The Australian Park Life project: Development of a nationally standardised spatial layer and public participatory GIS for greenspace in Australian capital cities.

This paper introduces the 'Australian Park Life Project' and describes a protocol to standardise the capture and collation of public open greenspace spatial data across Australian cities. This method will progress greenspace research allowing for unique coherent national analyses and comparative research across Australia. We also outline the development of the Park Life public participatory geographic information system (PPGIS) to spatially explore what, and how, public open greenspaces are being used by Australian communities. The combination of community crowdsourced spatial data providing location-specific information on the green public open spaces used, in combination with the national spatial layer of greenspace allows for unique analyses exploring the role of greenspace provision and design on use and represents a transformative strategy in shaping public open space policy and strategy.•A spatial layer of public open greenspace was created for the eight Australian State and Territory capital cities using a standardised data capture and collection method from local government planning schemes and land use data, and listings of managed and demarcated parks and reserves.•The Park Life public participatory geographic information system (PPGIS) was built to capture spatially referenced information on the use of greenspaces - specifically what spaces are used, and how they are used.

Benefits and challenges of geographic information systems (GIS) for data-driven outreach in black communities experiencing overdose disparities: results of a stakeholder focus group.

BACKGROUND: Black individuals in the U.S. face increasing racial disparities in drug overdose related to social determinants of health, including place-based features. Mobile outreach efforts work to mitigate social determinants by servicing geographic areas with low drug treatment and overdose prevention access but are often limited by convenience-based targets. Geographic information systems (GIS) are often used to characterize and visualize the overdose crisis and could be translated to community to guide mobile outreach services. The current study examines the initial acceptability and appropriateness of GIS to facilitate data-driven outreach for reducing overdose inequities facing Black individuals. METHODS: We convened a focus group of stakeholders (N = 8) in leadership roles at organizations conducting mobile outreach in predominantly Black neighborhoods of St. Louis, MO. Organizations represented provided adult mental health and substance use treatment or harm reduction services. Participants were prompted to discuss current outreach strategies and provided feedback on preliminary GIS-derived maps displaying regional overdose epidemiology. A reflexive approach to thematic analysis was used to extract themes. RESULTS: Four themes were identified that contextualize the acceptability and utility of an overdose visualization tool to mobile service providers in Black communities. They were: 1) importance of considering broader community context; 2) potential for awareness, engagement, and community collaboration; 3) ensuring data relevance to the affected community; and 4) data manipulation and validity concerns. CONCLUSIONS: There are several perceived benefits of using GIS to map overdose among mobile providers serving Black communities that are overburdened by the overdose crisis but under resourced. Perceived potential benefits included informing location-based targets for services as well as improving awareness of the overdose crisis and facilitating collaboration, advocacy, and resource allocation. However, as GIS-enabled visualization of drug overdose grows in science, public health, and community settings, stakeholders must consider concerns undermining community trust and benefits, particularly for Black communities facing historical inequities and ongoing disparities.

Spatial dependence of non-traumatic out-of-hospital cardiac arrest in a Swiss region: A retrospective analysis.

Background: Out-of-hospital cardiac arrest (OHCA) incidence and survival often vary within regions according to patient-related and contextual factors. This study aims to establish the overall spatial dependence of incidence, bystander cardiopulmonary resuscitation (BCPR) and 48-h survival of OHCA with their associated demographic and socioeconomic characteristics in a Swiss region. Methods: We conducted a retrospective study using data of all OHCAs recorded between 2007 and 2019 in the canton of Vaud and, more specifically, in the Lausanne area. Provision of BCPR and 48-h survival were analysed using Getis-Ord Gi statistics and OHCA incidence by local Moran's I with empirical Bayes standardised rates. Demographic and socioeconomic characteristics were compared between incidence clusters generated by local Moran's I method. Results: Significant spatial variations of OHCA incidence, BCPR and 48-h mortality were observed. Although BCPR was statistically more likely in rural areas, 48-h survival was improved in a few main cities. At the cantonal level, postcode areas with a higher incidence of OHCAs were less densely inhabited with lower salary levels, more Swiss citizens, and an older population. At city level, small area variations were detected within urban neighbourhoods. The more affected hectares with more OHCAs were less inhabited, with a better median salary, more Swiss citizens, and off-centre. Conclusions: Spatial variations associated with demographic and socioeconomic factors were observed for OHCA incidence and survival, with sparsely populated areas particularly at risk. These data suggest an unmet need for targeted prevention interventions and structural modifications of the existing prehospital system at the cantonal level.

A GIS software-based method to identify public health data belonging to address-defined communities.

OBJECTIVE: This communication presents the results of defining a tribal health jurisdiction by a combination of tribal affiliation and case address. METHODS: Through a county-tribal partnership, GIS software and custom code were used to extract tribal data from county data by identifying reservation addresses in county extracts of COVID-19 case records from December 30, 2019, to December 31, 2022 (n = 374,653) and COVID-19 vaccination records from December 1, 2020, to April 18, 2023 (n = 2,355,058). RESULTS: The tool identified 1.91 times as many case records and 3.76 times as many vaccination records as filtering by tribal affiliation alone. DISCUSSION AND CONCLUSION: This method of identifying communities by patient address, in combination with tribal affiliation and enrollment, can help tribal health jurisdictions attain equitable access to public health data, when done in partnership with a data sharing agreement. This methodology has potential applications for other populations underrepresented in public health and clinical research.

Assessing the Environmental Impacts, Condition and Sustainability of Mountain Biking Trails in an Urban National Park.

Mountain biking is a popular recreational activity in natural areas, with thousands of formal trails designed, constructed and maintained by land managers. Increasingly, there are also rising numbers of informal trails created by riders. A challenge for land managers is identifying, assessing, and then mitigating environmental impacts created by trails, including in protected areas. Here we assessed mountain biking trails in a large, popular national park on the Gold Coast, Australia, addressing the currently limited research comparing the extent, environmental impacts, condition and sustainability of these trails. Impacts from the 31.4 km of formal and 33.7 km of informal trails through the forests in Nerang National Park (1659 ha) included soil erosion (16.48 m3) and loss of vegetation along and adjacent to the trails (90,955 m2). Formal trails were six times more popular and wider on average (1.1 m vs 0.7 m) than informal trails, but less incised than informal trails (4.6 cm deep vs 6.3 cm). Generalised Linear Models showed that Trail Grade, slope and alignment best-predicted trail condition, highlighting the importance of good trail design in minimising trail impacts. It is recommended most of the informal trails are closed and rehabilitated, as they were not well-designed, increase fragmentation and have environmental impacts, with some traversing ecologically sensitive areas. In addition, some formal trails need to be upgraded to deal with erosion and other impacts. More broadly, the increasing demand for mountain biking must be addressed, including exploring opportunities to promote areas outside of national parks while minimising environmental impacts and other challenges associated with the creation and use of informal mountain bike trails in protected areas.

Using Spatial Scan Statistics and Geographic Information Systems to Detect Monthly Human Mobility Clusters and Analyze Cluster Area Characteristics.

Introduction: This study evaluated the detection of monthly human mobility clusters and characteristics of cluster areas before the coronavirus disease 2019 (COVID-19) outbreak using spatial epidemiological methods, namely, spatial scan statistics and geographic information systems (GIS). Methods: The research area covers approximately 10.3 km2, with a population of about 350,000 people. Analysis was conducted using open data, with the exception of one dataset. Human mobility and population data were used on a 1-km mesh scale, and business location data were used to examine the area characteristics. Data from January to December 2019 were utilized to detect human mobility clusters before the COVID-19 pandemic. Spatial scan statistics were performed using SaTScan to calculate relative risk (RR). The detected clusters and other data were visualized in QGIS to explore the features of the cluster areas. Results: Spatial scan statistics identified 33 clusters. The detailed analysis focused on clusters with an RR exceeding 1.5. Meshes with an RR over 1.5 included one with clusters for 1 year which is identified in all months of the year, one with clusters for 9 months, three with clusters for 6 months, three with clusters for 3 months, and four with clusters for 1 month. September had the highest number of clusters (eight), followed by April and November (seven each). The remaining months had five or six clusters. Characteristically, the cluster areas included the vicinity of railway stations, densely populated business areas, ball game fields, and large-scale construction sites. Conclusions: Statistical analysis of human mobility clusters using open data and open-source tools is crucial for the advancement of evidence-based policymaking based on scientific facts, not only for novel infectious diseases but also for existing ones, such as influenza.

Spatial dynamics of COVID-19 in São Paulo: A cellular automata and GIS approach.

This study examines the spread of COVID-19 in São Paulo, Brazil, using a combination of cellular automata and geographic information systems to model the epidemic's spatial dynamics. By integrating epidemiological models with georeferenced data and social indicators, we analyse how the virus propagates in a complex urban setting, characterized by significant social and economic disparities. The research highlights the role of various factors, including mobility patterns, neighbourhood configurations, and local inequalities, in the spatial spreading of COVID-19 throughout São Paulo. We simulate disease transmission across the city's 96 districts, offering insights into the impact of network topology and district-specific variables on the spread of infections. The study seeks to fine-tune the model to extract epidemiological parameters for further use in a statistical analysis of social variables. Our findings underline the critical importance of spatial analysis in public health strategies and emphasize the necessity for targeted interventions in vulnerable communities. Additionally, the study explores the potential of mathematical modelling in understanding and mitigating the effects of pandemics in urban environments.

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review.

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

Using Social Vulnerability Indices to Predict Priority Areas for Prevention of Sudden Unexpected Infant Death in Cook County, IL: Cross-Sectional Study.

Background: The incidence of sudden unexpected infant death (SUID) in the United States has persisted at roughly the same level since the mid-2000s, despite intensive prevention efforts around safe sleep. Disparities in outcomes across racial and socioeconomic lines also persist. These disparities are reflected in the spatial distribution of cases across neighborhoods. Strategies for prevention should be targeted precisely in space and time to further reduce SUID and correct disparities. Objective: We sought to aid neighborhood-level prevention efforts by characterizing communities where SUID occurred in Cook County, IL, from 2015 to 2019 and predicting where it would occur in 2021-2025 using a semiautomated, reproducible workflow based on open-source software and data. Methods: This cross-sectional retrospective study queried geocoded medical examiner data from 2015-2019 to identify SUID cases in Cook County, IL, and aggregated them to "communities" as the unit of analysis. We compared demographic factors in communities affected by SUID versus those unaffected using Wilcoxon rank sum statistical testing. We used social vulnerability indicators from 2014 to train a negative binomial prediction model for SUID case counts in each given community for 2015-2019. We applied indicators from 2020 to the trained model to make predictions for 2021-2025. Results: Validation of our query of medical examiner data produced 325 finalized cases with a sensitivity of 95% (95% CI 93%-97%) and a specificity of 98% (95% CI 94%-100%). Case counts at the community level ranged from a minimum of 0 to a maximum of 17. A map of SUID case counts showed clusters of communities in the south and west regions of the county. All communities with the highest case counts were located within Chicago city limits. Communities affected by SUID exhibited lower median proportions of non-Hispanic White residents at 17% versus 60% (P<.001) and higher median proportions of non-Hispanic Black residents at 32% versus 3% (P<.001). Our predictive model showed moderate accuracy when assessed on the training data (Nagelkerke R2=70.2% and RMSE=17.49). It predicted Austin (17 cases), Englewood (14 cases), Auburn Gresham (12 cases), Chicago Lawn (12 cases), and South Shore (11 cases) would have the largest case counts between 2021 and 2025. Conclusions: Sharp racial and socioeconomic disparities in SUID incidence persisted within Cook County from 2015 to 2019. Our predictive model and maps identify precise regions within the county for local health departments to target for intervention. Other jurisdictions can adapt our coding workflows and data sources to predict which of their own communities will be most affected by SUID.

Visualizing Multilayer Spatiotemporal Epidemiological Data with Animated Geocircles.

OBJECTIVE: The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts. MATERIALS AND METHODS: We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings. RESULTS: Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards. DISCUSSION: We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation. CONCLUSION: CoronaViz's unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.

Behavioral activation for veterans with co-occurring alcohol use disorder and posttraumatic stress disorder: Basis and methodology for a pilot randomized controlled trial.

BACKGROUND: Nearly 2 million U.S. veterans live with co-occurring alcohol use disorder and posttraumatic stress disorder (AUD/PTSD). Extant AUD/PTSD treatments emphasize symptom reduction, sometimes overlooking psychosocial functioning improvements, and have dropout rates as high as 50 %. Additionally, current approaches to measuring psychosocial functioning are limited to self-report. This study protocol describes a 1:1 parallel, two-arm, pilot randomized controlled trial comparing Behavioral Activation (BA) psychotherapy to Relapse Prevention (RP) psychotherapy for veterans with AUD/PTSD. METHODS: Forty-six veterans with AUD/PTSD will be block-randomized to eight weekly, virtual, hour-long individual sessions of BA or RP. Clinical interview, self-report, and geospatial assessments will be administered at pre- and post-treatment. Select outcome and exploratory measures will be administered during treatment. Analyses will focus on trial feasibility, BA acceptability, and preliminary efficacy. Geospatial analyses will explore whether pre- to post-treatment changes in geospatial movement can be used to objectively measure treatment response. The study site and an independent Data and Safety Monitoring Board will monitor trial progress, safety, and quality. De-identified data from consenting participants will be submitted to a sponsor-designated data repository. CONCLUSION: If successful, this trial could help to provide veterans with AUD/PTSD with a more acceptable treatment option. Positive findings would also lay groundwork for testing BA in civilians with AUD/PTSD. Finally, by incorporating novel geospatial methods and technologies, this study could potentially yield a new approach to objectively measuring AUD/PTSD recovery that could be used in other clinical trials. This study was registered in ClinicalTrials.gov (NCT06249386).

Relationship Between Primary Care Physician Capacity and Usual Source of Care.

BACKGROUND: The NASEM Primary Care Report and Primary Care scorecard highlighted the importance of primary care physician (PCP) capacity and having a usual source of care (USC). However, research has found that PCP capacity and USC do not always correlate. This exploratory study compares geographic patterns and the characteristics of counties with similar rates of PCP capacity but varying rates of USC. METHODS: Our county-level, cross-sectional approach includes estimates from the Robert Graham Center and data from the Robert Wood Johnson County Health Rankings (CHR). We utilized conditional mapping methods to first identify US counties with the highest rates of social deprivation (SDI). Next, counties were stratified based on primary care physician (PCP) capacity and usual source of care (USC) terciles, allowing us to identify 4 types of counties: (1) High-Low (high PCP capacity, low USC); (2) High-High (high PCP capacity, high USC); (3) Low-High (low PCP capacity, high USC); and (4) Low-Low (low PCP capacity, low USC). We use t test to explore differences in the characteristics of counties with similar rates of primary care capacity. RESULTS: The results show clear geographic patterns: High-High counties are located primarily in the northern and northeastern US; High-Low counties are located primarily in the southwestern and southern US. Low-High counties are concentrated in the Appalachian and Great Lakes regions; Low-Low counties are concentrated in the southeastern US and Texas. Descriptive results reveal that rates of racial and ethnic minorities, the uninsured, and social deprivation are highest in counties with low rates of USC for both high PCP and low PCP areas. CONCLUSIONS: Recognizing PCP shortages and improving rates of USC are key strategies for increasing access to high-quality, primary care. Targeting strategies by geographic region will allow for tailored models to improve access to and continuity of primary care. For example, we found that many of the counties with the lowest rates of USC are found in non-Medicaid expansion states (Texas, Georgia, and Florida) with high rates of uninsured populations, suggesting that expanding Medicaid and improving access to health insurance are key strategies for increasing USC in these states.

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study.

Background: Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida. Methods: Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel. Results: A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida. Conclusion: Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

Census tract geospatial analysis comparing social determinants of health with tooth loss in California seniors: An ecologic study.

OBJECTIVES: Individual-level social determinant of health (SDOH) measures alone may insufficiently explain disparities in edentulism among seniors. Therefore, the authors examined the correlation of census tract-level SDOH and residential racial segregation measures with edentulism in Californian adults aged ≥65 years old. METHODS: Explanatory variables were obtained from Healthy Places Index (HPI), the National Cancer Institute and diversitydatakids.org. The edentulism outcome variable was obtained from CDC's PLACES small area estimates from the 2018 Behavioral Risk Factor Surveillance System data. Pearson and Spearman rank correlations were estimated. Multiple linear regression and multi-collinearity evaluations were performed. The Global Moran's I statistic assessed partial autocorrelation within census tracts. RESULTS: Pearson and Spearman correlations were similar, supporting robustness. HPI, an area measure of advantage, strongly negatively correlated with edentulism prevalence [correlation coefficient: -0.87; 95% confidence interval (CI): -0.87, -0.86]. A change of 1.0 in HPI corresponded to an estimated decrease in edentulism prevalence of 5.9% (linear model adjusted R2 = 0.78). Racially segregated census tracts with Hispanics or Blacks alone were positively correlated with edentulism prevalence [0.60, 95% CI: 0.58, 0.62; and 0.33, 95% CI: 0.31, 0.35, respectively]. The converse was seen in census tracts with non-Hispanic Whites alone [-0.57, 95% CI: -0.58, -0.55]. Global Moran's I statistic for edentulism (0.13) and HPI scores (0.19) were significant (both p < .001) indicating geospatial autocorrelation. CONCLUSIONS: Higher disadvantage and minority racial segregation within census tracts were positively correlated with edentulism prevalence. Future research and policy should consider possible interventions improving SDOH to reduce oral health inequities.

Innovative BIM technology application in the construction management of highway.

Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

The optimization of harm reduction services in Massachusetts through the use of GIS: Location-allocation analyses, 2019-2021.

BACKGROUND: Fatal opioid-related overdoses (OOD) continue to be a leading cause of preventable death across the US. Opioid Overdose Education and Naloxone Distribution programs (OENDs) play a vital role in addressing morbidity and mortality associated with opioid use, but access to such services is often inequitable. We utilized a geographic information system (GIS) and spatial analytical methods to inform prioritized placement of OEND services in Massachusetts. METHODS: We obtained addresses for OEND sites from the Massachusetts Department of Public Health and address-level fatal OOD data for January 2019 to December 2021 from the Massachusetts Registry of Vital Records and Statistics. Using location-allocation approaches in ArcGIS Pro, we created p-median models using locations of existing OEND sites and fatal OOD counts to identify areas that should be prioritized for future OEND placement. Variables included in our analysis were transportation mode, distance from public schools, race and ethnicity, and location feasibility. RESULTS: Three Massachusetts communities - Athol, Dorchester, and Fitchburg - were identified as priority sites for new OEND locations using location-allocation models based on capacity to maximize OOD prevention. Communities identified by the models for OEND placement had similar demographics and overdose rates (42.8 per 100,000 vs 40.1 per 100,000 population) to communities with existing OEND programs but lower naloxone kit distribution rates (2589 doses per 100,000 vs 3704 doses per 100,000). Further models demonstrated differential access based on location and transportation. CONCLUSION: Our analyses identified key areas of Massachusetts with greatest need for OEND services. Further, these results demonstrate the utility of using spatial epidemiological methods to inform public health recommendations.

Spatio-temporal characterization of PM10 concentration across Abu Dhabi Emirate (UAE).

The abundance and recurrence of particulate matter in Abu Dhabi Emirate (ADE), are often derived from different emission sources such as the combustion of hydrocarbon, producing much of the PM2.5 found in outdoor air, as well as a significant proportion of PM10. Wind-blown dust from open desert areas and construction sites, landfills and agriculture, brush/waste burning, and industrial sources, has contributed markedly to the problem of the spread of haze and the long-range movement of pollutants in the country. In this study, the spatio-temporal characterization of PM10 concentration across the Emirate was analyzed utilizing geospatial interpolation, spanning the period between 2013 and 2017. The results suggest that the fluctuations of the PM10 concentration can be decomposed into three dominant types, each characterizing different spatial and temporal variations. First, the western region with PM10 showing a peak concentration during the summer season i.e., when the winds are predominantly northerlies or northwesterly, and a minimal concentration during the winter season. Second, the central region with the PM10 exhibiting a concentration surge in July-August, as a result of a mix of strong winds and high temperatures. Third, the eastern region with a low concentration of PM10. Seasonally, this component exhibits two concentration maxima during quarters 2 and 3 (summer), and two minima during quarters 1 and 4 (winter). Indeed, the seasonal variability of PM10 concentration in desertic countries like the UAE is closely linked to the seasonal variation of heat waves and dust storms, which are characteristic of the dryland climate. During the summer months, the UAE experiences high temperatures and arid conditions, creating favorable conditions for the formation of heat waves. Furthermore, it was noticed that the PM10 concentration also fluctuated markedly throughout the study period with anomalies detected in open desert areas and regions characterized by extensive industrial operations.

GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities.

Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000-2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria's six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.

A Geographically Weighted Cost-effectiveness Analysis of Newborn Cytomegalovirus Screening.

Background: Early identification of newborns with congenital cytomegalovirus (CMV) is necessary to provide antiviral therapy and other interventions that can improve outcomes. Prior research demonstrates that universal newborn CMV screening would be the most cost-effective approach to identifying newborns who are infected. CMV is not uniformly prevalent, and it is uncertain whether universal screening would remain cost-effective in lower-prevalence neighborhoods. Our aim was to identify geographic heterogeneity in the cost-effectiveness of universal newborn CMV screening by combining a geospatial analysis with a preexisting cost-effectiveness analysis. Methods: This study used the CMV testing results and zip code location data of 96 785 newborns in 7 metropolitan areas who had been tested for CMV as part of the CMV and Hearing Multicenter Screening study. A hierarchical bayesian generalized additive model was constructed to evaluate geographic variability in the odds of CMV. The zip code-level odds of CMV were then used to weight the results of a previously published model evaluating universal CMV screening vs symptom-targeted screening. Results: The odds of CMV were heterogeneous over large geographic scales, with the highest odds in the southeastern United States. Universal screening was more cost-effective and afforded more averted cases of severe hearing loss than targeted testing. Universal screening remained the most cost-effective option even in areas with the lowest CMV prevalence. Conclusions: Universal newborn CMV screening is cost-effective regardless of underlying CMV prevalence and is the preferred strategy to reduce morbidity from congenital CMV.

Development of a Consolidated Health Facility Masterlist Using Data From Polio Electronic Surveillance in the World Health Organization African Region.

Geospatial data reporting from surveillance and immunization efforts is a key aspect of the World Health Organization (WHO) Global Polio Eradication Initiative in Africa. These activities are coordinated through the WHO Regional Office for Africa Geographic Information Systems Centre. To ensure the accuracy of field-collected data, the WHO Regional Office for Africa Geographic Information Systems Centre has developed mobile phone apps such as electronic surveillance (eSURV) and integrated supportive supervision (ISS) geospatial data collection programs. While eSURV and ISS have played a vital role in efforts to eradicate polio and control other communicable diseases in Africa, disease surveillance efforts have been hampered by incomplete and inaccurate listings of health care sites throughout the continent. To address this shortcoming, data compiled from eSURV and ISS are being used to develop, update, and validate a Health Facility master list for the WHO African region that contains comprehensive listings of the names, locations, and types of health facilities in each member state. The WHO and Ministry of Health field officers are responsible for documenting and transmitting the relevant geospatial location information regarding health facilities and traditional medicine sites using the eSURV and ISS form; this information is then used to update the Health Facility master list and is also made available to national ministries of health to update their respective health facility lists. This consolidation of health facility information into a single registry is expected to improve disease surveillance and facilitate epidemiologic research for the Global Polio Eradication Initiative, as well as aid public health efforts directed at other diseases across the African continent. This review examines active surveillance using eSURV at the district, country, and regional levels, highlighting its role in supporting polio surveillance and immunization efforts, as well as its potential to serve as a fundamental basis for broader public health initiatives and research throughout Africa.