Spatial Epidemiology Research Articles

A spatiotemporal analysis of the social determinants of health for COVID-19.

This research aims to uncover how the association between social determinants of health and COVID-19 cases and fatality rate have changed across time and space. To begin to understand these associations and show the benefits of analysing temporal and spatial variations in COVID-19, we utilized Geographically Weighted Regression (GWR). The results emphasize the advantages for using GWR in data with a spatial component, while showing the changing spatiotemporal magnitude of association between a given social determinant and cases or fatalities. While previous research has demonstrated the merits of GWR for spatial epidemiology, our study fills a gap in the literature, by examining a suite of variables across time to reveal how the pandemic unfolded across the US at a county-level spatial scale. The results speak to the importance of understanding the local effects that a social determinant may have on populations at the county level. From a public health perspective, these results can be used for an understanding of the disproportionate disease burden felt by different populations, while upholding and building upon trends observed in epidemiological literature.

Examining Increasing Racial Inequities in Opioid Overdose Deaths: a Spatiotemporal Analysis of Black and White Decedents in St. Louis, Missouri, 2011-2021.

The third wave of the opioid overdose crisis-defined by the proliferation of illicit fentanyl and its analogs-has not only led to record numbers of overdose deaths but also to unprecedented racial inequities in overdose deaths impacting Black Americans. Despite this racialized shift in opioid availability, little research has examined how the spatial epidemiology of opioid overdose death has also shifted. The current study examines the differential geography of OOD by race and time (i.e., pre-fentanyl versus fentanyl era) in St. Louis, Missouri. Data included decedent records from the local medical examiners suspected to involve opioid overdose (N = 4420). Analyses included calculating spatial descriptive analyses and conducting hotspot analyses (i.e., Gettis-Ord Gi*) stratified by race (Black versus White) and time (2011-2015 versus 2016-2021). Results indicated that fentanyl era overdose deaths were more densely clustered than pre-fentanyl era deaths, particularly those among Black decedents. Although hotspots of overdose death were racially distinct pre-fentanyl, they substantially overlapped in the fentanyl era, with both Black and White deaths clustering in predominantly Black neighborhoods. Racial differences were observed in substances involved in cause of death and other overdose characteristics. The third wave of the opioid crisis appears to involve a geographic shift from areas where White individuals live to those where Black individuals live. Findings demonstrate racial differences in the epidemiology of overdose deaths that point to built environment determinants for future examination. Policy interventions targeting high-deprivation communities are needed to reduce the burden of opioid overdose on Black communities.

Transmission risk of Oropouche fever across the Americas

BackgroundVector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance.MethodsTo better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas.ResultsModels based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks.ConclusionsHotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China.

This study aims to explore the spatiotemporal distribution of schistosomiasis in Jiangling County, and provide insights into the precise schistosomiasis control. The descriptive epidemiological method and Joinpoint regression model were used to analyze the changes in infection rates of humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County from 2005 to 2021. Spatial epidemiology methods were used to detect the spatiotemporal clustering of schistosomiasis transmission risk in Jiangling county. The infection rates in humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County decreased from 2005 to 2021 with statistically significant. The average density of living snails in Jiangling County was spatially clustered in each year, and the Moran's I varied from 0.10 to 0.26. The hot spots were mainly concentrated in some villages of Xionghe Town, Baimasi Town and Shagang Town. The mean center of the distribution of average density of living snails in Jiangling County first moved from northwest to southeast, and then returned from southeast to northwest after 2014. SDE azimuth fluctuated in the range of 111.68°-124.42°. Kernal density analysis showed that the high and medium-high risk areas of Jiangling County from 2005 to 2021 were mainly concentrated in the central and eastern of Jiangling County, and the medium-low and low risk areas were mainly distributed in the periphery of Jiangling County. The epidemic situation of schistosomiasis decreased significantly in Jiangling County from 2005 to 2021, but the schistosomiasis transmission risk still had spatial clustering in some areas. After transmission interruption, targeted transmission risk intervention strategies can be adopted according to different types of schistosomiasis risk areas.

The Analytic Solutions of the Fractional-Order Model for the Spatial Epidemiology of the COVID-19 Infection

This paper provides a mathematical fractional-order model that accounts for the mindset of patients in the transmission of COVID-19 disease, the continuous inflow of foreigners into the country, immunization of population subjects, and temporary loss of immunity by recovered individuals. The analytic solutions, which are given as series solutions, are derived using the fractional power series method (FPSM) and the residual power series method (RPSM). In comparison, the series solution for the number of susceptible members, using the FPSM, is proportional to the series solution, using the RPSM for the first two terms, with a proportional constant of ψ Γ n α + 1 , where ψ is the natural birth rate of the baby into the susceptible population, Γ is the gamma function, n is the n th term of the series, and α is the fractional order as the initial number of susceptible individuals approaches the population size of Ghana. However, the variation in the two series solutions of the number of members who are susceptible to the COVID-19 disease begins at the third term and continues through the remaining terms. This is brought on by the nonlinear function present in the equation for the susceptible subgroup. The similar finding is made in the series solution of the number of exposed individuals. The series solutions for the number of deviant people, the number of nondeviant people, the number of people quarantined, and the number of people recovered using the FPSM are unquestionably almost identical to the series solutions for same subgroups using the RPSM, with the exception that these series solutions have initial conditions of the subgroup of the population size. It is observed that, in this paper, the series solutions of the nonlinear system of fractional partial differential equations (PDEs) provided by the RPSM are more in line with the field data than the series solutions provided by the FPSM.

Narrative minireview of the spatial epidemiology of substance use disorder in the United States: Who is at risk and where?

Drug overdose is the leading cause of death by injury in the United States. The incidence of substance use disorder (SUD) in the United States has increased steadily over the past two decades, becoming a major public health problem for the country. The drivers of the SUD epidemic in the United States have changed over time, characterized by an initial heroin outbreak between 1970 and 1999, followed by a painkiller outbreak, and finally by an ongoing synthetic opioid outbreak. The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic. Moreover, the geospatial distribution of the SUD epidemic is not homogeneous. The United States has specific locations where vulnerable communities at high risk of SUD are concentrated, reaffirming the multifactorial socioeconomic nature of this epidemic. A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies. This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods, revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks. By analyzing the epidemic of SUD-related deaths, we also describe the epidemic behavior in areas with high incidence of cases. Finally, we describe public health interventions that can be effective for demographic groups, and we discuss future challenges in the study and control of the SUD epidemic in the country.

Retrospective investigation of Newcastle disease reported in Türkiye between 2017-2019

This study was aimed to understand the spatial and seasonal epidemiology of Newcastle disease (ND) in Türkiye using the outbreak data between 2017-2019 and also to calculate the case-fatality rates of this disease. It was also aimed to produce the maps by using Geographical Information Systems (GIS). Data were obtained from the World Animal Health Information System (WAHIS) database of the World Organization for Animal Health (OIE). Total number of 220 outbreaks of ND were registered in 47 provinces of Türkiye between this years. Accordingly, 88,372 poultry birds transmitted the disease. The highest number of ND outbreaks, cases and deaths was reported in The Black Sea Region. According to the regions there was a statistically significant difference in the number of outbreaks (P<0.05), but there was no statistically significant difference in terms of cases and deaths (P>0.05). On the other hand the highest number of ND outbreaks, cases and deaths was reported in the spring season. As a result of the comparisons according to the seasons, there was a statistically significant difference in the number of deaths (P<0.05), but there was no statistically significant difference in terms of outbreaks and cases (P>0.05). The spatial and seasonal distributions identified in this study should be taken into account while attempting to control the disease. Also, it is thought that the creation of spatial maps based on ND outbreaks that are common in Türkiye will contribute to the determination of the areas where precautions should be taken against the disease.

Epidemiology and economic impact of disease-related losses on commercial catfish farms: A seven-year case study from Alabama, USA

The objectives of this study were to determine statewide production losses caused by diseases, identify the most incident catfish diseases affecting Alabama (AL) farm-raised catfish production in freshwater ponds and their annual pattern, and determine disease-related economic impacts. The Alabama Fish Farming Center (AFFC) conducted seven annual retrospective questionnaire surveys of AL catfish farmers since 2015 to assess the epidemiological situation of farm-raised catfish fish diseases and their economic impacts. Across the study (2015–2021), the annual number of survey respondents ranged from 64 to 74, with a total of 482 respondents. The yearly survey response rate ranged from 95.6 to 100% (mean: 98.2%). The annual survey coverage area ranged from 6410 to 7006 ha/year. About 97% of survey coverage area reported fish losses caused by a disease. Fish diseases resulted in the loss of 9 million fish/year (2.9 million kg/year; 436.7 kg/ha/year). Bacterial diseases resulted in >83% of catfish disease losses (>5 times losses to non-bacterial causes). The most incident statewide catfish disease was hypervirulent Aeromonas hydrophila (225 kg/ha/year), followed by columnaris (140 kg/ha/year), followed by Edwardsiella (52 kg/ha/year). The temporal epidemiological analysis results indicate that annual trends of proliferative gill disease and Bolbophorus disease were correlated with prolonged precipitation periods and total quantity of rainfall, respectively. Economic losses caused by fish losses to bacterial diseases, non-bacterial diseases, production losses due to reduced feeding during outbreaks, and expenditures associated with catfish diseases were responsible for 54.5%, 10.8%, 22.0%, and 12.7% of total direct economic impacts attributed to AL farm-raised catfish industry, respectively. Total disease-related financial losses in west AL were 11.1 million USD/year (1651 USD/ha/year), representing about 9.5% of food-size catfish sales in AL. The present study provides the first extensive assessment of epidemiology and health economics of farm-raised catfish disease in the freshwater pond environment for a substantial duration. More studies focusing on disease losses at the county- and farm-level would provide useful information regarding the spatial epidemiology of diseases and their underlying risk factors.

Geographic Information System (GIS) and Healthcare: An Overview

GIS (Geographic Information System) are computer-based tools used to visualize, analyze, accumulate and explicate geographic data. The determinants of health status are often correlated and analogous. Establishing GIS-based approaches and programmes can predict and analyze the complexity of web of causation of many health issues. GIS can be used to efficaciously investigate health along with its physical, social and cultural environments. Mapping functions of GIS can be used to plot health attributes for better visualization, exploration, and modeling of health patterns. GIS based healthcare helps in explaining and describing health outcomes, health disparities, healthcare access and how health care delivery can be improved. GIS can also be used to bring all spatial data under one umbrella of “Geo Data Bank” that could provide easy accessibility and help in better utility of healthcare services. In India, the adoption and use of digital spatial methods like GIS in the field of healthcare has lagged. However, the “Computer Aided Utility Mapping Project for six cities” in India is a significant benefit for the GIS users in the country which can be used by healthcare sector in the near future for the upliftment of healthcare facility. The primary focus of the paper is to provide details about benefits of using GIS-based analytical approaches in healthcare planning, the successes, challenges, remedies and future perspective of GIS in healthcare delivery. Keywords: GIS, healthcare planning, location allocation modeling, public health, spatial analysis, spatial epidemiology.

Molecular Transmission Network of Newly Reported HIV Infections in Pengzhou, Sichuan Province: A Study Based on Genomics and Spatial Epidemiology.

The objective of this study was to understand the molecular transmission characteristics of newly reported HIV infections in the city of Pengzhou, Sichuan Province, to analyze the risk factors of transmission network and spatial clustering and the transmission characteristics, and to provide a scientific basis for precision prevention and intervention. Anticoagulated whole blood was collected from newly reported HIV infections in Pengzhou from March 2019 to August 2021. After the plasma was isolated, the HIV-1 pol gene was amplified and sequenced by reverse transcriptase polymerase chain reaction (PCR). The obtained gene sequences were used to construct a maximum likelihood phylogenetic tree for the analysis of virus subtypes, and a molecular transmission network was constructed using the genetic distance method to evaluate the transmission pattern of people living with HIV/AIDS in Pengzhou. A logistic regression model was used to find out the potential risk factors for entering the molecular transmission network with the number of nodes ≥ 2. Spatial analysis is used to show the geographical pattern of the proportion of newly reported HIV infections entering the molecular transmission network, and a flow map is used to show the intensity of transmission within and between townships. A total of 463 newly reported HIV-infection sequences were obtained in this study, including 237 cases (51.19%) of CRF01_ AE, 159 cases (34.34%) of CRF07_BC, 45 cases (9.72%) of B, 15 cases (3.24%) of CRF08_BC and 7 cases (1.5%) of others. The number of clusters was the highest when the gene distance was 0.009, with a total of 246 sequences entering the network, forming 54 clusters, and the network entry rate was 55.36%. There were 170 sequences with more than two nodes in the network sequence. The logistic regression showed that compared with age < 50 years old, age ≥ 50 years old has a higher risk of transmission (OR = 3.43, 95% CI = 2.06-5.71); compared with farmers, the risk of transmission within industry is lower (OR = 0.046, 95% CI = 0.25-0.87); and compared with CRF07_BC, CRF01_AE (OR = 6.09, 95% CI = 3.60-10.30) and B (OR = 20.31, 95% CI = 8.94-46.13) had a higher risk of transmission. Men aged ≥ 50 years are mainly clustered with women between 50 and 70 years of age. In addition to being clustered with gay men, there are nine (50%) and three (16.7%) chains of transmission between gay men and heterosexual men and women, respectively. In the geographical space, there is no hot spot clustering of the molecular propagation network. The subtype B was mainly distributed in the town of Tianpeng and formed transmission networks in eastern Pengzhou;0020CRF01_AE is mainly distributed in the town of Lichun and formed transmission networks in the west and north of Pengzhou. This study reveals the characteristics and influencing factors of molecular network transmission in the region, as well as the spatial transmission characteristics of newly reported HIV infections in recent years, and reveals the geographical differences in HIV-1 transmission. The results provide a scientific basis for the development of local AIDS-specific intervention measures.

Longitudinal trajectories of region-level suicide mortality in Tokyo, Japan, 2011 to 2021

Background: Suicide mortality in Japan has declined over a period of more than 10 years, however, differences in longitudinal trajectories at a regional level are not well characterized. Objective was to clarify the longitudinal suicide mortality trajectories at the regional level in Tokyo from 2011 to 2021 by considering spatial smoothing, before and during the COVID-19 pandemic. Methods: This longitudinal cross-sectional analysis used fifty-four regions in Tokyo, Japan. Suicide mortality trends used data from the Cabinet Office of the Japanese government from 2011 to 2021. Regional social and environmental characteristics were used as 10 covariates. Empirical Bayes estimates for the standardized mortality ratio were obtained. A conditional autoregressive (CAR) model was applied to capture the spatial correlation for a crude and adjusted with 10 covariates using OpenBUGS. Spatial clusters were also identified by FlexScan, SaTScan, and Tango’s test. Results: Longitudinal trajectories for both males and females were similar to a decreasing trend in all Japan until 2019. In 2020, the beginning of the COVID-19 pandemic, the age-specific suicide deaths were the highest among those in their 20s. However, those were the highest among males in their 50s in 2021. The results of the CAR models adjusted for 10 covariates detected several regions as having higher suicide rates, but those regions were somewhat varied. Conclusion: During the COVID-19 pandemic, both sexes in their 20s and males in their 50s showed a tendency toward an increase in suicides. The detected regions by spatial epidemiology varied with sex.

Lessons Learned from Designing an Aeronautical Reconnaissance Coverage Geographic Information System Dashboard in the Geographic Information System-Naïve Environment to Fit the Spatial Epidemiology Study

Background Since patient access to healthcare is controlled by geographic location, patient records routinely include street addresses, postal codes, or other location elements. By linking spatial, numeric, and categorical health data through a single interface, the aeronautical reconnaissance coverage geographic information system (ArcGIS) dashboard adds the dimension of location and the quality of health information needed at the point of care. Its functions may be used to answer questions that would be difficult to answer without integrating raw patient data with location intelligence technology. However, the provision of geographic information system (GIS) functionalities at the point of care has not been thoroughly investigated. Aim This paper evaluates the ArcGIS dashboard functionalities through a case study of disruption in care for international patient populations due to the coronavirus disease-2019 (COVID-19) pandemic based on their home locations at the tertiary hospital in the U. S. Since the data used in this case study comes from the hospital health data repository, constructing the dashboard afforded an opportunity to explore the provision of GIS functionalities in the GIS–naïve hospital environment. Methods The model used the loose-coupling approach where two components (electronic health records and GIS data) were integrated and visualized through dashboard linking operations and programming languages. The process resulted in three data-driven dashboard elements: area map, attribute table, and statistical plots, enabling simultaneous exploration of various properties of spatial data. Conclusion This paper underpins the potential benefits of the provision of GIS functionalities in the electronic medical records to improve health outcomes, lower costs, and advance spatial epidemiology research. Additionally, it evaluates dashboard scaling limitations and solutions for overcoming the implementation barriers and proposes the future integration of the GIS dashboard with electronic health records by using the health level seven (HL7) fast healthcare interoperability resources (FHIR).

Spatial epidemiology of acute respiratory infections in children under 5 years and associated risk factors in India: District-level analysis of health, household, and environmental datasets.

In India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India. Data on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors. Acute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI. To decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

Early Genomic Surveillance and Phylogeographic Analysis of Getah Virus, a Reemerging Arbovirus, in Livestock in China.

Getah virus (GETV) mainly causes disease in livestock and may pose an epidemic risk due to its expanding host range and the potential of long-distance dispersal through animal trade. Here, we used metagenomic next-generation sequencing (mNGS) to identify GETV as the pathogen responsible for reemerging swine disease in China and subsequently estimated key epidemiological parameters using phylodynamic and spatially-explicit phylogeographic approaches. The GETV isolates were able to replicate in a variety of cell lines, including human cells, and showed high pathogenicity in a mouse model, suggesting the potential for more mammal hosts. We obtained 16 complete genomes and 79 E2 gene sequences from viral strains collected in China from 2016 to 2021 through large-scale surveillance among livestock, pets, and mosquitoes. Our phylogenetic analysis revealed that three major GETV lineages are responsible for the current epidemic in livestock in China. We identified three potential positively selected sites and mutations of interest in E2, which may impact the transmissibility and pathogenicity of the virus. Phylodynamic inference of the GETV demographic dynamics identified an association between livestock meat consumption and the evolution of viral genetic diversity. Finally, phylogeographic reconstruction of GETV dispersal indicated that the sampled lineages have preferentially circulated within areas associated with relatively higher mean annual temperature and pig population density. Our results highlight the importance of continuous surveillance of GETV among livestock in southern Chinese regions associated with relatively high temperatures. IMPORTANCE Although livestock is known to be the primary reservoir of Getah virus (GETV) in Asian countries, where identification is largely based on serology, the evolutionary history and spatial epidemiology of GETV in these regions remain largely unknown. Through our sequencing efforts, we provided robust support for lineage delineation of GETV and identified three major lineages that are responsible for the current epidemic in livestock in China. We further analyzed genomic and epidemiological data to reconstruct the recent demographic and dispersal history of GETV in domestic animals in China and to explore the impact of environmental factors on its genetic diversity and its diffusion. Notably, except for livestock meat consumption, other pig-related factors such as the evolution of live pig transport and pork production do not show a significant association with the evolution of viral genetic diversity, pointing out that further studies should investigate the potential contribution of other host species to the GETV outbreak. Our analysis of GETV demonstrates the need for wider animal species surveillance and provides a baseline for future studies of the molecular epidemiology and early warning of emerging arboviruses in China.

Spatial epidemiology and the detection of spatial clusters of dengue in Paraíba: a comparison between the flexible Scan and circular Scan methods

Resumo Introdução Estatísticas espaciais são usadas para auxiliar gestores de saúde na tomada de decisão, informando a taxa de ocorrência de agravos na população e destacando quando estas alcançam valores além do esperado. Objetivo Compreender o funcionamento e aplicabilidade das Estatísticas Espaciais Scan flexível e Scan circular, comparando seus resultados na detecção de aglomerados espaciais usando dados epidemiológicos reais do dengue no estado da Paraíba – Brasil. Método Descreveu-se o processo detalhado da aplicação das estatísticas Scan flexível e Scan circular para a detecção de áreas significativas de risco (aglomerados) do dengue na Paraíba, nos anos de 2009 a 20013, por meio dos software FLeXScan e SaTScan. Resultados Ambos os métodos revelaram o oeste do estado como a região com maior frequência de aglomerados detectados com alto risco, em todos os anos analisados, levando-se em consideração os mapas de risco de incidência do dengue na Paraíba, nos anos de 2009 a 2013. Conclusão As estatísticas Scan flexível e Scan circular são praticamente similares quanto à eficiência na detecção de aglomerados do dengue. Entretanto, verificaram-se problemas de superestimação no método Scan circular e subestimação no método Scan flexível na detecção dos aglomerados. Destacou-se ainda o auxílio destas estatísticas espaciais aos gestores de saúde quanto à localização das regiões de agravo da doença, tornando mais efetivo o direcionamento das ações de combate de forma politicamente correta.

Larval survey of the dengue-endemic area in Samarinda: guide to determine risk containers

Dengue has been reported in Indonesia since1968; the cases tend to increase and spread every year. From 2015-2019 almost all districts in Indonesia had dengue cases. This study aimed to determine the value of the entomological index and its relationship with the treatment of various types of containers and spatial epidemiology. A larval survey was conducted to determine the presence of larvae in water containers. Logistic regression and spatial data analysis were performed to see the distribution and make a buffer area map of &lt;em&gt;Aedes&lt;/em&gt; &lt;em&gt;sp&lt;/em&gt;. spreading risk. The types of containers observed in this study were water tubs, drums, buckets, refrigerator containers, dispenser containers, and other categories. There were 221 containers found containing &lt;em&gt;Aedes sp.&lt;/em&gt; larvae. Container types affected the presence of larvae with adjusted ORs of 2.779 (95% CI: 1.441-5.360) on buckets, 9.812 (95% CI: 1.249-77.051) on refrigerator holders, and 0.301 (95% CI: 0.147-0.617) on dispenser holders, while the other variables were constant. The spatial analysis found that many houses are at risk of dengue transmission within a radius of 100 m. The discovery of containers as potential breeding sites for &lt;em&gt;Aedes sp.&lt;/em&gt; provides a chance for an increase in dengue cases in Samarinda.

Spatial epidemiology of bacterial meningitis in the Upper West Region of Ghana: Analysis of disease surveillance data 2018-2020.

The use of a Geographic Information System in identifying meningitis hotspots in the Upper West Region (UWR) remains underutilized, making spatial targeting of meningitis hotspots difficult. We therefore utilized surveillance data enabled with GIS technology to target meningitis outbreaks in the UWR. Secondary data analysis was conducted in the study. The dynamics of bacterial meningitis in space and time were studied using epidemiological data from 2018 to 2020. Spot map and choropleths were used to depict the distribution of cases in the region. Moran's I statistics were used to assess spatial autocorrelation. Getis-Ord Gi*(d) and Anselin Local Moran's statistics were used to identify hotspots and spatial outliers within the study area. A Geographic Weighted Regression model was also used to examine how socio bio-climatic conditions influence the spread of meningitis. There were 1176 cases of bacterial meningitis, 118 deaths, and 1058 survivors between 2018 and 2020. Nandom municipality had the highest Attack Rate (AR) at 492/100,000 persons, followed by Nadowli-Kaleo district at 314/100,000 persons. Jirapa had the highest case fatality rate (CFR) at 17%. The spatio-temporal analysis showed spatial diffusion of meningitis prevalence from the western half of the UWR to the east with a significant number of hotpots and cluster outliers. Bacterial meningitis does not occur at random. Populations (10.9%) under sub-districts identified as hotspots are exceptionally at higher risk of outbreaks. Targeted interventions should be directed towards clustered hotspots, focusing on zones with low prevalence fenced off by high prevalence zones.

Assessment of Environmental Impacts on Health: Examples from the Pacific Basin.

Assessing environmental impacts on health in the Pacific Basin is challenged by significantly varying data types - quantities, qualities, and paucities - because of varying geographic sizes, environments, biodiversity, ecological assets, and human population densities, with highly varied and unequal socio-economic development and capacity to respond to environmental and health challenges. We discuss three case-based methodological examples from Pacific Basin environmental health impact assessments. These methods could be used to improve environmental health evidence at all country and regional levels across a spectrum of big data availability to no data. These methods are, 1) a risk assessment of airborne particulate matter in Korea based on the chemical composition of these particulates; 2) the use of system dynamics to appraise the influences of a range of environmental health determinants on child health outcomes in remote Solomon Islands; and 3) precision environmental public health methodologies based on comprehensive data collection, analyses, and modelling (including Bayesian belief networks and spatial epidemiology) increasing precision for good environmental health decision making to prevent and control a zoonotic disease in Fiji Islands. We show that while a common theme across the three examples is the value of high quality and quantity data to support stronger policy decisions and appropriate prioritizing of investment, it is also clear that for many countries in the Pacific Basin, sufficient data will remain a challenge to inform decision makers about environmental impact on health.

Evidence-Based Conceptual Collection of Methods for Spatial Epidemiology and Analysis to Enhance Cancer Surveillance and Public Health.

(1) Background: Although spatial statistics are often used by cancer epidemiologists, there is not yet an established collection of methods to serve their needs. We aimed to develop an evidence-based cancer-oriented conceptual collection of methods for spatial analysis; (2) Methods: A triangulation of approaches was used; literature review, consensus meetings (expert panel), and testing the selected methods on “training” databases. The literature review was conducted in three databases. This approach guided the development of a collection of methods that was subsequently commented on by the expert panel and tested on “training data” of cancer cases obtained from the Cancer Registry of Crete based on three epidemiological scenarios: (a) low prevalence cancers, (b) high prevalence cancers, (c) cancer and risk factors; (3) Results: The final spatial epidemiology conceptual collection of methods covered: data preparation/testing randomness, data protection, mapping/visualizing, geographic correlation studies, clustering/surveillance, integration of cancer data with socio-economic, clinical and environmental factors. Some of the tests/techniques included in the conceptual collection of methods were: buffer and proximity analysis, exploratory spatial analysis and others. All suggested that statistical models were found to fit well (R2 = 0.72–0.96) in “training data”; Conclusions: The proposed conceptual collection of methods provides public health professionals with a useful methodological framework along with recommendations for assessing diverse research questions of global health.

Exploring blue spaces’ effects on childhood leukaemia inci-dence: A population-based case-control study in Spain

Background and aim: Blue spaces have been a key part of human evolution, providing resources and helping economies develop. To date, no studies have been carried out to explore how they may be linked to paediatric oncological diseases. The aim of this study was to explore the possible relationship of residential proximity to natural and urban blue spaces on childhood leukaemia. Methods: A population-based case control study were conducted in four regions of Spain across the period 2000-2018. 936 incident cases and 5,616 controls were included, individually matched by sex, year-of-birth and place-of-residence. An exposure proxy with four distances (250m, 500m, 750m and 1 km) to blue spaces was built using the geographical coordinates of the participants’ home residences. Odds ratios (ORs) and 95% confidence intervals (95%CIs), blue space exposure were calculated for overall childhood leukaemia, and the acute lymphoblastic (ALL) and acute myeloblastic leukaemia (AML) subtypes, with adjustment for socio-demographic and environmental covariates. Results: A decrease in overall childhood leukaemia and ALL-subtype incidence was found as we came nearer to children’s places of residence, showing, for the study as a whole, a reduced incidence at 250m (odds ratio (OR)=0.77; 95%CI=0.60-0.97), 500m (OR=0.78; 95%CI=0.65-0.93), 750m (OR=0.80; 95%CI=0.69-0.93) and 1000m (OR=0.84; 95%CI=0.72-0.97). AML model results showed an increasing incidence at closest to subjects’ homes (OR at 250m = 1.06; 95%CI=0.63-1.71). Conclusions: Our results suggest a possible association between lower childhood-leukaemia incidence and blue-space proximity. This study is a first approach to blue spaces’ possible effects on childhood leukaemia incidence; consequently, it is necessary to continue studying these spaces – while taking into account more individualized data and other possible environmental risk factors. Keywords: urban blue spaces; environmental factors; childhood cancer; childhood leukaemia; incidence; spatial epidemiology.