Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes

Coleman, Marlize; Coleman, Michael; Mabuza, Aaron; Kok, Gerdalize; Coetzee, Maureen; Dürrheim, David N

doi:10.1186/1475-2875-8-68

Cited by 171 publications

(145 citation statements)

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“…Because of its ability of both identifying localised clusters and evaluating their significance, the spatial scan statistics becomes more prominent relative to other statistical methods for disease clustering. Space-time scan statistics have been applied previously in several mosquito-borne diseases such as malaria (Coleman et al, 2009), Japanese encephalitis (Impoinvil et al, 2011) and Barmah Forest virus (Naish et al, 2011). However, this study is the first attempt to apply SaTScan on dengue in Australia.…”

Section: Spatial Scan Statistics -Satscanmentioning

confidence: 99%

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Naish

Tong

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: Clusters, Dengue, Geographical information systems, Spatiotemporal, Spatial autocorrelation ABSTRACT:Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992Queensland in -1993. This study explored spatio-temporal distribution and clustering of locally-acquired dengue cases in Queensland State, Australia and identified target areas for effective interventions. A computerised locally-acquired dengue case dataset was collected from Queensland Health for Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Dengue hot spots were detected using SatScan method. Descriptive spatial analysis showed that a total of 2,398 locally-acquired dengue cases were recorded in central and northern regions of tropical Queensland. A seasonal pattern was observed with most of the cases occurring in autumn. Spatial and temporal variation of dengue cases was observed in the geographic areas affected by dengue over time. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in tropical Queensland, Australia. There is a clear evidence for the existence of statistically significant clusters of dengue and these clusters varied over time. These findings enabled us to detect and target dengue clusters suggesting that the use of geospatial information can assist the health authority in planning dengue control activities and it would allow for better design and implementation of dengue management programs.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Section: Spatial Scan Statistics -Satscanmentioning

confidence: 99%

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Naish

Tong

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For example, spatial approaches through an identification of the local clustering of malaria cases have been used to identify 'hot spots' of malaria cases in an area (14) and to guide malaria control and response efforts (15) . The application of the spatial clustering approach also emphasized that a smaller proportion of households carried most of the case burden in an area.…”

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confidence: 99%

Local spatial clustering of stunting and wasting among children under the age of 5 years: implications for intervention strategies

Gebreyesus

Mariam

Woldehanna

et al. 2015

Public Health Nutr.

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Objective: The present study aimed to evaluate the clustering of undernutrition indicators of children under the age of 5 years in relation to different scales. Design: A community-based cross-sectional study design was employed. We collected anthropometric data, geographic locations/elevations of households and other data from visited households. We used a retrospective purely spatial Poisson probability model to identify and locate clusters (high rates) of stunting and wasting using the software SaTScan™ version 9·1·1. We ran a logistic regression model to help evaluate the causes of clustering. Settings: Six villages in the Meskane Mareko District (38·45763°E, 8·042144°N) of southern Ethiopia. Subjects: We surveyed 2371 children aged <5 years, who were found in 1744 households. Results: We found a micro-level variation in the risk of stunting and wasting within the studied district. We found the most likely significant clusters for wasting and severe wasting in two of the six villages. For stunting, a single large cluster size of 390 cases (304·19 expected) in 756 households was identified (relative risk = 1·48, P < 0·01). For severe stunting, a single cluster size of 106 cases (69·39 expected) in 364 households was identified (relative risk = 1·69, P = 0·035). Conclusions: We conclude that the distribution of wasting and stunting was partly spatially structured. We identified distinct areas within and between villages that have a higher risk than the underlying at-risk population. Our analysis identified the spatial locations of high-risk areas for stunting that could be an input for geographically targeting and optimizing nutritional interventions. KeywordsClustering Undernutrition Stunting Wasting EthiopiaThe burden of undernutrition has declined progressively in developing countries, with the rate of reduction varying by region. The largest reductions have been in Asia, whereas a modest reduction in prevalence is documented in Africa (1) . Ethiopia has documented a significant decline in the prevalence of both stunting and underweight over the past 14 years (between 2000 and 2014). For example, the prevalence of stunting decreased by 31 % (from 58 % to 40 %), while the prevalence of underweight decreased by 39 % (from 41 % to 25 %), between 2000 and 2014 (2-5) .Although this is significant progress in the reduction of stunting and underweight prevalence, the current prevalence levels are still considerable. Based on the WHO recommended criteria for assessing the severity of stunting and underweight at the population level, the country is classified under a medium to high grade (prevalence range of 20-40 % for stunting and 10-30 % for underweight). To help ameliorate the current unacceptably high burden of undernutrition among children under the age of 5 years, the Government of Ethiopia has launched a national nutrition programme (June 2013-June 2015 prioritizing nutrition as a viable strategy for the country's growth and development plans (6) . Among other things, a reduction in the burden of ch...

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“…22 This scan statistic approach has been widely applied in identifying clustering regions and periods in vector-borne diseases like West Nile virus, 23 schistosomiasis, 24 dengue fever, 25 Barmah Forest virus, 26 and malaria. 5,14,27,28 However, little is known about the spatial pattern of malaria deaths. This study investigated the spatial distribution of reported malaria deaths in Yunnan, China, using GIS integrated with spatial analysis approaches.…”

Section: Introductionmentioning

confidence: 99%

Spatial Patterns of Malaria Reported Deaths in Yunnan Province, China

Bi¹,

Hu²,

Yang³

et al. 2013

The American Society of Tropical Medicine and Hygiene

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Abstract. Malaria has been a heavy social and health burden in the remote and poor areas in southern China. Analyses of malaria epidemic patterns can uncover important features of malaria transmission. This study identified spatial clusters, seasonal patterns, and geographic variations of malaria deaths at a county level in Yunnan, China, during 1991-2010. A discrete Poisson model was used to identify purely spatial clusters of malaria deaths. Logistic regression analysis was performed to detect changes in geographic patterns. The results show that malaria mortality had declined in Yunnan over the study period and the most likely spatial clusters (relative risk [RR] = 23.03-32.06, P 0.001) of malaria deaths were identified in western Yunnan along the China-Myanmar border. The highest risk of malaria deaths occurred in autumn (RR = 58.91, P 0.001) and summer (RR = 31.91, P 0.001). The results suggested that the geographic distribution of malaria deaths was significantly changed with longitude, which indicated there was decreased mortality of malaria in eastern areas over the last two decades, although there was no significant change in latitude during the same period. Public health interventions should target populations in western Yunnan along border areas, especially focusing on floating populations crossing international borders.

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Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes

Cited by 171 publications

References 39 publications

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Local spatial clustering of stunting and wasting among children under the age of 5 years: implications for intervention strategies

Spatial Patterns of Malaria Reported Deaths in Yunnan Province, China

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