Spirometric studies on the adult general population of Helsinki : bronchodilation responses, determinants, and intrasession repeatability of FEV1, FEV6, FVC, and forced expiratory time

Spirometry is the most widely used lung function test in the world. It is fundamental in diagnostic and functional evaluation of various pulmonary diseases. In the studies described in this thesis, the spirometric assessment of reversibility of bronchial obstruction, its determinants, and variation features are described in a general population sample from Helsinki, Finland. This study is a part of the FinEsS study, which is a collaborative study of clinical epidemiology of respiratory health between Finland (Fin), Estonia (Es), and Sweden (S). Asthma and chronic obstructive pulmonary disease (COPD) constitute the two major obstructive airways diseases. The prevalence of asthma has increased, with around 6% of the population in Helsinki reporting physician-diagnosed asthma. The main cause of COPD is smoking with changes in smoking habits in the population affecting its prevalence with a delay. Whereas airway obstruction in asthma is by definition reversible, COPD is characterized by fixed obstruction. Cough and sputum production, the first symptoms of COPD, are often misinterpreted for smokers cough and not recognized as first signs of a chronic illness. Therefore COPD is widely underdiagnosed. More extensive use of spirometry in primary care is advocated to focus smoking cessation interventions on populations at risk. The use of forced expiratory volume in six seconds (FEV6) instead of forced vital capacity (FVC) has been suggested to enable office spirometry to be used in earlier detection of airflow limitation. Despite being a widely accepted standard method of assessment of lung function, the methodology and interpretation of spirometry are constantly developing. In 2005, the ATS/ERS Task Force issued a joint statement which endorsed the 12% and 200 ml thresholds for significant change in forced expiratory volume in one second (FEV1) or FVC during bronchodilation testing, but included the notion that in cases where only FVC improves it should be verified that this is not caused by a longer exhalation time in post-bronchodilator spirometry. This elicited new interest in the assessment of forced expiratory time (FET), a spirometric variable not usually reported or used in assessment. In this population sample, we examined FET and found it to be on average 10.7 (SD 4.3) s and to increase with ageing and airflow limitation in spirometry. The intrasession repeatability of FET was the poorest of the spirometric variables assessed. Based on the intrasession repeatability, a limit for significant change of 3 s was suggested for FET during bronchodilation testing. FEV6 was found to perform equally well as FVC in the population and in a subgroup of subjects with airways obstruction. In the bronchodilation test, decreases were frequently observed in FEV1 and particularly in FVC. The limit of significant increase based on the 95th percentile of the population sample was 9% for FEV1 and 6% for FEV6 and FVC; these are slightly lower than the current limits for single bronchodilation tests (ATS/ERS guidelines). FEV6 was proven as a valid alternative to FVC also in the bronchodilation test and would remove the need to control duration of exhalation during the spirometric bronchodilation test.

Electrocardiographic repolarization variables in detecting myocardial infarction and ischemic injury : From body surface potential mapping to a single lead

The aim of the studies was to improve the diagnostic capability of electrocardiography (ECG) in detecting myocardial ischemic injury with a future goal of an automatic screening and monitoring method for ischemic heart disease. The method of choice was body surface potential mapping (BSPM), containing numerous leads, with intention to find the optimal recording sites and optimal ECG variables for ischemia and myocardial infarction (MI) diagnostics. The studies included 144 patients with prior MI, 79 patients with evolving ischemia, 42 patients with left ventricular hypertrophy (LVH), and 84 healthy controls. Study I examined the depolarization wave in prior MI with respect to MI location. Studies II-V examined the depolarization and repolarization waves in prior MI detection with respect to the Minnesota code, Q-wave status, and study V also with respect to MI location. In study VI the depolarization and repolarization variables were examined in 79 patients in the face of evolving myocardial ischemia and ischemic injury. When analyzed from a single lead at any recording site the results revealed superiority of the repolarization variables over the depolarization variables and over the conventional 12-lead ECG methods, both in the detection of prior MI and evolving ischemic injury. The QT integral, covering both depolarization and repolarization, appeared indifferent to the Q-wave status, the time elapsed from MI, or the MI or ischemia location. In the face of evolving ischemic injury the performance of the QT integral was not hampered even by underlying LVH. The examined depolarization and repolarization variables were effective when recorded in a single site, in contrast to the conventional 12-lead ECG criteria. The inverse spatial correlation of the depolarization and depolarization waves in myocardial ischemia and injury could be reduced into the QT integral variable recorded in a single site on the left flank. In conclusion, the QT integral variable, detectable in a single lead, with optimal recording site on the left flank, was able to detect prior MI and evolving ischemic injury more effectively than the conventional ECG markers. The QT integral, in a single-lead or a small number of leads, offers potential for automated screening of ischemic heart disease, acute ischemia monitoring and therapeutic decision-guiding as well as risk stratification.

Quantifying Time Awake Posturographically

This thesis focuses on the issue of testing sleepiness quantitatively. The issue is relevant to policymakers concerned with traffic- and occupational safety; such testing provides a tool for safety legislation and -surveillance. The findings of this thesis provide guidelines for a posturographic sleepiness tester. Sleepiness ensuing from staying awake merely 17 h impairs our performance as much as the legally proscribed blood alcohol concentration 0.5 does. Hence, sleepiness is a major risk factor in transportation and occupational accidents. The lack of convenient, commercial sleepiness tests precludes testing impending sleepiness levels contrary to simply breath testing for alcohol intoxication. Posturography is a potential sleepiness test, since clinical diurnal balance testing suggests the hypothesis that time awake could be posturographically estimable. Relying on this hypothesis this thesis examines posturographic sleepiness testing for instrumentation purposes. Empirical results from 63 subjects for whom we tested balance with a force platform during wakefulness for maximum 36 h show that sustained wakefulness impairs balance. The results show that time awake is posturographically estimable with 88% accuracy and 97% precision which validates our hypothesis. Results also show that balance scores tested at 13:30 hours serve as a threshold to detect excessive sleepiness. Analytical results show that the test length has a marked effect on estimation accuracy: 18 s tests suffice to identify sleepiness related balance changes, but trades off some of the accuracy achieved with 30 s tests. The procedure to estimate time awake relies on equating the subject s test score to a reference table (comprising balance scores tested during sustained wakefulness, regressed against time awake). Empirical results showed that sustained wakefulness explains 60% of the diurnal balance variations, whereas the time of day explains 40% of the balance variations. The latter fact implies that time awake estimations also must rely on knowing the local times of both test and reference scores.

Improving GPS time series for geodynamic studies

Accurate and stable time series of geodetic parameters can be used to help in understanding the dynamic Earth and its response to global change. The Global Positioning System, GPS, has proven to be invaluable in modern geodynamic studies. In Fennoscandia the first GPS networks were set up in 1993. These networks form the basis of the national reference frames in the area, but they also provide long and important time series for crustal deformation studies. These time series can be used, for example, to better constrain the ice history of the last ice age and the Earth s structure, via existing glacial isostatic adjustment models. To improve the accuracy and stability of the GPS time series, the possible nuisance parameters and error sources need to be minimized. We have analysed GPS time series to study two phenomena. First, we study the refraction in the neutral atmosphere of the GPS signal, and, second, we study the surface loading of the crust by environmental factors, namely the non-tidal Baltic Sea, atmospheric load and varying continental water reservoirs. We studied the atmospheric effects on the GPS time series by comparing the standard method to slant delays derived from a regional numerical weather model. We have presented a method for correcting the atmospheric delays at the observational level. The results show that both standard atmosphere modelling and the atmospheric delays derived from a numerical weather model by ray-tracing provide a stable solution. The advantage of the latter is that the number of unknowns used in the computation decreases and thus, the computation may become faster and more robust. The computation can also be done with any processing software that allows the atmospheric correction to be turned off. The crustal deformation due to loading was computed by convolving Green s functions with surface load data, that is to say, global hydrology models, global numerical weather models and a local model for the Baltic Sea. The result was that the loading factors can be seen in the GPS coordinate time series. Reducing the computed deformation from the vertical time series of GPS coordinates reduces the scatter of the time series; however, the long term trends are not influenced. We show that global hydrology models and the local sea surface can explain up to 30% of the GPS time series variation. On the other hand atmospheric loading admittance in the GPS time series is low, and different hydrological surface load models could not be validated in the present study. In order to be used for GPS corrections in the future, both atmospheric loading and hydrological models need further analysis and improvements.

Quantum Space-Time and Noncommutative Gauge Field Theories

Arguments arising from quantum mechanics and gravitation theory as well as from string theory, indicate that the description of space-time as a continuous manifold is not adequate at very short distances. An important candidate for the description of space-time at such scales is provided by noncommutative space-time where the coordinates are promoted to noncommuting operators. Thus, the study of quantum field theory in noncommutative space-time provides an interesting interface where ordinary field theoretic tools can be used to study the properties of quantum spacetime. The three original publications in this thesis encompass various aspects in the still developing area of noncommutative quantum field theory, ranging from fundamental concepts to model building. One of the key features of noncommutative space-time is the apparent loss of Lorentz invariance that has been addressed in different ways in the literature. One recently developed approach is to eliminate the Lorentz violating effects by integrating over the parameter of noncommutativity. Fundamental properties of such theories are investigated in this thesis. Another issue addressed is model building, which is difficult in the noncommutative setting due to severe restrictions on the possible gauge symmetries imposed by the noncommutativity of the space-time. Possible ways to relieve these restrictions are investigated and applied and a noncommutative version of the Minimal Supersymmetric Standard Model is presented. While putting the results obtained in the three original publications into their proper context, the introductory part of this thesis aims to provide an overview of the present situation in the field.

Diagnostic Tests Based on Quantile Residuals for Nonlinear Time Series Models

This thesis studies quantile residuals and uses different methodologies to develop test statistics that are applicable in evaluating linear and nonlinear time series models based on continuous distributions. Models based on mixtures of distributions are of special interest because it turns out that for those models traditional residuals, often referred to as Pearson's residuals, are not appropriate. As such models have become more and more popular in practice, especially with financial time series data there is a need for reliable diagnostic tools that can be used to evaluate them. The aim of the thesis is to show how such diagnostic tools can be obtained and used in model evaluation. The quantile residuals considered here are defined in such a way that, when the model is correctly specified and its parameters are consistently estimated, they are approximately independent with standard normal distribution. All the tests derived in the thesis are pure significance type tests and are theoretically sound in that they properly take the uncertainty caused by parameter estimation into account. -- In Chapter 2 a general framework based on the likelihood function and smooth functions of univariate quantile residuals is derived that can be used to obtain misspecification tests for various purposes. Three easy-to-use tests aimed at detecting non-normality, autocorrelation, and conditional heteroscedasticity in quantile residuals are formulated. It also turns out that these tests can be interpreted as Lagrange Multiplier or score tests so that they are asymptotically optimal against local alternatives. Chapter 3 extends the concept of quantile residuals to multivariate models. The framework of Chapter 2 is generalized and tests aimed at detecting non-normality, serial correlation, and conditional heteroscedasticity in multivariate quantile residuals are derived based on it. Score test interpretations are obtained for the serial correlation and conditional heteroscedasticity tests and in a rather restricted special case for the normality test. In Chapter 4 the tests are constructed using the empirical distribution function of quantile residuals. So-called Khmaladze s martingale transformation is applied in order to eliminate the uncertainty caused by parameter estimation. Various test statistics are considered so that critical bounds for histogram type plots as well as Quantile-Quantile and Probability-Probability type plots of quantile residuals are obtained. Chapters 2, 3, and 4 contain simulations and empirical examples which illustrate the finite sample size and power properties of the derived tests and also how the tests and related graphical tools based on residuals are applied in practice.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

What do hydrological time series tell about climate change?

Yhteenveto: Mitä hydrologiset mallit kertovat ilmaston muutoksesta?