1,721,004 research outputs found
Comparison of scoring systems for invasive pests using\ud ROC analysis and Monte Carlo simulation
No full textDifferent international plant protection organisations advocate different schemes for conducting pest risk assessments. Most of these schemes use structured questionnaire in which\ud
experts are asked to score several items using an ordinal scale. The scores are then combined\ud
using a range of procedures, such as simple arithmetic mean, weighted averages, multiplication of scores, and cumulative sums. The most useful schemes will correctly identify harmful\ud
pests and identify ones that are not. As the quality of a pest risk assessment can depend on\ud
the characteristics of the scoring system used by the risk assessors (i.e., on the number of\ud
points of the scale and on the method used for combining the component scores), it is important to assess and compare the performance of different scoring systems. In this article, we\ud
proposed a new method for assessing scoring systems. Its principle is to simulate virtual data\ud
using a stochastic model and, then, to estimate sensitivity and specificity values from these\ud
data for different scoring systems. The interest of our approach was illustrated in a case study\ud
where several scoring systems were compared. Data for this analysis were generated using a\ud
probabilistic model describing the pest introduction process. The generated data were then\ud
used to simulate the outcome of scoring systems and to assess the accuracy of the decisions\ud
about positive and negative introduction. The results showed that ordinal scales with at most\ud
5 or 6 points were sufficient and that the multiplication-based scoring systems performed better than their sum-based counterparts. The proposed method could be used in the future to\ud
assess a great diversity of scoring systems
An ingenuous Decision Support System (iDSS) approach for remote area pregnant women
No full textThis paper addresses development of an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s α and Classification Tree were incorporated in the iDSS. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcome of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women
Clinical prediction modelling in oral health: A review of study quality and empirical examples of model development
Full text linkBackground Substantial efforts have been made to improve the reproducibility and reliability of scientific findings in health research. These efforts include the development of guidelines for the design, conduct and reporting of preclinical studies (ARRIVE), clinical trials (ROBINS-I, CONSORT), observational studies (STROBE), and systematic reviews and meta-analyses (PRISMA). In recent years, the use of prediction modelling has increased in the health sciences. Clinical prediction models use information at the individual patient level to estimate the probability of a health outcome(s). Such models offer the potential to assist in clinical decision-making and to improve medical care. Guidelines such as PROBAST (Prediction model Risk Of Bias Assessment Tool) have been recently published to further inform the conduct of prediction modelling studies. Related guidelines for the reporting of these studies, such as TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) instrument, have also been developed. Since the early 2000s, oral health prediction models have been used to predict the risk of various types of oral conditions, including dental caries, periodontal diseases and oral cancers. However, there is a lack of information on the methodological quality and reporting transparency of the published oral health prediction modelling studies. As a consequence, and due to the unknown quality and reliability of these studies, it remains unclear to what extent it is possible to generalise their findings and to replicate their derived models. Moreover, there remains a need to demonstrate the conduct of prediction modelling studies in oral health field following the contemporary guidelines. This doctoral project addresses these issues using two systematic reviews and two empirical analyses. This thesis is the first comprehensive and systematic project reviewing the study quality and demonstrating the use of registry data and longitudinal cohorts to develop clinical prediction models in oral health. Aims • To identify and examine the quality of existing prediction modelling studies in the major fields of oral health.• To demonstrate the conduct and reporting of a prediction modelling study following current guidelines, incorporating machine learning algorithms and accounting for multiple sources of biases. Methods As one of the most prevalent oral conditions, chronic periodontitis was chosen as the exemplar pathology for the first part of this thesis. A systematic review was conducted to investigate the existing prediction models for the incidence and progression of this condition. Based upon this initial overview, a more comprehensive critical review was conducted to assess the methodological quality and completeness of reporting for prediction modelling studies in the field of oral health. The risk of bias in the existing literature was assessed using the PROBAST criteria, and the quality of study reporting was measured in accordance with the TRIPOD guidelines. Following these two reviews, this research project demonstrated the conduct and reporting of a clinical prediction modelling study using two empirical examples. Two types of analyses that are commonly used for two different types of outcome data were adopted: survival analysis for censored outcomes and logistic regression analysis for binary outcomes. Models were developed to 1) predict the three- and five-year disease-specific survival of patients with oral and pharyngeal cancers, based on 21,154 cases collected by a large cancer registry program in the US, the Surveillance, Epidemiology and End Results (SEER) program, and 2) to predict the occurrence of acute and persistent pain following root canal treatment, based on the electronic dental records of 708 adult patients collected by the National Practice-Based Research Network. In these two case studies, all prediction models were developed in five steps: (i) framing the research question; (ii) data acquisition and pre-processing; (iii) model generation; (iv) model validation and performance evaluation; and (v) model presentation and reporting. In accordance with the PROBAST recommendations, the risk of bias during the modelling process was reduced in the following aspects: • In the first case study, three types of biases were taken into account: (i) bias due to missing data was reduced by adopting compatible methods to conduct imputation; (ii) bias due to unmeasured predictors was tested by sensitivity analysis; and (iii) bias due to the initial choice of modelling approach was addressed by comparing tree-based machine learning algorithms (survival tree, random survival forest and conditional inference forest) with the traditional statistical model (Cox regression). • In the second case study, the following strategies were employed: (i) missing data were addressed by multiple imputation with missing indicator methods; (ii) a multilevel logistic regression approach was adopted for model development in order to fit Table of Contents xi the hierarchical structure of the data; (iii) model complexity was reduced using the Least Absolute Shrinkage and Selection Operator (LASSO) for predictor selection; and (iv) the models’ predictive performance was evaluated comprehensively by using the Area Under the Precision Recall Curve (AUPRC) in addition to the Area Under the Receiver Operating Characteristic curve (AUROC); (v) finally, and most importantly, given the existing criticism in the research community concerning the gender-based and racial bias in risk prediction models, we compared the models’ predictive performance built with different sets of predictors (including a clinical set, a sociodemographic set and a combination of both, the ‘general’ set). Results The first and second review studies indicated that, in the field of oral health, the popularity of multivariable prediction models has increased in recent years. Bias and variance are two components of the uncertainty (e.g., the mean squared error) in model estimation. However, the majority of the existing studies did not account for various sources of bias, such as measurement error and inappropriate handling of missing data. Moreover, non-transparent reporting and lack of reproducibility of the models were also identified in the existing oral health prediction modelling studies. These findings provided motivation to conduct two case studies aimed at demonstrating adherence to the contemporary guidelines and to best practice. In the third study, comparable predictive capabilities between Cox regression and the non-parametric tree-based machine learning algorithms were observed for predicting the survival of patients with oral and pharyngeal cancers. For example, the C-index for a Cox model and a random survival forest in predicting three-year survival were 0.82 and 0.84, respectively. A novelty of this study was the development of an online calculator designed to provide an open and transparent estimation of patients’ survival probability for up to five years after diagnosis. This calculator has clinical translational potential and could aid in patient stratification and treatment planning, at least in the context of ongoing research. In addition, the transparent reporting of this study was achieved by following the TRIPOD checklist and sharing all data and codes. In the fourth study, LASSO regression suggested that pre-treatment clinical factors were important in the development of one-week and six-month postoperative pain following root canal treatment. Among all the developed multilevel logistic models, models with a clinical set of predictors yielded similar predictive performance to models with a general set of predictors, while the models with sociodemographic predictors showed the weakest predictive ability. For example, for predicting one-week postoperative pain, the AUROC for models with clinical, sociodemographic and general predictors were 0.82, 0.68 and 0,84, respectively, and the AUPRC were 0.66, 0.40 and 0.72, respectively. Conclusion The significance of this research project is twofold. First, prediction models have been developed for potential clinical use in the context of various oral conditions. Second, this research represents the first attempt to standardise the conduct of this type of studies in oral health research. This thesis presents three conclusions: 1) Adherence to contemporary best practice guidelines such as PROBAST and TRIPOD is limited in the field of oral health research. In response, this PhD project disseminates these guidelines and leverages their advantages to develop effective prediction models for use in dentistry and oral health. 2) Use of appropriate procedures, accounting for and adapting to multiple sources of bias in model development, produces predictive tools of increased reliability and accuracy that hold the potential to be implemented in clinical practice. Therefore, for future prediction modelling research, it is important that data analysts work towards eliminating bias, regardless of the areas in which the models are employed. 3) Machine learning algorithms provide alternatives to traditional statistical models for clinical prediction purposes. Additionally, in the presence of clinical factors, sociodemographic characteristics contribute less to the improvement of models’ predictive performance or to providing cogent explanations of the variance in the models, regardless of the modelling approach. Therefore, it is timely to reconsider the use of sociodemographic characteristics in clinical prediction modelling research. It is suggested that this is a proportionate and evidence based strategy aimed at reducing biases in healthcare risk prediction that may be derived from gender and racial characteristics inherent in sociodemographic data sets.Thesis (Ph.D.) -- University of Adelaide, School of Public Health, 202
Effects of inequality, family investment and early childhood interventions on children cognitive and socio-emotional wellbeing in Indonesia
Full text linkBackground: Understanding inequality in children’s health and development is important because effects of disadvantage early in life may contribute to health disparities throughout life. Evidence shows that children who live in poorer families tend to have poorer cognitive outcomes and higher risk of behavioural problems compared to their peers from non-poor families. In low and middle income countries, children from poor families are more likely to be exposed to a multitude of risk factors that compromise healthy child development including lack of access to safe drinking water and improved sanitation, lack of access to health and education services, as well as inadequate learning environment at home. Whilst parental investment in children’s health and development often relies on resources that are available at home, effective interventions may protect children from negative consequences of living in poverty and increase investment in children’s health and development. Aims: The overall aim of this thesis is to investigate inequalities in cognitive function and socio-emotional well-being among Indonesian children, and how early childhood interventions might reduce these inequalities. The specific research questions are as follows: 1. What is the magnitude of socioeconomic inequality in Indonesian children’s cognitive function in 2000 and 2007? What factors contribute to the inequality? Does the inequality in children’s cognitive functioning change between 2000 and 2007 and what factors contribute to the change in inequality? 2. What is the effect of household per capita expenditure on Indonesian children cognitive function and does a cash transfer intervention increase cognitive function scores? 3. What is the association of poverty at ages 0-7 and poverty at 7-14 with children’s cognitive function at 7-14 years? What is the direct effect of poverty at 0-7 years on cognitive function at 7-14 years, and is this effect mediated through poverty at 7-14 and through school attendance and aspects of the child’s home environment? 4. What is the relative and combined effect of different hypothetical interventions such as improving standard of living through provision of piped water and improved sanitation, maternal mental health and a parenting program on children’s school readiness and socio-emotional wellbeing in Indonesia? Methods: This thesis used data from the Indonesian Family Life Survey (IFLS) and the Early Childhood Education and Development (ECED) project. IFLS was used in studies 1-3, where the study participants consisted of two cohorts who were recruited for cognitive testing, comprising children aged 7-14 in 2000 (born between 1993 and 1986) and children aged 7-14 in 2007 (born between 2000 and 1993). In study 4, data from the ECED was used. Herein, the study participants included children aged 4 in 2009 and followed up at ages 5 and 8. This thesis used a range of statistical approaches to answer the aims of this thesis including the relative concentration index, decomposition of concentration index, Oaxaca-type decomposition of change, an inverse probability of treatment weight of a marginal structural model, conventional regression analysis, decomposition analysis (direct and indirect effects) and parametric g-formula. Multiple imputation analysis was also performed where applicable. Results: In the first study, there were substantial reductions in inequality in children’s cognitive function between 2000 and 2007, but the burden of poor cognitive function was still higher among the disadvantaged. In both 2000 and 2007, household per capita expenditure was the largest single contributor to inequality in children’s cognitive function. However, improvements in maternal education, access to improved sanitation and household per capita expenditure were the main contributors to reductions in inequality in children’s cognitive function from 2000 to 2007. In study two, greater household per capita expenditure was associated with higher cognitive function but the effect size was small. Based on simulations of a hypothetical cash transfer intervention, an additional US$ 6-10/month of cash transfer for children from the poorest households in 2000 increased the mean cognitive function score by 6% but there was no overall effect of cash transfers at the total population level. In the third study, being exposed to poverty was associated with poor cognitive function score at any age, however, there was no evidence that being exposed to poverty at 0-7 had a larger effect on cognitive function than poverty at 7-14 years. From decomposition analysis, poverty at 0-7 had a larger direct effect on children’s cognitive function at 7-14 years than the effect of poverty at 0-7 that was mediated through poverty, school attendance and aspects of the child’s home environment at 7-14 years. Moreover, the effect of poverty at 0-7 on cognitive function at 7-14 years was largely mediated through pathways involving child’s home environment, school attendance and poverty at 7-14 than the mediated effect through poverty at 7-14 alone. From the final study, providing access to piped water as the main drinking water source, improved sanitation, maternal mental health and a parenting education program had positive effects on children’s school readiness and socio-emotional wellbeing in rural Indonesia. Intervention that combined multiple programs had a larger effect than any single intervention. In this study, a combination of provision of piped drinking water, improved sanitation, maternal mental health and a parenting education program is likely yield the largest effect, however, most of the effect was driven by provision of piped drinking water and improved sanitation. Conclusions: This thesis provides some evidence to fill the knowledge gap on inequalities in children’s cognitive and socio-emotional wellbeing in Indonesia. It has also attempted to generate evidence that is relevant for policy intervention that may help to reduce these inequalities. Providing early childhood intervention that combined multiple programs is likely to have the largest effect. More importantly, the early childhood intervention in Indonesia should start with providing greater access to piped drinking water and improved sanitation.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Public Health, 2016
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Socio-Economic Inequalities in Different Australian Dental Service Providers
Full text linkBackground Australia is a vast country with cultural, religious, racial, sexual and geographical diversity. Australia's Indigenous population has suffered many injustices in the past and to date. Australia also has a large culturally and linguistically diverse population (CALD) as an immigration destination. These challenges along with different socioeconomic status are sources of inequality in the Australian health system. Taking a specific lens at health services and considering that dental services have the least government support, and along with the minimum insurance coverage and the fact that only 15% of services are provided in the public sector, the importance of investigating inequalities in dental services is critical. Therefore, the aim of this PhD is to investigate the inequalities in dental services in Australia with a focus on different dental service sectors. Methods This project was carried out in two main phases with different methodologies. In the first phase, two scoping reviews were performed with a systematic approach. Determinants of inequalities in dental services were determined by qualitative content analysis. In the first study, determinants of utilisation and provision of dental services, and in the second study, the determinants of access were identified. Also, the role of access in achieving universal health coverage in dental services was discussed. In the second phase, statistical modelling was performed by considering this synthesised knowledge. Using Flexible Mediation Analysis and Ratio of Mediator Probability Weighting Approach, data from 4494 South Australian adults from the Dental Care and Oral Health Study were analysed. In the first empirical study, income based inequalities and financial burden in dental services were examined. The direct and indirect effect of income through mediators such as insurance, concession cards and dental service sectors (public/private) on avoiding or delaying dental services were investigated. The second empirical study investigated sociocultural inequality in Australian public and private sectors using a similar population. This study investigated the direct and indirect effects of education through mediators including oral health, smoking status, and tooth brushing on utilisation patterns of dental services. Results According to the review chapters, a conceptual model of inequality (named as the Triangle of Inequality) in dental services was designed in the third study. The second phase results showed that people with lower income experienced more financial burden to receive dental services regardless of their insurance, concession card holding status, and the dental service sectors they attend. The findings showed that people with less education received fewer dental services. Low education was associated with emergency and treatment visits. Examining the effect modification of the dental service sector, it was observed that utilisation of services was better among less educated people who visited the public sector. Conclusion These findings highlight the importance of income inequality and highlight the fact that facilitators such as insurance, concession card holding status, and the dental service sector are ineffective in reducing the financial burden on contemporary dental services. The findings of the second empirical study indicate that the public sector could improve the utilisation of dental services in people with low education.Thesis (Ph.D.) -- University of Adelaide, Adelaide Dental School, 202
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Reliability prediction using the non-parametric explicit hazard model : a case study
No full textSurvival probability prediction using covariate-based hazard approach is a known statistical methodology in engineering asset health management. We have previously reported the semi-parametric Explicit Hazard Model (EHM) which incorporates three types of information: population characteristics; condition indicators; and operating environment indicators for hazard prediction. This model assumes the baseline hazard has the form of the Weibull distribution. To avoid this assumption, this paper presents the non-parametric EHM which is a distribution-free covariate-based hazard model. In this paper, an application of the non-parametric EHM is demonstrated via a case study. In this case study, survival probabilities of a set of resistance elements using the non-parametric EHM are compared with the Weibull proportional hazard model and traditional Weibull model. The results show that the non-parametric EHM can effectively predict asset life using the condition indicator, operating environment indicator, and failure history
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