Potential selection bias associated with using geocoded birth records for epidemiologic research

Abstract

Purpose There is an increasing use of geocoded birth registry data in environmental epidemiology research. Ungeocoded records are routinely excluded. Methods We used classification and regression tree analysis (CART) and logistic regression to investigate potential selection bias associated with this exclusion among all singleton Florida births in 2009 (N=210,285). Results The rate of unsuccessful geocoding was 11.5% (n=24,171). This ranged between 0% to 100% across zip codes. Living in a rural zip code was the strongest predictor of being ungeocoded. Other predictors for geocoding status varied with urbanity status. In urban areas, maternal race [adjusted odds ratio (aOR) ranging between 1.08 for Hispanic to 1.18 for Black compared to White], maternal age [aOR: 1.16 (1.10-1.23) for ages 20-34 compared to <20], maternal nativity [aOR: 1.20 (1.15, 1.25) for Non-US vs. US born], delivery at a birth center [aOR: 1.72(1.49, 2.00 compared to hospital delivery)], multiparity [aOR: 0.91 (0.88, 0.94)], maternal smoking [aOR: 0.82 (0.76-0.88)] and having non-private insurance [aOR: 1.25 (1.20-1.30) for Medicaid vs. private insurance] were significantly associated with being ungeocoded. In rural areas, births delivered at birth center [aOR: 2.91(1.80-4.73)] or home [aOR: 1.94(1.28-2.95) had increased odds compared to hospital births. The characteristics predictive of being ungeocoded were also significantly associated with adverse birth outcomes such as low birthweight and preterm delivery, and the association for maternal age was different when ungeocoded births were included and excluded. Conclusions Geocoding status is not random. Women with certain exposure-outcome characteristics may be more likely to be ungeocoded and excluded, indicating potential selection bias.

Decentralized and reproducible geocoding and characterization of community and environmental exposures for multisite studies

“…Indeed, others have previously found that excluding nongeocoded addresses can result in selection bias for epidemiological research. 23 This study found that living in a rural zip code was highly associated with geocoding status. Furthermore, even among addresses only in urban zip codes, geocoding status was associated with numerous factors, including maternal race, maternal smoking, and having nonprivate insurance.…”

Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities

“…With increased accessibility to health delivery data by both public health and health care professionals, there has been an increase in the use of spatial analytics to monitor and understand the spread of disease and outbreak management responses 32 . For example, geographic information systems (GIS) allow communities to better utilize public health resources by targeting a community immunization response strategy to outbreak clusters 33 , although selection bias could be an issue, particularly for those from rural settings, certain ethnic or racial groups, and those with lower socio-economic status 34 . In addition, GIS surveillance can be assisted by social media.…”

Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses

“…Geocoding individual level records (by linking zip codes or census tracts, for example) is widely conducted in epidemiologic research to provide additional information, particularly when interested in environmental exposures (such as air pollution) or proxies for socioeconomic position (e.g., area-based deprivation). Ensuring accurate addresses when cleaning administrative data is crucial, as it has the potential to lead to mismatches, the consequences of which may be reduced sample sizes and increased selection and information biases [ 41 ].…”