Bayesian Networks to Compare Pest Control Interventions on Commodities Along Agricultural Production Chains

The production of an agricultural commodity involves a sequence of processes: planting/growing, harvesting, sorting/grading, postharvest treatment, packing, and exporting. A Bayesian network has been developed to represent the level of potential infestation of an agricultural commodity by a specified pest along an agricultural production chain. It reflects the dependency of this infestation on the predicted level of pest challenge, the anticipated susceptibility of the commodity to the pest, the level of impact from pest control measures as designed, and any variation from that due to uncertainty in measure efficacy. The objective of this Bayesian network is to facilitate agreement between national governments of the exporters and importers on a set of phytosanitary measures to meet specific phytosanitary measure requirements to achieve target levels of protection against regulated pests. The model can be used to compare the performance of different combinations of measures under different scenarios of pest challenge, making use of available measure performance data. A case study is presented using a model developed for a fruit fly pest on dragon fruit in Vietnam; the model parameters and results are illustrative and do not imply a particular level of fruit fly infestation of these exports; rather, they provide the most likely, alternative, or worst-case scenarios of the impact of measures. As a means to facilitate agreement for trade, the model provides a framework to support communication between exporters and importers about any differences in perceptions of the risk reduction achieved by pest control measures deployed during the commodity production chain.

Using a Bayesian network to clarify areas requiring research in a host-pathogen system

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease-driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host-pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious-disease systems.

Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors

Collected real-life clinical target volume (CTV) displacement data show that some patients undergoing external beam radiotherapy (EBRT) demonstrate significantly more fraction-to-fraction variability in their displacement ('random error') than others. This contrasts with the common assumption made by historical recipes for margin estimation for EBRT, that the random error is constant across patients. In this work we present statistical models of CTV displacements in which random errors are characterised by an inverse gamma (IG) distribution in order to assess the impact of random error variability on CTV-to-PTV margin widths, for eight real world patient cohorts from four institutions, and for different sites of malignancy. We considered a variety of clinical treatment requirements and penumbral widths. The eight cohorts consisted of a total of 874 patients and 27 391 treatment sessions. Compared to a traditional margin recipe that assumes constant random errors across patients, for a typical 4 mm penumbral width, the IG based margin model mandates that in order to satisfy the common clinical requirement that 90% of patients receive at least 95% of prescribed RT dose to the entire CTV, margins be increased by a median of 10% (range over the eight cohorts -19% to +35%). This substantially reduces the proportion of patients for whom margins are too small to satisfy clinical requirements.

Creating a Sustainability Scorecard as a predictive tool for measuring the complex social, economic and environmental impacts of industries, a case study: assessing the viability and sustainability of the dairy industry

Sustainability is a key driver for decisions in the management and future development of industries. The World Commission on Environment and Development (WCED, 1987) outlined imperatives which need to be met for environmental, economic and social sustainability. Development of strategies for measuring and improving sustainability in and across these domains, however, has been hindered by intense debate between advocates for one approach fearing that efforts by those who advocate for another could have unintended adverse impacts. Studies attempting to compare the sustainability performance of countries and industries have also found ratings of performance quite variable depending on the sustainability indices used. Quantifying and comparing the sustainability of industries across the triple bottom line of economy, environment and social impact continues to be problematic. Using the Australian dairy industry as a case study, a Sustainability Scorecard, developed as a Bayesian network model, is proposed as an adaptable tool to enable informed assessment, dialogue and negotiation of strategies at a global level as well as being suitable for developing local solutions.

Potential uses of Bayesian networks as tools for synthesis of systematic reviews of complex interventions

Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention. Bayesian networks are useful extensions to logic maps when initiating a review or to facilitate synthesis and bridge the gap between evidence acquisition and decision-making. Formal elicitation techniques allow development of BNs on the basis of expert opinion. Such applications are useful alternatives to 'empty' reviews, which identify knowledge gaps but fail to support decision-making. Where review evidence exists, it can inform the development of a BN. We illustrate the construction of a BN using a motivating example that demonstrates how BNs can ensure coherence, transparently structure the problem addressed by a complex intervention and assess sensitivity to context, all of which are critical components of robust reviews of complex interventions. We suggest that BNs should be utilised to routinely synthesise reviews of complex interventions or empty reviews where decisions must be made despite poor evidence.

Funnel plots and risk-adjusted count data adverse events. A limitation of indirect standardisation

Reporting of hospital adverse event data is becoming increasingly mandated and this has motivated work on methods for the analysis and display of these data for groups of institutions. Currently, the method preferred by many workers is the funnel plot. Often, indirect standardisation is employed to produce these plots. It appears that, when used to display binary data such as surgical site infection or mortality data, the method is satisfactory. Increasingly, these data are risk-adjusted. However, risk adjustment of these data usually involves individual patients undergoing the same or similar procedures and the method does not appear to mislead. However, when dealing with count data such as bacteraemias it appears that this method can mislead, particularly where methods for risk adjustment of these data are used. Information about the hospitals or units of interest rather than individual patients is employed. For example, one hospital may have plastic and cardiac surgery units in which bacteraemias occur infrequently whereas another may provide treatment for renal failure (including transplantation) and have a large haematology-oncology unit (also including transplantation), each of which would expect higher bacteraemia rates. Moreover, the hospitals and units within them may differ substantially in size. It is well known that indirect standardisation can give biased results when denominators differ substantially. We illustrate this difficulty with risk-adjusted bacteraemia data from the Queensland Health Centre for Healthcare Infection, Surveillance and Prevention (CHRISP) database.

Spatial and temporal clusters of Barmah Forest virus disease in Queensland, Australia

OBJECTIVE

To identify the spatial and temporal clusters of Barmah Forest virus (BFV) disease in Queensland in Australia, using geographical information systems and spatial scan statistic (SaTScan).

METHODS

We obtained BFV disease cases, population and statistical local areas (SLAs) boundary data from Queensland Health and Australian Bureau of Statistics, respectively, during 1992-2008 for Queensland. A retrospective Poisson-based analysis using SaTScan software and method was conducted to identify both purely spatial and space-time BFV disease high-rate clusters. A spatial cluster size of a proportion of the population and a 200 km radius and varying time windows from 1 to 12 months were chosen (for the space-time analysis).

RESULTS

The spatial scan statistic detected a most likely significant purely spatial cluster (including 23 SLAs) and a most likely significant space-time cluster (including 24 SLAs) in approximately the same location. Significant secondary clusters were also identified from both the analyses in several locations.

CONCLUSIONS

This study provides evidence of the existence of statistically significant BFV disease clusters in Queensland, Australia. The study also demonstrated the relevance and applicability of SaTScan in analysing ongoing surveillance data to identify clusters to facilitate the development of effective BFV disease prevention and control strategies in Queensland, Australia.

Effect of vitamin D supplementation on muscle strength: a systematic review and meta-analysis

This systematic review demonstrates that vitamin D supplementation does not have a significant effect on muscle strength in vitamin D replete adults. However, a limited number of studies demonstrate an increase in proximal muscle strength in adults with vitamin D deficiency.

INTRODUCTION

The purpose of this study is to systematically review the evidence on the effect of vitamin D supplementation on muscle strength in adults.

METHODS

A comprehensive systematic database search was performed. Inclusion criteria included randomised controlled trials (RCTs) involving adult human participants. All forms and doses of vitamin D supplementation with or without calcium supplementation were included compared with placebo or standard care. Outcome measures included evaluation of strength. Outcomes were compared by calculating standardised mean difference (SMD) and 95% confidence intervals.

RESULTS

Of 52 identified studies, 17 RCTs involving 5,072 participants met the inclusion criteria. Meta-analysis showed no significant effect of vitamin D supplementation on grip strength (SMD -0.02, 95%CI -0.15,0.11) or proximal lower limb strength (SMD 0.1, 95%CI -0.01,0.22) in adults with 25(OH)D levels > 25 nmol/L. Pooled data from two studies in vitamin D deficient participants (25(OH)D <25 nmol/L) demonstrated a large effect of vitamin D supplementation on hip muscle strength (SMD 3.52, 95%CI 2.18, 4.85).

CONCLUSION

Based on studies included in this systematic review, vitamin D supplementation does not have a significant effect on muscle strength in adults with baseline 25(OH)D >25 nmol/L. However, a limited number of studies demonstrate an increase in proximal muscle strength in adults with vitamin D deficiency.

Association between indoor air pollution measurements and respiratory health in women and children in Lao PDR

This article presents the results of a study on the association between measured air pollutants and the respiratory health of resident women and children in Lao PDR, one of the least developed countries in Southeast Asia. The study, commissioned by the World Health Organisation, included PM(10), CO and NO(2) measurements made inside 181 dwellings in nine districts within two provinces in Lao PDR over a 5-month period (12/05-04/06), and respiratory health information (via questionnaires and peak expiratory flow rate (PEFR) measurements) for all residents in the same dwellings. Adjusted odds ratios were calculated separately for each health outcome using binary logistic regression. There was a strong and consistent positive association between NO(2) and CO for almost all questionnaire-based health outcomes for both women and children. Women in dwellings with higher measured NO(2) had more than triple of the odds of almost all of the health outcomes, and higher concentrations of NO(2) and CO were significantly associated with lower PEFR. This study supports a growing literature confirming the role of indoor air pollution in the burden of respiratory disease in developing countries. The results will directly support changes in health and housing policy in Lao PDR.

PRACTICAL IMPLICATIONS

This is the first study that investigated indoor air quality and its impact within residential dwellings in Lao PDR, which is one of the poorest and least developed countries in south-east Asia, with a life-expectancy of 56 years in 2008. While there have been other studies published on indoor air quality in other developing countries, the situation in Laos is different because the majority of houses in Laos used wood stoves, and therefore, emissions from wood burning are the dominant sources of indoor air pollution. In other countries, and studies, while emission from wood burning was investigated, wood was rarely the main or the only fuel used, as the houses used in addition (or solely) dung, kerosene or coal. The study quantified, for the first time, concentrations in houses two provinces in Laos PDR and shed light on the impact of human activities and urban design on pollutant concentrations and respiratory health. This study contributes to the accumulation of evidence to provide more reliable estimates of risk and a more informed basis for decision-making by concerned governments and communities.

Pollutant concentrations within households in Lao PDR and association with housing characteristics and occupants' activities

The paper presents the results of a study conducted to investigate indoor air quality within residential dwellings in Lao PDR. Results from PM(10), CO, and NO(2) measurements inside 167 dwellings in Lao PDR over a five month period (December 2005-April 2006) are discussed as a function of household characteristics and occupant activities. Extremely high PM(10) and NO(2) concentrations (12 h mean PM(10) concentrations 1275 ± 98 μg m(-3) and 1183 ± 99 μg m(-3) in Vientiane and Bolikhamxay provinces, respectively; 12 h mean NO(2) concentrations 1210 ± 94 μg m(-3) and 561 ± 45 μg m(-3) in Vientiane and Bolikhamxay, respectively) were measured within the dwellings. Correlations, ANOVA analysis (univariate and multivariate), and linear regression results suggest a substantial contribution from cooking and smoking. The PM(10) concentrations were significantly higher in houses without a chimney compared to houses in which cooking occurred on a stove with a chimney. However, no significant differences in pollutant concentrations were observed as a function of cooking location. Furthermore, PM(10) and NO(2) concentrations were higher in houses in which smoking occurred, suggestive of a relationship between increased indoor concentrations and smoking (0.05 < p < 0.10). Resuspension of dust from soil floors was another significant source of PM(10) inside the house (634 μg m(-3), p < 0.05).

Sequential analysis of uncommon adverse outcomes

Sequential analysis of uncommon adverse outcomes (AEs) such as surgical site infections (SSIs) is desirable. Short postoperative lengths of stay (LOS) result in many SSIs occurring after discharge and they are often superficial. Deep and organ space (complex) SSIs occur less frequently but are detected more reliably and are suitable for monitoring wound care. Those occurring post-discharge usually require readmissison and can be counted accurately. Sequential analysis of meticillin-resistant Staphylococcus aureus bacteraemia is also needed. The key to prevention is to implement systems based on evidence, e.g. using 'bundles' and checklists. Regular mortality and morbidity audit meetings are required and these may need to be followed by independent audits. Sequential statistical analysis is desirable for data presentation, to detect changes, and to discourage tampering with processes when occasional AEs occur in a reliable system. Tabulations and cumulative observed minus expected (O-E) charts and funnel plots are valuable, supplemented in the presence of apparent 'runs' of AEs by cumulative sum analysis. Used prospectively, they may enable staff to visualise and detect patterns or shifts in rates and counts that might not otherwise be apparent.

Difference in mosquito species (Diptera: Culicidae) and the transmission of Ross River virus between coastline and inland areas in Brisbane, Australia

This study examined the distribution of major mosquito species and their roles in the transmission of Ross River virus (RRV) infection for coastline and inland areas in Brisbane, Australia (27 degrees 28' S, 153 degrees 2' E). We obtained data on the monthly counts of RRV cases in Brisbane between November 1998 and December 2001 by statistical local areas from the Queensland Department of Health and the monthly mosquito abundance from the Brisbane City Council. Correlation analysis was used to assess the pairwise relationships between mosquito density and the incidence of RRV disease. This study showed that the mosquito abundance of Aedes vigilax (Skuse), Culex annulirostris (Skuse), and Aedes vittiger (Skuse) were significantly associated with the monthly incidence of RRV in the coastline area, whereas Aedes vigilax, Culex annulirostris, and Aedes notoscriptus (Skuse) were significantly associated with the monthly incidence of RRV in the inland area. The results of the classification and regression tree (CART) analysis show that both occurrence and incidence of RRV were influenced by interactions between species in both coastal and inland regions. We found that there was an 89% chance for an occurrence of RRV if the abundance of Ae. vigilax was between 64 and 90 in the coastline region. There was an 80% chance for an occurrence of RRV if the density of Cx. annulirostris was between 53 and 74 in the inland area. The results of this study may have applications as a decision support tool in planning disease control of RRV and other mosquito-borne diseases.

Measurement of quality of life in women with breast cancer before and after adjuvant chemotherapy: A structural equation modelling

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Background: This study examines breast cancer patients’ individual changes in health related quality of life (HRQOL) measures over time and the relationships between these measures within the framework of Structural equation modeling (SEM). Methods: One hundred and fifty five Australian women with breast cancer were asked to complete the Functional Assessment of Cancer Therapy Breast, Version 4 (FACT-B) pre- and post-chemotherapy as measures of HRQOL. In SEM, growth curve modelling was required for the estimation of individual differences in developmental changes of HRQOL scales over time. A growth mixture model was also used for identifying the cross-group differences in the pattern of changes in a given population over time. Results: Reliability and validity of the FACT-G including the fatigue subscale and breast cancer subscale for evaluating Australian breast cancer patients was determined to be sufficient for research purposes. After adjustment for covariates, two subgroups of women were identified with respect of patterns of response, representing largely increasing and decreasing trends in HRQOL respectively. In the former group, on average 85% of breast cancer women reported changes over time with respect to individual HRQOL measures and 78% reported such changes with respect to combined HRQOL Scores. A small group of women reported changes with respect to HRQOL measures in the decreasing group. The greatest contribution to variation in HRQOL measures was in the breast cancer subscale for both individual and combined scores. Subsequent factor analysis of the breast cancer subscale revealed that with one exception, all items loaded onto three separate factors described as self-esteem, physical and psychological. The latter two improved over time, but self- esteem was largely unchanged. Conclusions: The study presents a technique that allows us to identify potential subgroups of study subjects that experience different trajectories of responses. The results of this study provide greater insight into HRQOL changes over time and can help to plan interventions in a better way, ultimately contributing to improvement in HRQOL of women with breast cancer.

No significant financial relationships to disclose.

Characterization of expiration air jets and droplet size distributions immediately at the mouth opening

Size distributions of expiratory droplets expelled during coughing and speaking and the velocities of the expiration air jets of healthy volunteers were measured. Droplet size was measured using the interferometric Mie imaging (IMI) technique while the particle image velocimetry (PIV) technique was used for measuring air velocity. These techniques allowed measurements in close proximity to the mouth and avoided air sampling losses. The average expiration air velocity was 11.7 m/s for coughing and 3.9 m/s for speaking. Under the experimental setting, evaporation and condensation effects had negligible impact on the measured droplet size. The geometric mean diameter of droplets from coughing was 13.5 μm and it was 16.0 μm for speaking (counting 1-100). The estimated total number of droplets expelled ranged from 947 to 2085 per cough and 112-6720 for speaking. The estimated droplet concentrations for coughing ranged from 2.4 to 5.2 cm^-3 per cough and 0.004-0.223 cm^-3 for speaking.

Characterization of expiration air jets and droplet size distributions immediately at the mouth opening

Size distributions of expiratory droplets expelled during coughing and speaking and the velocities of the expiration air jets of healthy volunteers were measured. Droplet size was measured using the interferometric Mie imaging (IMI) technique while the particle image velocimetry (PIV) technique was used for measuring air velocity. These techniques allowed measurements in close proximity to the mouth and avoided air sampling losses. The average expiration air velocity was 11.7 m/s for coughing and 3.9 m/s for speaking. Under the experimental setting, evaporation and condensation effects had negligible impact on the measured droplet size. The geometric mean diameter of droplets from coughing was 13.5 μm and it was 16.0 μm for speaking (counting 1-100). The estimated total number of droplets expelled ranged from 947 to 2085 per cough and 112-6720 for speaking. The estimated droplet concentrations for coughing ranged from 2.4 to 5.2 cm-3 per cough and 0.004-0.223 cm-3 for speaking.

Characterization of expiration air jets and droplet size distributions immediately at the mouth opening

Size distributions of expiratory droplets expelled during coughing and speaking and the velocities of the expiration air jets of healthy volunteers were measured. Droplet size was measured using the interferometric Mie imaging (IMI) technique while the particle image velocimetry (PIV) technique was used for measuring air velocity. These techniques allowed measurements in close proximity to the mouth and avoided air sampling losses. The average expiration air velocity was 11.7 m/s for coughing and 3.9 m/s for speaking. Under the experimental setting, evaporation and condensation effects had negligible impact on the measured droplet size. The geometric mean diameter of droplets from coughing was 13.5 μm and it was 16.0 μm for speaking (counting 1-100). The estimated total number of droplets expelled ranged from 947 to 2085 per cough and 112-6720 for speaking. The estimated droplet concentrations for coughing ranged from 2.4 to 5.2 cm^-3 per cough and 0.004-0.223 cm^-3 for speaking.

Ozone modifies associations between temperature and cardiovascular mortality: analysis of the NMMAPS data

OBJECTIVES

Both ambient ozone and temperature are associated with human health. However, few data are available on whether ozone modifies temperature effects. This study aims to explore whether ozone modified associations between maximum temperature and cardiovascular mortality in the USA.

METHODS

The authors obtained data from the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) website. They used two time-series Poisson regression models (a response surface model and a stratification model) to examine whether ozone modified associations between maximum temperature and cardiovascular mortality (CVM) in 95 large US communities during 1987-2000 in summer (June to September). Bayesian meta-analysis was used to pool estimates in each community.

RESULTS

The response surface model was used to examine the joint effects of temperature and ozone on CVM in summer. Results indicate that ozone positively modified the temperature-CVM associations across the different regions. The stratification model quantified the temperature-CVM associations across different levels of ozone. Results show that in general the higher the ozone concentration, the stronger the temperature-CVM associations across the communities. A 10 degrees C increase in temperature on the same day was associated with an increase in CVM by 1.17% and 8.31% for the lowest and highest level of ozone concentrations in all communities, respectively.

CONCLUSION

Ozone modified temperature effects in different regions in the USA. It is important to evaluate the modifying role of ozone when estimating temperature-related health impacts and to further investigate the reasons behind the regional variability and mechanism for the interaction between temperature and ozone.

A Bayesian approach to assess interaction between known risk factors: the risk of lung cancer from exposure to asbestos and smoking

We review the literature on the combined effect of asbestos exposure and smoking on lung cancer, and explore a Bayesian approach to assess evidence of interaction. Previous approaches have focussed on separate tests for an additive or multiplicative relation. We extend these approaches by exploring the strength of evidence for either relation using approaches which allow the data to choose between both models. We then compare the different approaches.

Assessing the combined effect of asbestos exposure and smoking on lung cancer: a Bayesian approach

We review the literature on the combined association between lung cancer and two environmental exposures, asbestos exposure and smoking, and explore a Bayesian approach to assess evidence of interaction between the exposures. The meta-analysis combines separate indices of additive and multiplicative relationships and multivariate relative risk estimates. By making inferences on posterior probabilities we can explore both the form and strength of interaction. This analysis may be more informative than providing evidence to support one relation over another on the basis of statistical significance. Overall, we find evidence for a more than additive and less than multiplicative relation.

Novel method for on-road emission factor measurements using a plume capture trailer

The method outlined provides for emission factor measurements to be made for unmodified vehicles driving under real world conditions at minimal cost. The method consists of a plume capture trailer towed behind a test vehicle. The trailer collects a sample of the naturally diluted plume in a 200 L conductive bag and this is delivered immediately to a mobile laboratory for subsequent analysis of particulate and gaseous emissions. The method offers low test turnaround times with the potential to complete much larger numbers of emission factor measurements than have been possible using dynamometer testing. Samples can be collected at distances up to 3 m from the exhaust pipe allowing investigation of early dilution processes. Particle size distribution measurements, as well as particle number and mass emission factor measurements, based on naturally diluted plumes are presented. A dilution profile relating the plume dilution ratio to distance from the vehicle tail pipe for a diesel passenger vehicle is also presented. Such profiles are an essential input for new mechanistic roadway air quality models.

Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome

AIM

HER-2/neu amplification occurs in 15-25% of breast carcinomas. This oncogene, also referred to as c-erbB-2, encodes a transmembrane tyrosine kinase receptor belonging to the epidermal growth factor receptor family. HER-2 over-expression is reported to be associated with a poor prognosis in breast carcinoma patients and in some studies is associated with a poorer response to anti-oestrogen therapy. These patients are less likely to benefit from CMF (cyclophosphamide, methotrexate, fluorouracil)-based chemotherapy compared with anthracycline-based chemotherapy. The aim of this study was to evaluate breast carcinomas to determine hormone receptor status and if there is a difference in breast cancer specific survival for HER-2 positive patients.

METHODS

A total of 591 breast carcinomas were evaluated using immunohistochemistry (IHC) for oestrogen receptor (ERp), progesterone receptor (PRp) and three different HER-2 antibodies (CB11, A0485 and TAB250). Percentage of tumour cells and intensity of staining for ERp were evaluated using a semiquantitative method.

RESULTS

Of the 591 tumours, 91 (15.4%) showed 3+ membrane staining for HER-2 with one or more antibodies. Of these 91 tumours, 41 (45.1%) were ERp+/PRp+, seven (7.7%) were ERp+/PR-, six (6.6%) were ERp-/PRp+ and 37 (40.7%) were ERp-/PR-. Of HER-2 positive tumours, 5.5% showed >80% 3+ staining for ERp compared with 31.8% of 0-2+ HER-2 tumours; 24.2% of HER-2-positive tumours showed 60% or more cells with 2+ or 3+ staining for ERp. Treatment data were available for 209 patients and no difference was observed in breast cancer specific survival (BCSS) with HER-2 status and tamoxifen.

CONCLUSION

Oestrogen receptor status cannot be used to select tumours for evaluation of HER-2 status, and oestrogen and progesterone receptor positivity does not preclude a positive HER-2 status. There is a higher proportion of ERp negative tumours associated with HER-2 positivity, however, more than 20% of HER-2 positive tumours show moderate or strong staining for ERp. HER-2 positive patients in this study did not show an adverse BCSS with tamoxifen treatment unlike some previous studies.

Improving the quality of patient care using reliability measures: a classification tree approach

This paper considers the application and interpretation of new reliability measures for a classification tree-based medical risk assessment tool. Following the construction of a classification tree reliability measures may then be used to provide an estimate of the precision of the classification and the probability in each terminal node of the classification tree. Identification of unreliable nodes (those that have low precision) in this application may indicate patient groups requiring closer monitoring or scenarios in which further information about the patient is required, thereby providing medical practitioners with an avenue for more informed decision making.

Confidence limits for the ratio of two rates based on likelihood scores: non-iterative method

This paper describes a method for creating a confidence interval for the ratio of rates using the score statistic. This non-iterative and easy to apply procedure produces confidence intervals that are suitable for use with Poisson data and simulation results indicate that it is close to the nominal level for a wide range of scenarios.

Bayesian nonparametric modeling using mixtures of triangular distributions

Nonparametric modeling is an indispensable tool in many applications and its formulation in an hierarchical Bayesian context, using the entire posterior distribution rather than particular expectations, increases its flexibility. In this article, the focus is on nonparametric estimation through a mixture of triangular distributions. The optimality of this methodology is addressed and bounds on the accuracy of this approximation are derived. Although our approach is more widely applicable, we focus for simplicity on estimation of a monotone nondecreasing regression on [0, 1] with additive error, effectively approximating the function of interest by a function having a piecewise linear derivative. Computationally accessible methods of estimation are described through an amalgamation of existing Markov chain Monte Carlo algorithms. Simulations and examples illustrate the approach.

Socioeconomic status and infant mortality in Australia: a national study of small urban areas, 1985-89

This study uses small-area data for the period 1985-89 to examine the relationship between socioeconomic status and infant mortality in each of the mainland State capital cities of Australia. An unweighted OLS regression analysis based on 195 Statistical Local Areas (SLAs) that recorded five or more deaths over the reference period shows that standardised infant mortality ratios were significantly higher in areas with greater concentrations of low income families. This relationship was independent of the effects of low birthweight, Aboriginality, ethnicity and variability between each of the capital cities. To test for the robustness of this result a sensitivity analysis was undertaken. This involved (a) performing a Principal Components Analysis on a wide range of sociodemographic variables to derive factor scales that were subsequently included in a regression analysis, (b) using weighted least-squares regression and a Poisson generalised linear model and (c) including in the analysis all SLAs irrespective of the number of infant deaths. The sensitivity analysis supported the results of this study, thus validating the observed association between the socioeconomic characteristics of urban areas and their rate of infant mortality. Despite marked reductions in overall rates of infant mortality over the last century in Australia. socioeconomic disparities were still evident during the mid-to-late 1980s. Whether and to what extent this situation persisted during the early-to-mid 1990s will be known in the near future when the next collection of area-based data are publicly released. The results of this study, therefore, represent an important baseline against which more contemporary national trends can be monitored.