Twin study of adolescent genetic susceptibility to mosquito bites using ordinal and comparative rating data

Ordinal and comparative rating measures of mosquito attraction and mosquito bite frequency and symptoms were administered in a self-report questionnaire format to a sample of 197 monozygotic and 326 dizygotic Australian adolescent twin pairs at age 12 between 1992 and 1999, in order to investigate the environmental and possibly genetic determinants of variation between individuals. Repeat measures were obtained from the twin pairs at age 14. Ordinal variable measures, although providing some support for genetic effects on mosquito susceptibility, were affected by low repeatability. However, analysis of a comparative rating variable compared with your twin, who is bitten by mosquitoes more often? indicated a strong genetic influence on frequency of being bitten by mosquitoes, with no significant differences observed between males and females. Comparative rating questionnaire items are a potentially valuable tool for complementing and improving the results obtained from more conventional absolute measures. (C) 2000 Wiley-Liss, Inc.

Ontological clarity and comprehension in health data models

Conceptual modeling forms an important part of systems analysis. If this is done incorrectly or incompletely, there can be serious implications for the resultant system, specifically in terms of rework and useability. One approach to improving the conceptual modelling process is to evaluate how well the model represents reality. Emergence of the Bunge-Wand-Weber (BWW) ontological model introduced a platform to classify and compare the grammar of conceptual modelling languages. This work applies the BWW theory to a real world example in the health arena. The general practice computing group data model was developed using the Barker Entity Relationship Modelling technique. We describe an experiment, grounded in ontological theory, which evaluates how well the GPCG data model is understood by domain experts. The results show that with the exception of the use of entities to represent events, the raw model is better understood by domain experts

Use of routine data for determination of the population pharmacokinetics and enteral biovailability of phenytoin in neonates and infants with seizures

Objective: To investigate the population pharmacokinetics and the enteral bioavailability of phenytoin in neonates and infants with seizures. Methods: Data (5 mg kg-1 day-1) from 83 patients were obtained retrospectively from the medical records following written ethical approval. A one-compartment model was fitted to the data using NONMEM with FOCE-interaction. Between-subject variability (BSV) and interoccasion variability (IOV) were modelled exponentially together with a log transform-both-sides exponential residual unexplained variance (RUV) model. Covariates in nested models were screened for significance (X2, 1, 0.01). Model validity was determined by bootstrapping with replacement (N=500 samples) from the dataset. Results: The parameters of final pharmacokinetic were: Clearance (L h-1) = 0.826.(current Weight [kg]/70)0.75.(1+0.0692.(Postnatal age [days]-11)); Volume of distribution (L) = 74.2.(current Weight [kg]/70); Enteral bioavailability = 0.76; Absorption rate constant (h-1) = 0.167. BSV for clearance and volume of distribution were 74.2% and 65.6%, respectively. The IOV in clearance was 54.4%. The RUV was 51.1%. Final model parameters deviated from mean bootstrap estimates by

Rule induction for data exploration

This paper describes how the statistical technique of cluster analysis and the machine learning technique of rule induction can be combined to explore a database. The ways in which such an approach alleviates the problems associated with other techniques for data analysis are discussed. We report the results of experiments carried out on a database from the medical diagnosis domain. Finally we describe the future developments which we plan to carry out to build on our current work.

Transmission of image data over digital networks involving mobile terminals

There is a growing demand for data transmission over digital networks involving mobile terminals. An important class of data required for transmission over mobile terminals is image information such as street maps, floor plans and identikit images. This sort of transmission is of particular interest to the service industries such as the Police force, Fire brigade, medical services and other services. These services cannot be applied directly to mobile terminals because of the limited capacity of the mobile channels and the transmission errors caused by the multipath (Rayleigh) fading. In this research, transmission of line diagram images such as floor plans and street maps, over digital networks involving mobile terminals at transmission rates of 2400 bits/s and 4800 bits/s have been studied. A low bit-rate source encoding technique using geometric codes is found to be suitable to represent line diagram images. In geometric encoding, the amount of data required to represent or store the line diagram images is proportional to the image detail. Thus a simple line diagram image would require a small amount of data. To study the effect of transmission errors due to mobile channels on the transmitted images, error sources (error files), which represent mobile channels under different conditions, have been produced using channel modelling techniques. Satisfactory models of the mobile channel have been obtained when compared to the field test measurements. Subjective performance tests have been carried out to evaluate the quality and usefulness of the received line diagram images under various mobile channel conditions. The effect of mobile transmission errors on the quality of the received images has been determined. To improve the quality of the received images under various mobile channel conditions, forward error correcting codes (FEC) with interleaving and automatic repeat request (ARQ) schemes have been proposed. The performance of the error control codes have been evaluated under various mobile channel conditions. It has been shown that a FEC code with interleaving can be used effectively to improve the quality of the received images under normal and severe mobile channel conditions. Under normal channel conditions, similar results have been obtained when using ARQ schemes. However, under severe mobile channel conditions, the FEC code with interleaving shows better performance.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

Watermarking biomedical time series data

This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidence (in terms of privacy and security) in an individual’s diverse bio-profile (the digital fingerprint of an individual’s medical history), ensure belief that the data being analysed does indeed belong to the correct person, and also that it is not being accessed by unauthorised personnel. Embedding information inside single channel biomedical time series data is more difficult than the standard application for images due to the reduced redundancy. A data hiding approach which has an in built capability to protect against illegal data snooping is developed. The capability of this secure method is enhanced by embedding not just a single message but multiple messages into an example one-dimensional EEG signal. Embedding multiple messages of similar characteristics, for example identities of clinicians accessing the medical record helps in creating a log of access while embedding multiple messages of dissimilar characteristics into an EPHR enhances confidence in the use of the EPHR. The novel method of embedding multiple messages of both similar and dissimilar characteristics into a single channel EEG demonstrated in this thesis shows how this embedding of data boosts the implementation and use of the EPHR securely.

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

Data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit

The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

Levonorgestrel intrauterine system versus medical therapy for menorrhagia

ABSTRACT: Menorrhagia is a common problem that interferes with a woman’s physical, emotional, and social life. Evidence to guide physicians for decision about therapy for heavy menstrual bleeding is lacking. One treatment option, the levonorgestrel-releasing intrauterine system (levonorgestrel-IUS), has been available in the United States since 2009. Updated meta-analyses comparing the levonorgestrel-IUS with nonhormonal and hormonal treatments showed that the levonorgestrel-IUS produced a greater reduction in menstrual blood loss at 3 to 12 months of follow-up. It is not clear whether these short-term benefits persist. Moreover, the rates of discontinuation of the levonorgestrel-IUS at 2 years are as high as 28%, and effects on bleeding-related quality of life are not known. This pragmatic, multicenter, randomized trial compared the effectiveness of the levonorgestrel-IUS with that of usual medical treatment among women with menorrhagia in a primary care setting. A total of 571 women with menorrhagia were randomized to treatment with levonorgestrel-IUS (n = 285) or usual medical treatment (n = 286). Usual treatment was tranexamic acid, mefenamic acid, combined estrogen-progestogen, or progesterone alone. The primary study outcome measure was the patient-reported score on the condition-specific Menorrhagia Multi-Attribute Scale (MMAS) assessed over a 2-year period. The MMAS scores range from 0 to 100, with lower scores indicating greater severity. Summary MMAS scores were assessed at 6, 12, and 24 months. Secondary outcome measures included general health-related quality of life, sexual-activity scores, and surgical intervention. There was a significant improvement in total MMAS scores from baseline to 6 months in both the levonorgestrel-IUS group and the usual-treatment group; the mean increase was 32.7 and 21.4 points, respectively; P < 0.001 for both comparisons. Over the 2-year follow-up, improvements were maintained in both groups but were significantly greater in the levonorgestrel-IUS group (mean between-group difference, 13.4 points; 95% confidence interval, 9.9–16.9; P < 0.001). Significantly greater improvements in all MMAS domains (practical difficulties, social life, psychological health, physical health, work and daily routine, and family life and relationships) occurred with the levonorgestrel-IUS than with the usual treatment (P < 0.001 with the use of a test for trend). This was also found for 7 of the 8 quality-of-life domains. At the 2-year end point, almost twice as many women were still using the levonorgestrel-IUS than were those receiving the usual medical treatment (64% vs 38%, P < 0.001). No significant between-group differences were noted in the rates of surgical intervention or sexual-activity scores as well as in the frequency of serious adverse events. These data show that levonorgestrel-IUS is more effective than usual medical treatment in improving the quality of life of women with menorrhagia in a primary care setting.

Estimating the efficiency of healthcare facilities providing HIV/AIDS treatment in Zambia:a data envelopment approach

Zambia and many other countries in Sub-Saharan Africa face a key challenge of sustaining high levels of coverage of AIDS treatment under prospects of dwindling global resources for HIV/AIDS treatment. Policy debate in HIV/AIDS is increasingly paying more focus to efficiency in the use of available resources. In this chapter, we apply Data Envelopment Analysis (DEA) to estimate short term technical efficiency of 34 HIV/AIDS treatment facilities in Zambia. The data consists of input variables such as human resources, medical equipment, building space, drugs, medical supplies, and other materials used in providing HIV/AIDS treatment. Two main outputs namely, numbers of ART-years (Anti-Retroviral Therapy-years) and pre-ART-years are included in the model. Results show the mean technical efficiency score to be 83%, with great variability in efficiency scores across the facilities. Scale inefficiency is also shown to be significant. About half of the facilities were on the efficiency frontier. We also construct bootstrap confidence intervals around the efficiency scores.

Using DEA to estimate potential savings at GP units at medical specialty level

The aim of this paper is to identify benchmark cost-efficient General Practitioner (GP) units at delivering health care in the Geriatric and General Medicine (GMG) specialty and estimate potential cost savings. The use of a single medical specialty makes it possible to reflect more accurately the medical condition of the List population of the Practice so as to contextualize its expenditure on care for patients. We use Data Envelopment Analysis (DEA) to estimate the potential for cost savings at GP units and to decompose these savings into those attributable to the reduction of resource use, to altering the mix of resources used and to those attributable to securing better resource 'prices'. The results reveal a considerable potential for savings of varying composition across GP units. © 2013 Elsevier Ltd.