Significant Space-time Clusters Research Articles

Clusters of Jail Incarcerations in U.S. Counties: 2010–2018

This study investigates the spatiotemporal variations in jail incarcerations in addition to associations with several risk factors and jail incarceration counts at the county level for the period 2010–2018 in the contiguous USA. The disease surveillance software SaTScanTM was used to identify and test purely spatial and spatiotemporal variations in jail incarceration. Significant spatial and space-time clusters with elevated relative risk for jail incarceration were found in analysis. Additionally, a negative binomial regression model was used to predict jail incarcerations counts based on several covariates and found significant and nonrandom spatial clusters of jail incarceration that are explained after adjusting for these covariates. The results in this study provide useful information on possible associations in geographical areas where jail incarceration rates are higher than expected and demonstrate significant correlations between jail incarceration counts and several covariates. The study and its conclusions provide an epidemiological framework for identifying and addressing geographic patterns of unusually high jail incarceration rates in the United States and provide evidence of appropriate locations to further investigate underlying causes of disproportionate incarceration. Supplementary files for this article are available online.

Sporadic Shiga Toxin-Producing Escherichia coli-Associated Pediatric Hemolytic Uremic Syndrome, France, 2012-2021.

Shiga toxin-producing Escherichia coli-associated pediatric hemolytic uremic syndrome (STEC-HUS) remains an important public health risk in France. Cases are primarily sporadic, and geographic heterogeneity has been observed in crude incidence rates. We conducted a retrospective study of 1,255 sporadic pediatric STEC-HUS cases reported during 2012-2021 to describe spatiotemporal dynamics and geographic patterns of higher STEC-HUS risk. Annual case notifications ranged from 109 to 163. Most cases (n = 780 [62%]) were in children <3 years of age. STEC serogroups O26, O80, and O157 accounted for 78% (559/717) of cases with serogroup data. We identified 13 significant space-time clusters and 3 major geographic zones of interest; areas of southeastern France were included in >5 annual space-time clusters. The results of this study have numerous implications for outbreak detection and investigation and research perspectives to improve knowledge of environmental risk factors associated with geographic disparities in STEC-HUS in France.

Geographic variation in HCV treatment penetration among people who inject drugs in Baltimore, MD.

We evaluated geographic heterogeneity in hepatitis C virus (HCV) treatment penetration among people who inject drug (PWID) across Baltimore, MD since the advent of direct-acting antivirals (DAAs) using space-time clusters of HCV viraemia. Using data from a community-based cohort of PWID, the AIDS Linked to the IntraVenous Experience (ALIVE) study, we identified space-time clusters with higher-than-expected rates of HCV viraemia between 2015 and 2019 using scan statistics. We used Poisson regression to identify covariates associated with HCV viraemia and used the regression-fitted values to detect adjusted space-time clusters of HCV viraemia in Baltimore city. Overall, in the cohort, HCV viraemia fell from 77% in 2015 to 64%, 49%, 39% and 36% from 2016 to 2019. In Baltimore city, the percentage of census tracts where prevalence of HCV viraemia was ≥85% dropped from 57% to 34%, 25%, 22% and 10% from 2015 to 2019. We identified two clusters of higher-than-expected HCV viraemia in the unadjusted analysis that lasted from 2015 to 2017 in East and West Baltimore and one adjusted cluster of HCV viraemia in West Baltimore from 2015 to 2016. Neither differences in age, sex, race, HIV status, nor neighbourhood deprivation were able to explain the significant space-time clusters. However, residing in a cluster with higher-than-expected viraemia was associated with age, sex, educational attainment and higher levels of neighbourhood deprivation. Nearly 4 years after DAAs became available, HCV treatment has penetrated all PWID communities across Baltimore city. While nearly all census tracts experienced improvements, change was more gradual in areas with higher levels of poverty.

Spatial analysis of human and livestock anthrax in Dien Bien province, Vietnam (2010-2019) and the significance of anthrax vaccination in livestock.

Anthrax is a serious zoonosis caused by Bacillus anthracis, which primarily affects wild herbivorous animals with spillover into humans. The disease occurs nearly worldwide but is poorly reported in Southeast Asian countries. In Vietnam, anthrax is underreported, and little is known about its temporal and spatial distributions. This paper examines the spatio-temporal distribution and epidemiological characteristics of human and livestock anthrax from Dien Bien province, Vietnam from 2010 to 2019. We also aim to define the role of livestock vaccination in reducing human cases. Historical anthrax data were collected by local human and animal health sectors in the province. Spatial rate smoothing and spatial clustering analysis, using Local Moran's I in GeoDa and space-time scan statistic in SaTScan, were employed to address these objectives. We found temporal and spatial overlap of anthrax incidence in humans and livestock with hotspots of human anthrax in the east. We identified three significant space-time clusters of human anthrax persisting from 2010 to 2014 in the east and southeast, each with high relative risk. Most of the human cases were male (69%), aged 15-59 years (80%), involved in processing, slaughtering, or eating meat of sick or dead livestock (96.9%) but environmental and unknown exposure were reported. Animal reports were limited compared to humans and at coarser spatial scale, but in areas with human case clusters. In years when livestock vaccination was high (>~25%), human incidence was reduced, with the opposite effect when vaccine rates dropped. This indicates livestock vaccination campaigns reduce anthrax burden in both humans and livestock in Vietnam, though livestock surveillance needs immediate improvement. These findings suggest further investigation and measures to strengthen the surveillance of human and animal anthrax for other provinces of Vietnam, as well as in other countries with similar disease context.

Space-Time Clustering with the Space-Time Permutation Model in SaTScan™ Applied to Building Permit Data Following the 2011 Joplin, Missouri Tornado

Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to (1) compare the observed versus expected frequency (chi-square) of permit issuance before and after the EF5 2011 tornado; (2), determine if significant space-time clusters of permits existed using the SaTScan™ cluster analysis program (version 9.7); and (3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event, and one (residential) showed significance for nine of the 10 years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.

Evidence of transgenerational effects on autism spectrum disorder using multigenerational space-time cluster detection

BackgroundTransgenerational epigenetic risks associated with complex health outcomes, such as autism spectrum disorder (ASD), have attracted increasing attention. Transgenerational environmental risk exposures with potential for epigenetic effects can be effectively identified using space-time clustering. Specifically applied to ancestors of individuals with disease outcomes, space-time clustering characterized for vulnerable developmental stages of growth can provide a measure of relative risk for disease outcomes in descendants.Objectives(1) Identify space-time clusters of ancestors with a descendent with a clinical ASD diagnosis and matched controls. (2) Identify developmental windows of ancestors with the highest relative risk for ASD in descendants. (3) Identify how the relative risk may vary through the maternal or paternal line.MethodsFamily pedigrees linked to residential locations of ASD cases in Utah have been used to identify space-time clusters of ancestors. Control family pedigrees of none-cases based on age and sex have been matched to cases 2:1. The data have been categorized by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3957 children, both parents, and maternal and paternal grandparents were identified. Bernoulli space-time binomial relative risk (RR) scan statistic was used to identify clusters. Monte Carlo simulation was used for statistical significance testing.ResultsTwenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from the maternal and paternal lines at a p-value < 0.05. The paternal grandparents carry the greatest RR (2.86–2.96) during birth and childhood in the 1950’s–1960, which represent the smallest size clusters, and occur in urban areas. Additionally, seven statistically significant clusters with RR < 1 were relatively large in area, covering more rural areas of the state.ConclusionThis study has identified statistically significant space-time clusters during critical developmental windows that are associated with ASD risk in descendants. The geographic space and time clusters family pedigrees with over 3 + generations, which we refer to as a person’s geographic legacy, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. Our novel use of space-time clustering can be applied to any disease where family pedigree data is available.

Spatial and space-time clusters of suicides in the contiguous USA (2000–2019)

The present study investigates the spatiotemporal variations in suicide mortality and tests associations between several covariates and suicides for the years 20002019 in the contiguous USA. The epidemiological disease surveillance software was used to identify spatiotemporal variations in suicide mortality rates and to test for significant spatial and space-time clusters with elevated relative suicide risk. The analysis was done with age-adjusted suicide mortality counts data from the Centers for Disease Control (CDC) with International Classification of Diseases (ICD)-10 codes. Specifically, data with codes ICD-10 codes X60-X84.9 and Y87.0, plus ICD-10 113 codes from the CDC, was used. Fourteen significant spatial clusters and five significant space-time clusters of suicide in the contiguous USA were found, including nine significant bivariate spatial clusters of suicide deaths and opioid deaths. Based on these data, there exist significant and non-random suicide mortality clusters after adjusting for multiple covariates or risk factors. The covariates studied provide evidence to develop a better understanding of possible associations in geographical areas where the suicide mortality rates are higher than expected. In addition, there is a significant association between several of the studied risk factors and suicide mortality. While most suicide clusters are also opioid clusters, there exist some clusters with high opioid deaths that are not suicide clusters. These results have the potential to provide a scientific framework that is based on surveillance, allowing health agencies to intervene and reduce elevated rates of suicides in selected counties in the U.S. The study is limited due to the resolution of the data at the county level, and some covariate data was unavailable for the entire period of the study.

Prospective spatial-temporal clusters of COVID-19 in local communities: case study of Kansas City, Missouri, United States.

Kansas City, Missouri, became one of the major United States hotspots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of positive cases in Kansas City, MO, the spatial-temporal analysis of data has been less investigated. However, it is critical to detect emerging clusters of COVID-19 and enforce control and preventive policies within those clusters. We conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO data to detect significant space-time clusters of COVID-19 positive cases at the zip code level in Kansas City, MO. The analysis focused on daily infected cases in four equal periods of 3 months. We detected temporal patterns of emerging and re-emerging space-time clusters between March 2020 and February 2021. Three statistically significant clusters emerged in the first period, mainly concentrated in downtown. It increased to seven clusters in the second period, spreading across a broader region in downtown and north of Kansas City. In the third period, nine clusters covered large areas of north and downtown Kansas City, MO. Ten clusters were present in the last period, further extending the infection along the State Line Road. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and allocating resources (e.g., vaccines and testing sites). As more data become available, statistical clustering can be used as a COVID-19 surveillance tool to measure the effects of vaccination.

Detection of temporal, spatial and spatiotemporal clustering of malaria incidence in northwest Ethiopia, 2012\u20132020

Malaria is one of Ethiopia's most targeted communicable diseases for elimination. Malaria transmission varies significantly across space and time; and Ethiopia had space–time disparity in its transmission intensities. Considering heterogeneity and transmission intensity at the district level could play a crucial role in malaria prevention and elimination. This study aimed to explore temporal, spatial, and spatiotemporal clusters of malaria incidence in northwest Ethiopia. The analysis is based on monthly malaria surveillance data of districts and collected from the Amhara public health institute. The Kulldorff's retrospective space–time scan statistics using a discrete Poisson model were used to detect temporal, spatial, and space–time clusters of malaria incidence with and without adjusting the altitude + LLIN arm. Monthly malaria incidence had seasonal variations, and higher seasonal indices occurred in October and November. The temporal cluster occurred in the higher transmission season between September and December annually. The higher malaria incidence risk occurred between July 2012 and December 2013 (LLR = 414,013.41, RR = 2.54, P < 0.05). The purely spatial clustering result revealed that the most likely cluster occurred in the north and northwest parts of the region while secondary clusters varied in years. The space–time clusters were detected with and without considering altitude + LLIN arm. The most likely space–time cluster was concentrated in northwestern and western parts of the region with a high-risk period between July 2012 and December 2013 (LLR = 880,088.3, RR = 5.5, P < 0.001). We found eight significant space–time clusters using the altitude + LLIN arm. The most likely space–time cluster occurred in the western and northwestern parts of the region in July 2012–December 2013 (LLR = 886,097.7, RR = 5.55, P < 0.05). However, secondary clusters were located in eastern, northwestern, western parts of regions, which had different cases and relative risks in each cluster. Malaria transmission had temporal, spatial, and space–time variation in the region at the district level. Hence, considering these variations and factors contributing to malaria stratification would play an indispensable role in preventing and controlling practices that ultimately leads to malaria eliminations.

Evidence for Transgenerational Effects on Autism Spectrum Disorder Using Multigenerational Space-time Cluster Detection

BACKGROUND AND AIM: Space-time cluster analyses are commonly used in spatial epidemiology. There is evidence of transgenerational factors associated with ASD. Space-time clusters of parents and grandparents of ASD cases may provide evidence of transgenerational effects and generate hypotheses regarding ancestral conditions associated with ASD. The Aim of this study is to identify and describe space-time clusters of ancestors of individuals with a clinical diagnosis of ASD. METHODS: Parents and grandparents of diagnosed ASD individuals were identified using the Utah Registry of Autism Developmental Disabilities (URADD) linked to their pedigrees in the Utah Population Database (UPDB). Inclusion criteria for ASD case ancestors included (1) three generations of continuous Utah residency, and (2) first reported case of ASD in the family. Ancestors of individuals without ASD were selected based on a 2:1 random sample matched by age, time-period, and sex to the ASD cases. Residential addresses of the ancestors for their perinatal, childhood and adolescent time periods were compiled from administrative databases linked to the UPDB and geocoded. Bernoulli space-time scan statistic was used to identify clusters for each type of ancestor and developmental window. RESULTS:The study included 7,914 parents and 15,828 grandparents generated from 3,957 ASD cases. Over 90% of the addresses were geocoded to street address. Twenty statistically significant space-time clusters (p-value 0.05) were identified (13 increased relative risk (RR) and 7 with RR1). The four strongest clusters (RR = 2.74 – 2.96) were from paternal grandparents at the perinatal time-period spanning 1 to 10 years and covering 8 to 132 km2 in predominantly urban residential areas. Of the 13 clusters with RR1 (range=1.27 – 2.96) 11 began between 1946 and 1960, nine were grandparents. CONCLUSIONS:Multiple very high relative risk space-time clusters of ASD ancestors during early life provide evidence of potential transgenerational effects on ASD in grandchildren. KEYWORDS: Space-time clustering, Multigenerational, Autism Spectrum Disorder, Transgenerational Effects

Detection of COVID-19 case clusters in Qu\xe9bec, May\u2013October 2020

ObjectivesThe Quebec Public Health Institute (INSPQ) was mandated to develop an automated tool for detecting space-time COVID-19 case clusters to assist regional public health authorities in identifying situations that require public health interventions. This article aims to describe the methodology used and to document the main outcomes achieved.MethodsNew COVID-19 cases are supplied by the “Trajectoire de santé publique” information system, geolocated to civic addresses and then aggregated by day and dissemination area. To target community-level clusters, cases identified as residents of congregate living settings are excluded from the cluster detection analysis. Detection is performed using the space-time scan statistic and Poisson statistical model, and implemented in the SaTScan software. Information on detected clusters is disseminated daily via an online interactive mapping interface.ResultsThe number of clusters detected tracked with the number of new cases. Slightly more than 4900 statistically significant (p ≤ 0.01) space-time clusters were detected over 14 health regions from May to October 2020. The Montréal region was the most affected.ConclusionConsidering the objective of timely cluster detection, the use of near-real-time health surveillance data of varying quality over time and by region constitutes an acceptable compromise between timeliness and data quality. This tool serves to supplement the epidemiologic investigations carried out by regional public health authorities for purposes of COVID-19 management and prevention.

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.

A Spatio-temporal distribution analysis of vesicular stomatitis outbreak in Ecuador, 2018

Vesicular stomatitis (VS) is a viral disease primarily affecting cattle, swine, and equine causing economic losses. It is of particular interest because its outward signs are similar to those of foot-and-mouth disease. Outbreaks of VS occurred in several herds in Ecuador in 2018, affecting principally bovines. In this sense, the present study was conducted to characterize the temporal and spatial dynamics of Vesicular stomatitis occurrence between January and December 2018. During the study period, 583 animals with symptoms of VS were reported. In this way, tissue samples were collected, VS was diagnosed, and outbreaks were defined as herds with a confirmed positive test for the disease. Outbreaks were georeferenced, and Space-time clusters were used to determine zones where the number of reported outbreaks was more significant than expected. A space-time permutation scan statistic (STPSS) was used to identify hot spots of space-time interaction within patterns of the cases reported. Standard Monte Carlo Critical Value was used to test for the cluster's significance. A total of 399 outbreaks were presented in 18 provinces. Spatial scan statistics allowed the detection of four significant space-time clusters of VS outbreaks. The highest incidence was reported around week 35 and week 44, which were observed outbreaks increase in the country's north region. In this sense, clusters coincided with the areas with the highest incidence of outbreaks. Besides, maps showed places where the disease is not shared. The information showed in the present study may contribute to prevents VS spread into regions of Ecuador that is only sporadically affected by the disease. Monitoring in affected zones may lead to quick responses to possible outbreaks issuing alerts when there is a greater than typical risk of spreading the disease

Detection of spatial and spatio-temporal Salmonella Heidelberg and Salmonella Typhimurium human case clusters focused around licensed abattoirs in Ontario in 2015, and their potential relation to known outbreaks.

Salmonellosis is one of several zoonotic diseases for which individuals with occupational animal contact, including abattoir workers, are at an increased risk. If meat is contaminated during slaughter, this can increase the risk of enteric illness for consumers. In this study, we investigated whether reported cases of Salmonella Heidelberg and Typhimurium were clustered around abattoirs in Ontario in 2015 and whether there was any evidence (laboratory/exposure) to suggest an abattoir at the centre of a cluster might be the source of exposure. Data for each reported case of S.Heidelberg and S.Typhimurium in Ontario in 2015 were collected. Multi-focused and non-focused spatial and space-time cluster detection tests were performed for each serotype, with and without cases linked to known outbreaks, using Poisson and space-time permutation models. Focused tests included the location of abattoirs operational in all or part of 2015. Laboratory data and exposure information were used to explore the relatedness of cases within identified clusters. Focused spatial tests identified clusters of S.Heidelberg and S.Typhimurium around abattoirs. Focused space-time permutation tests identified 2 significant space-time clusters of S.Heidelberg; one cluster (n=11 cases) included 8 of 9 cases associated with a known outbreak and the other cluster (n=18 cases) was not part of a previously identified outbreak. Review of laboratory and risk factor information suggested that cases within each cluster shared a common exposure. Cases were not asked about goat or sheep meat consumption. The focused cluster test, particularly with the space-time permutation model, could assist in identifying outbreaks associated with a particular physical location, such as an abattoir. Improvements to the current case investigation process, such as consistent collection and reporting of high-risk occupation information and more detailed food consumption history, could assist in outbreak identification when coupled with this statistic.

Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia

Dengue has been a perennial public health problem in Medan city, North Sumatera, despite the widespread implementation of dengue control. Understanding the spatial and temporal pattern of dengue is critical for effective implementation of dengue control strategies. This study aimed to characterize the epidemiology and spatio-temporal patterns of dengue in Medan City, Indonesia. Data on dengue incidence were obtained from January 2016 to December 2019. Kulldorff’s space-time scan statistic was used to identify dengue clusters. The Getis-Ord Gi* and Anselin Local Moran’s I statistics were used for further characterisation of dengue hotspots and cold spots. Results: A total of 5556 cases were reported from 151 villages across 21 districts in Medan City. Annual incidence in villages varied from zero to 439.32 per 100,000 inhabitants. According to Kulldorf’s space-time scan statistic, the most likely cluster was located in 27 villages in the south-west of Medan between January 2016 and February 2017, with a relative risk (RR) of 2.47. Getis-Ord Gi* and LISA statistics also identified these villages as hotpot areas. Significant space-time dengue clusters were identified during the study period. These clusters could be prioritized for resource allocation for more efficient prevention and control of dengue.

Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong

Identifying the space-time patterns of areas with a higher risk of transmission and the associated built environment and demographic characteristics during the COVID-19 pandemic is critical for developing targeted intervention measures in response to the pandemic. This study aims to identify areas with a higher risk of COVID-19 transmission in different periods in Hong Kong and analyze the associated built environment and demographic factors using data of individual confirmed cases. We detect statistically significant space-time clusters of COVID-19 at the Large Street Block Group (LSBG) level in Hong Kong between January 23 and April 14, 2020. Two types of high-risk areas are identified (residences of and places visited by confirmed cases) and two types of cases (imported and local cases) are considered. The demographic and built environment features for the identified high-risk areas are further examined. The results indicate that high transport accessibility, dense and high-rise buildings, a higher density of commercial land and higher land-use mix are associated with a higher risk for places visited by confirmed cases. More green spaces, higher median household income, lower commercial land density are linked to a higher risk for the residences of confirmed cases. The results in this study not only can inform policymakers to improve resource allocation and intervention strategies but also can provide guidance to the public to avoid conducting high-risk activities and visiting high-risk places.

Spatial and space-time clusters of brain tumor and CNS incidence and mortality in the contiguous USA: 2000–2014

Abstract Objective Investigate geographical variations in brain/central nervous system (CNS) cancer incidence and mortality, and test associations between covariates and brain/CNS cancer for the years 2000–2014 in the contiguous USA. Methods The disease surveillance software (SaTScanTM) was used to measure observable geographical variation in brain/CNS cancer incidence and mortality rates and identify and test spatial and space-time clusters with elevated relative risk. The analysis was done with age-adjusted brain/CNS cancer mortality and incidence counts data, including ICD codes C710–C719, C379, C740–C749, and C750–C759. The cancer data were then adjusted for covariates or risk factors, such as obesity, smoking, poverty, race, arsenic, air pollution, land pollution, water pollution, gender, health insurance, diabetes, glyphosate, superfund site density, and proximity to nuclear power plants. Results Several significant spatial clusters of brain/CNS cancer exist for incidence in the Northeast and the Northwest, while spatial mortality clusters cover additional parts of the US. There also exist several significant space-time clusters for incidence and mortality. Conclusions There exist significant and non-random brain/CNS cancer incidence and mortality clusters after adjusting for multiple covariates or risk factors. The thirteen covariates studied can assist in a better understanding of possible associations in geographical areas where the incidence or mortality rates are higher than expected. There is a significant association between several of the studied risk factors and the incidence rates of brain/CNS cancers, and the mortality rates. These results have the potential to provide a scientific framework that is based on surveillance, allowing health agencies to intervene to try to reduce the elevated rates of brain/CNS cancer in some parts of the contiguous US.

Geospatial analysis of malaria mortality in the kintampo health and demographic surveillance area of central Ghana

ABSTRACT Malaria remains a menace to the existence of humanity in most contexts. Geospatial analysis of malaria mortality is crucial to identifying clusters of high disease burden and areas with limited access to malaria care for targeted control and remedial interventions. This study identified spatial and space-time clusters of malaria mortality in the Kintampo area of central Ghana. We used 1301 malaria deaths archived from 2005 to 2017 and Global Positioning System (GPS) point locations of the sub-districts in which these deaths occurred for our analysis. Mortality risks were smoothed and mapped using the Spatial Empirical Bayesian smoothing technique in Geoda (version 1.12.1.161) whereas spatial and spatio-temporal clustering analysis was done using SaTScan (version 9.6). Malaria mortality risks ranged between 1.2 and 2.4 deaths per 1000 population for persons of all ages and between 3.3 and 6.0 deaths per 1000 population for children under five years of age by sub-district. Two spatial clusters were detected for all-age malaria mortality with only the primary cluster (RR = 1.42, p = 0.001) being statistically significant. Also, two statistically significant space-time clusters were detected for all-age malaria mortality in the study area. The most likely cluster occurred between 2006 and 2011 in five sub-districts with a relative risk of 2.12 (p < 0.001) whilst the secondary cluster which had a relative risk of 2.47 (p < 0.001) occurred between 2005 and 2010 in four sub-districts. Similarly, only the most likely spatial cluster of under-five malaria mortality was statistically significant (RR = 1.36, p = 0.024). Furthermore, three spatio-temporal clusters of under-five malaria mortality were detected in the study area. The primary and second secondary clusters were statistically significant whilst the first secondary cluster had borderline significance. The primary cluster (RR = 4.49, p = 0.002) occurred in two sub-districts between 2006 and 2007. The first secondary cluster (RR = 2.21, P = 0.005) covered four sub-districts and was detected between 2006 and 2011 whereas the second secondary cluster (RR = 2.51, p = 0.003) covered two sub-districts between 2008 and 2013. Ultimately, our analysis identified a number of substantial spatial and apace-time clusters of malaria mortality in the study context, which could aid in the strategic planning, implementation and monitoring of targeted malaria control interventions.

Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

BackgroundAlthough effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016.MethodsGeographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model.ResultsResults showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P < 0.05) spatial and space-time clusters of malaria in specific geographic regions. The detected primary clusters persisted in the eastern region of the study area over the six year study period while the temporal pattern of malaria reflected the seasonality of the disease where clusters were detected within particular months of the year.ConclusionsGeographic regions characterised by clusters of high rates were identified as malaria high risk areas. The results of this study could be useful in prioritizing resource allocation in high-risk areas for malaria control and elimination particularly in resource limited settings such as Zimbabwe. The results of this study are also useful to guide further investigation into the possible determinants of persistence of high clusters of malaria cases in particular geographic regions which is useful in reducing malaria burden in such areas.

Long-Term Epidemiological Dynamics of Japanese Encephalitis Infection in Gansu Province, China: A Spatial and Temporal Analysis.

.The incidence of Japanese encephalitis (JE) has greatly declined in China. However, JE incidence has significantly increased in Gansu in recent years, on the top of ranks among all provinces in China. To explore the spatial spread and resurgence of JE transmission in Gansu in the past 60 years, we collected yearly data on reported JE in each county (1958–2017) and monthly data on JE cases (1968–2017), respectively. We grouped the dataset into six categories, each consisting of a 10-year period between 1958 and 2017. Spatial cluster analysis was applied to identify the potential space–time clusters of JE incidence, and logistic regression models were used to identify the spatial and temporal dispersion of JE. Japanese encephalitis incidence in Gansu showed an upward trend from 1970 to 1977 and peaked in 1974, then declined, and fluctuated over the study period until an outbreak again in 2017. Japanese encephalitis incidence for the first 30-year period (1958–1987) peaked in September each year and thereafter peaked in July and August during 1988–2017. Spatial cluster analysis showed the geographical range of JE transmission fluctuated over the past 60 years. The high-incidence clusters of JE were primarily concentrated in the southeast of Gansu. We found significant space–time clustering characteristics of JE in Gansu, and the geographical range of notified JE cases has significantly expanded over recent years. The potential rebound of JE transmission occurred in 2016–2017 should be placed on the top priority of government work during the control and prevention of JE in Gansu, China.