Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data

2019 ◽  
Vol 133 ◽  
pp. 28-39
Author(s):  
Myeonggyun Lee ◽  
Inkyung Jung
Keyword(s):  
Likelihood Ratio ◽  
Outcome Data ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Restricted Likelihood ◽  
Spatial Scan ◽  
Ordinal Outcome

Spatial scan statistics can be dangerous

2021 ◽  
Vol 30 (1) ◽  
pp. 75-86
Author(s):  
Toshiro Tango
Keyword(s):  
Disease Surveillance ◽  
Epidemiological Studies ◽  
Scan Statistics ◽  
Mortality Data ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Spatial Scan Statistics ◽  
Restricted Likelihood ◽  
Risk Of Disease ◽  
Spatial Scan

Spatial scan statistics are widely used tools for the detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff along with SaTScan software has been used in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect non-circular, irregularly shaped clusters, many authors have proposed non-circular spatial scan statistics. Above all, the flexible spatial scan statistic proposed by Tango and Takahashi along with FleXScan software has also been used. However, it does not seem to be well recognized that these spatial scan statistics, especially SaTScan, tend to detect the most likely cluster, much larger than the true cluster by absorbing neighboring regions with nonelevated risk of disease occurrence. Therefore, if researchers reported the detected most likely cluster as they are, it might lead to a criticism to them due to the fact that some regions with nonelevated risk are included in the detected most likely cluster. In this paper, to avoid detecting such undesirable and misleading clusters which might cause a social concern, we shall propose the use of the restricted likelihood ratio proposed by Tango and illustrate the procedure with two kinds of mortality data in Japan.

Spatial Scan Statistics to Assess Sampling Strategy of Antimicrobial Resistance Monitoring Program

2009 ◽  
Vol 6 (1) ◽  
pp. 15-21 ◽  
Author(s):  
A.R. Vieira ◽  
H. Houe ◽  
H.C. Wegener ◽  
D.M.A. Lo Fo Wong ◽  
R. Bødker ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Monitoring Program ◽  
Sampling Strategy ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Resistance Monitoring ◽  
Spatial Scan

Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters

2006 ◽  
Vol 15 (2) ◽  
pp. 428-442 ◽  
Author(s):  
Luiz Duczmal ◽  
Martin Kulldorff ◽  
Lan Huang
Keyword(s):  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Spatial Scan

A genetic algorithm for irregularly shaped spatial scan statistics

2007 ◽  
Vol 52 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Luiz Duczmal ◽  
André L.F. Cançado ◽  
Ricardo H.C. Takahashi ◽  
Lupércio F. Bessegato
Keyword(s):  
Genetic Algorithm ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Spatial Scan

Space–time patterns ofCampylobacterspp. colonization in broiler flocks, 2002–2006

2010 ◽  
Vol 138 (9) ◽  
pp. 1336-1345 ◽  
Author(s):  
M. E. JONSSON ◽  
M. NORSTRÖM ◽  
M. SANDBERG ◽  
A. K. ERSBØLL ◽  
M. HOFSHAGEN
Keyword(s):  
Risk Factors ◽  
Relative Risk ◽  
Function Analysis ◽  
Space Time ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Factors Associated ◽  
Time Patterns ◽  
Broiler Flock ◽  
Spatial Scan

SUMMARYThis study was performed to investigate space–time patterns ofCampylobacterspp. colonization in broiler flocks in Norway. Data on theCampylobacterspp. status at the time of slaughter of 16 054 broiler flocks from 580 farms between 2002 and 2006 was included in the study. Spatial relative risk maps together with maps of space–time clustering were generated, the latter by using spatial scan statistics. These maps identified the same areas almost every year where there was a higher risk for a broiler flock to test positive forCampylobacterspp. during the summer months. A modifiedK-function analysis showed significant clustering at distances between 2·5 and 4 km within different years. The identification of geographical areas with higher risk forCampylobacterspp. colonization in broilers indicates that there are risk factors associated withCampylobacterspp. colonization in broiler flocks varying with region and time, e.g. climate, landscape or geography. These need to be further explored. The results also showed clustering at shorter distances indicating that there are risk factors forCampylobacterspp. acting in a more narrow scale as well.

A multidimensional spatial scan statistics approach to movement pattern comparison

2018 ◽  
Vol 32 (7) ◽  
pp. 1304-1325 ◽  
Author(s):  
Yizhao Gao ◽  
Ting Li ◽  
Shaowen Wang ◽  
Myeong-Hun Jeong ◽  
Kiumars Soltani
Keyword(s):  
Movement Pattern ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Pattern Comparison ◽  
Spatial Scan

scholarly journals Spatial distribution of iron rich foods consumption and its associated factors among children aged 6–23 months in Ethiopia: spatial and multilevel analysis of 2016 Ethiopian demographic and health survey

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Sofonyas Abebaw Tiruneh ◽  
Belete Achamyelew Ayele ◽  
Getachew Yideg Yitbarek ◽  
Desalegn Tesfa Asnakew ◽  
Melaku Tadege Engidaw ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Health Survey ◽  
Associated Factors ◽  
Household Wealth ◽  
Demographic And Health Survey ◽  
Scan Statistics ◽  
Nutritional Deficiencies ◽  
Spatial Scan Statistics ◽  
Developmental Problems ◽  
Spatial Scan

Abstract Background Micronutrient deficiencies are the most prevalent nutritional deficiencies that cause serious developmental problems in the globe. The aim of this study was to assess the spatial distribution of iron rich foods consumption and its associated factors among children aged 6–23 months in Ethiopia. Methods The data retrieved from the standard Ethiopian Demographic and Health Survey 2016 dataset with a total sample size of 3055 children aged 6–23 months. Spatial scan statistics done using Kuldorff’s SaTScan version 9.6 software. ArcGIS version 10.7 software used to visualize spatial distribution for poor consumption of iron rich foods. Multilevel mixed-effects logistic regression analysis employed to identify the associated factors for good consumption of iron-rich foods. Level of statistical significance was declared at a two-sided P-value < 0.05. Results Overall, 21.41% (95% CI: 19.9–22.9) of children aged 6–23 months had good consumption of iron rich foods in Ethiopia. Poor consumption of iron rich foods highly clustered at Southern Afar, Southeastern Amhara and Tigray, and the Northern part of Somali Regional States of Ethiopia. In spatial scan statistics, children aged 6–23 months living in the most likely cluster were 21% more likely vulnerable to poor consumption of iron rich foods than those living outside the window (RR = 1.21, P-value < 0.001). Child aged 12–17 months (AOR = 1.90, 95% CI: 1.45–2.49) and 18–23 months (AOR = 2.05, 95% CI: 1.55–2.73), primary (AOR = 1.42, 95% CI:1.06–1.87) and secondary and above (AOR = 2.26, 95% CI: 1.47–3.46) mother’s education level, rich (AOR = 1.49, 95% CI: 1.04–2.13) and middle (AOR = 1.83, 95% CI: 1.31–2.57) household wealth status, Amhara (AOR = 0.24, 95% CI: 0.09–0.60), Afar (AOR = 0.38, 95% CI: 0.17–0.84), and Harari (AOR = 2.11, 95% CI: 1.02–4.39) regional states of Ethiopia were statistically significant factors for good consumption of iron rich foods. Conclusion Overall, the consumption of iron rich foods was low and spatially non-random in Ethiopia. Federal Ministry of Health and other stakeholders should give prior attention to the identified hot spot areas to enhance the consumption of iron rich foods among children aged 6–23 months.

Spatial scan statistics with overdispersion

2011 ◽  
Vol 31 (8) ◽  
pp. 762-774 ◽  
Author(s):  
Tonglin Zhang ◽  
Zuoyi Zhang ◽  
Ge Lin
Keyword(s):  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Spatial Scan
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