Yingchun Zhou

Prevalence and risk factors of childhood allergic diseases in eight metropolitan cities in China: a multicenter study.

BackgroundSeveral studies conducted during the past two decades suggested increasing trend of childhood allergic diseases in China. However, few studies have provided detailed description of geographic variation and explored risk factors of these diseases. This study investigated the pattern and risk factors of asthma, allergic rhinitis and eczema in eight metropolitan cities in China.MethodsWe conducted a cross-sectional survey during November-December 2005 in eight metropolitan cities in China. A total of 23791 children aged 6-13 years participated in this survey. Questions from the standard questionnaire of the International Study of Asthma and Allergies in Children (ISAAC) were used to examine the pattern of current asthma, allergic rhinitis and eczema. Logistic regression analyses were performed to assess the risk factors for childhood allergies.ResultsThe average prevalence of childhood asthma, allergic rhinitis and eczema across the eight cities was 3∙3% (95% Confidence interval (CI): 3∙1%, 3∙6%), 9∙8% (95% CI: 9∙4%, 10∙2%) and 5∙5% (95% CI: 5∙2%, 5∙8%), respectively. Factors related to lifestyle, mental health and socio-economic status were found to be associated with the prevalence of childhood allergies. These risk factors were unevenly distributed across cities and disproportionately affected the local prevalence.ConclusionsThere was apparent geographic variation of childhood allergies in China. Socio-environmental factors had strong impacts on the prevalence of childhood allergies; but these impacts differed across regions. Thus public health policies should specifically target at the local risk factors for each individual area.

Functional data analytic approach of modeling ECG T-wave shape to measure cardiovascular behavior

The T-wave of an electrocardiogram (ECG) represents the ventricular repolarization that is critical in restoration of the heart muscle to a pre-contractile state prior to the next beat. Alterations in the T-wave reflect various cardiac conditions; and links between abnormal (prolonged) ventricular repolarization and malignant arrhythmias have been documented. Cardiac safety testing prior to approval of any new drug currently relies on two points of the ECG waveform: onset of the Q-wave and termination of the T-wave; and only a few beats are measured. Using functional data analysis, a statistical approach extracts a common shape for each subject (reference curve) from a sequence of beats, and then models the deviation of each curve in the sequence from that reference curve as a four-dimensional vector. The representation can be used to distinguish differences between beats or to model shape changes in a subjects T-wave over time. This model provides physically interpretable parameters characterizing T-wave shape, and is robust to the determination of the endpoint of the T-wave. Thus, this dimension reduction methodology offers the strong potential for definition of more robust and more informative biomarkers of cardiac abnormalities than the QT (or QT corrected) interval in current use.

A new functional data‐based biomarker for monitoring cardiovascular behavior

Cardiac safety assessment in drug development concerns the ventricular repolarization (represented by electrocardiogram (ECG) T-wave) abnormalities of a cardiac cycle, which are widely believed to be linked with torsades de pointes, a potentially life-threatening arrhythmia. The most often used biomarker for such abnormalities is the prolongation of the QT interval, which relies on the correct annotation of onset of QRS complex and offset of T-wave on ECG. A new biomarker generated from a functional data-based methodology is developed to quantify the T-wave morphology changes from placebo to drug interventions. Comparisons of T-wave-form characters through a multivariate linear mixed model are made to assess cardiovascular risk of drugs. Data from a study with 60 subjects participating in a two-period placebo-controlled crossover trial with repeat ECGs obtained at baseline and 12 time points after interventions are used to illustrate this methodology; different types of wave form changes were characterized and motivated further investigation.

Disease Classification and Biomarker Discovery Using ECG Data

In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA), SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively.

Marking the Ends of ECG T-Waves for Assessing Cardiac Safety: Algorithms and Experts

The prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval endpoints. However, selected beats of data show that the difference between two experts’ marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave. This article develops a Bayesian approach combining both slope and curvature information. We show that the difference between the automatic Bayesian marks and either of the experts’ marks is not statistically larger than the difference between two experts’ marks, thus this approach is successful in closely approximating the experts’ results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.

Functional Mixed Effects Model for the Analysis of Dose-Titration Studies

ABSTRACT Functional data analysis, which analyzes data that can be represented by curves or images, has many potential applications in clinical trials. Motivated by a real example, this study constructs a functional mixed effects model for analyzing a clinical outcome that is observed continuously over a long period of time. A penalized spline (P-spline)-based method is applied to obtain the estimators of the mean function and the time-varying coefficients. Simulation studies are conducted to investigate the consistency, efficiency, and robustness of the method. To illustrate the use of the method, a real data analysis is performed and produces interpretable results.

Local functional data model for characterizing shape variation of similar curves

ABSTRACT “Similar curves” in the present article refers to a family of curves whose major shape are similar, but who have variation coming from curve-specific sources. The goal here is to develop a general methodology to describe small changes among similar curves. Previous methods mainly focus on dimension reduction through FPCA, which are not appropriate for quantifying local variation. Here, we consider a local functional data model which divides data into segments adaptively and models each segment with a shape invariant model. Such model has great flexibility in characterizing local variation of curves, as illustrated by simulation and real data examples.

Placebo-controlled assessment of somnolence effect of cetirizine: a meta-analysis

It has been found that the most common adverse reaction which occurs more frequently on cetirizine than on placebo is somnolence. However, the somnolence rate varied widely among different studies. The objective of this study was to assess the somnolence effect of cetirizine 10 mg daily compared to placebo in patients aged 6 years and older using meta‐analysis and explore the sources of heterogeneity among different studies.

Multiple Source Annotation of ECG T-Waves for Measuring QT in Drug Development

QT interval (adjusted for heart rate) of electrocardiogram (ECG) is the current measure for assessing cardiac safety of noncardiac drugs in drug development. It measures the length between the onset of the Q-wave and the offset of the T-wave. Many single-lead methods are developed to annotate the wave boundaries. While they agree quite closely on the onsets of the Q-waves, often times they differ by large margins on the offsets of the T-waves, since the T-waves are more variable. We propose three methods to combine the annotation results from multiple sources, which can either be annotations from different leads or annotations using different methods. The three methods are the meta-analysis methods for integrating independent and dependent sources and the Bayes-expectation-maximization (EM) algorithm method. The results from these information integrated methods are much better than those obtained from single-source methods, which is illustrated by a simulation study and real-data applications.

Environmental risk factor assessment: a multilevel analysis of childhood asthma in China

BackgroundRapid changes in socioeconomic environment and their diverse patterns in China raise a question: how socio-environmental factors affect childhood asthma in China. We performed a multilevel analysis based on a 2005 national survey to understand the association between environmental factors and asthma, and to provide insights on developing prevention strategies.MethodsA multi-center, cross-sectional survey was conducted in 2018 school-aged children chosen from eight Chinese cities. Children of 6–13 years old were chosen randomly from schools of 39 centers in 8 cities. The multilevel analysis was made to assess both individual-level and city-level risk factors. The effect of gross domestic product (GDP) was further investigated by analysis of the factors.ResultsAnalysis of city-level environmental factors showed that GDP [adjusted odds ratio (OR)=1.88], particulate matter with aerodynamic diameter ≤10 μm (PM10) (adjusted OR=1.37), and average humidity (adjusted OR=1.33) were strong risk factors. Further analysis of the factors decomposed GDP into two major factors, the first represented by urban construction, energy consumption, nitrogen dioxide concentration, and the second represented by health-system coverage. This suggested that the negative effects of GDP outweighed its positive effects on asthma.ConclusionsThe prevalence of childhood asthma varies significantly in the eight Chinese cities. Socioenvironmental factors such as GDP, PM10 and average humidity are strong risk factors controlling individual attributes, suggesting that balance is needed between public health and economic development in China.