9 resultados para Risk Indicators

em University of Queensland eSpace - Australia


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Background: To investigate the association between selected social and behavioural (infant feeding and preventive dental practices) variables and the presence of early childhood caries in preschool children within the north Brisbane region. Methods: A cross sectional sample of 2515 children aged four to five years were examined in a preschool setting using prevalence (percentage with caries) and severity (dmft) indices. A self-administered questionnaire obtained information regarding selected social and behavioural variables. The data were modelled using multiple logistic regression analysis at the 5 per cent level of significance. Results: The final explanatory model for caries presence in four to five year old children included the variables breast feeding from three to six months of age (OR=0.7, CI=0.5, 1.0), sleeping with the bottle (OR=1.9, CI=1.5, 2.4), sipping from the bottle (OR=1.6, CI=1.2, 2.0), ethnicity other than Caucasian (OR=1.9, CI=1.4, 2.5), annual family income $20,000-$35,000 (OR = 1.7, CI=1.3, 2.3) and annual family income less than $20,000 (OR=2.1, CI=1.5, 2.8). Conclusion: A statistical model for early childhood caries in preschool children within the north Brisbane region has been constructed using selected social and behavioural determinants. Epidemiological data can be used for improved public oral health service planning and resource allocation within the region.

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Molecular analysis of invasive breast cancer and its precursors has furthered our understanding of breast cancer progression. In the past few years, new multi-step pathways of breast cancer progression have been delineated through genotypic-phenotypic correlations. Nuclear grade, more than any other pathological feature, is strongly associated with the number and pattern of molecular genetic abnormalities in breast cancer cells. Thus, there are two distinct major pathways to the evolution of low- and high-grade invasive carcinomas: whilst the former consistently show oestrogen receptor (ER) and progesterone receptor (PgR) positivity and 16q loss, the latter are usually ER/PgR-negative and show Her-2 over-expression/amplification and complex karyotypes. The boundaries between the evolutionary pathways of well-differentiated/low-grade ductal and lobular carcinomas have been blurred, with changes in E-cadherin expression being one of the few distinguishing features between the two. In addition, lesions long thought to be precursors of breast carcinomas, such as hyperplasia of usual type, are currently considered mere risk indicators, whilst columnar cell lesions are now implicated as non-obligate precursors of atypical ductal hyperplasia (ADH) and well-differentiated ductal carcinoma in situ (DCIS). However, only through the combination of comprehensive morphological analysis and cutting-edge molecular tools can this knowledge be translated into clinical practice and patient management. Copyright (C) 2005 Pathological Society of Great Britain and Ireland. Published by John Wiley Sons, Ltd.

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Introduction: There is currently a need for research into indicators that could be used by non-clinical professionals working with young people, to inform the need for referral for further clinical assessment of those at risk of suicide. Method: Participants of this repeated measures longitudinal study, were 2603, 2485, and 2246 school students aged 13, 14, and 15, respectively, from 27 South Australian Schools. Results: Perceived academic performance, self-esteem and locus of control are significantly associated with suicidality. Further, logistic regression of longitudinal results suggests that perceived academic performance, over and above self-esteem and locus of control, in some instances, is a good long-term predictor of suicidality. (C) 2004 Published by Elsevier Ltd. on behalf of The Association for Professionals in Services for Adolescents.

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Background: Hospital performance reports based on administrative data should distinguish differences in quality of care between hospitals from case mix related variation and random error effects. A study was undertaken to determine which of 12 diagnosis-outcome indicators measured across all hospitals in one state had significant risk adjusted systematic ( or special cause) variation (SV) suggesting differences in quality of care. For those that did, we determined whether SV persists within hospital peer groups, whether indicator results correlate at the individual hospital level, and how many adverse outcomes would be avoided if all hospitals achieved indicator values equal to the best performing 20% of hospitals. Methods: All patients admitted during a 12 month period to 180 acute care hospitals in Queensland, Australia with heart failure (n = 5745), acute myocardial infarction ( AMI) ( n = 3427), or stroke ( n = 2955) were entered into the study. Outcomes comprised in-hospital deaths, long hospital stays, and 30 day readmissions. Regression models produced standardised, risk adjusted diagnosis specific outcome event ratios for each hospital. Systematic and random variation in ratio distributions for each indicator were then apportioned using hierarchical statistical models. Results: Only five of 12 (42%) diagnosis-outcome indicators showed significant SV across all hospitals ( long stays and same diagnosis readmissions for heart failure; in-hospital deaths and same diagnosis readmissions for AMI; and in-hospital deaths for stroke). Significant SV was only seen for two indicators within hospital peer groups ( same diagnosis readmissions for heart failure in tertiary hospitals and inhospital mortality for AMI in community hospitals). Only two pairs of indicators showed significant correlation. If all hospitals emulated the best performers, at least 20% of AMI and stroke deaths, heart failure long stays, and heart failure and AMI readmissions could be avoided. Conclusions: Diagnosis-outcome indicators based on administrative data require validation as markers of significant risk adjusted SV. Validated indicators allow quantification of realisable outcome benefits if all hospitals achieved best performer levels. The overall level of quality of care within single institutions cannot be inferred from the results of one or a few indicators.

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Reliable, comparable information about the main causes of disease and injury in populations, and how these are changing, is a critical input for debates about priorities in the health sector. Traditional sources of information about the descriptive epidemiology of diseases, injuries and risk factors are generally incomplete, fragmented and of uncertain reliability and comparability. Lack of a standardized measurement framework to permit comparisons across diseases and injuries, as well as risk factors, and failure to systematically evaluate data quality have impeded comparative analyses of the true public health importance of various conditions and risk factors. As a consequence the impact of major conditions and hazards on population health has been poorly appreciated, often leading to a lack of public health investment. Global disease and risk factor quantification improved dramatically in the early 1990s with the completion of the first Global Burden of Disease Study. For the first time, the comparative importance of over 100 diseases and injuries, and ten major risk factors, for global and regional health status could be assessed using a common metric (Disability-Adjusted Life Years) which simultaneously accounted for both premature mortality and the prevalence, duration and severity of the non-fatal consequences of disease and injury. As a consequence, mental health conditions and injuries, for which non-fatal outcomes are of particular significance, were identified as being among the leading causes of disease/injury burden worldwide, with clear implications for policy, particularly prevention. A major achievement of the Study was the complete global descriptive epidemiology, including incidence, prevalence and mortality, by age, sex and Region, of over 100 diseases and injuries. National applications, further methodological research and an increase in data availability have led to improved national, regional and global estimates for 2000, but substantial uncertainty around the disease burden caused by major conditions, including, HIV, remains. The rapid implementation of cost-effective data collection systems in developing countries is a key priority if global public policy to promote health is to be more effectively informed.

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Background Evidence on the relative influence of childhood vs adulthood socioeconomic conditions on obesity risk is limited and equivocal. The objective of this study was to investigate associations of several indicators of mothers', fathers', and own socioeconomic status, and intergenerational social mobility, with body mass index (BMI) and weight change in young women. Methods This population-based cohort study used survey data provided by 8756 women in the young cohort (aged 18-23 years at baseline) of the Australian Longitudinal Study on Women's Health. In 1996 and 2000, women completed mailed surveys in which they reported their height and weight, and their own, mother's, and father's education and occupation. Results Multiple linear regression models showed that both childhood and adulthood socioeconomic status were associated with women's BMI and weight change, generally in the hypothesized (inverse) direction, but the associations varied according to socioeconomic status and weight indicator. Social mobility was associated with BMI (based on father's socioeconomic status) and weight change (based on mother's socioeconomic status), but results were slightly less consistent. Conclusions Results suggest lasting effects of childhood socioeconomic status on young women's weight status, independent of adult socioeconomic status, although the effect may be attenuated among those who are upwardly socially mobile. While the mechanisms underlying these associations require further investigation, public health strategies aimed at preventing obesity may need to target families of low socioeconomic status early in children's lives.

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Objective: Childhood injury remains the single most important cause of mortality in children aged between 1-14 years in many countries. It has been proposed that lower socio-economic status (SES) and poorer housing contribute to potential hazards in the home environment. This study sought to establish whether the prevalence of observed hazards in and around the home was differentially distributed by SES, in order to identify opportunities for injury prevention. Methods: This study was a cross-sectional, random sample survey of primary school children from 32 schools in Brisbane. Interviews and house audits were conducted between July 2000 and April 2003 to collect information on SES (income, employment and education) and previously identified household hazards. Results: There was evidence of a relationship between prevalence of household environmental hazards and household SES; however, the magnitude and direction of this relationship appeared to be hazard-specific. Household income was related to play equipment characteristics, with higher SES groups being more likely to be exposed to risk. All three SES indicators were associated with differences in the home safety characteristics, with the lower SES groups more likely to be exposed to risk. Conclusion:The differential distribution of environmental risk factors by SES of household may help explain the SES differential in the burden of injury and provides opportunities for focusing efforts to address the problem.

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Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.