Geospatial cryptography: enabling researchers to access private, spatially referenced, human subjects data for cancer control and prevention.

As the volume, accuracy and precision of digital geographic information have increased, concerns regarding individual privacy and confidentiality have come to the forefront. Not only do these challenge a basic tenet underlying the advancement of science by posing substantial obstacles to the sharing of data to validate research results, but they are obstacles to conducting certain research projects in the first place. Geospatial cryptography involves the specification, design, implementation and application of cryptographic techniques to address privacy, confidentiality and security concerns for geographically referenced data.

Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome.

The exposome, defined as the totality of an individual's exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic geographic information science (Genetic GISc) that is founded on the exposome, genome+ and behavome.

Investigation of spatio-temporal cancer clusters using residential histories in a case-control study of non-Hodgkin lymphoma in the United States.

Background: Non-Hodgkin lymphoma (NHL) is an enigmatic disease with few known risk factors. Spatio-temporal epidemiologic analyses have the potential to reveal patterns that may give clues to new risk factors worthy of investigation. We sought to investigate clusters of NHL through space and time based on life course residential histories.

Local bladder cancer clusters in southeastern Michigan accounting for risk factors, covariates and residential mobility.

Background: In case control studies disease risk not explained by the significant risk factors is the unexplained risk. Considering unexplained risk for specific populations, places and times can reveal the signature of unidentified risk factors and risk factors not fully accounted for in the case-control study. This potentially can lead to new hypotheses regarding disease causation.

Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark.

Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics.

Space-time clusters of breast cancer using residential histories: a Danish case-control study.

Background: A large proportion of breast cancer cases are thought related to environmental factors. Identification of specific geographical areas with high risk (clusters) may give clues to potential environmental risk factors. The aim of this study was to investigate whether clusters of breast cancer existed in space and time in Denmark, using 33 years of residential histories.

Residential mobility and breast cancer in Marin County, California, USA.

Marin County (California, USA) has among the highest incidences of breast cancer in the U.S. A previously conducted case-control study found eight significant risk factors in participants enrolled from 1997-1999.

Space-time clustering of non-hodgkin lymphoma using residential histories in a Danish case-control study.

Non-Hodgkin lymphoma (NHL) is a frequent cancer and incidence rates have increased markedly during the second half of the 20(th) century; however, the few established risk factors cannot explain this rise and still little is known about the aetiology of NHL. Spatial analyses have been applied in an attempt to identify environmental risk factors, but most studies do not take human mobility into account. The aim of this study was to identify clustering of NHL in space and time in Denmark, using 33 years of residential addresses.

Performance of cancer cluster Q-statistics for case-control residential histories.

Few investigations of health event clustering have evaluated residential mobility, though causative exposures for chronic diseases such as cancer often occur long before diagnosis. Recently developed Q-statistics incorporate human mobility into disease cluster investigations by quantifying space- and time-dependent nearest neighbor relationships. Using residential histories from two cancer case-control studies, we created simulated clusters to examine Q-statistic performance.

A research agenda: does geocoding positional error matter in health GIS studies?

Until recently, little attention has been paid to geocoding positional accuracy and its impacts on accessibility measures; estimates of disease rates; findings of disease clustering; spatial prediction and modeling of health outcomes; and estimates of individual exposures based on geographic proximity to pollutant and pathogen sources. It is now clear that positional errors can result in flawed findings and poor public health decisions. Yet the current state-of-practice is to ignore geocoding positional uncertainty, primarily because of a lack of theory, methods and tools for quantifying, modeling, and adjusting for geocoding positional errors in health analysis.

Forecasting the effect of the Amethyst initiative on college drinking.

Background: A number of college presidents have endorsed the Amethyst Initiative, a call to consider lowering the minimum legal drinking age (MLDA). Our objective is to forecast the effect of the Amethyst Initiative on college drinking.

Methods: A system model of college drinking simulates MLDA changes through (i) a decrease in heavy episodic drinking (HED) because of the lower likelihood of students drinking in unsupervised settings where they model irresponsible drinking (misperception), and (ii) an increase in overall drinking among currently underage students because of increased social availability of alcohol (wetness).

Spatial clustering of leukemia and type 1 diabetes in children in Denmark.

It has been proposed that type 1 diabetes (T1D) and leukemia in children may cluster in space and time due to common spatially mediated etiologies. We investigated this hypothesis and clustering of both diseases separately in Danish children aged 0-14 years, using 1,168 leukemia cases diagnosed in the period 1980-2006, 2,443 T1D cases diagnosed 1996-2006, and population-based controls matched on age, gender, and time of diagnosis. Residential histories from birth to diagnosis were collected.

Geographic boundary analysis in spatial and spatio-temporal epidemiology: perspective and prospects.

Geographic boundary analysis is a relatively new approach that is just beginning to be applied in spatial and spatio-temporal epidemiology to quantify spatial variation in health outcomes, predictors and correlates; generate and test epidemiologic hypotheses; to evaluate health-environment relationships; and to guide sampling design. Geographic boundaries are zones of rapid change in the value of a spatially distributed variable, and mathematically may be defined as those locations with a large second derivative of the spatial response surface. Here we introduce a pattern analysis framework based on Value, Change and Association questions, and boundary analysis is shown to fit logically into Change and Association paradigms.

Heavy episodic drinking on college campuses: does changing the legal drinking age make a difference?

Objective: This article extends the compartmental model previously developed by Scribner et al. in the context of college drinking to a mathematical model of the consequences of lowering the legal drinking age.

Method: Using data available from 32 U.

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.

Incorporating individual-level distributions of exposure error in epidemiologic analyses: an example using arsenic in drinking water and bladder cancer.

Purpose: Epidemiologic analyses traditionally rely on point estimates of exposure for assessing risk despite exposure error. We present a strategy that produces a range of risk estimates reflecting distributions of individual-level exposure.

Methods: Quantitative estimates of exposure and its associated error are used to create for each individual a normal distribution of exposure estimates which is then sampled using Monte Carlo simulation.

Evaluation of the risk of neighbourhood infection of H7N1 Highly Pathogenic Avian Influenza in Italy using Q statistic.

Exposure to the risk of neighbourhood infection was estimated for the H7N1 Highly Pathogenic Avian Influenza (HPAI) epidemic that affected Northern Italy between 1999 and 2000. The two most affected regions (Lombardy and Veneto) were analyzed and the epidemic was divided into three phases. Q statistics were used to evaluate exposure to the risk of neighbourhood infection using two measures.

Lifetime exposure to arsenic in drinking water and bladder cancer: a population-based case-control study in Michigan, USA.

Objective: Arsenic in drinking water has been linked with the risk of urinary bladder cancer, but the dose-response relationships for arsenic exposures below 100 microg/L remain equivocal. We conducted a population-based case-control study in southeastern Michigan, USA, where approximately 230,000 people were exposed to arsenic concentrations between 10 and 100 microg/L.

Methods: This study included 411 bladder cancer cases diagnosed between 2000 and 2004, and 566 controls recruited during the same period.

Local indicators of geocoding accuracy (LIGA): theory and application.

Background: Although sources of positional error in geographic locations (e.g. geocoding error) used for describing and modeling spatial patterns are widely acknowledged, research on how such error impacts the statistical results has been limited.

A systems approach to college drinking: development of a deterministic model for testing alcohol control policies.

Objective: The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts.

Breast and prostate cancer survival in Michigan: can geographic analyses assist in understanding racial disparities?

Background: Racial disparities in survival from breast and prostate cancer are well established; however, the roles of societal/socioeconomic factors and innate/genetic factors in explaining the disparities remain unclear. One approach for evaluating the relative importance of societal and innate factors is to quantify how the magnitude of racial disparities changes according to the geographic scales at which data are aggregated. Disappearance of racial disparities for some levels of aggregation would suggest that modifiable factors not inherent at the individual level are responsible for the disparities.

Spatial cluster analysis of early stage breast cancer: a method for public health practice using cancer registry data.

Objectives: Cancer registries are increasingly mapping residences of patients at time of diagnosis, however, an accepted protocol for spatial analysis of these data is lacking. We undertook a public health practice-research partnership to develop a strategy for detecting spatial clusters of early stage breast cancer using registry data.

Methods: Spatial patterns of early stage breast cancer throughout Michigan were analyzed comparing several scales of spatial support, and different clustering algorithms.

Cluster morphology analysis.

Most disease clustering methods assume specific shapes and do not evaluate statistical power using the applicable geography, at-risk population, and covariates. Cluster Morphology Analysis (CMA) conducts power analyses of alternative techniques assuming clusters of different relative risks and shapes. Results are ranked by statistical power and false positives, under the rationale that surveillance should (1) find true clusters while (2) avoiding false clusters.