研究了在医疗资源有限的条件下, 如何在多组人群中探测某疾病最强地理聚集性的问题. 为了有效地探测疾病爆发, 只需特别监测和评估某些特定人群即可, 这类人群称为具有最强聚集性的人群(most severe cluster, MSC). 由于各组人群会相互影响, 因而提出了一种多元正态空间扫描统计量(multivariate normal scan statistic, MNSS)模型, 并以美国纽约州肺癌患者为例验证了该模型的适用性.
This paper focuses on the problem of detecting ageographical cluster with the most severe status in multiple groups of populations with the consideration of limited medical resources. In an early stage of a disease, an outbreak may only be present in some specific population group. Therefore, to efficiently detect the outbreak, specific groups need to be particularly monitored and evaluated. The objective of detection as the most severe cluster (MSC) is defined. Considering interaction among population groups, a multivariate normal scan statistic is proposed. The method is applied to an example of lung cancer in New York State to detect the MSC with a high mortality rate at the aggregate level.
[1] Kulldorff M. A spatial scan statistic [J]. Communications in Statistics: Theory and Methods, 1997, 26(6): 1481-1496.
[2] Turnbull G W, Iwano E J, Burnett W S, et al. Monitoring for clusters of disease: application to Leukemia incidence in upstate New York [J]. American Journal of Epidemiology, 1990,
132(S1): 136-143.
[3] Chen J, Roth R E, Naito A T, et al. Geovisual analytics to enhance spatial scan statistic interpretation: an analysis to US cervical cancer mortality [J]. International Journal of Health
Geographics, 2008, 7: 57.
[4] Osei F B, Duker A A. Spatial dependency of V. cholera prevalence on open space refuse dumps in Kumasi, Ghana: a spatial statistical modeling [J]. International Journal of Health
Geographics, 2008, 7: 62.
[5] Rubinsky-Elefant G, Silva-Nunes M, Malafronte R S, et al. Human toxocariasis in rural Brazilian Amazonia: seroprevalence, risk factors, and spatial distribution [J]. American Journal
of Tropical Medicine and Hygiene, 2008, 79: 93-98.
[6] Heres L, Brus D J, Hagenaars T J. Spatial analysis of BSE cases in the Netherlands [J]. BMC Veterinary Research, 2008, 4: 21.
[7] Reinhardt M, Elias J, Albert J, et al. EpiScanGIS: an online geographic surveillance system for meningococcal disease [J]. International Journal of Health Geographics, 2008, 7: 33.
[8] Gay E, Senoussi R, Barnouin J. A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score [J]. Veterinary Research, 2007, 38: 585-596.
[9] Huang L, Kulldorff M, Gregorio D. A spatial scan statistic for survival data [J]. Biometrics, 2007, 63: 109-118.
[10] Huang L, Pickle L W, Stinchcomb D, et al. Detection of spatial clusters: application to cancer survival as a continuous outcome [J]. Epidemiology, 2007, 18: 73-87.
[11] Kulldorff M, Huang L, Kevin K. A scan statistic for continuous data based on the normal probability model [J]. International Journal of Health Geographics, 2009, 8: 58.
[12] Huang L, Ram C T, Zhao H Z, et al. Weighted normal spatial scan statistic for heterogeneous population data [J]. Journal of the American Statistical Association, 2009, 104: 886-898.
[13] Kulldorff M, Lan H, Pickle L, et al. An elliptic spatial scan statistic [J]. Statistics in Medicine, 2006, 25(22): 3929-3943.
[14] Toshiro T, Kunihiko T. A flexibly shaped spatial scan statistic for detecting clusters [J]. International Journal of Health Geographics, 2005, 4: 11.
