554 resultados para Health statistics

em Queensland University of Technology - ePrints Archive


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Despite the rhetoric that students with learning difficulties are adequately supported within schools, the evidence suggests that they continue to experience school failure with devastating consequences. Students with learning difficulties are disproportionately represented as juvenile delinquents, as the unemployed and in mental health statistics. However, the defining of this group remains confused and imprecise and has not been a national priority. This has repercussions for both secondary schools and for the students themselves. This paper highlights research related to teaching practices, policies and school structure and their effects on the academic outcomes and emotional well being of students with learning difficulties. Finally, it makes a number of recommendations to change the status quo for these students.

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Today in Australia, 75% of all Indigenous Australians reside in urban and peri-urban areas. In Brisbane, Indigenous Australians now number just over 45,000, and this number is rapidly increasing. Undertaking research with urban based Indigenous Australians is a relatively new phenomenon. Most past research with Indigenous people has been carried out in remote and regional areas. This paper focuses on a Participation Action Research project undertaken with Indigenous women in the highly urbanised area of North Brisbane. The project takes on the challenge of undertaking urban based Indigenous research. It opts not to centre on poor Indigenous women’s health statistics but instead centres on Indigenous women’s wellness and ways to talk about and work towards wellness. Through the cycles of dialogue with Indigenous women these concepts were teased out and manifested in two highly successful Women’s Wellness Summits. This paper will outline aspects of this project.

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Objective To make a comprehensive estimation of the burden of malignant neoplasm on village residents in Linqu County by the means of DALY (Disability-adjusted life year). Methods DALYs, YLLs and YLDs were calculated following the procedures developed for the Global Burden of Disease (GBD) study to ensure comparability, based on the cancer registration data of Linqu villages during 1998-2004, in order to measure the burden of various caners. Results All cancers were responsible for 20.00 DALYs for every thousand population in Linqu County (24.82 for men and 14.96 for women). 92.5% of the losses were due to premature death and 7.5% to disability. 31.5% of the DALYs happened among 45-59 age group. The top 10 cancers for DALYs were stomach cancer, lung cancer, liver cancer, oesophagus cancer, leukemia,colon/rectum cancer, brain cancer, pancreas cancer, breast cancer and bone cancer in turn. Only stomach cancer, lung cancer and liver cancer together account for 69.3% of total DALYs due to malignant neoplasm. The burden of malignant neoplasm was on rising recent years. Conclusions The burden of disease of malignant neoplasm is mainly because of premature death. Stomach cancer, lung cancer and liver cancer lead to heavier burden than the global and national levels. Abstract in Chinese 目的 应用伤残调整寿命年(DALY)对临朐县农村恶性肿瘤疾病负担进行评价. 方法 以1998~2004年临朐县农村肿瘤登记资料为基础,利用全球疾病负担研究中使用的专门公式计算恶性肿瘤的YLLs、YLDs和DALYs,以此评价各类恶性肿瘤的疾病负担. 结果 临朐农村每千人每年因恶性肿瘤造成20.0个DALYs损失(男性24.82,女性14.96),其中92.5%为早死所致,7.5%因残疾所致;恶性肿瘤主要导致45~59岁人群的DALYs损失,占31.5%;恶性肿瘤疾病负担前10位依次为胃癌、肺癌、肝癌、食道癌、白血病、肠癌、脑癌、胰腺癌、乳腺癌和骨癌,其中仅胃癌、肺癌和肝癌三大肿瘤DALYs就占全部肿瘤的69.3%;临朐县农村恶性肿瘤疾病负担有进一步上升的趋势. 结论 恶性肿瘤疾病负担主要由早死所致,胃癌、肺癌、肝癌等主要癌症疾病负担高于全球和中国区水平.

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In this rural population, we found birth rate was 12% with a male-female ratio of 1.08. The infant mortality was 11.5% which was much lower than the national average.

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Initial estimates of the burden of disease in South Africa in 20001 have been revised on the basis of additional data to estimate the disability-adjusted life-years (DALYs) for single causes for the first time in South Africa. The findings highlight the fact that despite uncertainty in the estimates, they provide important information to guide public health responses to improve the health of the nation...

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Background: High levels of wealth inequality with improved health statistics in South Africa (SA) provide an important opportunity to investigate non-communicable diseases (NCDs) among the poor. Aims: This paper uses two distinct national data sets to contrast patterns of mortality in rich and poor areas and explore the associations between poverty, risk factors, health care and selected NCDs diseases in South African adults. Methods: Causes of premature mortality in 1996 experienced in the poorest magisterial districts are compared with those in the richest, using average household wealth to classify districts. Logistic and multinomial regression are used to investigate the association of a household asset index and selected chronic conditions, related risk factors and healthcare indicators using data from the 1998 South African Demographic and Health Survey. Results: NCDs accounted for 39% and 33% of premature mortality in rich and poor districts respectively. The household survey data showed that the risk factors hypertension and obesity increased with increasing wealth, while most of the lifestyle factors, such as light smoking, domestic exposure to ``smoky'' fuels and alcohol dependence were associated with poverty. Treatment status for hypertension and asthma was worse for poor people than for rich people. Conclusions: The study suggests that NCDs and lifestyle-related risk factors are prevalent among the poor in SA and treatment for chronic diseases is lacking for poor people. The observed increase in hypertension and obesity with wealth suggests that unless comprehensive health promotion strategies are implemented, there will be an unmanageable chronic disease epidemic with future socioeconomic development in SA.

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Objective: To investigate measures aimed at defining the nutritional status of cystic fibrosis (CF) populations, this study compared standard anthropometric measurements and total body potassium (TBK) as indicators of malnutrition. Methods: Height, weight, and TBK measurements of 226 children with CF from Royal Children's Hospital, Brisbane, Australia, were analyzed. Z scores for height for age, weight for age, and weight for height were analyzed by means of the National Centre for Health Statistics reference. TBK was measured by means of whole body counting and compared with predicted TBK for age. Two criteria were evaluated with respect to malnutrition: (1) a z score < -2.0 and (2) a TBK for age <80% of predicted. Results: Males and females with CF had lower mean height-for-age and weight-for-age z scores than the National Centre for Health Statistics reference (P < .01), but mean weight-for-height z score was not significantly different. There were no significant gender differences. According to anthropometry, only 7.5% of this population were underweight and 7.6% were stunted. However, with TBK as an indicator of nutritional status, 29.9% of males and 22.0% of females were malnourished. Conclusion: There are large differences in the percentage of patients with CF identified as malnourished depending on whether anthropometry or body composition data are used as the nutritional indicator. At an individual level, weight-based indicators are not sensitive indicators of suboptimal nutritional status in CF, significantly underestimating the extent of malnutrition. Current recommendations in which anthropometry is used as the indicator of malnutrition in CF should be revised.

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Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

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Overweight and obesity are two of the most important emerging public health issues in our time and regarded by the World Health Organisation [WHO] (1998) as a worldwide epidemic. The prevalence of obesity in the USA is the highest in the world, and Australian obesity rates fall into second place. Currently, about 60% of Australian adults are overweight (BMI „d 25kg/m2). The socio-demographic factors associated with overweight and/or obesity have been well demonstrated, but many of the existing studies only examined these relationships at one point of time, and did not examine whether significant relationships changed over time. Furthermore, only limited previous research has examined the issue of the relationship between perception of weight status and actual weight status, as well as factors that may impact on people¡¦s perception of their body weight status. Aims: The aims of the proposed research are to analyse the discrepancy between perceptions of weight status and actual weight status in Australian adults; to examine if there are trends in perceptions of weight status in adults between 1995 to 2004/5; and to propose a range of health promotion strategies and furth er research that may be useful in managing physical activity, healthy diet, and weight reduction. Hypotheses: Four alternate hypotheses are examined by the research: (1) there are associations between independent variables (e.g. socio -demographic factors, physical activity and dietary habits) and overweight and/or obesity; (2) there are associations between the same independent variables and the perception of overweight; (3) there are associations between the same independent variables and the discrepancy between weight status and perception of weight status; and (4) there are trends in overweight and/or obesity, perception of overweight, and the discrepancy in Australian adults from 1995 to 2004/5. Conceptual Framework and Methods: A conceptual framework is developed that shows the associations identified among socio -demographic factors, physical activity and dietary habits with actual weight status, as well as examining perception of weight status. The three latest National Health Survey data bases (1995 , 2001 and 2004/5) were used as the primary data sources. A total of 74,114 Australian adults aged 20 years and over were recruited from these databases. Descriptive statistics, bivariate analyses (One -Way ANOVA tests, unpaired t-tests and Pearson chi-square tests), and multinomial logistic regression modelling were used to analyse the data. Findings: This research reveals that gender, main language spoken at home, occupation status, household structure, private health insurance status, and exercise are related to the discrepancy between actual weight status and perception of weight status, but only gender and exercise are related to the discrepancy across the three time point s. The current research provides more knowledge about perception of weight status independently. Factors which affect perception of overweight are gender, age, language spoken at home, private health insurance status, and diet ary habits. The study also finds that many factors that impact overweight and/or obesity also have an effect on perception of overweight, such as age, language spoken at home, household structure, and exercise. However, some factors (i.e. private health insurance status and milk consumption) only impact on perception of overweight. Furthermore, factors that are rel ated to people’s overweight are not totally related to people’s underestimation of their body weight status in the study results. Thus, there are unknown factors which can affect people’s underestimation of their body weight status. Conclusions: Health promotion and education activities should provide education about population health education and promotion and education for particular at risk sub -groups. Further research should take the form of a longitudinal study design ed to examine the causal relationship between overweight and/or obesity and underestimation of body weight status, it should also place more attention on the relationships between overweight and/or obesity and dietary habits, with a more comprehensive representation of SES. Moreover, further research that deals with identification of characteristics about perception of weight status, in particular the underestimation of body weight status should be undertaken.

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The refractive error of a human eye varies across the pupil and therefore may be treated as a random variable. The probability distribution of this random variable provides a means for assessing the main refractive properties of the eye without the necessity of traditional functional representation of wavefront aberrations. To demonstrate this approach, the statistical properties of refractive error maps are investigated. Closed-form expressions are derived for the probability density function (PDF) and its statistical moments for the general case of rotationally-symmetric aberrations. A closed-form expression for a PDF for a general non-rotationally symmetric wavefront aberration is difficult to derive. However, for specific cases, such as astigmatism, a closed-form expression of the PDF can be obtained. Further, interpretation of the distribution of the refractive error map as well as its moments is provided for a range of wavefront aberrations measured in real eyes. These are evaluated using a kernel density and sample moments estimators. It is concluded that the refractive error domain allows non-functional analysis of wavefront aberrations based on simple statistics in the form of its sample moments. Clinicians may find this approach to wavefront analysis easier to interpret due to the clinical familiarity and intuitive appeal of refractive error maps.

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Seasonal patterns have been found in a remarkable range of health conditions, including birth defects, respiratory infections and cardiovascular disease. Accurately estimating the size and timing of seasonal peaks in disease incidence is an aid to understanding the causes and possibly to developing interventions. With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonal effects on health is timely. This is the first book on statistical methods for seasonal data written for a health audience. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarising and modelling these data. It has a practical focus and uses interesting examples to motivate and illustrate the methods. The statistical procedures and example data sets are available in an R package called ‘season’. Adrian Barnett is a senior research fellow at Queensland University of Technology, Australia. Annette Dobson is a Professor of Biostatistics at The University of Queensland, Australia. Both are experienced medical statisticians with a commitment to statistical education and have previously collaborated in research in the methodological developments and applications of biostatistics, especially to time series data. Among other projects, they worked together on revising the well-known textbook "An Introduction to Generalized Linear Models," third edition, Chapman Hall/CRC, 2008. In their new book they share their knowledge of statistical methods for examining seasonal patterns in health.

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Emergency Health Services (EHS), encompassing hospital-based Emergency Departments (ED) and pre-hospital ambulance services, are a significant and high profile component of Australia’s health care system and congestion of these, evidenced by physical overcrowding and prolonged waiting times, is causing considerable community and professional concern. This concern relates not only to Australia’s capacity to manage daily health emergencies but also the ability to respond to major incidents and disasters. EHS congestion is a result of the combined effects of increased demand for emergency care, increased complexity of acute health care, and blocked access to ongoing care (e.g. inpatient beds). Despite this conceptual understanding there is a lack of robust evidence to explain the factors driving increased demand, or how demand contributes to congestion, and therefore public policy responses have relied upon limited or unsound information. The Emergency Health Services Queensland (EHSQ) research program proposes to determine the factors influencing the growing demand for emergency health care and to establish options for alternative service provision that may safely meet patient’s needs. The EHSQ study is funded by the Australian Research Council (ARC) through its Linkage Program and is supported financially by the Queensland Ambulance Service (QAS). This monograph is part of a suite of publications based on the research findings that examines the existing literature, and current operational context. Literature was sourced using standard search approaches and a range of databases as well as a selection of articles cited in the reviewed literature. Public sources including the Australian Institute of Health and Welfare (AIHW), the Council of Ambulance Authorities (CAA) Annual Reports, Australian Bureau of Statistics (ABS) and Department of Health and Ageing (DoHA) were examined for trend data across Australia.

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As the development of ICD-11 progresses, the Australian Bureau of Statistics is beginning to consider what will be required to successfully implement the new version of the classification. This paper will present early thoughts on the following: building understanding amongst the user community of upcoming changes and the implications of those changes; the need for training of coders and data users; development of analytical methods and conduct of comparability studies; processes to test, accept and implement new or updated coding software; assessment of coding quality; changes to data analyses and reporting processes; updates to regular publications; and assessing the resources required for successful implementation.

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Background: Although class attendance is linked to academic performance, questions remain about what determines students’ decisions to attend or miss class. Aims: In addition to the constructs of a common decision-making model, the theory of planned behaviour, the present study examined the influence of student role identity and university student (in-group) identification for predicting both the initiation and maintenance of students’ attendance at voluntary peer-assisted study sessions in a statistics subject. Sample: University students enrolled in a statistics subject were invited to complete a questionnaire at two time points across the academic semester. A total of 79 university students completed questionnaires at the first data collection point, with 46 students completing the questionnaire at the second data collection point. Method: Twice during the semester, students’ attitudes, subjective norm, perceived behavioural control, student role identity, in-group identification, and intention to attend study sessions were assessed via on-line questionnaires. Objective measures of class attendance records for each half-semester (or ‘term’) were obtained. Results: Across both terms, students’ attitudes predicted their attendance intentions, with intentions predicting class attendance. Earlier in the semester, in addition to perceived behavioural control, both student role identity and in-group identification predicted students’ attendance intentions, with only role identity influencing intentions later in the semester. Conclusions: These findings highlight the possible chronology that different identity influences have in determining students’ initial and maintained attendance at voluntary sessions designed to facilitate their learning.