Mapping the results of local statistics

The application of geographically weighted regression (GWR) – a local spatial statistical technique used to test for spatial nonstationarity – has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity.geographically weighted regression, local statistics, mapping, nonstationarity

Social Isolation, Residential Stability, and Opioid Use Disorder among Older Medicare Beneficiaries: Metropolitan and Non-Metropolitan County Comparison

Research has shown that the prevalence of opioid use disorder (OUD) may rise substantially as society ages, but this issue receives the least attention in the literature. To address this gap, this study utilizes county-level data from multiple data sources (1) to investigate whether social isolation is associated with OUD prevalence among older Medicare beneficiaries, (2) to examine whether and how residential stability moderates the association between social isolation and OUD prevalence in US counties, and (3) to determine if there are any differences in these associations between metropolitan and nonmetropolitan counties. The results show that social isolation is a significant factor for county-level OUD prevalence, regardless of metropolitan status. In addition, counties with high residential stability have low prevalence of OUD among older adults and this association is stronger in metropolitan than in nonmetropolitan counties. Nonetheless, high levels of residential stability reinforce the positive relationship between social isolation and OUD prevalence. As a result, when developing policies and interventions aimed at reducing OUD among older adults, place of residence must be taken into account

Unemployment and Opioid-Related Mortality Rates in U.S. Counties: Investigating Social Capital and Social Isolation–Smoking Pathways

We examine two mechanisms—social capital and sociobehavior—potentially linking unemployment rates to opioid-related mortality and investigate whether the mechanisms differ geographically by the pace of the opioid crisis. Applying path analysis techniques to 2015–2017 opioid-related mortality in U.S. counties (N = 2,648), we find that (1) high unemployment rates are not directly associated with opioid-related mortality rates; (2) high unemployment rates are negatively associated with social capital, and low social capital contributes to high opioid-related mortality; (3) high unemployment rates increase social isolation and the prevalence of smoking, which is positively related to opioid-related mortality; and (4) the pathways are stronger among counties in the states experiencing a rapid growth in opioid-related mortality rates than among those states that are not. Our findings offer insight into how unemployment rates shape the opioid crisis and suggest that the relationship between unemployment and opioid-related mortality is complex

Contextual Despair

This document provides a summary of contextual variables at the tract, county, and state level that may be relevant to operationalizing different dimensions of “despair” in Add Health respondents’ environment, per the “deaths of despair” hypothesis advanced by Case and Deaton (2015; 2020)

Looking Back, Looking Forward: Progress and Prospect for Spatial Demography

In 2011 a specialist meeting on the “Future Directions in Spatial Demography” was held in Santa Barbara, California (Matthews, Goodchild, & Janelle, 2012).1 This specialist meeting was the capstone to a multi-year National Institutes of Health training grant that had supported workshops in advanced spatial analysis methods increasing used by population scientists.2 Early-career scholars who had participated in the training workshops and senior demographers and geographers drawn from across the United States participated in the specialist meeting.3 The application process to attend the 2011 meeting, required that each of the forty-one attendees submit a statement that reviewed challenges and identifed new directions for spatial demography, including gaps in current knowledge regarding innovations in geospatial data, spatial statistical methods, and the integration of data and models to enhance the science of spatial demography in population and health research. Reading again some of the ruminations of these scholars is an interesting exercise in its own right. The level of optimism back in 2011 was high, and especially regarding anticipated changes in computational capacity, leveraging big data (including volunteered geographic information), developments in data systems (including new data high resolution data products and online resources such as multi-scale map interfaces and dashboards), and in methods such as time–space models, agent-based models, microsimulation, and small-area estimation. There were also several challenges identifed including, but not limited to, study designs, data integration, data validation, confdentiality, non-representative data, historic data, defnitions of place, residential selection and mobility as well as two overarching challenges related to the role and contribution of spatial demographers in interdisciplinary population and health research, and many, many comments on training issues. Substantively the attendees research focused on all forms of interaction between people and place (and the reciprocal relations between the people in social, built, and physical environment contexts) covering the gamut of demographic processes from reproductive health to mortality, though with perhaps an overrepresentation of researchers in areas related to population and environment research, racial and residential segregation, and migration.The R25 Training Grant was funded through the Eunice Kennedy Shriver National Institutes of Child Health and Human Development (NICHD 5R-25 HD057002; Principal Investigator: Stephen A. Matthews).

Looking Back, Looking Forward: Progress and Prospect for Spatial Demography

In 2011 a specialist meeting on the “Future Directions in Spatial Demography” was held in Santa Barbara, California (Matthews, Goodchild, & Janelle, 2012).1 This specialist meeting was the capstone to a multi-year National Institutes of Health training grant that had supported workshops in advanced spatial analysis methods increasing used by population scientists.2 Early-career scholars who had participated in the training workshops and senior demographers and geographers drawn from across the United States participated in the specialist meeting.3 The application process to attend the 2011 meeting, required that each of the forty-one attendees submit a statement that reviewed challenges and identifed new directions for spatial demography, including gaps in current knowledge regarding innovations in geospatial data, spatial statistical methods, and the integration of data and models to enhance the science of spatial demography in population and health research. Reading again some of the ruminations of these scholars is an interesting exercise in its own right. The level of optimism back in 2011 was high, and especially regarding anticipated changes in computational capacity, leveraging big data (including volunteered geographic information), developments in data systems (including new data high resolution data products and online resources such as multi-scale map interfaces and dashboards), and in methods such as time–space models, agent-based models, microsimulation, and small-area estimation. There were also several challenges identifed including, but not limited to, study designs, data integration, data validation, confdentiality, non-representative data, historic data, defnitions of place, residential selection and mobility as well as two overarching challenges related to the role and contribution of spatial demographers in interdisciplinary population and health research, and many, many comments on training issues. Substantively the attendees research focused on all forms of interaction between people and place (and the reciprocal relations between the people in social, built, and physical environment contexts) covering the gamut of demographic processes from reproductive health to mortality, though with perhaps an overrepresentation of researchers in areas related to population and environment research, racial and residential segregation, and migration.The R25 Training Grant was funded through the Eunice Kennedy Shriver National Institutes of Child Health and Human Development (NICHD 5R-25 HD057002; Principal Investigator: Stephen A. Matthews).

The effects of histone H4 tail acetylations on cation-induced chromatin folding and self-association

Understanding the molecular mechanisms behind regulation of chromatin folding through covalent modifications of the histone N-terminal tails is hampered by a lack of accessible chromatin containing precisely modified histones. We study the internal folding and intermolecular self-association of a chromatin system consisting of saturated 12-mer nucleosome arrays containing various combinations of completely acetylated lysines at positions 5, 8, 12 and 16 of histone H4, induced by the cations Na+, K+, Mg2+, Ca2+, cobalt-hexammine3+, spermidine3+ and spermine4+. Histones were prepared using a novel semi-synthetic approach with native chemical ligation. Acetylation of H4-K16, but not its glutamine mutation, drastically reduces cation-induced folding of the array. Neither acetylations nor mutations of all the sites K5, K8 and K12 can induce a similar degree of array unfolding. The ubiquitous K+, (as well as Rb+ and Cs+) showed an unfolding effect on unmodified arrays almost similar to that of H4-K16 acetylation. We propose that K+ (and Rb+/Cs+) binding to a site on the H2B histone (R96-L99) disrupts H4K16 ε-amino group binding to this specific site, thereby deranging H4 tail-mediated nucleosome–nucleosome stacking and that a similar mechanism operates in the case of H4-K16 acetylation. Inter-array self-association follows electrostatic behavior and is largely insensitive to the position or nature of the H4 tail charge modification

2011 Specialist Meeting-Future Directions in Spatial Demography Future Directions in Spatial Demography

rained as a macro-demographer with a strong training background in spatial statistics and mortality research, I am particularly interested in the questions of what new methodological developments in spatial analysis are possible in the near future, and how these new developments evolve from current mainstream spatial demography. I prepared this document to briefly discuss the limitations in current spatial analysis methods and development in spatial data, respectively. Drawing on these discussions, I will answer the two questions above by elaborating on the future challenges facing macro-demographers. The essence of demography is to study the collective, rather than individual, behaviors [1]. As such, demographic changes and questions have traditionally been studied using data aggregated to various geographic units (e.g., counties and states), which is considered a macrodemographer's perspective Since the late 1990s, macro-demographers have benefited from the development of userfriendly spatial analysis tools by Fotheringham et al. [15] has given macro-demographers cause to reflect on the conventional one-model-fits-all approach. Second, the ability to incorporate a temporal dimension into regression models is limited. While some developers have endeavored to address this issue, most space-time analyses are exploratory or descriptive, such as STARS by Rey and Janika

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

[[abstract]]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.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SSCI[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]GB

Spatializing health research: what we know and where we are headin

Beyond individual-level factors, researchers have adopted a spatial perspective to explore potentially modifiable environmental determinants of health. A spatial perspective can be integrated into health research by incorporating spatial data into studies or analysing georeferenced data. Given the rapid changes in data collection methods and the complex dynamics between individuals and environment, we argue that geographical information system (GIS) functions have shortcomings with respect to analytical capability and are limited when it comes to visualizing the temporal component in spatio-temporal data. In addition, we maintain that relatively little effort has been made to handle spatial heterogeneity. To that end, health researchers should be persuaded to better justify the theoretical meaning underlying the spatial matrix in analysis, while spatial data collectors, GIS specialists, spatial analysis methodologists and the different breeds of users should be encouraged to work together making health research move forward through addressing these issue