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Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico

Author

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  • Kulldorff, M.
  • Athas, W.F.
  • Feuer, E.J.
  • Miller, B.A.
  • Key, C.R.

Abstract

Objectives. This article presents a space-time scan statistic, useful for evaluating space-time cluster alarms, and illustrates the method on a recent brain cancer cluster alarm in Los Alamos, NM. Methods. The space-time scan statistic accounts for the preselection bias and multiple testing inherent in a cluster alarm. Confounders and time trends can be adjusted for. Results. The observed excess of brain cancer in Los Alamos was not statistically significant. Conclusions. The space-time scan statistic is useful as a screening tool for evaluating which cluster alarms merit further investigation and which clusters are probably chance occurrences.

Suggested Citation

  • Kulldorff, M. & Athas, W.F. & Feuer, E.J. & Miller, B.A. & Key, C.R., 1998. "Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico," American Journal of Public Health, American Public Health Association, vol. 88(9), pages 1377-1380.
    • Handle: RePEc:aph:ajpbhl:1998:88:9:1377-1380_5
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    Cited by:

    1. Hoje Kang, 2010. "Detecting agglomeration processes using space–time clustering analyses," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(2), pages 291-311, October.
    2. Sami Ullah & Hanita Daud & Sarat C Dass & Hadi Fanaee-T & Alamgir Khalil, 2018. "An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    3. Nicholas Malizia, 2013. "Inaccuracy, Uncertainty and the Space-Time Permutation Scan Statistic," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-15, February.
    4. A. D. Gibson & G. Yale & J. Corfmat & M. Appupillai & C. M. Gigante & M. Lopes & U. Betodkar & N. C. Costa & K. A. Fernandes & P. Mathapati & P. M. Suryawanshi & N. Otter & G. Thomas & P. Ohal & I. Ai, 2022. "Elimination of human rabies in Goa, India through an integrated One Health approach," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    6. Shino Shiode & Narushige Shiode, 2022. "Network-Based Space-Time Scan Statistics for Detecting Micro-Scale Hotspots," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    7. Shixin Wang & Wenjun Li & Yi Zhou & Fuli Yan & Futao Wang & Wenliang Liu, 2015. "Space–time evolution of historical drought hazards in eastern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(3), pages 2027-2047, July.
    8. Fenyang Tang & Yuejia Cheng & Changjun Bao & Jianli Hu & Wendong Liu & Qi Liang & Ying Wu & Jessie Norris & Zhihang Peng & Rongbin Yu & Hongbing Shen & Feng Chen, 2014. "Spatio-Temporal Trends and Risk Factors for Shigella from 2001 to 2011 in Jiangsu Province, People's Republic of China," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    9. Ken Silver & Richard Clapp, 2006. "Environmental Surveillance at Los Alamos: An Independent Reassessment of Historical Data," Risk Analysis, John Wiley & Sons, vol. 26(4), pages 893-906, August.
    10. Yukio Sadahiro, 2022. "A method for evaluating the degree of spatial and temporal avoidance in spatial point patterns," Journal of Geographical Systems, Springer, vol. 24(2), pages 241-260, April.
    11. Sami Ullah & Hanita Daud & Sarat C. Dass & Hadi Fanaee-T & Husnul Kausarian & Alamgir, 2020. "Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019," IJERPH, MDPI, vol. 17(4), pages 1-10, February.
    12. Lan Huang & Martin Kulldorff & David Gregorio, 2007. "A Spatial Scan Statistic for Survival Data," Biometrics, The International Biometric Society, vol. 63(1), pages 109-118, March.
    13. Suyan Yi & Hongwei Wang & Shengtian Yang & Ling Xie & Yibo Gao & Chen Ma, 2021. "Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Its Response to Climate Factors in the Ili River Valley Region of China," IJERPH, MDPI, vol. 18(4), pages 1-13, February.
    14. Zhezhe Cui & Dingwen Lin & Virasakdi Chongsuvivatwong & Jinming Zhao & Mei Lin & Jing Ou & Jinghua Zhao, 2019. "Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-15, May.
    15. Yingqi Zhao & Donglin Zeng & Amy H. Herring & Amy Ising & Anna Waller & David Richardson & Michael R. Kosorok, 2011. "Detecting Disease Outbreaks Using Local Spatiotemporal Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1508-1517, December.
    16. Root, Elisabeth Dowling & Meyer, Robert E. & Emch, Michael E., 2009. "Evidence of localized clustering of gastroschisis births in North Carolina, 1999-2004," Social Science & Medicine, Elsevier, vol. 68(8), pages 1361-1367, April.

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