arXiv subjects

King-Fai Li

Publications and source records attributed to King-Fai Li.

3 recordsLinked to original sources

arXiv2026

A Hierarchical Multilevel Inference Framework for Structural Cardiovascular Risk Modeling: County-Scale Analysis of Cardiovascular Mortality in Ohio and Pennsylvania (1999-2020)

Cardiovascular mortality is shaped by interacting demographic, environmental, and structural processes operating across multiple spatial scales. Conventional epidemiologic analyses often rely on aggregate summaries or single-model formulations that obscure hierarchical variation and contextual heterogeneity. We present a reproducible multilevel statistical inference framework integrating Normal (age-adjusted), Poisson (count-based), and population-offset Poisson models to quantify cardiovascular mortality across nested geographic units while separating demographic effects from structural variation. The framework was applied to county-level mortality data from Ohio and Pennsylvania (1999-2020) using MLwiN hierarchical models for seven cardiovascular disease (CVD) subtypes. Fixed effects included year, sex, race, PM2.5, and O3, while county-level random intercepts captured spatial heterogeneity. Complete model equations are provided in the Supplementary Material. The framework reveals complementary perspectives on cardiovascular risk unavailable from a single model. Age-adjusted mortality declined more rapidly in Pennsylvania than Ohio, whereas Poisson models identified post-2010 stagnation or reversal for several CVD subtypes. Black populations experienced elevated mortality risks, males exhibited higher mortality than females, and PM2.5 showed stronger associations with ischemic and hypertensive mortality in Pennsylvania. Population-offset models reduced unexplained variance while preserving county-level structural disparities. Beyond cardiovascular epidemiology, this work introduces a generalizable hierarchical statistical framework for structurally nested health systems. The methodology provides a scalable foundation for disease surveillance, environmental health assessment, health equity research, reproducible statistical analysis, and AI-assisted scientific inference.

stat.AP↗

arXiv2023

Solar Cycle as a Distinct Line of Evidence Constraining Earth's Transient Climate Response

Severity of warming predicted by climate models depends on their Transient Climate Response (TCR). Inter-model spread of TCR has persisted at ~100% of its mean for decades. Existing observational constraints of TCR are based on observed historical warming to historical forcing and their uncertainty spread is just as wide, mainly due to forcing uncertainty, and especially that of aerosols. Contrary, no aerosols are involved in solar-cycle forcing, providing an independent, tighter, constraint. Here, we define a climate sensitivity metric: time-dependent response regressed against time-dependent forcing, allowing phenomena with dissimilar time variations, such as the solar cycle with 11-year cyclic forcing, to be used to constrain TCR, which has a linear time-dependent forcing. We find a theoretical linear relationship between the two. The latest coupled atmosphere-ocean climate models obey the same linear relationship statistically. The proposed observational constraint on TCR is about 1/3 as narrow as existing constraints. The central estimate, 2.2$^\circ$C, is at the midpoint of the spread of the latest generation of climate models, which are more sensitive than those of the previous generations.

physics.ao-ph↗

arXiv2020

The Improvement of the Air Quality due to Traffic Halting in Los Angeles and Potential Health Care Risk during the COVID-19 Outbreak

Background: On March 19, 2020, the government of California ordered all 40 million Californians to stay at home in the coming weeks as the result of the escalation of the coronavirus disease 2019 (COVID-19) pandemic. As lockdowns were implemented, the significant changes caused by these restrictions brought a dramatic improvement in air quality in metropolitan cities such as Los Angeles (LA Basin).Methods: We use real-time data from The South Coast Air Quality Management District (South Coast AQMD), and the California Department of Transportation to evaluate the drivers of the pollution sources. We also mapped monthly spatial variations and constructed hourly heatmaps of those pollutants in 2020 to understand the impacts of the lockdown on different locations and times of day in the LA Basin. Results: Compared to the same dates in 2019, traffic flow on highways in the Los Angeles Basin dropped by 20.86 % when the stay at home order was initiated and it continued to decrease along with dramatic declines in NO2, CO, and PM2.5. The correlation (Pierson r) between truck flow change and changes of NO2, CO, and PM2.5 is statistically significant. Conclusion: The declines in truck flow are mainly responsible for the drop of NO2 and CO, with traffic having a slightly smaller effect on PM2.5. The lockdowns provided a large-scale experiment into air quality research. The result of this research would provide an important reference for the policy markers regarding truck management in light of air quality control to prepare for the 2028 Summer Olympics in LA.

physics.ao-ph↗