Neighborhood Deprivation and Rurality Associated With Patient-Reported Outcomes and Survival in Men With Prostate Cancer in NRG Oncology RTOG 0415

Impact of Respiratory Dust on Health: A Comparison Based on the Toxicity of PM2.5, Silica, and Nanosilica

Respiratory dust of different particle sizes in the environment causes diverse health effects when entering the human body and makes acute or chronic damage through multiple systems and organs. However, the precise toxic effects and potential mechanisms induced by dust of different particle sizes have not been systematically summarized. In this study, we described the sources and characteristics of three different particle sizes of dust: PM2.5 (<2.5 μm), silica (<5 μm), and nanosilica (<100 nm). Based on their respective characteristics, we further explored the main toxicity induced by silica, PM2.5, and nanosilica in vivo and in vitro. Furthermore, we evaluated the health implications of respiratory dust on the human body, and especially proposed potential synergistic effects, considering current studies. In summary, this review summarized the health hazards and toxic mechanisms associated with respiratory dust of different particle sizes. It could provide new insights for investigating the synergistic effects of co-exposure to respiratory dust of different particle sizes in mixed environments.

Analysis of travel burden and travel support among patients treated at a comprehensive cancer center in the Southeastern United States

INTRODUCTION

Travel burden leads to worse cancer outcomes. Understanding travel burden and the level and types of travel support provided at large cancer centers is critical for developing systematic programs to alleviate travel burden. This study analyzed patients who received travel assistance, including their travel burden, types and amount of travel support received, and factors that influenced these outcomes.

METHODS

We analyzed 1063 patients who received travel support from 1/1/2021 to 5/1/2023 at Winship Cancer Institute, in which ~18,000 patients received cancer care annually. Travel burden was measured using distance and time to Winship sites from patients' residential address. Travel support was evaluated using the monetary value of total travel support and type of support received. Patients' sociodemographic and clinical factors were extracted from electronic medical records. Area-level socioeconomic disadvantage was coded by the Area Deprivation Index using patient ZIP codes.

RESULTS

On average, patients traveled 57.2 miles and 67.3 min for care and received $74.1 in total for travel support. Most patients (88.3%) received travel-related funds (e.g., gas cards), 5% received direct rides (e.g., Uber), 3.8% received vouchers for taxi or public transportation, and 3% received combined travel support. Male and White had longer travel distance and higher travel time than female and other races, respectively. Patients residing in more disadvantaged neighborhoods had an increased travel distance and travel time. Other races and Hispanics received more travel support ($) than Black and White patients or non-Hispanics. Patients with higher travel distance and travel time were more like to receive travel-related financial support.

CONCLUSION

Among patients who received travel support, those from socioeconomically disadvantaged neighborhoods had greater travel burden. Patients with greater travel burden were more likely to receive travel funds versus other types of support. Further understanding of the impact of travel burden and travel support on cancer outcomes is needed.

Relationship between area-level socioeconomic status and health-related quality of life among cancer survivors

Area-level socioeconomic status (SES) impacts cancer outcomes, such as stage at diagnosis, treatments received, and mortality. However, less is known about the relationship between area-level SES and health-related quality of life (HRQOL) for cancer survivors. To assess the additive value of area-level SES data and the relative contribution of area- and individual-level SES for estimating cancer survivors' HRQOL, we conducted a secondary analysis of data from a population-based survey study of cancer survivors (the Measuring Your Health [MY-Health] Study). Multilevel multinomial logistic regression models were used to examine the relationships between individual-level SES, area-level SES as measured by the Centers for Disease Control and Prevention's Social Vulnerability Index, and HRQOL group membership (high, average, low, or very low HRQOL). Area-level SES did not significantly increase model estimation accuracy compared to models using only individual-level SES. However, area-level SES could be an appropriate proxy when the individual-level SES is missing.