Zhiyong Hu

Objective: COVID-19 has a lasting impact on mental health, particularly within the Hispanic/Latinx communities. This paper empirically investigates the post-COVID-19 presence and severities of depression, one of the most common mental health disorders, among adults in a predominantly U.S.-born Hispanic/Latinx community in South Texas composed primarily of Mexican Americans. Methods: Multiple statistic regression models were applied to data from 515 adults in Nueces County who completed all questions in a survey from convenience sampling between June 2022 and May 2023. Depression was assessed using both standard PHQ-2 and PHQ-9 measurements. Results: Of the 515 participants, 377 (64.5%) were Hispanic, and 441 (85.6%) had a high school education or higher, reflecting the county’s demographics. About half of the participants (47%) reported mild/moderate to severe depression. The regression model estimation results reveal that female participants, those not in full-time employment, and individuals with disabilities were more likely to feel depressed after COVID-19. Middle-aged adults demonstrated greater resilience to depression compared to other age groups. Notably, non-Hispanic participants in the study reported higher levels of depression compared to their Hispanic counterparts. Additionally, COVID-19-related experiences, such as testing positive for the virus, being hospitalized, or having a history of depression before COVID-19, were associated with higher levels of reported depression. Conclusions: COVID-19 has significantly impacted the mental health of this predominantly U.S.-born Hispanic/Latinx community. These findings can assist healthcare providers and policymakers in developing targeted strategies to tailor interventions aimed at enhancing mental health well-being, reducing disparities, and fostering overall improvement within the Hispanic/Latinx community.

This paper outlines the methods, results, and statistical analysis of a model we developed to demonstrate the feasibility of applying remote sensor meteorological data to navigation by using meteorological contour matching (METCOM). Terrain contour matching (TERCOM), a contemporary navigation system, possesses inherent performance flaws that may be resolved and improved by METCOM for subsonic and hypersonic missile or aircraft navigation. Remote sensor imagery data for this model was accessed from the Geostationary Operational Environmental Satellites-R Series operated by the National Oceanic and Atmospheric Administration by using Amazon Web Services through a script we developed in Python. Data processed for the model included imagery data and corresponding geospatial data from the legacy atmospheric profile products: legacy vertical temperature and legacy vertical moisture. Our analysis of the model included an error assessment to determine model accuracy, geostatistical analysis through semivariograms, meteorological signal of model data, and a combinatorial analysis to evaluate navigation performance. We conducted a model assessment which indicated an accuracy of 66.2% in the data used as a combined result of instrument error and interference of cloud formations. Results of the remaining analysis offered methods to evaluate METCOM performance and compare different meteorological data products. These results allowed us to statistically compare METCOM and TERCOM, yielding several indications of improved performance including an increase by a factor of at least 13.5 in data variability and contourability. The analysis we conducted served as a proof of concept to justify further research into the feasibility and application of METCOM.

This research explores the use of PM2.5 gird derived from remote sensing for assessing the effect of long-term exposure to PM2.5 (ambient air pollution of particulate matter with an aerodynamic diameter of 2.5 μm or less) on stroke, adjusting for unhealthy behaviors and medical risk factors. Health data was obtained from the newly published CDC “500 Cities Project” which provides city- and census tract-level small area estimates for chronic disease risk factors, and clinical preventive service use for the largest 500 cities in the United States. PM2.5 data was acquired from the “The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), V1 (1998–2012)” datasets. Average PM2.5 were calculated for each city using a GIS zonal statistics function. Map data visualization and pattern comparison, univariate linear regression, and a multivariate linear regression model fitted using a generalized linear model via penalized maximum likelihood found that long-term exposure to ambient PM2.5 may increase the risk of stroke. Increasing physical activity, reducing smoking and body weight, enough sleeping, controlling diseases such as blood pressure, coronary heart disease, diabetes, and cholesterol, may mitigate the effect. PM2.5 grids derived from moderate resolution satellite remote sensing imagery may offer a unique opportunity to fill the data gap due to limited ground monitoring at broader scales. The evidence of raised stroke prevalence risk in high PM2.5 areas would support targeting of policy interventions on such areas to reduce pollution levels and protect human health.

There are several known factors that cause ischemic heart disease. However, the part played by air pollution still remains something of a mystery. Recent attention has focused on the chronic effect of particulate matter on heart disease. Satellite-derived aerosol optical depth (AOD) was found to be correlated with PM2.5 in the eastern US. The objective of this study was to examine if there is an association between aerosol air pollution as indicated by AOD and chronic ischemic heart disease (CIHD) in the eastern US.

Numerous studies have found adverse health effects of acute and chronic exposure to fine particulate matter (PM2.5). Air pollution epidemiological studies relying on ground measurements provided by monitoring networks are often limited by sparse and unbalanced spatial distribution of the monitors. Studies have found correlations between satellite aerosol optical depth (AOD) and PM2.5 in some land regions. Satellite aerosol data may be used to extend the spatial coverage of PM2.5 exposure assessment. This study was to investigate correlation between PM2.5 and AOD in the conterminous USA, to derive a spatially complete PM2.5 surface by merging satellite AOD data and ground measurements based on the potential correlation, and to examine if there is an association of coronary heart disease with PM2.5.