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This study examines exposure to four contextual Determinants of Health (cDOH): healthcare access (Medically Underserved Areas), socioeconomic condition (Area Deprivation Index), air pollution (Nitrogen Dioxide (NO2), Particulate Matter 2.5 (PM 2.5) and Particulate Matter 10 (PM 10)), and walkability (National Walkability Index) among residents of gentrifying and not gentrifying lower income neighborhoods in central cities for the 100 largest metropolitan regions in the US using their location in 2006 and 2019 based on individual level consumer trace data. Individuals who lived in gentrifying neighborhoods as of 2006 had more favorable cDOH in terms of MUA, ADI and Walkability Index and similar levels of pollution. Between 2006 and 2019, they experienced worse changes in MUAs, ADI, and Walkability Index but a greater improvement in exposure to air pollutants. The negative changes are driven by movers, while stayers actually experience a relative improvement in MUAs and ADI and larger improvements in exposure to air pollutants. The findings indicate that gentrification may contribute to health disparities through changes in exposure to cDOH through mobility to communities with worse cDOH among residents of gentrifying neighborhoods although results in terms of exposure to health pollutants are mixed. [ABSTRACT FROM AUTHOR]

BACKGROUND Timely, accurate, and precise demographic estimates at various levels of geography are crucial for planning, policymaking, and analysis. In the United States, data from the decennial census and annual American Community Survey (ACS) serve as the main sources for subnational demographic estimates. While estimates derived from these sources are widely regarded as accurate, their timeliness is limited and variability sizable for small geographic units like towns and neighborhoods. OBJECTIVE This paper investigates the potential for using nonrepresentative consumer trace data assembled by commercial vendors to produce valid and timely estimates. We focus on data purchased from Data Axle, which contains the names and addresses of over 150 million Americans annually. METHODS We identify the predictors of over- and undercounts of households as measured with consumer trace data and compare a range of calibration approaches to assess the extent to which systematic errors in the data can be adjusted for over time. We also demonstrate the utility of the data for predicting contemporaneous (nowcasting) tract-level household counts in the 2020 Decennial Census. RESULTS We find that adjusted counts at the county, ZIP Code Tabulation Areas (ZCTA), and tract levels deviate from ACS survey-based estimates by an amount roughly equivalent to the ACS margins of error. Machine-learning methods perform best for calibration of countyand tract-level data. The estimates are stable over time and across regions of the country. We also find that when doing nowcasts, incorporating Data Axle estimates improved prediction bias relative to using the most recent ACS five-year estimates alone. CONTRIBUTION Despite its affordability and timeliness compared to survey-based measures, consumer trace data remains underexplored by demographers. This paper examines one consumer trace data source and demonstrates that challenges with representativeness can be overcome to produce household estimates that align with survey-based estimates and improve demographic forecasts. At the same time, the analysis also underscores the need for researchers to examine the limits of the data carefully before using them for specific applications. [ABSTRACT FROM AUTHOR]