Nicole White

Concentrations of organochlorine pesticides in pooled human serum by age and gender

&NA; Organochlorine pesticides (OCPs) have been used for many decades in Australia with cessation of selected persistent and bioaccumulative OCPs ranging from the 1970 s to as recently as 2007. The specific aims of this study were to use samples representative of an Australian population to assess age and gender differences in the concentration of OCPs in human blood sera and to investigate temporal trends in these chemicals. Serum was collected from de‐identified, surplus pathology samples over five time periods (2002/03, 2006/07, 2008/09, 2010/11 and 2012/13), with 183 serum pools made from 12,175 individual samples; 26 pools in 2002/03, 85 pools in 2006/07 and 24 pools each in 2008/09, 2010/11 and 2012/13. Samples were analyzed for hexachlorobenzene (HCB), &bgr;‐hexachlorocyclohexane (&bgr;‐HCH), &ggr; ‐hexachlorocyclohexane (lindane) (&ggr;‐HCH), oxy‐chlordane, trans‐nonachlor, p,p′‐DDE, o,p′‐DDT, p,p′‐DDT and Mirex. Stratification criteria included gender and age (0–4; 5–15; 16–30; 31–45; 46–60; and >60 years) with age additionally stratified by adults >16 years and children 0–4 and 5–15 years. All pools from all collection periods had detectable concentrations of OCPs with a detection frequency of >60% for HCB, &bgr;‐HCH, trans‐nonachlor, p,p′‐DDT and p,p′‐DDE. The overall OCP concentrations increased with age with the highest concentrations in the >60 years groups. Females did not have higher mean OCP concentrations than males except for HCB concentrations (p=0.0006). Temporal trends showed overall decreasing serum concentrations by collection period with the exception of an increase in OCP concentrations between 2006/07 and 2008/09. Excluding this data point, HCB decreased from year to year by 7–76%; &bgr;‐HCH concentrations decreased by 14 – 38%; trans‐nonachlor concentrations decreased by 10 – 65%; p,p′‐DDE concentrations decreased by 6 – 52%; and p,p′‐DDT concentrations decreased by 7 – 30%. The results indicate that OCP concentrations have decreased over time as is to be expected following the phase out of these chemicals in Australia. HighlightsThe concentrations of OCPs in Australia, collected over a decade, are analyzed.There was a significant decrease in OCP levels from 2002/03 to 2012/13.Results show higher levels of OCPs in older age groups.In the age groups, OCP levels are elevated in females.

Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

BackgroundSpatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences.MethodsWe present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions.ResultsChoice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application.ConclusionsSelecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

Cardiac surgery in indigenous Australians - How wide is 'The Gap'?

BACKGROUND Cardiovascular disease remains the leading cause of mortality in the Indigenous Australian population. Limited research exists in regards to cardiac surgery in the Aboriginal and Torres Strait Islander (ATSI) population. We aimed to investigate risk profiles, surgical pathologies, surgical management and short term outcomes in a contemporary group of patients. METHODS Variables were assessed for 557 consecutive patients who underwent surgery at our institution between August 2008 and March 2010. RESULTS 19.2% (107/557) of patients were of Indigenous origin. ATSI patients were significantly younger at time of surgery (mean age 54.1±13.23 vs. 63.1±12.46; p=<0.001) with higher rates of preventable risk factors. Rheumatic heart disease (RHD) was the dominant valvular pathology observed in the Indigenous population. Significantly higher rates of left ventricular impairment and more diffuse coronary artery disease were observed in ATSI patients. A non-significant trend towards higher 30-day mortality was observed in the Indigenous population (5.6% vs. 3.1%; p=0.244). CONCLUSIONS Cardiac surgery is generally required at a younger age in the Indigenous population with patients often presenting with more advanced disease. Despite often more advanced disease, surgical outcomes do not differ significantly from non-Indigenous patients. Continued focus on preventative strategies for coronary artery disease and RHD in the Indigenous population is required.

Overfitting Bayesian Mixture Models with an Unknown Number of Components

This paper proposes solutions to three issues pertaining to the estimation of finite mixture models with an unknown number of components: the non-identifiability induced by overfitting the number of components, the mixing limitations of standard Markov Chain Monte Carlo (MCMC) sampling techniques, and the related label switching problem. An overfitting approach is used to estimate the number of components in a finite mixture model via a Zmix algorithm. Zmix provides a bridge between multidimensional samplers and test based estimation methods, whereby priors are chosen to encourage extra groups to have weights approaching zero. MCMC sampling is made possible by the implementation of prior parallel tempering, an extension of parallel tempering. Zmix can accurately estimate the number of components, posterior parameter estimates and allocation probabilities given a sufficiently large sample size. The results will reflect uncertainty in the final model and will report the range of possible candidate models and their respective estimated probabilities from a single run. Label switching is resolved with a computationally light-weight method, Zswitch, developed for overfitted mixtures by exploiting the intuitiveness of allocation-based relabelling algorithms and the precision of label-invariant loss functions. Four simulation studies are included to illustrate Zmix and Zswitch, as well as three case studies from the literature. All methods are available as part of the R package Zmix, which can currently be applied to univariate Gaussian mixture models.

Spatial modelling of type II diabetes outcomes: a systematic review of approaches used

With the rising incidence of type II diabetes mellitus (DM II) worldwide, methods to identify high-risk geographical areas have become increasingly important. In this comprehensive review following Cochrane Collaboration guidelines, we outline spatial methods, outcomes and covariates used in all spatial studies involving outcomes of DM II. A total of 1894 potentially relevant citations were identified. Studies were included if spatial methods were used to explore outcomes of DM II or type I and 2 diabetes combined. Descriptive tables were used to summarize information from included studies. Ten spatial studies conducted in the USA, UK and Europe met selection criteria. Three studies used Bayesian generalized linear mixed modelling (GLMM), three used classic generalized linear modelling, one used classic GLMM, two used geographic information systems mapping tools and one compared case:provider ratios across regions. Spatial studies have been effective in identifying high-risk areas and spatial factors associated with DM II outcomes in the USA, UK and Europe, and would be useful in other parts of the world for allocation of additional services to detect and manage DM II early.

Probabilistic subgroup identification using Bayesian finite mixture modelling: A case study in Parkinson's disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a persons membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinsons disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinsons Disease Rating Scale (UPDRS).

Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease

In this paper, the goal of identifying disease subgroups based on differences in observed symptom profile is considered. Commonly referred to as phenotype identification, solutions to this task often involve the application of unsupervised clustering techniques. In this paper, we investigate the application of a Dirichlet process mixture model for this task. This model is defined by the placement of the Dirichlet process on the unknown components of a mixture model, allowing for the expression of uncertainty about the partitioning of observed data into homogeneous subgroups. To exemplify this approach, an application to phenotype identification in Parkinsons disease is considered, with symptom profiles collected using the Unified Parkinsons Disease Rating Scale.

Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation

Objectives To compare two Bayesian models capable of identifying unusual and unstable temporal patterns in spatiotemporal data. Setting Annual counts of mammography screening users from each statistical local area (SLA) in Brisbane, Australia, recorded between 1997 and 2008 inclusive. Primary outcome measures Mammography screening counts. Results The temporal trends of 91 SLAs (58%) were dissimilar from the overall common temporal trend. SLAs that followed the common temporal trend also tended to have stable temporal trends. SLAs with unstable temporal trends tended to be situated farther from the city and farther from mammography screening facilities. Conclusions This paper demonstrates the usefulness of the two models in identifying unusual and unstable temporal trends, and the synergy obtained when both models are applied to the same data set. An analysis of these models has provided interesting insights into the temporal trends of mammography screening counts and has shown several possible avenues for further research, such as extending the models to allow for multiple common temporal trends and accounting for additional spatiotemporal heterogeneity.

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

BACKGROUND Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. METHODS The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. RESULTS Strong spatial patterns were observed in the underlying risk factor estimates for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48 to 2.00) and females (median RR range across SLAs 0.53-1.80), with high risks observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final 2 years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. CONCLUSION The modelled underlying risks appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.