Racial/ethnic and income disparities in neighborhood-level broadband access in 905 US cities, 2017-2021.

OBJECTIVES: Broadband access is an essential social determinant of health, the importance of which was made apparent during the COVID-19 pandemic. We sought to understand disparities in broadband access within cities and identify potential solutions to increase urban access. STUDY DESIGN: This was a descriptive secondary analysis using multi-year cross-sectional survey data. METHODS: Data were obtained from the City Health Dashboard and American Community Survey. We studied broadband access in 905 large US cities, stratifying neighborhood broadband access by neighborhood median household income and racial/ethnic composition. RESULTS: In 2017, 30% of urban households across 905 large US cities did not have access to high-speed broadband internet. After controlling for median household income, broadband access in majority Black and Hispanic neighborhoods was 10-15% lower than in majority White or Asian neighborhoods. Over time, lack of broadband access in urban households decreased from 30% in 2017 to 24% in 2021, but racial and income disparities persisted. CONCLUSIONS: As an emerging social determinant, broadband access impacts health across the life course, affecting students' ability to learn and adults' ability to find and retain jobs. Resolving lack of broadband access remains an urban priority. City policymakers can harness recent infrastructure funding opportunities to reduce broadband access disparities.

Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C)1 to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis. © 2022 IEEE.

COVID-19 public health data reporting and homelessness: challenges and future directions

Data on COVID-19 among people experiencing homelessness has been extremely limited, despite known unique risks faced by this population. Gaps in data collection, monitoring, and reporting across multiple systems and government levels create challenges to understanding how COVID-19 is impacting people experiencing homelessness. This article examines these challenges and gaps in public health data on homelessness, presents examples of best practices, and offers policy recommendations for data collection, sharing, and dissemination of pandemic-related health metrics by homelessness status.