A framework for widespread replication of a highly spatially resolved childhood lead exposure risk model.
BACKGROUND
Preventive approaches to childhood lead poisoning are critical for addressing this longstanding environmental health concern. Moreover, increasing evidence of cognitive effects of blood lead levels < 10 microg/dL highlights the need for improved exposure prevention interventions.


OBJECTIVE
Geographic information system-based childhood lead exposure risk models, especially if executed at highly resolved spatial scales, can help identify children most at risk of lead exposure, as well as prioritize and direct housing and health-protective intervention programs. However, developing highly resolved spatial data requires labor-and time-intensive geocoding and analytical processes. In this study we evaluated the benefit of increased effort spent geocoding in terms of improved performance of lead exposure risk models.


METHODS
We constructed three childhood lead exposure risk models based on established methods but using different levels of geocoded data from blood lead surveillance, county tax assessors, and the 2000 U.S. Census for 18 counties in North Carolina. We used the results to predict lead exposure risk levels mapped at the individual tax parcel unit.


RESULTS
The models performed well enough to identify high-risk areas for targeted intervention, even with a relatively low level of effort on geocoding.


CONCLUSIONS
This study demonstrates the feasibility of widespread replication of highly spatially resolved childhood lead exposure risk models. The models guide resource-constrained local health and housing departments and community-based organizations on how best to expend their efforts in preventing and mitigating lead exposure risk in their communities.
Kim
Dohyeong
D
Nicholas School of the Environment, Duke University, Durham, NC 27708, USA.
Galeano
M Alicia Overstreet
MA
Hull
Andrew
A
Miranda
Marie Lynn
ML
D002648:Child; D004781:Environmental Exposure; D006801:Humans; D007854:Lead / Q000633:toxicity*; D015233:Models, Statistical; D009657:North Carolina; D012107:Research Design; D018570:Risk Assessment
D007854:Lead