Household Hardships, Public Programs, and Their Associations with the Health and Development of Very Young Children: Insights from Children’s HealthWatch

America’s low-income families struggle to protect their children from multiple threats to their health and growth. Many research and advocacy groups explore the health and educational effects of food insecurity, but less is known about these effects on very young children. Children’s HealthWatch, a group of pediatric clinicians and public health researchers, has continuously collected data on the effects of food insecurity alone and in conjunction with other household hardships since 1998. The group’s peer reviewed research has shown that a number of economic risks at the household level, including food, housing and energy insecurity, tend to be correlated. These insecurities alone or in conjunction increase the risk that a young child will suffer various negative health consequences, including increases in lifetime hospitalizations, parental report of fair or poor health,1 or risk for developmental delays.2 Child food insecurity is an incremental risk indicator above and beyond the risk imposed by household-level food insecurity. The Children’sHealthwatch research also suggests public benefits programs modify some of these effects for families experiencing hardships. This empirical evidence is presented in a variety of public venues outside the usual scientific settings, such as congressional hearings, to support the needs of America’s most vulnerable population through policy change. Children’s HealthWatch research supports legislative solutions to food insecurity, including sustained funding for public programs and re-evaluation of the use of the Thrifty Food Plan as the basis of SNAP benefits calculations. Children’s HealthWatch is one of many models to support the American Academy of Pediatrics’ call to “stand up, speak up, and step up for children.”3 No isolated group or single intervention will solve child poverty or multiple hardships. However, working collaboratively each group has a role to play in supporting the health and well-being of young children and their families. 1. Cook JT, Frank DA, Berkowitz C, et al. Food insecurity is associated with adverse health outcomes among human infants and toddlers. J Nutr. 2004;134:1432-1438. 2. Rose-Jacobs R, Black MM, Casey PH, et al. Household food insecurity: associations with at-risk infant and toddler development. Pediatrics. 2008;121:65-72. 3. AAP leader says to stand up, speak up, and step up for child health [news release]. Boston, MA: American Academy of Pediatrics; October 11, 2008. http://www2.aap.org/pressroom/nce/nce08childhealth.htm. Accessed January 1, 2012.

Establishing occupational and environmental health design requirements for Lunar and Mars settlements

In a study of Lunar and Mars settlement concepts, an analysis was made of fundamental design assumptions in five technical areas against a model list of occupational and environmental health concerns. The technical areas included the proposed science projects to be supported, habitat and construction issues, closed ecosystem issues, the "MMM" issues--mining, material-processing, and manufacturing, and the human elements of physiology, behavior and mission approach. Four major lessons were learned. First it is possible to relate public health concerns to complex technological development in a proactive design mode, which has the potential for long-term cost savings. Second, it became very apparent that prior to committing any nation or international group to spending the billions to start and complete a lunar settlement, over the next century, that a significantly different approach must be taken from those previously proposed, to solve the closed ecosystem and "MMM" problems. Third, it also appears that the health concerns and technology issues to be addressed for human exploration into space are fundamentally those to be solved for human habitation of the earth (as a closed ecosystem) in the 21st century. Finally, it is proposed that ecosystem design modeling must develop new tools, based on probabilistic models as a step up from closed circuit models. ^

Mouse models and quantitative trait loci of susceptibility to bleomycin- and radiation-induced pulmonary fibrosis

Radiotherapy involving the thoracic cavity and chemotherapy with the drug bleomycin are both dose limited by the development of pulmonary fibrosis. From evidence that there is variation in the population in susceptibility to pulmonary fibrosis, and animal data, it was hypothesized that individual variation in susceptibility to bleomycin-induced, or radiation-induced, pulmonary fibrosis is, in part, genetically controlled. In this thesis a three generation mouse genetic model of C57BL/6J (fibrosis prone) and C3Hf/Kam (fibrosis resistant) mouse strains and F1 and F2 (F1 intercross) progeny derived from the parental strains was developed to investigate the genetic basis of susceptibility to fibrosis. In the bleomycin studies the mice received 100 mg/kg (125 for females) of bleomycin, via mini osmotic pump. The animals were sacrificed at eight weeks following treatment or when their breathing rate indicated respiratory distress. In the radiation studies the mice were given a single dose of 14 or 16 Gy (Co$\sp{60})$ to the whole thorax and were sacrificed when moribund. The phenotype was defined as the percent of fibrosis area in the left lung as quantified with image analysis of histological sections. Quantitative trait loci (QTL) mapping was used to identify the chromosomal location of genes which contribute to susceptibility to bleomycin-induced pulmonary fibrosis in C57BL/6J mice compared to C3Hf/Kam mice and to determine if the QTL's which influence susceptibility to bleomycin-induced lung fibrosis in these progenitor strains could be implicated in susceptibility to radiation-induced lung fibrosis. For bleomycin, a genome wide scan revealed QTL's on chromosome 17, at the MHC, (LOD = 11.7 for males and 7.2 for females) accounting for approximately 21% of the phenotypic variance, and on chromosome 11 (LOD = 4.9), in male mice only, adding 8% of phenotypic variance. The bleomycin QTL on chromosome 17 was also implicated for susceptibility to radiation-induced fibrosis (LOD = 5.0) and contributes 7% of the phenotypic variance in the radiation study. In conclusion, susceptibility to both bleomycin-induced and radiation-induced pulmonary fibrosis are heritable traits, and are influenced by a genetic factor which maps to a genomic region containing the MHC. ^

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Evaluation of 9 biomarkers for predicting 10-year cardiovascular risk in patients undergoing coronary angiography: findings from the LUdwigshafen RIsk and Cardiovascular Health (LURIC) study.

BACKGROUND Conventional factors do not fully explain the distribution of cardiovascular outcomes. Biomarkers are known to participate in well-established pathways associated with cardiovascular disease, and may therefore provide further information over and above conventional risk factors. This study sought to determine whether individual and/or combined assessment of 9 biomarkers improved discrimination, calibration and reclassification of cardiovascular mortality. METHODS 3267 patients (2283 men), aged 18-95 years, at intermediate-to-high-risk of cardiovascular disease were followed in this prospective cohort study. Conventional risk factors and biomarkers were included based on forward and backward Cox proportional stepwise selection models. RESULTS During 10-years of follow-up, 546 fatal cardiovascular events occurred. Four biomarkers (interleukin-6, neutrophils, von Willebrand factor, and 25-hydroxyvitamin D) were retained during stepwise selection procedures for subsequent analyses. Simultaneous inclusion of these biomarkers significantly improved discrimination as measured by the C-index (0.78, P = 0.0001), and integrated discrimination improvement (0.0219, P<0.0001). Collectively, these biomarkers improved net reclassification for cardiovascular death by 10.6% (P<0.0001) when added to the conventional risk model. CONCLUSIONS In terms of adverse cardiovascular prognosis, a biomarker panel consisting of interleukin-6, neutrophils, von Willebrand factor, and 25-hydroxyvitamin D offered significant incremental value beyond that conveyed by simple conventional risk factors.

Finite element analyses of human vertebral bodies embedded in polymethylmethalcrylate or loaded via the hyperelastic intervertebral disc models provide equivalent predictions of experimental strength

Quantitative computer tomography (QCT)-based finite element (FE) models of vertebral body provide better prediction of vertebral strength than dual energy X-ray absorptiometry. However, most models were validated against compression of vertebral bodies with endplates embedded in polymethylmethalcrylate (PMMA). Yet, loading being as important as bone density, the absence of intervertebral disc (IVD) affects the strength. Accordingly, the aim was to assess the strength predictions of the classic FE models (vertebral body embedded) against the in vitro and in silico strengths of vertebral bodies loaded via IVDs. High resolution peripheral QCT (HR-pQCT) were performed on 13 segments (T11/T12/L1). T11 and L1 were augmented with PMMA and the samples were tested under a 4° wedge compression until failure of T12. Specimen-specific model was generated for each T12 from the HR-pQCT data. Two FE sets were created: FE-PMMA refers to the classical vertebral body embedded model under axial compression; FE-IVD to their loading via hyperelastic IVD model under the wedge compression as conducted experimentally. Results showed that FE-PMMA models overestimated the experimental strength and their strength prediction was satisfactory considering the different experimental set-up. On the other hand, the FE-IVD models did not prove significantly better (Exp/FE-PMMA: R²=0.68; Exp/FE-IVD: R²=0.71, p=0.84). In conclusion, FE-PMMA correlates well with in vitro strength of human vertebral bodies loaded via real IVDs and FE-IVD with hyperelastic IVDs do not significantly improve this correlation. Therefore, it seems not worth adding the IVDs to vertebral body models until fully validated patient-specific IVD models become available.

The Effectiveness of Alexander Technique on Music Performance and Musicians' Health and Well-being – a Systematic Review

Purpose Musicians often suffer injuries related to their music playing. Therefore, some use the Alexander Technique (AT), a psycho-physical method that helps to release unnecessary muscle tension and re-educates non-beneficial movement patterns through enhanced kinaesthetic awareness. According to a recent review AT may be effective for chronic back pain. This review aimed to evaluate the evidence for the effectiveness of AT lessons on music performance and musicians’ health and well-being. Methods The following electronic databases were searched up to July 2012 for relevant literature: PUBMED, Google Scholar, CINAHL and EMBASE. The search criteria were "Alexander technique" AND "music*" [all fields]. References were searched, and experts and societies of AT or musicians' medicine contacted for further publications. Results 100 studies were identified. 35 studies were included for further analysis, 5 of which were randomised controlled trials (RCTs), 5 controlled but not randomised, 5 not controlled, 5 qualitative case studies, 2 mixed-models (RCT and case studies), 7 surveys, 4 qualitative case reports and 2 unpublished pilot studies. 13 to 72 musicians participated per RCT. In 5 RCTs AT groups received between 12 and 20 one-to-one lessons. In 4 RCTs control groups received no interventions. Primary outcomes were performance anxiety, performance, "use" as well as respiratory function and pain. Performance anxiety decreased by AT in 3 of 4 RCTs. Music performance was improved by AT in 1 RCT, yet in 2 RCTs comparing neurofeedback (NF) to AT, only NF showed improvements. Conclusions To investigate the effectiveness of AT in musicians a variety of study designs and outcome measures have been used. Evidence from RCTs suggests that AT may improve performance anxiety in musicians. Effects on music performance, body use and respiratory function yet remain inconsistent. Future trials with well-established study designs are warranted to further and more reliably explore the potential of AT as a relatively low cost and low risk method in the interest of musicians.

Apoptosis of hippocampal neurons in organotypic slice culture models: direct effect of bacteria revisited

Neurons of the hippocampal dentate gyrus selectively undergo programmed cell death in patients suffering from bacterial meningitis and in experimental models of pneumococcal meningitis in infant rats. In the present study, a membrane-based organotypic slice culture system of rat hippocampus was used to test whether this selective vulnerability of neurons of the dentate gyrus could be reproduced in vitro. Apoptosis was assessed by nuclear morphology (condensed and fragmented nuclei), by immunochemistry for active caspase-3 and deltaC-APP, and by proteolytic caspase-3 activity. Co-incubation of the cultures with live pneumococci did not induce neuronal apoptosis unless cultures were kept in partially nutrient-deprived medium. Complete nutrient deprivation alone and staurosporine independently induced significant apoptosis, the latter in a dose-response way. In all experimental settings, apoptosis occurred preferentially in the dentate gyrus. Our data demonstrate that factors released by pneumococci per se failed to induce significant apoptosis in vitro. Thus, these factors appear to contribute to a multifactorial pathway, which ultimately leads to neuronal apoptosis in bacterial meningitis.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Clinical versus pre-clinical FE models for vertebral body strength predictions

The finite element analysis is an accepted method to predict vertebral body compressive strength. This study compares measurements obtained from in vitro tests with the ones from two different simulation models: clinical quantitative computer tomography (QCT) based homogenized finite element (hFE) models and pre-clinical high-resolution peripheral QCT-based (HR-pQCT) hFE models. About 37 vertebral body sections were prepared by removing end-plates and posterior elements, scanned with QCT (390/450μm voxel size) as well as HR-pQCT (82μm voxel size), and tested in compression up to failure. Non-linear viscous damage hFE models were created from QCT/HT-pQCT images and compared to experimental results based on stiffness and ultimate load. As expected, the predictability of QCT/HR-pQCT-based hFE models for both apparent stiffness (r2=0.685/0.801r2=0.685/0.801) and strength (r2=0.774/0.924r2=0.774/0.924) increased if a better image resolution was used. An analysis of the damage distribution showed similar damage locations for all cases. In conclusion, HR-pQCT-based hFE models increased the predictability considerably and do not need any tuning of input parameters. In contrast, QCT-based hFE models usually need some tuning but are clinically the only possible choice at the moment.