Severe weather events and cryptosporidiosis in Aotearoa New Zealand: A case series of space-time clusters

Occurrence of cryptosporidiosis has been associated with weather conditions in many settings internationally. We explored statistical clusters of human cryptosporidiosis and their relationship with severe weather events in New Zealand (NZ). Notified cases of cryptosporidiosis from 1997 to 2015 were obtained from the national surveillance system. Retrospective space-time permutation was used to identify statistical clusters. Cluster data were compared to severe weather events in a national database. SaTScan analysis detected 38 statistically significant cryptosporidiosis clusters. Around a third (34.2%, 13/38) of these clusters showed temporal and spatial alignment with severe weather events. Of these, nearly half (46.2%, 6/13) occurred in the spring. Only five (38%, 5/13) of these clusters corresponded to a previously reported cryptosporidiosis outbreak. This study provides additional evidence that severe weather events may contribute to the development of some cryptosporidiosis clusters. Further research on this association is needed as rainfall intensity is projected to rise in NZ due to climate change. The findings also provide further arguments for upgrading the quality of drinking water sources to minimize contamination with pathogens from runoff from livestock agriculture.

Space-time clustering of COVID-19 cases in the United States veteran population

PURPOSE

To assess the distribution and clustering of coronavirus disease 2019 (COVID-19) testing and incidence over space and time, U.S. Department of Veteran's Affairs (VA) data were used to describe where and when veterans experienced highest proportions of test positivity.

METHODS

Data for 6,342,455 veterans who utilized VA services between January 1, 2018, and September 30, 2021, were assessed for COVID-19 testing and test positivity. Testing and positivity proportions by county were mapped and focused-cluster tests identified significant clustering around VA facilities. Spatial cluster analysis also identified where and when veterans experienced highest proportions of test positivity.

RESULTS

Within the veterans study population and our time window, 21.3% received at least one COVID-19 test, and 20.4% of those tested had at least one positive test. There was statistically significant clustering of testing around VA facilities, revealing regional variation in testing practices. Veterans experienced highest test positivity proportions between November 2020 and January 2021 in a cluster of states in the Midwest, compared to those who received testing outside of the identified cluster (RR: 3.45).

CONCLUSIONS

Findings reflect broad regional trends in COVID-19 positivity which can inform VA policy and resource allocation. Additional analysis is needed to understand patterns during Delta and Omicron variant periods.

Clusters of malaria cases at sub-district level in endemic area in Java Island, Indonesia

Malaria remains one of the essential public health problems in Indonesia. The year 2015 was originally set as the elimination target in Java Island, but there are still several regencies on Java reporting malaria cases. Spatial technology helps determine local variations in malaria transmission, control risk areas and assess the outcome of interventions. Information on distribution patterns of malaria at the sub-district level, presented as spatial, temporal, and spatiotemporal data, is vital in planning control interventions. Information on malaria transmission at the sub-district level in three regencies in Java (Banyumas, Kebumen, and Purbalingga) was collected from the Agency for Regional Development (Bappeda), the Population and Civil Registration Agency (Disdukcapil) and Statistics Indonesia (BPS). Global spatial autocorrelation and space-time clustering was investigated together with purely spatial and purely temporal analyses using geographical information systems (GIS) by ArcGis 10.2 and SaTScan 8.0 to detect areas at high risk of malaria. Our results show that malaria was spatially clustered in the study area in central Java, in particular in the Banyumas and Purbalingga regencies. The temporal analysis revealed that malaria clusters predominantly appeared in the period January-April. The results of the spatiotemporal analysis showed that there was one most likely malaria cluster and three secondary clusters in southern central Java. The most likely cluster was located in Purbalingga Regency covering one sub-district and remaining from the beginning of 2016 to the end of 2018. The approach used can assist the setting of resource priorities to control and eliminate malaria.

Global, regional and national incidence, mortality and disability-adjusted life-years of skin cancers and trend analysis from 1990 to 2019: An analysis of the Global Burden of Disease Study 2019

BACKGROUND

Information about global and local epidemiology and trends of skin cancers is limited, which increases the difficulty of cutaneous cancer control.

METHODS

To estimate the global spatial patterns and temporal trends of skin cancer burden. Based on the GBD 2019, we collected and analyzed numbers and age-standardized rates (ASR) of skin cancer incidence, disability-adjusted life years (DALYs) and mortality (ASIR, ASDR, and ASMR) in 204 countries from 1990 through 2019 were estimated by age, sex, subtype (malignant skin melanoma [MSM], squamous-cell carcinoma [SCC], and basal-cell carcinoma [BCC]), Socio-demographic Index (SDI), region, and country. Temporal trends in ASR were also analyzed using estimated annual percentage change.

RESULTS

Globally, in 2019, there were 4.0 million BCC, 2.4 million SCC, and 0.3 million MSM. There were approximately 62.8 thousand deaths and 1.7 million DALYs due to MSM, and 56.1 thousand deaths and 1.2 million DALYs were attributed to SCC, respectively. The men had higher ASR of skin cancer burden than women. The age-specific rates of global skin cancer burden were higher in the older adults, increasing trends observed from 55 years old. Geographically, the numbers and ASR of skin cancers varied greatly across countries, with the largest burden of ASIR in high SDI regions. However, an unexpected increase was observed in some regions from 1990 to 2019, such as East Asia, and Sub-Saharan Africa. Although there was a slight decrease of the ASMR and ASDR, the global ASIR of MSM dramatically increased, 1990-2019. Also, there was a remarkable increase in ASR of BCC and SCC burden.

CONCLUSIONS

Skin cancer remains a major global public health threat. Reducing morbidity and mortality strategies such as primary and secondary prevention should be reconsidered, especially in the most prevalent and unexpected increased regions, especially for those areas with the greatest proportions of their population over age 55.

Spatial clustering and temporal trend analysis of international migrants diagnosed with tuberculosis in Brazil

Background

Tuberculosis (TB) in migrants is of concern to health authorities worldwide and is even more critical in Brazil, considering the country´s size and long land borders. The aim of the study was to identify critical areas in Brazil for migrants diagnosed with TB and to describe the temporal trend in this phenomenon in recent years.

Methods

This is an ecological study that used spatial analysis and time series analysis. As the study population, all cases of migrants diagnosed with TB from 2014 to 2019 were included, and Brazilian municipalities were considered as the unit of ecological analysis. The Getis-Ord Gi* technique was applied to identify critical areas, and based on the identified clusters, seasonal-trend decomposition based on loess (STL) and Prais-Winsten autoregression were used, respectively, to trace and classify temporal trend in the analyzed series. In addition, several municipal socioeconomic indicators were selected to verify the association between the identified clusters and social vulnerability.

Results

2,471 TB cases were reported in migrants. Gi* analysis showed that areas with spatial association with TB in immigrants coincide with critical areas for TB in the general population (coast of the Southeast and North regions). Four TB clusters were identified in immigrants in the states of Amazonas, Roraima, São Paulo, and Rio de Janeiro, with an upward trend in most of these clusters. The temporal trend in TB in immigrants was classified as increasing in Brazil (+ 60.66% per year [95% CI: 27.21–91.85]) and in the clusters in the states of Amazonas, Roraima, and Rio de Janeiro (+1.01, +2.15, and + 2.90% per year, respectively). The cluster in the state of São Paulo was the only one classified as stationary. The descriptive data on the municipalities belonging to the clusters showed evidence of the association between TB incidence and conditions of social vulnerability.

Conclusions

The study revealed the critical situation of TB among migrants in the country. Based on the findings, health authorities might focus on actions in regions identified, stablishing an intensive monitoring and following up, ensuring that these cases concluded their treatment and avoiding that they could spread the disease to the other regions or scenarios. The population of migrants are very dynamic, therefore strategies for following up them across Brazil are really urgent to manage the tuberculosis among international migrants in an efficient and proper way.

Congenital Hypothyroidism: Space-Time Clustering of Thyroid Dysgenesis Indicates a Role for Environmental Factors in Disease Etiology

Background: The etiology of most cases of congenital hypothyroidism (CHT) due to thyroid dysgenesis (DG) is unknown. If transient environmental factors can impact on thyroid gland development, then clustering of cases in time and/or space may occur, and this would be more likely in thyroid DG than dyshormonogenesis (DHG). Methods: The newborn screening program for CHT in Scotland is linked to a central database that includes case details such as postcode. The etiology of CHT is investigated in many cases of CHT using scintigraphy and/or ultrasonography. We looked for evidence of a change in CHT incidence with year of birth and according to season of the year. We then undertook space-time clustering analysis (using a method based on K-functions, with nearest neighbor thresholds) of CHT in Scotland between 1979 and 2015. We also looked for evidence of overall changes associated with sex and area-based birth density. Results: Of 531 cases with CHT during the study period, 290 cases had been categorized as DG (n = 229) or DHG (n = 61) following more detailed investigation. The incidence of CHT increased with year of birth and was in part linked to changing methodology, but there was no seasonality. There was no evidence of overall space-time clustering (p = 0.06), but there was evidence of clustering in babies with DG (p = 0.007). This picture appeared to be most closely linked to underlying thyroid gland hypoplasia rather than thyroid gland agenesis or ectopia. There was significant space-time clustering for both males and females, but clustering was restricted to lesser birth density areas. There was also evidence of clustering for unknown cases (p < 0.001). Clustering of these cases was restricted to females but was present for cases from both greater and lesser birth density areas. There was no evidence of clustering in cases of DHG. Conclusions: These data suggest that an unidentified environmental factor or factors may be involved in the etiology of thyroid DG in Scotland. The variation in CHT incidence observed internationally may reflect environmental as well as genetic factors.

Hierarchical networks of food exchange in the black garden ant Lasius niger

In most eusocial insects, the division of labor results in relatively few individuals foraging for the entire colony. Thus, the survival of the colony depends on its efficiency in meeting the nutritional needs of all its members. Here, we characterize the network topology of a eusocial insect to understand the role and centrality of each caste in this network during the process of food dissemination. We constructed trophallaxis networks from 34 food-exchange experiments in black garden ants (Lasius niger). We tested the influence of brood and colony size on (i) global indices at the network level (i.e., efficiency, resilience, centralization, and modularity) and (ii) individual values (i.e., degree, strength, betweenness, and the clustering coefficient). Network resilience, the ratio between global efficiency and centralization, was stable with colony size but increased in the presence of broods, presumably in response to the nutritional needs of larvae. Individual metrics highlighted the major role of foragers in food dissemination. In addition, a hierarchical clustering analysis suggested that some domestics acted as intermediaries between foragers and other domestics. Networks appeared to be hierarchical rather than random or centralized exclusively around foragers. Finally, our results suggested that networks emerging from social insect interactions can improve group performance and thus colony fitness.

Premature mortality of gastrointestinal cancer in Iran: trends and projections 2001-2030

BACKGROUND

The present study was conducted to determine the trend and projection of premature mortality from gastrointestinal cancers (GI cancers) at national and subnational levels in Iran.

METHODS

Employing the data obtained from Iranian Death Registry System (DRS) and population data from census, the mortality rates of GI cancers was calculated among 30-70 age groups. The trends of esophageal, colon and rectum, gallbladder, pancreases, stomach, and liver cancer premature mortalities were estimated and projected at the national and subnational levels from 2001 to 2030. Then, Spatio-temporal model was used to project spatial and temporal correlations.

RESULTS

The overall mortality rate of GI cancers was higher in males than in females, indicating 6.1, 3.9 and 3.9% per 100,000 individuals among males in 2001, 2015 and 2030 respectively and 3.8, 3.1 and 3.7 per 100,000 individuals among females in the same time-frame. The overall mortality rate of GI cancers in males was decreasing until 2015 and will remain stationary into 2030; however, the rate will be increasing among females in both time-frames. Also, there was a considerable variation in the mortality trends of different cancers. Pancreatic, gallbladder, and liver cancers were shown to have an increasing trend while a drop was observed in the mortality rates of stomach, colon and rectum, and esophageal cancers.

CONCLUSION

Variation of GI cancers patterns and trends around the country indicated that a more comprehensive control plan is needed to include the predicted variations.

Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.

Analysis of the Breast Cancer Mortality Rate in Portugal Over a Decade: Spatiotemporal Clustering Analysis

INTRODUCTION

Breast cancer is the first cause of cancer-related death in Portuguese women. This study aimed to characterize female breast cancer mortality in Portugal in the period between 2002 and 2013, with a special focus on spatiotemporal patterns.

MATERIAL AND METHODS

The breast cancer mortality rate was studied using descriptive analysis (unadjusted and age-adjusted), and spatiotemporal clustering analyses.

RESULTS

In 2002 - 2013 the breast cancer mortality rate was 28.47/100 000 inhabitants and the age-adjusted mortality rate was 19.46/100 000 inhabitants. In this period the Lisbon region (urban), Alentejo and Algarve (rural) presented higher breast cancer mortality rate, but Madeira (urban), Lisbon and Algarve had higher age-adjusted mortality rate. In the spatiotemporal analysis, the overall mortality rate showed an increasing trend of 1.218%/year, without spatial variations. Also, different patterns were detected in the < 50, 50 - 64 and ≥ 65 age-groups (+ 0.725%, - 1.781% and + 0.896%, respectively). One temporal (2004 - 2006) and one spatiotemporal cluster (North coast) presented significantly lower mortality rate than expected for the period and/or area (26.2 and 16.1/100 000 inhabitants, respectively). Conversely, two spatiotemporal clusters, located in the city of Lisbon (2002 - 2007) and in the Centre region (2008 - 2013), presented significantly higher breast cancer mortality rate than expected (48.6 and 34.9/100 000 inhabitants, relative risk: 1.74 and 1.26, respectively).

DISCUSSION

The annual female crude and adjusted breast cancer mortality rate matched previous publications. However the annual increase detected in the unadjusted rate clashes with the published literature. Overall, the presence of spatiotemporal clusters supports the uneven distribution of the breast cancer mortality reported previously in the different Portuguese regions.

CONCLUSION

This study identified areas and trends of the female breast cancer mortality rate, showing high spatiotemporal variations that must support further detailed studies/interventions.

Spatial-Temporal Cluster Analysis of Childhood Cancer in California

BACKGROUND

The observance of nonrandom space-time groupings of childhood cancer has been a concern of health professionals and the general public for decades. Many childhood cancers are suspected to have initiated in utero; therefore, we examined the spatial-temporal randomness of the birthplace of children who later developed cancer.

METHODS

We performed a space-time cluster analysis using birth addresses of 5,896 cases and 23,369 population-based, age-, sex-, and race/ethnicity-matched controls in California from 1997 to 2007, evaluating 20 types of childhood cancer and three a priori designated subgroups of childhood acute lymphoblastic leukemia (ALL). We analyzed data using a newly designed semiparametric analysis program, ClustR, and a common algorithm, SaTScan.

RESULTS

We observed evidence for nonrandom space-time clustering for ALL diagnosed at 2-6 years of age in the South San Francisco Bay Area (ClustR P = 0.04, SaTScan P = 0.07), and malignant gonadal germ cell tumors in a region of Los Angeles (ClustR P = 0.03, SaTScan P = 0.06). ClustR did not identify evidence of clustering for other childhood cancers, although SaTScan suggested some clustering for Hodgkin lymphoma (P = 0.09), astrocytoma (P = 0.06), and retinoblastoma (P = 0.06).

CONCLUSIONS

Our study provides evidence that childhood ALL diagnosed at 2-6 years and malignant gonadal germ cell tumors sporadically occurs in nonrandom space-time clusters. Further research is warranted to identify epidemiologic features that may inform the underlying etiology.

Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015-2019

The number of tuberculosis (TB) cases in Pakistan ranks fifth in the world. The National TB Control Program (NTP) has recently reported more than 462,920 TB patients in Khyber Pakhtunkhwa province, Pakistan from 2002 to 2017. This study aims to identify spatial and space-time clusters of TB cases in Khyber Pakhtunkhwa province Pakistan during 2015-2019 to design effective interventions. The spatial and space-time cluster analyses were conducted at the district-level based on the reported TB cases from January 2015 to April 2019 using space-time scan statistics (SaTScan). The most likely spatial and space-time clusters were detected in the northern rural part of the province. Additionally, two districts in the west were detected as the secondary space-time clusters. The most likely space-time cluster shows a tendency of spread toward the neighboring districts in the central part, and the most likely spatial cluster shows a tendency of spread toward the neighboring districts in the south. Most of the space-time clusters were detected at the start of the study period 2015-2016. The potential TB clusters in the remote rural part might be associated to the dry-cool climate and lack of access to the healthcare centers in the remote areas.

Space-time clustering of childhood leukemia in Colombia: a nationwide study

BACKGROUND

Leukemia is the most common cancer in childhood. The estimated incidence rate of childhood leukemia in Colombia is one of the highest in America and little is known about its spatial distribution.

PURPOSE

To explore the presence of space-time clustering of childhood leukemia in Colombia.

METHODS

We included children less than 15 years of age with confirmed diagnosis of acute leukemia reported to the national surveillance system for cancer between 2009 and 2017. Kulldorff's spatio-temporal scan statistics were used with municipality and year of diagnosis as units for spatial and temporal analysis.

RESULTS

There were 3846 cases of childhood leukemia between 2009 and 2017 with a specific mean incidence rate of 33 cases per million person-years in children aged 0-14 years. We identified five spatial clusters of childhood leukemia in different regions of the country and specific time clustering during the study period.

CONCLUSION

Childhood leukemia seems to cluster in space and time in some regions of Colombia suggesting a common etiologic factor or conditions to be studied.

Analysis of inpatients' characteristics and spatio-temporal distribution of liver cancer incidence in Wuwei, Gansu Province, from 1995 to 2016: A long-term span retrospective study

This long-term span retrospective study aimed to determine the incidence and spatial-temporal trends of liver cancer in Wuwei city from 1995 to 2016 to provide scientific knowledge on the prevention and treatment of liver cancer.Data from the medical records of liver cancer patients treated in 12 sentinel hospitals in Wuwei city were extracted. SAS and Joinpoint software were used for data analysis, ArcGIS 10.2 software was used to make geographical distribution map, and SaTScan 9.4 software was used for clustering area detection.Of 2271 patients with liver cancer (average age, 58.73 years), 17.7% were 60 to 64 years of age. Majority were males (1680, 74%), with a sex ratio of 2.84. Joinpoint regression analysis from 1995 to 2016 revealed that the standardized liver cancer rate increased [annual average rate of change (AAPC) = 12.80% (95% CI: 9.5%-16.7%)], with a joinpoint in 2009. From 1995 to 2009, the change in overall APC was statistically significant [APC = 16.7 (95% CI: 12.3%-21.3%)]. The average incidence was the highest in Hongshagang Town. After 2005, the incidence gradually increased in each township. Five clusters of liver cancer were noted in Wuwei, including 37 townships.Males had a higher standardized liver cancer rate. After 2009, increase in the incidence of liver cancer was less rapid. The incidence of liver cancer in townships of Wuwei city was non-random, with certain spatial aggregations, covering 37 townships.

A multivariate-based variable selection framework for clustering traffic conflicts in a brazilian freeway

More than one million people die or suffer non-fatal injuries annually due to road accidents around the world. Understanding the causes that give rise to different types of conflict events, as well as their characteristics, can help researchers and traffic authorities to draw up strategies aimed at mitigating collision risks. This paper proposes a framework for grouping traffic conflicts relying on similar profiles and factors that contribute to conflict occurrence using self-organizing maps (SOM). In order to improve the quality of the formed groups, we developed a novel variable importance index relying on the outputs of the nonlinear principal component analysis (NLPCA) that intends to identify the most informative variables for grouping collision events. Such index guides a backward variable selection procedure in which less relevant variables are removed one-by-one; after each removal, the clustering quality is assessed via the Davies-Bouldin (DB) index. The proposed framework was applied to a real-time dataset collected from a Brazilian highway aimed at allocating traffic conflicts into groups presenting similar profiles. The selected variables suggest that lower average speeds, which are typically verified during congestion events, contribute to conflict occurrence. Higher variability on speed (denoted by high standard deviation, and speed's coefficient of variation levels on that variable), which are also perceived in the assessed freeway near to congestion periods, also contribute to conflicts.

Opioid-related diagnoses and HIV, HCV and mental disorders: using Pennsylvania hospitalisation data to assess community-level relationships over space and time

BACKGROUND

We assessed the community-level spatiotemporal connexions between hospitalisations for common opioid comorbidities (HIV, hepatitis C (HCV) and mental disorders) and opioid-related hospitalisations in the current and previous year.

METHODS

We used Bayesian hierarchical spatiotemporal Poisson regression with conditionally autoregressive spatial effects to assess counts of HCV-related, HIV-related and mental disorder-related hospitalisations at the ZIP code level from 2004 to 2014 in Pennsylvania. Models included rates of current-year and previous-year opioid-related hospitalisations as well as covariates measuring demographic and environmental characteristics.

RESULTS

After adjusting for measures of demographic and environmental characteristics, current-year and previous-year opioid-related hospitalisations were associated with higher risk of HCV, HIV and mental disorders. The relative risks and 95% credible intervals for previous-year opioid-related hospitalisations were 1.092 (1.078 to 1.106) for HCV, 1.098 (1.068 to 1.126) for HIV and 1.020 (1.013 to 1.027) for mental disorders.

CONCLUSION

Previous-year opioid-related hospitalisations are connected to common comorbid conditions such as HCV, HIV and mental disorders, illustrating some of the broader health-related impacts of the opioid epidemic. Public health interventions focused on the opioid epidemic must consider individual community needs and comorbid diagnoses.

Estimating under-five mortality in space and time in a developing world context

Accurate estimates of the under-five mortality rate in a developing world context are a key barometer of the health of a nation. This paper describes a new model to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is wishing to estimate under-five mortality rate across regions and years and to investigate the association between the under-five mortality rate and spatially varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980-2014 using data from the Demographic and Health Surveys, which use stratified cluster sampling. We use a binomial likelihood with fixed effects for the urban/rural strata and random effects for the clustering to account for the complex survey design. Smoothing is carried out using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in Kenya in the under-five mortality rate in the period 1980-2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. In exploratory work, we examine whether a variety of spatial covariate surfaces can explain the variability in under-five mortality rate. Temperature, precipitation, a measure of malaria infection prevalence, and a measure of nearness to cities were candidates for inclusion in the covariate model, but the interplay between space, time, and covariates is complex.

Spatiotemporal Variation and Hot Spot Detection of Visceral Leishmaniasis Disease in Kashi Prefecture, China

Visceral leishmaniasis (VL) remains a serious public health problem in China. To explore the temporal, spatial, and spatiotemporal characteristics of visceral leishmaniasis (VL), the spatial and spatiotemporal clustering distribution and their relationships with the surrounding geographic environmental factors were analyzed. In this study, the average nearest-neighbor distance (ANN), Ripley's K-function and Moran's I statistics were used to evaluate spatial autocorrelation in the VL distribution of the existing case patterns. Getis⁻Ord Gi* was used to identify the hot-spot and cold-spot areas based on Geographic Information System (GIS), and spatiotemporal retrospective permutation scan statistics was used to detect the spatiotemporal clusters. The results indicated that VL continues to be a serious public health problem in Kashi Prefecture, China, particularly in the north-central region of Jiashi County, which is a relatively high-risk area in which hot spots are distributed. Autumn and winter months were the outbreak season for VL cases. The detection of spatial and spatiotemporal patterns can provide epidemiologists and local governments with significant information for prevention measures and control strategies.

Multivariate random parameter Tobit modeling of crashes involving aging drivers, passengers, bicyclists, and pedestrians: Spatiotemporal variations

The increase in 65 years and older population in the United States compels the investigation of the crashes involving all aging (65+) roadway users (drivers, passengers, bicyclists, and pedestrians) in order to ensure their safety. As such, the objective of this research is to provide a spatiotemporal comparative investigation of the crashes involving these aging roadway users in Florida via concurrently using the same set of predictors in order to obtain comparable findings among them. First, a new metric, namely Crash Rate Difference (CRD) approach is developed, which enables one to capture potential spatial and temporal (e.g., weekend and weekday) variations in crash rates of aging user-involved crashes. Second, a multivariate random parameter Tobit model is utilized to determine the factors that drive both the crash occurrence probability and the crash rate of 65+ roadway users, accounting for the unobserved heterogeneity. Findings show that there are statistically significant heterogeneous effects of predictors on the crash rates of different roadway users, which evidences the unobserved heterogeneity across observations. Results also indicate that the presence of facilities such as hospitals, religious facilities, or supermarkets is very influential on crash rates of 65+ roadway users, advocating that roadways around these facilities should be particularly scrutinized by road safety stakeholders. Interestingly, the effect of these facilities on crashes also differs significantly between weekdays and weekends. Moreover, the roadway segments with high crash rates vary temporally depending on whether it is a weekday or a weekend. These findings regarding the spatiotemporal variations clearly indicate the need to develop and design better traffic safety measures and plans addressing these specific roadway segments, which can be tailored to alleviate traffic safety problems for 65+ roadway users.

Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016

Vector-borne diseases (VBDs) infect over one billion people and are responsible for over one million deaths each year, globally. Chikungunya (CHIK) and Dengue Fever (DENF) are emerging VBDs due to overpopulation, increases in urbanization, climate change, and other factors. Colombia has recently experienced severe outbreaks of CHIK AND DENF. Both viruses are transmitted by the Aedes mosquitoes and are preventable with a variety of surveillance and vector control measures (e.g. insecticides, reduction of open containers, etc.). Spatiotemporal statistics can facilitate the surveillance of VBD outbreaks by informing public health officials where to allocate resources to mitigate future outbreaks. We utilize the univariate Kulldorff space-time scan statistic (STSS) to identify and compare statistically significant space-time clusters of CHIK and DENF in Colombia during the outbreaks of 2015 and 2016. We also utilize the multivariate STSS to examine co-occurrences (simultaneous excess incidences) of DENF and CHIK, which is critical to identify regions that may have experienced the greatest burden of VBDs. The relative risk of CHIK and DENF for each Colombian municipality belonging to a univariate and multivariate cluster is reported to facilitate targeted interventions. Finally, we visualize the results in a three-dimensional environment to examine the size and duration of the clusters. Our approach is the first of its kind to examine multiple VBDs in Colombia simultaneously, while the 3D visualizations are a novel way of illustrating the dynamics of space-time clusters of disease.

Space-time clustering and associated risk factors of pulmonary tuberculosis in southwest China

BACKGROUND

Pulmonary tuberculosis (PTB,both smear positive and smear negative) is an airborne infectious disease of major public health concern in China and other parts of the world where PTB endemicity is reported. This study aims at identifying PTB spatio-temporal clusters and associated risk factors in Zhaotong prefecture-level city, located in southwest China, where the PTB notification rate was higher than the average rate in the entire country.

METHODS

Space-time scan statistics were carried out using PTB registered data in the nationwide TB online registration system from 2011 to 2015, to identify spatial clusters. PTB patients diagnosed between October 2015 and February 2016 were selected and a structured questionnaire was administered to collect a set of variables that includes socio-economic status, behavioural characteristics, local environmental and biological characteristics. Based on the discovery of detailed town-level spatio-temporal PTB clusters, we divided selected subjects into two groups including the cases that resides within and outside identified clusters. Then, logistic regression analysis was applied comparing the results of variables between the two groups.

RESULTS

A total of 1508 subjects consented and participated in the survey. Clusters for PTB cases were identified in 38 towns distributed over south-western Zhaotong. Logistic regression analysis showed that history of chronic bronchitis (OR = 3.683, 95% CI: 2.180-6.223), living in an urban area (OR = 5.876, 95% CI: 2.381-14.502) and using coal as the main fuel (OR = 9.356, 95% CI: 5.620-15.576) were independently associated with clustering. While, not smoking (OR = 0.340, 95% CI: 0.137-0.843) is the protection factor of spatial clustering.

CONCLUSIONS

We found PTB specially clustered in south-western Zhaotong. The strong associated factors influencing the PTB spatial cluster including: the history of chronic bronchitis, living in the urban area, smoking and the use of coal as the main fuel for cooking and heating. Therefore, efforts should be made to curtail these associated factors.

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.

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.

Typhoid fever acquired in the United States, 1999-2010: epidemiology, microbiology, and use of a space-time scan statistic for outbreak detection

Although rare, typhoid fever cases acquired in the United States continue to be reported. Detection and investigation of outbreaks in these domestically acquired cases offer opportunities to identify chronic carriers. We searched surveillance and laboratory databases for domestically acquired typhoid fever cases, used a space-time scan statistic to identify clusters, and classified clusters as outbreaks or non-outbreaks. From 1999 to 2010, domestically acquired cases accounted for 18% of 3373 reported typhoid fever cases; their isolates were less often multidrug-resistant (2% vs. 15%) compared to isolates from travel-associated cases. We identified 28 outbreaks and two possible outbreaks within 45 space-time clusters of ⩾2 domestically acquired cases, including three outbreaks involving ⩾2 molecular subtypes. The approach detected seven of the ten outbreaks published in the literature or reported to CDC. Although this approach did not definitively identify any previously unrecognized outbreaks, it showed the potential to detect outbreaks of typhoid fever that may escape detection by routine analysis of surveillance data. Sixteen outbreaks had been linked to a carrier. Every case of typhoid fever acquired in a non-endemic country warrants thorough investigation. Space-time scan statistics, together with shoe-leather epidemiology and molecular subtyping, may improve outbreak detection.

Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark

Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population.

Spatiotemporal trends of cases of pandemic influenza A(H1N1)pdm09 in Argentina, 2009-2012

The aim of this paper was to analyze the spatiotemporal variations of cases of influenza A(H1N1)pdm09 in Argentina. A space-time permutation scan statistic was performed to test the non-randomness in the interaction between space and time in reported influenza A(H1N1)pdm09 cases. In 2009, two clusters were recorded in the east of Buenos Aires Province (May and June) and in the central and northern part of Argentina (July and August). Between 2011 and 2012, clusters near areas bordering other countries were registered. Within the clusters, in 2009, the high notification rates were first observed in the school-age population and then extended to the older population (15-59 years). From 2011 onwards, higher rates of reported cases of influenza A(H1N1)pdm09 occurred in children under five years in center of the country. Two stages of transmission of influenza A(H1N1)pdm09 can be characterized. The first stage had high rates of notification and a possible interaction with individuals from other countries in the major cities of Argentina (pattern of hierarchy), and the second stage had an increased interaction in some border areas without a clear pattern of hierarchy. These results suggest the need for greater coordination in the Southern Cone countries, in order to implement joint prevention and vaccination policies.

Epidemiological Characteristics and Spatial-Temporal Clusters of Hand, Foot, and Mouth Disease in Zhejiang Province, China, 2008-2012

Hand, foot and mouth disease (HFMD) is one of the major public health concerns in China. Being the province with high incidence rates of HFMD, the epidemiological features and the spatial-temporal patterns of Zhejiang Province were still unknown. The objective of this study was to investigate the epidemiological characteristics and the high-incidence clusters, as well as explore some potential risk factors. The surveillance data of HFMD during 2008-2012 were collected from the communicable disease surveillance network system of Zhejiang Provincial Center for Disease Control and Prevention. The distributions of age, gender, occupation, season, region, pathogen's serotype and disease severity were analyzed to describe the epidemiological features of HFMD in Zhejiang Province. Seroprevalence survey for human enterovirus 71 (EV71) in 549 healthy children of Zhejiang Province was also performed, as well as 27 seroprevalence publications between 1997 and 2015 were summarized. The spatial-temporal methods were performed to explore the clusters at county level. Furthermore, pathogens' serotypes such as EV71 and coxsackievirus A16 (Cox A16) and meteorological factors were analyzed to explore the potential factors associated with the clusters. A total of 454,339 HFMD cases were reported in Zhejiang Province during 2008-2012, including 1688 (0.37%) severe cases. The annual average incidence rate was 172.98 per 100,000 (ranged from 72.61 to 270.04). The male-to-female ratio for mild cases was around 1.64:1, and up to 1.87:1 for severe cases. Of the total cases, children aged under three years old and under five years old accounted for almost 60% and 90%, respectively. Among all enteroviruses, the predominant serotype was EV71 (49.70%), followed by Cox A16 (26.05%) and other enteroviruses (24.24%) for mild cases. In severe cases, EV71 (82.85%) was the major causative agent. EV71 seroprevalence survey in healthy children confirmed that occult infection was common in children. Furthermore, literature summary for 26 seroprevalence studies during 1997-2015 confirmed that 0-5 years group showed lowest level of EV71 seroprevalence (29.1% on average) compared to the elder children (6-10 years group: 54.6%; 11-20 years group: 61.8%). Global positive spatial autocorrelation patterns (Moran's Is>0.25, P<0.05) were discovered not only for mild cases but also for severe cases, and local positive spatial autocorrelation patterns were revealed for counties from the eastern coastal and southern regions. The retrospective space-time cluster analysis also confirmed these patterns. Risk factors analyses implied that more EV71 and less sunshine were associated with the clusters of HFMD in Zhejiang Province. Our study confirmed that Zhejiang Province was one of the highly epidemic provinces in China and that the epidemiological characteristics of HFMD were similar to other provinces. Occult infection in elder children and adults was one of the important reasons why most HFMD cases were children aged under-five. Combining the results of spatial autocorrelation analysis and the space-time cluster analysis, the major spatial-temporal clusters were from the eastern coastal and southern regions. The distribution of pathogens' serotypes and the level of sunshine could be risk factors for, and serve as an early warning of, the outbreak of HFMD in Zhejiang Province.

Geographical spread of influenza incidence in Spain during the 2009 A(H1N1) pandemic wave and the two succeeding influenza seasons

The aim of this study was to monitor the spatio-temporal spread of influenza incidence in Spain during the 2009 pandemic and the following two influenza seasons 2010-2011 and 2011-2012 using a Bayesian Poisson mixed regression model; and implement this model of geographical analysis in the Spanish Influenza Surveillance System to obtain maps of influenza incidence for every week. In the pandemic wave the maps showed influenza activity spreading from west to east. The 2010-2011 influenza epidemic wave plotted a north-west/south-east pattern of spread. During the 2011-2012 season the spread of influenza was geographically heterogeneous. The most important source of variability in the model is the temporal term. The model of spatio-temporal spread of influenza incidence is a supplementary tool of influenza surveillance in Spain.