 consecutive patients presenting in Leicester since 1988 were reviewed. Univariate analysis of prognostic variables was performed using a Cox proportional hazards regression model. Statistically significant variables were analysed further in a forward, stepwise multivariate model. EORTC and CALGB prognostic groups were derived, Kaplan-Meier survival curves plotted, and survival rates were calculated from life tables. Results Significant poor prognostic factors in univariate analysis included male sex, older age, weight loss, chest pain, poor performance status, low haemoglobin, leukocytosis, thrombocytosis, and non-epithelial cell type (p<0.05). The prognostic significance of cell type, haemoglobin, white cell count, performance status, and sex were retained in the multivariate model. Overall median survival was 5.9 (range 0-34.3) months. One and two year survival rates were 21.3% (95% CI 13.9 to 28.7) and 3.5% (0 to 8.5), respectively. Median, one, and two year survival data within prognostic groups in Leicester were equivalent to the EORTC and CALGB series. Survival curves were successfully stratified by the prognostic groups. Conclusions This study validates the EORTC and CALGB prognostic scoring systems which should be used both in the assessment of survival data of series in different countries and in the stratification of patients into randomised clinical studies.

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Tumour angiogenesis has been recently recognised as one of the most important prognostic factors in lung cancer. Although a variety of angiogenic factors have been identified, the angiogenesis process remains poorly understood. Bcl-2, c-erbB-2 and p53 are well-known oncogenes involved in non- small-cell lung cancer pathogenesis. A direct correlation of thymidine phosphorylase (TP) and of vascular endothelial growth factor (VEGF) with intratumoural angiogenesis has been reported. In the present study we investigated the possible regulatory role if bcl-2, c-erB-2 proteins in angiogenesis and in VEGF and TP expression in non-small-cell lung cancer. Two hundred sixteen specimens from T1,2-NO, 1 staged patients treated with surgery alone were immunohistochemically examined. Bcl-2 and c-erbB-2 were significantly inversely related to each other (P = 0.04) and both were inversely associated with microvessel density (P < 0.02). High TP and VEGF reactivity was statistically related to loss of bcl-2 expression (P < 0.01). A significant co-expression of c-erbB-2 with TP was noted (P = 0.01). However, TP expression was related to high angiogenesis only in cases with absence of c-erB-2 expression (P < 0.0001). c-erbB-2 expression in poorly vascularised tumours was linked with poor outcome (P = 0.03). The present study provides strong evidence that the bcl-2 gene has a suppressive function over genes involved in both angiogenesis (VEGF and TP) and cell migration (c- erbB-2) in NSCLC. TP and c-erbB-2 proteins are significantly, and often simultaneously, expressed in bcl-2 negative cases. However, expression of the c-erbB-2 abolishes the TP-related angiogenic activity. Whether this is a result of a direct activity of the c-erbB-2 protein or a consequence of a c- erbB-2-related immune response remains to be further investigated.

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Background Up-to-date evidence on levels and trends for age-sex-specific all-cause and cause-specific mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specific all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specific causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65·3 years (UI 65·0–65·6) in 1990, to 71·5 years (UI 71·0–71·9) in 2013, while the number of deaths increased from 47·5 million (UI 46·8–48·2) to 54·9 million (UI 53·6–56·3) over the same interval. Global progress masked variation by age and sex: for children, average absolute differences between countries decreased but relative differences increased. For women aged 25–39 years and older than 75 years and for men aged 20–49 years and 65 years and older, both absolute and relative differences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10·7%, from 4·3 million deaths in 1990 to 4·8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specific mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.

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Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

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A travel article about lifestyle and architecture in Vancouver, Canada. I arrived in Vancouver on a Sunday morning and followed the smell of crepes to a café that had opened early. It was warm, and the outside tables were filling up. The others had a last-night look about them, except a family that smiled as they took the table next to mine. There were no shoppers yet, but the mall was busy with walkers and cyclists. Here and there the down-and-out found seats in the sun; their shadows ran the length of shop fronts...

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OBJECTIVE: To evaluate the scored Patient-generated Subjective Global Assessment (PG-SGA) tool as an outcome measure in clinical nutrition practice and determine its association with quality of life (QoL). DESIGN: A prospective 4 week study assessing the nutritional status and QoL of ambulatory patients receiving radiation therapy to the head, neck, rectal or abdominal area. SETTING: Australian radiation oncology facilities. SUBJECTS: Sixty cancer patients aged 24-85 y. INTERVENTION: Scored PG-SGA questionnaire, subjective global assessment (SGA), QoL (EORTC QLQ-C30 version 3). RESULTS: According to SGA, 65.0% (39) of subjects were well-nourished, 28.3% (17) moderately or suspected of being malnourished and 6.7% (4) severely malnourished. PG-SGA score and global QoL were correlated (r=-0.66, P<0.001) at baseline. There was a decrease in nutritional status according to PG-SGA score (P<0.001) and SGA (P<0.001); and a decrease in global QoL (P<0.001) after 4 weeks of radiotherapy. There was a linear trend for change in PG-SGA score (P<0.001) and change in global QoL (P=0.003) between those patients who improved (5%) maintained (56.7%) or deteriorated (33.3%) in nutritional status according to SGA. There was a correlation between change in PG-SGA score and change in QoL after 4 weeks of radiotherapy (r=-0.55, P<0.001). Regression analysis determined that 26% of the variation of change in QoL was explained by change in PG-SGA (P=0.001). CONCLUSION: The scored PG-SGA is a nutrition assessment tool that identifies malnutrition in ambulatory oncology patients receiving radiotherapy and can be used to predict the magnitude of change in QoL.