Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data.

Geographically Weighted Regression (GWR) has gained widespread popularity across various disciplines for investigating spatial heterogeneity with respect to data relationships in georeferenced datasets. However, GWR is typically limited to the analysis of continuous dependent variables, which are assumed to follow a symmetric normal distribution. In many fields, nonnegative continuous data are often observed and may contain substantial amounts of zeros followed by a right-skewed distribution of positive values.

Human Motor Endplate Survival after Chronic Peripheral Nerve Injury.

Objective: Degeneration of motor endplates (MEPs) in denervated muscle is thought to be a key factor limiting functional regeneration after peripheral nerve injury (PNI) in humans. However, there is currently no paradigm to determine MEP status in denervated human muscle to estimate likelihood of reinnervation success. Here, we present a quantitative analysis of MEP status in biopsies of denervated muscles taken during nerve repair surgery and ensuing functional recovery.

Preoperative Muscle Biopsy to Assess Motor End Plate Integrity as a Predictor for Successful Nerve Transfer: A Case Report.

Case: A 60-year-old right-hand-dominant man was referred for persistent right deltoid weakness, lateral shoulder numbness, and severe functional deficit 3 months after undergoing proximal humerus open reduction and internal fixation with plate and fibular strut allograft. Deltoid muscle biopsy demonstrated motor end plate (MEP) degeneration. After partial radial-to-axillary nerve transfer, repeat deltoid muscle biopsy revealed successful regeneration of MEPs with reinnervation of deltoid confirmed with postnerve transfer electromyography.

Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties.

Purpose: Research on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks.

Methods: Analyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups-High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk-and then apply both conventional (i.

Exploring heterogeneities with geographically weighted quantile regression: An enhancement based on the bootstrap approach.

Geographically weighted quantile regression (GWQR) has been proposed as a spatial analytical technique to simultaneously explore two heterogeneities, one of spatial heterogeneity with respect to data relationships over space and one of response heterogeneity across different locations of the outcome distribution. However, one limitation of GWQR framework is that the existing inference procedures are established based on asymptotic approximation, which may suffer computation difficulties or yield incorrect estimates with finite samples. In this paper, we suggest a bootstrap approach to address this limitation.

When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization.

Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilisation in the United States of America (USA) and found that most of the relationships between late or no prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study was to address this concern by including both spatial homogeneity and heterogeneity into the analysis.

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

Background: Obesity 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.

Purpose: This 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.

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.

Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data.

In recent years, techniques have been developed to explore spatial non-stationarity and to model the entire distribution of a regressand. The former is mainly addressed by geographically weighted regression (GWR), and the latter by quantile regression (QR). However, little attention has been paid to combining these analytical techniques.

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.

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.

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.

'Zuoyuezi' after caesarean in China: an interview survey.

While studying caesarean decision-making in China, the increasing popularity of 'zuoyuezi' emerged. This paper addresses this development, the reasons and implications. We tape-recorded semi-structured interviews in three industrial cities in China.