Mapping the Mental Health of Residents After the 2013 Boston Marathon Bombings

Postdisaster mental health is typically assessed and treated on an individual basis. Ecological assessments, however, can be a more cost-effective means to identify and promote mental health after a disaster. In this study, the spatial patterning of acute stress scores, probable posttraumatic stress disorder (PTSD), and fears and worries among a representative sample of Boston metropolitan area participants (N = 788) was examined using data collected 2-4 weeks to 2 years after the 2013 Boston Marathon bombings. Findings indicate significant clustering of acute stress scores, Moran's I = 0.24, z = 2.91, p = .004; fears and worries, Moran's I = 0.25, z = 2.39, p = .017; and probable PTSD at Wave 2, Moran's I = 0.49, z = 5.16; p < .001, and at Wave 5, Moran's I = 0.26, z = 2.51, p = .012, in the Boston metropolitan area, with high distress clusters found near downtown Boston and the attack site. These results suggest that disaster mental health is not uniformly distributed across space. Instead, patterns emerge to identify persons and neighborhoods at risk for poor mental health outcomes.

An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan

Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space.

Spatial and space-time clustering of tuberculosis in Gurage Zone, Southern Ethiopia

INTRODUCTION

Spatial targeting is advocated as an effective method that contributes for achieving tuberculosis control in high-burden countries. However, there is a paucity of studies clarifying the spatial nature of the disease in these countries. This study aims to identify the location, size and risk of purely spatial and space-time clusters for high occurrence of tuberculosis in Gurage Zone, Southern Ethiopia during 2007 to 2016.

MATERIALS AND METHODS

A total of 15,805 patient data that were retrieved from unit TB registers were included in the final analyses. The spatial and space-time cluster analyses were performed using the global Moran's I, Getis-Ord [Formula: see text] and Kulldorff's scan statistics.

RESULTS

Eleven purely spatial and three space-time clusters were detected (P <0.001).The clusters were concentrated in border areas of the Gurage Zone. There were considerable spatial variations in the risk of tuberculosis by year during the study period.

CONCLUSIONS

This study showed that tuberculosis clusters were mainly concentrated at border areas of the Gurage Zone during the study period, suggesting that there has been sustained transmission of the disease within these locations. The findings may help intensify the implementation of tuberculosis control activities in these locations. Further study is warranted to explore the roles of various ecological factors on the observed spatial distribution of tuberculosis.

Geographical distribution and spatio-temporal patterns of hospitalization due to dengue infection at a leading specialist hospital in Malaysia

Increasing numbers of dengue infection worldwide have led to a rise in deaths due to complications caused by this disease. We present here a cross-sectional study of dengue patients who attended the Emergency and Trauma Department of Ampang Hospital, one of Malaysia's leading specialist hospitals. The objective was to search for potential clustering of severe dengue, in space and/or time, among the annual admissions with the secondary objective to describe the spatio-temporal pattern of all dengue cases admitted to this hospital. The dengue status of the patients was confirmed serologically with the geographic location of the patients determined by residency, but not more specific than the street level. A total of 1165 dengue patients were included in the analysis using SaTScan software. The mean age of these patients was 27.8 years, with a standard deviation of 14.2 years and an age range from 1 to 77 years, among whom 54 (4.6%) were cases of severe dengue. A cluster of general dengue cases was identified occurring from October to December in the study year of 2015 but the inclusion of severe dengue in that cluster was not statistically significant (P=0.862). The standardized incidence ratio was 1.51. General presence of dengue cases was, however, detected to be concentrated at the end of the year, which should be useful for hospital planning and management if this pattern holds.

Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, USA, 2014-2015

Each day, the New York City Department of Health and Mental Hygiene uses the free SaTScan software to apply prospective space–time permutation scan statistics to strengthen early outbreak detection for 35 reportable diseases. This method prompted early detection of outbreaks of community-acquired legionellosis and shigellosis.

Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space-time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend. A Correction has been published: https://doi.org/10.4081/gh.2023.1232

Spatial variation and hot-spots of district level diarrhea incidences in Ghana: 2010-2014

BACKGROUND

Diarrhea is a public health menace, especially in developing countries. Knowledge of the biological and anthropogenic characteristics is abundant. However, little is known about its spatial patterns especially in developing countries like Ghana. This study aims to map and explore the spatial variation and hot-spots of district level diarrhea incidences in Ghana.

METHODS

Data on district level incidences of diarrhea from 2010 to 2014 were compiled together with population data. We mapped the relative risks using empirical Bayesian smoothing. The spatial scan statistics was used to detect and map spatial and space-time clusters. Logistic regression was used to explore the relationship between space-time clustering and urbanization strata, i.e. rural, peri-urban, and urban districts.

RESULTS

We observed substantial variation in the spatial distribution of the relative risk. There was evidence of significant spatial clusters with most of the excess incidences being long-term with only a few being emerging clusters. Space-time clustering was found to be more likely to occur in peri-urban districts than in rural and urban districts.

CONCLUSION

This study has revealed that the excess incidences of diarrhea is spatially clustered with peri-urban districts showing the greatest risk of space-time clustering. More attention should therefore be paid to diarrhea in peri-urban districts. These findings also prompt public health officials to integrate disease mapping and cluster analyses in developing location specific interventions for reducing diarrhea.

Evaluating the Brazilian zero tolerance drinking and driving law: Time series analyses of traffic-related mortality in three major cities

OBJECTIVE

A zero tolerance alcohol restriction law was adopted in Brazil in 2008. In order to assess the effectiveness of this intervention, the present study compares specific mortality in 2 time series: 1980-2007 and 2008-2013.

METHODS

Data on mortality and population were gathered from official Brazilian Ministry of Health information systems. Segmented regression analyses were carried out separately for 3 major Brazilian capitals: Belo Horizonte, Rio de Janeiro, and São Paulo.

RESULTS

In 2 cities (Belo Horizonte and Rio de Janeiro) there were no significant changes in mortality rate trends in 2 periods, 1980 to 2007 and 2008 to 2013, where the observed rates did not differ significantly from predicted rates. In São Paulo, a decreasing trend until 2007 unexpectedly assumed higher levels after implementation of the law.

CONCLUSION

There is no evidence of reduced traffic-related mortality in the 3 major Brazilian capitals 5.5 years after the zero tolerance drinking and driving law was adopted.

[Spatial distribution and clustering in birth defects from 2010 to 2013 in Shaanxi Province]

To explore the spatial distribution and clustering in birth defects from 2010 to 2013 in Shaanxi Province.
 Methods: Spatial distribution was used to describe the birth defects, while ordinary Kriging method was used to predict the status of birth defects in Shaanxi province. The spatial characteristics for the birth defects at the county/district level were analyzed by spatial autocorrelation.
 Results: The overall incidence of birth defects was 219.196/10 000; Birth defect did not appear to be a random distribution but show a significant spatial aggregation. Spatial interpolation predicted the geographic distribution for occurrence of birth defects in Shaanxi Province. Local autocorrelation analysis showed nine "hot spot areas" for birth defects, such as Qian County, Liquan County, Yongshou County, Bin County, Fufeng County, Jingyang County, Chunhua County, Wugong County and Xingping City, and seven "cold spot areas" including Jia County, Yuyang District, Mizhi County, Suide County, Wubu County, Qingjian County and Zizhou District.
 Conclusion: There are spatial clustering in birth defects from 2010 to 2013 in Shaanxi Province. Spatial interpolation and spatial autocorrelation can be used to predict the spatial features of birth defects in the whole province and provide evidence for the further intervention.

Space-Time Clustering of Childhood Leukemia: Evidence of an Association with ETV6-RUNX1 (TEL-AML1) Fusion

BACKGROUND

Many studies have observed space-time clustering of childhood leukemia (CL) yet few have attempted to elicit etiological clues from such clustering. We recently reported space-time clustering of CL around birth, and now aim to generate etiological hypotheses by comparing clustered and nonclustered cases. We also investigated whether the clustering resulted from many small aggregations of cases or from a few larger clusters.

METHODS

We identified cases of persons born and diagnosed between 1985 and 2014 at age 0-15 years from the Swiss Childhood Cancer Registry. We determined spatial and temporal lags that maximized evidence of clustering based on the Knox test and classified cases born within these lags from another case as clustered. Using logistic regression adjusted for child population density, we determined whether clustering status was associated with age at diagnosis, immunophenotype, cytogenetic subtype, perinatal and socioeconomic characteristics, and pollution sources.

RESULTS

Analyses included 1,282 cases of which 242 were clustered (born within 1 km and 2 years from another case). Of all investigated characteristics only the t(12;21)(p13;q22) translocation (resulting in ETV6-RUNX1 fusion) differed significantly in prevalence between clustered and nonclustered cases (40% and 25%, respectively; adjusted OR 2.54 [1.52-4.23]; p = 0.003). Spatio-temporal clustering was driven by an excess of aggregations of two or three children rather than by a few large clusters.

CONCLUSION

Our findings suggest ETV6-RUNX1 is associated with space-time clustering of CL and are consistent with an infection interacting with that oncogene in early life leading to clinical leukemia.

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation

PURPOSE

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables.

METHODS

In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina.

RESULTS

Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation.

CONCLUSIONS

Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer.

Ambient Fine Particulate Matter, Outdoor Temperature, and Risk of Metabolic Syndrome

Ambient air pollution and temperature have been linked with cardiovascular morbidity and mortality. Metabolic syndrome and its components-abdominal obesity, elevated fasting blood glucose concentration, low high-density lipoprotein cholesterol concentration, hypertension, and hypertriglyceridemia-predict cardiovascular disease, but the environmental causes are understudied. In this study, we prospectively examined the long-term associations of air pollution, defined as particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM_2.5), and temperature with the development of metabolic syndrome and its components. Using covariate-adjustment Cox proportional hazards models, we estimated associations of mean annual PM_2.5 concentration and temperature with risk of incident metabolic dysfunctions between 1993 and 2011 in 587 elderly (mean = 70 (standard deviation, 7) years of age) male participants in the Normative Aging Study. A 1-μg/m³ increase in mean annual PM_2.5 concentration was associated with a higher risk of developing metabolic syndrome (hazard ratio (HR) = 1.27, 95% confidence interval (CI): 1.06, 1.52), an elevated fasting blood glucose level (HR = 1.20, 95% CI: 1.03, 1.39), and hypertriglyceridemia (HR = 1.14, 95% CI: 1.00, 1.30). Our findings for metabolic syndrome and high fasting blood glucose remained significant for PM_2.5 levels below the Environmental Protection Agency's health-safety limit (12 μg/m³). A 1°C increase in mean annual temperature was associated with a higher risk of developing elevated fasting blood glucose (HR = 1.33, 95% CI: 1.14, 1.56). Men living in neighborhoods with worse air quality-with higher PM_2.5 levels and/or temperatures than average-showed increased risk of developing metabolic dysfunctions.

A million-plus neuron model of the hippocampal dentate gyrus: Dependency of spatio-temporal network dynamics on topography

This paper describes a million-plus granule cell compartmental model of the rat hippocampal dentate gyrus, including excitatory, perforant path input from the entorhinal cortex, and feedforward and feedback inhibitory input from dentate interneurons. The model includes experimentally determined morphological and biophysical properties of granule cells, together with glutamatergic AMPA-like EPSP and GABAergic GABAA-like IPSP synaptic excitatory and inhibitory inputs, respectively. Each granule cell was composed of approximately 200 compartments having passive and active conductances distributed throughout the somatic and dendritic regions. Modeling excitatory input from the entorhinal cortex was guided by axonal transport studies documenting the topographical organization of projections from subregions of the medial and lateral entorhinal cortex, plus other important details of the distribution of glutamatergic inputs to the dentate gyrus. Results showed that when medial and lateral entorhinal cortical neurons maintained Poisson random firing, dentate granule cells expressed, throughout the million-cell network, a robust, non-random pattern of spiking best described as spatiotemporal "clustering". To identify the network property or properties responsible for generating such firing "clusters", we progressively eliminated from the model key mechanisms such as feedforward and feedback inhibition, intrinsic membrane properties underlying rhythmic burst firing, and/or topographical organization of entorhinal afferents. Findings conclusively identified topographical organization of inputs as the key element responsible for generating a spatio-temporal distribution of clustered firing. These results uncover a functional organization of perforant path afferents to the dentate gyrus not previously recognized: topography-dependent clusters of granule cell activity as "functional units" that organize the processing of entorhinal signals.

A family cluster of hepatitis A virus due to an uncommon IA strain circulating in Campania (southern Italy), not associated with raw shellfish or berries: a wake-up call to implement vaccination against hepatitis A?

Hepatitis A virus is a widely occurring disease, with different prevalence rates between countries in the North and West and those in the South and East. In Italy endemicity is low/medium, but not homogeneously distributed: in the northern/central regions a large hepatitis A outbreak due to genotype IA, related to the consumption of contaminated mixed frozen berries, occurred between 2013 and 2014, whereas in southern Italian regions recurrent outbreaks of hepatitis A, due to the IB genotype, still result from consumption of raw seafood. In 2014 an uncommon genotype IA strain was isolated from five patients (2 adults and 3 children) with hepatitis A, living in the surroundings of Naples (Campania) who did not have any of the most common risk factors for hepatitis A in Italy, such as consumption of raw shellfish or frozen berries, or travel to endemic countries. Moreover, based on the analysis of viral sequences obtained, this strain differed from several others in the national database, which had been recently isolated during Italian outbreaks. This case report reinforces the need to implement both information campaigns about the prevention of hepatitis A and vaccination programmes in childhood; in addition, it would be suitable to sequence strains routinely not only during large outbreaks of hepatitis A in order to obtain a more detailed national database of HAV strains circulating in Italy.

Circulating Influenza Virus and Adverse Pregnancy Outcomes: A Time-Series Study

Individual-level epidemiologic studies of pregnancy outcomes after maternal influenza are limited in number and quality and have produced inconsistent results. We used a time-series design to investigate whether fluctuation in influenza virus circulation was associated with short-term variation in population-level rates of preterm birth, stillbirth, and perinatal death in Ontario between 2003 and 2012. Using Poisson regression, we assessed the association between weekly levels of circulating influenza virus and counts of outcomes offset by the number of at-risk gestations during 3 gestational exposure windows. The rate of preterm birth was not associated with circulating influenza level in the week preceding birth (adjusted rate ratio = 1.01, 95% confidence interval: 1.00, 1.02) or in any other exposure window. These findings were robust to alternate specifications of the model and adjustment for potential confounding. Stillbirth and perinatal death rates were similarly not associated with gestational exposure to influenza circulation during late pregnancy. We could not assess mortality outcomes relative to early gestational exposure because of missing dates of conception for many stillbirths. In this time-series study, population-level influenza circulation was not associated with short-term variation in rates of preterm birth, stillbirth, or perinatal death.

Surveillance of individual level disease maps

Methods for the production of individual (address) level disease maps are often retrospective; they estimate a map of the average relative risk of disease over a study period. However, recently, epidemiologists have started to look at weekly or monthly reports of disease and assess them for any change in the distribution of relative risk. For example, in the United States of America, the Centre for Disease Control and Prevention now routinely collects information on over 50 notifiable diseases every week. In this paper we present a method for the detection of a sudden change in the geographical distribution of the disease in a prospective study. The method is based on an estimate of the directional derivative of the conditional probability of a case, given either a case or control has occurred. It is based on standard kernel approaches to nonparametric regression and it is readily applied in any standard statistical software package. Two simulated examples of sudden clustering around a fixed point are provided.

Assessing surveillance using sensitivity, specificity and timeliness

Monitoring ongoing processes of illness to detect sudden changes is an important aspect of practical epidemiology and medicine more generally. Most commonly, the monitoring has been restricted to a unidimensional stream of data over time. In such situations, analytic results from the industrial process monitoring have suggested optimal approaches to monitor the data streams. Data streams including spatial location as well as temporal sequence are becoming available. Monitoring methods that incorporate spatial data may prove superior to those that ignore it. However, analytically, optimal methods for spatial surveil-lance data may not exist. In the present article, we introduce and discuss evaluation metrics that can be used to compare the performance of statistical methods of surveillance. Our general approach is to generalize receiver operating characteristic (ROC) curves to incorporate the time of detection in addition to the usual test characteristics of sensitivity and specificity. In addition to weighting ordinary ROC curves by two measures of timeliness, we describe three three-dimensional generalizations of ROC curves that result in timeliness-ROC surfaces. Working in the context of surveillance of cases of disease to detect a sudden outbreak, we demonstrate these in an artificial example and in a previously described simulation context and show how the metrics differ. We also discuss the differences and under which circumstances one might prefer a given method.

Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting

OBJECTIVES

To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013.

METHODS

Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs).

RESULTS

During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved.

CONCLUSIONS

Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks.

[Spatial-temporal distribution feature of measles in Zhejiang province, 2013]

OBJECTIVE

To study the spatial-temporal dynamical features of measles in Zhejiang province.

METHODS

Data was from the China Disease Surveillance Information System and China Immunization Program Information Management System. Power-law method on spatial-temporal-multicomponent model was used to analyze the epidemic characteristics of measles in the districts of Zhejiang province.

RESULTS

The incidence of measles in Zhejiang province was 2.72/100 000 (1 494 cases) in 2013. Compared to the first order adjacent matrix, Power-law method showed a lower value of Akaike information criterion. The follow-up impact from the previous measles epidemic was strong to the Keqiao, Xiaoshan and Yuecheng districts with the autoregressive component as 1.39, 0.88 and 0.77, respectively. Local risk of measles seemed high in Keqiao, Qujiang and Xiaoshan districts with the endemic component as 4.06, 3.74 and 3.55, respectively. Impact of the epidemic to the nearby districts was large in Keqiao, Shangyu districts and Jiande city with epidemic components as 3.08, 2.54 and 2.21, respectively.

CONCLUSION

The spatial-temporal feature of measles in several districts of Zhejiang province appeared heterogeneous, suggesting the specific strategies should be taken to control the epidemics of measles.

Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka

The aim of the present study was to identify geographical areas and time periods of potential clusters of dengue cases based on ecological, socio-economic and demographic factors in northern Sri Lanka from January 2010 to December 2013. Remote sensing (RS) was used to develop an index comprising rainfall, humidity and temperature data. Remote sensing data gathered by the AVNIR-2 instrument onboard the ALOS satellite were used to detect urbanisation, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analysed RS data and databases were integrated into a geographical information system (GIS) enabling space-time clustering analysis. Our results indicate that increases in the number of combinations of ecological, socio-economic and demographic factors that are present or above the average contribute to significantly high rates of space-time dengue clusters. The spatio-temporal association that consolidates the two kinds of associations into one can ensure a more stable model for forecasting. An integrated spatiotemporal prediction model at a smaller level using ecological, socioeconomic and demographic factors could lead to substantial improvements in dengue control and prevention by allocating the right resources to the appropriate places at the right time.

Pulmonary tuberculosis space-time clustering and spatial variation in temporal trends in Portugal, 2000-2010: an updated analysis

Portugal, a medium- to low-level endemic country (21·6 cases/100 000 population in 2012), has one of the highest European Union tuberculosis (TB) incidences. Although incidence is declining progressively, the country's heterogeneity in both regional endemics and their evolution suggests the importance of a better understanding of subnational epidemiology to customize TB control efforts. We aimed to update knowledge on municipality-years pulmonary TB incidence clustering, identify areas with different time trends, and show the potential of combining complementary clustering methods in control of infectious diseases. We used national surveillance municipality-level data (mainland Portugal, 2000-2010). Space-time clustering and spatial variation in temporal trends methods were applied. Space-time critical clusters identified (P < 0·001) were still the Lisbon and Oporto regions. The global incidence declined at a 5·81% mean annual percentage change, with high space-time heterogeneity and distinct time trend clusters (P < 0·001). Municipalities with incidences declining more rapidly belonged to critical areas. In particular, the Oporto trend cluster had a consistent -8·98% mean annual percentage change. Large space-time heterogeneities were identified, with critical incidences in the greater Lisbon and Oporto regions, but declining more rapidly in these regions. Oporto showed a consistent, steeper decrease and could represent a good example of local control strategy. Combining results from these approaches gives promise for prospects for infectious disease control and the design of more effective, focused interventions.

Detection and investigation of temporal clusters of congenital anomaly in Europe: seven years of experience of the EUROCAT surveillance system

Detection and investigation of congenital anomaly clusters is one part of surveillance to detect new or changing teratogenic exposures in the population. The EUROCAT (European Surveillance of Congenital Anomalies) cluster monitoring system and results are described here. Monitoring was conducted annually from 2007 to 2013 for 18 registries covering an annual birth population up to 0.5 million births. For each registry and 72 anomaly subgroups, the scan "moving window" technique was used to detect clusters in time occurring within the last 2 years based on estimated date of conception. Registries conducted preliminary investigations using a standardised protocol to determine whether there was cause for concern, and expert review was used at key points. 165 clusters were detected, a rate of 3.4% of all 4823 cluster tests performed over 7 years, more than expected by chance. Preliminary investigations of 126 new clusters confirmed that 35% were an unusual aggregation of cases, while 56% were explained by data quality or diagnostic issues, and 9% were not investigated. For confirmed clusters, the registries' course of action was continuing monitoring. Three confirmed clusters continued to grow in size for a limited period in subsequent monitoring. This system is best suited to early detection of exposures which are sudden, widespread and/or highly teratogenic, and was reassuring in demonstrating an absence of a sustained exposure of this type. Such proactive monitoring can be run efficiently without overwhelming the surveillance system with false positives, and serves an additional purpose of data quality control.

A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data

Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system.

Spatial-temporal clustering of companion animal enteric syndrome: detection and investigation through the use of electronic medical records from participating private practices

There is interest in the potential of companion animal surveillance to provide data to improve pet health and to provide early warning of environmental hazards to people. We implemented a companion animal surveillance system in Calgary, Alberta and the surrounding communities. Informatics technologies automatically extracted electronic medical records from participating veterinary practices and identified cases of enteric syndrome in the warehoused records. The data were analysed using time-series analyses and a retrospective space-time permutation scan statistic. We identified a seasonal pattern of reports of occurrences of enteric syndromes in companion animals and four statistically significant clusters of enteric syndrome cases. The cases within each cluster were examined and information about the animals involved (species, age, sex), their vaccination history, possible exposure or risk behaviour history, information about disease severity, and the aetiological diagnosis was collected. We then assessed whether the cases within the cluster were unusual and if they represented an animal or public health threat. There was often insufficient information recorded in the medical record to characterize the clusters by aetiology or exposures. Space-time analysis of companion animal enteric syndrome cases found evidence of clustering. Collection of more epidemiologically relevant data would enhance the utility of practice-based companion animal surveillance.