Cancer clusters in the USA: what do the last twenty years of state and federal investigations tell us?

Condon, Suzanne K.; Sullivan, Jan; Netreba, Brenda

doi:10.3109/10408444.2012.743504

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Equation 1 () is used to identify when and where an individual is the center of a local cluster. The simulation analysis [18] was undertaken to help account for multiple testing bias arising from the large number of statistical tests conducted in Q -statistics run across residential histories. In the simulation analysis we created clusters and evaluated the predictive capability of different versions of the local and global Q-statistics.…”

Section: Methodsmentioning

confidence: 99%

“…Latency is a difficult barrier in investigating spatial cancer clusters, as individuals are often highly mobile and will move residences more than once over the course of their life [16–18]. Several methodologies have recently been developed that help solve this difficult statistical task, with two of the most promising being generalized additive models (GAMs) with a loess smoother [19] and Q-statistics [20–22].…”

Section: Introductionmentioning

confidence: 99%

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Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark

et al. 2015

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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. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark

et al. 2015

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“…The intention of CDC’s guidelines and many states’ cluster response protocols is to screen and prioritize reports to limit investigations to those most likely to produce meaningful results [27,37]. Similarly, Condon et al [29] noted that health agencies have a responsibility to the public to respond to community concerns, and that interactions in the course of an investigation provide opportunities to educate an engaged group of citizens on the frequency, etiology, and prevention of cancer, as well as on exposure issues of concern. Further, without this engagement, health agencies might miss the rare instances where cancer cluster investigations using current methodologies might be productive.…”

Section: What Is a Cancer Cluster And What Are The Goals Of Investmentioning

confidence: 99%

“…Further, without this engagement, health agencies might miss the rare instances where cancer cluster investigations using current methodologies might be productive. This engagement allows health agencies to address environmental issues or other locally important cancer-related factors, such as screening [29]. …”

Section: What Is a Cancer Cluster And What Are The Goals Of Investmentioning

confidence: 99%

“…While goals, methods and conclusions of cluster studies are debated in both the scientific literature [8,25,29,30,31] and the popular press [32,33,34,35], investigations of reported residential clusters rarely provide definitive answers about disease etiology [8,36,37,38,39,40]. Further, it is inherently difficult to study a cluster for a disease with complex etiology and long latency such as most cancers.…”

Section: Introductionmentioning

confidence: 99%

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Cancer Cluster Investigations: Review of the Past and Proposals for the Future

Goodman

LaKind

Fagliano

et al. 2014

IJERPH

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Residential clusters of non-communicable diseases are a source of enduring public concern, and at times, controversy. Many clusters reported to public health agencies by concerned citizens are accompanied by expectations that investigations will uncover a cause of disease. While goals, methods and conclusions of cluster studies are debated in the scientific literature and popular press, investigations of reported residential clusters rarely provide definitive answers about disease etiology. Further, it is inherently difficult to study a cluster for diseases with complex etiology and long latency (e.g., most cancers). Regardless, cluster investigations remain an important function of local, state and federal public health agencies. Challenges limiting the ability of cluster investigations to uncover causes for disease include the need to consider long latency, low statistical power of most analyses, uncertain definitions of cluster boundaries and population of interest, and in- and out-migration. A multi-disciplinary Workshop was held to discuss innovative and/or under-explored approaches to investigate cancer clusters. Several potentially fruitful paths forward are described, including modern methods of reconstructing residential history, improved approaches to analyzing spatial data, improved utilization of electronic data sources, advances using biomarkers of carcinogenesis, novel concepts for grouping cases, investigations of infectious etiology of cancer, and “omics” approaches.

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Die kartografische Darstellung regionaler Unterschiede in der Morbidität

Pritzkuleit

Eisemann

Katalinic

2017

Bundesgesundheitsbl

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The cancer registry in Germany collects area-wide small-area data that can be presented in themes (disease mapping). Because of the occurrence of random extreme values of rates, mapping without prior spatial-statistical data analysis is problematic from a methodological and risk-communicative viewpoint - the extreme values easily mislead the card reader and obscure actual spatial patterns.The problem of data instability can generally be met by aggregation or by smoothing. The cancer atlas for Schleswig-Holstein is based on data from 1142 municipalities (median population: 721) for the diagnostic years 2001-2010. Maps for incidence (as a standardized incidence ratio), mortality (as a standardized mortality ratio), and relative survival (as a relative excess risk) were smoothed by using a Bayesian method (BYM model). The maps show that spatial differences can be made visible by smoothing.Data aggregation is the methodically simpler way, but means a loss of information. The atlas shows that small-scale mapping is possible while preserving the entire spatial information. The method of smoothing is complex, but useful for generating hypotheses. The spatial patterns found are complex, difficult to interpret, and require the collaboration of specialists from different professions, because of the diverse influencing factors (data collection, lifestyle factors, early detection, risk factors, etc.). The effort required to explain the methodology in a language easy to understand should not be underestimated.

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Cancer clusters in the USA: what do the last twenty years of state and federal investigations tell us?

Cited by 5 publications

References 3 publications

Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark

Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark

Cancer Cluster Investigations: Review of the Past and Proposals for the Future

Die kartografische Darstellung regionaler Unterschiede in der Morbidität

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