Vivian Yi-Ju Chen

Examining non-stationary effects of social determinants on cardiovascular mortality after cold surges in Taiwan.

Details about the impact of extreme cold on cardiovascular mortality are little known in the countries with warm winters like Taiwan. This study aimed to examine the ecological associations between various social determinants and cardiovascular mortality after cold surges in Taiwan with a spatial perspective focusing on spatial non-stationarity. The mortality rates at township level in Taiwan were observed from 1997 to 2003. Five social determinants (social disadvantage, lack of economic opportunity, stability, sensitive group, and rurality) were created with the 2000 Taiwan Census data. We analyzed the data using Geographically Weighted Poisson Regression. On average, an immediate increase in cardiovascular mortality was found right after cold surges. All of the five determinants were found to have spatial non-stationary effects on the cardiovascular mortality rates after cold surges. This finding provided an empirical basis for developing public health programs with local emphases on the impacts of extreme cold.

Is environmental disclosure an effective strategy on establishment of environmental legitimacy for organization

Purpose – The purpose of this paper is to investigate the relationship between level of environmental disclosure and establishment of a legitimacy image of operation among Japanese firms after implementation of the Kyoto Protocol. Design/methodology/approach – This study uses a sample consisting of 208 firms listed in the Japan Nikkei Stock Index 500 and adopts three-stage least-squares (3SLS) to explore the relationship between environmental news exposure, environmental disclosure in corporate social responsibility (CSR) reports, and environmental legitimacy. Findings – Results indicate that firms from environmentally-sensitive industries can significantly improve their perceived legitimacy by releasing CSR reports; firms with better prior environmental legitimacy will be more active in environmental disclosure and establish better environmental legitimacy in the next period; firms with better carbon reduction performance tend to have higher levels of environmental disclosure. In terms of carbon reductio...

Cold surge: A sudden and spatially varying threat to health?

While cold surge is one of the most conspicuous features of the winter monsoon in East Asia, its impact on human health remains underexplored. Based on the definition by the Central Weather Bureau in Taiwan, we identified four cold surges between 2000 and 2003 and collected the cardiovascular disease mortality data 2 weeks before and 2 weeks after these events. We attempted to answer the following research questions: 1) whether the cold surges impose an adverse and immediate effect on cardiovascular mortality; 2) whether the people living in temperate zones have a higher tolerance of extreme temperature drop than those in the subtropics. With geographic weighting techniques, we not only found that the cardiovascular disease mortality rates increased significantly after the cold surges, but also discovered a spatially varying pattern of tolerance to cold surges. Even within a small study area such as Taiwan, human reaction to severe weather drop differs across space. Needless to say, in the U.S., these findings should be considered in redirecting policy to address populations living in warm places when extreme temperature drops occur.

Stochastic variability in stress, sleep duration and sleep quality across the distribution of body mass index: Insights from quantile regression

BackgroundObesity has become a problem in the USA and identifying modifiable factors at the individual level may help to address this public health concern. A burgeoning literature has suggested that sleep and stress may be associated with obesity; however, little is know about whether these two factors moderate each other and even less is known about whether their impacts on obesity differ by gender.PurposeThis study investigates whether sleep and stress are associated with body mass index (BMI) respectively, explores whether the combination of stress and sleep is also related to BMI, and demonstrates how these associations vary across the distribution of BMI values.MethodsWe analyze the data from 3,318 men and 6,689 women in the Philadelphia area using quantile regression (QR) to evaluate the relationships between sleep, stress, and obesity by gender.ResultsOur substantive findings include: (1) high and/or extreme stress were related to roughly an increase of 1.2 in BMI after accounting for other covariates; (2) the pathways linking sleep and BMI differed by gender, with BMI for men increasing by 0.77–1 units with reduced sleep duration and BMI for women declining by 0.12 unit with 1 unit increase in sleep quality; (3) stress- and sleep-related variables were confounded, but there was little evidence for moderation between these two; (4) the QR results demonstrate that the association between high and/or extreme stress to BMI varied stochastically across the distribution of BMI values, with an upward trend, suggesting that stress played a more important role among adults with higher BMI (i.e., BMI > 26 for both genders); and (5) the QR plots of sleep-related variables show similar patterns, with stronger effects on BMI at the upper end of BMI distribution.ConclusionsOur findings suggested that sleep and stress were two seemingly independent predictors for BMI and their relationships with BMI were not constant across the BMI distribution.

SAS macro programs for geographically weighted generalized linear modeling with spatial point data: Applications to health research

An increasing interest in exploring spatial non-stationarity has generated several specialized analytic software programs; however, few of these programs can be integrated natively into a well-developed statistical environment such as SAS. We not only developed a set of SAS macro programs to fill this gap, but also expanded the geographically weighted generalized linear modeling (GWGLM) by integrating the strengths of SAS into the GWGLM framework. Three features distinguish our work. First, the macro programs of this study provide more kernel weighting functions than the existing programs. Second, with our codes the users are able to better specify the bandwidth selection process compared to the capabilities of existing programs. Third, the development of the macro programs is fully embedded in the SAS environment, providing great potential for future exploration of complicated spatially varying coefficient models in other disciplines. We provided three empirical examples to illustrate the use of the SAS macro programs and demonstrated the advantages explained above.

Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties

The U.S. has experienced a resurgence of income inequality in the past decades. The evidence regarding the mortality implications of this phenomenon has been mixed. This study employs a rarely used method in mortality research, quantile regression (QR), to provide insight into the ongoing debate of whether income inequality is a determinant of mortality and to investigate the varying relationship between inequality and mortality throughout the mortality distribution. Analyzing a U.S. dataset where the five-year (1998-2002) average mortality rates were combined with other county-level covariates, we found that the association between inequality and mortality was not constant throughout the mortality distribution and the impact of inequality on mortality steadily increased until the 80th percentile. When accounting for all potential confounders, inequality was significantly and positively related to mortality; however, this inequality-mortality relationship did not hold across the mortality distribution. A series of Wald tests confirmed this varying inequality-mortality relationship, especially between the lower and upper tails. The large variation in the estimated coefficients of the Gini index suggested that inequality had the greatest influence on those counties with a mortality rate of roughly 9.95 deaths per 1000 population (80th percentile) compared to any other counties. Furthermore, our results suggest that the traditional analytic methods that focus on mean or median value of the dependent variable can be, at most, applied to a narrow 20 percent of observations. This study demonstrates the value of QR. Our findings provide some insight as to why the existing evidence for the inequality-mortality relationship is mixed and suggest that analytical issues may play a role in clarifying whether inequality is a robust determinant of population health.

A Generalized Correlation Coefficient for the General Two-Treatment Crossover Design

This article extends the correlation methodology developed by Chinchilli et al. (2005) for the 2 × 2 crossover design to more complex crossover designs for clinical trials. We describe how the methodology can be adapted to a general type of two-treatment crossover design which includes either at least two sequences or at least two treatment periods or both. We then derive the asymptotic theory for the corresponding correlation statistics, investigate the statistical accuracy of the estimators via bootstrap analyses, and demonstrate their use with two real data examples.