Novel approaches to elevated blood lead level using geospatial methods and the electronic medical record

Purpose: Fine geographic scale geospatial technologies now offer opportunities to approach neighborhood / residential lead exposure in an active rather than passive manner. Previous GIS approaches tend to concentrate on census tracts or zip codes, which miss both contextual understanding of the problem and make intervention impractical. We show how a spatially encoded video camera can help blood lead level researchers, clinicians and public health planners become more proactive in their prediction and intervention strategies.

Study Questions:  To what degree do hotspots of elevated child blood level extracted from electronic medical records (EMR) overlay with building and street scale environmental variables? Can neighborhood geonarratives conducted in conjunction with spatial video reveal nuances within these hotspots?

Methods:  Locations of children with elevated blood lead levels (EBLL) were extracted from EMR data for Akron Children’s Hospital for the period 2007 to 2014. Fine scale hotspot clusters were identified using several techniques in a GIS and linked spatio-temporally with building scale assessments of eight neighborhoods. Geonarratives, which are community member and professional conversations recorded while driving around the neighborhood, were mapped using novel coding developed at Kent State University to reveal contextual layers both explaining why these hotspots exist, and identified impediments to intervention. 

Results:  Fine-scale clusters of EBLL can be found at varying intensities throughout Akron, Ohio. These hotspots display visual building characteristics that can be used to develop a preemptive diagnostic tool for identifying where children with EBLL are likely living.  Mapped narratives reveal specific challenges of the resident population and add local context to the problem and solutions.

Discussion:  The novel methods employed here demonstrate a vast improvement to mapping age of residence as a way to show where children are at greatest risk for EBLL.  The technology and the method are translatable to other environments and health conditions.