Richard Casey Sadler1, Jenny LaChance1, Mona Hanna-Attisha1. 1. Richard Casey Sadler is with the Division of Public Health and Department of Family Medicine, Michigan State University, Flint. Jenny LaChance is with Hurley Medical Center Research, Flint. Mona Hanna-Attisha is with the Pediatric Residency Program, Hurley Medical Center, and the Department of Pediatrics and Human Development, Michigan State University, Flint.
Abstract
OBJECTIVES: To highlight contextual factors tied to increased blood lead level (BLL) risk following the lead-in-water contamination in Flint, Michigan. METHODS: Using geocoded BLL data collected in 2013 and 2015 and areal interpolation, we predicted BLLs at every residential parcel in the city. We then spatially joined social and built environmental variables to link the parcels with neighborhood-level factors that may influence BLLs. RESULTS: When we compared levels before and during the water crisis, we saw the highest estimates of predicted BLLs during the water crisis and the greatest changes in BLLs in neighborhoods with the longest water residence time in pipes (μ = 2.30 µg/dL; Δ = 0.45 µg/dL), oldest house age (μ = 2.22 µg/dL; Δ = 0.37 µg/dL), and poorest average neighborhood housing condition (μ = 2.18 µg/dL; Δ = 0.44 µg/dL). CONCLUSIONS: Key social and built environmental variables correlate with BLL; such information can continue to guide response by prioritizing older, deteriorating neighborhoods with the longest water residence time in pipes.
OBJECTIVES: To highlight contextual factors tied to increased blood lead level (BLL) risk following the lead-in-water contamination in Flint, Michigan. METHODS: Using geocoded BLL data collected in 2013 and 2015 and areal interpolation, we predicted BLLs at every residential parcel in the city. We then spatially joined social and built environmental variables to link the parcels with neighborhood-level factors that may influence BLLs. RESULTS: When we compared levels before and during the water crisis, we saw the highest estimates of predicted BLLs during the water crisis and the greatest changes in BLLs in neighborhoods with the longest water residence time in pipes (μ = 2.30 µg/dL; Δ = 0.45 µg/dL), oldest house age (μ = 2.22 µg/dL; Δ = 0.37 µg/dL), and poorest average neighborhood housing condition (μ = 2.18 µg/dL; Δ = 0.44 µg/dL). CONCLUSIONS: Key social and built environmental variables correlate with BLL; such information can continue to guide response by prioritizing older, deteriorating neighborhoods with the longest water residence time in pipes.
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