Pre- and post-move exposure to air pollution and neighborhood socioeconomic status
Nearly 10 % of the U.S. population moves in a given year. We aimed to examine differences in neighborhood socioeconomic status (nSES) and ambient air pollution concentrations before and after a residential move, whether change varies by distance of the move, and whether moving is a potential source...
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| Published in: | The Science of the total environment Vol. 1004; p. 180749 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.11.2025
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| ISSN: | 0048-9697, 1879-1026, 1879-1026 |
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| Abstract | Nearly 10 % of the U.S. population moves in a given year. We aimed to examine differences in neighborhood socioeconomic status (nSES) and ambient air pollution concentrations before and after a residential move, whether change varies by distance of the move, and whether moving is a potential source of bias in estimating relationships between contextual exposures and outcomes.
We used data from participants in the Atherosclerosis Risk in Communities (ARIC) Study with geocoded addresses at Visit 2 (Visit 2; 1990–1992) and Visit 3 (Visit 3; 1993–1995). We quantified nSES using the 1990 census and estimated concentrations of fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2). We defined short-distance and long-distance movers as having moved less and more than the median move distance of 4.3 miles, respectively. We quantified Visits 2 to 3 change in nSES and air pollution for short and long-distance movers, examined change in air pollution after accounting for temporal trends, and conducted a quantitative bias analysis to estimate the bias introduced by not accounting for moving in example studies of PM2.5 and health.
On average, compared to non-movers, short-distance movers relocated to neighborhoods with better nSES and similar air pollution concentrations, while long-distance movers relocated to neighborhoods with worse nSES but lower exposure to air pollution. The quantitative bias analysis suggested there was little to no bias in the PM2.5-health relationship from not accounting for relocation, likely due to relatively little overall difference in PM2.5 exposures observed with relocation.
Change in nSES and air pollution with a residential move differs by move distance. However, not accounting for relocation may not lead to significant bias in the relationship with the health outcome. Understanding the extent to which contextual exposures change after a move can clarify whether accounting for bias due to relocation is necessary.
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•Moving changes exposure to neighborhood SES (nSES) and air pollution•Short-distance moves led to better nSES, with little change in pollution levels.•Long-distance moves led to worse nSES but lower air pollution exposures.•PM2.5-health relationship showed minimal bias from not accounting for moving. |
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| AbstractList | Nearly 10 % of the U.S. population moves in a given year. We aimed to examine differences in neighborhood socioeconomic status (nSES) and ambient air pollution concentrations before and after a residential move, whether change varies by distance of the move, and whether moving is a potential source of bias in estimating relationships between contextual exposures and outcomes.
We used data from participants in the Atherosclerosis Risk in Communities (ARIC) Study with geocoded addresses at Visit 2 (Visit 2; 1990–1992) and Visit 3 (Visit 3; 1993–1995). We quantified nSES using the 1990 census and estimated concentrations of fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2). We defined short-distance and long-distance movers as having moved less and more than the median move distance of 4.3 miles, respectively. We quantified Visits 2 to 3 change in nSES and air pollution for short and long-distance movers, examined change in air pollution after accounting for temporal trends, and conducted a quantitative bias analysis to estimate the bias introduced by not accounting for moving in example studies of PM2.5 and health.
On average, compared to non-movers, short-distance movers relocated to neighborhoods with better nSES and similar air pollution concentrations, while long-distance movers relocated to neighborhoods with worse nSES but lower exposure to air pollution. The quantitative bias analysis suggested there was little to no bias in the PM2.5-health relationship from not accounting for relocation, likely due to relatively little overall difference in PM2.5 exposures observed with relocation.
Change in nSES and air pollution with a residential move differs by move distance. However, not accounting for relocation may not lead to significant bias in the relationship with the health outcome. Understanding the extent to which contextual exposures change after a move can clarify whether accounting for bias due to relocation is necessary.
[Display omitted]
•Moving changes exposure to neighborhood SES (nSES) and air pollution•Short-distance moves led to better nSES, with little change in pollution levels.•Long-distance moves led to worse nSES but lower air pollution exposures.•PM2.5-health relationship showed minimal bias from not accounting for moving. Nearly 10 % of the U.S. population moves in a given year. We aimed to examine differences in neighborhood socioeconomic status (nSES) and ambient air pollution concentrations before and after a residential move, whether change varies by distance of the move, and whether moving is a potential source of bias in estimating relationships between contextual exposures and outcomes.INTRODUCTIONNearly 10 % of the U.S. population moves in a given year. We aimed to examine differences in neighborhood socioeconomic status (nSES) and ambient air pollution concentrations before and after a residential move, whether change varies by distance of the move, and whether moving is a potential source of bias in estimating relationships between contextual exposures and outcomes.We used data from participants in the Atherosclerosis Risk in Communities (ARIC) Study with geocoded addresses at Visit 2 (Visit 2; 1990-1992) and Visit 3 (Visit 3; 1993-1995). We quantified nSES using the 1990 census and estimated concentrations of fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2). We defined short-distance and long-distance movers as having moved less and more than the median move distance of 4.3 miles, respectively. We quantified Visits 2 to 3 change in nSES and air pollution for short and long-distance movers, examined change in air pollution after accounting for temporal trends, and conducted a quantitative bias analysis to estimate the bias introduced by not accounting for moving in example studies of PM2.5 and health.METHODSWe used data from participants in the Atherosclerosis Risk in Communities (ARIC) Study with geocoded addresses at Visit 2 (Visit 2; 1990-1992) and Visit 3 (Visit 3; 1993-1995). We quantified nSES using the 1990 census and estimated concentrations of fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2). We defined short-distance and long-distance movers as having moved less and more than the median move distance of 4.3 miles, respectively. We quantified Visits 2 to 3 change in nSES and air pollution for short and long-distance movers, examined change in air pollution after accounting for temporal trends, and conducted a quantitative bias analysis to estimate the bias introduced by not accounting for moving in example studies of PM2.5 and health.On average, compared to non-movers, short-distance movers relocated to neighborhoods with better nSES and similar air pollution concentrations, while long-distance movers relocated to neighborhoods with worse nSES but lower exposure to air pollution. The quantitative bias analysis suggested there was little to no bias in the PM2.5-health relationship from not accounting for relocation, likely due to relatively little overall difference in PM2.5 exposures observed with relocation.RESULTSOn average, compared to non-movers, short-distance movers relocated to neighborhoods with better nSES and similar air pollution concentrations, while long-distance movers relocated to neighborhoods with worse nSES but lower exposure to air pollution. The quantitative bias analysis suggested there was little to no bias in the PM2.5-health relationship from not accounting for relocation, likely due to relatively little overall difference in PM2.5 exposures observed with relocation.Change in nSES and air pollution with a residential move differs by move distance. However, not accounting for relocation may not lead to significant bias in the relationship with the health outcome. Understanding the extent to which contextual exposures change after a move can clarify whether accounting for bias due to relocation is necessary.CONCLUSIONSChange in nSES and air pollution with a residential move differs by move distance. However, not accounting for relocation may not lead to significant bias in the relationship with the health outcome. Understanding the extent to which contextual exposures change after a move can clarify whether accounting for bias due to relocation is necessary. Nearly 10 % of the U.S. population moves in a given year. We aimed to examine differences in neighborhood socioeconomic status (nSES) and ambient air pollution concentrations before and after a residential move, whether change varies by distance of the move, and whether moving is a potential source of bias in estimating relationships between contextual exposures and outcomes. We used data from participants in the Atherosclerosis Risk in Communities (ARIC) Study with geocoded addresses at Visit 2 (Visit 2; 1990-1992) and Visit 3 (Visit 3; 1993-1995). We quantified nSES using the 1990 census and estimated concentrations of fine particulate matter (PM ), ozone (O ) and nitrogen dioxide (NO ). We defined short-distance and long-distance movers as having moved less and more than the median move distance of 4.3 miles, respectively. We quantified Visits 2 to 3 change in nSES and air pollution for short and long-distance movers, examined change in air pollution after accounting for temporal trends, and conducted a quantitative bias analysis to estimate the bias introduced by not accounting for moving in example studies of PM and health. On average, compared to non-movers, short-distance movers relocated to neighborhoods with better nSES and similar air pollution concentrations, while long-distance movers relocated to neighborhoods with worse nSES but lower exposure to air pollution. The quantitative bias analysis suggested there was little to no bias in the PM -health relationship from not accounting for relocation, likely due to relatively little overall difference in PM exposures observed with relocation. Change in nSES and air pollution with a residential move differs by move distance. However, not accounting for relocation may not lead to significant bias in the relationship with the health outcome. Understanding the extent to which contextual exposures change after a move can clarify whether accounting for bias due to relocation is necessary. |
| ArticleNumber | 180749 |
| Author | Whitsel, Eric A. Park, Eun Sug Smith, Richard L. Stewart, James D. Liu, Chelsea Stapp, Emma K. Lynch, Katie M. Bennett, Erin E. Kaufman, Joel D. Power, Melinda C. Xu, Xiaohui Ying, Qi |
| Author_xml | – sequence: 1 givenname: Chelsea surname: Liu fullname: Liu, Chelsea email: chelsea.liu@uth.tmc.edu organization: Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC, USA – sequence: 2 givenname: Erin E. surname: Bennett fullname: Bennett, Erin E. email: erinbennett@gwu.edu organization: Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC, USA – sequence: 3 givenname: Katie M. surname: Lynch fullname: Lynch, Katie M. email: kmlynch@gwu.edu organization: Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC, USA – sequence: 4 givenname: Xiaohui surname: Xu fullname: Xu, Xiaohui email: xiaohui.xu@tamu.edu organization: Department of Epidemiology & Biostatistics, Texas A&M Health Science Center School of Public Health, College Station, TX, USA – sequence: 5 givenname: Eun Sug surname: Park fullname: Park, Eun Sug email: e-park@tti.tamu.edu organization: Texas A&M Transportation Institute, College Station, TX, USA – sequence: 6 givenname: Qi surname: Ying fullname: Ying, Qi organization: Zachry Department of Civil & Environmental Engineering, Texas A&M University, College Station, TX, USA – sequence: 7 givenname: Richard L. surname: Smith fullname: Smith, Richard L. email: rls@email.unc.edu organization: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA – sequence: 8 givenname: James D. surname: Stewart fullname: Stewart, James D. email: j.stewart@unc.edu organization: Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA – sequence: 9 givenname: Eric A. surname: Whitsel fullname: Whitsel, Eric A. email: eric_whitsel@med.unc.edu organization: Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA – sequence: 10 givenname: Joel D. surname: Kaufman fullname: Kaufman, Joel D. email: joelk@uw.edu organization: Department of Environmental & Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, WA, USA – sequence: 11 givenname: Emma K. surname: Stapp fullname: Stapp, Emma K. email: emma.stapp@gwu.edu organization: Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC, USA – sequence: 12 givenname: Melinda C. surname: Power fullname: Power, Melinda C. email: power@gwu.edu organization: Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC, USA |
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| SubjectTerms | Air Pollutants - analysis Air Pollution - statistics & numerical data Environmental Exposure - statistics & numerical data Humans Neighborhood Characteristics - statistics & numerical data Nitrogen Dioxide - analysis Ozone - analysis Particulate Matter - analysis Residence Characteristics - statistics & numerical data Social Class United States |
| Title | Pre- and post-move exposure to air pollution and neighborhood socioeconomic status |
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