245046 Impact of incomplete geographic locators on small-area predictive models of invasive pneumococcal disease (IPD) rates in New Mexico (NM), 2004-2010

Tuesday, November 1, 2011: 2:35 PM

Karen Scherzinger, MS , Institute for Public Health, Emerging Infections Program, University of New Mexico, Albuquerque, NM
Joseph C. Bareta, MS , Epidemiology and Response Division, New Mexico Department of Health, Santa Fe, NM
Megin Nichols, DVM, MPH , Epidemiology and Response Division, New Mexico Department of Health, Santa Fe, NM
Joan Baumbach, MD, MS, MPH , Epidemiology and Response Division, New Mexico Department of Health, Santa Fe, NM
Small-area spatial analysis of health events adds information about disease predictors when individual-level information is unavailable. The US Census Bureau's American Community Survey (ACS) provides small-area demographic, social, and economic data. Rural states often have difficulty geo-locating individuals with post office box addresses. We sought to determine the impact of non-geocoded cases (NGCs) on a regression model predicting block-group-level invasive pneumococcal disease (IPD). New Mexico (NM) IPD cases identified through Emerging Infections Program (NMEIP) active surveillance during 2004-2010 were geocoded (ArcGIS 9.3). NGCs were assigned to the postal code centroid block-group. Block-group-level IPD incidence rates were calculated. Educational attainment, English-speaking ability, vehicle availability, poverty status, average household size, median year house built, and household plumbing data were obtained from ACS 5-year estimates (2005-2009). We compared two regression models, one excluding NGCs and the second assigning them. NMEIP identified 2,391 IPD cases; 30% were unmatched at the street level. NGCs were more likely to be non-white, younger, and reside in rural counties. A regression model with NGCs removed identified low educational attainment and older housing as significant block-group IPD rate predictors (p<0.05), whereas the second model with assigned NGCs predicted increased rates with decreased English fluency and incomplete plumbing facilities (p<0.05). Our analysis indicated that excluding NGCs resulted in differing predictive models. Health researchers should recognize potential bias when excluding cases. More rigorous population-weighted geo-imputation might produce better predictive models; however, error might be reduced by postal code assignment in the absence of adequate resources for more complex analyses.

Learning Areas:

Learning Objectives:
Differentiate procedures for geocoding PO Box addresses to small areas. Identify sources of error in predictive models using different geocoding procedures.

Keywords: Geographic Information Systems, Methodology

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am the special projects coordinator for the New Mexico Emerging Infections Program (NMEIP). NMEIP is a program for active, population-based surveillance of specified organisms.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.