Application of GIS technology in public health: successes and challenges

Geographic information system and information visualization capacity building: Successful polio eradication and current and future challenges in the COVID-19 era for the World Health Organization's African region

Despite a half-century-long global eradication effort, polio continues to have a devastating impact on individuals and communities worldwide, especially in low-income countries affected by conflict or geographic barriers to immunization programs. In response, the World Health Organization (WHO) Global Polio Eradication Initiative (GPEI) employs disease surveillance and vaccination campaigns coordinated through the WHO Regional Office for Africa (AFRO) Geographic Information System (GIS) Centre. Established in 2017, the AFRO GIS Centre played a key role in the eradication of wild-type polioviruses (WPVs) in 2020, but the COVID-19 pandemic, emergence of circulating vaccine-derived polioviruses, and transmission of WPV1 from Central Asia have led to a resurgence of polio in Sub-Saharan Africa. The AFRO GIS comprises a set of mobile device or cloud-based tools for geospatial data collection, analysis, and visualization. Using tools such as Auto-Visual Acute Flaccid Paralysis Detection and Reporting, electronic surveillance, and Integrated Supportive Supervision, GIS personnel collect polio case numbers and locations, track field worker activities, follow the movements of nomadic populations vulnerable to polio and other diseases, and determine needs for further healthcare deployments. The system is location specific and operates in real time, enabling the AFRO GIS to promptly target its responses to polio, COVID-19, Ebola virus disease, and other public health crises and natural disasters. The present review describes the components of the AFRO GIS and how the AFRO GIS Centre coordinated on-the-ground polio eradication efforts to help secure Africa's certification as WPV free. It also examines current and prospective challenges regarding other disease outbreaks in the COVID-19 era and how the AFRO GIS Centre is addressing these ongoing public health needs.

Geographical Information System: Strengthening the Delivery of Medical Education

In the field of medical education, geographic information system (GIS) can contribute in several ways to strengthen the process of delivery of education. As medical institutions plan to integrate GIS in the delivery of medical education, there can be some challenges that must be given due attention to optimize the benefits. In conclusion, the integration of GIS in medical education can expose medical students to enriching learning experiences. Acknowledging the importance and merits of GIS, there is an immense need to explore the possibility and introduce it in medical education to benefit medical students and widen their perspective on health care.

The evolution of Kenya's animal health surveillance system and its potential for efficient detection of zoonoses

Introduction

Animal health surveillance systems in Kenya have undergone significant changes and faced various challenges throughout the years.

Methods

In this article, we present a comprehensive overview of the Kenya animal health surveillance system (1944 to 2024), based on a review of archived documents, a scoping literature review, and an examination of past surveillance assessments and evaluation reports.

Results

The review of archived documents revealed key historical events that have shaped the surveillance system. These include the establishment of the Directorate of Veterinary Services in 1895, advancements in livestock farming, the implementation of mandatory disease control interventions in 1944, the growth of veterinary services from a section to a ministry in 1954, the disruption caused by the Mau Mau insurrection from 1952 to 1954, which led to the temporary halt of agriculture in certain regions until 1955, the transition of veterinary clinical services from public to private, and the progressive privatization plan for veterinary services starting in 1976. Additionally, we highlight the development of electronic surveillance from 2003 to 2024. The scoping literature review, assessments and evaluation reports uncovered several strengths and weaknesses of the surveillance system. Among the strengths are a robust legislative framework, the adoption of technology in surveillance practices, the existence of a formal intersectoral coordination platform, the implementation of syndromic, sentinel, and community-based surveillance methods, and the presence of a feedback mechanism. On the other hand, the system's weaknesses include the inadequate implementation of strategies and enforcement of laws, the lack of standard case definitions for priority diseases, underutilization of laboratory services, the absence of formal mechanisms for data sharing across sectors, insufficient resources for surveillance and response, limited integration of surveillance and laboratory systems, inadequate involvement of private actors and communities in disease surveillance, and the absence of a direct supervisory role between the national and county veterinary services.

Discussion and recommendations

To establish an effective early warning system, we propose the integration of surveillance systems and the establishment of formal data sharing mechanisms. Furthermore, we recommend enhancing technological advancements and adopting artificial intelligence in surveillance practices, as well as implementing risk-based surveillance to optimize the allocation of surveillance resources.

COVID-19 Vaccination Site Accessibility, United States, December 11, 2020-March 29, 2022

During December 11, 2020-March 29, 2022, the US government delivered ≈700 million doses of COVID-19 vaccine to vaccination sites, resulting in vaccination of ≈75% of US adults during that period. We evaluated accessibility of vaccination sites. Sites were accessible by walking within 15 minutes by 46.6% of persons, 30 minutes by 74.8%, 45 minutes by 82.8%, and 60 minutes by 86.7%. When limited to populations in counties with high social vulnerability, accessibility by walking was 55.3%, 81.1%, 86.7%, and 89.4%, respectively. By driving, lowest accessibility was 96.5% at 15 minutes. For urban/rural categories, the 15-minute walking accessibility between noncore and large central metropolitan areas ranged from 27.2% to 65.1%; driving accessibility was 79.9% to 99.5%. By 30 minutes driving accessibility for all urban/rural categories was >95.9%. Walking time variations across jurisdictions and between urban/rural areas indicate that potential gains could have been made by improving walkability or making transportation more readily available.

Utilizing geospatial artificial intelligence to map cancer disparities across health regions

We have developed an innovative tool, the Intelligent Catchment Analysis Tool (iCAT), designed to identify and address healthcare disparities across specific regions. Powered by Artificial Intelligence and Machine Learning, our tool employs a robust Geographic Information System (GIS) to map healthcare outcomes and disease disparities. iCAT allows users to query publicly available data sources, health system data, and treatment data, offering insights into gaps and disparities in diagnosis and treatment paradigms. This project aims to promote best practices to bridge the gap in healthcare access, resources, education, and economic opportunities. The project aims to engage local and regional stakeholders in data collection and evaluation, including patients, providers, and organizations. Their active involvement helps refine the platform and guides targeted interventions for more effective outcomes. In this paper, we present two sample illustrations demonstrating how iCAT identifies healthcare disparities and analyzes the impact of social and environmental variables on outcomes. Over time, this platform can help communities make decisions to optimize resource allocation.

A Scoping Review on GIS Technologies Applied to Farmed Fish Health Management

Finfish aquaculture, one of the fastest growing intensive sectors worldwide, is threatened by numerous transmissible diseases that may have devastating impacts on its economic sustainability. This review (2010-2022) used a PRISMA extension for scoping reviews and a text mining approach to explore the extent to which geographical information systems (GIS) are used in farmed fish health management and to unveil the main GIS technologies, databases, and functions used to update the spatiotemporal data underpinning risk and predictive models in aquatic surveillance programmes. After filtering for eligibility criteria, the literature search provided 54 records, highlighting the limited use of GIS technologies for disease prevention and control, as well as the prevalence of GIS application in marine salmonid farming, especially for viruses and parasitic diseases typically associated with these species. The text mining generated five main research areas, underlining a limited range of investigated species, rearing environments, and diseases, as well as highlighting the lack of GIS-based methodologies at the core of such publications. This scoping review provides a source of information for future more detailed literature analyses and outcomes to support the development of geospatial disease spread models and expand in-field GIS technologies for the prevention and mitigation of fish disease epidemics.

A GIS workflow for the identification of corridors of geomorphic river recovery across landscapes

The provision of a simplified GIS workflow to analyse the Open Access NSW River Styles database provides non-technical GIS users in river management with the ability to quickly and efficiently obtain information to assist them in catchment-scale rehabilitation prioritisation. Publicly available proprietary GIS software, standard GIS tools, and a packaged digital elevation model are used to demonstrate the ease of analysis for those with some GIS skills, to establish where corridors of geomorphic river recovery occur or could be built at-scale. Rather than a 'single use' report, this novel application of GIS methods is designed to be used by those responsible for river management, replicated across landscapes and adjusted according to preferences. Decision making becomes more cost effective, and adaptive to local circumstances and changing river management priorities. The method could also be adjusted and applied to other river monitoring and condition datasets where polyline data layers are available.

Geoclimatic risk factors for childhood asthma hospitalization in southwest of Iran

BACKGROUND

Asthma is a chronic respiratory disease resulting from a complex interaction between genetic and environmental factors. Among environmental factors, climatic and geographical variations have an important role in increasing asthma hospitalization. The current study aimed to investigate the effect of geoclimatic factors on the occurrence of childhood asthma hospitalization in Fars province, southwest Iran.

METHOD

We mapped the addresses of 211 hospitalized patients with childhood asthma (2016-2019) and investigated the effects of different temperature models, mean annual rainfall and humidity, number of frosty and rainy days, evaporation, slope, and land covers on the occurrence of childhood asthma hospitalization using a geographical information system. The Kriging and Spline methods have been used for generating interpolated models. Data were analyzed using logistic regression.

RESULTS

In the multivariate model, urban setting was recognized as the most important childhood asthma hospitalization predictor (p < 0.001, odds ration [OR] = 35.044, confidence interval [CI] = 9.096-135.018). The slope was considered the determinant of childhood asthma hospitalization when analyzed independently and its increase was associated with decreased childhood asthma hospitalization (p  = 0.01, OR = 0.914, CI = 0.849-0.984).

CONCLUSION

In the current study, the urban setting was the most important risk factor associated with increased childhood asthma hospitalization.

The development and validation of a dashboard prototype for real-time suicide mortality data

Introduction/Aim

Data visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface.

Materials and Methods

Individual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008-2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the "rsatscan" and "shiny" packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface.

Results

Using the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence.

Discussion

The challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making.

Conclusions

The results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.

Temporal and spatial characterization of myopia in China

Purpose

The aim of this study was to characterize the temporal and spatial distribution of myopia among students aged 7-18 years, by analyzing the aggregation area and providing the basis for the prevention and control of myopia in China.

Methods

A database for the spatial analysis of myopia in China during 1995-2014 was established using ArcGIS10.0 software as a platform for data management and presentation. A spatial autocorrelation analysis of myopia was undertaken, and a temporal and spatial scan analysis was performed using SaTScan9.5 software.

Results

Our data demonstrated that the prevalence of myopia in China in 1995, 2000, 2005, 2010, and 2014 was 35.9, 41.5, 48.7, 57.3, and 57.1%, respectively, thus indicating a gradual upward trend. The prevalence of myopia was analyzed in various provinces (municipalities and autonomous regions), and the highest was found in Jiangsu Province, with an average Moran's I index of 0.244295 in China (P ≤ 0.05). According to the local Moran's I autocorrelation analysis, there was a spatial aggregation of myopia prevalence among students in the entire country, with Shandong, Jiangsu, Anhui, and Shanghai being classified as high-high aggregation areas, while Hainan and Guangxi were classified as low-low aggregation areas. In addition, the Getis-Ord General G results of the global hotspot analysis showed a countrywide myopia prevalence index of 0.035020 and a Z score of 1.7959 (P = 0.07251). Because the myopia prevalence correlation difference was not statistically significant, there were no "positive hotspots" or "negative hotspots." The local hotspot analysis shows that Shandong and Jiangsu belong to high-value aggregation areas, while Hainan and Guizhou belong to low-value aggregation areas. Further analysis using time-space scanning showed 15 aggregation regions in five stages, with four aggregation regions having statistically significant differences (P ≤ 0.05). However, the aggregation range has changed over time. Overall, from 1995 to 2014, the aggregation areas for the myopia prevalence in Chinese students have shifted from the northwest, north, and northeast regions to the southeast regions.

Conclusion

Our data demonstrate that, from 1995 to 2014, the prevalence of myopia increased in students aged 7-18 years in China. In addition, the prevalence of myopia is randomly distributed in various provinces (municipalities and autonomous regions) and exhibits spatial aggregation. Also, the gathering area is gradually shifting to the southeast, with the existence of high-risk areas. It is, therefore, necessary to focus on this area and undertake targeted prevention and control measures.

Progress on application of spatial epidemiology in ophthalmology

Most ocular diseases observed with cataract, chlamydia trachomatis, diabetic retinopathy, and uveitis, have their associations with environmental exposures, lifestyle, and habits, making their distribution has certain temporal and spatial features based essentially on epidemiology. Spatial epidemiology focuses on the use of geographic information systems (GIS), global navigation satellite systems (GNSS), and spatial analysis to map spatial distribution as well as change the tendency of diseases and investigate the health services status of populations. Recently, the spatial epidemic approach has been applied in the field of ophthalmology, which provides many valuable key messages on ocular disease prevention and control. This work briefly reviewed the context of spatial epidemiology and summarized its progress in the analysis of spatiotemporal distribution, non-monitoring area data estimation, influencing factors of ocular diseases, and allocation and utilization of eye health resources, to provide references for its application in the prevention and control of ocular diseases in the future.

Revisiting health promotion settings: An innovative model from Sri Lanka to integrate healthy settings using mHealth

The health promotion settings approach has been recognised as an effective method of health promotion in the recent era, and mobile health (mHealth) is a highly evolving field in the health sector. The health promotion settings are shifting the focus away from the individuals and moving towards a more holistic model of health promotion. We identified five settings in Sri Lanka to promote a mHealth model, including villages, schools, preschools, workplaces, and hospitals. The specified model using mHealth helps monitor the activities at various levels of healthcare, including regional, district and national levels. The model also maps the location of the healthy settings, which provide a visual picture to the policymakers, helpful in planning and decision-making.

Availability of open data for spatial public health research

Background: Preventive and health-promoting policies can guide (place- and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) have become available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives are providing data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers. Objectives: This paper aims to provide an overview of open data resources available in the context of space and health to date. It also describes the technical and legal conditions for using open data. Results: An up-to-date summary of results including information on relevant data access and terms of use is provided along with a web visualization. All data is available for further use under an open license.

Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping

Schistosomiasis is a neglected tropical disease (NTD) found throughout tropical and subtropical Africa. In Madagascar, the condition is widespread and endemic in 74% of all administrative districts in the country. Despite the significant burden of the disease, high-resolution risk maps have yet to be produced to guide national control programs. This study used an ecological niche modeling (ENM) and precision mapping approach to estimate environmental suitability and disease transmission risk. The results show that suitability for schistosomiasis is widespread and covers 264,781 km2 (102,232 sq miles). Covariates of significance to the model were the accessibility to cities, distance to water, enhanced vegetation index (EVI), annual mean temperature, land surface temperature (LST), clay content, and annual precipitation. Disease transmission risk is greatest in the central highlands, tropical east coast, arid-southwest, and northwest. An estimated 14.9 million people could be at risk of schistosomiasis; 11.4 million reside in rural areas, while 3.5 million are in urban areas. This study provides valuable insight into the geography of schistosomiasis in Madagascar and its potential risk to human populations. Because of the focal nature of the disease, these maps can inform national surveillance programs while improving understanding of areas in need of medical interventions.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

[Relevance of health geographic research for dermatology]

The association between geographic and medical aspects is a well-known phenomenon, which also occurs in dermatological research. This article reviews the field of health geography, the history of the association between spatial location and health, and focuses on current areas of research. Research focusing on explaining regional variations in health refer to individual aspects and needs, population factors, environmental factors, and health care delivery structures in specific regions, as well as the interaction between them. Regional healthcare research is primarily concerned with access to health services and on the utilisation of those services. Methodologically, the analysis of geodata and the application of geographic information systems (GIS) and spatial modelling play a major role in this field. Dermatological research and dermatological practice can benefit from the findings of the regional analysis of access, utilisation, and variations in order to obtain a more detailed picture of care and thus to optimise care.

Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study

The Special Capital Region of Jakarta is the epicentre of the transmission of COVID‐19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID‐19 incidence and related socio‐demographic factors explaining the variations of COVID‐19 incidence at local level. COVID‐19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID‐19 dashboard. Global and local spatial clustering of COVID‐19 incidence was examined using the Moran's I and local Moran analysis. Socio‐demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID‐19 incidence was evaluated. The COVID‐19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID‐19 high‐risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20–49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012–1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041–1.050), number of households (IRR = 1.196; 95% CI = 1.193–1.200), access to metered water for washing, and the main occupation of the residents were village level socio‐demographic factors associated with the risk of COVID‐19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high‐risk COVID‐19.

Temporal and Spatial Characteristics of Cataract Surgery Rates in China

OBJECTIVE

The aim of the current study was to explore the spatial and temporal distribution characteristics of registered cases of cataract surgery in China from 2013 to 2017.

METHODS

A database for spatial analysis of cataract surgery in China in 2013-2017 was established using ArcGIS10.0 software as a platform for data management and presentation. Spatial autocorrelation analysis of cataract surgery was undertaken, and temporal and spatial scan analysis was done using SaTScan 9.5 software.

RESULTS

From 2013 to 2017, annual cataract surgery rates (CSRs) in China were 1200, 1400, 1782, 2070, and 2205 per 1 million population, indicating a gradually increasing trend. Local Moran's I autocorrelation analysis showed that there was spatial clustering of CSR in China, with Anhui being a low-high clustering region. Findings of global hotspot analysis Getis-Ord General G showed that General G index of national CSR was <0.01, Z = 1.12, P = 0.26. Findings of staged spatial-temporal scan analysis indicated that 18 areas of aggregation were found in 2 stages. Observed differences in each clustering area were statistically significant (P < 0.05).

CONCLUSION

CSRs in China showed increasing trend year by year and were randomly distributed, with spatial clustering, and Anhui was reported as a low-high clustering region. However, high-risk areas still persist, requiring focused attention and targeted prevention and control measures.

Environmental Factors Affecting Cognitive Function among Community-Dwelling Older Adults: A Longitudinal Study

Although neighborhood environmental factors have been found to be associated with cognitive decline, few longitudinal studies have focused on their effect on older adults living in rural areas. This longitudinal study aimed to investigate the role of neighborhood environmental factors in cognitive decline among rural older adults. The data of 485 older adults aged ≥60 years who were living in Unnan City in Japan and had participated in two surveys conducted between 2014 and 2018 were analyzed. Cognitive function was assessed using the Cognitive Assessment for Dementia, iPad version 2. Elevation, hilliness, residential density, and proximity to a community center were determined using geographic information system. We applied a generalized estimating equation with odds ratios (OR) and 95% confidence intervals (CIs) of cognitive decline in the quartiles of neighborhood environmental factors. A total of 56 (11.6%) participants demonstrated a decrease in cognitive function at follow up. Elevation (adjusted OR 2.58, 95% CI (1.39, 4.77) for Q4 vs. Q1) and hilliness (adjusted OR 1.93, 95% CI (1.03, 3.63) for Q4 vs. Q1) were associated with a higher likelihood of cognitive decline. The second quartiles of residential density showed significantly lower likelihoods of cognitive decline compared with the first quartiles (adjusted OR 0.36, 95% CI (0.19, 0.71) for Q2 vs. Q1). Thus, an elevated hilly environment and residential density predicted cognitive decline among rural older adults.

Spatial analysis of malaria in Kotabaru, South Kalimantan, Indonesia: an evaluation to guide elimination strategies

BACKGROUND

Malaria remains a significant public health concern in Indonesia. Knowledge about spatial patterns of the residual malaria hotspots is critical to help design elimination strategies in Kotabaru district, South Kalimantan, Indonesia.

METHODS

Laboratory-confirmed malaria cases from 2012 to 2016 were analysed to examine the trend in malaria cases. Decomposition analysis was performed to assess seasonality. Annual spatial clustering of the incidence and hotspots were identified by Moran's I and the local indicator for spatial association, respectively.

RESULTS

The annual parasite incidence of malaria was significantly reduced by 87% from 2012 to 2016. Plasmodium vivax infections were significantly much more prevalent over time, followed by Plasmodium falciparum infections (p<0.001). The monthly seasonality of P. vivax and P. falciparum was distinct. High incidence was spatially clustered identified in the north, west and parts of south Kotabaru. Two persistent and four re-emerging high-risk clusters were identified during the period. Despite the significant reduction in the incidence of malaria, the residual high-risk villages remained clustered in the northern part of Kotabaru.

CONCLUSIONS

A spatially explicit decision support system is needed to support surveillance and control programs in the identified high-risk areas to succeed in the elimination goal of 2030.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

mHealth Technology Translation in a Limited Resources Community-Process, Challenges, and Lessons Learned From a Limited Resources Community of Chiang Mai Province, Thailand

This report aims to provide practical advice about the implementation of a public health monitoring system using both geographic information system technology and mobile health, a term used for healthcare delivery via mobile devices. application amongst household residents and community stakeholders in the limited resource community. A public health monitoring system was implemented in a semi-rural district in Thailand. The challenges encountered during implementation were documented qualitatively in a series of monthly focus group discussions, several community hearings, and many targeted interviews. In addition, lessons learned from the expansion of the program to 75 other districts throughout Thailand were also considered. All challenges proved solvable yielding several key pieces of advice for future project implementation teams. Specifically, communication between team members, anticipating technological challenges, and involvement of community members are critical. The problems encountered in our project were mainly related to the capabilities of the data collectors and technical issues of mobile devices, internet coverage, and the GIS application itself. During the implementation phase, progressive changes needed to be made to the system promptly, in parallel with community team building in order to get the highest public health impact.

Geographic Accessibility of HIV Preventive Services for Young Latino Men in Miami, Florida: A Cross-Sectional Study

ABSTRACT

The U.S. HIV incidence has decreased or stabilized among all subgroups except for young Latino men who have sex with men (YLMSM). Previous research has suggested that Latinos experience geographic accessibility barriers to YLMSM-friendly HIV prevention services. We aimed to characterize the geographic accessibility of young Latinos ages 15-29 years to HIV preventive services in Miami-Dade County, the domestic HIV epicenter. Using ArcMap, we created a density map of 18 YLMSM-friendly HIV programs, then used Network Analysis Tools to generate service areas describing time and travel distance for walking, public transit, and driving. Our results show that accessibility to YLMSM-friendly HIV prevention services by YLMSM varies by mode of transportation. Of YLMSM, HIV prevention services are available to 2% by walking, 19% by public transit, and 70% by driving. To increase accessibility, future public health interventions should use geographic information system and geodemographic data to identify areas for culturally appropriate service expansion.

Place and health infrastructure in the Gulf Cooperation Council: A systematic scoping review of GIS applications in health

The rising burden of non-communicable diseases is taxing health systems globally. Using data science and information systems is necessary to support public health practices. Geographic Information Systems (GIS) are key to inform and help guide public health policies related to place (i.e. location or where one lives) and how it affects health. Despite the increasing use of GIS for public health globally, its applications to health in the Gulf Cooperation Council (GCC) states remains largely unknown. This systematic scoping review aimed to uncover how GIS has been used in the GCC states to understand "place" and "health". A comprehensive search of the literature was performed in PubMed, Scopus, Science Citation Index Expanded, ScienceDirect, Embase, IEEE Xplore, and ACM Digital Library during June 2020. All journal articles involving the use of GIS for human health applications in the GCC states published in English in peerreviewed scientific journals were considered. After removing duplicates and applying eligibility criteria, qualitative content analysis was performed for 24 of 630 studies. GIS uses in the GCC states were categorized as health access and planning (n=9), health risk analysis (n=8), disease surveillance (n=6) and community health profiling (n=1). The majority of the uncovered evidence in this study focused on the Kingdom of Saudi Arabia. The results of this study indicate a deficiency of published evidence regarding the use of GIS in support of public health in other GCC states. This stands to compromise planning and strategic decision making in health risk analysis, disease surveillance, community health profiling, health services provision and health interventions.

Starter Kit for Geotagging and Geovisualization in Health Care: Resource Paper

BACKGROUND

Geotagging is the process of attaching geospatial tags to various media data types. In health care, the goal of geotagging is to gain a better understanding of health-related questions applied to populations. Although there has been a prevalence of geographic information in public health, in order to effectively use and expand geotagging across health care there is a requirement to understand other factors such as the disposition, standardization, data sources, technologies, and limitations.

OBJECTIVE

The objective of this document is to serve as a resource for new researchers in the field. This report aims to be comprehensive but easy for beginners to understand and adopt in practice. The optimal geocodes, their sources, and a rationale for use are suggested. Geotagging's issues and limitations are also discussed.

METHODS

A comprehensive review of technical instructions and articles was conducted to evaluate guidelines for geotagging, and online resources were curated to support the implementation of geotagging practices. Summary tables were developed to describe the available geotagging resources (free and for fee) that can be leveraged by researchers and quality improvement personnel to effectively perform geospatial analyses primarily targeting US health care.

RESULTS

This paper demonstrated steps to develop an initial geotagging and geovisualization project with clear structure and instructions. The geotagging resources were summarized. These resources are essential for geotagging health care projects. The discussion section provides better understanding of geotagging's limitations and suggests suitable way to approach it.

CONCLUSIONS

We explain how geotagging can be leveraged in health care and offer the necessary initial resources to obtain geocodes, adjustment data, and health-related measures. The resources outlined in this paper can support an individual and/or organization in initiating a geotagging health care project.

Environmental, climatic and host population risk factors of human cystic echinococcosis in southwest of Iran

BACKGROUND

Cystic echinococcosis (CE), a worldwide zoonotic disease, is affected by various biological and environmental factors. We investigated dog/livestock populations, climatic and environmental factors influencing the distribution of human CE cases in Fars province, southwest Iran.

METHODS

We mapped the addresses of 266 hospitalised CE patients (2004-2014) and studied the effects of different temperature models, mean annual rainfall and humidity, number of frosty days, slope, latitude, land covers, close proximity to nomads travel routes, livestock and dog densities on the occurrence of CE using geographical information systems approach. Data were analyzed by logistic regression.

RESULTS

In the multivariate model predicting CE, living in an urban setting and densities of cattle and dogs were the most important CE predictors, sequentially. Dry (rained) farm, density of camel and sheep, close proximity to nomads travel routes, humidity, and slope also were considered as the determinants of CE distribution, when analyzed independently. Slope had a negative correlation with CE while temperature, frost days and latitude were not associated with CE.

CONCLUSIONS

In our study, an urban setting was the most important risk factor and likely due to a combination of the high density of key life cycle hosts, dogs and livestock, a large human susceptible population and the high number of abattoirs. Farmland and humidity were highly suggestive risk factors and these conditions support the increased survival of Echinococcus granulosus eggs in the soil. These findings support the development of strategies for control of disease. More research is needed test optimal interventions.

Interactive web-based tool for evaluating the spread of bovine tuberculosis and brucellosis in Colombia

Background: Bovine tuberculosis (BTB) and brucellosis are associated with devastating losses in the livestock sector in Colombia and even in developed countries. Real-time disease surveillance is a key strategy to control and eradicate infectious disease outbreaks. Objective: To design an epidemiological tool for monitoring BTB and brucellosis in Colombia. Methods: An interactive platform for disease mapping of BTB and brucellosis during an observation period between years 2004 and 2019 was designed. Results: Our analysis showed that the provinces of Cundinamarca and Valle del Cauca are regions affected by BTB and brucellosis epidemics, respectively (p<0.001). Furthermore, increased case detection of BTB was reported in 2012 and brucellosis in 2019 (p<0.001). Conclusions: This epidemiological platform allows tracking BTB and tuberculosis hotspots, identifying trends over time, and provides useful information to animal health authorities for designing new strategies in control programs.

Environmental Health Burdens and Socioeconomic Status in Rhode Island: Using Geographic Information Systems to Examine Health Disparities in Medical School

Race and class are major predictors of health outcomes in the United States. Health disparities among racial and low-income minorities often have environmental etiologies. Using Rhode Island as a case study, we geocoded and visualized several environmental determinants of health via Geographic Information Systems (GIS) in the entire state and conducted a geospatial analysis to determine whether or not patterns existed along racial and class lines. The variables that we geocoded include elementary schools, fast food restaurants, Superfund sites, and community parks. From a census tract level, we then analyzed the racial and income makeup of each geocoded site. We discovered that, on average, the worst-performing elementary schools, fast food restaurants, and Superfund sites in Rhode Island were clustered in neighborhoods with a larger black population and lower household income. Conversely, community parks and the best elementary schools in Rhode Island tended to be located near neighborhoods with a larger White population and higher household income. Our results provide additional evidence for the pervasiveness of the unequal distribution of environmental health burdens between low-income, minority communities and affluent, predominantly White communities. This summer experiential student project demonstrates the feasibility of incorporating GIS as a practical tool for learning health disparities material at a U.S. medical school. Our study also highlights the value of digital technology and citizen science in helping the public recognize and understand the various environmental factors that perpetuate health disparities.

Using GIS for Disease Mapping and Clustering in Jeddah, Saudi Arabia

Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to produce mapping and cluster modeling of three diseases in Jeddah, Saudi Arabia: diabetes, asthma, and hypertension. Data about these diseases were obtained from health centers’ registered patient records. These data were spatially evaluated using several spatial–statistical analytical models, including kernel and hotspot models. These models were created to explore and display the disparate patterns of the selected diseases and to illustrate areas of high concentration, and may be invaluable in understanding local patterns of diseases and their geographical associations.

Analysis of Heavy Metal Contamination of Agricultural Soils and Related Effect on Population Health-A Case Study for East River Basin in China

To understand the heavy metal pollution in agricultural soils along the East River basin and assess the pollution related health effect to local residents, interviews and archived data were obtained to identify the study sites affected by polluted tailing. Soil samples were collected and tested for heavy metal content and the Comprehensive Pollution Index (CPI). The degree of pollution of agricultural soils in the area was assessed using GIS-based Spatial distribution map of heavy metals and the trend of soil heavy metal risk. Two villages (Matian and Zhudui) near the East River were included in this study for health effect assessment. A total of 193 residents aged 15 or above from each village were tested for the present status of chronic diseases. Convenient sampling method was used to collect blood samples from 78 residents for heavy metal concentration. The contents of Pb, Cd, As, Zn, and Cu in the agricultural soils were all over the standards with a moderate to severe CPI. Among these metals, Cd was the highest followed by Pb, and Cu was the lowest. The contents of Pb, Cd, As, and Zn tend to be higher in soils closer to the river. The prevalence of chronic diseases was over 30%, which is significantly higher than the report from the national central region (23.15%). The average blood lead level (BLL) among children under 14 years is 7.42 μg/dL. Although the adults in Matian village had a significantly higher BLL (χ2 = 8.70, p = 0.03) as compared to Zhudui village, there was no significant difference for the prevalence of chronic diseases between the two villages (χ2 = 3.23, p = 0.09). The mean BLL of children and the proportion of children with BLL ≥ 10 µg/dL in this study are equivalent to the national average. The higher BLL concentration and prevalence of chronic diseases in adults might be due to their long-term exposure to heavy metal contamination environment and higher background level of heavy metals. Findings from this study will form the baseline information for local government to the development of effective approaches to control the heavy metal contamination and reduce the pollution related adverse health effect on local residents.

A space-time analysis of recurrent malnutrition-related hospitalisations in Kilifi, Kenya for children under-5 years

Background

Globally, malnutrition underlies 45% of under-5 s mortality, mainly from potentiating common infections such as diarrhoea and pneumonia. Malnutrition as a public health problem is not evenly disbursed because of disparities in food insecurity and health, and children commonly suffer recurrent episodes of opportunistic infections. We aimed to understand better the spatial and temporal structure of multiple paediatric hospital admissions associated with malnutrition-related illnesses. This paper aimed to investigate the spatial-temporal variations in malnutrition-related recurrent morbidity of children under-5 years from the Kilifi County in Kenya between 2002 and 2015.

Methods

The study included data from children under-5 years old who had more than one admission to a rural district hospital in Kenya within the Kilifi Health and Demographic Surveillance System (KHDSS). The primary outcome was a malnutrition-related admission, based on wasting (WHZ < -2) or nutritional oedema. Individual, household and environmental level covariates were examined as exposures. We first fitted a SARIMA model for the temporality, and the Moran’s Index affirmed spatial clustering in malnutrition admissions. Kulldorf Statistics using SaTScan were applied to detect hotspots. Then, bivariate analysis was done using repeated values tabulation and analysis of covariance (ANCOVA). Inferential analysis was done using a mixed effect multivariable negative-binomial regression model, adjusting for spatiotemporal random effects.

Results

A total of 2821 children were admitted more than once, giving a total of 6375 admissions. Of these 6375 admissions, 1866 were malnutrition-related, and 3.9% (109/2821) of the children with repeat admissions died. There was a seasonal pattern of re-admissions, peaking from May to July over the years. Hotspots were found in both the Northern and Southern areas of the KHDSS, while the areas near Kilifi Town were least affected. We found that disease severity was most likely associated with a malnutrition re-admission to the hospital.

Conclusion

Disease severity was strongly associated with admission with malnutrition but its effect reduced after adjusting for the spatial and temporal random effects. Adjusting for clustering in space and in time (spatial-temporal) in models helps to improve the understanding of recurrent hospitalisations involving malnutrition.

Electronic supplementary material

The online version of this article (10.1186/s40795-019-0296-5) contains supplementary material, which is available to authorized users.

Clustering and spatial heterogeneity of bovine tuberculosis at the livestock/wildlife interface areas in Namwala District of Zambia

BACKGROUND AND AIM

Bovine tuberculosis (bTB) remains a major public health issue in Zambia and has been exacerbated by human immunodeficiency virus prevalence and consumption of unpasteurized milk in the Southern Province of the country. The prevalence of bTB has been established to be linked to Kafue Lechwe, which act as reservoir hosts and share grazing fields with domestic cattle. No studies have so far used geographic information system (GIS) to investigate the relationship between the reservoir hosts (Kafue Lechwe) and domestic animals. This study, therefore, aimed to apply GIS to investigate the spatial distribution of bTB in Namwala District of the Southern Province of the country.

MATERIALS AND METHODS

To investigate the spatial distribution of bTB, geographical positioning system (GPS) coordinates representing 96 cattle herds across 20 independent villages were captured alongside risk factor data. The 96 herds were based on abattoir reports of condemned carcasses and a trace back. Positive herds were confirmed by cross-reference to purified protein derivative tests conducted by the District Veterinary Office. The GPS coordinates were transferred into ArcView 3.2 and laid on the map of Namwala District alongside physical features, including national parks, game management areas, and flood plains. Questionnaires were administered across 96 independent households to assess risk factors of bTB transmission.

RESULTS

The results revealed a "clustered" spatial distribution of the disease in cattle in Namwala District of Zambia, particularly significant in the eastern interface areas of the district (p=0.006 using Moran's I). Abattoir to production area trace back revealed a herd-level prevalence of 36.4% (95% CI=26.7-46.3%) among cattle herds in Namwala District, whereas individual animal prevalence ranged from 0% to 14% (95% CI=2.4-26.2%). Further, GPS data indicated that the majority of the positive herds were located at the livestock/wildlife interface area. Contacts with wildlife, coupled with sharing grazing, and watering points were found to be significant risk factors for bTB transmission.

CONCLUSION

This study demonstrated the presence of bTB in cattle and associated spatial risk factors. In particular, bTB was observed to be a function of animal location within the livestock/wildlife interface area. GIS is thus an applicable and important tool in studying disease distribution.

Non-vaccine strategies for cholera prevention and control: India's preparedness for the global roadmap

BACKGROUND

Recently World Health Organization's Global Task Force on Cholera Control (GTFCC) has published a global roadmap for prevention and control of cholera. We review preparedness of existing governmental non-vaccine programs and strategies for cholera prevention and control in India. We also describe strengths and gaps in the context of implementation of the global roadmap.

METHODS

We reviewed published literature on non-vaccine based strategies for prevention and control of cholera in India and analyzed strengths and weaknesses of Government of India's major anti-cholera and ante-diarrhea initiatives under Integrated Disease Surveillance Program (IDSP), National Rural Health Mission (NRHM), and other disease surveillance platforms.

RESULTS

The first strategy of the WHO global roadmap, namely, preparedness for early detection and outbreak containment, has been addressed by the IDSP. NRHM complements IDSP activities by focusing on sanitation, hygiene, nutrition, and safe drinking water. We identified the need to adopt stricter case definitions and data validation protocols. Multi-sectoral approach to prevent cholera occurrences and re-occurrences [the second suggested strategy in the global roadmap], highlights identification of hotspots and implementing strategies based on transmission dynamics. We recommend development of comprehensive models by integrating data sources beyond the national programs to eliminate cholera hotspots in India. Implementing the third proposed strategy in the global roadmap, coordinated technical support, resource mobilization, and partnerships at local and global levels, has major challenges in India due to structural issues related to health systems and health programs.

CONCLUSION

Even with a robust public health infrastructure, absence of a national cholera program might have resulted in lack of specific focus and concerted efforts for cholera prevention and control in India. A National Taskforce for Cholera Control must develop India-specific 'National Cholera Prevention and Response Road Map' with an appropriate administrative and financially viable framework for its implementation.

Behavioral mapping of children's physical activities and social behaviors in an indoor preschool facility: methodological challenges in revealing the influence of space in play

BACKGROUND

GIS (Geographic Information Systems) based behavior maps are useful for visualizing and analyzing how children utilize their play spaces. However, a GIS needs accurate locational information to ensure that observations are correctly represented on the layout maps of play spaces. The most commonly used tools for observing and coding free play among children in indoor play spaces require that locational data be collected alongside other play variables. There is a need for a practical, cost-effective approach for extending most tools for analyzing free play by adding geospatial locational information to children's behavior data collected in indoor play environments.

RESULTS

We provide a non-intrusive approach to adding locational information to behavior data acquired from video recordings of preschool children in their indoor play spaces. The gridding technique showed to be a cost-effective method of gathering locational information about children from video recordings of their indoor physical activities and social behaviors. Visualizing the proportions of categories and observed intervals was done using bubble pie charts which allowed for the merging of multiple categorical information on one map. The addition of locational information to other play activity and social behavior data presented the opportunity to assess what types of equipment or play areas may encourage different physical activities and social behaviors among preschool children.

CONCLUSIONS

Gridding is an effective method for providing locational data when analyzing physical activities and social behaviors of preschool children in indoor spaces. It is also reproducible for most GIS behavior mapping focusing on indoor environments. This bypasses the need to have positioning devices attached to children during observations, which can raise ethical considerations regarding children's privacy and methodological implications with children playing less naturally. It also supports visualizations on behavior maps making them easier to interpret.

Network analysis of intra-hospital transfers and hospital onset clostridium difficile infection

OBJECTIVES

To explore how social network analysis (SNA) can be used to analyse intra-hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment.

METHODS

A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS.

RESULTS

The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor.

DISCUSSION

This study confirmed the importance that intra-hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals.

CONCLUSION

Utilising SNA and GIS analysis in conjunction with one another provided a data-rich environment in which the risk inherent in intra-hospital transfer networks was quantified, visualised and interpreted for potential interventions.

Returning to our roots: The use of geospatial data for nurse-led community research

In the early 20th century, public health nurse, Lillian Wald, addressed the social determinants of health (SDOH) through her work in New York City and her advocacy to improve policy in workplace conditions, education, recreation, and housing. In the early 21st century, addressing the SDOH is a renewed priority and provides nurse researchers with an opportunity to return to our roots. The purpose of this methods paper is to examine how the incorporation of geospatial data and spatial methodologies in community research can enhance the analyses of the complex relationships between social determinants and health. Geospatial technologies, software for mapping and working with geospatial data, statistical methods, and unique considerations are discussed. An exemplar for using geospatial data is presented regarding associations between neighborhood greenspace, neighborhood violence, and children's asthma control. This innovative use of geospatial data illustrates a new frontier in investigating nontraditional connections between the environment and SDOH outcomes.

Assessing the combined effects of household type and insecticide effectiveness for kala-azar vector control using indoor residual spraying: a case study from North Bihar, India

BACKGROUND

Indoor residual spraying (IRS) is the mainstay for vector control intervention of visceral leishmaniasis (VL) in India. Little is known on the control effects of IRS on different household types. Here, we assessed if IRS with insecticides has an equal residual and interventional effect on all household types in a village. We also developed a combined spatial-risk map and a sand fly, Phlebotomus argentipes density analytical model based on household characteristics, insecticide susceptibility and IRS-status to explore the spatio-temporal distributions of the vector at a micro-scale level.

METHODS

This study was carried out in two villages of Mahnar block in Vaishali district, Bihar. IRS using two insecticides [dichlorodiphenyltrichloroethane (DDT 50%) and synthetic pyrethroid (SP 5%)] was evaluated for VL-vector (P. argentipes) control. Temporal residual efficacy of the insecticides on different wall-surface types was evaluated using the cone-bioassay technique according to WHO guidelines. Insecticide susceptibility of local P. argentipes was explored using the tube-bioassay method. Pre- and post-IRS sand fly densities were monitored in human dwellings and animal shelters using Centers for Disease Control light-traps installed between 18:00-6:00 h. A best-fit model for sand fly density analysis was developed using multiple logistic regression analysis. Geographical information system based spatial analysis techniques were employed to map the household type distribution of insecticide susceptibility of the vector, and IRS-status of the households to interpret the spatio-temporal distributions of P. argentipes.

RESULTS

Phlebotomus argentipes was highly susceptible to SP (100%) but showed high resistance to DDT with a 49.1% mortality rate. SP-IRS has been reported as having better community acceptance than DDT-IRS in all household types. Residual efficacies were varied between wall-surfaces; both insecticides failed to achieve the duration of IRS effectiveness recommended by the WHO. Reduction in P. argentipes counts due to SP-IRS was higher than DDT-IRS between household groups (i.e. sprayed and sentinel), in all intervals post-IRS. Combined spatial risk-maps revealed a better control effect of SP-IRS on sand flies than DDT-IRS in all household types risk-zones. The multilevel logistic regression analysis explored five risk-factors that were strongly associated with the density of P. argentipes.

CONCLUSIONS

The results contribute to furthering current understanding of IRS-practices for control of visceral leishmaniasis in endemic Bihar, which may help in future actions for improvements.

Traditional and Nontraditional Collaborations to Improve Population Health Using Geospatial Information SystemMaps: Analysis of the Opioid Crisis

INTRODUCTION

The study aims were to analyze interprofessional practice collaborations among traditional and nontraditional health care providers and to educate nurse practitioner preceptors and students on population health, specifically, implementation of geospatial information system (GIS) maps and the correlation with the opioid crisis.

METHODS

A descriptive analysis was used to examine New York State data on the opioid crisis in comparison to outcome data from GIS maps of opioid use in two boroughs in New York City. Web-based modules were designed for analysis of GIS maps of opioid use near practice settings.

RESULTS

New York State data provided context for local opioiduse, while GIS maps identified specific areas of the New York City boroughs that were most affected by the opioid epidemic.

DISCUSSION

The importance of local GIS maps is that the information is available in real-time, and thus interventions can be designed, evaluated, and changed quickly to meet the immediate needs of the community.

Spatial diffusion of the 2015-2016 Zika, dengue and chikungunya epidemics in Rio de Janeiro Municipality, Brazil

Different countries, especially Brazil, that have faced recurrent dengue epidemics for decades and chikungunya epidemics since 2014, have had to restructure their health services to combat a triple epidemic of arboviruses - Zika, dengue and Chikungunya - transmitted by the same vector, mainly Aedes aegypti, in 2015-2016. Several efforts have been made to better understand these three arboviruses. Spatial analysis plays an important role in the knowledge of disease dynamics. The knowledge of the patterns of spatial diffusion of these three arboviruses during an epidemic can contribute to the planning of surveillance actions and control of these diseases. This study aimed to identify the spatial diffusion processes of these viruses in the context of the triple epidemic in 2015-2016 in Rio de Janeiro, Brazil. Two study designs were used: cross-sectional and ecological. Sequential Kernel maps, nearest-neighbour ratios calculated cumulatively over time, Moran global autocorrelation correlograms, and local autocorrelation changes over time were used to identify spatial diffusion patterns. The results suggested an expansion diffusion pattern for the three arboviruses during 2015-2016 in Rio de Janeiro. These findings can be considered for more effective control measures and for new studies on the dynamics of these three arboviruses.

[What potential do geographic information systems have for population-wide health monitoring in Germany? : Perspectives and challenges for the health monitoring of the Robert Koch Institute]

Geographic information systems (GISs) are computer-based systems with which geographical data can be recorded, stored, managed, analyzed, visualized and provided. In recent years, they have become an integral part of public health research. They offer a broad range of analysis tools, which enable innovative solutions for health-related research questions. An analysis of nationwide studies that applied geographic information systems underlines the potential this instrument bears for health monitoring in Germany. Geographic information systems provide up-to-date mapping and visualization options to be used for national health monitoring at the Robert Koch Institute (RKI). Furthermore, objective information on the residential environment as an influencing factor on population health and on health behavior can be gathered and linked to RKI survey data at different geographic scales. Besides using physical information, such as climate, vegetation or land use, as well as information on the built environment, the instrument can link socioeconomic and sociodemographic data as well as information on health care and environmental stress to the survey data and integrate them into concepts for analyses. Therefore, geographic information systems expand the potential of the RKI to present nationwide, representative and meaningful health-monitoring results. In doing so, data protection regulations must always be followed. To conclude, the development of a national spatial data infrastructure and the identification of important data sources can prospectively improve access to high quality data sets that are relevant for the health monitoring.

mvmapper: Interactive spatial mapping of genetic structures

Characterizing genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata are not always easily integrated into these methods in a user-friendly fashion. Here, we present a deployable Python-based web-tool, mvmapper, for visualizing and exploring results of multivariate analyses in geographic space. This tool can be used to map results of virtually any multivariate analysis of georeferenced data, and routines for exporting results from a number of standard methods have been integrated in the R package adegenet, including principal components analysis (PCA), spatial PCA, discriminant analysis of principal components, principal coordinates analysis, nonmetric dimensional scaling and correspondence analysis. mvmapper's greatest strength is facilitating dynamic and interactive exploration of the statistical and geographic frameworks side by side, a task that is difficult and time-consuming with currently available tools. Source code and deployment instructions, as well as a link to a hosted instance of mvmapper, can be found at https://popphylotools.github.io/mvMapper/.

A generic method for improving the spatial interoperability of medical and ecological databases

BACKGROUND

The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution.

METHODS

Firstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index.

RESULTS

We used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table's validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas.

CONCLUSIONS

Our innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.

Spatial Distribution of Pemphigus Occurrence over Five Decades in Southeastern Brazil

Well-defined locations of pemphigus cases support the hypothesis of environmental factors' involvement in its etiopathogenesis; however, these foci have never been described using specialized geographical tools. This is the first report to geo-reference pemphigus cases in a high-prevalence Brazilian region using geographic information systems. We aimed to report the spatio-temporal behavior of pemphigus foliaceus (PF) and vulgaris (PV) in southeastern Brazil, over the last five decades to describe geographical clusters, as well as to characterize the land use in the city with the highest number of cases. Patients were identified from 1965 to 2014. Maps were developed using ArcGIS software and organized into decades from 1965 to 2014. Ribeirão Preto was identified as the city with the greatest number of cases. Land use was analyzed within a 2 km-buffer surrounding the residence of each patient. A total of 426 cases of pemphigus were identified. PF was the predominant form (285 cases); notwithstanding, the number of new cases of PV rose, overtaking the number of new cases of PF in the last decade studied. Agricultural area (42%) and exposed soil (33.2%) are the most predominant land uses in Ribeirão Preto surrounding patients' residences. This study shows high-confidence geographical foci of PF and PV, as well as provides evidence of an increase of both clinical forms over the last five decades. All cases of PV and PF are in proximity to rivers and agricultural areas which reinforce the hypothesis that environmental factors play a role in pemphigus etiopathogenesis.

Geospatial distribution and risk factors for bovine cysticercosis in the state of Rondônia, Brazil

ABSTRACT: This study focused on assessing the prevalence, geospatial distribution and risk factors for bovine cysticercosis in cattle from the state of Rondônia, Brazil, through the years 2012 to 2015. The prevalence established was 0.014% (95% C.I. 0.013-0.014), with a higher detection of unviable cysticerci (84.80%). The municipalities of Itapuã do Oeste, Candeias do Jamari, Nova Brasilândia D’Oeste, Pimenteiras do Oeste, Porto Velho, Nova Mamoré, Urupá and Guajará-Mirim had higher risk (OR>1; p<0.05) for cysticercosis occurrence compared with the municipality of Castanheiras (OR=1). The Administrative Regions of Porto Velho, Guajará-Mirim, Colorado D’Oeste, Cacoal, Ji-Paraná had higher risk (OR>1; p<0.05) for cysticercosis occurrence in the slaughtered animals than those reared in Ariquemes Administrative Region (OR=1). Some variables such as human population density (OR=2.15; 2.15-2.16), percentage of urban houses with inappropriate sewage system (OR=1.91, 1.91-.1.92) and percentage of inappropriate rural sewage system (OR=1.14, 1.14-1.14) were significantly associated (p<0.05) with the occurrence of bovine cysticercosis. In conclusion, the prevalence of bovine cysticercosis in the state of Rondônia was 0.014% (95% C.I. 0.013-0.014) and higher-risk areas were identified, providing useful information to Official Sanitary Inspection System in order to improve cysticercosis detection. Also, human population density and the lack of appropriate sewage system in urban and rural areas are closely related to bovine cysticercosis occurrence in this state.

Spatial analysis of bovine cysticercosis in the state of Mato Grosso do Sul, Brazil - The needs of interventions in animal and human populations

The Taenia saginata-cysticercosis complex is an important zoonosis for public health and an important cause of economical losses for beef supply chain in developing and industrialized countries. Despite some risk factors for high-occurrence areas have already been described, further studies are required to better understand its global epidemiology. So, this study focused on establishing the prevalence, spatial distribution and environmental and human population aspects correlated with bovine cysticercosis prevalence in the state of Mato Grosso do Sul, Brazil. To this, we used data of 7,415,998 animals slaughtered from all 79 municipalities located in this state. Cysticercosis prevalence showed spatial distribution dependence (Moran Index=0.375, p=0.01) and some areas presented higher risk. Positive correlations of bovine cysticercosis prevalence with human population density (R=0.5712, p=3.85 e-08) and rainfall index (R=0.631, p=0.03) were observed. A negative correlation (R=-0.4637, p=2.096 e-05) with cattle population size was also established. These results highlight the importance of adopting sanitary measures to prevent environment contamination, besides other prophylactic measures like the treatment of human patients in order to reduce the Taenia saginata-cysticercosis complex occurrence.