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

Jacquez, Geoffrey M.; Kaufmann, Andreas; Meliker, Jaymie R.; Goovaerts, Pierre; AvRuskin, Gillian A.; Nriagu, Jerome O.

doi:10.1186/1476-069x-4-4

Cited by 58 publications

(82 citation statements)

References 58 publications

(42 reference statements)

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“…The Q-statistic was first used to describe and explain spatial and space-time clustering of chronic diseases in human medicine (Jacquez et al, 2005). The statistic relied on the concept of residential history of patients, i.e.…”

Section: Q Statisticmentioning

confidence: 99%

See 1 more Smart Citation

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

Mulatti

Kitron

Jacquez

et al. 2010

Preventive Veterinary Medicine

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Section: Q Statisticmentioning

confidence: 99%

“…In this study, Q statistic (Jacquez et al, 2005, Jacquez andMeliker, 2009) was used since it relaxes the abovementioned assumptions. Here, the non-independence of IFs is not critical since IFs were considered as potential sources of infection for susceptible farms.…”

Section: Introductionmentioning

confidence: 99%

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

Mulatti

Kitron

Jacquez

et al. 2010

Preventive Veterinary Medicine

View full text Add to dashboard Cite

“…Next, a bladder cancer case-control study in southeastern Michigan is described, followed by an application of these methods to this dataset to illustrate the approach. Jacquez et al (2005Jacquez et al ( , 2006 develop global, local, and focused tests for case-control clustering of residential histories that account for covariates and other risk factors. Readers unfamiliar with Q-statistics may wish to refer to the original works.…”

Section: Methodsmentioning

confidence: 99%

Space–time clustering of case–control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects

Meliker

Jacquez

2007

Stoch Environ Res Risk Assess

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Our research group recently developed Q-statistics for evaluating space-time clustering in casecontrol studies with residential histories. This technique relies on time-dependent nearest-neighbor relationships to examine clustering at any moment in the life-course of the residential histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness, each individual's probability of being a case is based instead on his/her risk factors and covariates. In this paper, we extend this approach to illustrate how alternative temporal orientations (e.g., years prior to diagnosis/recruitment, participant's age, and calendar year) influence a spatial clustering pattern. These temporal orientations are valuable for shedding light on the duration of time between clustering and subsequent disease development (known as the empirical induction period), and for revealing age-specific susceptibility windows and calendar year-specific effects. An ongoing population-based bladder cancer case-control study is used to demonstrate this approach. Data collection is currently incomplete and therefore no inferences should be drawn; we analyze these data to demonstrate these novel methods. Maps of space-time clustering of bladder cancer cases are presented using different temporal orientations while accounting for covariates and known risk factors. This systematic approach for evaluating space-time clustering has the potential to generate novel hypotheses about environmental risk factors and provides insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects.

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“…points aggregated to boundaries) (Mostashari et al, 2003). For every data type numerous spatial cluster analysis methods exist and vary broadly with respect to assumptions and interpretation (Moore and Carpenter, 1999;Jacquez et al, 2005;Kulldorff, 2006). It is speculated that since the development of early algorithms, hundreds of new methods, and variants of existing ones have been introduced, providing researchers with more robust statistical and analytical capabilities (Kulldorff, 2006).…”

Section: Introductionmentioning

confidence: 99%

A scoping review of spatial cluster analysis techniques for point-event data

et al. 2013

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Abstract. Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

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

Cited by 58 publications

References 58 publications

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

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

Space–time clustering of case–control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects

A scoping review of spatial cluster analysis techniques for point-event data

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