arXiv subjects

Kevin Josey

Publications and source records attributed to Kevin Josey.

3 recordsLinked to original sources

arXiv2025

Excess risk of heat-related hospitalization associated with temperature and PM2.5 among older adults

Background: With rising temperatures and an aging population, understanding how to prevent heat-related illness among older adults will be increasingly crucial. Despite biological plausibility, no study to date has investigated whether fine particulate matter air pollution (PM2.5) contributes to the risk of hospitalization with a diagnosis code indicating heat-related illness, referred to as heat-related hospitalization. This study aims to fill this gap by investigating the independent and combined effects of temperature and PM2.5 on heat-related hospitalization risk. Methods: We identified Medicare fee-for-service beneficiaries in the contiguous United States who experienced a heat-related hospitalization between 2008 and 2016. Using a case-crossover design and Bayesian conditional logistic regression, we characterized the associations of temperature and PM2.5 with heat-related hospitalization. We then estimated the relative excess risk due to interaction to quantify the additive interaction of simultaneous exposure to heat and PM2.5. Results: We observed 112,969 heat-related hospitalizations. Fixing PM2.5 at the case day median, the odds ratio for increasing temperature from its case day median to the 95th percentile was 1.05 (95% CI: 1.03, 1.06). Fixing temperature at the case day median, the odds ratio for increasing PM2.5 from its median to the 95th percentile was 1.01 (95% CI: 0.99, 1.04). The relative excess risk due to interaction for simultaneous median-to-95th percentile increases in temperature and PM2.5 was 0.03 (95% CI: 0.01, 0.06). Conclusions: Our study is the first to observe synergism between temperature and PM2.5 associated with the risk of heat-related hospitalization. These findings highlight the importance of considering air pollution in effective public health and clinical interventions to prevent heat-related illness.

stat.AP↗

arXiv2023

Causal Estimation of Exposure Shifts with Neural Networks

A fundamental task in causal inference is estimating the effect of distribution shift in the treatment variable. We refer to this problem as shift-response function (SRF) estimation. Existing neural network methods for causal inference lack theoretical guarantees and practical implementations for SRF estimation. In this paper, we introduce Targeted Regularization for Exposure Shifts with Neural Networks (TRESNET), a method to estimate SRFs with robustness and efficiency guarantees. Our contributions are twofold. First, we propose a targeted regularization loss for neural networks with theoretical properties that ensure double robustness and asymptotic efficiency specific to SRF estimation. Second, we extend targeted regularization to support loss functions from the exponential family to accommodate non-continuous outcome distributions (e.g., discrete counts). We conduct benchmark experiments demonstrating TRESNET's broad applicability and competitiveness. We then apply our method to a key policy question in public health to estimate the causal effect of revising the US National Ambient Air Quality Standards (NAAQS) for PM 2.5 from 12 ${\mu}g/m^3$ to 9 ${\mu}g/m^3$. This change has been recently proposed by the US Environmental Protection Agency (EPA). Our goal is to estimate the reduction in deaths that would result from this anticipated revision using data consisting of 68 million individuals across the U.S.

cs.LG↗

arXiv2023

An empirical process framework for covariate balance in causal inference

We propose a new perspective for the evaluation of matching procedures by considering the complexity of the function class they belong to. Under this perspective we provide theoretical guarantees on post-matching covariate balance through a finite sample concentration inequality. We apply this framework to coarsened exact matching as well as matching using the propensity score and suggest how to apply it to other algorithms. Simulation studies are used to evaluate the procedures.

math.ST↗