Spatial Scan Statistic Method Research Articles

Density Based Spatial Clustering of Applications and Spatial Pattern Analysis In Mapping the Distribution of ISPA Disease in Bireuen Regency

ISPA is an infectious disease transmitted through the air. ISPA disease can be detected through the regional distribution maps of disease. ISPA disease can be prevented in the early detection before the disease gets worse. This can prevent the spread of the disease further. Data on ISPA cases in 2019 to 2021 in Bireuen Regency from the medical records of dr. Fauziah Bireuen from an average of 13.18 to 59.24 per year. This research aims to determine the clusters of ISPA spread areas across the district of Bireuen and to analyze the distribution of disease patterns with Spatial Pattern Analysis and to map the respiratory diseases in each area with Flexibly Shaped Spatial Scan Statistics. The methodology in this research are collecting the data at the hospital for each area of ISPA patients and the data processed by using DBSCAN to obtain the cluster points on the map. The result of the DBSCAN model are clusters. The cluster results are next processed with the distribution of hotspot point on the map using the Spatial Pattern Analysis model, and the map of the prone of the spread of ISPA in Bireuen Regency is processed by using the Flexibly Shaped Spatial Scan Statistic method. Information from the map obtained that the first 4 clusters of the area are formed, namely 3 clusters and 1 outlier. Cluster 1 and outliers consist of 6 sub-districts, cluster 2 consist of 4 sub-districts and cluster 3 only has 1 sub-district. There are 6 hotspot areas in 2019, 5 hotspot areas in 2020, and 6 hotspot areas in 2021. The results of this research are each ISPA disease clustering map shows the results of the ISPA distribution on the map and areas that are prone to ISPA disease in Bireuen Regency are Jeunieb and Peusangan

Spatiotemporally comparative analysis of three common infectious diseases in China during 2013–2015

BackgroundDengue fever (DF), influenza, and hand, foot, and mouth disease (HFMD) have had several various degrees of outbreaks in China since the 1900s, posing a serious threat to public health. Previous studies have found that these infectious diseases were often prevalent in the same areas and during the same periods in China.MethodsThis study combined traditional descriptive statistics and spatial scan statistic methods to analyze the spatiotemporal features of the epidemics of DF, influenza, and HFMD during 2013–2015 in mainland China at the provincial level.ResultsDF got an intensive outbreak in 2014, while influenza and HFMD were stable from 2013 to 2015. DF mostly occurred during August–November, influenza appeared during November–next March, and HFMD happened during April–November. The peaks of these diseases form a year-round sequence; Spatially, HFMD generally has a much higher incidence than influenza and DF and covers larger high-risk areas. The hotspots of influenza tend to move from North China to the southeast coast. The southeastern coastal regions are the high-incidence areas and the most significant hotspots of all three diseases.ConclusionsThis study suggested that the three diseases can form a year-round sequence in southern China, and the southeast coast of China is a particularly high-risk area for these diseases. These findings may have important implications for the local public health agency to allocate the prevention and control resources.

237Spatial distribution and correlates of HIV prevalence in South Africa

Abstract Background South Africa has a high HIV prevalence and generalized HIV epidemic. It is now well established that the HIV epidemic and its drivers are highly heterogeneous, even in generalized HIV epidemic settings. Methods This study uses data from South African HIV/AIDS, Behavioural Risks, Sero-status, and Mass Media Impact (SABSSM), 2005, 2008 and 2012 surveys. To identify spatial clusters, we used the spatial scan statistic method in SaTScan, assuming discrete Poisson distributions. Poisson regression models were used to explore the municipality-level correlates of HIV prevalence and a logistic regression model was used to determine individual-level correlates of HIV infection. Results Between 2005 and 2012, There was significant geographical variation in estimated HIV prevalence (range = <1.0%–27.5%). Eight, five and six significant overlapping high-risk spatial clusters of high HIV prevalence were detected in 2005, 2008 and 2012, respectively. HIV prevalence is clustered in the central and north-eastern regions of South Africa. Living in municipalities with high percentage of black South Africans, higher poverty index, higher population aged 25-49, and higher early sexual debut were associated with HIV prevalence, while living in municipalities with higher percentage male circumcision and a high percentage married were associated with low risk of HIV. Logistic regression revealed race, sex and mobility as correlates of HIV infection. Conclusions HIV prevalence is highly spatially heterogenous and affected by various municipal-level factors. Key messages Identification of the spatial clusters of HIV prevalence and contextual factors should inform targeted interventions that are necessary to bringing HIV infections under control.

Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method

ABSTRACTThe spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method’s sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method’s sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.

Detecting spatial clusters of HIV and hepatitis coinfections.

BackgroundPeople with HIV infection in the United States are often affected by chronic viral hepatitis. These coinfected people with either HBV or HCV are at increased risk for serious, life-threatening complications. Coinfections with viral hepatitis may also complicate the delivery of anti-retroviral therapy (ART) by escalating the risk of drug-related hepatoxicity. According to the Centers for Disease Control and Prevention (CDC), approximately 10 percent of people with HIV in the United States also have HBV, and 25 percent also have HCV coinfection. With the advent of highly active antiretroviral therapy (HAART) and the increased life-expectancy of HIV patients, clinicians are more likely to be confronted with issues related to co-infection and the management challenges that they present, especially in resource-limited settings. The purpose of this analysis was to identify geographical clusters of HIV- (HBV/HCV) co-infection and compared to the geographical clusters of not co-infected using DC, Department of Health surveillance data. The results of the analysis will be used to target resources to areas at risk.MethodsHIV and Hepatitis surveillance data were matched among cases diagnosed between 1980 and 2016. HIV-hepatitis co-infected and the not co-infected spatial clusters were detected using discrete Poisson model. Kulldorff’s spatial scan statistic method was implemented in the free software tool called SaTScan which has been widely adopted for detecting disease cluster. The analysis was conducted by tracts, but for visualization, ease of interpretation and assist in policy making the tract map was overlaid with the ward map using ArcGIS 10.5.1.ResultsBetween 1980 and 2016, there were 12,965 diagnosed cases of HIV, of which 2,316 HIV/Hepatitis matches were identified. Of the 2316 co-infected people living in DC, 25 percent (N = 590) of people had HBV, and 75 percent (N = 1,726) had HCV. Out of 12,965 diagnosed cases, remaining 10,649 did not have any co-infections (not co-infected). IDU (27.16 percent) and MSM (32.86 percent) were the highest mode of transmission for co-infected population. African-American were reported 83.64 percent (N = 1,937) among co-infection population. Three clusters were identified for both co-infected population in DC. The largest cluster radius for co-infected analysis covers wards 6, 7 and 8 as well as large parts of 2 and 5 (p < 0.001). Multiple clusters were identified for not co-infected population (p < 0.001). IDU (n = 450) was the highest mode of transmission for the co-infected clusters. For all clusters combined of not co-infected population highest mode of transmission were MSM (n = 2,534). This analysis also showed racial disparity, economic deprivation and lack of education were prominent in the co-infected clusters.ConclusionWe identified locations of high risk clusters where enhanced hepatitis and HIV prevention, treatment, and care can help combat the epidemic. The clusters radius expands into the neighboring state of Maryland as well. The findings from this analysis will be used to target area based public health policy and healthcare interventions for HIV-hepatitis. It is recommended based on the analysis that needle exchange programs can successfully control new HIV infections as well as hepatitis co-infections.

Geographic access to radiation therapy facilities and disparities of early-stage breast cancer treatment.

Few studies of breast cancer treatment have focused on the Northern Plains of the United States, an area with a high mastectomy rate. This study examined the association between geographic access to radiation therapy facilities and receipt of breast cancer treatments among early-stage breast cancer patients in South Dakota. Based on 4,209 early-stage breast cancer patients diagnosed between 2001 and 2012 in South Dakota, the study measured geographic proximity to radiation therapy facilities using the shortest travel time for patients to the closest radiation therapy facility. Two-level logistic regression models were used to estimate for early stage cases i) the odds of mastectomy versus breast conserving surgery (BCS); ii) the odds of not receiving radiation therapy after BCS versus receiving follow-up radiation therapy. Covariates included race/ethnicity, age at diagnosis, tumour grade, tumour sequence, year of diagnosis, census tract-level poverty rate and urban/rural residence. The spatial scan statistic method was used to identify geographic areas with significantly higher likelihood of experiencing mastectomy. The study found that geographic accessibility to radiation therapy facilities was negatively associated with the likelihood of receiving mastectomy after adjustment for other covariates, but not associated with radiation therapy use among patients receiving BCS. Compared with patients travelling less than 30 minutes to a radiation therapy facility, patients travelling more than 90 minutes were about 1.5 times more likely to receive mastectomy (odds ratio, 1.51; 95% confidence interval, 1.08-2.11) and patients travelling more than 120 minutes were 1.7 times more likely to receive mastectomy (odds ratio, 1.70; 95% confidence interval, 1.19-2.42). The study also identified a statistically significant cluster of patients receiving mastectomy who were located in south-eastern South Dakota, after adjustment for other factors. Because geographic proximity to treatment facilities plays an important role on the treatment for early-stage breast cancer patients, this study has important implications for developing targeted intervention to reduce disparities in breast cancer treatment in South Dakota.

A hybrid method for fast detection of spatial disease clusters in irregular shapes

Detection of spatial disease clusters in irregular shapes has generated considerable interest among public health researchers and policymakers. The existing methods have varying issues such as enormous computing workloads, peculiar cluster shapes, and high subjectivity of parameters. To support fast detection of irregularly shaped clusters, we are proposing a hybrid method combining Tango’s restricted likelihood ratio as the test statistic and Assuncao et al.’s dynamic Minimum Spanning Tree method as the search strategy. We discuss the advantages and the implementation of the hybrid method, and systematically compare its performance with other three well-known scan-based cluster detection methods, including Tango’s method, Assuncao et al.’s method, and Kulldorff’s circular spatial scan statistic method. Using simulated data of six cluster models combining two disease incidence levels and three true cluster shapes, the performance of the methods is evaluated in terms of statistical power, geographic accuracy, and computational intensity. The experimental results indicate that our hybrid method with 0.2 as the screening level value has the third highest average statistical power and the best average geographic accuracy among the four methods with all of the tested parameters. The four methods are then applied to the county-level lung cancer incidence data of Georgia from 1998 to 2005, and all find a significant cluster in northwestern Georgia but varying in shape and size.

Irregularly Shaped Cluster Detection Using a CPSO Distribution-Free Spatial Scan Statistic

Classical spatial scan statistics are based on pre-defined shapes for scanning windows and specific distribution models and are used to detect latent cluster(s). However, the pre-defined windows (especially circularly shaped windows) may not be suitable for real situations, and the specific distribution models are inadequate for real clusters in which the exact distributions of the test statistics are only known in special cases. To generate more reasonable results, we propose a spatial scan statistic method with an irregularly shaped scanning window. A combinatorial particle swarm optimization method is used to optimize this window. A distribution-free concentration index is constructed to measure the difference between inside cluster and outside cluster. A compactness penalty function is employed to avoid generating clusters in a tree-structure. Simulation data sets are used to test the proposed method, and the results show the feasibility of our method.

Spatial Pattern Detection of Tuberculosis: A Case Study of Si Sa Ket Province, Thailand.

This retrospective population-based study was conducted to analyze spatial patterns of tuberculosis (TB) incidence in Si Sa Ket province, Thailand. TB notification data from 2004 to 2008 collected from TB clinics throughout the province was used along with population data to reveal a descriptive epidemiology of TB incidences. Global clustering patterns of the occurrence were assessed by using global spatial autocorrelation techniques. Additionally, local spatial pattern detection was performed by using local spatial autocorrelation and spatial scan statistic methods. The findings indicated clusters of the disease occurred in the study area. More specifically, significantly high-rate clusters were mostly detected in Mueang Si Sa Ket and Khukhan districts, which are located in the northwestern part of the province, while significantly low-rate clusters were persistent in Kantharalak and Benchalak districts, which are located at the southeastern area.

Spatial, temporal, and spatiotemporal analysis of malaria in Hubei Province, China from 2004-2011.

BackgroundMalaria remains a public health concern in Hubei Province despite the significant decrease in malaria incidence over the past decades. Furthermore, history reveals that malaria transmission is unstable and prone to local outbreaks in Hubei Province. Thus, understanding spatial, temporal, and spatiotemporal distribution of malaria is needed for the effective control and elimination of this disease in Hubei Province.MethodsAnnual malaria incidence at the county level was calculated using the malaria cases reported from 2004 to 2011 in Hubei Province. Geographical information system (GIS) and spatial scan statistic method were used to identify spatial clusters of malaria cases at the county level. Pure retrospective temporal analysis scanning was performed to detect the temporal clusters of malaria cases with high rates using the discrete Poisson model. The space-time cluster was detected with high rates through the retrospective space-time analysis scanning using the discrete Poisson model.ResultsThe overall malaria incidence decreased to a low level from 2004 to 2011. The purely spatial cluster of malaria cases from 2004 to 2011 showed that the disease was not randomly distributed in the study area. A total of 11 high-risk counties were determined through Local Moran’s I analysis from 2004 to 2011. The method of spatial scan statistics identified different 11 significant spatial clusters between 2004 and 2011. The space-time clustering analysis determined that the most likely cluster included 13 counties, and the time frame was from April 2004 to November 2007.ConclusionsThe GIS application and scan statistical technique can provide means to detect spatial, temporal, and spatiotemporal distribution of malaria, as well as to identify malaria high-risk areas. This study could be helpful in prioritizing resource assignment in high-risk areas for future malaria control and elimination.

A Fast Method for Making Candidate Clusters in Spatial Scan Statistic Method

A Fast Method for Making Candidate Clusters in Spatial Scan Statistic Method

A spatial scan statistic for nonisotropic two‐level risk cluster

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster.

Analysis of simultaneous space-time clusters of Campylobacter spp. in humans and in broiler flocks using a multiple dataset approach.

BackgroundCampylobacteriosis is the most frequently reported zoonosis in the EU and the epidemiology of sporadic campylobacteriosis, especially the routes of transmission, is to a great extent unclear. Poultry easily become colonised with Campylobacter spp., being symptom-less intestinal carriers. Earlier it was estimated that internationally between 50% and 80% of the cases could be attributed to chicken as a reservoir. In a Norwegian surveillance programme all broiler flocks under 50 days of age were tested for Campylobacter spp. The aim of the current study was to identify simultaneous local space-time clusters each year from 2002 to 2007 for human cases of campylobacteriosis and for broiler flocks testing positive for Campylobacter spp. using a multivariate spatial scan statistic method. A cluster occurring simultaneously in humans and broilers could indicate the presence of common factors associated with the dissemination of Campylobacter spp. for both humans and broilers.ResultsLocal space-time clusters of humans and broilers positive for Campylobacter spp. occurring simultaneously were identified in all investigated years. All clusters but one were identified from May to August. Some municipalities were included in clusters all years.ConclusionsThe simultaneous occurrence of clusters of humans and broilers positive for Campylobacter spp. combined with the knowledge that poultry meat has a nation-wide distribution indicates that campylobacteriosis cases might also be caused by other risk factors than consumption and handling of poultry meat.Broiler farms that are positive could contaminate the environment with further spread to new broiler farms or to humans living in the area and local environmental factors, such as climate, might influence the spread of Campylobacter spp. in an area. Further studies to clarify the role of such factors are needed.

Spatial Clusters and Variability Analysis of Tree Mortality

Spatial patterns of tree mortality are important factors in analysis of forest health and sustainable forest management. We analyzed spatial cluster characteristics of forest tree mortality among hardwoods, softwoods, and all species groups between 1989 and 1997 in Georgia. We used for this purpose the 1989 and 1997 USDA Forest Service Forest Inventory and Analysis (FIA) permanent sample plot data, spatial scan statistic and semivariogram analysis methods, and geographic information system (GIS) techniques. In Georgia, forest tree mortality for softwoods and all species groups formed clusters in North Georgia in 1989. These clusters became more widespread, but the aggregation intensity of tree mortality was decreased compared with that of the 1989 clusters. Another finding is that the locations for mortality clusters of softwoods were almost the same as those of all species combined both in 1989 and in 1997. In 1989 the forest tree mortality of softwoods and all species groups agglomerated with small clusters forming in north Georgia. In 1997, these clusters became more widespread, but the magnitudes of tree mortality within them decreased compared with those of 1989 clusters. Semivariogram analysis suggests that hardwoods, softwoods, and all species combined followed a similar trend for spatial variation in that they fit a spherical model of tree mortality. However, the strengths of spatial dependence were significantly different among the hardwoods, softwoods, and all species in 1989 and 1997. The higher the agglomeration of tree mortality, the stronger the spatial dependence. Spatial analysis of tree damage indicates that agents of tree damage were also agglomerated in the state. Eight kinds of tree damage, possibly combined with other kinds of damage, directly determined the spatial patterns of forest tree mortality. Out of these eight, only vegetation competition, insects, and weather caused the formation of the north Georgia tree mortality clusters.

Visualization of the spatial scan statistic using nested circles

We propose a technique for the display of results of Kulldorff's spatial scan statistic and related cluster detection methods that provides a greater degree of informational content. By simultaneously considering likelihood ratio and relative risk, it is possible to identify focused sub-clusters of higher (or lower) relative risk among broader regional excesses or deficits. The result is a map with a nested or contoured appearance. Here the technique is applied to prostate cancer mortality data in counties within the contiguous United States during the period 1970–1994. The resulting map shows both broad and localized patterns of excess and deficit, which complements a choropleth map of the same data.