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Characterization of Regional Poison Center Utilization Through Geospatial Mapping
Travis D Olives1,2,3, Bjorn Westgard2,3,4, Lila W Steinberg1,2,3
1Minnesota Poison Control System, Minneapolis, Minnesota.
Poison center (PC) utilization varies by demographics. Geospatial mapping revealed disparities in PC access not seen in regression analysis, highlighting areas for targeted resource allocation.
Area of Science:
- Public Health
- Geospatial Analysis
- Health Services Research
Background:
- Penetrance, a measure of poison center (PC) utilization, historically reflects annual human exposure calls per 1000 persons.
- PC utilization exhibits significant variation based on sociodemographic factors and geographic location.
Purpose of the Study:
- To characterize the geospatial distribution of PC calls.
- To assess the contribution of geospatial mapping in understanding PC utilization patterns.
Main Methods:
- Retrospective analysis of 156,805 non-healthcare exposure calls from a regional PC over five years.
- Geocoding of exposure data (substance, demographics, ZIP Code) linked to US Census data (income, education, age, language).
- Spatial apportionment to census tracts and analysis using linear regression and geospatial mapping.
Main Results:
- Penetrance varied widely (0.081 - 38.47 calls/1000 persons/year).
- Higher educational attainment (>8th grade), and larger non-Hispanic Black and American Indian populations were associated with increased PC penetrance.
- Geospatial mapping revealed significant penetrance variability not identified through regression analysis, with low explanatory power (R2 = 0.054) for regression models.
Conclusions:
- PC call penetrance is influenced by sociodemographic factors, notably higher in areas with greater proportions of non-Hispanic Black or American Indian residents and higher educational attainment.
- Geospatial mapping offers a novel approach to identify previously unrecognized variations in PC utilization and potential disparities in healthcare access.
- Integrating geospatial mapping with sociodemographic data can guide the targeted allocation of PC resources to underserved communities and highlight healthcare access inequities.
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