Severe lower respiratory tract infection in infants and toddlers from a non-affluent population: viral etiology and co-detection as risk factors

Abstract Background Lower respiratory tract infection (LRTI) is a major cause of pediatric morbidity and mortality, especially among non-affluent communities. In this study we determine the impact of respiratory viruses and how viral co-detections/infections can affect clinical LRTI severity in children in a hospital setting. Methods Patients younger than 3 years of age admitted to a tertiary hospital in Brazil during the months of high prevalence of respiratory viruses had samples collected from nasopharyngeal aspiration. These samples were tested for 13 different respiratory viruses through real-time PCR (rt-PCR). Patients were followed during hospitalization, and clinical data and population characteristics were collected during that period and at discharge to evaluate severity markers, especially length of hospital stay and oxygen use. Univariate regression analyses identified potential risk factors and multivariate logistic regressions were used to determine the impact of specific viral detections as well as viral co-detections in relation to clinical outcomes. Results We analyzed 260 episodes of LRTI with a viral detection rate of 85% (n = 222). Co-detection was observed in 65% of all virus-positive episodes. The most prevalent virus was Respiratory Syncytial Virus (RSV) (54%), followed by Human Metapneumovirus (hMPV) (32%) and Human Rhinovirus (HRV) (21%). In the multivariate models, infants with co-detection of HRV + RSV stayed 4.5 extra days (p = 0.004), when compared to infants without the co-detection. The same trends were observed for the outcome of days of supplemental oxygen use. Conclusions Although RSV remains as the main cause of LRTI in infants our study indicates an increase in the length of hospital stay and oxygen use in infants with HRV detected by RT-PCR compared to those without HRV. Moreover, one can speculate that when HRV is detected simultaneously with RSV there is an additive effect that may be reflected in more severe clinical outcome. Also, our study identified a significant number of children infected by recently identified viruses, such as hMPV and Human Bocavirus (HBov), and this is a novel finding for poor communities from developing countries.

Irradiated disks and planet population synthesis

Recent planet population synthesis models (Alibert et al. 2010, submitted) have emphasized the key role played by the proto-planetary disk properties in determining the overall planet population characteristics. We present a disk model that takes into account viscous heating and irradiation by a central star. We consider the case of an equilibrium flaring angle. We illustrate the consequences of the resulting changes in the disk structure on the planet population by the synthetic populations corresponding to each of the different structures.

A categorical data analysis approach to estimation of demographic characteristics for Texas county populations

The need for timely population data for health planning and Indicators of need has Increased the demand for population estimates. The data required to produce estimates is difficult to obtain and the process is time consuming. Estimation methods that require less effort and fewer data are needed. The structure preserving estimator (SPREE) is a promising technique not previously used to estimate county population characteristics. This study first uses traditional regression estimation techniques to produce estimates of county population totals. Then the structure preserving estimator, using the results produced in the first phase as constraints, is evaluated.^ Regression methods are among the most frequently used demographic methods for estimating populations. These methods use symptomatic indicators to predict population change. This research evaluates three regression methods to determine which will produce the best estimates based on the 1970 to 1980 indicators of population change. Strategies for stratifying data to improve the ability of the methods to predict change were tested. Difference-correlation using PMSA strata produced the equation which fit the data the best. Regression diagnostics were used to evaluate the residuals.^ The second phase of this study is to evaluate use of the structure preserving estimator in making estimates of population characteristics. The SPREE estimation approach uses existing data (the association structure) to establish the relationship between the variable of interest and the associated variable(s) at the county level. Marginals at the state level (the allocation structure) supply the current relationship between the variables. The full allocation structure model uses current estimates of county population totals to limit the magnitude of county estimates. The limited full allocation structure model has no constraints on county size. The 1970 county census age - gender population provides the association structure, the allocation structure is the 1980 state age - gender distribution.^ The full allocation model produces good estimates of the 1980 county age - gender populations. An unanticipated finding of this research is that the limited full allocation model produces estimates of county population totals that are superior to those produced by the regression methods. The full allocation model is used to produce estimates of 1986 county population characteristics. ^

1970 census of population and housing; evaluation and research program. The medicare record check: an evaluation of the coverage of persons 65 years of age and over in the 1970 census.

"PHC(E)-7."

1980 census of population.

Issued June-November 1981.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Reliability prediction using the non-parametric explicit hazard model : a case study

Survival probability prediction using covariate-based hazard approach is a known statistical methodology in engineering asset health management. We have previously reported the semi-parametric Explicit Hazard Model (EHM) which incorporates three types of information: population characteristics; condition indicators; and operating environment indicators for hazard prediction. This model assumes the baseline hazard has the form of the Weibull distribution. To avoid this assumption, this paper presents the non-parametric EHM which is a distribution-free covariate-based hazard model. In this paper, an application of the non-parametric EHM is demonstrated via a case study. In this case study, survival probabilities of a set of resistance elements using the non-parametric EHM are compared with the Weibull proportional hazard model and traditional Weibull model. The results show that the non-parametric EHM can effectively predict asset life using the condition indicator, operating environment indicator, and failure history.

Asset health prediction using condition indicators and operating environment indicators : a case study

Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.

Cardiac ARIA Index: Measuring Accessibility to Cardiovascular Services in Rural and Remote Australia via Applied Geographic Spatial Technology

Cardiovascular disease (CVD) continues to impose a heavy burden in terms of cost, disability and death in Australia. Evidence suggests that increasing remoteness, where cardiac services are scarce, is linked to an increased risk of dying from CVD. Fatal CVD events are reported to be between 20% and 50% higher in rural areas compared to major cities. The Cardiac ARIA project, with its extensive use of geographic Information Systems (GIS), ranks each of Australia’s 20,387 urban, rural and remote population centres by accessibility to essential services or resources for the management of a cardiac event. This unique, innovative and highly collaborative project delivers a powerful tool to highlight and combat the burden imposed by cardiovascular disease (CVD) in Australia. Cardiac ARIA is innovative. It is a model that could be applied internationally and to other acute and chronic conditions such as mental health, midwifery, cancer, respiratory, diabetes and burns services. Cardiac ARIA was designed to: 1. Determine by expert panel, what were the minimal services and resources required for the management of a cardiac event in any urban, rural or remote population locations in Australia using a single patient pathway to access care. 2. Derive a classification using GIS accessibility modelling for each of Australia’s 20,387 urban, rural and remote population locations. 3. Compare the Cardiac ARIA categories and population locations with census derived population characteristics. Key findings are as follows: • In the event of a cardiac emergency, the majority of Australians had very good access to cardiac services. Approximately 71% or 13.9 million people lived within one hour of a category one hospital. • 68% of older Australians lived within one hour of a category one hospital (Principal Referral Hospital with access to Cardiac Catheterisation). • Only 40% of indigenous people lived within one hour of the category one hospital. • 16% (74000) of indigenous people lived more than one hour from a hospital. • 3% (91,000) of people 65 years of age or older lived more than one hour from any hospital or clinic. • Approximately 96%, or 19 million, of people lived within one hour of the four key services to support cardiac rehabilitation and secondary prevention. • 75% of indigenous people lived within one hour of the four cardiac rehabilitation services to support cardiac rehabilitation and secondary prevention. Fourteen percent (64,000 persons) indigenous people had poor access to the four key services to support cardiac rehabilitation and secondary prevention. • 12% (56,000) of indigenous people were more than one hour from a hospital and only had access one the four key services (usually a medical service) to support cardiac rehabilitation and secondary prevention.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Not all systematic reviews are systematic : a meta-review of the quality of current systematic reviews and meta-analyses for remote monitoring in heart failure

Background/aims: Remote monitoring for heart failure has not only been evaluated in a large number of randomised controlled trials, but also in many systematic reviews and meta-analyses. The aim of this meta-review was to identify, appraise and synthesise existing systematic reviews that have evaluated the effects of remote monitoring in heart failure. Methods: Using a Cochrane methodology, we electronically searched all relevant online databases and search engines, performed a forward citation search as well as hand-searched bibliographies. Only fully published systematic reviews of invasive and/or non-invasive remote monitoring interventions were included. Two reviewers independently extracted data. Results: Sixty-five publications from 3333 citations were identified. Seventeen fulfilled the inclusion and exclusion criteria. Quality varied with A Measurement Tool to Assess Systematic Reviews (AMSTAR scores) ranging from 2 to 11 (mean 5.88). Seven reviews (41%) pooled results from individual studies for meta-analysis. Eight (47%) considered all non-invasive remote monitoring strategies. Four (24%) focused specifically on telemonitoring. Four (24%) included studies investigating both non-invasive and invasive technologies. Population characteristics of the included studies were not reported consistently. Mortality and hospitalisations were the most frequently reported outcomes 12 (70%). Only five reviews (29%) reported healthcare costs and compliance. A high degree of heterogeneity was reported in many of the meta-analyses. Conclusions: These results should be considered in context of two negative RCTs of remote monitoring for heart failure that have been published since the meta-analyses (TIM-HF and Tele-HF). However, high quality reviews demonstrated improved mortality, quality of life, reduction in hospitalisations and healthcare costs.

The regional distribution of anxiety disorders : implications for the Global Burden of Disease Study, 2010

Anxiety disorders are increasingly acknowledged as a global health issue however an accurate picture of prevalence across populations is lacking. Empirical data are incomplete and inconsistent so alternate means of estimating prevalence are required to inform estimates for the new Global Burden of Disease Study 2010. We used a Bayesian meta-regression approach which included empirical epidemiological data, expert prior information, study covariates and population characteristics. Reported are global and regional point prevalence for anxiety disorders in 2010. Point prevalence of anxiety disorders differed by up to three-fold across world regions, ranging between 2.1% (1.8-2.5%) in East Asia and 6.1% (5.1-7.4%) in North Africa/Middle East. Anxiety was more common in Latin America; high income regions; and regions with a history of recent conflict. There was considerable uncertainty around estimates, particularly for regions where no data were available. Future research is required to examine whether variations in regional distributions of anxiety disorders are substantive differences or an artefact of cultural or methodological differences. This is a particular imperative where anxiety is consistently reported to be less common, and where it appears to be elevated, but uncertainty prevents the reporting of conclusive estimates.

A 3-hour quantitative comparison of glucose-based versus rice-based oral rehydration solution intake by children with diarrhoea in Port Moresby General Hospital

Measurements were made of the intake of a WHO/UNICEF glucose-based and a rice cereal-based oral rehydration solution (ORS) by children with diarrhoea. Twenty children who presented to the Children's Outpatient Department at Port Moresby General Hospital with acute diarrhoea and mild dehydration were randomly assigned to an ORS and measurements were taken over the following 3 hours. For data analysis, the patients were paired by weight. Testing the means of the paired samples by t test showed that there was no significant difference between the amount of rice ORS and the amount of glucose ORS taken over 3 hours. The discovery of oral rehydration solution (ORS) for the treatment of diarrheal disease has been heralded as the most important medical discovery of the century. Cereal-based ORS is able to decrease stool output and the duration of diarrheal illness more than the standard glucose-based ORS, through the increased absorption provided by oligosaccharides without the imposition of a greater osmotic penalty. Moreover, the peptides in cereals enhance amino acid and water absorption, while providing nutritional benefits. UNICEF's glucose-based ORS is becoming more widely used in Papua New Guinea (PNG). 20 children aged 6-37 months (mean age, 15 months) who presented to the Children's Outpatient Department at Port Moresby General Hospital during September-October 1993 with acute diarrhea and mild dehydration were randomly assigned to receive either a rice-based ORS or standard glucose ORS, and measurements were taken over the following 3 hours. The patients were paired by weight for analysis. No statistically significant difference was found between the amount of rice ORS and the amount of glucose ORS taken over 3 hours.

Acceptability of a rice-based oral rehydration solution in Port Moresby General Hospital's Children's Outpatient Department

The guardians of children brought to the Port Moresby General Hospital's Children's Outpatient Department with a chief complaint of diarrhoeal disease were questioned regarding their preference of glucose-based vs rice-based oral rehydration solution (ORS) in order to determine the acceptability of a rice-based ORS. Of the 93 guardians interviewed, greater than 60% preferred the glucose-based solution in its mixability, appearance and taste, and 65% initially reported that their children preferred the taste of the glucose solution. However, after a 30-minute trial, only 58% of children still preferred the glucose solution. In a country where diarrhoeal disease is a leading cause of child death and guardians are the primary health care providers, the acceptability of an ORS is critical to the morbidity and mortality of Papua New Guinea's children. Killing an estimated 2.9 million children annually, diarrheal disease is the second leading cause of child mortality worldwide. Diarrheal disease is also the second leading cause of child mortality in Papua New Guinea (PNG), killing an average 193 inpatient children per year over the period 1984-90. However, despite the high level of diarrhea-related mortality and the proven efficacy of oral rehydration therapy (ORT) in managing diarrhea-related dehydration, standardized ORT has been underutilized in PNG. The current glucose-based oral rehydration solution (ORS) does not reduce the frequency or volume of a child's diarrhea, the most immediate concern of caregivers during episodes of illness. Cereal-based ORS, made from cereals which are commonly available as food staples in most countries, better address the short-term concerns of caregivers while offering a superior nutritional profile. A sample of guardians of children brought to the Port Moresby General Hospital's Children's Outpatient Department complaining of child diarrhea were asked about their preferences on glucose-based versus rice-based ORS in order to determine the acceptability of a rice-based ORS. More than 60% of the 93 guardians interviewed preferred the glucose-based solution for its mixability, appearance, and taste. 65% initially reported that their children preferred the taste of the glucose solution. However, after a 30-minute trial, only 58% of children still preferred the glucose solution.