Spatial-temporal disease mapping of illicit drug abuse or dependence in the presence of misaligned ZIP codes

• Type of Material: Article
• Authors: Li Zhu, National Cancer Institute; Lance Waller, Emory University; Juan Ma, Fayetteville State University
• Language: English
• Date: 2013-06-01
• Publisher: Springer Verlag (Germany)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2011 Springer Science+Business Media B.V. (outside the USA).
• Final Published Version (URL): http://dx.doi.org/10.1007/s10708-011-9429-3
• Title of Journal or Parent Work: GeoJournal
• ISSN: 0343-2521
• Volume: 78
• Issue: 3
• Start Page: 463
• End Page: 474
• Grant/Funding Information: Research and preparation of this manuscript was supported in part by a National Institute on Drug Abuse Grant Number R21 DA024341 (Paul Gruenewald, Principal Investigator).; The research of Li Zhu was conducted at the School of Rural Public Health, Texas A&M Health Science Center.; The work of Li Zhu and Lance Waller was supported by National Institutes of Health grant 5R01ES015525-02.
• Keywords: spatial-temporal disease mapping; illicit drugs; misaligned data; changing ZIP codes; Bayesian hierarchical method
• Research Categories: Health Sciences, Public Health; Geography

Abstract

Geo-referenced data often are collected in small, administrative units such as census enumeration districts or postal code areas. Such areas vary in geographic area and population size and may change over time. In research into drug-related health issues within the United States, U.S. Postal Service ZIP codes represent a commonly used unit for data collection, storage, and spatial analysis because of their widespread availability in health databases through patient contact and billing information. However, the ZIP code was developed for the specific purpose of delivering mail and may be changed at any time, and its design and development does not take into consideration problems that may arise in data collection, analysis, and presentation in health studies. In this paper, we propose a spatial hierarchical modeling approach to quantify trends within ZIP-code based counts when some fraction of ZIP codes change over the study period, that is, when the data are spatially misaligned across time. We propose a data vector approach and adjust the spatial auto-correlation structure within our Bayesian hierarchical model to provide inference for our misaligned data. We motivate and illustrate our approach to explore spatio-temporal patterns of amphetamine abuse and/or dependence in Tracy, California over the years 1995-2005. Uncertainty associated with misaligned data is modeled, quantified, and visualized. The approach offers a framework for further investigation into other risk factors in order to more fully understand the dynamics of illicit drug abuse or dependence across time and space in imperfectly measured data.