Endemic tuberculosis, the homeless, and public transportation: A merging of geographical information systems surveillance and the Houston Tuberculosis Initiative Surveillance project

To reach the goals established by the Institute of Medicine (IOM) and the Centers for Disease Control's (CDC) STOP TB USA, measures must be taken to curtail a future peak in Tuberculosis (TB) incidence and speed the currently stagnant rate of TB elimination. Both efforts will require, at minimum, the consideration and understanding of the third dimension of TB transmission: the location-based spread of an airborne pathogen among persons known and unknown to each other. This consideration will require an elucidation of the areas within the U.S. that have endemic TB. The Houston Tuberculosis Initiative (HTI) was a population-based active surveillance of confirmed Houston/Harris County TB cases from 1995–2004. Strengths in this dataset include the molecular characterization of laboratory confirmed cases, the collection of geographic locations (including home addresses) frequented by cases, and the HTI time period that parallels a decline in TB incidence in the United States (U.S.). The HTI dataset was used in this secondary data analysis to implement a GIS analysis of TB cases, the locations frequented by cases, and their association with risk factors associated with TB transmission. ^ This study reports, for the first time, the incidence of TB among the homeless in Houston, Texas. The homeless are an at-risk population for TB disease, yet they are also a population whose TB incidence has been unknown and unreported due to their non-enumeration. The first section of this dissertation identifies local areas in Houston with endemic TB disease. Many Houston TB cases who reported living in these endemic areas also share the TB risk factor of current or recent homelessness. Merging the 2004–2005 Houston enumeration of the homeless with historical HTI surveillance data of TB cases in Houston enabled this first-time report of TB risk among the homeless in Houston. The homeless were more likely to be US-born, belong to a genotypic cluster, and belong to a cluster of a larger size. The calculated average incidence among homeless persons was 411/100,000, compared to 9.5/100,000 among housed. These alarming rates are not driven by a co-infection but by social determinants. The unsheltered persons were hospitalized more days and required more follow-up time by staff than those who reported a steady housing situation. The homeless are a specific example of the increased targeting of prevention dollars that could occur if TB rates were reported for specific areas with known health disparities rather than as a generalized rate normalized over a diverse population. ^ It has been estimated that 27% of Houstonians use public transportation. The city layout allows bus routes to run like veins connecting even the most diverse of populations within the metropolitan area. Secondary data analysis of frequent bus use (defined as riding a route weekly) among TB cases was assessed for its relationship with known TB risk factors. The spatial distribution of genotypic clusters associated with bus use was assessed, along with the reported routes and epidemiologic-links among cases belonging to the identified clusters. ^ TB cases who reported frequent bus use were more likely to have demographic and social risk factors associated with poverty, immune suppression and health disparities. An equal proportion of bus riders and non-bus riders were cultured for Mycobacterium tuberculosis, yet 75% of bus riders were genotypically clustered, indicating recent transmission, compared to 56% of non-bus riders (OR=2.4, 95%CI(2.0, 2.8), p<0.001). Bus riders had a mean cluster size of 50.14 vs. 28.9 (p<0.001). Second order spatial analysis of clustered fingerprint 2 (n=122), a Beijing family cluster, revealed geographic clustering among cases based on their report of bus use. Univariate and multivariate analysis of routes reported by cases belonging to these clusters found that 10 of the 14 clusters were associated with use. Individual Metro routes, including one route servicing the local hospitals, were found to be risk factors for belonging to a cluster shown to be endemic in Houston. The routes themselves geographically connect the census tracts previously identified as having endemic TB. 78% (15/23) of Houston Metro routes investigated had one or more print groups reporting frequent use for every HTI study year. We present data on three specific but clonally related print groups and show that bus-use is clustered in time by route and is the only known link between cases in one of the three prints: print 22. (Abstract shortened by UMI.)^

Effects of various pollutant mixtures on immune responses of the blue mussel (Mytilus edulis) collected at a salinity gradient in Danish coastal waters, links to supplementary material

The Baltic Sea is a semi-enclosed sea with a steady salinity gradient (3 per mil-30 per mil). Organisms have adapted to such low salinities, but are suspected to be more susceptible to stress. Within the frame of the integrated environmental monitoring BONUS + project "BEAST" the applicability of immune responses of the blue mussel was investigated in Danish coastal waters. The sampling sites were characterised by a salinity range (11-19 per mil) and different mixtures of contaminants (metals, PAHs and POPs), according to chemical analysis of mussel tissues. Variation partitioning (redundancy analysis) was applied to decompose salinity and contamination effects. The results indicated that cellular immune responses (total and differential haemocyte count, phagocytic activity and apoptosis) were mainly influenced by contaminants, whereas humoral factors (haemolytic activity) were mainly impacted by salinity. Hence, cellular immune functions may be suitable as biomarkers in monitoring programmes for the Baltic Sea and other geographic regions with salinity variances of the studied range.

Framework for risk assessment for maritime transportation systems

Maritime accidents involving ships carrying passengers may pose a high risk with respect to human casualties. For effective risk mitigation, an insight into the process of risk escalation is needed. This requires a proactive approach when it comes to risk modelling for maritime transportation systems. Most of the existing models are based on historical data on maritime accidents, and thus they can be considered reactive instead of proactive. This paper introduces a systematic, transferable and proactive framework estimating the risk for maritime transportation systems, meeting the requirements stemming from the adopted formal definition of risk. The framework focuses on ship-ship collisions in the open sea, with a RoRo/Passenger ship (RoPax) being considered as the struck ship. First, it covers an identification of the events that follow a collision between two ships in the open sea, and, second, it evaluates the probabilities of these events, concluding by determining the severity of a collision. The risk framework is developed with the use of Bayesian Belief Networks and utilizes a set of analytical methods for the estimation of the risk model parameters. The model can be run with the use of GeNIe software package. Finally, a case study is presented, in which the risk framework developed here is applied to a maritime transportation system operating in the Gulf of Finland (GoF). The results obtained are compared to the historical data and available models, in which a RoPax was involved in a collision, and good agreement with the available records is found.

Innnovation and new business models for the regional development: travel and transportation

Regional development could present different strategies: •Relocation of industry clusters •Foreign Direct Investment attraction •Innovation based on new business models The Regional Government of Madrid (3rd largest GDP in the EU) selected strategic industries to compete & innovate: •Travel & Transportation •Aerospace •Nanotech. & •Biotech. •ICTs. •Energy

Methodologies for Designing and Developing New Concepts in Vertical Transportation

Escalator and moving walkway are multibody systems with a design of more than a century. Developed methodology allows studying and improving any subsystem of both systems. In addition, new concepts can be developed and tested without the necessity and cost of a real construction. CITEF (Railway Technologies Research Centre) has been modelling escalators for more than four years. Several complex and innovative models has been developed to characterize static, kinematic and dynamic escalator behaviour. The high number of mechanical elements that are part of escalators complicate modelling task. In this way, methodologies and tools have been developed in order to automate these task and saving computational and time costs. Developed methodologies have been validated with the results of comparing real measurements and simulated outputs from a dynamic model.

High-Speed Rail Versus Air Transportation: Case Study of Madrid–Barcelona, Spain.

Travel time savings, better quality of the supplied services, greater comfort for the users, and improved accessibility are the main factors of success of High Speed Rail(HSR)links. This paper presents the results from a revealed and stated preference survey conducted to both HSR and air transport users in the Madrid Barcelona corridor. The data gathered from the stated preference survey was used to calibrate a modal choice model aiming at explaining competition between HSR and air transportation in the corridor. From the model, the authors obtain that prices and service frequency are the most important variables to compete with the other mode. In addition, they found that check-in and security controls at the airport are a crucial variable for the users in their modal choice. Other policies, such as the improvement of parking facilities at the train stations, play a secondary role.

Transportation infrastructure impacts on firm location: the effect of a new metro line in the suburbs of Madrid

Firm location patterns emerge as a consequence of multiple factors, including firm considerations, labor force availability, market opportunities, and transportation costs. Many of these factors are influenced by changes in accessibility wrought by new transportation infrastructure. In this paper we use spatial statistical techniques and a micro-level data base to evaluate the effects of Madrid?s metro line 12 (known as Metrosur) expansion on business location patterns. The case study is the municipality of Alcorcon, which is served by the new metro line since 2003. Specifically, we explore the location patterns by different industry sectors, to evaluate if the new metro line has encouraged the emergence of a ?Metrosur spatial economy?. Our results indicate that the pattern of economic activity location is related to urban accessibility and that agglomeration, through economies of scale, also plays an important role. The results presented in this paper provide evidence useful to inform efficient transportation, urban, and regional economic planning.

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Pollutant concentrations and Meteorological data classification by Neural Networks

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.

Unsupervised method to classify PM10 pollutant concentrations

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Modeling impact assessment of transport policies through a multiregional input-output integrated approach

El objetivo de esta investigación es desarrollar una metodología para estimar los potenciales impactos económicos y de transporte generados por la aplicación de políticas en el sector transporte. Los departamentos de transporte y otras instituciones gubernamentales relacionadas se encuentran interesadas en estos análisis debido a que son presentados comúnmente de forma errónea por la insuficiencia de datos o por la falta de metodologías adecuadas. La presente investigación tiene por objeto llenar este vacío haciendo un análisis exhaustivo de las técnicas disponibles que coincidan con ese propósito. Se ha realizado un análisis que ha identificado las diferencias cuando son aplicados para la valoración de los beneficios para el usuario o para otros efectos como aspectos sociales. Como resultado de ello, esta investigación ofrece un enfoque integrado que incluye un modelo Input-Output de múltiples regiones basado en la utilidad aleatoria (RUBMRIO), y un modelo de red de transporte por carretera. Este modelo permite la reproducción con mayor detalle y realismo del transporte de mercancías que por medio de su estructura sectorial identifica los vínculos de las compras y ventas inter-industriales dentro de un país utilizando los servicios del transporte de mercancías. Por esta razón, el modelo integrado es aplicable a diversas políticas de transporte. En efecto, el enfoque se ha aplicado para estudiar los efectos macroeconómicos regionales de la implementación de dos políticas diferentes en el sistema de transporte de mercancías de España, tales como la tarificación basada en la distancia recorrida por vehículo-kilómetro (€/km) aplicada a los vehículos del transporte de mercancías, y para la introducción de vehículos más largos y pesados de mercancías en la red de carreteras de España. El enfoque metodológico se ha evaluado caso por caso teniendo en cuenta una selección de la red de carreteras que unen las capitales de las regiones españolas. También se ha tenido en cuenta una dimensión económica a través de una tabla Input-Output de múltiples regiones (MRIO) y la base de datos de conteo de tráfico existente para realizar la validación del modelo. El enfoque integrado reproduce las condiciones de comercio observadas entre las regiones usando el sistema de transporte de mercancías por carretera, y que permite por comparación con los escenarios de políticas, determinar las contribuciones a los cambios distributivos y generativos. Así pues, el análisis estima los impactos económicos en cualquier región considerando los cambios en el Producto Interno Bruto (PIB) y el empleo. El enfoque identifica los cambios en el sistema de transporte a través de todos los caminos de la red de transporte a través de las medidas de efectividad (MOEs). Los resultados presentados en esta investigación proporcionan evidencia sustancial de que en la evaluación de las políticas de transporte, es necesario establecer un vínculo entre la estructura económica de las regiones y de los servicios de transporte. Los análisis muestran que para la mayoría de las regiones del país, los cambios son evidentes para el PIB y el empleo, ya que el comercio se fomenta o se inhibe. El enfoque muestra cómo el tráfico se desvía en ambas políticas, y también determina detalles de las emisiones de contaminantes en los dos escenarios. Además, las políticas de fijación de precios o de regulación de los sistemas de transporte de mercancías por carretera dirigidas a los productores y consumidores en las regiones promoverán transformaciones regionales afectando todo el país, y esto conduce a conclusiones diferentes. Así mismo, este enfoque integrado podría ser útil para evaluar otras políticas y otros países en todo el mundo. The purpose of this research is to develop a methodological approach aimed at assessing the potential economic and transportation impacts of transport policies. Transportation departments and other related government parties are interested in such analysis because it is commonly misrepresented for the insufficiency of data and suitable methodologies available. This research is directed at filling this gap by making a comprehensive analysis of the available techniques that match with that purpose. The differences when they are applied for the valuation of user benefits or for other impacts as social matters have been identified. As a result, this research presents an integrated approach which includes both a random utility-based multiregional Input-Output model (RUBMRIO), and a road transport network model. This model accounts for freight transport with more detail and realism because its commodity-based structure traces the linkages of inter-industry purchases and sales that use freight services within a given country. For this reason, the integrated model is applicable to various transport policies. In fact, the approach is applied to study the regional macroeconomic effects of implementing two different policies in the freight transport system of Spain, such as a distance-based charge in vehicle-kilometer (€/km) for Heavy Goods Vehicles (HGVs), and the introduction of Longer and Heavier Vehicles (LHVs) in the road network of Spain. The methodological approach has been evaluated on a case by case basis considering a selected road network of highways linking the capitals of the Spanish regions. It has also considered an economic dimension through a Multiregional Input Output Table (MRIO) and the existing traffic count database used in the model validation. The integrated approach replicates observed conditions of trade among regions using road freight transport systems that determine contributions to distributional and generative changes by comparison with policy scenarios. Therefore, the model estimates economic impacts in any given area by considering changes in Gross Domestic Product (GDP), employment (jobs), and in the transportation system across all paths of the transport network considering Measures of effectiveness (MOEs). The results presented in this research provide substantive evidence that in the assessment of transport policies it is necessary to establish a link between the economic structure of regions and the transportation services. The analysis shows that for most regions in the country, GDP and employment changes are noticeable when trade is encouraged or discouraged. This approach shows how traffic is diverted in both policies, and also provides details of the pollutant emissions in both scenarios. Furthermore, policies, such as pricing or regulation of road freight transportation systems, directed to producers and consumers in regions will promote different regional transformations across the country, and this lead to different conclusions. In addition, this integrated approach could be useful to assess other policies and countries worldwide.