Using geographic information systems to assess risk for elevated blood lead levels in children.
OBJECTIVE
Targeted screening for childhood lead poisoning depends on assessment of risk factors including housing age. Using a geographic information system (GIS), we aim to determine high-risk regions in Charleston County, South Carolina, to assist public health officials in developing targeted lead-screening.


METHODS
Properties built before 1978 were geocoded (assigned latitude and longitude coordinates) from tax assessor data. Addresses of Charleston County children who have been screened for lead poisoning were also geocoded. Locations of all housing, lead poisoning cases, and negative screens were created as separate map layers. Prevalence ratios of lead poisoning cases were calculated, as were relative risks for each category of housing.


RESULTS
Maps of Charleston County were produced showing the location of old housing, where screening took place, and where cases were found. One thousand forty-four cases were identified. Twenty percent of children living in pre-1950 homes had elevated blood lead levels (EBLL). Children living in pre-1950 housing were 3.9 times more likely to have an EBLL than children living in post-1977 housing. There was no difference in risk of living in a 1950-1977 home vs. a post-1977 home. A large number of cases were also found in an area of newer houses, but near a potential point source. Eighty-two percent of all screens were from children in post-1977 homes.


CONCLUSIONS
Children living in pre-1950 housing were at higher risk for lead poisoning. GIS is useful in identifying areas of risk and unexpected clustering from potential point sources and may be useful for public health officials in developing targeted screening programs.
Roberts
James R
JR
Department of Pediatrics, Medical University of South Carolina, Charleston, SC 29425, USA. robertsj@musc.edu
Hulsey
Thomas C
TC
Curtis
Gerald B
GB
Reigart
J Routt
JR
D002648:Child; D002669:Child Welfare / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D040362:Geographic Information Systems; D006798:Housing / Q000145:classification* / Q000592:standards; D006801:Humans; D007854:Lead / Q000097:blood; D007855:Lead Poisoning / Q000453:epidemiology*; D011177:Postal Service; D015995:Prevalence; D040442:Public Health Informatics; D018570:Risk Assessment; D012984:Software; D013022:South Carolina / Q000453:epidemiology
D007854:Lead