Spatio-temporal Cluster Analysis Research Articles

Magnetoencephalography reveals an increased non-target P3a, but not target P3b, that is associated with high non-clinical psychosocial deficits

Auditory processing deficits are frequently identified in autism and schizophrenia, and the two disorders have been shown to share psychosocial difficulties. This study used magnetoencephalography to investigate auditory processing differences for those with a high degree of a non-clinical autistic and schizotypal trait phenotype, Social Disorganisation (SD). Participants were 18 low (9 female) and 19 high (9 female) SD scorers (18–40 years) who completed a three-stimulus auditory oddball paradigm of speech sounds (standard: 100ms ‘o’, deviant: 150ms ‘o’, novel: 150ms ‘e’). Spatio-temporal cluster analysis revealed increased amplitude for the high SD group in a left (p = 0.006) and a right (p = 0.020) hemisphere cluster in response to the novel non-target. No cluster differences were found in response to the target deviant. These findings suggest that those with a high degree of the SD phenotype recruit more cortical resources when processing unattended, novel speech stimuli, which may lead to psychosocial deficits.

Adapted estimate of neural activity based on blood-oxygen-level dependent signal by a model-free spatio-temporal clustering analysis

In this study, we detected brain activity by comparing the overall temporal response of the blood oxygen level referring to hemodynamic response with a modeled hemodynamic response (MHR). However, in a conventional analysis by statistical parametric mapping (SPM) method, the MHR is assumed to be a fixed-response function, which may bias the conclusions about brain activation, such as the shapes of the response curve or the different response delays to stimuli. Therefore, to improve detection efficacy, we applied a spatio-temporal clustering analysis (sTCA) to determine the MHR, which is calculated from the prospective voxels with no a priori information about the experiment design. With the sTCA method, these prospective voxels are detected by the feature with the largest temporal clustering within which these voxels react simultaneously, irrespective of where the variant hemodynamic response occurs. This estimated MHR (eMHR) is then applied to search for brain activation. Preliminary results show that the eMHR signal response closely resembles the real signal response of the target area. Moreover, the activation detection using eMHR method is more sensitive for the human visual and motor tasks than that with the canonical hemodynamic response embedded in the SPM analysis as the default MHR (dMHR). The more precise location of brain activation made possible by the improved sensitivity should provide helpful information about the stimulation of neuron activity.

Spatio-temporal clustering analysis and its determinants of hand, foot and mouth disease in Hunan, China, 2009\u20132015

BackgroundHand, foot and mouth disease (HFMD) is one of the highest reported infectious diseases with several outbreaks across the world. This study aimed at describing epidemiological characteristics, investigating spatio-temporal clustering changes, and identifying determinant factors in different clustering areas of HFMD.MethodsDescriptive statistics was used to evaluate the epidemic characteristics of HFMD from 2009 to 2015. Spatial autocorrelation and spatio-temporal cluster analysis were used to explore the spatial temporal patterns. An autologistic regression model was employed to explore determinants of HFMD clustering.ResultsThe incidence rates of HFMD ranged from 54.31/10 million to 318.06/10 million between 2009 and 2015 in Hunan. Cases were mainly prevalent in children aged 5 years and even younger, with an average male-to-female sex ratio of 1.66, and two epidemic periods in each year. Clustering areas gathered in the northern regions in 2009 and in the central regions from 2010 to 2012. They moved to central-southern regions in 2013 and 2014 and central-western regions in 2015. The significant risk factors of HFMD clusters were rainfall (OR = 2.187), temperature (OR = 4.329) and humidity (OR = 2.070). The protect factor was wind speed (OR = 0.258).ConclusionsThe HFMD incidence from 2009 to 2015 in Hunan showed a new spatiotemporal clustering tendency, with the shifting trend of clustering areas toward south and west. Meteorological factors showed a strong association with HFMD clustering, which may assist in predicting future spatial-temporal clusters.

Mortality in Danish Swine herds: Spatio-temporal clusters and risk factors

The aim of this study was to explore spatio-temporal mortality patterns in Danish swine herds from December 2013 to October 2015, and to discuss the use of mortality data for syndromic surveillance in Denmark. Although it has previously been assessed within the context of syndromic surveillance, the value of mortality data generated on a regular and mandatory basis from all swine herds remains unexplored in terms of swine surveillance in Denmark.A total of 5010 farms were included in the analysis, corresponding to 1896 weaner herds, 1490 sow herds and 3839 finisher herds. The spatio-temporal analysis included data description for spatial, temporal, and spatio-temporal cluster analysis for three age groups: weaners (up to 30kg), sows and finishers. Logistic regression models were used to assess the potential factors associated with finisher and weaner herds being included within multiple-herd clusters.The spatio-temporal distribution of mortality changed over time, and suggested a general increase in mortality for the months of January and July for the three age groups. A large number of single-herd clusters (i.e. clusters with only one herd), and fewer multiple-herd clusters (i.e. clusters with at least two herds) were found. The herd size affected whether weaner herds were within multiple-herd clusters, and factors such farm type, SPF status and presence of atrophic rhinitis had an impact on finisher herds being inside vs. outside multiple-herd clusters in the univariable analysis. However, due to a strong correlation between variables, only farm type remained in the multivariable analysis for the finisher herds.The higher mortality observed for the months of January and July could be linked to infrequent updates of the data used to calculate mortality. The presence of single-herd clusters might indicate welfare and disease issues, while multiple-herd clusters could suggest the presence of infectious diseases within the cluster area. The impact of farm type is linked to the fact that larger farms specialize in only one age group, with high biosecurity and more specialized personnel, and subsequently a lower mortality.Mortality data have a potential use in disease surveillance. However, detected clusters might not be due to disease, but the result of changes such as herd management practices. Further analysis to explore other spatio-temporal monitoring methods is needed before mortality data can be incorporated into a Danish disease monitoring system.

Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach

BackgroundHepatitis C virus (HCV) infections have increased during the past decade but little is known about geographic clustering patterns.MethodsWe used a unique analytical approach, combining geographic information systems (GIS), spatial epidemiology, and statistical modeling to identify and characterize HCV hotspots, statistically significant clusters of census tracts with elevated HCV counts and rates. We compiled sociodemographic and HCV surveillance data (n = 99,780 cases) for Massachusetts census tracts (n = 1464) from 2002 to 2013. We used a five-step spatial epidemiological approach, calculating incremental spatial autocorrelations and Getis-Ord Gi* statistics to identify clusters. We conducted logistic regression analyses to determine factors associated with the HCV hotspots.ResultsWe identified nine HCV clusters, with the largest in Boston, New Bedford/Fall River, Worcester, and Springfield (p < 0.05). In multivariable analyses, we found that HCV hotspots were independently and positively associated with the percent of the population that was Hispanic (adjusted odds ratio [AOR]: 1.07; 95% confidence interval [CI]: 1.04, 1.09) and the percent of households receiving food stamps (AOR: 1.83; 95% CI: 1.22, 2.74). HCV hotspots were independently and negatively associated with the percent of the population that were high school graduates or higher (AOR: 0.91; 95% CI: 0.89, 0.93) and the percent of the population in the “other” race/ethnicity category (AOR: 0.88; 95% CI: 0.85, 0.91).ConclusionWe identified locations where HCV clusters were a concern, and where enhanced HCV prevention, treatment, and care can help combat the HCV epidemic in Massachusetts. GIS, spatial epidemiological and statistical analyses provided a rigorous approach to identify hotspot clusters of disease, which can inform public health policy and intervention targeting. Further studies that incorporate spatiotemporal cluster analyses, Bayesian spatial and geostatistical models, spatially weighted regression analyses, and assessment of associations between HCV clustering and the built environment are needed to expand upon our combined spatial epidemiological and statistical methods.

Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China

BackgroundThe Poyang Lake Region, one of the major epidemic sites of schistosomiasis in China, remains a severe challenge. To improve our understanding of the current endemic status of schistosomiasis and to better control the transmission of the disease in the Poyang Lake Region, it is important to analyse the clustering pattern of schistosomiasis and detect the hotspots of transmission risk.ResultsBased on annual surveillance data, at the village level in this region from 2009 to 2014, spatial and temporal cluster analyses were conducted to assess the pattern of schistosomiasis infection risk among humans through purely spatial (Local Moran’s I, Kulldorff and Flexible scan statistic) and space-time scan statistics (Kulldorff). A dramatic decline was found in the infection rate during the study period, which was shown to be maintained at a low level. The number of spatial clusters declined over time and were concentrated in counties around Poyang Lake, including Yugan, Yongxiu, Nanchang, Xingzi, Xinjian, De’an as well as Pengze, situated along the Yangtze River and the most serious area found in this study. Space-time analysis revealed that the clustering time frame appeared between 2009 and 2011 and the most likely cluster with the widest range was particularly concentrated in Pengze County.ConclusionsThis study detected areas at high risk for schistosomiasis both in space and time at the village level from 2009 to 2014 in Poyang Lake Region. The high-risk areas are now more concentrated and mainly distributed at the river inflows Poyang Lake and along Yangtze River in Pengze County. It was assumed that the water projects including reservoirs and a recently breached dyke in this area were partly to blame. This study points out that attempts to reduce the negative effects of water projects in China should focus on the Poyang Lake Region.

Spatio-temporal source cluster analysis reveals fronto-temporal auditory change processing differences within a shared autistic and schizotypal trait phenotype.

Social Disorganisation (SD) is a shared autistic and schizotypal phenotype that is present in the subclinical population. Auditory processing deficits, particularly in mismatch negativity/field (MMN/F) have been reported across both spectrum disorders. This study investigates differences in MMN/F cortical spatio-temporal source activity between higher and lower quintiles of the SD spectrum.Sixteen low (9 female) and 19 high (9 female) SD subclinical adults (18–40years) underwent magnetoencephalography (MEG) during an MMF paradigm where standard tones (50ms) were interrupted by infrequent duration deviants (100ms).Spatio-temporal source cluster analysis with permutation testing revealed no difference between the groups in source activation to the standard tone. To the deviant tone however, there was significantly reduced right hemisphere fronto-temporal and insular cortex activation for the high SD group (p= 0.038). The MMF, as a product of the cortical response to the deviant minus that to the standard, did not differ significantly between the high and low Social Disorganisation groups.These data demonstrate a deficit in right fronto-temporal processing of an auditory change for those with more of the shared SD phenotype, indicating that right fronto-temporal auditory processing may be associated with psychosocial functioning.

Spatio-temporal variation of mood and anxiety symptom treatments in Christchurch in the context of the 2010/11 Canterbury earthquake sequence

This article explores the spatio-temporal variation of mood and anxiety treatments in the context of a severe earthquake sequence. The aim was to examine a possible earthquake exposure effect, identify populations at risk and areas with particularly large mood and anxiety treatment rate increases or decreases in the affected Christchurch urban area. A significantly stronger increase of mood and anxiety treatments among residents in Christchurch compared to others in New Zealand have been found, as well as children and elderly identified as especially vulnerable. Spatio-temporal cluster analysis and Bayesian spatio-temporal modelling revealed little changes in mood and anxiety treatment patterns for most parts of the city, whereas areas in the less affected north and northwest showed the strongest increases in risk. This effect may be linked to inner-city mobility activity as a consequence of the earthquakes, but also different levels of community cohesion after the disaster, which merit further research.

Statistical Modeling of Ultrawideband MIMO Propagation Channel in a Warehouse Environment

This paper describes an extensive propagation channel measurement campaign in a warehouse environment for line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The measurement setup employs a vector network analyzer operating in the 2–8-GHz frequency band combined with an $8 \times 8$ virtual multiple-input multiple-output (MIMO) antenna array. We develop a comprehensive statistical propagation channel model based on high-resolution extraction of multipath components and subsequent spatiotemporal clustering analysis. The intracluster direction of departure (DoD), direction of arrival (DoA), and the time of arrival (ToA) are independent, both for the LOS and NLOS scenarios. The intracluster DoD and DoA can be approximated by the Laplace distribution, and the intracluster ToA can be approximated by an exponential mixture distribution. The intercluster analysis, however, shows a dependency between the cluster DoD, DoA, and ToA. To capture this dependency, we separately model the clusters caused by single and multiple bounce scattering along the aisles in the warehouse. The intercluster DoD distribution follows a Laplace distribution, while the cluster DoA conditioned on the DoD is approximated by a Gaussian mixture distribution. The model was validated using the capacity and delay-spread values.

Spatiotemporal Dynamics of Scrub Typhus Transmission in Mainland China, 2006-2014.

BackgroundScrub typhus is endemic in the Asia-Pacific region including China, and the number of reported cases has increased dramatically in the past decade. However, the spatial-temporal dynamics and the potential risk factors in transmission of scrub typhus in mainland China have yet to be characterized.ObjectiveThis study aims to explore the spatiotemporal dynamics of reported scrub typhus cases in mainland China between January 2006 and December 2014, to detect the location of high risk spatiotemporal clusters of scrub typhus cases, and identify the potential risk factors affecting the re-emergence of the disease.MethodMonthly cases of scrub typhus reported at the county level between 2006 and 2014 were obtained from the Chinese Center for Diseases Control and Prevention. Time-series analyses, spatiotemporal cluster analyses, and spatial scan statistics were used to explore the characteristics of the scrub typhus incidence. To explore the association between scrub typhus incidence and environmental variables panel Poisson regression analysis was conducted.ResultsDuring the time period between 2006 and 2014 a total of 54,558 scrub typhus cases were reported in mainland China, which grew exponentially. The majority of cases were reported each year between July and November, with peak incidence during October every year. The spatiotemporal dynamics of scrub typhus varied over the study period with high-risk clusters identified in southwest, southern, and middle-eastern part of China. Scrub typhus incidence was positively correlated with the percentage of shrub and meteorological variables including temperature and precipitation.ConclusionsThe results of this study demonstrate areas in China that could be targeted with public health interventions to mitigate the growing threat of scrub typhus in the country.

Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform

Objective: The outbreak of the Ebola epidemic in West Africa in 2014 exerted enormous global public reaction via the Internet and social media. This study aimed to investigate and evaluate the public reaction to Ebola in China and identify the primitive correlation between possible influence factors caused by the outbreak of Ebola in West Africa and Chinese public attention via Internet surveillance. Methods: Baidu Index (BDI) and Sina Micro Index (SMI) were collected from their official websites, and the disease-related data were recorded from the websites of the World Health Organization (WHO), U.S. Centers for Disease Control and Prevention (CDC), and U.S. National Ministries of Health. The average BDI of Internet users in different regions were calculated to identify the public reaction to the Ebola outbreak. Spearman’s rank correlation was used to check the relationship of epidemic trends with BDI and SMI. Additionally, spatio-temporal analysis and autocorrelation analysis were performed to detect the clustered areas with the high attention to the topic of “Ebola”. The related news reports were collected from authoritative websites to identify potential patterns. Results: The BDI and the SMI for “Ebola” showed a similar fluctuating trend with a correlation coefficient = 0.9 (p < 0.05). The average BDI in Beijing, Tibet, and Shanghai was higher than other cities. However, the disease-related indicators did not identify potential correlation with both indices above. A hotspot area was detected in Tibet by local autocorrelation analysis. The most likely cluster identified by spatiotemporal cluster analysis was in the northeast regions of China with the relative risk (RR) of 2.26 (p ≤ 0.01) from 30 July to 14 August in 2014. Qualitative analysis indicated that negative news could lead to a continuous increase of the public’s attention until the appearance of a positive news report. Conclusions: Confronted with the risk of cross-border transmission of the infectious disease, online surveillance might be used as an innovative approach to perform public communication and health education through examining the public’s reaction and attitude.

Deconstructing the Construction Industry: A Spatiotemporal Clustering Approach to Profitability Modeling

AbstractIn spite of the strong influence of the construction industry on the national health of the United States’ economy, very little research has specifically aimed at evaluating the key performance parameters and trends (KPPT) of the industry. Due to this knowledge gap, concerns have been constantly raised over lack of accurate measures of KPPT. To circumvent these challenges, this study investigates and models the macroeconomic KPPT of the industry through spatiotemporal clustering modeling. This study specifically aims to analyze the industry in 14 of its subsectors and subsequently, by 51 geographic spatial areas at a 15-year temporal scale. KPPT and their interdependence were firstly examined by utilizing the interpolated comprehensive U.S. economic census data. A hierarchical spatiotemporal clustering analysis was then performed to create predictive models that can reliably determine firm’s profitability as a function of the key parameters. Lastly, the robustness of the predictive models was test...

Applying Science: Opportunities to Inform Disease Management Policy with Cooperative Research within a One Health Framework.

The ongoing Ebola outbreak in West Africa and the current saiga antelope die off in Kazakhstan each represent very real and difficult to manage public or veterinary health crises. They also illustrate the importance of stable and funded surveillance and sound policy for intervention or disease control. While these two events highlight extreme cases of infectious disease (Ebola) or (possible) environmental exposure (saiga), diseases such as anthrax, brucellosis, tularemia, and plague are all zoonoses that pose risks and present surveillance challenges at the wildlife-livestock–human interfaces. These four diseases are also considered important actors in the threat of biological terror activities and have a long history as legacy biowarfare pathogens. This paper reviews recent studies done cooperatively between American and institutions within nations of the Former Soviet Union (FSU) focused on spatiotemporal, epidemiological, and ecological patterns of these four zoonoses. We examine recent studies and discuss the possible ways in which techniques, including ecological niche modeling, disease risk modeling, and spatiotemporal cluster analysis, can inform disease surveillance, control efforts, and impact policy. Our focus is to posit ways to apply science to disease management policy and actual management or mitigation practices. Across these examples, we illustrate the value of cooperative studies that bring together modern geospatial and epidemiological analyses to improve our understanding of the distribution of pathogens and diseases in livestock, wildlife, and humans. For example, ecological niche modeling can provide national level maps of pathogen distributions for surveillance planning, while space-time models can identify the timing and location of significant outbreak events for defining active control strategies. We advocate for the need to bring the results and the researchers from cooperative studies into the meeting rooms where policy is negotiated and use these results to inform future disease surveillance and control or eradication campaigns.

Geographic Expansion of Lyme Disease in the Southeastern United States, 2000-2014.

Background. The majority of Lyme disease cases in the United States are acquired on the east coast between northern Virginia and New England. In recent years the geographic extent of Lyme disease has been expanding, raising the prospect of Lyme disease becoming endemic in the southeast.Methods. We collected confirmed and probable cases of Lyme disease from 2000 through 2014 from the Virginia Department of Health and North Carolina Department of Public Health and entered them in a geographic information system. We performed spatial and spatiotemporal cluster analyses to characterize Lyme disease expansion.Results. There was a marked increase in Lyme disease cases in Virginia, particularly from 2007 onwards. Northern Virginia experienced intensification and geographic expansion of Lyme disease cases. The most notable area of expansion was to the southwest along the Appalachian Mountains with development of a new disease cluster in the southern Virginia mountain region.Conclusions. The geographic distribution of Lyme disease cases significantly expanded in Virginia between 2000 and 2014, particularly southward in the Virginia mountain ranges. If these trends continue, North Carolina can expect autochthonous Lyme disease transmission in its mountain region in the coming years.

Spatial and Temporal Clustering of Chikungunya Virus Transmission in Dominica.

Using geo-referenced case data, we present spatial and spatio-temporal cluster analyses of the early spread of the 2013–2015 chikungunya virus (CHIKV) in Dominica, an island in the Caribbean. Spatial coordinates of the locations of the first 417 reported cases observed between December 15th, 2013 and March 11th, 2014, were captured using the Global Positioning System (GPS). We observed a preponderance of female cases, which has been reported for CHIKV outbreaks in other regions. We also noted statistically significant spatial and spatio-temporal clusters in highly populated areas and observed major clusters prior to implementation of intensive vector control programs suggesting early vector control measures, and education had an impact on the spread of the CHIKV epidemic in Dominica. A dynamical identification of clusters can lead to local assessment of risk and provide opportunities for targeted control efforts for nations experiencing CHIKV outbreaks.

Seropositivity to Toxoplasma infection in sheep samples submitted to Animal and Plant Health Agency laboratories between 2005 and 2012

Ovine serum samples submitted to Animal and Plant Health Agency (APHA) (formerly the Animal Health and Veterinary Laboratories Agency) – Weybridge regional laboratories in England and Wales for diagnostic and...

Spatial distribution of lightning associated with low-shear thunderstorm environments in the central Appalachian region

Differential heating, the preferential warming of terrain by insolation based on its gradient and aspect, has been observed to play an instrumental role in thunderstorm formation in weak flow regimes. In this study, spatiotemporal cluster analysis was used to group total lightning observations into likely thunderstorms, which were then categorized into lightning-defined (LD) storm modes. Flashes associated with the 351 LD single-cell storms identified were then imported into a geographic information system to compare their frequency against the presence of east- and south-facing surfaces with above-average gradients, termed “enhanced diurnal heating surfaces” (EDHSs). The influence of EDHSs on LD single-cell total lightning and storm frequency was examined via a series of pooled t-tests at eight different spatial scales. The results indicate that EDHSs possessed a significantly greater frequency of LD single-cell storms, 37.6% greater than non-EDHSs, with the relationship best observed at a 15-km resolution. The analysis was then repeated but with terrain aspect ignored. The elimination of aspect as an EDHS requirement removed all statistical significance from the initial results, suggesting that aspect is indeed a key contributor to single-cell thunderstorm formation within areas of complex terrain.

Space–time cluster analysis of the non-pathogenic infectious salmon anemia virus (HPR0 ISAV) in Chile, 2011–2012

Abstract In 2007, Chile reported its first case of infectious salmon anemia (ISA), caused by ISA virus (ISAV), in sea-cage reared Atlantic salmon. By 2010, the ISA disease outbreak was under control, but similarly to other countries, Chile continues to occasionally detect the non-pathogenic variant of ISAV (HPR0 ISAV). The Chilean salmon industry has been including gill samples in their routine ISAV monitoring program to improve the detection of HRP0 ISAV, which provides an opportunity to better assess the distribution of HPR0 ISAV and describe its occurrence in the Chilean marine aquaculture industry. A risk and a relative risk surface map were generated for the predominant regions of the Chilean aquaculture industry, and demonstrated areas of increased risk to HPR0 ISAV for the entire surveillance period. The observed occurrence and seasonal patterns of HPR0 ISAV were similar to those previously reported in Chile and in other countries. Further, spatiotemporal cluster analysis showed that the areas with a high-risk of HPR0 ISAV changed during the surveillance period (highly prevalent and causing non-clinical transient infections), and cases in clusters were initially observed within companies, followed by neighboring farms affecting other companies. Monitoring HPR0 ISAV in marine phase salmon production highlighted possible transmission patterns within the Chilean aquaculture industry and identified higher-risk areas associated with circulating orthomyxoviruses.

Geographic variation of clinically diagnosed mood and anxiety disorders in Christchurch after the 2010/11 earthquakes

The 22nd February 2011 Christchurch earthquake killed 185 people, injured over 8000, damaged over 100,000 buildings and on-going aftershocks maintained high anxiety levels. This paper examines the dose of exposure effect of earthquake damage assessments, earthquake intensity measures, liquefaction and lateral spreading on mood and anxiety disorders in Christchurch after this event. We hypothesise that such disorders are more likely to develop in people who have experienced greater exposure to these impacts within their neighborhood than others who have been less exposed, but also live in the city. For this purpose, almost all clinically diagnosed incident and relapsed cases in Christchurch in a 12 months period after the 2011 earthquake were analysed. Spatio-temporal cluster analysis shows that people living in the widely affected central and eastern parts after the 2010/11 earthquakes have a 23% higher risk of developing a mood or anxiety disorder than people living in other parts of the city. Generally, mood and anxiety-related disorders increase with closer proximity to damage from liquefaction and moderate to major lateral spreading, as well as areas that are more likely to suffer from damage in future earthquakes.

Effects of Residential Exposure to Physical Earthquake Damage Caused by the 2010/2011 Canterbury Earthquakes on Mood and Anxiety Disorders in Christchurch

Effects of Residential Exposure to Physical Earthquake Damage Caused by the 2010/2011 Canterbury Earthquakes on Mood and Anxiety Disorders in ChristchurchAbstract Number:1761 Daniel Hogg*, Simon Kingham, Thomas Wilson, Edward Griffin, Michael Ardagh Daniel Hogg* GeoHealth Laboratory, Department of Geography, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , Simon Kingham GeoHealth Laboratory, Department of Geography, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , Thomas Wilson Department of Geological Sciences, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , Edward Griffin GeoHealth Laboratory, Department of Geography, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , and Michael Ardagh Canterbury District Health Board (CDHB), New Zealand, E-mail Address: [email protected] Search for more papers by this author AbstractObjective: In the 2010/2011 Canterbury earthquake sequence 185 people were killed, over 8,000 injured, over 100,000 buildings have been damaged and on-going aftershocks kept the city on edge maintaining high anxiety levels. This project examined whether mood and anxiety disorders up to sixteen months after the devastating 22nd February 2011 earthquake were more likely to develop in people who have been exposed to greater physical earthquake damage to their neighborhood than those who have experienced less damage but also live in Christchurch.Methods: Based on nearly all the residential population and clinical mood and anxiety diagnoses in Christchurch, spatio-temporal scan analysis with SaTScanTM was used to detect clusters of mood and anxiety between July 1, 2009 and June 30, 2012. Multivariate logistic regression was used to assess the relationship between those disorders and the extent of damage to the neighbourhood measured by the Canterbury Earthquake Recovery Authority (CERA) land zoning, lateral spreading, liquefaction and the hazard intensity measures Modified Mercalli Intensity (MMI) and Peak Ground Acceleration (PGA).Results: Spatio-temporal cluster analysis showed that people living in the more damaged central and eastern parts of the city have a 24% higher risk of developing a mood or anxiety disorder than people living in less damaged parts of the city. Multivariate logistic regression showed that mood and anxiety disorders increased with increasing earthquake intensity and resultant increased extent of damage. Overall, people who haven’t had a prior mood or anxiety disorder are at greater risk of developing such a disorder by living in closer proximity to more affected areas. Furthermore this suggests that living in closer proximity to areas that are more likely to suffer from damage in future earthquakes is likely to increase the risk of getting a mood or anxiety disorder in the population, and thus could benefit from targeted health interventions.