Lifetime residential history collection and processing for environmental data linkages in the ABCD study

By using geospatial information such as participants' residential history along with external datasets of environmental exposures, ongoing studies can enrich their cohorts to investigate the role of the environment on brain-behavior health outcomes. However, challenges may arise if clear guidance and key quality control steps are not taken at the outset of data collection of residential information. Here, we detail the protocol development aimed at improving the collection of lifetime residential address information from the Adolescent Brain Cognitive Development (ABCD) Study. This protocol generates a workflow for minimizing gaps in residential information, improving data collection processes, and reducing misclassification error in exposure estimates.

Construction of ecological network in Qujing city based on MSPA and MCR models

With the rapid advancement of urbanization and industrialization, ecological patches within cities and towns are fragmented and ecological corridors are cut off, regional ecological security is threatened and sustainable development is hindered. Building an ecological network that conforms to regional realities can connect fragmented patches, protect biodiversity and regional characteristics, and provide scientific reference for regional ecological protection and ecological network planning. By taking Qilin District, the main urban area of Qujing City as an example, and using geospatial data as the main data source, based on morphological spatial pattern analysis (MSPA) and minimum cumulative resistance (MCR), this study identified ecological source areas, extracted ecological corridors, and build & optimize ecological networks. (1) All landscape types are identified based on MSPA, the proportion of core area was the highest among all landscape types, which was 80.69%, combined with the connectivity evaluation, 14 important ecological source areas were selected. (2) 91 potential ecological corridors were extracted through MCR and gravity models, there were 16 important ones. (3) The network connectivity analysis method is used to calculate the α, β, and γ indexes of the ecological network before optimization, which were 2.36, 6.5, and 2.53, while after optimization, α, β and γ indices were 3.8, 9.5 and 3.5, respectively. The combined application of MSPA-MCR model and ecological network connectivity analysis evaluation is conducive to improving the structure and functionality of ecological network.

An innovative strategy for deworming dogs in Mediterranean areas highly endemic for cystic echinococcosis

BACKGROUND

Cystic echinococcosis (CE), caused by the larval stage of Echinococcus granulosus sensu lato, is a zoonotic parasitic disease of economic and public health importance worldwide, especially in the Mediterranean area. Canids are the main definitive hosts of the adult cestode contaminating the environment with parasite eggs released with feces. In rural and peri-urban areas, the risk of transmission to livestock as well as humans is high because of the free-roaming behavior of owned/not owned dogs. Collecting data on animal movements and behavior using GPS dataloggers could be a milestone to contain the spread of this parasitosis. Thus, this study aims to develop a comprehensive control strategy, focused on deworming a dog population in a pilot area of southern Italy (Campania region) highly endemic for CE.

METHODS

Accordingly, five sheep farms, tested to be positive for CE, were selected. In each sheep farm, all shepherd dogs present were treated every 2 months with praziquantel. Furthermore, 15 GPS dataloggers were applied to sheep and dogs, and their movements were tracked for 1 month; the distances that they traveled and their respective home ranges were determined using minimum convex polygon (MCP) analysis with a convex hull geometry as output.

RESULTS

The results showed that the mean daily walking distances traveled by sheep and dogs did not significantly differ. Over 90% of the point locations collected by GPS fell within 1500 mt of the farm, and the longest distances were traveled between 10:00 and 17:00. In all the sheep farms monitored, the area traversed by the animals during their daily activities showed an extension of < 250 hectares. Based on the home range of the animals, the area with the highest risk of access from canids (minimum safe convex polygon) was estimated around the centroid of each farm, and a potential scheme for the delivery of praziquantel-laced baits for the treatment of not owned dogs gravitating around the grazing area was designed.

CONCLUSIONS

This study documents the usefulness of geospatial technology in supporting parasite control strategies to reduce disease transmission.

Evaluating COVID-19 Risk to Essential Workers by Occupational Group: A Case Study in Massachusetts

Occupational exposure to SARS-CoV-2 varies by profession, but "essential workers" are often considered in aggregate in COVID-19 models. This aggregation complicates efforts to understand risks to specific types of workers or industries and target interventions, specifically towards non-healthcare workers. We used census tract-resolution American Community Survey data to develop novel essential worker categories among the occupations designated as COVID-19 Essential Services in Massachusetts. Census tract-resolution COVID-19 cases and deaths were provided by the Massachusetts Department of Public Health. We evaluated the association between essential worker categories and cases and deaths over two phases of the pandemic from March 2020 to February 2021 using adjusted mixed-effects negative binomial regression, controlling for other sociodemographic risk factors. We observed elevated COVID-19 case incidence in census tracts in the highest tertile of workers in construction/transportation/buildings maintenance (Phase 1: IRR 1.32 [95% CI 1.22, 1.42]; Phase 2: IRR: 1.19 [1.13, 1.25]), production (Phase 1: IRR: 1.23 [1.15, 1.33]; Phase 2: 1.18 [1.12, 1.24]), and public-facing sales and services occupations (Phase 1: IRR: 1.14 [1.07, 1.21]; Phase 2: IRR: 1.10 [1.06, 1.15]). We found reduced case incidence associated with greater percentage of essential workers able to work from home (Phase 1: IRR: 0.85 [0.78, 0.94]; Phase 2: IRR: 0.83 [0.77, 0.88]). Similar trends exist in the associations between essential worker categories and deaths, though attenuated. Estimating industry-specific risk for essential workers is important in targeting interventions for COVID-19 and other diseases and our categories provide a reproducible and straightforward way to support such efforts.

Predicting emergency health care demands due to respiratory diseases

BACKGROUND

Timely care in the health sector is essential for the recovery of patients, and even more so in the case of a health emergency. In these cases, appropriate management of human and technical resources is essential. These are limited and must be mobilised in an optimal and efficient manner.

OBJECTIVE

This paper analyses the use of the health emergency service in a city, Jaén, in the south of Spain. The study is focused on the most recurrent case in this service, respiratory diseases.

METHODS

Machine Learning algorithms are used in which the input variables are multisource data and the target attribute is the prediction of the number of health emergency demands that will occur for a selected date. Health, social, economic, environmental, and geospatial data related to each of the emergency demands were integrated and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel.

RESULTS

Predictive models of emergency demand due to respiratory disseases were generated with am absolute error better than 35 %.

CONCLUSIONS

This model helps to make decisions on the efficient sizing of emergency health resources to manage and respond in the shortest possible time to patients with respiratory diseases requiring urgent care in the city of Jaén.

COVID-19 contact tracking based on person reidentification and geospatial data

Efficient contact tracing is a crucial step in preventing the spread of COVID-19. However, the current methods rely heavily on manual investigation and truthful reporting by high-risk individuals. Mobile applications and Bluetooth-based contact tracing methods have also been adopted, but privacy concerns and reliance on personal data have limited their effectiveness. To address these challenges, in this paper, a geospatial big data method that combines person reidentification and geospatial information for contact tracing is proposed. The proposed real-time person reidentification model can identify individuals across multiple surveillance cameras, and the surveillance data is fused with geographic information and mapped onto a 3D geospatial model to track movement trajectories. After real-world verification, the proposed method achieves a first accuracy rate of 91.56%, a first-five accuracy rate of 97.70%, and a mean average precision of 78.03% with an inference speed of 13 ms per image. Importantly, the proposed method does not rely on personal information, mobile phones, or wearable devices, avoiding the limitations of existing contact tracing schemes and providing significant implications for public health in the post-COVID-19 era.

Geospatial techniques for monitoring and mitigating climate change and its effects on human health

This article begins by briefly examining the multitude of ways in which climate and climate change affect human health and wellbeing. It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in the measurement, analysis and modelling of climate change and its effects on human health. Geospatial techniques are proving indispensable for making more accurate assessments and estimates, predicting future trends more reliably, and devising more optimised climate change adaptation and mitigation plans.

Disparity in HIV Service Interruption in the Outbreak of COVID-19 in South Carolina

To examine HIV service interruptions during the COIVD-19 outbreak in South Carolina (SC) and identify geospatial and socioeconomic correlates of such interruptions, we collected qualitative, geospatial, and quantitative data from 27 Ryan White HIV clinics in SC in March, 2020. HIV service interruptions were categorized (none, minimal, partial, and complete interruption) and analyzed for geospatial heterogeneity. Nearly 56% of the HIV clinics were partially interrupted and 26% were completely closed. Geospatial heterogeneity of service interruption existed but did not exactly overlap with the geospatial pattern of COVID-19 outbreak. The percentage of uninsured in the service catchment areas was significantly correlated with HIV service interruption (F = 3.987, P = .02). This mixed-method study demonstrated the disparity of HIV service interruptions in the COVID-19 in SC and suggested a contribution of existing socioeconomic gaps to this disparity. These findings may inform the resources allocation and future strategies to respond to public health emergencies.

Two judging criteria to check validity of a model for filling gaps caused by incomplete geospatial data

Many models can be used to fill the gaps caused by incomplete geospatial data. But not all are valid. To study the validity of geospatial information diffusion model, in this article, two judging criteria are suggested to check if a model is valid for filling a gap unit. The root mean squared error of a model with a given sample after removing a test point is called datum error of the model. The error between real value and estimated value of the test point is called forecasting error of the model. The first criterion says that, when the average forecasting error is less than the average datum error, the model is invalid. The second criterion says that, the smaller the errors, the more valid the model. The results of computer simulation show that geospatial information diffusion model is more valid than the geographically weighted regression and the back propagation neural network.

A cross-sectional ecological analysis of international and sub-national health inequalities in commercial geospatial resource availability

BACKGROUND

Commercial geospatial data resources are frequently used to understand healthcare utilisation. Although there is widespread evidence of a digital divide for other digital resources and infra-structure, it is unclear how commercial geospatial data resources are distributed relative to health need.

METHODS

To examine the distribution of commercial geospatial data resources relative to health needs, we assembled coverage and quality metrics for commercial geocoding, neighbourhood characterisation, and travel time calculation resources for 183 countries. We developed a country-level, composite index of commercial geospatial data quality/availability and examined its distribution relative to age-standardised all-cause and cause specific (for three main causes of death) mortality using two inequality metrics, the slope index of inequality and relative concentration index. In two sub-national case studies, we also examined geocoding success rates versus area deprivation by district in Eastern Region, Ghana and Lagos State, Nigeria.

RESULTS

Internationally, commercial geospatial data resources were inversely related to all-cause mortality. This relationship was more pronounced when examining mortality due to communicable diseases. Commercial geospatial data resources for calculating patient travel times were more equitably distributed relative to health need than resources for characterising neighbourhoods or geocoding patient addresses. Countries such as South Africa have comparatively high commercial geospatial data availability despite high mortality, whilst countries such as South Korea have comparatively low data availability and low mortality. Sub-nationally, evidence was mixed as to whether geocoding success was lowest in more deprived districts.

CONCLUSIONS

To our knowledge, this is the first global analysis of commercial geospatial data resources in relation to health outcomes. In countries such as South Africa where there is high mortality but also comparatively rich commercial geospatial data, these data resources are a potential resource for examining healthcare utilisation that requires further evaluation. In countries such as Sierra Leone where there is high mortality but minimal commercial geospatial data, alternative approaches such as open data use are needed in quantifying patient travel times, geocoding patient addresses, and characterising patients' neighbourhoods.

Mapping elemental contamination on Palmyra Atoll National Wildlife Refuge

Palmyra Atoll, once a WWII U.S. Navy air station, is now a U.S. National Wildlife Refuge with nearly 50km² of coral reef and 275ha of emergent lands with forests of Pisonia grandis trees and colonies of several bird species. Due to the known elemental and organic contamination from chemicals associated with aviation, power generation and transmission, waste management, and other air station activities, a screening survey to map elemental concentrations was conducted. A map of 1944 Navy facilities was georeferenced and identifiable features were digitized. These data informed a targeted survey of 25 elements in soils and sediment at locations known or suspected to be contaminated, using a hand-held X-ray fluorescence spectrometer. At dozens of locations, concentrations of elements exceeded established soil and marine sediment thresholds for adverse ecological effects. Results were compiled into a publically available geospatial dataset to inform potential remediation and habitat restoration activities.

Effects of ensemble and summary displays on interpretations of geospatial uncertainty data

Ensemble and summary displays are two widely used methods to represent visual-spatial uncertainty; however, there is disagreement about which is the most effective technique to communicate uncertainty to the general public. Visualization scientists create ensemble displays by plotting multiple data points on the same Cartesian coordinate plane. Despite their use in scientific practice, it is more common in public presentations to use visualizations of summary displays, which scientists create by plotting statistical parameters of the ensemble members. While prior work has demonstrated that viewers make different decisions when viewing summary and ensemble displays, it is unclear what components of the displays lead to diverging judgments. This study aims to compare the salience of visual features – or visual elements that attract bottom-up attention – as one possible source of diverging judgments made with ensemble and summary displays in the context of hurricane track forecasts. We report that salient visual features of both ensemble and summary displays influence participant judgment. Specifically, we find that salient features of summary displays of geospatial uncertainty can be misunderstood as displaying size information. Further, salient features of ensemble displays evoke judgments that are indicative of accurate interpretations of the underlying probability distribution of the ensemble data. However, when participants use ensemble displays to make point-based judgments, they may overweight individual ensemble members in their decision-making process. We propose that ensemble displays are a promising alternative to summary displays in a geospatial context but that decisions about visualization methods should be informed by the viewer’s task.

A Global Meta-Analysis of the Value of Ecosystem Services Provided by Lakes

This study presents the first meta-analysis on the economic value of ecosystem services delivered by lakes. A worldwide data set of 699 observations drawn from 133 studies combines information reported in primary studies with geospatial data. The meta-analysis explores antagonisms and synergies between ecosystem services. This is the first meta-analysis to incorporate simultaneously external geospatial data and ecosystem service interactions. We first show that it is possible to reliably predict the value of ecosystem services provided by lakes based on their physical and geographic characteristics. Second, we demonstrate that interactions between ecosystem services appear to be significant for explaining lake ecosystem service values. Third, we provide an estimation of the average value of ecosystem services provided by lakes: between 106 and 140 USD$2010 per respondent per year for non-hedonic price studies and between 169 and 403 USD$2010 per property per year for hedonic price studies.

Extreme urban-rural temperatures in the coastal city of Turku, Finland: Quantification and visualization based on a generalized additive model

Fundamental knowledge on the determinants of air temperatures across spatial and temporal scales is essential in climate change mitigation and adaptation. Spatial-based statistical modelling provides an efficient approach for the analysis and prediction of air temperatures in human-modified environments at high spatial accuracy. The aim of the study was firstly, to analyse the environmental factors affecting extreme air temperature conditions in a coastal high-latitude city and secondly, to explore the applicability of generalized additive model (GAM) in the study of urban-rural temperatures. We utilized air temperature data from 50 permanent temperature logger stations and extensive geospatial environmental data on different scales from Turku, SW Finland. We selected five temperature situations (cases) and altogether 12 urban and natural explanatory variables for the analyses. The results displayed that (i) water bodies and topographical conditions were often more important than urban variables in controlling the spatial variability of extreme air temperatures, (ii) case specificity of the explanatory variables and their scales should be considered in the analyses and (iii) GAM was highly suitable in quantifying and visualizing the relations between urban-rural temperatures and environmental determinants at local scales. The results promote the use of GAMs in spatial-based statistical modelling of air temperature in future.