Survival Voting and Minority Political Rights

The health of American democracy has literally been challenged. The global pandemic has powerfully exposed a long-standing truth: electoral policies that are frequently referred to as convenience voting are really a mode of survival voting for millions of Americans. As our data show, racial minorities are overrepresented among voters whose health is most vulnerable, and politicians have leveraged these health disparities to subordinate the political voice of racial minorities. To date, data about racial disparities in health has played a very limited role in assessing voting rights. A new health lens on the racial impacts of voting rules would beneficially inform—and perhaps even fundamentally alter—how we address several common voting rights issues. A new focus on the disparate health effects of voting rules, grounded in the kind of solid empirical evidence we provide, could reinvigorate the Voting Rights Act (VRA) by providing new avenues for assessing voting rights, for litigating and judging voter suppression claims under section 2, and even informing a new coverage formula in a modified section 5. This evidence arrives at a critical juncture for the VRA which has been stripped of much of its bite by the Supreme Court and is currently being debated in Congress. The clear and compelling story told by our data are a clarion call to legislators, courts, and litigators to reconceptualize and strengthen voting rights by accounting for the barriers that health disparities pose to minority access to the ballot

Disaster Vulnerability

Vulnerability drives disaster law, yet the literature lacks both an overarching analysis of the different aspects of vulnerability and a nuanced examination of the factors that shape disaster outcomes. Though central to disaster law and policy, vulnerability often lurks in the shadows of a disaster, evident only once the worst is past and the bodies have been counted. The COVID-19 pandemic is a notable exception to this historical pattern: from the beginning of the pandemic, it has been clear that the virus poses different risks to different people, depending on vulnerability variables. This most recent pandemic experience thus provides a useful vantage point for analyzing vulnerability. Drawing on empirical data from the pandemic and experiences from past disasters, this Article identifies and discusses the policy implications of three dimensions of disaster vulnerability: the geography of vulnerability, competing or conflicting vulnerabilities, and political vulnerability. First, it explores the geography of vulnerability, using statistical analysis and geographic information system (GIS) mapping. The Article presents an innovative COVID-19 vulnerability index that identifies the country’s most vulnerable counties and the leading driver of vulnerability for each county. It demonstrates how this index could have informed voter accommodations during the 2020 elections and mask mandates throughout the pandemic. The Article also shows how, going forward, similar modeling could make disaster management more proactive and better able to anticipate needs and prioritize disaster mitigation and response resources. Second, this Article explores competing or conflicting vulnerabilities––situations where policy-makers must prioritize one vulnerable group or one aspect of vulnerability over another. To illustrate this, it considers two other policy challenges: school closures and vaccine distribution. Finally, the Article explores political vulnerability, analyzing how disasters make already-vulnerable groups even more vulnerable to certain harms, including political neglect, stigmatization, disenfranchisement, and displacement. In sum, this Article draws upon the costly lessons of COVID-19 to suggest a more robust framework for policy-makers to assess and respond to vulnerability in future disaster

Disaster Vulnerability

Vulnerability drives disaster law, yet the literature lacks both an overarching analysis of the different aspects of vulnerability and a nuanced examination of the factors that shape disaster outcomes. Though central to disaster law and policy, vulnerability often lurks in the shadows of a disaster, evident only once the worst is past and the bodies have been counted. The COVID-19 pandemic is a notable exception to this historical pattern: from the beginning of the pandemic, it has been clear that the virus poses different risks to different people, depending on vulnerability variables. This most recent pandemic experience thus provides a useful vantage point for analyzing vulnerability. Drawing on empirical data from the pandemic and experiences from past disasters, this Article identifies and discusses the policy implications of three dimensions of disaster vulnerability: the geography of vulnerability, competing or conflicting vulnerabilities, and political vulnerability. First, it explores the geography of vulnerability, using statistical analysis and geographic information system (GIS) mapping. The Article presents an innovative COVID-19 vulnerability index that identifies the country’s most vulnerable counties and the leading driver of vulnerability for each county. It demonstrates how this index could have informed voter accommodations during the 2020 elections and mask mandates throughout the pandemic. The Article also shows how, going forward, similar modeling could make disaster management more proactive and better able to anticipate needs and prioritize disaster mitigation and response resources. Second, this Article explores competing or conflicting vulnerabilities––situations where policy-makers must prioritize one vulnerable group or one aspect of vulnerability over another. To illustrate this, it considers two other policy challenges: school closures and vaccine distribution. Finally, the Article explores political vulnerability, analyzing how disasters make already-vulnerable groups even more vulnerable to certain harms, including political neglect, stigmatization, disenfranchisement, and displacement. In sum, this Article draws upon the costly lessons of COVID-19 to suggest a more robust framework for policy-makers to assess and respond to vulnerability in future disasters

Genetic Population Structure Analysis in New Hampshire Reveals Eastern European Ancestry

Genetic structure due to ancestry has been well documented among many divergent human populations. However, the ability to associate ancestry with genetic substructure without using supervised clustering has not been explored in more presumably homogeneous and admixed US populations. The goal of this study was to determine if genetic structure could be detected in a United States population from a single state where the individuals have mixed European ancestry. Using Bayesian clustering with a set of 960 single nucleotide polymorphisms (SNPs) we found evidence of population stratification in 864 individuals from New Hampshire that can be used to differentiate the population into six distinct genetic subgroups. We then correlated self-reported ancestry of the individuals with the Bayesian clustering results. Finnish and Russian/Polish/ Lithuanian ancestries were most notably found to be associated with genetic substructure. The ancestral results were further explained and substantiated using New Hampshire census data from 1870 to 1930 when the largest waves of European immigrants came to the area. We also discerned distinct patterns of linkage disequilibrium (LD) between the genetic groups in the growth hormone receptor gene (GHR). To our knowledge, this is the first time such an investigation has uncovered a strong link between genetic structure and ancestry in what would otherwise be considered a homogenous US population

Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

Abstract A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5 T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome-wide association studies (GWAS). One hundred fortytwo measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality-controlled 1 Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Authorship_list.pdf). NIH Public Access Author Manuscript Neuroimage. Author manuscript; available in PMC 2010 November 1. Published in final edited form as: Neuroimage. maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10 −7 and p<10 −6 ). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome-wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication

Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

ABSTRACT A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome wide association studies (GWAS). 142 measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10 -7 and p<10 -6 ). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication. Shen et al.

Walking

Data collected from walkers

Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression.

Low atmospheric pressure may increase depression and suicide through inducing hypoxia. Previous studies have not evaluated the geographic variation of this relationship across the United States. Analyses were based on three groupings of age-adjusted completed suicide rates (all suicide, firearm-related suicide, non-firearm-related suicide) from 2286 counties in the United States. Multiple regression was used to determine the overall relationship between atmospheric pressure and completed suicide rates. Geographically weighted regression (GWR) models were used to obtain local coefficient estimates. A negative correlation between atmospheric pressure and completed suicide rates was observed for all three suicide groupings (p-value <0.0001). Significant, negative GWR coefficient estimates were located in the West and Northeast for the all suicides and firearm-related suicides, and in the Midwest for non-firearm-related suicides

Driving Windows Down

Data collected from driving with windows down (open