Ethical considerations for drug abuse epidemiological research

The mortality and morbidity caused by alcohol, tobacco and illicit drug misuse represents a significant public health burden (Ezzati et al., 2002). A key part of the public health response is the collection of epidemiological and social science data to define at-risk populations to identify opportunities for intervention and to evaluate the effectiveness of policies in preventing or treating drug misuse and drug-related harm. The systematic use of epidemiological and social science research methods to study illicit drug use is barely 40 years old in the United States and United Kingdom, which have pioneered this approach. Because of the sensitive nature of epidemiological research on illicit drug use a unique set of ethical challenges need to be explicitly addressed by the field. Although ethics guidelines have been proposed (Council for International Organizations of Medical Sciences, 1991), scholarship on the ethics of epidemiology is scant, and consensus on core values not yet achieved (Coughlin, 2000).

Matching prescription claims with medication data for nursing home residents: Implications for prescriber feedback, drug utilisation studies and selection of prescription claims database

Medication data retrieved from Australian Repatriation Pharmaceutical Benefits Scheme (RPBS) claims for 44 veterans residing in nursing homes and Pharmaceutical Benefits Scheme (PBS) claims for 898 nursing home residents were compared with medication data from nursing home records to determine the optimal time interval for retrieving claims data and its validity. Optimal matching was achieved using 12 weeks of RPBS claims data, with 60% of medications in the RPBS claims located in nursing home administration records, and 78% of medications administered to nursing home residents identified in RPBS claims. In comparison, 48% of medications administered to nursing home residents could be found in 12 weeks of PBS data, and 56% of medications present in PBS claims could be matched with nursing home administration records. RPBS claims data was superior to PBS, due to the larger number of scheduled items available to veterans and the veteran's file number, which acts as a unique identifier. These findings should be taken into account when using prescription claims data for medication histories, prescriber feedback, drug utilisation, intervention or epidemiological studies. (C) 2001 Elsevier Science Inc. All rights reserved.

Long-term trends in sunshine duration and its association with schizophrenia birth rates and age at first registration - data from Australia and the Netherlands

Back ground. Based on the well-described excess of schizophrenia births in winter and spring, we hypothesised that individuals with schizophrenia (a) would be more likely to be born during periods of decreased perinatal sunshine, and (b) those born during periods of less sunshine would have an earlier age of first registration. Methods. We undertook an ecological analysis of long-term trends in perinatal sunshine duration and schizophrenia birth rates based on two mental health registers (Queensland. Australia n = 6630; The Netherlands n = 24, 474). For each of the 480 months between 1931 and 1970, the agreement between slopes of the trends in psychosis and long-term sunshine duration series were assessed. Age at first registration was assessed by quartiles of long-term trends in perinatal sunshine duration, Males and females were assessed separately. Results. Both the Dutch and Australian data showed a statistically significant association between falling long-term trends in sunshine duration around the time of birth and rising schizophrenia birth rates for males only. In both the Dutch and Australian data there were significant associations between earlier age of first registration and reduced long-term trends in sunshine duration around the time of birth for both males and females, Conclusions. A measure of long-term trends in perinatal sunshine duration was associated with two epidemiological features of schizophrenia in two separate data sets. Exposures related to sunshine duration warrant further consideration in schizophrenia research. (C) 2002 Elsevier Science B.V. All rights reserved.

Epidemiological analysis of electroconvulsive therapy in Victoria, Australia

Objective: To determine the population-based utilization rate of electroconvulsive therapy (ECT) in Victoria between 1998-1999, to examine the characteristics of the ECT treated group, and to identify patient factors independently associated with differential rates of ECT treatment. Method: Electroconvulsive therapy is reported under statute in Victoria, Australia. Crude, age-adjusted and age-sex specific utilization rates were calculated using this statutory data for the 1998-1999 financial year and estimated mid-year populations from the Australian Bureau of Statistics. Descriptive characteristics of those treated with ECT were derived from the statutory data. Patient factors associated with an increased likelihood of ECT in the public sector were explored with logistic regression analysis, using non-ECT treated mental health patients from the Victorian Psychiatric Case Register as the reference population. Results: The crude treated-person and age-adjusted rates for the State (both public and private sectors) were 39.9 and 44.0 persons per 100 000 resident population per annum, respectively. The crude and age-adjusted administration rates were 330.3 and 362.6 ECT administrations per 100 000 resident population per annum, respectively. Age-sex specific rates varied by age and sex, with rates generally increasing with age and female sex. Overall, 62.8% of the treated group were women, 32.9% aged over 64, and 75.2% had depression. Diagnosis, age and sex each independently predicted ECT in the public sector, with diagnosis the most important factor, followed by age then sex. Conclusions: Despite decades of use, the appropriate rate of ECT utilization is still unclear. Further research should be directed at exploring the factors, including provider variables, determining ECT treatment.

Morbidity due to Schistosoma mansoni: an epidemiological assessment of distended abdomen syndrome in Ugandan school children with observations before and 1-year after anthelminthic chemotherapy

The objectives of this study were to determine the prevalence and distribution of distended abdomens among Ugandan school children across a range of eco-epidemiological settings and to investigate the relationship between distended abdomens and helminth infections, in particular Schistosoma mansoni, before and 1-year after anthelminthic treatment. A cross-sectional survey was conducted on 4354 school children across eight districts, with a longitudinal 1-year follow-up of 2644 children (60.7%). On both occasions, parasitological, biometrical and clinical data were collected for each child. Baseline prevalence of S. mansoni and hookworms was 44.3% and 51.8%, respectively. Distended abdomens, defined as an abdominal circumference ratio (ACR) >1.05, were observed in 2.5% of the sampled children, several of whom presented with particularly severe distensions necessitating hospital referral. ACR scores were highly overdispersed between districts and schools. Multivariate regression analysis revealed that S. mansoni infection accounted for only a small fraction of ACR variation, suggesting that either single point prevalence and intensity measures failed to reflect this more chronically evolved morbidity and/or that other interacting factors were involved, e.g. malnutrition and malaria. At 1-year follow-up, ACR scores showed an overall trend of regression towards the mean, potentially indicative of amelioration following chemotherapy, but geographic overdispersion still remained. © 2006 Royal Society of Tropical Medicine and Hygiene.

Using epidemiological models to estimate the health effects of diet behaviour change: the example of tailored fruit and vegetable promotion

Objective: To explore the use of epidemiological modelling for the estimation of health effects of behaviour change interventions, using the example of computer-tailored nutrition education aimed at fruit and vegetable consumption in The Netherlands. Design: The effects of the intervention on changes in consumption were obtained from an earlier evaluation study. The effect on health outcomes was estimated using an epidemiological multi-state life table model. input data for the model consisted of relative risk estimates for cardiovascular disease and cancers, data on disease occurrence and mortality, and survey data on the consumption of fruits and vegetables. Results: if the computer-tailored nutrition education reached the entire adult population and the effects were sustained, it could result in a mortality decrease of 0.4 to 0.7% and save 72 to 115 life-years per 100000 persons aged 25 years or older. Healthy life expectancy is estimated to increase by 32.7 days for men and 25.3 days for women. The true effect is likely to lie between this theoretical maximum and zero effect, depending mostly on durability of behaviour change and reach of the intervention. Conclusion: Epidemiological models can be used to estimate the health impact of health promotion interventions.

Migrating eprints.org data to a Fez repository

This document records the process of migrating eprints.org data to a Fez repository. Fez is a Web-based digital repository and workflow management system based on Fedora (http://www.fedora.info/). At the time of migration, the University of Queensland Library was using EPrints 2.2.1 [pepper] for its ePrintsUQ repository. Once we began to develop Fez, we did not upgrade to later versions of eprints.org software since we knew we would be migrating data from ePrintsUQ to the Fez-based UQ eSpace. Since this document records our experiences of migration from an earlier version of eprints.org, anyone seeking to migrate eprints.org data into a Fez repository might encounter some small differences. Moving UQ publication data from an eprints.org repository into a Fez repository (hereafter called UQ eSpace (http://espace.uq.edu.au/) was part of a plan to integrate metadata (and, in some cases, full texts) about all UQ research outputs, including theses, images, multimedia and datasets, in a single repository. This tied in with the plan to identify and capture the research output of a single institution, the main task of the eScholarshipUQ testbed for the Australian Partnership for Sustainable Repositories project (http://www.apsr.edu.au/). The migration could not occur at UQ until the functionality in Fez was at least equal to that of the existing ePrintsUQ repository. Accordingly, as Fez development occurred throughout 2006, a list of eprints.org functionality not currently supported in Fez was created so that programming of such development could be planned for and implemented.

Environmental Risk Factors for Parkinson's Disease

Parkinson’s disease (PD) is a progressive, degenerative, neurological disease. The progressive disability associated with PD results in substantial burdens for those with the condition, their families and society in terms of increased health resource use, earnings loss of affected individuals and family caregivers, poorer quality of life, caregiver burden, disrupted family relationships, decreased social and leisure activities, and deteriorating emotional well-being. Currently, no cure is available and the efficacy of available treatments, such as medication and surgical interventions, decreases with longer duration of the disease. Whilst the cause of PD is unknown, genetic and environmental factors are believed to contribute to its aetiology. Descriptive and analytical epidemiological studies have been conducted in a number of countries in an effort to elucidate the cause, or causes, of PD. Rural residency, farming, well water consumption, pesticide exposure, metals and solvents have been implicated as potential risk factors for PD in some previous epidemiological studies. However, there is substantial disagreement between the results of existing studies. Therefore, the role of environmental exposures in the aetiology of PD remains unclear. The main component of this thesis consists of a case-control study that assessed the contribution of environmental exposures to the risk of developing PD. An existing, previously unanalysed, dataset from a local case-control study was analysed to inform the design of the new case-control study. The analysis results suggested that regular exposure to pesticides and head injury were important risk factors for PD. However, due to the substantial limitations of this existing study, further confirmation of these results was desirable with a more robustly designed epidemiological study. A new exposure measurement instrument (a structured interviewer-delivered questionnaire) was developed for the new case-control study to obtain data on demographic, lifestyle, environmental and medical factors. Prior to its use in the case-control study, the questionnaire was assessed for test-retest repeatability in a series of 32 PD cases and 29 healthy sex-, age- and residential suburb-matched electoral roll controls. High repeatability was demonstrated for lifestyle exposures, such as smoking and coffee/tea consumption (kappas 0.70-1.00). The majority of environmental exposures, including use of pesticides, solvents and exposure to metal dusts and fumes, also showed high repeatability (kappas >0.78). A consecutive series of 163 PD case participants was recruited from a neurology clinic in Brisbane. One hundred and fifty-one (151) control participants were randomly selected from the Australian Commonwealth Electoral Roll and individually matched to the PD cases on age (± 2 years), sex and current residential suburb. Participants ranged in age from 40-89 years (mean age 67 years). Exposure data were collected in face-to-face interviews. Odds ratios and 95% confidence intervals were calculated using conditional logistic regression for matched sets in SAS version 9.1. Consistent with previous studies, ever having been a regular smoker or coffee drinker was inversely associated with PD with dose-response relationships evident for packyears smoked and number of cups of coffee drunk per day. Passive smoking from ever having lived with a smoker or worked in a smoky workplace was also inversely related to PD. Ever having been a regular tea drinker was associated with decreased odds of PD. Hobby gardening was inversely associated with PD. However, use of fungicides in the home garden or occupationally was associated with increased odds of PD. Exposure to welding fumes, cleaning solvents, or thinners occupationally was associated with increased odds of PD. Ever having resided in a rural or remote area was inversely associated with PD. Ever having resided on a farm was only associated with moderately increased odds of PD. Whilst the current study’s results suggest that environmental exposures on their own are only modest contributors to overall PD risk, the possibility that interaction with genetic factors may additively or synergistically increase risk should be considered. The results of this research support the theory that PD has a multifactorial aetiology and that environmental exposures are some of a number of factors to contribute to PD risk. There was also evidence of interaction between some factors (eg smoking and welding) to moderate PD risk.

Test–retest repeatability of self-reported environmental exposures in Parkinson’s disease cases and healthy controls

There is substantial disagreement among published epidemiological studies regarding environmental risk factors for Parkinson’s disease (PD). Differences in the quality of measurement of environmental exposures may contribute to this variation. The current study examined the test–retest repeatability of self-report data on risk factors for PD obtained from a series of 32 PD cases recruited from neurology clinics and 29 healthy sex-, age-and residential suburb-matched controls. Exposure data were collected in face-to-face interviews using a structured questionnaire derived from previous epidemiological studies. High repeatability was demonstrated for ‘lifestyle’ exposures, such as smoking and coffee/tea consumption (kappas 0.70–1.00). Environmental exposures that involved some action by the person, such as pesticide application and use of solvents and metals, also showed high repeatability (kappas>0.78). Lower repeatability was seen for rural residency and bore water consumption (kappa 0.39–0.74). In general, we found that case and control participants provided similar rates of incongruent and missing responses for categorical and continuous occupational, domestic, lifestyle and medical exposures.

A simple data logger for student-designed rocket experiments.

The final-year project for Mechanical & Space Engineering students at UQ often involves the design and flight testing of an experiment. This report describes the design and use of a simple data logger that should be suitable for collecting data from the students' flight experiments. The exercise here was taken as far as the construction of a prototype device that is suitable for ground-based testing, say, the static firing of a hybrid rocket motor.

Equational Reasoning as a Tool for Data Analysis

A combination of deductive reasoning, clustering, and inductive learning is given as an example of a hybrid system for exploratory data analysis. Visualization is replaced by a dialogue with the data.

Leadership Attributes and Cultural Values in Australia and New Zealand Compared: An Initial Report Based on GLOBE Data.

This paper reports a comparative study of Australian and New Zealand leadership attributes, based on the GLOBE (Global Leadership and Organizational Behavior Effectiveness) program. Responses from 344 Australian managers and 184 New Zealand managers in three industries were analyzed using exploratory and confirmatory factor analysis. Results supported some of the etic leadership dimensions identified in the GLOBE study, but also found some emic dimensions of leadership for each country. An interesting finding of the study was that the New Zealand data fitted the Australian model, but not vice versa, suggesting asymmetric perceptions of leadership in the two countries.

Selection bias in gene extraction on the basis of microarray gene-expression data

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.