Open Access Research article

Adjusting for geographic variation in observational comparative effectiveness studies: a case study of antipsychotics using state Medicaid data

Elisabeth Dowling Root1*, Deborah SK Thomas2, Elizabeth J Campagna3 and Elaine H Morrato4

Author Affiliations

1 Department of Geography and Institute for Behavioral Science, University of Colorado at Boulder, Boulder, CO 80309, USA

2 Department of Geography and Environmental Sciences, University of Colorado Denver, Denver, CO, USA

3 Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA

4 Colorado Health Outcomes Program, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA

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BMC Health Services Research 2014, 14:355  doi:10.1186/1472-6963-14-355

Published: 27 August 2014



Area-level variation in treatment and outcomes may be a potential source of confounding bias in observational comparative effectiveness studies. This paper demonstrates how to use exploratory spatial data analysis (ESDA) and spatial statistical methods to investigate and control for these potential biases. The case presented compares the effectiveness of two antipsychotic treatment strategies: oral second-generation antipsychotics (SGAs) vs. long-acting paliperiodone palmitate (PP).


A new-start cohort study was conducted analyzing patient-level administrative claims data (8/1/2008–4/30/2011) from Missouri Medicaid. ESDA techniques were used to examine spatial patterns of antipsychotic prescriptions and outcomes (hospitalization and emergency department (ED) visits). Likelihood of mental health-related outcomes were compared between patients starting PP (N = 295) and oral SGAs (N = 8,626) using multilevel logistic regression models adjusting for patient composition (demographic and clinical factors) and geographic region.


ESDA indicated significant spatial variation in antipsychotic prescription patterns and moderate variation in hospitalization and ED visits thereby indicating possible confounding by geography. In the multilevel models for this antipsychotic case example, patient composition represented a stronger source of confounding than geographic context.


Because geographic variation in health care delivery is ubiquitous, it could be a comparative effectiveness research (CER) best practice to test for possible geographic confounding in observational data. Though the magnitude of the area-level geography effects were small in this case, they were still statistically significant and should therefore be examined as part of this observational CER study. More research is needed to better estimate the range of confounding due to geography across different types of observational comparative effectiveness studies and healthcare utilization outcomes.

Comparative effectiveness research; Antipsychotics; Mental health; Small area variation; Geographic information systems; Spatial data analysis; Multilevel modeling