PyQMC: An all-Python real-space quantum Monte Carlo module in PySCF.

We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.

Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California.

With a population of forty million and substantial geographic variation in sociodemographics and health services, California is an important setting in which to study disparities. Its population (37.5 percent White, 39.1 percent Latino, 5.3 percent Black, and 14.4 percent Asian) experienced 59,258 COVID-19 deaths through April 14, 2021-the most of any state. We analyzed California's racial/ethnic disparities in COVID-19 exposure risks, testing rates, test positivity, and case rates through October 2020, combining data from 15.4 million SARS-CoV-2 tests with subcounty exposure risk estimates from the American Community Survey. We defined "high-exposure-risk" households as those with one or more essential workers and fewer rooms than inhabitants. Latino people in California are 8.1 times more likely to live in high-exposure-risk households than White people (23.6 percent versus 2.9 percent), are overrepresented in cumulative cases (3,784 versus 1,112 per 100,000 people), and are underrepresented in cumulative testing (35,635 versus 48,930 per 100,000 people). These risks and outcomes were worse for Latino people than for members of other racial/ethnic minority groups. Subcounty disparity analyses can inform targeting of interventions and resources, including community-based testing and vaccine access measures. Tracking COVID-19 disparities and developing equity-focused public health programming that mitigates the effects of systemic racism can help improve health outcomes among California's populations of color.

An Integrated Assessment Model for Valuing Water Quality Changes in the U.S.

The US Environmental Protection Agency (EPA) often requires expertise from environmental assessors, hydrologists, economists, and others to analyze the benefits of regional and national policy decisions related to changes in water quality. This led EPA to develop two models to form an Integrated Assessment Model (IAM): HAWQS is a web-based water quantity and quality modeling systems and BenSPLASH is a modeling platform for quantifying the economic benefits of changes in water quality. This paper discusses the development of the component models and applies HAWQS and BenSPLASH to a case study in the Republican River Basin.

The economic burden of exposure to secondhand smoke for child and adult never smokers residing in U.S. public housing.

OBJECTIVE: The World Health Organization (WHO) reports that nonsmokers experience disease and death due to secondhand smoke (SHS) exposure in the home. We estimated the total excess burden and costs to society due to SHS exposure in U.S. public housing. METHODS: We quantified the public health burden for outcomes causally related to SHS exposure for nationally representative never-smoking residents in U.S. public housing using (1) WHO-recommended health outcomes and methodology, (2) publicly available and other large databases, and (3) published estimates of morbidity and mortality rates. We used published estimates of direct medical and nonmedical care costs and the value of productivity losses to estimate SHS-related societal costs for disease and death. We estimated the public health and economic burden for two serum cotinine limits of detection (LODs): 0.05 nanograms per milliliter (ng/mL) and 0.015 ng/mL. RESULTS: In 2011, an estimated 37,791 never-smoking child and adult U.S. public housing residents experienced illness and death due to SHS exposure at home based on an LOD=0.05 ng/mL (50,967 residents at LOD=0.015 ng/mL). Costs incurred by society for these illnesses and deaths totaled $183 million (LOD=0.05 ng/mL) and $267 million (LOD=0.015 ng/mL) annually. Of the total costs, direct costs (medical and nonmedical) accounted for $128 million and $176 million for LOD=0.05 ng/mL and LOD=0.015 ng/mL, respectively. Medical care accounted for the majority of direct costs-$110 million at LOD=0.05 ng/mL and $153 million at LOD=0.015 ng/mL. Adverse respiratory health outcomes accounted for approximately one-half (56% at LOD=0.05 ng/mL and 52% at LOD=0.015 ng/mL) of total societal costs. CONCLUSION: Implementing smoke-free policies in all U.S. public housing could save lives and decrease SHS-related morbidity and mortality in never-smoking residents, resulting in annual societal savings of $183 million at LOD=0.05 ng/mL and $267 million at LOD=0.015 ng/mL.

A flexible Bayesian model for studying gene-environment interaction.

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene-environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene-environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene-environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene-environment interaction based on the single-marker approach is far from significant.

Use of surveillance, epidemiology, and end results-medicare data to conduct case-control studies of cancer among the US elderly.

Cancer is an important cause of morbidity in the elderly, and many medical conditions and treatments influence cancer risk. The Surveillance, Epidemiology, and End Results (SEER)-Medicare database can be used to conduct population-based case-control studies that elucidate the etiology of cancer among the US elderly. SEER-Medicare links data on malignancies ascertained through SEER cancer registries to claims from Medicare, the US government insurance program for people over age 65 years. Under one approach described herein, elderly cancer cases are ascertained from SEER data (1987-2005). Matched controls are selected from a 5% random sample of Medicare beneficiaries. Risk factors of interest, including medical conditions and procedures, are identified by using linked Medicare claims. Strengths of this design include the ready availability of data, representative sampling from the US elderly population, and large sample size (e.g., under one scenario: 1,176,950 cases, including 221,389 prostate cancers, 185,853 lung cancers, 138,041 breast cancers, and 124,442 colorectal cancers; and 100,000 control subjects). Limitations reflect challenges in exposure assessment related to Medicare claims: restricted range of evaluable risk factors, short time before diagnosis/selection for ascertainment, and inaccuracies in claims. With awareness of limitations, investigators have in SEER-Medicare data a valuable resource for epidemiologic research on cancer etiology.