Significant Space-time Clusters Research Articles

Spatio-temporal analysis of malaria incidence in the Peruvian Amazon Region between 2002 and 2013

Malaria remains a major public health problem in the Peruvian Amazon where the persistence of high-risk transmission areas (hotspots) challenges the current malaria control strategies. This study aimed at identifying significant space-time clusters of malaria incidence in Loreto region 2002–2013 and to determine significant changes across years in relation to the control measures applied. Poisson regression and purely temporal, spatial, and space-time analyses were conducted. Three significantly different periods in terms of annual incidence rates (AIR) were identified, overlapping respectively with the pre-, during, and post- implementation control activities supported by PAMAFRO project. The most likely space-time clusters of malaria incidence for P. vivax and P. falciparum corresponded to the pre- and first two years of the PAMAFRO project and were situated in the northern districts of Loreto, while secondary clusters were identified in eastern and southern districts with the latest onset and the shortest duration of PAMAFRO interventions. Malaria in Loreto was highly heterogeneous at geographical level and over time. Importantly, the excellent achievements obtained during 5 years of intensified control efforts totally vanished in only 2 to 3 years after the end of the program, calling for sustained political and financial commitment for the success of malaria elimination as ultimate goal.

Spatiotemporal clustering of dengue cases in Thiruvananthapuram district, Kerala

Dengue cases are increasing in Kerala since 2010. Information on clustering of cases across locations and time periods is vital for disease surveillance and timely control. The objective is to study spatiotemporal clustering of dengue cases and their climatic and physioenvironmental correlates in Thiruvananthapuram district during 2010-2014. Health department data on reported cases of dengue were obtained from January 2011 to June 2014. Cases were individually geocoded, using Google Earth. Moran's I index was estimated to analyze spatial autocorrelation using GeoDa software. Space-time clustering across 178 geo-divisions within the district was analyzed using SaTScan software. Correlation analysis was done for space-time clustering with climatic variables. Definite spatial and temporal trends were found on analysis of a total of 8279 dengue cases. Significant spatial autocorrelation (Moran's I = 0.32, P< 0.01) and space-time clusters with very high log-likelihood ratios (P < 0.01) were found across geo-divisions. Pallichal panchayat was the most likely cluster in every year. The monthly incidence of dengue cases showed a significant positive association (P < 0.05) with a 2-month lag of mean minimum temperature (ρ = 0.39), 1-month lag of rainfall (ρ = 0.33), and 1-month lag of humidity (ρ = 0.38). Dengue occurrences showed an inverse association (P < 0.01) with mean maximum temperatures of the respective months (ρ= -0.48). Spatial analysis using epidemiological tools reveals spatial and temporal clustering of dengue cases within the district and their association with climatic parameters. This information can be used in controlling outbreaks in the future. This work upholds scope and feasibility of geospatial research in public health in India.

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand.

Influenza-like illness (ILI) is an acute respiratory disease that remains a public health concern for its ability to circulate globally affecting any age group and gender causing serious illness with mortality risk. Comprehensive assessment of the spatio-temporal dynamics of ILI is a prerequisite for effective risk assessment and application of control measures. Though meteorological parameters, such as rainfall, average relative humidity and temperature, influence ILI and represent crucial information for control of this disease, the relation between the disease and these variables is not clearly understood in tropical climates. The aim of this study was to analyse the epidemiology of ILI cases using integrated methods (space-time analysis, spatial autocorrelation and other correlation statistics). After 2009s H1N1 influenza pandemic, Phitsanulok Province in northern Thailand was strongly affected by ILI for many years. This study is based on ILI cases in villages in this province from 2005 to 2012. We used highly precise weekly incidence records covering eight years, which allowed accurate estimation of the ILI outbreak. Comprehensive methodology was developed to analyse the global and local patterns of the spread of the disease. Significant space-time clusters were detected over the study region during eight different periods. ILI cases showed seasonal clustered patterns with a peak in 2010 (P&gt;0.05-9.999 iterations). Local indicators of spatial association identified hotspots for each year. Statistically, the weather pattern showed a clear influence on ILI cases and it strongly correlated with humidity at a lag of 1 month, while temperature had a weaker correlation.

Revealing spatio-temporal patterns of rabies spread among various categories of animals in the Republic of Kazakhstan, 2010-2013.

This study estimated the basic reproductive ratio of rabies at the population level in wild animals (foxes), farm animals (cattle, camels, horses, sheep) and what we classified as domestic animals (cats, dogs) in the Republic of Kazakhstan (RK). It also aimed at forecasting the possible number of new outbreaks in case of emergence of the disease in new territories. We considered cases of rabies in animals in RK from 2010 to 2013, recorded by regional veterinary services. Statistically significant space-time clusters of outbreaks in three subpopulations were detected by means of Kulldorff Scan statistics. Theoretical curves were then fitted to epidemiological data within each cluster assuming exponential initial growth, which was followed up by calculation of the basic reproductive ratio R0. For farm animals, the value of R0 was 1.62 (1.11-2.26) and for wild animals 1.84 (1.08- 3.13), while it was close to 1 for domestic animals. Using the values obtained, an initial phase of possible epidemic was simulated in order to predict the expected number of secondary cases if the disease were introduced into a new area. The possible number of new cases for 20 weeks was estimated at 5 (1-16) for farm animals, 17 (1-113) for wild animals and about 1 in the category of domestic animals. These results have been used to produce set of recommendations for organising of preventive and contra-epizootic measures against rabies expected to be applied by state veterinarian services.

Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009-2012.

BackgroundTuberculosis (TB) is an infectious disease of major public health concern. The disease has demonstrated large space-time variations. This study aims to explore the space-time dynamics of TB cases in an economically and geographically dynamic province in China with specific references of TB control for policy makers.MethodsData on all reported TB cases from 2009 to 2012 were collected from the TB program at the Zhejiang Provincial Center for Disease Control and Prevention. We employed time series and exploratory spatial data analyses, including Moran’s I, Local Getis’s Gi*, and Kulldorff’s space-time scan statistics, to identify the temporal trends and spatial patterns of TB at a county level.ResultsA total of 147,941 TB cases were reported during 2009–2012 in Zhejiang. A higher proportion of TB cases were younger, male, and registered permanent residents among all TB cases notified in the province. TB cases were reported most frequently in April with small peaks in June, July, and October. This disease was spatially clustering with Moran’s I values ranged from 0.29 to 0.32 (p < 0.001). A most likely cluster and ten secondary clusters were identified, mainly concentrated in the southeast and west counties of the province.ConclusionsThis study identified seasonal patterns and significant space-time clusters of TB cases in Zhejiang, China. Poverty, migration, and seasonal effects may play important roles in potential clusters.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-016-0104-2) contains supplementary material, which is available to authorized users.

Wildland Arson as Clandestine Resource Management: A Space–Time Permutation Analysis and Classification of Informal Fire Management Regimes in Georgia, USA

Forest managers are increasingly recognizing the value of disturbance-based land management techniques such as prescribed burning. Unauthorized, "arson" fires are common in the southeastern United States where a legacy of agrarian cultural heritage persists amidst an increasingly forest-dominated landscape. This paper reexamines unauthorized fire-setting in the state of Georgia, USA from a historical ecology perspective that aims to contribute to historically informed, disturbance-based land management. A space-time permutation analysis is employed to discriminate systematic, management-oriented unauthorized fires from more arbitrary or socially deviant fire-setting behaviors. This paper argues that statistically significant space-time clusters of unauthorized fire occurrence represent informal management regimes linked to the legacy of traditional land management practices. Recent scholarship has pointed out that traditional management has actively promoted sustainable resource use and, in some cases, enhanced biodiversity often through the use of fire. Despite broad-scale displacement of traditional management during the 20th century, informal management practices may locally circumvent more formal and regionally dominant management regimes. Space-time permutation analysis identified 29 statistically significant fire regimes for the state of Georgia. The identified regimes are classified by region and land cover type and their implications for historically informed disturbance-based resource management are discussed.

Spatiotemporal transmission dynamics of hemorrhagic fever with renal syndrome in China, 2005-2012.

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem with 90% of the total reported cases in the world. This study describes the spatiotemporal dynamics of HFRS cases in China and identifies the regions, time, and populations at highest risk, which could help the planning and implementation of key preventative measures.MethodsData on all reported HFRS cases at the county level from January 2005 to December 2012 were collected from Chinese Center for Disease Control and Prevention. Geographic Information System-based spatiotemporal analyses including Local Indicators of Spatial Association and Kulldorff's space-time scan statistic were performed to detect local high-risk space-time clusters of HFRS in China. In addition, cases from high-risk and low-risk counties were compared to identify significant demographic differences.ResultsA total of 100,868 cases were reported during 2005–2012 in mainland China. There were significant variations in the spatiotemporal dynamics of HFRS. HFRS cases occurred most frequently in June, November, and December. There was a significant positive spatial autocorrelation of HFRS incidence during the study periods, with Moran's I values ranging from 0.46 to 0.56 (P<0.05). Several distinct HFRS cluster areas were identified, mainly concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties.ConclusionsThis study identified significant space-time clusters of HFRS in China during 2005–2012 indicating that preventative strategies for HFRS should be particularly focused on the northeastern, central, and eastern of China to achieve the most cost-effective outcomes.

Retrospective space-time cluster analysis of whooping cough, re-emergence in Barcelona, Spain, 2000-2011.

A retrospective, space-time study of whooping cough cases reported to the Public Health Agency of Barcelona, Spain between the years 2000 and 2011 is presented. It is based on 633 individual whooping cough cases and the 2006 population census from the Spanish National Statistics Institute, stratified by age and sex at the census tract level. Cluster identification was attempted using space-time scan statistic assuming a Poisson distribution and restricting temporal extent to 7 days and spatial distance to 500 m. Statistical calculations were performed with Stata 11 and SatScan and mapping was performed with ArcGis 10.0. Only clusters showing statistical significance (P <0.05) were mapped. The most likely cluster identified included five census tracts located in three neighbourhoods in central Barcelona during the week from 17 to 23 August 2011. This cluster included five cases compared with the expected level of 0.0021 (relative risk = 2436, P <0.001). In addition, 11 secondary significant space-time clusters were detected with secondary clusters occurring at different times and localizations. Spatial statistics is felt to be useful by complementing epidemiological surveillance systems through visualizing excess in the number of cases in space and time and thus increase the possibility of identifying outbreaks not reported by the surveillance system.

Spatiotemporal analysis of infant measles using population attributable risk in Shandong province, 1999-2008.

BackgroundReduction of measles incidence and mortality has been encouraging in China. However, it remains an important public health concern among infants. This study aimed to examine the space–time distribution pattern of infant measles occurrence for the period of 1999–2008 in Shandong, China.Methods and FindingsMeasles cases among infants aged younger than 1 year were obtained from the national infectious diseases reporting information system. A spatiotemporal analysis using population attributable risk percent (PAR%) was used to distinguish between multiple geographic clusters of potential interest. The analysis detected 29 statistically significant space–time clusters with the most likely cluster in Zaozhuang City from 2006 to 2008. Of the 28 secondary clusters, 22 were found in 2008. The map of PAR%, relative risk (RR) and space–time cluster analysis indicated that the clusters were generally unchanged, and were found south-west and north-west of Shandong. The Lanshan District in Linyi had the highest PAR%, while highest RR was in the Yicheng District in Zaozhuang.ConclusionThere were significant space-time clusters of infant measles in Shandong over the study period. PAR% is an effective way to analyze multiple clusters from their application like RR. Interrupting measles circulation and maintaining routine coverage over 95% may be the only effective strategy to achieve measles elimination.

The Clustering of Smear-Positive Tuberculosis in Dabat, Ethiopia: A Population Based Cross Sectional Study

BackgroundIn Ethiopia where tuberculosis epidemic remains high, studies that describe hotspots of the disease are unavailable. This study tried to detect the spatial distribution and clustering of smear-positive tuberculosis cases in Dabat, Ethiopia.Methods and FindingsA population-based cross sectional study conducted in the Dabat Health and Demographic Surveillance System site from October 2010 to September 2011 identified smear-positive tuberculosis cases. Trained field workers collected demographic and location data from each study participant through house-to-house visits. A spatial scan statistic was used to identify purely spatial and space–time clusters of tuberculosis among permanent residents. Two significant (p<0.001) spatial and space-time clusters were identified in the study district.ConclusionTuberculosis is concentrated in certain geographic locations in Dabat, Ethiopia. This kind of clustering can be common in the country, so the National Tuberculosis Control Program can be more effective by identifying such clusters and targeting interventions.

Spatio-temporal clusters of incident human brucellosis cases in Ecuador

This study aimed to determine whether variations in the incidence of reported cases of human brucellosis in Ecuador were clustered in space and time. In addition, the effects of cattle and small ruminant population density and other socio-economic factors on the incidence were investigated. Significant space–time clusters were found in the northern and southern highlands and parts of Ecuadorian Amazonia. Customs of people, cattle, goat and sheep population density appeared to influence the incidence of brucellosis. In this study, the incidence of reported cases of human brucellosis was found to be higher in the highlands (sierra) and in municipalities near Peru and Colombia. The results of this study highlight the need for prevention and control measures aimed at abating the incidence of brucellosis among livestock and humans.

Global statistical tests for clustered earthquake pattern discovery in Italy

It is a widely shared opinion that not only secondary earthquakes (aftershocks) but also main earthquakes tend to occur in time-space clusters. The importance of this assumption requires the application of statistical tools to objectively evaluate its coherence with the reality at different scales of size-space-time. Global tests allow us to select the data sets with significant space-time clustering in order to perform more in-depth analyses to detect cluster locations. According to different fixed magnitude thresholds, we perform two global statistical tests, the Knox test and the Jacquez test, based on the space-time distance between pairs of earthquakes under the null hypothesis of uniform distribution in time and space, and evaluate the significance of the possible clusters. We analyze subsets of historical Italian earthquakes drawn from the Parametric Catalog of Italian Earthquakes (CPTI04) with magnitude thresholds 4.5, 5.3 and 6.0, associated with the composite seismogenic sources of the Database of Individual Seismogenic Sources. Each subset is related to one of the eight tectonically homogeneous macroregions in which the Italian territory has been divided. Significant space-time clustering is found for all sets with a magnitude threshold of 4.5. This tendency decreases drastically or disappears when the cut off rises to 5.3, with the exception of two macroregions located in the Eastern Alps and the Calabrian Arc, respectively, where evidence of space-time interaction may refer to stress transfer among consecutive or adjacent faults. The link between clustering effect and tectonic behavior could guide the choice of different stochastic point processes to model the seismic activity.

Space‐time clusters of dengue fever in Bangladesh

To examine the space-time clustering of dengue fever (DF) transmission in Bangladesh using geographical information system and spatial scan statistics (SaTScan). We obtained data on monthly suspected DF cases and deaths by district in Bangladesh for the period of 2000-2009 from Directorate General of Health Services. Population and district boundary data of each district were collected from national census managed by Bangladesh Bureau of Statistics. To identify the space-time clusters of DF transmission a discrete Poisson model was performed using SaTScan software. Space-time distribution of DF transmission was clustered during three periods 2000-2002, 2003-2005 and 2006-2009. Dhaka was the most likely cluster for DF in all three periods. Several other districts were significant secondary clusters. However, the geographical range of DF transmission appears to have declined in Bangladesh over the last decade. There were significant space-time clusters of DF in Bangladesh over the last decade. Our results would prompt future studies to explore how social and ecological factors may affect DF transmission and would also be useful for improving DF control and prevention programs in Bangladesh.

Space-time clustering of childhood central nervous system tumours in Yorkshire, UK.

BackgroundWe specifically tested the aetiological hypothesis that a factor influencing geographical or temporal heterogeneity of childhood central nervous system (CNS) tumour incidence was related to exposure to a transient environmental agent.MethodsInformation was extracted on individuals aged 0-14 years, diagnosed with a CNS tumour between the 1st January 1974 and 31st December 2006 from the Yorkshire Specialist Register of Cancer in Children and Young People. Ordnance Survey eight-digit grid references were allocated to each case with respect to addresses at the time of birth and the time of diagnosis, locating each address to within 0.1 km. The following diagnostic groups were specified a priori for analysis: ependymoma; astrocytoma; primitive neuroectodermal tumours (PNETs); other gliomas; total CNS tumours. We applied the K-function method for testing global space-time clustering using fixed geographical distance thresholds. Tests were repeated using variable nearest neighbour (NN) thresholds.ResultsThere was statistically significant global space-time clustering for PNETs only, based on time and place of diagnosis (P = 0.03 and 0.01 using the fixed geographical distance and the variable NN threshold versions of the K-function method respectively).ConclusionsThere was some evidence for a transient environmental component to the aetiology of PNETs. However, a possible role for chance cannot be excluded.

Identifying equine premises at high risk of introduction of vector-borne diseases using geo-statistical and space-time analyses

The identification of premises that may play an important role in the introduction or spread of animal diseases is fundamental to the development of risk-based surveillance and control programs. A combination of geo-statistical and cluster analysis methods was used to identify geographical areas and periods of time at highest risk for introduction of the African horse sickness virus (AHSV) into the Castile and Leon (CyL) region of Spain. Risk was estimated based on the predicted premises-specific abundance of Culicoides spp., a vector for AHSV, and on the frequency of equine introductions from outside regions. The largest abundance of Culicoides spp. was observed between May and September in the northern region of CyL. Six significant (P-value <0.01) space-time clusters of equine premises were found, at which presence of Culicoides spp. was predicted and live equidae were introduced from outside CyL. The clusters included 37 equine premises and took place between April and December. These results will contribute to updating plans for prevention of AHSV introduction and spread in Spain. The methodological approach developed here may be adapted and applied to design and establish risk-based surveillance and control programs for Spain and other European countries.

Using Building Permits to Monitor Disaster Recovery: A Spatio-Temporal Case Study of Coastal Mississippi Following Hurricane Katrina

The pace of disaster recovery varies considerably from one place to another. Even when places suffer impacts from the same event, recovery studies often lack the spatial and temporal resolution to fully understand such local variability in the recovery process and patterns. This paper discusses the novel use of building permits and a spatial scan statistic to identify the spatial and temporal dimensions of recovery in coastal Mississippi following Hurricane Katrina. Our work identifies significant space-time clusters of recovery activity and indicates that the amount of damage experienced and the amount of pre-event housing strongly influence the timing and location of building permit clusters. This analytical method and the use of publicly available data are valuable for a better understanding of long-term recovery processes.

Are transient environmental agents involved in the aetiology of primary biliary cirrhosis?

Recently a space-time clustering analysis of cases of primary biliary cirrhosis (PBC) was carried out, using population-based data from a defined geographical region of northeast England. Space-time clustering analysis is a specialised descriptive statistical technique an d is observed when excess cases are found in highly localised geographical areas for very limited periods of time. The analyses revealed the presence of highly statistically significant space-time clustering which could not be attributed to an artefactual occurrence. This finding was interpreted as providing supportive evidence for the involvement of transient environmental agents in aetiology [1]. This Special Issue of Disease Markers is dedicated to PBC and specifically the role that transient environmental factors may play in aetiology. Current knowledge is reviewed by seven selected invited experts (and associates) in the field. The aetiology of PBC is far from clear. In this Special Issue of Disease Markers, the role of transient environmental agents is reviewed by the seven invited contributors. They each provide their own perspective to the ongoing debate. The possibility that the putative transient agent might be an infection is considered by some of the reviewers. Epidemiological studies have indicated that infections may either trigger PBC or lead to progression. Reviewers also examine the putative role of other transient environmental factors, including chemical exposures, contaminated drinking water and lifestyle factors.

Are Transient Environmental Agents Involved in the Cause of Primary Biliary Cirrhosis? Evidence from Space–Time Clustering Analysis†

The cause of primary biliary cirrhosis (PBC) is unclear. Both genetic and environmental factors are likely to contribute. Some studies have suggested that one or more infectious agents may be involved. To examine whether infections may contribute to the cause of PBC, we have analyzed for space-time clustering using population-based data from northeast England over a defined period (1987-2003). Space-time clustering is observed when excess cases of a disease are found within limited geographical areas at limited periods of time. If present, it is suggestive of the involvement of one or more environmental components in the cause of a disease and is especially supportive of infections. A second-order procedure based on K-functions was used to test for global space-time clustering using residential addresses at the time of diagnosis. The Knox method determined the spatiotemporal range over which global clustering was strongest. K-function tests were repeated using nearest neighbor thresholds to adjust for variations in population density. Individual space-time clusters were identified using Kulldorff's scan statistic. Analysis of 1015 cases showed highly statistically significant space-time clustering (P < 0.001). Clustering was most marked for cases diagnosed within 1-4 months of one another. A number of specific space-time clusters were identified. In conclusion, these novel results suggest that transient environmental agents may play a role in the cause of PBC.

Space‐time clustering of childhood cancer around the residence at birth

Previously, we identified space-time clustering in certain childhood cancers around diagnosis residence. These findings provided support for the involvement of environmental agents in etiological processes occurring close to diagnosis. We have reanalyzed the same British population-based dataset. The aim of the study was to determine whether there was space-time clustering around the residence at birth in relation to time of birth and separately from time of diagnosis. A total of 29,553 cases, diagnosed during the period 1969-1993, were examined by a second-order procedure based on K-functions. Locations were birth addresses, but separately, both dates of birth and diagnosis were analyzed. There was statistically significant space-time clustering for Hodgkin lymphoma (HL) and central nervous system (CNS) tumors (p=0.047 and 0.01, respectively, based on birth date) and for total leukemia at ages 1-4 years only, Non-Hodgkin lymphoma (NHL) and Wilms tumor (p=0.01, 0.02 and 0.006, respectively, based on diagnosis date). These results, interpreted together with other epidemiological evidence, suggest an etiological role for environmental factors focused around birth address for certain childhood cancers. For HL and CNS tumors, findings suggest that etiological exposures occurred at similar ages or in utero. For leukemia, NHL and Wilms tumor there is support for exposures occurring at similar times before diagnosis. For leukemia, HL, NHL and CNS tumors, but not Wilms tumor, the findings are consistent with infectious hypotheses.

Geographic Clustering of Nonmedical Exemptions to School Immunization Requirements and Associations With Geographic Clustering of Pertussis

School immunization requirements are important in controlling vaccine-preventable diseases in the United States. Forty-eight states offer nonmedical exemptions to school immunization requirements. Children with exemptions are at increased risk of contracting and transmitting vaccine-preventable diseases. The clustering of nonmedical exemptions can affect community risk of vaccine-preventable diseases. The authors evaluated spatial clustering of nonmedical exemptions in Michigan and geographic overlap between exemptions clusters and clusters of reported pertussis cases. Kulldorf's scan statistic identified 23 statistically significant census tract clusters for exemption rates and 6 significant census tract clusters for reported pertussis cases between 1993 and 2004. The time frames for significant space-time pertussis clusters were August 1993-September 1993, August 1994-February 1995, May 1998-June 1998, April 2002, May 2003-July 2003, and June 2004-November 2004. Census tracts in exemptions clusters were more likely to be in pertussis clusters (odds ratio = 3.0, 95% confidence interval: 2.5, 3.6). The overlap of exemptions clusters and pertussis clusters remained significant after adjustment for population density, proportion of racial/ethnic minorities, proportion of children aged 5 years or younger, percentage of persons below the poverty level, and average family size (odds ratio = 2.7, 95% confidence interval: 2.2, 3.3). Geographic pockets of vaccine exemptors pose a risk to the whole community. In addition to monitoring state-level exemption rates, health authorities should be mindful of within-state heterogeneity.