Spatial Disease Cluster Detection: An Application to Childhood Asthma in Manitoba, Canada

Torabi, Mahmoud

doi:10.4172/2155-6180.s7-010

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Adjustments may be made when the expected number of cases varies by different factors such as year, age, and gender. The expected number of disease cases was then adjusted by year (1-10), age group ((0-5), (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), (41,88), (89+)) and gender (male, female). A review of the CSS, FSS, BYM, MLE, and LISA spatial cluster detection methods is given in the Appendix.…”

Section: Study Subjectsmentioning

confidence: 99%

“…These approaches were compared by analyzing childhood cancer data in the province of Alberta, Canada [22]. Recently, a frequentist approach based on the maximum likelihood estimation (MLE), via data cloning (DC) [23,24], was also proposed to obtain possible clusters [25] in an area of interest. Another cluster detection method is the local indicator of spatial association (LISA) [26].…”

Section: Introductionmentioning

confidence: 99%

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Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada

Torabi

Galloway

2014

IJGI

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Abstract:We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Bayesian disease mapping (BYM), maximum likelihood estimation (MLE), and local indicator of spatial association (LISA). Our results showed that there are some regions in southern Manitoba that are potential clusters of COPD cases. The FSS method identified more regions than the CSS and LISA methods and the BYM and MLE methods identified similar regions as potential clusters. Most of the regions identified by the MLE and BYM methods were also identified by the FSS method and most of the regions identified by the CSS method were also identified by most of the other methods. The CSS, FSS and LISA methods identify potential clusters but are not able to control for confounders at the same time. However, the BYM and MLE methods can simultaneously identify potential clusters and control for possible confounders. Overall, we recommend using the BYM and MLE methods for cluster detection in areas with similar population and structure of regions as those in Manitoba.

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Section: Study Subjectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada

Torabi

Galloway

2014

IJGI

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“…The utility of spatial clustering and mapping techniques in studying mosquito-borne infections has also been exemplified in spatial and spatio-temporal analysis of malaria in Brazil (Melchior and Neto 2016), spatial and space-time clustering of malaria mortality in rural Tanzania (Selemani et al 2015), space-time clustering of chikungunya and dengue fever in Colombia (Desjardins et al 2018), spatio-temporal clustering of childhood malaria in Mali (Gaudart et al 2006) and the detection of local clusters of malaria in South Africa (Coleman et al 2009). Cluster detection methods have also been applied in analysing other public health issues such as childhood asthma in Canada (Torabi 2012), cholera and childhood mortality in Ghana (Osei, Duker, and Stein 2012;Nettey et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Geospatial analysis of malaria mortality in the kintampo health and demographic surveillance area of central Ghana

et al. 2020

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Malaria remains a menace to the existence of humanity in most contexts. Geospatial analysis of malaria mortality is crucial to identifying clusters of high disease burden and areas with limited access to malaria care for targeted control and remedial interventions. This study identified spatial and space-time clusters of malaria mortality in the Kintampo area of central Ghana. We used 1301 malaria deaths archived from 2005 to 2017 and Global Positioning System (GPS) point locations of the sub-districts in which these deaths occurred for our analysis. Mortality risks were smoothed and mapped using the Spatial Empirical Bayesian smoothing technique in Geoda (version 1.12.1.161) whereas spatial and spatio-temporal clustering analysis was done using SaTScan (version 9.6). Malaria mortality risks ranged between 1.2 and 2.4 deaths per 1000 population for persons of all ages and between 3.3 and 6.0 deaths per 1000 population for children under five years of age by sub-district. Two spatial clusters were detected for all-age malaria mortality with only the primary cluster (RR = 1.42, p = 0.001) being statistically significant. Also, two statistically significant space-time clusters were detected for all-age malaria mortality in the study area. The most likely cluster occurred between 2006 and 2011 in five sub-districts with a relative risk of 2.12 (p < 0.001) whilst the secondary cluster which had a relative risk of 2.47 (p < 0.001) occurred between 2005 and 2010 in four sub-districts. Similarly, only the most likely spatial cluster of under-five malaria mortality was statistically significant (RR = 1.36, p = 0.024). Furthermore, three spatio-temporal clusters of under-five malaria mortality were detected in the study area. The primary and second secondary clusters were statistically significant whilst the first secondary cluster had borderline significance. The primary cluster (RR = 4.49, p = 0.002) occurred in two sub-districts between 2006 and 2007. The first secondary cluster (RR = 2.21, P = 0.005) covered four sub-districts and was detected between 2006 and 2011 whereas the second secondary cluster (RR = 2.51, p = 0.003) covered two sub-districts between 2008 and 2013. Ultimately, our analysis identified a number of substantial spatial and apace-time clusters of malaria mortality in the study context, which could aid in the strategic planning, implementation and monitoring of targeted malaria control interventions.

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“…Previously, three focused cluster detection methods (CSS, FSS and BYM) were applied to a dataset containing childhood asthma visits to a hospital in Manitoba, Canada in order to investigate potential clusters (Torabi, 2012). As an extension of that work, we studied the impact of spatial aggregation in this paper.…”

Section: Introductionmentioning

confidence: 99%

Examining the impact of the number of regions used in cluster detection methods: An application to childhood asthma visits to a hospital in Manitoba, Canada

Torabi

Galloway²

2018

Geospat Health

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The level of spatial aggregation is a major concern in cluster investigations. Combining regions to protect privacy may result in a loss of power and thus, can limit the information researchers can obtain. The impact of spatial aggregation on the ability to detect clusters is examined in this study, which shows the importance of choosing the correct level of spatial aggregation in cluster investigations. We applied the circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS) and Bayesian disease mapping (BYM) approaches to a dataset containing childhood asthma visits to a hospital in Manitoba, Canada, using three different levels of spatial aggregation. Specifically, we used 56, 67 and 220 regions in the analysis, respectively. It is expected that the three scenarios will yield different results and will highlight the importance of using the right level of spatial aggregation. The three methods (CSS, FSS, BYM) examined in this study performed similarly when detecting potential clusters. However, for different levels of spatial aggregation, the potential clusters identified were different. As the number of regions used in the analysis increased, the total area identified in the cluster decreased. In general, potential clusters were identified in the central and northern parts of Manitoba. Overall, it is crucial to identify the appropriate number of regions to study spatial patterns of disease as it directly affects the results and consequently the conclusions. Additional investigation through future work is needed to determine which scenario of spatial aggregation is best.

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Spatial Disease Cluster Detection: An Application to Childhood Asthma in Manitoba, Canada

Cited by 6 publications

References 18 publications

Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada

Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada

Geospatial analysis of malaria mortality in the kintampo health and demographic surveillance area of central Ghana

Examining the impact of the number of regions used in cluster detection methods: An application to childhood asthma visits to a hospital in Manitoba, Canada

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