Longitudinal analysis of ear infection and hearing impairment: findings from 6-year prospective cohorts of Australian children

BACKGROUND Middle ear infection is common in childhood. Despite its prevalence, there is little longitudinal evidence about the impact of ear infection, particularly its association to hearing loss. By using 6-year prospective data, we investigate the onset and impact over time of ear infection in Australian children. METHODS We analyse 4 waves of the Longitudinal Study of Australian Children (LSAC) survey collected in 2004, 2006, 2008, and 2010. There are two age cohorts in this study (B cohort aged 0/1 to 6/7 years N=4242 and K cohort aged 4/5 to 10/11 years N=4169). Exposure was parent-reported ear infection and outcome was parent-reported hearing problems. We modelled ear infection onset and subsequent impact on hearing using multivariate logistic regressions, reporting Adjusted Odds Ratios (AOR) and Confidence Intervals (95% CI). Separate analyses were reported for indigenous and non-indigenous children. RESULTS Associations of ear infections between waves were found to be very strong both among both indigenous and non-indigenous children in the two cohorts. Reported ear infections at earlier wave were also associated with hearing problems in subsequent wave. For example, reported ear infections at age 4/5 years among the K cohort were found to be predictors of hearing problems at age 8/9 years (AOR 4.0, 95% CI 2.2-7.3 among non-indigenous children and AOR 7.7 95% CI 1.0-59.4 among indigenous children). Number of repeated ear infections during the 6-year follow-up revealed strong dose-response relationships with subsequent hearing problems among non-indigenous children (AORs ranged from 4.4 to 31.7 in the B cohort and 4.4 to 51.0 in the K cohort) but not statistically significant among indigenous children partly due to small sample. CONCLUSIONS This study revealed the longitudinal impact of ear infections on hearing problems in both indigenous and non-indigenous children. These findings highlight the need for special attention and follow-up on children with repeated ear infections.This study is supported by an unconditional grant from the GlaxoSmithKline. We used confidentialised unit record from Growing Up in Australia – the Longitudinal Study of Australian Children (LSAC); a partnership between the Australian Government Department of Families, Housing, Community Services, and Indigenous Affairs (FaHCSIA), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the authors and should not be attributed to FaHCSIA, AIFS or the ABS

Emotional Work: A Psychological View

At work and in the family, people do emotional work to meet other people's emotional needs, improve their wellbeing, and maintain social harmony. Emotional work is unique and skilled work - it involves handling emotions and social relationships and its product is the change of feeling in others. ¶ The thesis extends the work of Erickson and Wharton (1993, 1997) and England (1992, England & Farkas, 1986) by adding a psychological perspective. Emotional work is defined in terms of behaviours. Three dimensions, companionship, help and regulation, distinguish whether positive or negative emotions in other people are the target of emotional work. Companionship builds positive emotions, whereas help and regulation repairs and regulates negative emotions. ¶ Two studies, the Public Service Study (n=448) and the Health Care Study (n=261), sample different work and family role contexts (spouse, parent, kinkeeper and friendship, manager, workmate and service roles). The Integrative Emotional Work (IEW) Inventory was developed to assess emotional work in these roles. ¶ ..

CycleCounter: an Efficient and Accurate UltraSPARC III CPU Simulation Module

This paper presents a novel technique for cycle-accurate simulation of the Central Processing Unit (CPU) of a modern superscalar processor, the UltraSPARC III Cu processor. The technique is based on adding a module to an existing fetch-decode-execute style of CPU simulator, rather than the traditional method of fully implementing the CPU pipeline and microarchitecture. The main functions of the module are the simulation of instruction grouping, register interlocks and the store buffer, and has a simple table-driven implementation which permits easy modification for exploring microarchitectural variations. The technique results on a 15--30\% loss of simulation speed, instead of a 10 $\times$ or greater performance loss by fully implementing the detailed micro-architecture. The accuracy of the technique is validated against an actual UltraSPARC III Cu processor, and achieves high levels of accuracy in cases of interest

Bundled Payment Initiative

In a typical payment model, a health plan makes a separate payment for each service rendered by a hospital or physician. Especially for joint replacements, this approach can result in uncoordinated care across hospitals and providers, rewarding the quantity of services rendered rather than the quality. The Bundled Payment Initiative will provide hospitals the financial opportunity to coordinate care across specialties and settings in order to provide the member a better episode of care. In this project, the health plan will provide a single bundled payment per member to the hospital that would cover all services and procedures rendered by the hospital, physicians, and other health care providers during the member’s inpatient stay as well as related services up to a determined period post-discharge. The desired outcome of this care model is to incentivize hospitals to provide more coordinated, higher quality care resulting in better health outcomes for our members

Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac

Accelerated methods for performing the LDLT decomposition

This paper describes the design, implementation and performance of parallel direct dense symmetric-indefinite matrix factorisation algorithms. These algorithms use the Bunch-Kaufman diagonal pivoting method. The starting point is numerically identical to LAPACK _sytrf() algorithm, but out-performs zsytrf() by approximately 15% for large matrices on the UltraSPARC family of processors. The first variant reduces symmetric interchanges, particularly important for parallel implementation, by taking into account the growth attained by any preceding columns that did not require any interchanges. However, it achieves the same growth bound. The second variant uses a lookahead technique with heuristic methods to predict whether interchanges are required over the next block column; if so, the block column can be eliminated using modified Cholesky methods, which can yield both computational and communication advantages. These algorithms yield best performance gains on `weakly indefinite' matrices (i.e. those which have generally large diagonal elements), which often arise from electro-magnetic field analysis applications. On UltraSPARC processors, the first variant generally achieves a 1--2% performance gain; the second is faster still for large matrices by 2% for complex double precision and 6% for double precision. However, larger performance gains are observed on distributed memory machines, where symmetric interchanges are relatively more expensive. On a 16 node 300MHz UltraSPARC-based Fujitsu AP3000, the first variant achieved a 10-15% improvement for small-moderate sized matrices, decreasing to 7% for large matrices. For N=10000 , it achieved a sustained speed of 5.6GFLOPs and a parallel speedup of 12.8

Socioeconomic disadvantage and onset of childhood chronic disabling conditions: a cohort study

OBJECTIVE To study the temporal relationship between socioeconomic disadvantage and onset of chronic disabling conditions in childhood. METHOD Using parent reported data from the Longitudinal Study of Australian Children, we compared children who developed a chronic disabling condition between the ages of 6/7 and 10/11 years with children without a chronic disabling condition at either age. Logistic regression models assessed association between onset of chronic disabling condition and household income quintiles at 6/7 years, adjusting for confounders. To study the consequences of chronic disabling condition onset for family finances, a linear regression model was fitted on change in household income adjusted for income at 6/7. We compared prevalence of family material hardship in the two groups between 6/7 and 10/11. RESULTS Of 4010 children present in both waves, complete data were available for 3629 of whom 233 (6.4%) developed a chronic disabling condition between 6/7 and 10/11. After adjustment for confounding, the children from the lowest income quintile were more than twice as likely to develop a chronic disabling condition as those from the highest income quintile. Onset of a chronic disabling condition was associated with a relatively smaller increase in household income over time, but no change in hardship prevalence. CONCLUSIONS Family socioeconomic disadvantage when children are aged 6/7 is associated with their development of a chronic disabling condition over the next 4 years and with adverse effects on household income.Lyndall Strazdins is supported by an Australian Research Council Future Fellowship FT110100686

An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach