Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94929/1/wrcr10188.pd

Global, local and focused geographic clustering for case-control data with residential histories

BACKGROUND: This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile. METHODS: Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only. RESULTS: Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study. CONCLUSION: Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account

Complex Systems Analysis using Space-Time Information Systems and Model Transition Sensitivity Analysis

Real-world systems are dynamic, complex and geographic, yet many mathematical modeling tools do not evaluate sensitivity of results to underlying assumptions, and GIS do not adequately represent time. This presentation describes two new approaches: Space-Time Information Systems (STIS), and Model Transition Sensitivity Analysis (MTSA). Current GIS are based on spatial data models that inadequately characterize the temporal dimension needed for effective representation of complex systems. They do not deal readily with spacetime georeferencing nor space-time queries, and are best suited to “snapshots ” of static systems. These deficiencies prompted many geographers to call for a “higher-dimensional GIS ” (a STIS) to better represent space-time dynamics. When formulating models of complex systems, critical choices are made regarding model type and complexity. Model type is the mathematical approach employed, for example, a deterministic model versus a stochastic model. Model complexity is determined by the amount of abstraction and simplification employed during model construction. A growing body of work demonstrates that choice of model type and complexity has substantial impacts on simulation results and on model-based decisions. This paper briefly describes STIS and MTSA approaches that allow researchers to more effectively represent complex systems and to evaluate the sensitivity of their results to underlying assumptions. 1

Accuracy of Commercially Available Residential Histories for Epidemiologic Studies

A key problem facing epidemiologists who wish to account for residential mobility in their analyses is the cost and difficulty of obtaining residential histories. Commercial residential history data of acceptable accuracy, cost, and coverage would be of great value. The present research evaluated the accuracy of residential histories from LexisNexis, Inc. The authors chose LexisNexis because the Michigan Cancer Registry has considered using their data, they have excellent procedures for privacy protection, and they make available residential histories at 25 cents per person. Only first and last name and address at last-known residence are required to access the residential history. The authors compared lifetime residential histories collected through the use of written surveys in a case-control study of bladder cancer in Michigan to the 3 residential addresses routinely available in the address history from LexisNexis. The LexisNexis address matches, as a whole, accounted for 71.5% of participants’ lifetime addresses. These results provided a level of accuracy that indicates routine use of residential histories from commercial vendors is feasible. More detailed residential histories are available at a higher cost but were not analyzed in this study. Although higher accuracy is desirable, LexisNexis data are a vast improvement over the assumption of immobile individuals currently used in many spatial and spatiotemporal studies