Salud sexual y reproductiva: aspectos de análisis para el departamento de Casanare

La sexualidad y la reproducción están íntimamente ligadas a la calidad de vida, tanto en el ámbito de lo individual como de lo social, por tanto la salud sexual y reproductiva representa un componente esencial para la evaluación de desarrollo de un país, y un tema prioritario en la agenda política mundial y nacional como lo evidencian los objetivos del milenio y de desarrollo sostenible; aunque el país ha dado enormes avances en el tema, aun se presentan grandes retos para el logro y goce efectivo de los derechos sexuales y reproductivos en la población. El objetivo del presente estudio es describir y analizar algunos indicadores de salud sexual y reproductiva en el departamento de Casanare, en el periodo 2008- 2015. Como metodología se usó un diseño descriptivo trasversal, los datos fueron tomados de Sivigila departamental, Estadísticas DANE y del Instituto Nacional de Salud. Se usó SPSS versión 18 y Excel para el análisis de los datos. Como resultados se encontraron altas tasas de prevalencia e incidencia para VIH, sífilis gestacional, y fecundidad en adolescentes en el departamento de Casanare que sobrepasan los indicadores nacionales y presentan tendencia creciente

Asociación de las violencias física, sexual y comunitaria con el embarazo en adolescentes: análisis multinivel

Objetivos Determinar si existe asociación entre la exposición a violencia, experimentada a nivel individual o municipal, y el embarazo adolescente en mujeres Colombianas entre 13 y 19 años de edad que contestaron la Encuesta de Demografía y Salud en el año 2010. Métodos Estudio de corte transversal, nacional y multinivel. Se tomaron datos de dos niveles jerárquicos: Nivel- 1: Datos individuales de una muestra representativa de 13.313 mujeres entre 13 y 19 años de edad provenientes de La Encuesta Nacional de Demografía y Salud del año 2010 y Nivel- 2: Datos municipales de 258 municipios provenientes de las estadísticas vitales del DANE. Resultados La prevalencia del embarazo adolescente fue del 16.8% IC 95% [16.2-17.4]. El análisis mostró que la asociación entre embarazo adolescente y violencia tanto individual, representada como violencia sexual [OR= 6.99 IC99% 4.80-10.10] y violencia física [OR= 1.74 IC99% 1.47-2.05] así como la violencia municipal medida con tasas de homicidios altas [OR= 1.99 IC99% 1.29-3.07] y muy altas [OR= 2.10 IC99% 1.21-3.61] se mantuvo estadísticamente significativa después de ajustar por las variables: Edad [OR= 1.81 IC99% 1.71-1.91], ocupación [OR= 1.62 IC99% 1.37-1.93], educación primaria o sin educación [OR= 2.20 IC99% 1.47-3.30], educación secundaria [OR= 1.70 IC99% 1.24-2.32], asistir al colegio [OR= 0.18 IC99% 0.15-0.21], conocimiento en la fisiología reproductiva [OR= 1.28 IC99% 1.06-1.54], el índice de riqueza Q1, Q2, Q3 [OR= 2.18 IC99% 1.42-3.34], [OR= 2.00 IC99% 1.39-2.28], [OR= 1.82 IC99% 1.92-2.25] y alto porcentaje de Necesidades básicas insatisfechas a nivel municipal [OR= 2.34 IC99% 1.55-3.52]. Conclusiones Este estudio mostró una relación significativamente estadística entre la violencia sexual y física con el inicio de relaciones sexuales y embarazo adolescente después de controlar por factores sociodemográficos y conocimientos en reproducción sexual en mujeres colombianas de 13 a 19 años en el año 2010. Esta asociación debe continuar siendo estudiada para lograr optimizar las estrategias de prevención y disminuir la tasa actual de embarazos adolescentes en el país y sus consecuencias.

Impacto de los Niveles de Tacrolimus en el Rechazo al Injerto y la Tasa de Filtración Glomerular en Pacientes con Trasplante Hepático realizado en la FCI-IC entre 2009 y 2014

Introducción: El tacrolimus es el medicamento de elección para evitar el rechazo al injerto hepático. Su dosis se ajusta a partir de los niveles séricos que se toman periódicamente para asegurar rango terapéutico. Además, niveles elevados se asocian con disfunción renal postrasplante. Sin embargo, no hay consenso frente a los niveles adecuados para pacientes con trasplante hepático. Objetivo: Determinar la relación entre los niveles de tacrolimus y la presencia de rechazo agudo al injerto hepático en pacientes con trasplante hepático realizado en la Fundación Cardioinfantil – Instituto de Cardiología (FCI-IC). Determinar la relación entre los niveles de tacrolimus y la TFG en pacientes con trasplante hepático realizado en la FCI-IC. Métodos: Estudio observacional tipo cohorte histórica en pacientes adultos con trasplante hepático realizado en la FCI-IC entre 2009-2014. Resultados: No se encontró una asociación estadísticamente significativa entre los niveles de tacrolimus y la presencia de rechazo agudo, en sus diferentes definiciones (OR=1,02, p=0,14 y OR=1,01, p=0,29) incluso al ajustar por otras covariables (OR=1,03, p=0,10 y OR=1,02, p=0,25). No fue posible corroborar el diagnóstico con biopsia porque no todos la tenían. Si bien la relación entre los niveles de tacrolimus y la TFG fue estadísticamente significativa (p≤0,001), tiene bajo impacto clínico, pues la TFG disminuyó menos de un punto por cada incremento en 1 ng/ml en los niveles de tacrolimus. Conclusiones: Se necesitan más estudios para establecer la relación entre la exposición a tacrolimus y estos desenlaces para definir si es seguro disminuir su dosis con el fin de reducir los eventos adversos.

Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

The enduring effects of psychodynamic treatments vis-à-vis alternative treatments: A multilevel longitudinal meta-analysis

Although evidence suggests that the benefits of psychodynamic treatments are sustained over time, presently it is unclear whether these sustained benefits are superior to non-psychodynamic treatments. Additionally, the extant literature comparing the sustained benefits of psychodynamic treatments compared to alternative treatments is limited with methodological shortcomings. The purpose of the current study was to conduct a rigorous test of the growth of the benefits of psychodynamic treatments relative to alternative treatments across distinct domains of change (i.e., all outcome measures, targeted outcome measures, non-targeted outcome measures, and personality outcome measures). To do so, the study employed strict inclusion criteria to identify randomized clinical trials that directly compared at least one bona fide psychodynamic treatment and one bona fide non-psychodynamic treatment. Hierarchical linear modeling (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2011) was used to longitudinally model the impact of psychodynamic treatments compared to non-psychodynamic treatments at post-treatment and to compare the growth (i.e., slope) of effects beyond treatment completion. Findings from the present meta-analysis indicated that psychodynamic treatments and non-psychodynamic treatments were equally efficacious at post-treatment and at follow-up for combined outcomes (k=20), targeted outcomes (k=19), non-targeted outcomes (k=17), and personality outcomes (k=6). Clinical implications, directions for future research, and limitations are discussed.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using Intra-class Correlation Coefficient (ICC) and Deviance Information Criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time, in good street lighting condition, involving pedestrian injuries are associated with a lower severity, while those in night time, at T/Y type intersections, on right-most lane, and installed with red light camera have larger odds of being severe. Moreover, heavy vehicles have a better resistance on severe crash, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.

Perceived collision risks in anchorages : a hierarchical ordered probit analysis

Navigational collisions are a major safety concern in many seaports. Despite the recent advances in port navigational safety research, little is known about harbor pilot’s perception of collision risks in anchorages. This study attempts to model such risks by employing a hierarchical ordered probit model, which is calibrated by using data collected through a risk perception survey conducted on Singapore port pilots. The hierarchical model is found to be useful to account for correlations in risks perceived by individual pilots. Results show higher perceived risks in anchorages attached to intersection, local and international fairway; becoming more critical at night. Lesser risks are perceived in anchorages featuring shoreline in boundary, higher water depth, lower density of stationary ships, cardinal marks and isolated danger marks. Pilotage experience shows a negative effect on perceived risks. This study indicates that hierarchical modeling would be useful for treating correlations in navigational safety data.

Hierarchical multiscale model for biomechanics analysis of microfilament networks

The mechanisms of force generation and transference via microfilament networks are crucial to the understandings of mechanobiology of cellular processes in living cells. However, there exists an enormous challenge for all-atom physics simulation of real size microfilament networks due to scale limitation of molecular simulation techniques. Following biophysical investigations of constitutive relations between adjacent globular actin monomers on filamentous actin, a hierarchical multiscale model was developed to investigate the biomechanical properties of microfilament networks. This model was validated by previous experimental studies of axial tension and transverse vibration of single F-actin. The biomechanics of microfilament networks can be investigated at the scale of real eukaryotic cell size (10 μm). This multiscale approach provides a powerful modeling tool which can contribute to the understandings of actin-related cellular processes in living cells.

Socio-demographic characteristics, modifiable lifestyle factors, and health as predictors of mental health in midlife and older Australian women: a multilevel investigation

Background Poor mental health is a significant cause of morbidity and mortality, yet debate continues about factors most likely to predict poor mental health outcomes. Objective This cohort study examines the influence of modifiable lifestyle factors, menopausal symptoms, and physical health on the mental health of midlife and older Australian women. Methods: Random sampling was used to recruit women aged 40-55, from rural and urban areas of Queensland, Australia. Overall, 340 women completed mailed surveys on socio-demographic characteristics, midlife symptoms (Greene Climacteric Scale©), modifiable lifestyle factors, and mental health (SF-12©) in 2001, 2004 and 2011. Hierarchical repeated-measure models were used to explore the correlates of poor mental health over time. Results The mean age [SD] at baseline was 55 [2.7] years, most were married (73%, n=248) and 18% were pre-menopausal. The model suggested that variance in mental health widened and showed a non-linear increase with age. Decrements in mental health were associated with an increase in midlife symptoms (Greene psychological scale, P <0.01; Greene somatic scale, P <0.05), time (P <0.01), poor physical health (P <0.01) and individual variance (P <0.01). Socio-demographics and lifestyle factors had little influence on mental health over time. Conclusion Findings suggest that while women’s mental health may decline during midlife, the effect is temporary; in older women, physical health and individual factors seem to be increasingly significant. This research highlights the importance of active health promotion as a means of enhancing both physical and mental health in midlife women.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

MRI-based strain and strain rate analysis of left ventricle: A modified hierarchical transformation model

Background Different from other indicators of cardiac function, such as ejection fraction and transmitral early diastolic velocity, myocardial strain is promising to capture subtle alterations that result from early diseases of the myocardium. In order to extract the left ventricle (LV) myocardial strain and strain rate from cardiac cine-MRI, a modified hierarchical transformation model was proposed. Methods A hierarchical transformation model including the global and local LV deformations was employed to analyze the strain and strain rate of the left ventricle by cine-MRI image registration. The endocardial and epicardial contour information was introduced to enhance the registration accuracy by combining the original hierarchical algorithm with an Iterative Closest Points using Invariant Features algorithm. The hierarchical model was validated by a normal volunteer first and then applied to two clinical cases (i.e., the normal volunteer and a diabetic patient) to evaluate their respective function. Results Based on the two clinical cases, by comparing the displacement fields of two selected landmarks in the normal volunteer, the proposed method showed a better performance than the original or unmodified model. Meanwhile, the comparison of the radial strain between the volunteer and patient demonstrated their apparent functional difference. Conclusions The present method could be used to estimate the LV myocardial strain and strain rate during a cardiac cycle and thus to quantify the analysis of the LV motion function.

System-level analysis of network interfaces for hierarchical MPSoCs

Network Interfaces (NIs) are used in Multiprocessor System-on-Chips (MPSoCs) to connect CPUs to a packet switched Network-on-Chip. In this work we introduce a new NI architecture for our hierarchical CoreVA-MPSoC. The CoreVA-MPSoC targets streaming applications in embedded systems. The main contribution of this paper is a system-level analysis of different NI configurations, considering both software and hardware costs for NoC communication. Different configurations of the NI are compared using a benchmark suite of 10 streaming applications. The best performing NI configuration shows an average speedup of 20 for a CoreVA-MPSoC with 32 CPUs compared to a single CPU. Furthermore, we present physical implementation results using a 28 nm FD-SOI standard cell technology. A hierarchical MPSoC with 8 CPU clusters and 4 CPUs in each cluster running at 800MHz requires an area of 4.56mm2.

Analysis of the hierarchical structure of the B. subtilis transcriptional regulatory network

The transcriptional regulation of gene expression is orchestrated by complex networks of interacting genes. Increasing evidence indicates that these `transcriptional regulatory networks' (TRNs) in bacteria have an inherently hierarchical architecture, although the design principles and the specific advantages offered by this type of organization have not yet been fully elucidated. In this study, we focussed on the hierarchical structure of the TRN of the gram-positive bacterium Bacillus subtilis and performed a comparative analysis with the TRN of the gram-negative bacterium Escherichia coli. Using a graph-theoretic approach, we organized the transcription factors (TFs) and sigma-factors in the TRNs of B. subtilis and E. coli into three hierarchical levels (Top, Middle and Bottom) and studied several structural and functional properties across them. In addition to many similarities, we found also specific differences, explaining the majority of them with variations in the distribution of s-factors across the hierarchical levels in the two organisms. We then investigated the control of target metabolic genes by transcriptional regulators to characterize the differential regulation of three distinct metabolic subsystems (catabolism, anabolism and central energy metabolism). These results suggest that the hierarchical architecture that we observed in B. subtilis represents an effective organization of its TRN to achieve flexibility in response to a wide range of diverse stimuli.