arXiv subjects

Deb Niemeier

Publications and source records attributed to Deb Niemeier.

3 recordsLinked to original sources

arXiv2025

Access to healthcare for people with Alzheimer's Diseases and related dementias

Background: Alzheimer's Disease and Related Dementias (ADRD) affects millions worldwide. Significant disparities exist in ADRD diagnosis and care, disproportionately impacting minority and socioeconomically vulnerable populations Objective: In this study, we investigate the relationship between ADRD density and accessibility to healthcare. We identify underserved and overserved areas in Maryland based on diagnosed cases and mortality due to ADRD, focusing on geographic disparities in care. Methods: 2023 Maryland ADRD patients were identified using ICD-10 codes from. Accessibility was measured using the Kernel Density Two-Step Floating Catchment Area (KD2SFCA) method. The Gini index and t-tests were used to analyze disparities between urban and rural areas. Hot Spot Analysis Getis-Ord Gi* and local bivariate relationships analysis were applied to assess spatial correlations. Principal component analysis (PCA) was applied to calculate the health risk index. Results: Hospital accessibility was unevenly distributed. Mortality rates from ADRD were higher in underserved areas with fewer hospitals. Hot spot analysis shows eastern and southern Maryland have zones with high mortality per population and per ADRD patient, surrounded by similarly high-rate zones. Central Maryland shows lower death rates per patient but more hospital facilities. In eastern Maryland, higher poverty areas are surrounded by zones with lower accessibility and higher health risk indices. Conclusion: Hospital accessibility is unevenly distributed, creating major rural disparities. Underserved regions in terms of access to healthcare facilities, particularly in eastern and southern Maryland, exhibit high ADRD mortality rates despite low diagnosis rates. This suggests that many ADRD cases remain undiagnosed, underdiagnosed, or subject to delayed treatment.

stat.AP↗

arXiv2024

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Due to Covid-19

COVID-19 has deeply affected daily life and travel behaviors. Understanding these changes is crucial, prompting an investigation into socio-demographic and socio-economic factors. This study used large-scale mobile device location data in Washington, D.C., Maryland, and Virginia (DMV area) to unveil the impacts of these variables on commute trip changes. It reflected short and long-term impacts through linear regression and geographically weighted regression models. Findings indicated that counties with a higher percentage of people using walking and biking during the initial phase of COVID-19 experienced greater reductions in commute trips. For the long-term effect in November, the impact of active modes became insignificant, and individuals using public modes showed more significant trip reductions. Positive correlations were observed between median income levels and reduced commute trips. Sectors requiring ongoing outdoor operations during the pandemic showed substantial negative correlations. In the DMV area, counties with a higher proportion of Democratic voters experienced less trip reduction. Applying Geographically Weighted Regression models captured local spatial relationships, showing the emergence of local correlations as the pandemic evolved, suggesting a geographical impact pattern. Initially global, the pandemic's impact on commuting behaviors became more influenced by spatial factors over time, showing localized effects.

stat.AP↗

arXiv2023

Quantifying disparities in air pollution exposures across the United States using home and work addresses

While human mobility plays a crucial role in determining air pollution exposures and health risks, research to-date has assessed risks based solely on residential location. Here we leveraged a database of ~ 130 million workers in the US and published PM2.5 data between 2011-2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures (W) relative to home exposures (H), as well as increasing exposures for non-white and less educated workers relative to the national average. Workplace exposure disparities were higher among racial and ethnic groups and job-types than by income, education, age, and sex. Not considering workplace exposures can lead to systematic underestimations in disparities to exposure among these subpopulations. We also quantified the error in assigning workers H, instead of a weighted home-and-work (HW) exposure. We observed that biases in associations between PM2.5 and health impacts by using H instead of HW were highest among urban, younger populations.

stat.AP↗