Line and point cluster models for spatial health data. (English) Zbl 1445.62274
Summary: Spatial cluster modelling of small area disease incidence and mortality has previously focused on clusters where excess risk is distributed around fixed points, and the aim is the reconstruction of these points (cluster centers). Often there is a need to assess clusters of a different form, such as around roads or river systems. These clusters are often linear or can be approximated by combinations of several linear segments. In this paper the recovery of point and line clusters is considered jointly. An example application is given where both linear or point clustering could be present.
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62M30 | Inference from spatial processes |
References:
| [1] | Choi, E.; Hall, P., Nonparametric approach to the analysis of space-time data on earthquake occurrences, J. Comput. Graphical Statist., 8, 733-748 (1999) |
| [2] | Cressie, N. A.C., Statistics for Spatial Data (1993), Wiley: Wiley New York |
| [3] | Cressie, N. C.; Lawson, A., Hierarchical probability models and Bayesian analysis of mine locations, Adv. Appl. Probab., 32, 2, 315-330 (2000) · Zbl 0954.62111 |
| [4] | Cuzick, J.; Hills, M., Clustering and clusters-summary, (Draper, G., Geographical Epidemiology of Childhood Leukaemia and Non- Hodgkin Lymphomas in Great Britain 1966-1983 (1991), HMSO: HMSO London), 123-125 |
| [5] | Diggle, P.; Rowlingson, B., A conditional approach to point process modelling of elevated risk, J. Roy. Statist. Soc. Ser. A, 157, 433-440 (1994) |
| [6] | Fraley, C.; Raftery, A., Model-based clustering, discriminant analysis, and density estimation, J. Amer. Statist. Assoc., 97, 611-631 (2002) · Zbl 1073.62545 |
| [7] | Hillerdal, G., Mesotelioma: cases associated with non-occupational and low dose exposures, Occup. Environ. Med., 56, 505-513 (1999) |
| [8] | Hossain, M.; Lawson, A. B., Local likelihood disease clustering: development and evaluation, Environ. Ecol. Statist., 12, 259-273 (2005) |
| [9] | Lawson, A.B., 1996. Markov chain Monte Carlo methods for spatial cluster processes. In: Computer Science and Statistics: Proceedings of the Interface, vol. 27, pp. 314-319.; Lawson, A.B., 1996. Markov chain Monte Carlo methods for spatial cluster processes. In: Computer Science and Statistics: Proceedings of the Interface, vol. 27, pp. 314-319. |
| [10] | Lawson, A. B., Cluster modelling of disease incidence via RJMCMC methods: a comparative evaluation, Statist. Med., 19, 2361-2376 (2000) |
| [11] | Lawson, A. B., Statistical Methods in Spatial Epidemiology, second Ed (2006), Wiley: Wiley New York · Zbl 1096.62118 |
| [12] | Lawson, A. B., Disease cluster detection: a critique and a Bayesian proposal, Statist. Med., 25, 897-916 (2006) |
| [13] | Lawson, A. B.; Clark, A., Markov chain Monte Carlo methods for putative sources of hazard and general clustering, (Lawson, A. B.; Böhning, D.; Lesaffre, E.; Biggeri, A.; Viel, J.-F.; Bertollini, R., Disease Mapping and Risk Assessment for Public Health (1999), Wiley: Wiley New York), 119-141, (Chapter 9) · Zbl 1072.62662 |
| [14] | Lawson, A. B.; Denison, D., Spatial cluster modelling: an overview, (Lawson, A. B.; Denison, D., Spatial Cluster Modelling (2002), CRC Press: CRC Press New York), 1-19, (Chapter 1) · Zbl 1046.62102 |
| [15] | Magnani, C.; Terracini, B.; Ivaldi, C.; Botta, M.; Mancini, A.; Andrion, A., Pleural malignant mesotelioma and non-occupational exposure to asbestos in Casale Monferrato, italy, Occup. Environ. Med., 52, 362-367 (1995) |
| [16] | Magnani, C.; Terracini, B.; Ivaldi, C.; Mancini, A.; Botta, M., Tumor mortality and from other causes in asbestos cement workers at the casale monferrato plant, La Medicina del Lavoro, 87, 133-146 (1996) |
| [17] | Magnani, C.; Ivaldi, C.; Botta, M.; Terracini, B., Pleural malignant mesotelioma and environmental asbestos exposure in Casale Monferrato Piedmont. Preliminary analysis of a case-control study, La Medicina del Lavoro, 88, 302-309 (1997) |
| [18] | Møller, J., Markov chain Monte Carlo and spatial point processes, (Barndorff Nielsen, O.; Kendall, W. S.; van Lieshout, M., Stochastic Geometry Likelihood and Computation (1999), CRC press: CRC press London), 141-172, (Chapter 4) |
| [19] | Ogata, Y., Space-time point-process models for earthquake occurences, Ann. Inst. Statist. Math., 50, 379-402 (1998) · Zbl 0947.62061 |
| [20] | Robert, C.; Casella, G., Monte Carlo Statistical Methods (2005), Springer: Springer Berlin |
| [21] | Scott, D. W., Multivariate Density Estimation (1992), Wiley: Wiley New York · Zbl 0850.62006 |
| [22] | Stephens, M., Bayesian analysis of mixture models with an unknown number of components—an alternative to reversible jump methods, Ann. Statist., 28, 40-74 (2000) · Zbl 1106.62316 |
| [23] | van Lieshout, M.; Baddeley, A., Extrapolating and interpolating spatial patterns, (Lawson, A. B.; Denison, D., Spatial Cluster Modelling (2002), CRC Press: CRC Press New York), 61-86, (Chapter 4) |
| [24] | Zhuang, J.; Ogata, Y.; Vere-Jones, D., Stochastic declustering of space-time earthquake occurrences, J. Amer. Statist. Assoc., 97, 369-380 (2002) · Zbl 1073.62558 |
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