Model for the analysis of binary time series of respiratory symptoms.
نویسندگان
چکیده
Environmental epidemiologic research on respiratory symptoms presents unique types of data, typically requiring simultaneous analysis of both time- and person-varying factors. In this paper, the authors propose a new, simple model that incorporates such factors and controls for each person's prior history of symptoms. The Yale Mother and Infant Health Study was undertaken to investigate the effects of ambient pollutant concentrations, meteorologic changes, and demographic variables on daily respiratory symptoms in both mothers and infants. This analysis was restricted to 673 mothers followed in southwestern Virginia from June 10 to August 31, 1995. Of the person-varying factors, husband's level of education, nested within marital status, and having pets in the home were related to an increased likelihood of new episodes; however, neither was related to duration of symptoms. Interestingly, women who were unmarried were least likely to have new episodes of respiratory symptoms, while those with the most highly educated spouses were most likely to have new episodes. Having pets in the home increased the likelihood of a new episode. Having a history of allergies and having children in day care were found to be related to the symptom of a runny or stuffy nose, in terms of both incidence and duration. The level of coarse particles was related to the incidence of new episodes of runny or stuffy nose, and a higher level prolonged the duration of symptoms. Sulfate level was not related to the incidence of new episodes but was associated with the duration of the episodes.
منابع مشابه
A New Nonlinear Specification of Structural Breaks for Money Demand in Iran
In a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. The use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. The simple and basic idea in this paper i...
متن کاملTime Series Modeling of Coronavirus (COVID-19) Spread in Iran
Various types of Coronaviruses are enveloped RNA viruses from the Corona-viridae family and part of the Coronavirinae subfamily. This family of viruses affects neurological, gastrointestinal, hepatic, and respiratory systems. Recently, a new memb-er of this family, named Covid-19, is moving around the world. The expansion of Covid-19 carries many risks, and its control requires strict planning ...
متن کاملA new approach to detect Life threatening cardiac arrhythmias using Sequential spectrum of Electrocardiogram signals
This study evaluates the discriminative power of sequential spectrum analysis of the short-term electrocardiogram (ECG) time series in separating normal and subjects with life threatening arrhythmias like, ventricular tachycardia/fibrillation (VT/VF). The raw ECG time series is transformed into a series of binary symbols and the binary occupancy or relative distribution of mono-sequences (i.e. ...
متن کاملA new approach to detect Life threatening cardiac arrhythmias using Sequential spectrum of Electrocardiogram signals
This study evaluates the discriminative power of sequential spectrum analysis of the short-term electrocardiogram (ECG) time series in separating normal and subjects with life threatening arrhythmias like, ventricular tachycardia/fibrillation (VT/VF). The raw ECG time series is transformed into a series of binary symbols and the binary occupancy or relative distribution of mono-sequences (i.e. ...
متن کاملSeismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
In this paper, we have tried to predict earthquake events in a cluster of seismic data on pacific ring of fire, using multivariate adaptive regression splines (MARS). The model is employed as either a predictor for a sequence prediction task, or a binary classifier for a sequence recognition problem, which could alternatively help to predict an event. Here, we explain that sequence prediction/r...
متن کاملInterpolating time series based on fuzzy cluster analysis problem
This study proposes the model for interpolating time series to use them to forecast effectively for future. This model is established based on the improved fuzzy clustering analysis problem, which is implemented by the Matlab procedure. The proposed model is illustrated by a data set and tested for many other datasets, especially for 3003 series in M3-Competition data. Comparing to the exist...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- American journal of epidemiology
دوره 151 12 شماره
صفحات -
تاریخ انتشار 2000