Uncovering geographic concentrations of elevated mesothelioma risks across Japan : Spatial epidemiological mapping of the asbestos-related disease

Disease mapping is an effective analytical approach to conducting epidemiological analysis as well as risk communications to share fundamental knowledge of existing/emerging epidemics. This article employs a series of spatial epidemiological techniques for enhanced disease mapping of the mesothelioma epidemic at the municipality level across Japan during the period between 1995 and 2004. The processing of data using spatial statistics is vital in the effective geovisualisation. The results revealed distinctive geographical concentrations of highly elevated mesothelioma risks, especially in areas with a history of prior asbestos-related manufacturing industries, such as textile, construction materials and shipbuilding factories

地理統計に基づくがん死亡の社会経済的格差の評価－市区町村別がん死亡と地理的剥奪指標との関連性－

要旨ありがん統計データおよびその解析原著論

The importance of scale in spatially varying coefficient modeling

While spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the "spatial scale" of each data relationship is crucially important to make SVC modeling more stable, and in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of scale. Models are examined through a series of Monte Carlo simulation experiments to assess the extent to which spatial scale influences model stability and the accuracy of their SVC estimates. The following models are studied: (i) geographically weighted regression (GWR) with a fixed distance or (ii) an adaptive distance bandwidth (GWRa), (iii) flexible bandwidth GWR (FB-GWR) with fixed distance or (iv) adaptive distance bandwidths (FB-GWRa), (v) eigenvector spatial filtering (ESF), and (vi) random effects ESF (RE-ESF). Results reveal that the SVC models designed to capture scale dependencies in local relationships (FB-GWR, FB-GWRa and RE-ESF) most accurately estimate the simulated SVCs, where RE-ESF is the most computationally efficient. Conversely GWR and ESF, where SVC estimates are naively assumed to operate at the same spatial scale for each relationship, perform poorly. Results also confirm that the adaptive bandwidth GWR models (GWRa and FB-GWRa) are superior to their fixed bandwidth counterparts (GWR and FB-GWR)

Package ‘GWmodel’

In GWmodel, we introduce techniques from a particular branch of spatial statis- tics,termed geographically-weighted (GW) models. GW models suit situa- tions when data are not described well by some global model, but where there are spatial re- gions where a suitably localised calibration provides a better description. GWmodel in- cludes functions to calibrate: GW summary statistics, GW principal components analy- sis, GW discriminant analysis and various forms of GW regression; some of which are pro- vided in basic and robust (outlier resistant) forms

Package ‘GWmodel’

In GWmodel, we introduce techniques from a particular branch of spatial statis- tics,termed geographically-weighted (GW) models. GW models suit situa- tions when data are not described well by some global model, but where there are spatial re- gions where a suitably localised calibration provides a better description. GWmodel in- cludes functions to calibrate: GW summary statistics, GW principal components analy- sis, GW discriminant analysis and various forms of GW regression; some of which are pro- vided in basic and robust (outlier resistant) forms

gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets

Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local relationships on a map. However, software dedicated to visualizing local spatial relations be-tween multiple variables in high dimensional datasets remains undeveloped. This paper introduces gwpcorMapper, a newly developed software application for mapping geographically weighted correlation and partial correlation in large multivariate datasets. gwpcorMap-per facilitates ESDA by giving researchers the ability to interact with map components that describe local correlative relationships. We built gwpcorMapper using the R Shiny framework. The software inherits its core algorithm from GWpcor, an R library for calculating the geographically weighted correlation and partial correlation statistics. We demonstrate the application of gwpcorMapper by using it to explore census data in order to find meaningful relationships that describe the work-life environment in the 23 special wards of Tokyo, Japan. We show that gwpcorMapper is useful in both variable selection and parameter tuning for geographically weighted statistics. gwpcorMapper highlights that there are strong statistically clear local variations in the relationship between the number of commuters and the total number of hours worked when considering the total population in each district across the 23 special wards of Tokyo. Our application demonstrates that the ESDA process with high-dimensional geospatial data using gwpcorMapper has applications across multiple fields.Comment: 18 pages, 8 figures, 2 table

Distinct associations of different sedentary behaviors with health-related attributes among older adults

Objective: Leisure-time sedentary behaviors (LTSBs) have been associated adversely with health outcomes. However, limited research has focused on different categories of LTSB. We aimed at identifying categories of LTSBs and examining their separate associations with indices of health among Japanese older adults. Methods: A postal survey collected data on self-reported health, psychological distress, body mass index, moderate-to-vigorous physical activity (MVPA), LTSBs (five behaviors) and socio-demographic characteristics from 1,580 Japanese older adults (67% response rate; 65-74 years) in 2010. Exploratory factor analysis was used to classify LTSBs. Odds ratios (ORs) were calculated for associations of LTSB categories with self-reported health, psychological distress, overweight, and lower MVPA. Data were analyzed in 2013. Results: Two categories of LTSB: passive sedentary time (consisting of TV time, listening or talking while sitting, and sitting around) and mentally-active sedentary time (consisting of computer-use and reading books or newspapers) were identified. Higher passive sedentary time was associated with a higher odds of being overweight (OR: 1.39, [95% CI: 1.08-1.80]), and lower MVPA (1.26, [1.02-1.54]). Higher mentally-active sedentary time was associated with lower odds of lower MVPA (0.70, [0.57-0.86]). Conclusions: Two types of sedentary time-passive and mentally-active-may play different roles in older adults' well-being