Using small area estimation and geographic information systems technology to target health services for the uninsured

The three articles that comprise this dissertation describe how small area estimation and geographic information systems (GIS) technologies can be integrated to provide useful information about the number of uninsured and where they are located. Comprehensive data about the numbers and characteristics of the uninsured are typically only available from surveys. Utilization and administrative data are poor proxies from which to develop this information. Those who cannot access services are unlikely to be fully captured, either by health care provider utilization data or by state and local administrative data. In the absence of direct measures, a well-developed estimation of the local uninsured count or rate can prove valuable when assessing the unmet health service needs of this population. However, the fact that these are “estimates” increases the chances that results will be rejected or, at best, treated with suspicion. The visual impact and spatial analysis capabilities afforded by geographic information systems (GIS) technology can strengthen the likelihood of acceptance of area estimates by those most likely to benefit from the information, including health planners and policy makers. ^ The first article describes how uninsured estimates are currently being performed in the Houston metropolitan region. It details the synthetic model used to calculate numbers and percentages of uninsured, and how the resulting estimates are integrated into a GIS. The second article compares the estimation method of the first article with one currently used by the Texas State Data Center to estimate numbers of uninsured for all Texas counties. Estimates are developed for census tracts in Harris County, using both models with the same data sets. The results are statistically compared. The third article describes a new, revised synthetic method that is being tested to provide uninsured estimates at sub-county levels for eight counties in the Houston metropolitan area. It is being designed to replicate the same categorical results provided by a current U.S. Census Bureau estimation method. The estimates calculated by this revised model are compared to the most recent U.S. Census Bureau estimates, using the same areas and population categories. ^

HPV vaccine coverage in Texas counties: An application of multilevel, small area estimation

Health departments, research institutions, policy-makers, and healthcare providers are often interested in knowing the health status of their clients/constituents. Without the resources, financially or administratively, to go out into the community and conduct health assessments directly, these entities frequently rely on data from population-based surveys to supply the information they need. Unfortunately, these surveys are ill-equipped for the job due to sample size and privacy concerns. Small area estimation (SAE) techniques have excellent potential in such circumstances, but have been underutilized in public health due to lack of awareness and confidence in applying its methods. The goal of this research is to make model-based SAE accessible to a broad readership using clear, example-based learning. Specifically, we applied the principles of multilevel, unit-level SAE to describe the geographic distribution of HPV vaccine coverage among females aged 11-26 in Texas.^ Multilevel (3 level: individual, county, public health region) random-intercept logit models of HPV vaccination (receipt of ≥ 1 dose Gardasil® ) were fit to data from the 2008 Behavioral Risk Factor Surveillance System (outcome and level 1 covariates) and a number of secondary sources (group-level covariates). Sampling weights were scaled (level 1) or constructed (levels 2 & 3), and incorporated at every level. Using the regression coefficients (and standard errors) from the final models, I simulated 10,000 datasets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county and respective demographic subgroup. For simplicity, I only provide coverage estimates (and 95% confidence intervals) for counties.^ County-level coverage among females aged 11-17 varied from 6.8-29.0%. For females aged 18-26, coverage varied from 1.9%-23.8%. Aggregated to the state level, these values translate to indirect state estimates of 15.5% and 11.4%, respectively; both of which fall within the confidence intervals for the direct estimates of HPV vaccine coverage in Texas (Females 11-17: 17.7%, 95% CI: 13.6, 21.9; Females 18-26: 12.0%, 95% CI: 6.2, 17.7).^ Small area estimation has great potential for informing policy, program development and evaluation, and the provision of health services. Harnessing the flexibility of multilevel, unit-level SAE to estimate HPV vaccine coverage among females aged 11-26 in Texas counties, I have provided (1) practical guidance on how to conceptualize and conduct modelbased SAE, (2) a robust framework that can be applied to other health outcomes or geographic levels of aggregation, and (3) HPV vaccine coverage data that may inform the development of health education programs, the provision of health services, the planning of additional research studies, and the creation of local health policies.^

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. ^

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

How has financial deepening affected poverty reduction in India? : empirical analysis using state-level panel data

This paper examines empirically whether financial deepening has contributed to poverty reduction in India. Using unbalanced panel data for 28 states and union territories between 1973 and 2004, we estimate models in which the poverty ratio is explained by financial deepening, controlling for international openness, inflation rate, and economic growth. From the dynamic generalised method of moments (GMM) estimation, we find that financial deepening and economic growth alleviate poverty, while international openness and the inflation rate have the opposite effect. These results are robust to changes in the poverty ratios in rural areas, urban areas, and the whole economy.

A Multiresolution Image Alignment Technique Based on Direct Methods for Pose Estimation of Aerial Vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi-Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles' state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle's state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle's state for more than one minute, at real-time frame rates based, only on visual information.

Modal contribution and state space order selection in operational modal analysis

The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.

Estimating the modal parameters from multiple measurement setups using a joint state space model

Computing the modal parameters of structural systems often requires processing data from multiple non-simultaneously recorded setups of sensors. These setups share some sensors in common, the so-called reference sensors, which are fixed for all measurements, while the other sensors change their position from one setup to the next. One possibility is to process the setups separately resulting in different modal parameter estimates for each setup. Then, the reference sensors are used to merge or glue the different parts of the mode shapes to obtain global mode shapes, while the natural frequencies and damping ratios are usually averaged. In this paper we present a new state space model that processes all setups at once. The result is that the global mode shapes are obtained automatically, and only a value for the natural frequency and damping ratio of each mode is estimated. We also investigate the estimation of this model using maximum likelihood and the Expectation Maximization algorithm, and apply this technique to simulated and measured data corresponding to different structures.

Analysis of microscopic magnitudes of radiative blast waves launched in xenon clusters with collisional-radiative steady-state simulations

Radiative shock waves play a pivotal role in the transport energy into the stellar medium. This fact has led to many efforts to scale the astrophysical phenomena to accessible laboratory conditions and their study has been highlighted as an area requiring further experimental investigations. Low density material with high atomic mass is suitable to achieve radiative regime, and, therefore, low density xenon gas is commonly used for the medium in which the radiative shocks such as radiative blast waves propagate. In this work, by means of collisional-radiative steady-state calculations, a characterization and an analysis of microscopic magnitudes of laboratory blast waves launched in xenon clusters are made. Thus, for example, the average ionization, the charge state distribution, the cooling time or photon mean free paths are studied. Furthermore, for a particular experiment, the effects of the self-absorption and self-emission in the specific intensity emitted by the shock front and that is going through the radiative precursor are investigated. Finally, for that experiment, since the electron temperature is not measured experimentally, an estimation of this magnitude is made both for the shock shell and the radiative precursor.

Joint statistical analysis of multiple measurement setups for the estimation of vibrational modes

Computing the modal parameters of large structures in Operational Modal Analysis often requires to process data from multiple non simultaneously recorded setups of sensors. These setups share some sensors in common, the so-called reference sensors that are fixed for all the measurements, while the other sensors are moved from one setup to the next. One possibility is to process the setups separately what result in different modal parameter estimates for each setup. Then the reference sensors are used to merge or glue the different parts of the mode shapes to obtain global modes, while the natural frequencies and damping ratios are usually averaged. In this paper we present a state space model that can be used to process all setups at once so the global mode shapes are obtained automatically and subsequently only a value for the natural frequency and damping ratio of each mode is computed. We also present how this model can be estimated using maximum likelihood and the Expectation Maximization algorithm. We apply this technique to real data measured at a footbridge.

Using the EM algorithm to estimate the state space model for OMAX

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm that is applied to the estimation of modal parameters from system input and output data. The effectiveness of this structural identification method is evaluated through numerical simulation. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the simulated structure are estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates the modal parameters with precision in the presence of 20% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Proprioceptive force estimation and control for teleoperation in radioactive environments

A medida que se incrementa la energía de los aceleradores de partículas o iones pesados como el CERN o GSI, de los reactores de fusión como JET o ITER, u otros experimentos científicos, se va haciendo cada vez más imprescindible el uso de técnicas de manipulación remota para la interacción con el entorno sujeto a la radiación. Hasta ahora la tasa de dosis radioactiva en el CERN podía tomar valores cercanos a algunos mSv para tiempos de enfriamiento de horas, que permitían la intervención humana para tareas de mantenimiento. Durante los primeros ensayos con plasma en JET, se alcanzaban valores cercanos a los 200 μSv después de un tiempo de enfriamiento de 4 meses y ya se hacía extensivo el uso de técnicas de manipulación remota. Hay una clara tendencia al incremento de los niveles de radioactividad en el futuro en este tipo de instalaciones. Un claro ejemplo es ITER, donde se esperan valores de 450 Sv/h en el centro del toroide a los 11 días de enfriamiento o los nuevos niveles energéticos del CERN que harán necesario una apuesta por niveles de mantenimiento remotos. En estas circunstancias se enmarca esta tesis, que estudia un sistema de control bilateral basado en fuerza-posición, tratando de evitar el uso de sensores de fuerza/par, cuyo contenido electrónico los hace especialmente sensitivos en estos ambientes. El contenido de este trabajo se centra en la teleoperación de robots industriales, que debido a su reconocida solvencia y facilidad para ser adaptados a estos entornos, unido al bajo coste y alta disponibilidad, les convierte en una alternativa interesante para tareas de manipulación remota frente a costosas soluciones a medida. En primer lugar se considera el problema cinemático de teleoperación maestro-esclavo de cinemática disimilar y se desarrolla un método general para la solución del problema en el que se incluye el uso de fuerzas asistivas para guiar al operador. A continuación se explican con detalle los experimentos realizados con un robot ABB y que muestran las dificultades encontradas y recomendaciones para solventarlas. Se concluye el estudio cinemático con un método para el encaje de espacios de trabajo entre maestro y esclavo disimilares. Posteriormente se mira hacia la dinámica, estudiándose el modelado de robots con vistas a obtener un método que permita estimar las fuerzas externas que actúan sobre los mismos. Durante la caracterización del modelo dinámico, se realizan varios ensayos para tratar de encontrar un compromiso entre complejidad de cálculo y error de estimación. También se dan las claves para modelar y caracterizar robots con estructura en forma de paralelogramo y se presenta la arquitectura de control deseada. Una vez obtenido el modelo completo del esclavo, se investigan diferentes alternativas que permitan una estimación de fuerzas externas en tiempo real, minimizando las derivadas de la posición para minimizar el ruido. Se comienza utilizando observadores clásicos del estado para ir evolucionando hasta llegar al desarrollo de un observador de tipo Luenberger-Sliding cuya implementación es relativamente sencilla y sus resultados contundentes. También se analiza el uso del observador propuesto durante un control bilateral simulado en el que se compara la realimentación de fuerzas obtenida con las técnicas clásicas basadas en error de posición frente a un control basado en fuerza-posición donde la fuerza es estimada y no medida. Se comprueba como la solución propuesta da resultados comparables con las arquitecturas clásicas y sin embargo introduce una alternativa para la teleoperación de robots industriales cuya teleoperación en entornos radioactivos sería imposible de otra manera. Finalmente se analizan los problemas derivados de la aplicación práctica de la teleoperación en los escenarios mencionados anteriormente. Debido a las condiciones prohibitivas para todo equipo electrónico, los sistemas de control se deben colocar a gran distancia de los manipuladores, dando lugar a longitudes de cable de centenares de metros. En estas condiciones se crean sobretensiones en controladores basados en PWM que pueden ser destructivas para el sistema formado por control, cableado y actuador, y por tanto, han de ser eliminadas. En este trabajo se propone una solución basada en un filtro LC comercial y se prueba de forma extensiva que su inclusión no produce efectos negativos sobre el control del actuador. ABSTRACT As the energy on the particle accelerators or heavy ion accelerators such as CERN or GSI, fusion reactors such as JET or ITER, or other scientific experiments is increased, it is becoming increasingly necessary to use remote handling techniques to interact with the remote and radioactive environment. So far, the dose rate at CERN could present values near several mSv for cooling times on the range of hours, which allowed human intervention for maintenance tasks. At JET, they measured values close to 200 μSv after a cooling time of 4 months and since then, the remote handling techniques became usual. There is a clear tendency to increase the radiation levels in the future. A clear example is ITER, where values of 450 Sv/h are expected in the centre of the torus after 11 days of cooling. Also, the new energetic levels of CERN are expected to lead to a more advanced remote handling means. In these circumstances this thesis is framed, studying a bilateral control system based on force-position, trying to avoid the use of force/torque sensors, whose electronic content makes them very sensitive in these environments. The contents of this work are focused on teleoperating industrial robots, which due its well-known reliability, easiness to be adapted to these environments, cost-effectiveness and high availability, are considered as an interesting alternative to expensive custom-made solutions for remote handling tasks. Firstly, the kinematic problem of teloperating master and slave with dissimilar kinematics is analysed and a new general approach for solving this issue is presented. The solution includes using assistive forces in order to guide the human operator. Coming up next, I explain with detail the experiments accomplished with an ABB robot that show the difficulties encountered and the proposed solutions. This section is concluded with a method to match the master’s and slave’s workspaces when they present dissimilar kinematics. Later on, the research studies the dynamics, with special focus on robot modelling with the purpose of obtaining a method that allows to estimate external forces acting on them. During the characterisation of the model’s parameters, a set of tests are performed in order to get to a compromise between computational complexity and estimation error. Key points for modelling and characterising robots with a parallelogram structure are also given, and the desired control architecture is presented. Once a complete model of the slave is obtained, different alternatives for external force estimation are review to be able to predict forces in real time, minimizing the position differentiation to minimize the estimation noise. The research starts by implementing classic state observers and then it evolves towards the use of Luenberger- Sliding observers whose implementation is relatively easy and the results are convincing. I also analyse the use of proposed observer during a simulated bilateral control on which the force feedback obtained with the classic techniques based on the position error is compared versus a control architecture based on force-position, where the force is estimated instead of measured. I t is checked how the proposed solution gives results comparable with the classical techniques and however introduces an alternative method for teleoperating industrial robots whose teleoperation in radioactive environments would have been impossible in a different way. Finally, the problems originated by the practical application of teleoperation in the before mentioned scenarios are analysed. Due the prohibitive conditions for every electronic equipment, the control systems should be placed far from the manipulators. This provokes that the power cables that fed the slaves devices can present lengths of hundreds of meters. In these circumstances, overvoltage waves are developed when implementing drives based on PWM technique. The occurrence of overvoltage is very dangerous for the system composed by drive, wiring and actuator, and has to be eliminated. During this work, a solution based on commercial LC filters is proposed and it is extensively proved that its inclusion does not introduce adverse effects into the actuator’s control.