Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic
BMC Infectious Diseases, ISSN: 1471-2334, Vol: 7, Issue: 1, Page: 26
2007
- 52Citations
- 132Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations52
- Citation Indexes49
- 49
- CrossRef29
- Policy Citations3
- Policy Citation3
- Captures132
- Readers132
- 132
Article Description
Background: Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. Methods: TB cases among residents in Fukuoka Prefecture between 1999 and 2004 (n = 9,119) were geocoded at the census tract level (n = 109) based on residence at the time of diagnosis. The spatial and space-time scan statistics were then used to identify clusters of census tracts with elevated proportions of TB cases. Results: In the purely spatial analyses, the most likely clusters were in the Chikuho coal mining area (in 1999, 2002, 2003, 2004), the Kita-Kyushu industrial area (in 2000), and the Fukuoka urban area (in 2001). In the space-time analysis, the most likely cluster was the Kita-Kyushu industrial area (in 2000). The north part of Fukuoka Prefecture was the most likely to have a cluster with a significantly high occurrence of TB. Conclusion: The spatial and space-time scan statistics are effective ways of describing circular disease clusters. Since, in reality, infectious diseases might form other cluster types, the effectiveness of the method may be limited under actual practice. The sophistication of the analytical methodology, however, is a topic for future study. © 2007 Onozuka and Hagihara; licensee BioMed Central Ltd.
Bibliographic Details
Springer Science and Business Media LLC
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