A scan statistic for continuous data based on the normal probability model

A Hypothesis Test for Detecting Spatial Patterns in Categorical Areal Data

The vast growth of spatial datasets in recent decades has fueled the development of many statistical methods for detecting spatial patterns. Two of the most commonly studied spatial patterns are clustering, loosely defined as datapoints with similar attributes existing close together, and dispersion, loosely defined as the semi-regular placement of datapoints with similar attributes. In this work, we develop a hypothesis test to detect spatial clustering or dispersion at specific distances in categorical areal data. Such data consists of a set of spatial regions whose boundaries are fixed and known (e.g., counties) associated with a categorical random variable (e.g. whether the county is rural, micropolitan, or metropolitan). We propose a method to extend the positive area proportion function (developed for detecting spatial clustering in binary areal data) to the categorical case. This proposal, referred to as the categorical positive areal proportion function test, can detect various spatial patterns, including homogeneous clusters, heterogeneous clusters, and dispersion. Our approach is the first method capable of distinguishing between different types of clustering in categorical areal data. After validating our method using an extensive simulation study, we use the categorical positive area proportion function test to detect spatial patterns in Boulder County, Colorado USA biological, agricultural, built and open conservation easements.

An exploratory study on spatiotemporal clustering of suicide in Korean adolescents

BACKGROUND

Adolescent suicides are more likely to form clusters than those of other age groups. However, the definition of a cluster in the space-time dimension has not been established, neither are the factors contributing to it well known. Therefore, this study aimed to identify space-time clusters in adolescent suicides in Korea and to examine the differences between clustered and non-clustered cases using novel statistical methods.

METHODS

From 2016 to 2020, the dates and locations, including specific addresses from which the latitude and longitude of all student suicides (aged 9-18 years) in Korea were obtained through student suicide reports. Sociodemographic characteristics of the adolescents who died by suicide were collected, and the individual characteristics of each student who died by suicide were reported by teachers using the Strengths and Difficulties Questionnaire (SDQ). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis was used to assess the clustering of suicides.

RESULTS

We identified 23 clusters through the data analysis of 652 adolescent suicides using DBSCAN. By comparing the size of each cluster, we identified 63 (9.7%) spatiotemporally clustered suicides among adolescents, and the temporal range of these clusters was 7-59 days. The suicide cluster group had a lower economic status than the non-clustered group. There were no significant differences in other characteristics between the two groups.

CONCLUSION

This study has defined the space-time cluster of suicides using a novel statistical method. Our findings suggest that when an adolescent suicide occurs, close monitoring and intervention for approximately 2 months are needed to prevent subsequent suicides. Future research using DBSCAN needs to involve a larger sample of adolescents from various countries to further corroborate these findings.

Identification of Spatial Clusters of Undervaccination Patterns Among Children Aged <24 Months Using Immunization Information System Data, Montana, 2015-2019

OBJECTIVE

Spatial clustering of undervaccination leads to increased risk of vaccine-preventable diseases. We identified spatial clustering of undervaccination patterns among children aged <24 months in Montana.

METHODS

We used Montana's immunization information system data to analyze deidentified vaccination records of children aged <24 months born from January 2015 through November 2017. We measured 3 outcomes that were not mutually exclusive: not completing the combined 7-vaccine series by age 24 months, having an undervaccination pattern indicative of parental hesitancy, and having an undervaccination pattern indicative of structural barriers to timely vaccination. Using geomasked residential addresses, we conducted separate Bernoulli spatial scans with a randomization P < .01 to identify spatial clusters consisting of ≥100 children for each outcome and calculated the relative risk of having the undervaccination pattern inside versus outside the cluster.

RESULTS

Of 31 201 children aged <24 months included in our study, 11 712 (37.5%) had not completed the combined 7-vaccine series by age 24 months, and we identified 5 spatial clusters of this outcome across Montana. We identified 4 clusters of undervaccination patterns indicative of parental vaccine hesitancy, all in western Montana. The cluster with the largest relative risk (2.3) had a radius of 23.7 kilometers (n = 762 children, P < .001). We also identified 4 clusters of undervaccination patterns indicative of structural barriers, with 3 of the largest clusters in eastern Montana.

CONCLUSION

In Montana, different strategies to increase routine and timely childhood vaccination are needed in distinct areas of this large and predominantly rural state. Immunization information system data can pinpoint areas where interventions to increase vaccination uptake are needed.

A Bayesian Spatial Scan Statistic for Multinomial Data

Spatial scan statistics are commonly used to detect clustering. We present a Bayesian spatial scan statistic for multinomial data. After validating our method with a simulation study, we use it to detect clusters of SARS-CoV-2 infection/immunity in South Carolina.

Assessing the short-term effects of PM2.5 and O3 on cardiovascular mortality using high-resolution exposure: a time-stratified case cross-over study in Southwestern China

Air pollution is a major risk factor of cardiovascular disease (CVD). To date, limited studies have estimated the effects of ambient air pollution on CVD mortality using high-resolution exposure assessment, which might fail to capture the spatial variation in exposure and introduce bias in results. Besides, the three-year action plan (TYAP, 2018-2020) was released; thus, the constitution and health effect of air pollutants may have changed. In this study, we estimated the short-term effect exposed to particulate matters with parameter less than 2.5 µm (PM2.5) and ozone (O3) with 0.05° × 0.05° resolution on CVD mortality and measured the influence of TYAP in the associations. We used random forest models with spatial weight matrices to attain high-resolution pollutant concentrations and conditional Poisson regression to assess the relationship between air pollution and cardiovascular mortality. With an increase of 10 µg/m3 in PM2.5 and O3 during 2018-2021 in the Sichuan Basin (SCB), CVD mortality increased 1.0134 (95% CI 1.0102, 1.0166) and 1.0083 (95% CI 1.0060, 1.0107), respectively, using high-resolution air pollutant concentration, comparing to 1.0070 (95% CI 1.0052, 1.0087) and 1.0057 (95% CI 1.0037, 1.0078) using data from air quality monitoring stations (AQMs). After TYAP, the relative risk (RR) due to PM2.5 rose up to 1.0149 (95% CI 1.0054, 1.0243), and the RR due to O3 rose up to 1.0089 (95% CI 1.0030, 1.0148) in Sichuan Province. We found significantly positive association of cardiovascular mortality and air pollution in Sichuan Province. And using high-resolution exposure would be more accurate to estimate the effect of air pollution on CVD. After TYAP, the cardiovascular mortality risk estimation due to PM2.5 decreased in elderly in SCB, and the risk due to O3 increased in Sichuan Province.

Detecting the spatial clustering of exposure-response relationships with estimation error: a novel spatial scan statistic

Detecting the spatial clustering of the exposure-response relationship (ERR) between environmental risk factors and health-related outcomes plays important roles in disease control and prevention, such as identifying highly sensitive regions, exploring the causes of heterogeneous ERRs, and designing region-specific health intervention measures. However, few studies have focused on this issue. A possible reason is that the commonly used cluster-detecting tool, spatial scan statistics, cannot be used for multivariate spatial datasets with estimation error, such as the ERR, which is often defined by a vector with its covariance estimated by a regression model. Such spatial datasets have been produced in abundance in the last decade, which suggests the importance of developing a novel cluster-detecting tool applicable for multivariate datasets with estimation error. In this work, by extending the classic scan statistic, we developed a novel spatial scan statistic called the estimation-error-based scan statistic (EESS), which is applicable for both univariate and multivariate datasets with estimation error. Then, a two-stage analytic process was proposed to detect the spatial clustering of ERRs in practical studies. A published motivating example and a simulation study were used to validate the performance of EESS. The results show that the clusters detected by EESS can efficiently reflect the clustering heterogeneity and yield more accurate ERR estimates by adjusting for such heterogeneity.

Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic

BACKGROUND

Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model.

RESULTS

We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting.

CONCLUSIONS

Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.

Spatiotemporal Patterns of African Swine Fever in Wild Boar in the Russian Federation (2007-2022): Using Clustering Tools for Revealing High-Risk Areas

African swine fever (ASF) is an infectious disease that affects both domestic pigs (DPs) and wild boar (WB). The WB population plays an important role in the spread of ASF as the WB acts as a natural reservoir of the virus and transmits it to other susceptible wild and domestic pigs. Our study was aimed at revealing the areas with a high concentration of the WB population, and their potential relationships with the grouping of ASF cases in WB during the course of the ASF spread in the Russian Federation (2007-2022). We collected the annual data on WB numbers by municipalities within the regions of the most intensive ASF spread. We then conducted spatiotemporal analysis to identify clustering areas of ASF cases and compare them with the territories with a high density of WB population. We found that some of the territories with elevated ASF incidence in WB demonstrated spatial and temporal coincidence with the areas with a high WB population density. We also visualized the zones ("emerging hot spots") with a statistically significant rise in the WB population density in recent years, which may be treated as areas of paramount importance for the application of surveillance measures and WB population control.

Spatial-temporal analysis of pulmonary tuberculosis in Hubei Province, China, 2011-2021

BACKGROUND

Pulmonary tuberculosis (PTB) is an infectious disease of major public health problem, China is one of the PTB high burden counties in the word. Hubei is one of the provinces having the highest notification rate of tuberculosis in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Hubei province for targeted intervention on TB epidemics.

METHODS

The data on PTB cases were extracted from the National Tuberculosis Information Management System correspond to population in 103 counties of Hubei Province from 2011 to 2021. The effect of PTB control was measured by variation trend of bacteriologically confirmed PTB notification rate and total PTB notification rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level of Hubei.

RESULTS

A total of 436,955 cases were included in this study. The total PTB notification rate decreased significantly from 81.66 per 100,000 population in 2011 to 52.25 per 100,000 population in 2021. The peak of PTB notification occurred in late spring and early summer annually. This disease was spatially clustering with Global Moran's I values ranged from 0.34 to 0.63 (P< 0.01). Local spatial autocorrelation analysis indicated that the hot spots are mainly distributed in the southwest and southeast of Hubei Province. Using the SaTScan 10.0.2 software, results from the staged spatial-temporal analysis identified sixteen clusters.

CONCLUSIONS

This study identified seasonal patterns and spatial-temporal clusters of PTB cases in Hubei province. High-risk areas in southwestern Hubei still exist, and need to focus on and take targeted control and prevention measures.

Utilizing Residential History to Examine Heterogeneous Exposure Trajectories: A Latent Class Mixed Modeling Approach Applied to Mesothelioma Patients

Background

Life-course exposure assessment, as opposed to a one-time snapshot assessment based on the address at cancer diagnosis, has become increasingly possible with available cancer patients' residential history data. To demonstrate a novel application of residential history data, we examined the heterogeneous trajectories of the nonasbestos air toxic exposures among mesothelioma patients, and compared the patients' residential locations with the spatiotemporal clusters estimated from the National Air Toxic Assessment (NATA) data.

Methods

Patients' residential histories were obtained by linking mesothelioma cases diagnosed during 2011-2015 in the New York State (NYS) Cancer Registry to LexisNexis administrative data and inpatient claims data. To compare cancer risks over time, yearly relative exposure (RE) was calculated by dividing the NATA cancer risk at individual census tracts by the NYS average and subtracting 1. We used a latent class mixed model to identify distinct exposure trajectories among patients with a 15-year residential history prior to cancer diagnosis (n = 909). We further examined patient characteristics by the latent trajectory groups using bivariate comparisons and a logistic regression model. The spatiotemporal clusters of RE were generated based on all NATA data (n = 72,079) across the contiguous United States and using the SaTScan software.

Results

The median number of addresses lived was 2 (IQR, 1-4), with a median residential duration of 8 years (IQR, 4.7-13.2 years). We identified 3 distinct exposure trajectories: persistent low exposure (27%), decreased low exposure (41%), and increased high exposure (32%). Patient characteristics did not differ across trajectory groups, except for race and Hispanic ethnicity (P < .0001) and residential duration (P = .03). Compared to their counterparts, non-Hispanic White patients had a significantly lower odds of belonging to the increased high exposure group (adjusted odds ratio, 0.14; 95% CI, 0.09-0.23) than the persistent low exposure and decreased low exposure groups. Patients in the increased high exposure group tended to reside in New York City (NYC), which was covered by one of the high-RE clusters. On the other hand, patients in the persistent low exposure group tended to reside outside of NYC within NYS, which was largely covered by 2 low-RE clusters.

Conclusion

Using mesothelioma as an example, we quantified the heterogeneous trajectories of nonasbestos air toxic exposure based on patients' residential histories. We found that patients' race and ethnicity differed across the latent groups, likely reflecting the differences in patients' residential mobility before their cancer diagnoses. Our method can be used to study cancer types that do not have a clear etiology and may have a higher attributable risk due to environmental exposures as well as socioeconomic conditions.

Harnessing heterogeneity in space with statistically guided meta-learning

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity-an intrinsic characteristic embedded in spatial data-poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. Moreover, we propose a spatial moderator to generalize learned space partitionings to new test regions. Finally, we extend the framework by integrating meta-learning-based training strategies into both spatial transformation and moderation to enhance knowledge sharing and adaptation among different processes. Experiment results on real-world datasets show that the framework can effectively capture flexibly shaped heterogeneous footprints and substantially improve prediction performances.

Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019

The study analyzed the current status and changing trends of PM_2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM_2.5 pollution during 2015-2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China's annual grade II air quality standard (35 μg/m³). The spatial distribution of PM_2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM_2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.

Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics

Hotspot detection is an important exploratory technique to identify areas with high concentrations of crime and help deploy crime-reduction resources. Although a variety of methods have been developed to detect crime hotspots, few studies have systematically evaluated the performance of various methods, especially in terms of the ability to detect complex-shaped crime hotspots. Therefore, in this study, a comparative study of hotspot detection approaches while simultaneously considering the concentration and shape characteristics was conducted. Firstly, we established a framework for quantitatively evaluating the performance of hotspot detection for cases with or without the "ground truth". Secondly, accounting for the concentration and shape characteristics of the hotspot, we additionally defined two evaluation indicators, which can be used as a supplement to existing evaluation indicators. Finally, four classical hotspot-detection methods were quantitatively compared on the synthetic and real crime data. Results show that the proposed evaluation framework and indicators can describe the size, concentration and shape characteristics of the detected hotspots, thus supporting the quantitative comparison of different methods. From the selected methods, the AMOEBA (A Multidirectional Optimal Ecotope-Based Algorithm) method was more accurate in describing the concentration and shape characteristics and was powerful in discovering complex hotspots.

Genetic characterization and epidemiological analysis of the first lumpy skin disease virus outbreak in Mongolia, 2021

Novel lumpy skin disease virus (LSDV) strains of recombinant origin are on the rise in South East Asia following the first emergence in 2017, and published evidence demonstrates that such genetic lineages currently dominate the circulation. Mongolia reported first LSD outbreaks in 2021 in a north-eastern region sharing borders with Russia and China. For each of 59 reported LSDV outbreaks, the number of susceptible animals ranged from 8 to 8600 with a median of 572, while the number of infected animals ranged from one to 355 with a median of 14. Phylogenetic inferences revealed a close relationship of LSDV Mongolia/2021 with recombinant vaccine-like LSDV strains from Russia, China, Taiwan, Thailand and Vietnam. These findings support the published data that the circulating strain of LSDV belongs to the dominant recombinant lineage recently established in the region.

Geographical distribution and space-time clustering of human illnesses with major Salmonella serotypes in Florida, USA, 2017-2018

Nontyphoidal salmonellosis is the leading reported foodborne illness in Florida. Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 whole-genome sequenced Salmonella isolates within seven major serotypes (Enteritidis, Newport, Javiana, Sandiego, Braenderup, Typhimurium and I 4,[5],12:i:-) with the metadata obtained from Florida Department of Health during 2017-2018. Geographically, the distribution of incidence rates varied distinctively between serotypes. Illnesses with Enteritidis and Newport serotypes were widespread in Florida. The incidence rate for Javiana was relatively higher in the north compared to the south. Typhimurium was concentrated in the northwest, while I 4,[5],12:i:-, the monophasic variant of Typhimurium was limited to the south. We also evaluated space-time clustering of isolates at the zip code level using scan statistic models. Space-time clusters were detected for each major serotype during 2017-2018. The multinomial scan statistic found the risk of illness with Javiana was higher in the north and southwest in the fall of 2017 compared to other major serotypes. This serotype-specific clustering analysis will assist in further unpacking the associations between distinct reservoirs and illnesses with major serotypes in Florida.

A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics

Background

In public health and epidemiology, spatial scan statistics can be used to identify spatial cluster patterns of health-related outcomes from population-based health survey data. Although it is appropriate to consider the complex sample design and sampling weight when analyzing complex sample survey data, the observed survey responses without these considerations are often used in many studies related to spatial cluster detection.

Methods

We conducted a simulation study to investigate which data type from complex survey data is more suitable for use by comparing the spatial cluster detection results of three approaches: (1) individual-level data, (2) weighted individual-level data, and (3) aggregated data.

Results

The results of the spatial cluster detection varied depending on the data type. To compare the performance of spatial cluster detection, sensitivity and positive predictive value (PPV) were evaluated over 100 iterations. The average sensitivity was high for all three approaches, but the average PPV was higher when using aggregated data than when using individual-level data with or without sampling weights.

Conclusions

Through the simulation study, we found that use of aggregate-level data is more appropriate than other types of data, when searching for spatial clusters using spatial scan statistics on population-based health survey data.

Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition

Count data occur widely in many bio-surveillance and healthcare applications, e.g. the numbers of new patients of different types of infectious diseases from different cities/counties/states repeatedly over time, say, daily/weekly/monthly. For this type of count data, one important task is the quick detection and localization of hot-spots in terms of unusual infectious rates so that we can respond appropriately. In this paper, we develop a method called Poisson assisted Smooth Sparse Tensor Decomposition (PoSSTenD), which not only detect when hot-spots occur but also localize where hot-spots occur. The main idea of our proposed PoSSTenD method is articulated as follows. First, we represent the observed count data as a three-dimensional tensor including (1) a spatial dimension for location patterns, e.g. different cities/countries/states; (2) a temporal domain for time patterns, e.g. daily/weekly/monthly; (3) a categorical dimension for different types of data sources, e.g. different types of diseases. Second, we fit this tensor into a Poisson regression model, and then we further decompose the infectious rate into two components: smooth global trend and local hot-spots. Third, we detect when hot-spots occur by building a cumulative sum (CUSUM) control chart and localize where hot-spots occur by their LASSO-type sparse estimation. The usefulness of our proposed methodology is validated through numerical simulation studies and a real-world dataset, which records the annual number of 10 different infectious diseases from 1993 to 2018 for 49 mainland states in the United States.

A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.

Identification of spatio-temporal clusters of lung cancer cases in Pennsylvania, USA: 2010-2017

Background

It is known that geographic location plays a role in developing lung cancer. The objectives of this study were to examine spatio-temporal patterns of lung cancer incidence in Pennsylvania, to identify geographic clusters of high incidence, and to compare demographic characteristics and general physical and mental health characteristics in those areas.

Method

We geocoded the residential addresses at the time of diagnosis for lung cancer cases in the Pennsylvania Cancer Registry diagnosed between 2010 and 2017. Relative risks over the expected case counts at the census tract level were estimated using a log-linear Poisson model that allowed for spatial and temporal effects. Spatio-temporal clusters with high incidence were identified using scan statistics. Demographics obtained from the 2011–2015 American Community Survey and health variables obtained from 2020 CDC PLACES database were compared between census tracts that were part of clusters versus those that were not.

Results

Overall, the age-adjusted incidence rates and the relative risk of lung cancer decreased from 2010 to 2017 with no statistically significant space and time interaction. The analyses detected 5 statistically significant clusters over the 8-year study period. Cluster 1, the most likely cluster, was in southeastern PA including Delaware, Montgomery, and Philadelphia Counties from 2010 to 2013 (log likelihood ratio = 136.6); Cluster 2, the cluster with the largest area was in southwestern PA in the same period including Allegheny, Fayette, Greene, Washington, and Westmoreland Counties (log likelihood ratio = 78.6). Cluster 3 was in Mifflin County from 2014 to 2016 (log likelihood ratio = 25.3), Cluster 4 was in Luzerne County from 2013 to 2016 (log likelihood ratio = 18.1), and Cluster 5 was in Dauphin, Cumberland, and York Counties limited to 2010 to 2012 (log likelihood ratio = 17.9). Census tracts that were part of the high incidence clusters tended to be densely populated, had higher percentages of African American and residents that live below poverty line, and had poorer mental health and physical health when compared to the non-clusters (all p < 0.001).

Conclusions

These high incidence areas for lung cancer warrant further monitoring for other individual and environmental risk factors and screening efforts so lung cancer cases can be identified early and more efficiently.

A Geographical Analysis of Socioeconomic and Environmental Drivers of Physical Inactivity in Post Pandemic Cities: The Case Study of Chicago, IL, USA

The pandemic’s lockdown has made physical inactivity unavoidable, forcing many people to work from home and increasing the sedentary nature of their lifestyle. The link between spatial and socio-environmental dynamics and people’s levels of physical activity is critical for promoting healthy lifestyles and improving population health. Most studies on physical activity or sedentary behaviors have focused on the built environment, with less attention to social and natural environments. We illustrate the spatial distribution of physical inactivity using the space scan statistic to supplement choropleth maps of physical inactivity prevalence in Chicago, IL, USA. In addition, we employ geographically weighted regression (GWR) to address spatial non-stationarity of physical inactivity prevalence in Chicago per census tract. Lastly, we compare GWR to the traditional ordinary least squares (OLS) model to assess the effect of spatial dependency in the data. The findings indicate that, while access to green space, bike lanes, and living in a diverse environment, as well as poverty, unsafety, and disability, are associated with a lack of interest in physical activities, limited language proficiency is not a predictor of an inactive lifestyle. Our findings suggest that physical activity is related to socioeconomic and environmental factors, which may help guide future physical activity behavior research and intervention decisions, particularly in identifying vulnerable areas and people.

Spatio-temporal disease risk estimation using clustering-based adjacency modelling

Conditional autoregressive models are typically used to capture the spatial autocorrelation present in areal unit disease count data when estimating the spatial pattern in disease risk. This correlation is represented by a binary neighbourhood matrix based on a border sharing specification, which enforces spatial correlation between geographically neighbouring areas. However, enforcing such correlation will mask any discontinuities in the disease risk surface, thus impeding the detection of clusters of areas that exhibit higher or lower risks compared to their neighbours. Here we propose novel methodology to account for these clusters and discontinuities in disease risk via a two-stage modelling approach, which either forces the clusters/discontinuities to be the same for all time periods or allows them to evolve dynamically over time. Stage one constructs a set of candidate neighbourhood matrices to represent a range of possible cluster/discontinuity structures in the data, and stage two estimates an appropriate structure(s) by treating the neighbourhood matrix as an additional parameter to estimate within a Bayesian spatio-temporal disease mapping model. The effectiveness of our novel methodology is evidenced by simulation, before being applied to a new study of respiratory disease risk in Greater Glasgow, Scotland from 2011 to 2017.

Spatial distribution and determinants of thyroid cancer incidence from 1999 to 2013 in Korea

We evaluated the spatial variation in thyroid cancer incidence and its determinants in Korea considering its importance in cancer prevention and control. This study was based on the ecological design with cancer incidence data by administrative district from the National Cancer Center and regional characteristics generated from the Korea Community Health Survey Data. We identified spatial clusters of thyroid cancer incidences based on spatial scan statistics. Determinants of regional variation in thyroid cancer incidence were assessed using the Besag-York-Mollie model with integrated nested Laplace approximations. Spatial clusters for low and high thyroid cancer incidences were detected in the northeastern and southwestern regions, respectively. Regional variations in thyroid cancer incidence can be attributed to the prevalence of recipients of basic livelihood security (coefficient, - 1.59; 95% credible interval [CI], - 2.51 to - 0.67), high household income (coefficient, 0.53; 95% CI, 0.31 to 0.76), heavy smoking (coefficient, - 0.91; 95% CI, - 1.59 to - 0.23), thyroid dysfunction (coefficient, 3.24; 95% CI, 1.47 to 5.00), and thyroid cancer screening (coefficient, 0.38; 95% CI, 0.09 to 0.67). This study presented the spatial variations in thyroid cancer incidence, which can be explained by the prevalence of socioeconomic factors, thyroid cancer screening, thyroid dysfunction, and smoking.

Spatial epidemiologic analysis of the liver cancer and gallbladder cancer incidence and its determinants in South Korea

BACKGROUND

There have been reports on regional variation in prevalence of hepatitis B and C, and Clonorchis sinensis (C. sinensis) infection, which indicates potential of spatial variation in liver cancer and gallbladder cancer incidence in Korea. Therefore, we aimed to assess the regional variation of liver and gallbladder cancer incidence and its determinants based on the regional distribution of risk factors, including hepatitis B infection in Korea.

METHODS

This study used an ecological study design and district-level cancer incidence statistics generated by the National Cancer Center. Spatial clusters of liver and gallbladder cancer incidence were detected based on spatial scan statistics using SaTScan™ software. We set the size of maximum spatial scanning window of 25 and 35% of the population at risk for analyses of liver and gallbladder cancer, respectively. Significance level of 0.05 was used to reject the null hypothesis of no cluster. We fitted the Besag-York-Mollie model using integrated nested Laplace approximations to assess factors that influence the regional variation in cancer incidence.

RESULTS

Spatial clusters with high liver cancer incidence rates were detected in the southwestern and southeastern regions of Korea. High gallbladder cancer incidence rates are clustered in the southeastern region. Regional liver cancer incidence can be accounted for the prevalence of high household income (coefficient, - 0.10; 95% credible interval [CI], - 0.18 to - 0.02), marital status (coefficient, - 0.14; 95% CI, - 0.25 to - 0.03), the incidence of hepatitis B (coefficient, 0.87; 95% CI, 0.29 to 1.44), and liver cancer screening (coefficient, 0.06; 95% CI, 0.00 to 0.12), while gallbladder cancer incidence was related to the prevalence of high household income (coefficient, - 0.03; 95% CI, - 0.05 to 0.00) and living close to a river with a high prevalence of liver fluke infection (coefficient, 0.55; 95% CI, 0.14 to 0.96).

CONCLUSIONS

This study demonstrated geographic variation in liver and gallbladder cancer incidence, which can be explained by determinants such as hepatitis B, income, marital status, and living near a river.

Spatiotemporal Analysis of West Nile Virus Epidemic in South Banat District, Serbia, 2017-2019

West Nile virus (WNV) is an arthropod-born pathogen, which is transmitted from wild birds through mosquitoes to humans and animals. At the end of the 20th century, the first West Nile fever (WNF) outbreaks among humans in urban environments in Eastern Europe and the United States were reported. The disease continued to spread to other parts of the continents. In Serbia, the largest number of WNV-infected people was recorded in 2018. This research used spatial statistics to identify clusters of WNV infection in humans and animals in South Banat County, Serbia. The occurrence of WNV infection and risk factors were analyzed using a negative binomial regression model. Our research indicated that climatic factors were the main determinant of WNV distribution and were predictors of endemicity. Precipitation and water levels of rivers had an important influence on mosquito abundance and affected the habitats of wild birds, which are important for maintaining the virus in nature. We found that the maximum temperature of the warmest part of the year and the annual temperature range; and hydrographic variables, e.g., the presence of rivers and water streams were the best environmental predictors of WNF outbreaks in South Banat County.

Optimizing the maximum reported cluster size in the spatial scan statistic for survival data

Background

The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results.

Results

We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data.

Conclusions

Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-021-00286-w.

Environmental characteristics around the household and their association with hookworm infection in rural communities from Bahir Dar, Amhara Region, Ethiopia

Soil-Transmitted Helminths (STH) are highly prevalent Neglected Tropical Disease in Ethiopia, an estimated 26 million are infected. Geographic Information Systems and Remote Sensing (RS) technologies assist data mapping and analysis, and the prediction of the spatial distribution of infection in relation to environmental variables. The influence of socioeconomic, environmental and soil characteristics on hookworm infection at the individual and household level is explored in order to identify spatial patterns of infection in rural villages from Zenzelema (Amhara region). Inhabitants greater than 5 years old were recruited in order to assess the presence of STH. Socioeconomic and hookworm infection variables at the household level and environmental variables and soil characteristics using RS were obtained. The dominant STH found was hookworm. Individuals which practiced open defecation and those without electricity had a significant higher number of hookworm eggs in their stool. Additionally, adults showed statistically higher hookworm egg counts than children. Nonetheless, the probability of hookworm infection was not determined by socioeconomic conditions but by environmental characteristics surrounding the households, including a combination of vigorous vegetation and bare soil, high temperatures, and compacted soils (high bulk density) with more acidic pH, given a pH of 6.0 is optimal for hatching of hookworm eggs. The identification of high-risk environmental areas provides a useful tool for planning, targeting and monitoring of control measures, including not only children but also adults when hookworm is concerned.

Area-Level Clustering of Shiga Toxin-Producing Escherichia coli Infections and Their Socioeconomic and Demographic Factors in Ontario, Canada: An Ecological Study

Shiga toxin-producing Escherichia coli (STEC) infections are an important health burden for human populations in Ontario and worldwide. We assessed 452 STEC cases that were reported to Ontario's reportable disease surveillance system between 2015 and 2017. A retrospective scan statistic using a Poisson model was used to detect high-rate STEC clusters at the forward sortation area (FSA; the first three digits of a postal code) level. A significant spatial cluster in the southwest region of Ontario was identified. A case-case logistic regression analysis was applied to compare FSA-level socioeconomic and demographic characteristics among STEC cases included inside the spatial cluster with cases outside of the cluster. Cases included in the spatial cluster had higher odds of living in FSAs with a low median family income, low proportion of lone-parent families, and low proportion of the visible minority population. In addition, STEC cases inside the cluster had higher odds of coming from rural FSAs. Our study demonstrated that STEC cases were spatially clustered in Ontario and their clustering was associated with FSA-level socioeconomic and demographic determinants of cases.

Risk for African Swine Fever Introduction Into Kazakhstan

African swine fever (ASF) is a disease of swine that is endemic to some African countries and that has rapidly spread since 2007 through many regions of Asia and Europe, becoming endemic in some areas of those continents. Since there is neither vaccine nor treatment for ASF, prevention is an important action to avoid the economic losses that this disease can impose on a country. Although the Republic of Kazakhstan has remained free from the disease, some of its neighbors have become ASF-infected, raising concerns about the potential introduction of the disease into the country. Here, we have identified clusters of districts in Kazakhstan at highest risk for ASF introduction. Questionnaires were administered, and districts were visited to collect and document, for the first time, at the district level, the distribution of swine operations and population in Kazakhstan. A snowball sampling approach was used to identify ASF experts worldwide, and a conjoint analysis model was used to elicit their opinion in relation to the extent at which relevant epidemiological factors influence the risk for ASF introduction into disease-free regions. The resulting model was validated using data from the Russian Federation and Mongolia. Finally, the validated model was used to rank and categorize Kazakhstani districts in terms of the risk for serving as the point of entry for ASF into the country, and clusters of districts at highest risk of introduction were identified using the normal model of the spatial scan statistic. Results here will help to allocate resources for surveillance and prevention activities aimed at early detecting a hypothetical ASF introduction into Kazakhstan, ultimately helping to protect the sanitary status of the country.

Clustered spatio-temporal varying coefficient regression model

In regression analysis for spatio-temporal data, identifying clusters of spatial units over time in a regression coefficient could provide insight into the unique relationship between a response and covariates in certain subdomains of space and time windows relative to the background in other parts of the spatial domain and the time period of interest. In this article, we propose a varying coefficient regression method for spatial data repeatedly sampled over time, with heterogeneity in regression coefficients across both space and over time. In particular, we extend a varying coefficient regression model for spatial-only data to spatio-temporal data with flexible temporal patterns. We consider the detection of a potential cylindrical cluster of regression coefficients based on testing whether the regression coefficient is the same or not over the entire spatial domain for each time point. For multiple clusters, we develop a sequential identification approach. We assess the power and identification of known clusters via a simulation study. Our proposed methodology is illustrated by the analysis of a cancer mortality dataset in the Southeast of the U.S.

Towards precision public health: Geospatial analytics and sensitivity/specificity assessments to inform liver cancer prevention

Objectives

Liver cancer (LC) continues to rise, partially due to limited resources for prevention. To test the precision public health (PPH) hypothesis that fewer areas in need of LC prevention could be identified by combining existing surveillance data, we compared the sensitivity/specificity of standard recommendations to target geographic areas using U.S. Census demographic data only (percent (%) Hispanic, Black, and those born 1950–1959) to an alternative approach that couples additional geospatial data, including neighborhood socioeconomic status (nSES), with LC disease statistics.

Methods

Pennsylvania Cancer Registry data from 2007-2014 were linked to 2010 U.S. Census data at the Census tract (CT) level. CTs in the top 80th percentile for 3 standard demographic variables, %Hispanic, %Black, %born 1950–1959, were identified. Spatial scan statistics (SatScan) identified CTs with significantly elevated incident LC rates (p-value<0.05), adjusting for age, gender, diagnosis year. Sensitivity, specificity, and positive predictive value (PPV) of a CT being located in an elevated risk cluster and/or testing positive/negative for at least one standard variable were calculated. nSES variables (deprivation, stability, segregation) significantly associated with LC in regression models (p < 0.05) were systematically evaluated for improvements in sensitivity/specificity.

Results

9,460 LC cases were diagnosed across 3,217 CTs. 1,596 CTs were positive for at least one of 3 standard variables. 5 significant elevated risk clusters (CTs = 402) were identified. 324 CTs were positive for a high risk cluster AND standard variable (sensitivity = 92%; specificity = 37%; PPV = 17.4%). Incorporation of 3 new nSES variables with one standard variable (%Black) further improved sensitivity (93%), specificity (62.9%), and PPV (26.3%).

Conclusions

We introduce a quantitative assessment of PPH by applying established sensitivity/specificity assessments to geospatial data. Coupling existing disease cluster and nSES data can more precisely identify intervention targets with a liver cancer burden than standard demographic variables. Thus, this approach may inform prioritization of limited resources for liver cancer prevention.

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Precision Public Health calls for linking surveillance data to identify fewer neighborhoods for intervention.

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Sensitivity/specificity methods can measure the utility of Precision Public Health by identifying optimal data combinations.

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Select combinations of linked Census and liver cancer registry data reduced neighborhood targets more than Census data alone.

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Precision Public Health improves the prioritization of liver cancer prevention efforts.

Quantitative phenotype scan statistic (QPSS) reveals rare variant associations with Alzheimer's disease endophenotypes

BACKGROUND

Current sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants. Scan statistic approaches have previously been adapted to genetic sequencing data. Unlike currently-employed association tests, scan-statistic-based approaches can both localize clusters of disease-related variants and, subsequently, examine the phenotype association within the resulting cluster. In this study, we present a novel Quantitative Phenotype Scan Statistic (QPSS) that extends an approach for dichotomous phenotypes to continuous outcomes in order to identify genomic regions where rare quantitative-phenotype-associated variants cluster.

RESULTS

We demonstrate the performance and practicality of QPSS with extensive simulations and an application to a whole-genome sequencing (WGS) study of cerebrospinal fluid (CSF) biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using QPSS, we identify regions of rare variant enrichment associated with levels of AD-related proteins, CSF Aβ_1-42 and p-tau_181P.

CONCLUSIONS

QPSS is implemented under the assumption that causal variants within a window have the same direction of effect. Typical self-contained tests employ a null hypothesis of no association between the target variant set and the phenotype. Therefore, an advantage of the proposed competitive test is that it is possible to refine a known region of interest to localize disease-associated clusters. The definition of clusters can be easily adapted based on variant function or annotation.

The Proximity of Spatial Clusters of Low Birth Weight and Risk Factors: Defining a Neighborhood for Focused Interventions

BACKGROUND

Low birth weight (LBW) is associated with significant mortality and morbidity and remains a significant preventable problem. Risk factors include socioeconomic, demographics, and characteristics of the environment. Spatial analysis can uncover unusual frequencies of health problems in neighborhoods, eventually leading to insights for targeted interventions.

OBJECTIVES

This study's goals were to 1. Evaluate the geographic distribution of spatial clusters of LBW births and maternal risk factors. 2. Determine the spatial relationship between risk factors and LBW.

METHODS

This study obtained data on LBW newborns and risk factors from 19,013 births over 5 years (2012-2016) for Escambia County Census Tracts, extracted from FloridaCharts.com. Software was used to detect significant spatial clusters; these clusters were then plotted on a map. Poisson regression determined the statistical relationship between Census Tract risk factors and LBW. A separate analysis of the LBW cluster controlling for risk factors was also performed.

RESULTS

All risk factor clusters resided in similar locations as the LBW cluster. The multiple Poisson regression model containing all risk factors fully explained the LBW cluster. On bivariate Poisson regression all risk factors in the Census Tract were significantly related to LBW whereas in multivariable Poisson regression, the proportion of births to African American women in the Census Tract remained significant after adjusting for other risk factors (p < 0.001).

CONCLUSIONS FOR PRACTICE

Clusters of LBW and risk factors were located in the same region of the county, with the proportion of births to African American women in the Census Tract remaining significant on multiple Poisson Regression. Targeted interventions should be directed at the geographic level.

A spatial study of oral & pharynx cancer mortality and incidence in the U.S.A.: 2000-2015

This is a national scale study of spatial oral and pharynx cancer mortality and incidence clusters in the contiguous U.S.A. Spatial and space-time analyses of incidence and mortality rates of oral and pharynx cancers in the contiguous U.S.A. were done at the county resolution, using mortality data for the years 2000-2014 and incidence data for 2001-2015. The disease surveillance software SaTScan™ is used to identify significant cancer clusters that are non-random. In addition to a cluster analysis, regression analysis was used to adjust cancer incidence and mortality for several covariates or risk factors. This is the first study of the contiguous U.S.A. for oral and pharynx cancer in which mortality and incidence rates are studied together. The geographic clustering for mortality is somewhat different from the clustering for incidence. There exist several significant clusters in the contiguous U.S.A., both for oral and pharynx cancer incidence and for mortality. Some of the significant clusters persisted even after adjusting for several key risk factors. These clusters areas suggest a need for further investigation to identify some local concerns or needs to further address such cancer types in those specific sites. We identified statistically significant spatial and space-time clusters of oral and pharynx cancer for mortality and also for incidence in the contiguous US at the county resolution. The most important risk factors for oral cancer incidence are diabetes, alcohol drinking, and obesity, while the top risk factors for mortality are race, cervical cancer, diabetes, and alcohol drinking.

Housing Conditions, Neighborhood Physical Environment, and Secondhand Smoke Exposure at Home: Evidence from Chinese Rural-to-Urban Migrant Workers

Over the past two decades, health-related issues among rural-to-urban migrant workers in China have been widely discussed and documented by public health scholars. However, little, if any, scholarly attention has been paid to migrant workers’ secondhand smoke (SHS) exposure at home. This study aims to explore the contours of SHS exposure at home and investigate the effects of inadequate housing conditions and poor neighborhood physical environments on such in-home exposure among Chinese migrant workers. A respondent-driven sampling method was employed to interview 1854 rural-to-urban migrant workers from the period June 2017 to June 2018 in Chengdu, China. The results indicate that Chinese migrant workers are at high risk of SHS exposure at home. Migrant workers who live in homes with inadequate conditions, such as substandard housing and crowdedness, are especially at high risk of SHS exposure at home. Moreover, poor neighborhood physical environments are significantly and positively associated with SHS exposure at home. These findings suggest that strategies that can help improve housing conditions and neighborhood physical environments should be developed and promoted to protect rural-to-urban migrant workers from SHS exposure at home.

Impact of community piped water coverage on re-infection with urogenital schistosomiasis in rural South Africa

Previously, we demonstrated that coverage of piped water in the seven years preceding a parasitological survey was strongly predictive of Schistosomiasis haematobium infection in a nested cohort of 1976 primary school children (Tanser, 2018). Here, we report on the prospective follow up of infected members of this nested cohort (N = 333) for two successive rounds following treatment. Using a negative binomial regression fitted to egg count data, we found that every percentage point increase in piped water coverage was associated with 4.4% decline in intensity of re-infection (incidence rate ratio = 0.96, 95% CI: 0.93–0.98, p=0.004) among the treated children. We therefore provide further compelling evidence in support of the scaleup of piped water as an effective control strategy against Schistosoma haematobium transmission.

The Comparison of Three Statistical Models for Syndromic Surveillance in Cattle Using Milk Production Data

Two vector-borne infections have emerged and spread throughout the north-western part of Europe in the last decade: Bluetongue virus serotype-8 (BTV-8) and the Schmallenberg virus (SBV). The objective of the current study was to compare three statistical methods when applied in a syndromic surveillance context for the early detection of emerging diseases in cattle in the Netherlands. Since BTV-8 and SBV both have a negative effect on milk production in dairy cattle, routinely collected bulk milk recordings were used to compare the three statistical methods in their potential to detect drops in milk production during a period of seven years in which BTV-8 and SBV emerged. A Cusum algorithm, Bayesian disease mapping model, and spatiotemporal cluster analysis using the space-time scan statistic were performed and their performance in terms of sensitivity and specificity was compared. Spatiotemporal cluster analysis performed best for early detection of SBV in cattle in the Netherlands with a relative sensitivity of 71% compared to clinical surveillance and 100% specificity in a year without major disease outbreaks. Sensitivity to detect BTV-8 was low for all methods. However, many alerts of reduced milk production were generated several weeks before the week in which first clinical suspicions were reported. It cannot be excluded that these alerts represent the actual first signs of BTV-8 infections in cattle in the Netherlands thus leading to an underestimation of the sensitivity of the syndromic surveillance methods relative to the clinical surveillance in place.

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.

A survey of trace metal burdens in increment cores from eastern cottonwood (Populus deltoides) across a childhood cancer cluster, Sandusky County, OH, USA

A dendrochemical study of cottonwood trees (Populus deltoides) was conducted across a childhood cancer cluster in eastern Sandusky County (Ohio, USA). The justification for this study was that no satisfactory explanation has yet been put forward, despite extensive local surveys of aerosols, groundwater, and soil. Concentrations of eight trace metals were measured by ICP-MS in microwave-digested 5-year sections of increment cores, collected during 2012 and 2013. To determine whether the onset of the first cancer cases could be connected to an emergence of any of these contaminants, cores spanning the period 1970-2009 were taken from 51 trees of similar age, inside the cluster and in a control area to the west. The abundance of metals in cottonwood tree annual rings served as a proxy for their long-term, low-level accumulation from the same sources whereby exposure of the children may have occurred. A spatial analysis of cumulative metal burdens (lifetime accumulation in the tree) was performed to search for significant 'hotspots', employing a scan statistic with a mask of variable radius and center. For Cd, Cr, and Ni, circular hotspots were found that nearly coincide with the cancer cluster and are similar in size. No hotspots were found for Co, Cu, and Pb, while As and V were largely below method detection limits. Whereas our results do not implicate exposure to metals as a causative factor, we conclude that, after 1970, cottonwood trees have accumulated more Cd, Cr, and Ni, inside the childhood cancer cluster than elsewhere in Sandusky County.

EpiExploreR: A Shiny Web Application for the Analysis of Animal Disease Data

Emerging and re-emerging infectious diseases are a significant public and animal health threat. In some zoonosis, the early detection of virus spread in animals is a crucial early warning for humans. The analyses of animal surveillance data are therefore of paramount importance for public health authorities to identify the appropriate control measure and intervention strategies in case of epidemics. The interaction among host, vectors, pathogen and environment require the analysis of more complex and diverse data coming from different sources. There is a wide range of spatiotemporal methods that can be applied as a surveillance tool for cluster detection, identification of risk areas and risk factors and disease transmission pattern evaluation. However, despite the growing effort, most of the recent integrated applications still lack of managing simultaneously different datasets and at the same time making available an analytical tool for a complete epidemiological assessment. In this paper, we present EpiExploreR, a user-friendly, flexible, R-Shiny web application. EpiExploreR provides tools integrating common approaches to analyze spatiotemporal data on animal diseases in Italy, including notified outbreaks, surveillance of vectors, animal movements data and remotely sensed data. Data exploration and analysis results are displayed through an interactive map, tables and graphs. EpiExploreR is addressed to scientists and researchers, including public and animal health professionals wishing to test hypotheses and explore data on surveillance activities.

High-resolution micro-epidemiology of parasite spatial and temporal dynamics in a high malaria transmission setting in Kenya

Novel interventions that leverage the heterogeneity of parasite transmission are needed to achieve malaria elimination. To better understand spatial and temporal dynamics of transmission, we applied amplicon next-generation sequencing of two polymorphic gene regions (csp and ama1) to a cohort identified via reactive case detection in a high-transmission setting in western Kenya. From April 2013 to July 2014, we enrolled 442 symptomatic children with malaria, 442 matched controls, and all household members of both groups. Here, we evaluate genetic similarity between infected individuals using three indices: sharing of parasite haplotypes on binary and proportional scales and the L1 norm. Symptomatic children more commonly share haplotypes with their own household members. Furthermore, we observe robust temporal structuring of parasite genetic similarity and identify the unique molecular signature of an outbreak. These findings of both micro- and macro-scale organization of parasite populations might be harnessed to inform next-generation malaria control measures.

Here, Nelson et al. use amplicon next-generation sequencing of two P. falciparum polymorphic gene regions to investigate the genetic similarity of parasite populations across time and space in a pediatric cohort in Kenya. They identify both micro- and macro-scale structuring of malaria parasites in this high-transmission setting, which could inform future intervention strategies.

Spatial scan statistics for matched case-control data

Spatial scan statistics are widely used for cluster detection analysis in geographical disease surveillance. While this method has been developed for various types of data such as binary, count, and continuous data, spatial scan statistics for matched case-control data, which often arise in spatial epidemiology, have not been considered. We propose spatial scan statistics for matched case-control data. The proposed test statistics consider the correlations between matched pairs. We evaluate the statistical power and cluster detection accuracy of the proposed methods through simulations compared to the Bernoulli-based method. We illustrate the proposed methods using a real data example. The simulation study clearly revealed that the proposed methods had higher power and higher accuracy for detecting spatial clusters for matched case-control data than the Bernoulli-based spatial scan statistic. The cluster detection result of the real data example also appeared to reflect a higher power of the proposed methods. The proposed methods are very useful for spatial cluster detection for matched case-control data.

Risks of cattle babesiosis (Babesia bovis) outbreaks in a semi-arid region of Argentina

The epidemiology of Babesia bovis was studied in terms of enzootic stability/instability and husbandry and abiotic factors influencing B. bovis transmission rate in northeastern Santiago del Estero province, Argentina. The area is of limited suitability for its only vector in Argentina, the tick Rhipicephalus microplus. The proportion of calf herds in a state of enzootic stability/instability to B. bovis was determined and husbandry practices and abiotic factors associated with variations in B. bovis transmission rates were explored using a cross-sectional observational study design. Daily probability of infection (inoculation rate, h) with B. bovis was calculated from age-specific seroprevalence via ELISAi in 58 herds of 4.5-8.5-month-old calves. Herds were considered to be in enzootic instability (EI) when h < 0.005, and therefore inferred to be at risk of babesiosis outbreaks. Husbandry practices associated with differences in B. bovis transmission were analyzed using generalized linear models. Sixty-two percent of herds were found to be in an EI situation for B. bovis. Calves raised exclusively on permanent pastures -where higher cattle density is achieved- were exposed to higher B. bovis inoculation rates (h = 0.0063, 95% CI 0.0032-0.0123) than those reared under forage combinations (h = 0.0024, 95% CI 0.0011-0.0051) (P =  0.05). In addition, calves from herds located in the area of intermediate suitability for R. microplus development were more likely to become infected with B. bovis (h = 0.0067, 95% CI 0.0037-0.0121) than those reared in the ecologically unfavorable area for the vector (h = 0.0023, 95% CI 0.0010-0.0049) (P =  0.02). Neither the frequency of treatment with acaricides nor the use of long-acting acaricides to control R. microplus influenced the inoculation rate (P =  0.99 and P =  0.26, respectively). This result indicates that current R. microplus control schemes are not effective in reducing B. bovis transmission. Enzootic instability still prevails in the study area despite the drastic changes occurred in cattle production system. However, 38% of herds did reach enzootic stability; therefore, a specific epidemiological status cannot be assumed at a regional level. Yearly determination of the immunological status of each calf cohort is considered a proper approach to decision-making in vaccination against B. bovis.

GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.