Alternative regression models to assess increase in childhood BMI

<p>Abstract</p> <p>Background</p> <p>Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations.</p> <p>Methods</p> <p>Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity.</p> <p>Results</p> <p>GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models.</p> <p>Conclusion</p> <p>GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.</p

AIDS-Related Tuberculosis in Rio de Janeiro, Brazil

BACKGROUND: We studied the incidence of tuberculosis, AIDS, AIDS deaths and AIDS-TB co-infection at the population level in Rio de Janeiro, Brazil where universal and free access to combination antiretroviral therapy has been available since 1997. METHODOLOGY/PRINCIPAL FINDINGS: This was a retrospective surveillance database match of Rio de Janeiro databases from 1995-2004. Proportions of tuberculosis occurring within 30 days and between 30 days and 1 year after AIDS diagnosis were determined. Generalized additive models fitted with cubic splines with appropriate estimating methods were used to describe rates and proportions over time. Overall, 90,806 tuberculosis cases and 16,891 AIDS cases were reported; 3,125 tuberculosis cases within 1 year of AIDS diagnosis were detected. Tuberculosis notification rates decreased after 1997 from a fitted rate (fR per 100,000) of 166.5 to 138.8 in 2004. AIDS incidence rates increased 26% between 1995 and 1998 (30.7 to 38.7) followed by a 33.3% decrease to 25.8 in 2004. AIDS mortality rates decreased dramatically after antiretroviral therapy was introduced between 1995 (27.5) and 1999 (13.4). The fitted proportion (fP) of patients with tuberculosis diagnosed within one year of AIDS decreased from 1995 (24.4%) to 1998 (15.2%), remaining stable since. Seventy-five percent of tuberculosis diagnoses after an AIDS diagnosis occurred within 30 days of AIDS diagnosis. CONCLUSIONS/SIGNIFICANCE: Our results suggest that while combination ART should be considered an essential component of the response to the HIV and HIV/tuberculosis epidemics, it may not be sufficient alone to prevent progression from latent TB to active disease among HIV-infected populations. When tuberculosis is diagnosed prior to or at the same time as AIDS and ART has not yet been initiated, then ART is ineffective as a tuberculosis prevention strategy for these patients. Earlier HIV/AIDS diagnosis and ART initiation may reduce TB incidence in HIV/AIDS patients. More specific interventions will be required if HIV-related tuberculosis incidence is to continue to decline

Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201

Antecedents of hospital admission for deliberate self-harm from a 14-year follow-up study using data-linkage

Antecedents of hospital admission for deliberate self-harm from a 14-year follow-up study using data-linkageFrancis Mitrou1 email, Jennifer Gaudie1 email, David Lawrence1,2 email, Sven R Silburn1,2 email, Fiona J Stanley1 email and Stephen R Zubrick1,2 email1 Telethon Institute for Child Health Research, Centre for Child Health Research, The University of Western Australia. PO Box 855, West Perth, WA. 6872, Australia2 Centre for Developmental Health, Curtin Health Innovation Research Institute, Curtin University of Technology, Perth, Western Australia, Australiaauthor email corresponding author emailBMC Psychiatry 2010, 10:82doi:10.1186/1471-244X-10-82The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-244X/10/82Received: 22 April 2010Accepted: 18 October 2010Published: 18 October 2010© 2010 Mitrou et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Production of CXC and CC chemokines by human antigen-presenting cells in response to Lassa virus or closely related immunogenic viruses, and in cynomolgus monkeys with lassa fever.

International audienceThe pathogenesis of Lassa fever (LF), a hemorrhagic fever endemic to West Africa, remains unclear. We previously compared Lassa virus (LASV) with its genetically close, but nonpathogenic homolog Mopeia virus (MOPV) and demonstrated that the strong activation of antigen-presenting cells (APC), including type I IFN production, observed in response to MOPV probably plays a crucial role in controlling infection. We show here that human macrophages (MP) produce large amounts of CC and CXC chemokines in response to MOPV infection, whereas dendritic cells (DC) release only moderate amounts of CXC chemokines. However, in the presence of autologous T cells, DCs produced CC and CXC chemokines. Chemokines were produced in response to type I IFN synthesis, as the levels of both mediators were strongly correlated and the neutralization of type I IFN resulted in an inhibition of chemokine production. By contrast, LASV induced only low levels of CXCL-10 and CXCL-11 production. These differences in chemokine production may profoundly affect the generation of virus-specific T-cell responses and may therefore contribute to the difference of pathogenicity between these two viruses. In addition, a recombinant LASV (rLASV) harboring the NP-D389A/G392A mutations, which abolish the inhibition of type I IFN response by nucleoprotein (NP), induced the massive synthesis of CC and CXC chemokines in both DC and MP, confirming the crucial role of arenavirus NP in immunosuppression and pathogenicity. Finally, we confirmed, using PBMC samples and lymph nodes obtained from LASV-infected cynomolgus monkeys, that LF was associated with high levels of CXC chemokine mRNA synthesis, suggesting that the very early synthesis of these mediators may be correlated with a favourable outcome

The Crystal Structure and RNA-Binding of an Orthomyxovirus Nucleoprotein

Genome packaging for viruses with segmented genomes is often a complex problem. This is particularly true for influenza viruses and other orthomyxoviruses, whose genome consists of multiple negative-sense RNAs encapsidated as ribonucleoprotein (RNP) complexes. To better understand the structural features of orthomyxovirus RNPs that allow them to be packaged, we determined the crystal structure of the nucleoprotein (NP) of a fish orthomyxovirus, the infectious salmon anemia virus (ISAV) (genus Isavirus). As the major protein component of the RNPs, ISAV-NP possesses a bi-lobular structure similar to the influenza virus NP. Because both RNA-free and RNA-bound ISAV NP forms stable dimers in solution, we were able to measure the NP RNA binding affinity as well as the stoichiometry using recombinant proteins and synthetic oligos. Our RNA binding analysis revealed that each ISAV-NP binds ,12 nts of RNA, shorter than the 24ﾖ28 nts originally estimated for the influenza A virus NP based on population average. The 12-nt stoichiometry was further confirmed by results from electron microscopy and dynamic light scattering. Considering that RNPs of ISAV and the influenza viruses have similar morphologies and dimensions, our findings suggest that NP-free RNA may exist on orthomyxovirus RNPs, and selective RNP packaging may be accomplished through direct RNA-RNA interactions

A New Approach to Age-Period-Cohort Analysis Using Partial Least Squares Regression: The Trend in Blood Pressure in the Glasgow Alumni Cohort

Due to a problem of identification, how to estimate the distinct effects of age, time period and cohort has been a controversial issue in the analysis of trends in health outcomes in epidemiology. In this study, we propose a novel approach, partial least squares (PLS) analysis, to separate the effects of age, period, and cohort. Our example for illustration is taken from the Glasgow Alumni cohort. A total of 15,322 students (11,755 men and 3,567 women) received medical screening at the Glasgow University between 1948 and 1968. The aim is to investigate the secular trends in blood pressure over 1925 and 1950 while taking into account the year of examination and age at examination. We excluded students born before 1925 or aged over 25 years at examination and those with missing values in confounders from the analyses, resulting in 12,546 and 12,516 students for analysis of systolic and diastolic blood pressure, respectively. PLS analysis shows that both systolic and diastolic blood pressure increased with students' age, and students born later had on average lower blood pressure (SBP: −0.17 mmHg/per year [95% confidence intervals: −0.19 to −0.15] for men and −0.25 [−0.28 to −0.22] for women; DBP: −0.14 [−0.15 to −0.13] for men; −0.09 [−0.11 to −0.07] for women). PLS also shows a decreasing trend in blood pressure over the examination period. As identification is not a problem for PLS, it provides a flexible modelling strategy for age-period-cohort analysis. More emphasis is then required to clarify the substantive and conceptual issues surrounding the definitions and interpretations of age, period and cohort effects

Developing Literacy Learning Model Based on Multi Literacy, Integrated, and Differentiated Concept at Primary School

The main issue addressed in this research is the low writing skills of primary school students. One of the reasons for this condition is that the existing model of writing literacy learning is not appropriate. The purpose of this study is to explain MID-based literacy teaching model and the impact of the model in increasing primary school students\u27 writing skills. This study used combined methods of exploratory type. The samples were elementary school students coming from six schools with three different characteristics. Based on the data analysis, it can be concluded that the implementation of MID-based literacy learning model has proven to signi cantly contribute to the improvement of students\u27 writing skills. Taking place in all sample schools, the improvement may suggest that the model ts not only to students with high- ability but also those with low-ability. Therefore, the MID-based literacy learning model is needed to improve the ability to write various text types appropriately