An international survey of daily disposable contact lens prescribing

Purpose The aim was to determine the extent of daily disposable contact lens prescribing worldwide and to characterise the associated demographics and fitting patterns. Methods Up to 1,000 survey forms were sent to contact lens fitters in up to 40 countries between January and March every year for five consecutive years (2007 to 2011). Practitioners were asked to record data relating to the first 10 contact lens fits or refits performed after receiving the survey form. Survey data collected since 1996 were also analysed for seven nations to assess daily disposable lens fitting trends since that time. Results Data were collected in relation to 97,289 soft lens fits, of which 23,445 (24.1 per cent) were with daily disposable lenses and 73,170 (75.9 per cent) were with reusable lenses. Daily disposable lens prescribing ranged from 0.6 per cent of all soft lenses in Nepal to 66.2 per cent in Qatar. Compared with reusable lens fittings, daily disposable lens fittings can be characterised as follows: older age (30.0 ± 12.5 versus 29.3 ± 12.3 years for reusable lenses); males are over-represented; a greater proportion of new fits versus refits; 85.9 per cent hydrogel; lower proportion of toric and presbyopia designs and a higher proportion of part-time wear. There has been a continuous increase in daily disposable lens prescribing between 1996 and 2011. The proportion of daily disposable lens fits (as a function of all soft lens fits) is positively related to the gross domestic product at purchasing power parity per capita (r2 = 0.55, F = 46.8, p < 0.0001). Conclusions The greater convenience and other benefits of daily disposable lenses have resulted in this modality capturing significant market share. The contact lens field appears to be heading toward a true single-use-only, disposable lens market.

International survey of rigid contact lens fitting

Purpose To determine the extent of rigid contact lens fitting worldwide and to characterize the associated demographics and fitting patterns. Methods Survey forms were sent to contact lens fitters in up to 40 countries between January and March every year for five consecutive years (2007 to 2011). Practitioners were asked to record data relating to the first 10 contact lens fits or refits performed after receiving the survey form. Survey data collected between 1996 and 2011 were also analyzed to assess rigid lens fitting trends in seven nations during this period. Results Data were obtained for 12,230 rigid and 100,670 soft lens fits between 2007 and 2011. Overall, rigid lenses represented 10.8% of all contact lens fits, ranging from 0.2% in Lithuania to 37% in Malaysia. Compared with soft lens fits, rigid lens fits can be characterized as follows: older age (rigid, 37.3 ± 15.0 years; soft, 29.8 ± 12.4 years); fewer spherical and toric fits; more bifocal/multifocal fits; less frequent replacement (rigid, 7%; soft, 85%); and less part-time wear (rigid, 4%; soft, 10%). High-Dk (contact lens oxygen permeability) (36%) and mid-Dk (42%) materials are predominantly used for rigid lens fitting. Orthokeratology represents 11.5% of rigid contact lens fits. There has been a steady decline in rigid lens fitting between 1996 and 2011. Conclusions Rigid contact lens prescribing is in decline but still represents approximately 10% of all contact lenses fitted worldwide. It is likely that rigid lenses will remain as a viable, albeit increasingly specialized, form of vision correction.

An international survey of toric contact lens prescribing

Objectives To characterize toric contact lens prescribing worldwide. Methods Up to 1,000 survey forms were sent to contact lens fitters in up to 39 countries between January and March every year for 5 consecutive years (2007–2011). Practitioners were asked to record data relating to the first 10 contact lens fits or refits performed after receiving the survey form. Only data for toric and spherical soft lens fits were analyzed. Survey data collected since 1996 were also analyzed for 7 nations to assess toric lens fitting trends since that time. Results Data were collected in relation to 21,150 toric fits (25%) and 62,150 spherical fits (75%). Toric prescribing ranged from 6% of lenses in Russia to 48% in Portugal. Compared with spherical fittings, toric fittings can be characterized as follows: older age (29.8 ± 11.4 years vs. 27.6 ± 10.8 years for spherical lenses); men are overrepresented (38% vs. 34%); greater proportion of new fits (39% vs. 32%); use of silicone hydrogel lenses (49% vs. 39%); and lower proportion of daily disposable lenses (14% vs. 28%). There has been a continuous increase in toric lens prescribing between 1996 and 2011. The proportion of toric lens fits was positively related to the gross domestic product at purchasing power parity per capita for year 2011 (r2 = 0.21; P=0.004). Conclusions At the present time, in the majority of countries surveyed, toric soft contact lens prescribing falls short of that required to correct clinically significant astigmatism (≥0.75 diopters) in all lens wearers.

The impact of OCR accuracy on automated cancer classification of pathology reports

Objective To evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology reports. Method Scanned images of pathology reports were converted to electronic free-text using a commercial OCR system. A state-of-the-art cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR reports. Results The employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type, etc. Conclusions The automatic cancer classification system used in this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned images through OCR software. However, issues emerge when considering the extraction of cancer notification items.

Classification of pathology reports for cancer registry notifications

Objective: To develop a system for the automatic classification of pathology reports for Cancer Registry notifications. Method: A two pass approach is proposed to classify whether pathology reports are cancer notifiable or not. The first pass queries pathology HL7 messages for known report types that are received by the Queensland Cancer Registry (QCR), while the second pass aims to analyse the free text reports and identify those that are cancer notifiable. Cancer Registry business rules, natural language processing and symbolic reasoning using the SNOMED CT ontology were adopted in the system. Results: The system was developed on a corpus of 500 histology and cytology reports (with 47% notifiable reports) and evaluated on an independent set of 479 reports (with 52% notifiable reports). Results show that the system can reliably classify cancer notifiable reports with a sensitivity, specificity, and positive predicted value (PPV) of 0.99, 0.95, and 0.95, respectively for the development set, and 0.98, 0.96, and 0.96 for the evaluation set. High sensitivity can be achieved at a slight expense in specificity and PPV. Conclusion: The system demonstrates how medical free-text processing enables the classification of cancer notifiable pathology reports with high reliability for potential use by Cancer Registries and pathology laboratories.

Clinician-driven automated classification of limb fractures from free-text radiology reports

The aim of this research is to report initial experimental results and evaluation of a clinician-driven automated method that can address the issue of misdiagnosis from unstructured radiology reports. Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to disperse information resources and vast amounts of manual processing of unstructured information, a point-of-care accurate diagnosis is often difficult. A rule-based method that considers the occurrence of clinician specified keywords related to radiological findings was developed to identify limb abnormalities, such as fractures. A dataset containing 99 narrative reports of radiological findings was sourced from a tertiary hospital. The rule-based method achieved an F-measure of 0.80 and an accuracy of 0.80. While our method achieves promising performance, a number of avenues for improvement were identified using advanced natural language processing (NLP) techniques.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology

Background Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult. Aims The aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports. Method A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach. Results The automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80. Conclusion While the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP) and machine learning techniques.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

The international politics of disease reporting : towards post-Westphalianism?

Since the outbreak of Severe Acute Respiratory Syndrome (SARS) in 2003, there has been much discussion about whether the international community has moved into a new post-Westphalian era, where states increasingly recognize certain shared norms that guide what they ought to do in responding to infectious disease outbreaks. In this article I identify this new obligation as the ‘duty to report’, and examine competing accounts on the degree to which states appreciate this new obligation are considered by examining state behaviour during the H5N1 human infectious outbreaks in East Asia (since 2004). The article examines reporting behaviour for H5N1 human infectious cases in Cambodia, China, Indonesia, Thailand and Vietnam from 2004 to 2010. The findings lend strong support to the claim that East Asian states have come to accept and comply with the duty to report infectious disease outbreaks and that the assertions of sovereignty in response to global health governance frameworks have not systematically inhibited reporting compliance.

International Handbook of Entrepreneurship and HRM

This invaluable reference tool has been designed in response to the growing recognition that too little is known about the intersection between entrepreneurship and human resource management. Paying particular attention to the ‘people’ side of venture emergence and development, it offers unique insights into the role that human resource management (HRM) plays in small and entrepreneurial firms.

The rise and fall of the international law of maritime terrorism : the ghost of piracy is still hunting!

Maritime terrorism is a serious threat to global security. A major debate in this regard is the treating of acts of maritime terrorism as piracy by some scholars and a rejection of this view by others. Moreover, the international law of maritime terrorism suffers from fundamental definitional issues, much like the international law of terrorism. This article examines the current international law of maritime terrorism with a particular emphasis on the debate regarding the applicability of the international law of piracy in the case of maritime terrorism. It argues that the international law of piracy is not applicable in the enforcement and prosecution of maritime terrorists on the high seas. International treaties on terrorism and the post-September 11 developments relating to international laws on terrorism have created a workable international legal framework for combating maritime terrorism, despite some bottlenecks.