Recruiting and engaging new mothers in nutrition research studies : lessons from the Australian NOURISH randomised controlled trial

Background: Despite important implications for the budgets, statistical power and generalisability of research findings, detailed reports of recruitment and retention in randomised controlled trials (RCTs) are rare. The NOURISH RCT evaluated a community-based intervention for first-time mothers that promoted protective infant feeding practices as a primary prevention strategy for childhood obesity. The aim of this paper is to provide a detailed description and evaluation of the recruitment and retention strategies used. Methods: A two stage recruitment process designed to provide a consecutive sampling framework was used. First time mothers delivering healthy term infants were initially approached in postnatal wards of the major maternity services in two Australian cities for consent to later contact (Stage 1). When infants were about four months old mothers were re-contacted by mail for enrolment (Stage 2), baseline measurements (Time 1) and subsequent random allocation to the intervention or control condition. Outcomes were assessed at infant ages 14 months (Time 2) and 24 months (Time 3). Results: At Stage 1, 86% of eligible mothers were approached and of these women, 76% consented to later contact. At Stage 2, 3% had become ineligible and 76% could be recontacted. Of the latter, 44% consented to full enrolment and were allocated. This represented 21% of mothers screened as eligible at Stage 1. Retention at Time 3 was 78%. Mothers who did not consent or discontinued the study were younger and less likely to have a university education. Conclusions: The consent and retention rates of our sample of first time mothers are comparable with or better than other similar studies. The recruitment strategy used allowed for detailed information from non-consenters to be collected; thus selection bias could be estimated. Recommendations for future studies include being able to contact participants via mobile phone (particular text messaging), offering home visits to reduce participant burden and considering the use of financial incentives to support participant retention.

Review of statistical methods for disease mapping

Cancer poses an undeniable burden to the health and wellbeing of the Australian community. In a recent report commissioned by the Australian Institute for Health and Welfare(AIHW, 2010), one in every two Australians on average will be diagnosed with cancer by the age of 85, making cancer the second leading cause of death in 2007, preceded only by cardiovascular disease. Despite modest decreases in standardised combined cancer mortality over the past few decades, in part due to increased funding and access to screening programs, cancer remains a significant economic burden. In 2010, all cancers accounted for an estimated 19% of the country's total burden of disease, equating to approximately $3:8 billion in direct health system costs (Cancer Council Australia, 2011). Furthermore, there remains established socio-economic and other demographic inequalities in cancer incidence and survival, for example, by indigenous status and rurality. Therefore, in the interests of the nation's health and economic management, there is an immediate need to devise data-driven strategies to not only understand the socio-economic drivers of cancer but also facilitate the implementation of cost-effective resource allocation for cancer management...

Systematic variation of the severity of motor vehicle accident-related traumatic brain injury vignettes produces different post-concussion symptom reports

This study investigated the specificity of the post-concussion syndrome (PCS) expectation-as-etiology hypothesis. Undergraduate students (n = 551) were randomly allocated to one of three vignette conditions. Vignettes depicted either a very mild (VMI), mild (MI), or moderate-to-severe (MSI) motor vehicle-related traumatic brain injury (TBI). Participants reported the PCS and PTSD symptoms that they imagined the depicted injury would produce. Secondary outcomes (knowledge of mild TBI, and the perceived undesirability of TBI) were also assessed. After data screening, the distribution of participants by condition was: VMI (n = 100), MI (n = 96), and MSI (n = 71). There was a significant effect of condition on PCS symptomatology, F(2, 264) = 16.55, p < .001. Significantly greater PCS symptomatology was expected in the MSI condition compared to the other conditions (MSI > VMI; medium effect, r = .33; MSI > MI; small-to-medium effect, r = .22). The same pattern of group differences was found for PTSD symptoms, F(2, 264) = 17.12, p < .001. Knowledge of mild TBI was not related to differences in expected PCS symptoms by condition; and the perceived undesirability of TBI was only associated with reported PCS symptomatology in the MSI condition. Systematic variation in the severity of a depicted TBI produces different PCS and PTSD symptom expectations. Even a very mild TBI vignette can elicit expectations of PCS symptoms.

On the statistical determination of optimal camera configurations in large scale surveillance networks

The selection of optimal camera configurations (camera locations, orientations etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we introduce a statistical formulation of the optimal selection of camera configurations as well as propose a Trans-Dimensional Simulated Annealing (TDSA) algorithm to effectively solve the problem. We compare our approach with a state-of-the-art method based on Binary Integer Programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than 2 alternative heuristics designed to deal with the scalability issue of BIP.

Comparative study of analytical rental model and statistical models for predicting house rental levels

The need for a house rental model in Townsville, Australia is addressed. Models developed for predicting house rental levels are described. An analytical model is built upon a priori selected variables and parameters of rental levels. Regression models are generated to provide a comparison to the analytical model. Issues in model development and performance evaluation are discussed. A comparison of the models indicates that the analytical model performs better than the regression models.

Improving the measurement and surveillance of child abuse in Queensland emergency departments

There are no population studies of prevalence or incidence of child maltreatment in Australia. Child protection data gives some understanding but is restricted by system capacity and definitional issues across jurisdictions. Child protection data currently suggests that numbers of reports are increasing yearly, and the child protection system then becomes focussed on investigating all reports and diluting available resources for those children who are most in need of intervention. A public health response across multiple agencies enables responses to child safety across the entire population. All families are targeted at the primary level; examples include ensuring all parents know the dangers of shaking a baby or teaching children to say no if a situation makes them uncomfortable. The secondary level of prevention targets families with a number of risk factors, for example subsidised child care so children aren't left unsupervised after school when both parents have to be at work or home visiting for drug-addicted parents to ensure children are cared for. The tertiary response then becomes the responsibility of the child protection system and is reserved for those children where abuse and neglect are identified. This model requires that child safety is seen in a broader context than just the child protection system, and increasingly health professionals are being identified as an important component in the public health framework. If all injury is viewed as preventable and considered along a continuum of 'accidental' through to 'inflicted', it becomes possible to conceptualise child maltreatment in an injury context. Parental intent may not be to cause harm to the child, but by lack of insight or concern about risk, the potential for injury is high. The mechanisms for unintentional and intentional injury overlap and some suggest that by segregating child abuse (with the possible exception of sexual abuse) from unintentional injury, child abuse is excluded from the broader injury prevention initiative that is gaining momentum in the community. This research uses a public health perspective, specifically that of injury prevention, to consider the problem of child abuse. This study employed a mixed method design that incorporates secondary data analysis, data linkage and structured interviews of different professional groups. Datasets from the Queensland Injury Surveillance Unit (QISU) and The Department of Child Safety (DCS) were evaluated. Coded injury data was grouped according to intent of injury according to those with a code that indicated the ED presentation was due to child abuse, a code indicating that the injury was possibly due to abuse or, in the third group, the intent code indicated that the injury was unintentional and not due to abuse. Primary data collection from ED records was undertaken and information recoded to assess reliability and completeness. Emergency department data (QISU) was linked to Department of Child Safety Data to examine concordance and data quality. Factors influencing the collection and collation of these data were identified through structured interview methodology and analysed using qualitative methods. Secondary analysis of QISU data indicated that codes lacking specific information on the injury event were more likely to also have an intent code indicating abuse than those records where there was specific information on the injury event. Codes for abuse appeared in only 1.2% of the 84,765 records analysed. Unintentional injury was the most commonly coded intent (95.3%). In the group with a definite abuse code assigned at triage, 83% linked to a record with DCS and cases where documentation indicated police involvement were significantly more likely to be associated with a DCS record than those without such documentation. In those coded with an unintentional injury code, 22% linked to a DCS record with cases assigned an urgent triage category more likely to link than those with a triage category for resuscitation and children who presented to regional or remote hospitals more likely to link to a DCS record than those presenting to urban hospitals. Twenty-nine per cent of cases with a code indicating possible abuse linked to a DCS record. In documentation that indicated police involvement in the case, a code for unspecified activity when compared to cases with a code indicating involvement in a sporting activity and children less than 12 months of age compared to those in the 13-17 year old age group were all variables significantly associated with linkage to a DCS record. Only 13% of records contained documentation indicating that child abuse and neglect were considered in the diagnosis of the injury despite almost half of the sample having a code of abuse or possible abuse. Doctors and nurses were confident in their knowledge of the process of reporting child maltreatment but less confident about identifying child abuse and neglect and what should be reported. Many were concerned about implications of reporting, for the child and family and for themselves. A number were concerned about the implications of not reporting, mostly for the wellbeing of the child and a few in terms of their legal obligations as mandatory reporters. The outcomes of this research will help improve the knowledge of barriers to effective surveillance of child abuse in emergency departments. This will, in turn, ensure better identification and reporting practises; more reliable official statistical collections and the potential of flagging high-risk cases to ensure adequate departmental responses have been initiated.

A Systematic review of a statistical vacuum circuit breaker model for condition monitoring

Vacuum circuit breaker (VCB) overvoltage failure and its catastrophic failures during shunt reactor switching have been analyzed through computer simulations for multiple reignitions with a statistical VCB model found in the literature. However, a systematic review (SR) that is related to the multiple reignitions with a statistical VCB model does not yet exist. Therefore, this paper aims to analyze and explore the multiple reignitions with a statistical VCB model. It examines the salient points, research gaps and limitations of the multiple reignition phenomenon to assist with future investigations following the SR search. Based on the SR results, seven issues and two approaches to enhance the current statistical VCB model are identified. These results will be useful as an input to improve the computer modeling accuracy as well as the development of a reignition switch model with point-on-wave controlled switching for condition monitoring

Internet usage trends in medical informatics

Technological growth in the 21st century is exponential. Simultaneously, development of the associated risk, uncertainty and user acceptance are scattered. This required appropriate study to establish people accepting controversial technology (PACT). The Internet and services around it, such as World Wide Web, e-mail, instant messaging and social networking are increasingly becoming important in many aspects of our lives. Information related to medical and personal health sharing using the Internet is controversial and demand validity, usability and acceptance. Whilst literature suggest, Internet enhances patients and physicians’ positive interactions some studies establish opposite of such interaction in particular the associated risk. In recent years Internet has attracted considerable attention as a means to improve health and health care delivery. However, it is not clear how widespread the use of Internet for health care really is or what impact it has on health care utilisation. Estimated impact of Internet usage varies widely from the locations locally and globally. As a result, an estimate (or predication) of Internet use and their effects in Medical Informatics related decision-making is impractical. This open up research issues on validating and accepting Internet usage when designing and developing appropriate policy and processes activities for Medical Informatics, Health Informatics and/or e-Health related protocols. Access and/or availability of data on Internet usage for Medical Informatics related activities are unfeasible. This paper presents a trend analysis of the growth of Internet usage in medical informatics related activities. In order to perform the analysis, data was extracted from ERA (Excellence Research in Australia) ranked “A” and “A*” Journal publications and reports from the authenticated public domain. The study is limited to the analyses of Internet usage trends in United States, Italy, France and Japan. Projected trends and their influence to the field of medical informatics is reviewed and discussed. The study clearly indicates a trend of patients becoming active consumers of health information rather than passive recipients.

Improving statistical analysis of matched case-control studies

Matched case–control research designs can be useful because matching can increase power due to reduced variability between subjects. However, inappropriate statistical analysis of matched data could result in a change in the strength of association between the dependent and independent variables or a change in the significance of the findings. We sought to ascertain whether matched case–control studies published in the nursing literature utilized appropriate statistical analyses. Of 41 articles identified that met the inclusion criteria, 31 (76%) used an inappropriate statistical test for comparing data derived from case subjects and their matched controls. In response to this finding, we developed an algorithm to support decision-making regarding statistical tests for matched case–control studies.

Post-disaster reconstruction: a statistical overview of the barriers for community participation in Afghanistan

When a community already torn by an event such as a prolonged war, is then hit by a natural disaster, the negative impact of this subsequent disaster in the longer term can be extremely devastating. Natural disasters further damage already destabilised and demoralised communities, making it much harder for them to be resilient and recover. Communities often face enormous challenges during the immediate recovery and the subsequent long term reconstruction periods, mainly due to the lack of a viable community involvement process. In post-war settings, affected communities, including those internally displaced, are often conceived as being completely disabled and are hardly ever consulted when reconstruction projects are being instigated. This lack of community involvement often leads to poor project planning, decreased community support, and an unsustainable completed project. The impact of war, coupled with the tensions created by the uninhabitable and poor housing provision, often hinders the affected residents from integrating permanently into their home communities. This paper outlines a number of fundamental factors that act as barriers to community participation related to natural disasters in post-war settings. The paper is based on a statistical analysis of, and findings from, a questionnaire survey administered in early 2012 in Afghanistan.

Sampling stored-product insect pests : a comparison of four statistical sampling models for probability of pest detection

Background: Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult owing to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution that is sufficiently flexible to capture variations in the spatial distribution of the target species. Results: Comparisons are made of the accuracy of four probability-of-detection sampling models - the negative binomial model,1 the Poisson model,1 the double logarithmic model2 and the compound model3 - for detection of insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, it is shown that, of the four models examined, the compound model performed the best over a broad range of insect spatial distributions and densities. In particular, this model predicted well the number of samples required when insect density was high and clumped within experimental storages. Conclusions: This paper reinforces the need for effective sampling programs designed to detect insects over a broad range of spatial distributions. The compound model is robust over a broad range of insect densities and leads to substantial improvement in detection probabilities within highly variable systems such as grain storage.

A statistical strategy to select optimal structural health monitoring data in operational modal analysis

Operational modal analysis (OMA) is prevalent in modal identifi cation of civil structures. It asks for response measurements of the underlying structure under ambient loads. A valid OMA method requires the excitation be white noise in time and space. Although there are numerous applications of OMA in the literature, few have investigated the statistical distribution of a measurement and the infl uence of such randomness to modal identifi cation. This research has attempted modifi ed kurtosis to evaluate the statistical distribution of raw measurement data. In addition, a windowing strategy employing this index has been proposed to select quality datasets. In order to demonstrate how the data selection strategy works, the ambient vibration measurements of a laboratory bridge model and a real cable-stayed bridge have been respectively considered. The analysis incorporated with frequency domain decomposition (FDD) as the target OMA approach for modal identifi cation. The modal identifi cation results using the data segments with different randomness have been compared. The discrepancy in FDD spectra of the results indicates that, in order to fulfi l the assumption of an OMA method, special care shall be taken in processing a long vibration measurement data. The proposed data selection strategy is easy-to-apply and verifi ed effective in modal analysis.

Development and use of risk adjusted statistical process control tools for the monitoring and improvement of clinical outcomes in interventional cardiology

This thesis explored the development of statistical methods to support the monitoring and improvement in quality of treatment delivered to patients undergoing coronary angioplasty procedures. To achieve this goal, a suite of outcome measures was identified to characterise performance of the service, statistical tools were developed to monitor the various indicators and measures to strengthen governance processes were implemented and validated. Although this work focused on pursuit of these aims in the context of a an angioplasty service located at a single clinical site, development of the tools and techniques was undertaken mindful of the potential application to other clinical specialties and a wider, potentially national, scope.

Improving the statistical preparation for measuring soil N2O flux by closed chamber

Nitrous oxide emissions from soil are known to be spatially and temporally volatile. Reliable estimation of emissions over a given time and space depends on measuring with sufficient intensity but deciding on the number of measuring stations and the frequency of observation can be vexing. The question of low frequency manual observations providing comparable results to high frequency automated sampling also arises. Data collected from a replicated field experiment was intensively studied with the intention to give some statistically robust guidance on these issues. The experiment had nitrous oxide soil to air flux monitored within 10 m by 2.5 m plots by automated closed chambers under a 3 h average sampling interval and by manual static chambers under a three day average sampling interval over sixty days. Observed trends in flux over time by the static chambers were mostly within the auto chamber bounds of experimental error. Cumulated nitrous oxide emissions as measured by each system were also within error bounds. Under the temporal response pattern in this experiment, no significant loss of information was observed after culling the data to simulate results under various low frequency scenarios. Within the confines of this experiment observations from the manual chambers were not spatially correlated above distances of 1 m. Statistical power was therefore found to improve due to increased replicates per treatment or chambers per replicate. Careful after action review of experimental data can deliver savings for future work.

Young children's statistical reasoning : a tale of two contexts

This thesis explored the knowledge and reasoning of young children in solving novel statistical problems, and the influence of problem context and design on their solutions. It found that young children's statistical competencies are underestimated, and that problem design and context facilitated children's application of a wide range of knowledge and reasoning skills, none of which had been taught. A qualitative design-based research method, informed by the Models and Modeling perspective (Lesh & Doerr, 2003) underpinned the study. Data modelling activities incorporating picture story books were used to contextualise the problems. Children applied real-world understanding to problem solving, including attribute identification, categorisation and classification skills. Intuitive and metarepresentational knowledge together with inductive and probabilistic reasoning was used to make sense of data, and beginning awareness of statistical variation and informal inference was visible.