AI Article Synopsis
- Imputation of missing spatial data in health records can help connect health information with environmental factors, but few studies have explored its effects on epidemiological conclusions.
- In a study analyzing fine particulate matter and respiratory hospitalizations in New York, researchers imputed patient Census tracts and found generally higher PM exposures and high accuracy in exposure classification.
- While hazard ratios showed only slight variations, they were more pronounced in urban areas, indicating geo-imputation could be a useful method for enhancing health studies with missing spatial data.
Article Abstract
Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM) and respiratory hospitalizations in New York State (2000-2005). We observed non-significantly higher PM exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (-0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.
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| http://dx.doi.org/10.1016/j.sste.2019.100322 | DOI Listing |
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