Spatial distribution and mass transport of Perfluoroalkyl Substances (PFAS) in surface water: A statewide evaluation of PFAS occurrence and fate in Alabama

Per- and polyfluoroalkyl substances (PFAS) have been previously detected near suspected sources in Alabama, but the overall extent of contamination across the state is unknown. This study evaluated the spatial distribution of 17 PFAS within the ten major river basins in Alabama and provided insights into their transport and fate through a mass flux analysis. Six PFAS were identified in 65 out of the 74 riverine samples, with mean ∑₆PFAS levels of 35.2 ng L^-1. The highest ∑₆PFAS concentration of 237 ng L^-1 was detected in the Coosa River, a transboundary river that receives discharges from multiple sources in Alabama and Georgia. PFAS distribution was not observed to be uniform across the state: while the Coosa, Alabama, and Chattahoochee rivers presented relatively high mean ∑₆PFAS concentrations of 191, 100 and 28.8 ng L^-1, respectively, PFAS were not detected in the Conecuh, Escatawpa, and Yellow rivers. Remaining river systems presented mean ∑₆PFAS concentrations between 7.94 and 24.7 ng L^-1. Although the short-chain perfluoropentanoic acid (PFPeA) was the most detected analyte (88%), perfluorobutanesulfonic acid (PFBS) was the substance with the highest individual concentration of 79.4 ng L^-1. Consistent increases in the mass fluxes of PFAS were observed as the rivers flowed through Alabama, reaching up to 63.3 mg s^-1, indicating the presence of numerous sources across the state. Most of the mass inputs would not have been captured if only aqueous concentrations were evaluated, since concentration is usually heavily impacted by environmental conditions. Results of this study demonstrate that mass flux is a simple and powerful complementary approach that can be used to broadly understand trends in the transport and fate of PFAS in large river systems.

Efficacies of insecticide dog collars against visceral leishmaniasis in low and high-income areas and the effects for non-collared neighbor dogs

Previous studies demonstrated that insecticide collars are highly effective in reducing canine visceral leishmaniasis (CVL); however, it is unclear if the efficacy differs by socioeconomic conditions across diverse communities. This study aimed fourfold: (i) to evaluate the protection of 4% impregnated deltamethrin collared (DMC) dogs in different areas of an endemic city for visceral leishmaniasis (VL); (ii) to analyze socioeconomic variables with the seroconversion rates; (iii) to analyze the indirect effect of DMC on untreated dogs in areas of intervention; and, (iv) to evaluate the potential transmission to other dogs in the same household when one positive dog is present. The study employed the municipality of Bauru, São Paulo, Brazil, as the area of interest and used Geographic Information System tools to fit binary logistic regression models.  Dogs were divided into three cohort studies: intervention with DMC (I), indirect effect of DMC (IE), and control (C). Pre-intervention, lower mean income was associated with higher rates of CVL and a 142% increase in the odds of transmission (OR = 1.42, p-value = 0.001, CI 1.14,1.77). Post-intervention, lower-income areas depicted greater efficacy (76%) than higher-income areas (45%). The overall efficacy of DMC in preventing CVL was 63%; however, seroconversion rates were higher for IE (6.02%) than C (3.78%), revealing the failure of the indirect protection of DMC to manage the spread of the disease among the general non-wearing DMC canine population living in the same area. The protected dogs may repel the vectors, and non-protected dogs attract them, creating a higher transmission rate for non-protected dogs. Greater seroconversion was observed for living with an infected dog (10.20% in IE and 8.75% in C) than for the indirect effect of DMC, demonstrating the social burden of CVL. Overall, uncollared dogs have three times higher odds of being infected with CVL than DMC dogs (p < 0.005), and uncollared dogs living with (an) infected dog(s) in the same household can reach 3.5 times higher odds than those living with negative ones (p < 0.005). The results may assist in enhancing public policies and minimizing inequality in low and middle-income countries that suffer from neglected diseases such as VL.

Spatial analysis of cardiovascular mortality and associated factors around the world

BACKGROUND

Cardiovascular disease (CVD) is one of the most serious health issues and the leading cause of death worldwide in both developed and developing countries. The risk factors for CVD include demographic, socioeconomic, behavioral, environmental, and physiological factors. However, the spatial distribution of these risk factors, as well as CVD mortality, are not uniformly distributed across countries. Therefore, the goal of this study is to compare and evaluate some models commonly used in mortality and health studies to investigate whether the CVD mortality rates in the adult population (over 30 years of age) of a country are associated with the characteristics of surrounding countries from 2013 to 2017.

METHODS

We present the spatial distribution of the age-standardized crude mortality rate from cardiovascular disease, as well as conduct an exploratory data analysis (EDA) to obtain a basic understanding of the behavior of the variables of interest. Then, we apply the ordinary least squares (OLS) to the country level dataset. As OLS does not take into account the spatial dependence of the data, we apply two spatial modelling techniques, that is, spatial lag and spatial error models.

RESULTS

Our empirical findings show that the relationship between CVD and income, as well as other socioeconomic variables, are important. In addition, we highlight the importance of understanding how changes in individual behavior across different countries might affect future trends in CVD mortality, especially related to smoking and dietary behaviors.

CONCLUSIONS

We argue that this study provides useful clues for policymakers establishing effective public health planning and measures for the prevention of deaths from cardiovascular disease. The reduction of CVD mortality can positively impact GDP growth because increasing life expectancy enables people to contribute to the economy of the country and its regions for longer.

Comparing spatial patterns of 11 common cancers in Mainland China

Background

A stronger spatial clustering of cancer burden indicates stronger environmental and human behavioral effects. However, which common cancers in China have stronger spatial clustering and knowledge gaps regarding the environmental and human behavioral effects have yet to be investigated. This study aimed to compare the spatial clustering degree and hotspot patterns of 11 common cancers in mainland China and discuss the potential environmental and behavioral risks underlying the patterns.

Methods

Cancer incidence data recorded at 339 registries in 2014 was obtained from the “China Cancer Registry Annual Report 2017”. We calculated the spatial clustering degree of the common cancers using the global Moran’s Index and identified the hotspot patterns using the hotspot analysis.

Results

We found that esophagus, stomach and liver cancer have a significantly higher spatial clustering degree (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05) than others. When by sex, female esophagus, male stomach, male esophagus, male liver and female lung cancer had significantly higher spatial clustering degree (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001). The spatial clustering degree of male liver was significantly higher than that of female liver cancer (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001), whereas the spatial clustering degree of female lung was significantly higher than that of male lung cancer (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001). The high-risk areas of esophagus and stomach cancer were mainly in North China, Huai River Basin, Yangtze River Delta and Shaanxi Province. The hotspots for liver and male liver cancer were mainly in Southeast China and south Hunan. Hotspots of female lung cancer were mainly located in the Pearl River Delta, Shandong, North and Northeast China. The Yangtze River Delta and the Pearl River Delta were high-risk areas for multiple cancers.

Conclusions

The top highly clustered cancer types in mainland China included esophagus, stomach and liver cancer and, by sex, female esophagus, male stomach, male esophagus, male liver and female lung cancer. Among them, knowledge of their spatial patterns and environmental and behavioral risk factors is generally limited. Potential factors such as unhealthy diets, water pollution and climate factors have been suggested, and further investigation and validation are urgently needed, particularly for male liver cancer. This study identified the knowledge gap in understanding the spatial pattern of cancer burdens in China and offered insights into targeted cancer monitoring and control.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13926-y.

Griottes: a generalist tool for network generation from segmented tissue images

Background

Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information.

Results

This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here, we introduce Griottes, an open-source tool to build the “network twin” of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library’s capacities are demonstrated on different image and data types.

Conclusions

This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.

Macroecological drivers of vascular plant species composition in semi-natural grasslands: A regional study from Lower Silesia (Poland)

In agricultural landscapes of Europe, species-rich semi-natural grasslands provide numerous ecosystem services, such as forage production, ground and surface water regulation, and carbon sequestration. In the face of the declining area of grasslands, information about the macroecological drivers of grasslands diversity is an important element of studies on their ecology and management. Here, we tested the effect of factors representing environmental gradients, landscape structure, human pressure, habitat continuity in time, and spatial structure on species composition of vascular plants in semi-natural grasslands. The analysis was performed using a multivariate approach for 689 vegetation plots distributed across ~20,000 km² in the Lower Silesia region (Poland, Central Europe). We found that species composition was significantly influenced by factors representing all studied groups. The most influential factors were temperature, long-term habitat continuity, and topography. Moreover, we captured the correlation between habitat conditions and habitat continuity. Old grasslands (existing since the 1940s) usually occurred in wet habitats, and medium grasslands (existing since the 1980s) existed in highlands in most cases. We highlighted the potential of freely available environmental databases, as well as historical topographic maps, in the exploration of patterns of species composition at a large spatial extent. The result show also the usefulness of vegetation databases in recognition of grassland complexes contributing the regional biodiversity.

The role of geographic information system and global positioning system in dementia care and research: a scoping review

Background

Geographic Information System (GIS) and Global Positioning System (GPS), vital tools for supporting public health research, provide a framework to collect, analyze and visualize the interaction between different levels of the health care system. The extent to which GIS and GPS applications have been used in dementia care and research is not yet investigated. This scoping review aims to elaborate on the role and types of GIS and GPS applications in dementia care and research.

Methods

A scoping review was conducted based on Arksey and O’Malley’s framework. All published articles in peer-reviewed journals were searched in PubMed, Scopus, and Web of Science, subject to involving at least one GIS/GPS approach focused on dementia. Eligible studies were reviewed, grouped, and synthesized to identify GIS and GPS applications. The PRISMA standard was used to report the study.

Results

Ninety-two studies met our inclusion criteria, and their data were extracted. Six types of GIS/GPS applications had been reported in dementia literature including mapping and surveillance (n = 59), data preparation (n = 26), dementia care provision (n = 18), basic research (n = 18), contextual and risk factor analysis (n = 4), and planning (n = 1). Thematic mapping and GPS were most frequently used techniques in the dementia field.

Conclusions

Even though the applications of GIS/GPS methodologies in dementia care and research are growing, there is limited research on GIS/GPS utilization in dementia care, risk factor analysis, and dementia policy planning. GIS and GPS are space-based systems, so they have a strong capacity for developing innovative research based on spatial analysis in the area of dementia. The existing research has been summarized in this review which could help researchers to know the GIS/GPS capabilities in dementia research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-022-00308-1.

Operations research applicability in spatial conservation planning

A large fraction of the current environmental crisis derives from the large rates of human-driven biodiversity loss. Biodiversity conservation questions human practices towards biodiversity and, therefore, largely conflicts with ordinary societal aspirations. Decisions on the location of protected areas, one of the most convincing conservation tools, reflect such a competitive endeavor. Operations Research (OR) brings a set of analytical models and tools capable of resolving the conflicting interests between ecology and economy. Recent technological advances have boosted the size and variety of data available to planners, thus challenging conventional approaches bounded on optimized solutions. New models and methods are needed to use such a massive amount of data in integrative schemes addressing a large variety of concerns. This study provides an overview on the past, present and future challenges that characterize spatial conservation models supported by OR. We discuss the progress of OR models and methods in spatial conservation planning and how those models may be optimized through sophisticated algorithms and computational tools. Moreover, we anticipate possible panoramas of modern spatial conservation studies supported by OR and we explore possible avenues for the design of optimized interdisciplinary collaborative platforms in the era of Big Data, through consortia where distinct players with different motivations and services meet. By enlarging the spatial, temporal, taxonomic and societal horizons of biodiversity conservation, planners navigate around multiple socioecological/environmental equilibria and are able to decide on cost-effective strategies to improve biodiversity persistence under complex environments.

Impact of financial development and renewable energy consumption on environmental sustainability: a spatial analysis in CEMAC countries

This research aims to examine the impact of financial development and renewable energy consumption on CO₂ emissions in CEMAC for the period 1990-2018. The study employs spatial analysis approach. As previous investigations have overlooked the effects of the spatial interactions in demonstrating environmental sustainability across nations, this paper gives the first comprehensive spatial analysis of CO₂ emissions among countries. To eliminate possible bias and inefficiency in estimated coefficients, the spatial autoregressive model is used to account for both geographical dependency and individual heterogeneity. The results revealed that under economic distance weights, the values of the spatial lag coefficients of financial development (FD), foreign direct investment (FDI), gross domestic product (GDP), and renewable energy consumption depict negative and statistical significance effects regarding spatial fixed effects in the SDM model, while trade openness (TO) shows a negative and insignificant impact. Other decomposition effects results show that FD had both direct and indirect significantly negative effects on environmental sustainability in CEMAC. This indicates that FD has a significant negative effect on CO₂ emissions in both the local country and its adjoining countries. On average, a unit change in FD in each country and its neighboring countries would reduce dioxide carbon by 0.071 and 0.066, respectively, and that of the whole region would be 0.137 units, meaning that environmental sustainability will improve. However, GDP recorded significantly positive direct and indirect effects on environmental sustainability. The direct and indirect effects of FDI on CO2 in both the local and neighboring countries were negative but statistically insignificant. On the other hand, the direct and indirect effects of TO on environmental sustainability were positive and statistically significant. Along with significant and statistical effects of financial development, GDP, FDI, renewable energy consumption, and TO, the findings reveal the presence of a positive spatial dependency of CO₂ emissions in CEMAC. This implies that policymakers in CEMAC countries lean to rely on their environmental sustainability decisions to assign financial and investments to that of neighboring nations. Furthermore, the findings indicate that adjacent countries' FD and REC have a considerable impact on a country's CO₂ emissions. Our findings offer significant political implications, implying that financial development and renewable energy consumption should be strengthened to meet environmental sustainability goals.

Identifying the spatiotemporal vulnerability of soils to antimicrobial contamination through land application of animal manure in Minnesota, United States

Antimicrobials may reach the soil environment from a variety of sources and pathways, including land application of human biosolids and animal manure. Once in soil, antimicrobials can affect the abundance and activity of soil microorganisms and exert selection pressures that enhance the emergence and spread of antimicrobial resistance (AMR). To mitigate the spread of AMR it is important to understand the spatial and temporal interactions between antimicrobials and soil. The goal of this study was to assess the vulnerability of Minnesota (U.S.) soil to contamination with specific antimicrobial compounds at temperatures experienced throughout the year. Soil contamination potential was estimated based upon specific antimicrobial drug binding and permanence, and average monthly temperature. Minnesota soil vulnerability was estimated by incorporating spatially explicit soil contamination potential, land cover type, and livestock density. Assessment of antimicrobials used in livestock production showed that soils are most vulnerable to antimicrobial contamination in southwestern Minnesota, to enrofloxacin, chlortetracycline, and oxytetracycline, and in the months of April and October. While the assessment herein was not based on actual on-farm antimicrobial use data and subsequent excretion of antimicrobial metabolites into the environment, this study provides an overview of the spatial and temporal potential for Minnesota soil to be contaminated by several antimicrobial drugs and demonstrates how specific vulnerability assessments might be conducted for geographic areas with known exposure (e.g., cropland fertilized with livestock manure and/or human biosolids). Such assessments might be used to identify best practices for mitigating antimicrobial exposure to soils and guide additional research to understand the role of environmental antimicrobial contamination in the problem of AMR.

A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000-2019) based on LUCAS, CORINE and GLAD Landsat

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with "urbanization" showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.

Trend, multivariate decomposition and spatial variations of unintended pregnancy among reproductive-age women in Ethiopia: evidence from demographic and health surveys

Background

The magnitude of unintended pregnancy is unacceptably high and more than half of it end up with abortions. This may limit lower and middle-income countries to achieve the sustainable development goal targets of reduction of neonatal and maternal mortalities. Evidence on trends and spatial distribution of unintended pregnancy is limited. Therefore, this study aimed to assess the trend, multivariate decomposition, and spatial variations of unintended pregnancy among reproductive-age women in Ethiopia from 2000 to 2016.

Methods

Ethiopian Demographic and Health Data of 2000 to 2016 were used. A total weighted sample of 30,780 reproductive-age women participated. A multivariate decomposition analysis was employed to identify factors contributing to the change in the rate of unintended pregnancy in Ethiopia for 20 years from (1996 to 2016). The concentration index and graph were used to assess wealth-related inequalities, whereas spatial analysis was done to identify the hotspot of unintended pregnancy in Ethiopia.

Results

The 20-year trend analysis showed that the magnitude of unintended pregnancy among reproductive-age women decreased by 13.19 percentage points (from 39.76% in 2000 to 26.57% in 2016 EDHS). About 84.97% of the overall decrement was due to the difference in coefficient of the variables, whereas the remaining 15.03% was due to the difference in composition of the respondent. The differences in coefficient of the variables were decomposed by living metropolitan cities, having previous terminated pregnancy, and not having exposure to media; whereas, the change due to the composition, was expressed by having a household size of 1–3, living in metropolitan cities, being multipara and grand para, being unmarried and having no terminated pregnancy. Moreover, unintended pregnancies were more clustered in Addis Ababa and disproportionately concentrated in the poor groups.

Conclusions

In Ethiopia, a substantial decrement in unintended pregnancy was observed in the past decade. More than four-fifths of this overall decrement was due to the difference in the coefficient of the variables. There was spatial clustering of unintended pregnancy in Ethiopia. A program intervention is needed for high-risk regions such as Addis Ababa. Health education and media campaign should perform for high-risk women such as those having terminated pregnancy, and professing Islam faith.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41182-022-00440-5.

Geographical variations of cancer incidence in Guadeloupe, French West Indies

Background

Geographical disparities in cancer incidence are observed at different scales and may highlight areas of high risk that need special attention to improve health policies. In Guadeloupe, a French archipelago in the Caribbean, environmental and socioeconomic factors are potential factors associated with cancer incidence. Our objective was to describe geographical variations of cancer incidence in Guadeloupe at a small-area level, in order to identify potential clusters.

Methods

We conducted spatial analyses for the 18 most frequent cancer sites, using data collected by the population-based cancer registry of Guadeloupe over the period 2008–2017. For each cancer sites, we used the Besag, York and Mollié model to estimate smoothed standardized incidence ratios (SIRs) at a sub-municipality level. In addition, we performed ascendant hierarchical clustering of these smoothed SIRs to describe the relationship between the different cancer sites and to identify geographical clusters.

Results

We observed geographical disparities with a spatial pattern that varied across cancer sites. Clustering of the smoothed SIRs showed aggregations between breast cancer and multiple myeloma, thyroid and stomach cancer, cervical and head and neck cancers, lung and rectal cancers, ovarian and endometrial cancers. Cluster analysis also identified six geographical clusters. Features of these clusters suggest alcohol consumption, exposure to pesticides, pollution generated by open landfills, and ethnicity as possible explanatory factors.

Discussion/conclusion

Our study provided for the first time an extensive description of geographical disparities in cancer incidence in Guadeloupe, in a region where socioeconomic and environmental issues are major concerns. Although the identification of underlying factors was out of the scope of the present study, we highlighted areas of special interest and put forward some hypotheses that warrant to be further investigated in more in-depth analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09886-6.

Environmental emissions influencing solar photovoltaic waste management in Australia: An optimised system network of waste collection facilities

The Australian urban construction electricity sector has witnessed a transformational effect in the use of small-scale solar photovoltaic (PV) systems in the past decade. Currently, Australia has one of the highest rates of rooftop solar PV users with over 20% of households connected. This will see a rapid growth in the volume of PV waste in the coming years when these PV systems come to their end-of-life or require replacement. The collection and transportation involved in solar PV waste treatment has a significant impact on the environmental sustainability of Australian cities while designing a holistic reverse logistic (RL) network may play an essential role in the reduction of the associated cost and environmental impacts. In this study, the Weibull distribution model is employed to forecast the PV waste in the next three decades in South Australia. The study further estimates the pollutant emission associated with the collection and transportation of the waste for recycling and recovery using hotspot analysis, location allocation modelling and vehicle routing problem. Generation of pollutants - Particulate Matter (PM), Carbon Monoxide (CO), Carbon dioxide (CO₂) and Nitrogen Oxides (NOx) associated with transport and energy consumption are estimated through three routing scenarios. Results indicate that, there will be 109,007 tons of PV waste generated in urban and suburban context in South Australia by 2050. Among the three routing scenarios generated, the third scenario with optimised transfer stations and an additional recycling facility showed more than 34% reduction in pollutant emission. Such additional PV waste management facilities require policy support and regulations to effectively manage solar PV waste treatment and logistics.

Frequency, spatial distribution, and genetic diversity of Blastocystis among referred individuals to a clinical laboratory: First report of subtype 9 in Brazil

The enteric protist Blastocystis has a worldwide distribution, however its prevalence in the human population is still underestimated, especially in developing countries where proper diagnosis is not performed in the routine of clinical laboratories. In this study, we aimed to assess the frequency, genetic diversity, and spatial distribution of Blastocystis isolates detected in fecal samples referred to a clinical laboratory for routine examination in inner São Paulo State, Brazil. A total of 348 leftover stool samples available for disposal from female and male individuals with age ranging from 3 months to 88 years were analyzed by both microscopic examination and PCR/sequencing of the SSU rRNA gene. The overall frequency of Blastocystis sp. was 31% (108/348), including 20.1% (70/348) and 31% (108/348) by microscopic examination and PCR/sequencing, respectively. Significant association was found only between Blastocystis infection and age, since the highest rate of positive samples was detected among 5-9 years old individuals (p < 0.0001). In addition, spatial distribution revealed a wide distribution of the positive samples, however they were densely concentrated in more populated areas. Seven subtypes were identified, namely ST1 (40.7%), ST2 (9.2%), ST3 (45.3%), ST4 (0.9%), ST6 (1.8%), ST7 (0.9%) and ST9 (0.9%). The intra-subtype analysis revealed a total of 25 different alleles previously reported. Here, the findings lead us to highlight the following aspects: (1) the identification of a ST9 isolate is a relevant finding since it is considered a very rare subtype in human infections as well as this is the first report in Brazil; (2) the high frequency of Blastocystis in fecal samples submitted for examination in a clinical laboratory points to the need to consider its search in routine parasitological examinations, (3) the spatial distribution of Blastocystis infection was not homogeneous but concentrated in more populated areas where the access for population to diagnostic services in healthcare is likely to be easier and, (4) the genetic variability of Blastocystis isolates suggests exposure of inhabitants living in inner municipalities to different sources of contamination involving anthroponotic and zoonotic transmission pathways.

What factors explain the increase in obesity in Brazil? An ecological analysis of contextual and behavioural components

OBJECTIVES

It has been suggested that contextual factors may be related to obesity; however, they have not yet been widely investigated. The main objective of this ecological time-series study was to analyse factors associated with the increase in obesity in the adult and elderly population in Brazil from 2006 to 2020.

STUDY DESIGN

This is an ecological time-series study. Data were collected by the Surveillance System for Risk and Protection Factors for Chronic Diseases by Telephone Survey (VIGITEL), the main health survey in Brazil.

METHODS

The outcome was the annual obesity growth rate (in percentage points). Independent variables were behavioural and contextual factors. Data analysis was performed using Prais-Winsten regression for temporal analyses, and Spearman correlation and crude and adjusted linear regression (beta and 95% confidence intervals [CIs]).

RESULTS

The annual obesity growth rate was 0.58 percentage points (p.p.) (95% CI: 0.54; 0.63) per year. Demographic density and the percentage of the population employed showed an inverse association with the growth of obesity. Variables such as Gross Domestic Product (GDP) per capita, Gini coefficient, urbanisation rate, percentage of the population with low level of education and percentage of the population without an income were directly associated with the increase in obesity rates. The variables maintained in the final model explained 81% of the growth in obesity in Brazil over the last 15 years (2006-2020).

CONCLUSIONS

The growth of obesity in Brazil was mostly explained by contextual factors, especially those of a socio-economic nature. Therefore, interventions to mitigate the increase in obesity must go beyond behavioural factors.

Leveraging data analytics to understand the relationship between restaurants' safety violations and COVID-19 transmission

This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants' safety violations and COVID-19 cases. We used location-based consumers' complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants' safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers' complaints. Third, spatial modeling of the complaints' geographic distributions was performed to identify the hotspots of consumers' complaints and COVID-19 cases. The results reveal a positive relationship between consumers' complaints about restaurants' safety violations and COVID-19 cases. In particular, consumers' complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers' behavioral shifts and hospitality businesses' adaptive practices during a pandemic.

Modeling spatial variation of gaseous air pollutants and particulate matters in a Metropolitan area using mobile monitoring data

Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-m interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R² and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.

Assessment of spatial-temporal changes in water bodies and its influencing factors using remote sensing and GIS - a model study in the southeast coast of India

Concerns have been raised about the threat of ecological imbalance due to the loss of water bodies in densely populated areas. The present study explored the changes in water bodies in terms of area, number, and size in northern districts of Tamil Nadu, India, between 1978 and 2018 using satellite data, geographic information system, spatial analysis, ground truth verification, and field validation. The analysis indicated that the water bodies' area has reduced by 3027 ha and 4363 ha in the Kancheepuram and Tiruvallur Districts, respectively. Almost 179 water bodies have entirely disappeared, and 628 water bodies have been partly converted for other purposes. Of the disappeared water bodies, small, medium, and large water bodies account for 53, 93, and 33, respectively. The main reason for the changes in water bodies was the conversion to agriculture and buildings. Overall, the water bodies' area and number have been reduced by 9% and 12%, respectively, while the population has grown by 37%. The water bodies lost due to anthropogenic activities demand the scientific inventory of water bodies and integrated water resources management at a state or national level with strict monitoring regulations to protect them.

Assessment of heavy metals should be performed before the development of the selenium-rich soil: A case study in China

The use of selenium (Se)-rich soils in China is an effective method for rural revitalization, but assessment of heavy metals is essential prior to the development of Se-rich soils. This study was focused on the Jiangjin district, a typical Se-rich area located in Sichuan Basin of China, to investigate contamination, influencing factors, and sources of As, Cr, Cu, Cd, Ni, Pb, Sb, and Zn based on 156 topsoil samples. This study analyzed and compared the enrichment factor (EF), Nemerow index (P_N), geographical information system (GIS), and positive matrix factorization (PMF). Results demonstrate that the average values of As, Cu, Cd, Sb, and Zn in topsoil were higher than the soil background values of western Chongqing by approximately 1.75, 1.11, 1.27, 1.71, and 2.58 times, respectively, indicating that some heavy metals have been enriched in the soils. The polluted areas of As, Cu, Cd, and Zn in topsoil were mainly distributed in the northern and central Jiangjin district, whereas high-Sb soils were located in the southeast. The Cr, Cu, Cd, Pb, and Sb were concentrated in Se-rich soils, indicating that heavy metals pollution should be carefully considered for the utilization of Se-rich soils. Four potential sources of heavy metals were found in this study area: 1) the parent materials (Cr, Ni, Cu); 2) industrial activities with high coal consumption (As); 3) mechanical and chemical industrial activities (Zn, Sb); and 4) transportation and agricultural activities (Pb, Cd). These observations provide a scientific basis for the development, utilization, and protection of Se-rich soil resources.

Methods and technologies for spatial analysis of regional ecosystems based on the watershed approach

The watershed management approach is an efficient tool for organizing and implementing natural resource management at the regional and interregional levels. Statistical processing of watershed information contributes to a comprehensive assessment of the environment of the river watersheds at different hierarchical levels. The purpose of this work is to use spatial analysis and data simulation for assessing the state of ecosystems in hydrologically defined geographical areas such as watersheds. The minimal watersheds of the "pilot" territory of four watersheds in Eastern Siberia with an average area of approximately 1ha are used as elementary territorial units. For each watershed, spatial analysis tools form a set of attributes of unique natural characteristics, which are obtained using ground and satellite data. Such a set of attributes can be used to assess the state of the territory employing various methods of mathematical modeling and statistical and cluster analysis. Watershed modeling of the territory was carried out based on the MERIT Hydro digital elevation model. Based on Terra MODIS satellite information, data on temperature, vegetation indices, and vegetation cover were obtained for each watershed. The NASA Global Precipitation Measurement reanalysis data were used to generate the data on accumulated precipitation for all elementary watersheds. Analysis of the seasonal dynamics of the surface temperature in the study area was made, and statistical distributions of temperature variations during the year were obtained for each watershed. Effective temperature sum was calculated. Statistical values and average annual characteristics of trends in several vegetation indices were obtained for each watershed. The necessary methods and technologies for generating, storing, and processing data on each watershed have been developed. Integr Environ Assess Manag 2022;00:1-8. © 2021 SETAC.

Spatial analysis of vaccine coverage on the first year of life in the northeast of Brazil

Background

Over time, vaccination has been consolidated as one of the most cost effective and successful public health interventions and a right of every human being. This study aimed to assess the spatial dynamics of the vaccine coverage (VC) rate of children aged < 1 year per municipality in the Brazilian Northeast at 2016 and 2017.

Methods

This is a mixed-type ecological study that use a Public domain data Health Information. Vaccine doses were obtained from the Information System of the Brazilian National Immunization Program, and live births from the Brazilian Information System of Live Births of the Brazilian Unified Health System. Descriptive analysis of the coverage of all the vaccines for each year of the study was conducted, and Mann–Whitney U test was used to compare VC between the study years. Chi-squared test was used to evaluate the association between the years and VC, which was stratified into four ranges, very low, low, adequate, and high. Spatial distribution was analyzed according to both each study year and vaccine and presented as thematic maps. Spatial autocorrelation was analyzed using Moran’s Global and Local statistics.

Results

Compared with 2017, 2016 showed better VC (p < 0.05), except for Bacillus Calmette–Guérin. In the spatial analysis of the studied vaccines, the Global Moran’s Index did not show any spatial autocorrelation (p > 0.05), but the Local Moran’s Index showed some municipalities, particularly the Sertão Paraibano region, with high VC, high similarity, and a positive influence on neighboring municipalities (p < 0.05). In contrast, most municipalities with low VC were concentrated in the Mata Paraibano region, negatively influencing their neighbors (p < 0.05).

Conclusion

Uneven geographic regions and clusters of low VC for children aged < 1 year in the State of Paraíba were spatially visualized. Health policy makers and planners need to urgently devise and coordinate an action plan directed at each state’s regions to fulfill the vaccination calendar, thereby reversing the vulnerability of this age group, which is at a higher risk of diseases preventable by vaccination.

The neighborhood environment and its association with the spatio-temporal footprint of tobacco consumption and changes in smoking-related behaviors in a Swiss urban area

This study aimed to evaluate the association of the neighborhood environment with the spatio-temporal dependence of tobacco consumption and changes in smoking-related behaviors in a Swiss urban area. Data were obtained from the CoLaus cohort (2003-2006, 2009-2012, and 2014-2017) in Lausanne, Switzerland. Local Moran's I was performed to assess the spatial dependence of tobacco consumption. Prospective changes in tobacco consumption and the location of residence of participants were assessed through Cox regressions. Analyses were adjusted by individual and neighborhood data. The neighborhood environment was spatially associated with tobacco consumption and changes in smoking-related behaviors independently of individual factors.

Evaluation of the surface water and sediment quality in the Duger basin (Burdur, Turkey) using multivariate statistical analyses and identification of heavy metals

The study aims to determine the chemical contents of the surface water of the Duger Creek and the sediments of the Duger basin, which feeds the Burdur Lake, the correlation between these contents, the potential ecological risks of these contents, the toxic heavy metal contents, and their spatial distribution by using X-ray fluorescence (XRF) spectroscopy method. The Duger basin, which has been selected as the study area, is located on the streams feeding the Burdur Lake, which has been determined as the Ramsar area in Turkey. Burdur Lake has a key role in the ecological balance due to its biological diversity, characteristic animal/plant fauna since it provides a suitable area for seasonal migration of waterfowl. Considering all these functions, the surface water and sediment quality of the Duger basin, which is located in the streams feeding Burdur Lake, is critical. In this study, elemental compositions of 20 water and 20 sediment samples collected from the Duger basin were determined by using the XRF spectroscopy method. Many elements that may have toxicological characteristics in terms of human and environmental health have been detected in the surface water and sediment samples from the Duger basin. These elements were found to have the following average values: Al (60.09 mg/L), Co (3.35 mg/L), Zn (3.35 mg/L), Cu (2.21 mg/L), Cr (0.14 mg/L). It is interpreted that they may be toxic because they exceed the limits specified in WHO 2004. In this context, the surface water in the Duger basin, which feeds the Burdur Lake, must be filtered before using it as drinking water or for agricultural and livestock purposes. New strategic planning is suggested to ensure the sustainability of the basin's surface water quality.

Spatial variation and determinates of dietary diversity among children aged 6-23 months in Ethiopia: spatial and multilevel analysis using Ethiopian Demography Health Survey (EDHS) 2019

BACKGROUND

Minimum dietary diversity is the consumption of five or more food groups from the eight World Health Organization recommended food groups. Adequately diversified diet, in terms of amount and composition, is critical for optimal growth, development, and long-term health outcomes in the first 2 years. Understanding the regional variation of dietary diversity and the underlying factors is crucial for developing and implementing interventions. However, the use of spatial approaches in dietary studies has not been widely established. Therefore, this study aimed to explore the spatial patterns and determinates of minimum dietary diversity practice among 6-23 months children in Ethiopia.

METHODS

Secondary data analysis was conducted based on the Demographic and Health Surveys data conducted in Ethiopia. A total weighted sample of 1578 children aged 6-23 months was included for this study. The Global Moran's I was estimated to look into the regional variation of dietary diversity and hotspot and cold spot areas. Further, multivariable multilevel logistic regression was used for factor analyses. Adjusted Odds Ratio with 95% CI was used to declare the strength and significance of the association.

RESULTS

Overall, 87.4% (95% CI: 85.7 to 88.9%) of children in 2019 had inadequate Minimum dietary diversity. We identified statistically significant clusters of high inadequate dietary diversity (hotspots) notably observed in Somali, Afar, Eastern and western Amhara, western Tigray, Benishangul, and Northeastern and western parts of the southern nations, nationality and peoples' regions. Inadequate dietary diversity was significantly higher among young children, uneducated mother, married women, younger mother, no postnatal check, community with higher level of poverty and community level uneducated woman.

CONCLUSION

According to the findings of this study inadequate Minimum dietary diversity for children as measured by World Health Organization dietary assessment shows high. Children's dietary diversity was distributed non-randomly in different districts across Ethiopia's regions. The findings of the study provided critical evidence about dietary diversity and associated factors. Hence, policy should focused on improve education status of Mother, boosting economic status of the community, increased maternal continuum of care and focused on young children nutrition may advance dietary diversity.

Measurement of contagion spatial spread probability in public places: A case study on COVID-19

The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein distance algorithm and Monte Carlo simulation. This method identifies the public places in which COVID-19 spreads and grows easily. The Wasserstein Distance algorithm is used to calculate the distribution similarity between COVID-19 cases and the public places. Further, we used hypothesis tests and Monte Carlo simulation to estimate the spatial spread probability of COVID-19 in different public places. We used Snow's data to test the stability and accuracy of this measurement. This verification proved that our method is reliable and robust. We applied our method to the detailed geographic data of COVID-19 cases and public places in Wuhan. We found that, rather than financial service institutions and markets, public buildings such as restaurants and hospitals in Wuhan are 95 percent more likely to be the public places of COVID-19 spread.

Spatial correlation among cultivated land intensive use and carbon emission efficiency: A case study in the Yellow River Basin, China

Considering the current global goal of carbon neutrality, the relationship between cultivated land intensive use (CLIU) and carbon emission efficiency (CEE) should be explored to address the global climate crisis and move toward a low-carbon future. However, previous work in this has been conducted at provincial/regional scales and few have identified the spatial correlation between CLIU and CEE at the scale of large river basins. Therefore, this study explored the spatiotemporal characteristics of CLIU, cultivated land carbon emissions (CLCE), and CEE, as well as the spatial correlation between CLIU and CEE in the Yellow River Basin (YRB), China. A comprehensive evaluation model, the Intergovernmental Panel on Climate Change (IPCC) coefficient methodology, existing data envelopment analysis model, and bivariate spatial autocorrelation models were used to analyze statistical data from 2005 to 2017. We found that the overall CLIU and CLCE values in the YRB exhibited a continuous increase; the average carbon emission total efficiency and carbon emission scale efficiency first decreased and then increased, and the average carbon emission pure technical efficiency gradually decreased. Areas of high CLCE were concentrated in eastern areas of the YRB, whereas those of high CLIU, carbon emission total efficiency, carbon emission scale efficiency, and carbon emission pure technical efficiency predominantly appeared in the eastern areas, followed by central and western areas of the YRB. Spatial analysis revealed a significant spatial dependence of CLIU on CEE. From a global perspective, the spatial correlations between CLIU and CEE changed from positive to negative with time. Moreover, the aggregation degree between CLIU and CEE gradually decreases with time, while the dispersion degree increases with time, and the spatial correlation gradually weakens. The local spatial autocorrelation further demonstrates that the number of high-low and low-high clusters between CLIU and CEE gradually increases over time, while the number of high-high and low-low clusters gradually decreased over time. Collectively, these findings can help policymakers formulate feasible low-carbon and efficient CLIU policies to promote win-win cooperation among regions.

Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020

In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May.

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016-April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Spatial statistical and environmental correlation analyses on vector density, vector infection index and Japanese encephalitis cases at the village and pigsty levels in Liyi County, Shanxi Province, China

Background

In the eco-epidemiological context of Japanese encephalitis (JE), geo-environmental features influence the spatial spread of the vector (Culex tritaeniorhynchus, Giles 1901) density, vector infection, and JE cases.

Methods

In Liyi County, Shanxi Province, China, the spatial autocorrelation of mosquito vector density, vector infection indices, and JE cases were investigated at the pigsty and village scales. The map and Enhanced Thematic Mapper (ETM) remote sensing databases on township JE cases and geo-environmental features were combined in a Geographic Information System (GIS), and the connections among these variables were analyzed with regression and spatial analyses.

Results

At the pigsty level, the vector density but not the infection index of the vector was spatially autocorrelated. For the pigsty vector density, the cotton field area was positively related, whereas the road length and the distance between pigsties and gullies were negatively related. In addition, the vector infection index was correlated with the pigsty vector density (PVD) and the number of pigs. At the village level, the vector density, vector infection index, and number of JE cases were not spatially autocorrelated. In the study area, the geo-environmental features, vector density, vector infection index, and JE case number comprised the Geo-Environment-Vector-JE (GEVJ) intercorrelation net system. In this system, pig abundance and cotton area were positive factors influencing the vector density first. Second, the infection index was primarily influenced by the vector density. Lastly, the JE case number was determined by the vector infection index and the wheat area.

Conclusions

This study provided quantitative associations among geo-environmental features, vectors, and the incidence of JE in study sties, one typical northern Chinese JE epidemiological area without rice cultivation. The results highlighted the importance of using a diverse range of environmental management methods to control mosquito disease vectors and provided useful information for improving the control of vector mosquitoes and reducing the incidence of JE in the northern Chinese agricultural context.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05305-8.

The assistance gaps in combating COVID-19 in Brazil: for whom, where and when vaccination occurs

BACKGROUND

Following the emergence of the COVID-19 pandemic, the number of infected Brazilian people has increased dramatically since February 2020, with Brazil being amongst the countries with the highest number of cases and deaths. Brazilian vaccination began in January 2021, aimed at priority groups. This study analysed the spatial and temporal evolution of vaccination in Brazil between the 3rd and 21st epidemiological weeks (EW) of 2021.

METHODS

Spatial and temporal analyses were performed comprising 19 EW. Cases were structured into priority groups-elderly population (EP); healthcare workers (HW); indigenous and quilombola populations (I/Q), dose, vaccine (CoronaVac or AstraZeneca), and place of vaccination. A sweep test was performed to identify vaccination rate clusters. Vaccination rates (VR) were calculated according to a spatial window for each Health Region, indicating clusters above/below expected VR. Based on the discrete Poisson probability model, spatial analysis was performed to detect high/low VR clusters, which were converted into Kernel maps. Points were generated from SaTScan analyses associated with Health Region centroids. Temporal analysis of VR was carried out to identify significant trends, and results were converted into temporal cluster graphs. P-value ≤ 0.05 was adopted.

RESULTS

Southeast region concentrated most of the vaccines of EP and HW, followed by the Northeast. The latter region had the largest contingent of I/Q vaccinated. In all priority age groups and all regions, a higher percentage of complete CoronaVac vaccination schedules were observed compared with AstraZeneca. The temporal analysis identified high VR clusters of CoronaVac first and second dose in the early weeks, except for the EP; of AstraZeneca first dose, only for HW in the early weeks, and for EP and I/Q in the final weeks; of AstraZeneca second dose for all priority groups in the final weeks. I/Q populations had the lowest general VR. The spatial profile of VR indicated significant regional cluster differences between the priority groups.

CONCLUSION

This study highlights the importance of establishing vaccination priority groups, considering the asymmetries that a pandemic can trigger, notably in vast geographic areas, to contemplate the main objective of vaccination: to prevent casualties.

Spatiotemporal analysis of highly pathogenic avian influenza (H5N1) outbreaks in poultry in Egypt (2006 to 2017)

Background

In Egypt, the highly pathogenic avian influenza (HPAI) subtype H5N1 is endemic and possesses a severe impact on the poultry. To provide a better understanding of the distributional characteristics of HPAI H5N1 outbreaks in Egypt, this study aimed to explore the spatiotemporal pattern and identify clusters of HPAI H5N1 outbreaks in Egypt from 2006 to 2017.

Results

The Epidemic curve (EC) was constructed through time series analysis; in which six epidemic waves (EWs) were revealed. Outbreaks mainly started in winter peaked in March and ended in summer. However, newly emerged thermostable clades (2.2.1.1 and 2.2.1.2) during the 4th EW enabled the virus to survive and cause infection in warmer months with a clear alteration in the seasonality of the epidemic cycle in the 5th EW. The endemic situation became more complicated by the emergence of new serotypes. As a result, the EC ended up without any specific pattern since the 6th EW to now. The spatial analysis showed that the highest outbreak density was recorded in the Nile Delta considering it as the ‘Hot spot’ region. By the 6th EW, the outbreak extended to include the Nile valley. From spatiotemporal cluster epidemics, clustering in the Delta was a common feature in all EWs with primary clusters consistently detected in the hot-spot region, but the location and size varied with each EW. The highest Relative Risk (RR) regions in an EW were noticed to contain the primary clusters of the next EW and were found to include stopover sites for migratory wild birds. They were in Fayoum, Dakahlia, Qalyobiya, Sharkia, Kafr_Elsheikh, Giza, Behera, Menia, and BeniSuef governorates. Transmission of HPAI H5N1 occurred from one location to another directly resulted in a series of outbreaks forming neighboring secondary clusters. The absence of geographical borders between the governorates in addition to non-restricted movements of poultry and low vaccination and surveillance coverage contributed to the wider spread of infection all over Egypt and to look like one epidemiological unit.

Conclusion

Our findings can help in better understanding of the characteristics of HPAI H5N1 outbreaks and the distribution of outbreak risk, which can be used for effective disease control strategies.

Graphical abstract

Spatial performance assessment of reed bed filtration in a constructed wetland

Constructed wetlands (CW) are implemented to improve water quality through filtration by plants (macrophytes), which sequester nutrients and contaminants. Macrophyte beds in CWs reduce the speed of water flow, aiming to improve the water quality by sedimentation and filtration with increasing distance from the inflow. Few studies have assessed spatial distribution and accumulation concentrations of nutrients and contaminants in CW macrophytes as a performance indicator for wetland functionality and management. Macrophytes and water were analysed for nutrient and contaminant accumulation in-situ at a stormwater-fed CW and water remediation site in South Australia. During the austral summer, macrophytes were sampled at 36 sites and water at 46 sites selected by a systematic GIS produced grid covering the entire wetland, which determined distance from the inflow for each site. A total of 144 Schoenoplectus validus (stems and roots) macrophyte samples (i.e. carbon-C, nitrogen-N, Trace elements) and 183 water samples (i.e. total suspended solids-TSS, total nitrogen-TN, total carbon-TC, nitrate-NO₃^-/ nitrite-NO₂^- and ammonia-NH₄⁺) were analysed. Concentrations of water chemistry parameters that significantly increased with distance away from inflow included; TC (P = 0.0008), TN (P = 0.0001), and NH₄⁺ (P = 0.0001), while there was significant decrease in TSS (P = 0.0001). The macrophyte S. validus significantly decreased in height (P = 0.0001) and biomass (P = 0.03) with distance from the inflow. Spatial mapping of nutrients and contaminants with distance from inflow identified increasing TC and C characteristics from inflow to outflow and identified where TSS were removed from the water column. Through this spatial assessment approach of the Oaklands CW, management has identified problem areas with flow regimes that require further investigation to enhance macrophyte water filtration performance which can be used in CWs elsewhere in the world.

Association of regional socioeconomic deprivation and rurality with global developmental delay in early childhood: Data from mandatory school entry examinations in Germany

BACKGROUND

From birth to young adulthood, health and development of young people are strongly linked to their living situation, including their family's socioeconomic position (SEP) and living environment. The impact of regional characteristics on development in early childhood beyond family SEP has been rarely investigated. This study aimed to identify regional predictors of global developmental delay at school entry taking family SEP into consideration.

METHOD

We used representative, population-based data from mandatory school entry examinations of the German federal state of Brandenburg in 2018/2019 with n=22,801 preschool children. By applying binary multilevel models, we hierarchically analyzed the effect of regional deprivation defined by the German Index of Socioeconomic Deprivation (GISD) and rurality operationalized as inverted population density of the children's school district on global developmental delay (GDD) while adjusting for family SEP (low, medium and high).

RESULTS

Family SEP was significantly and strongly linked to GDD. Children with the highest family SEP showed a lower odds for GDD compared to a medium SEP (female: OR=4.26, male: OR=3.46) and low SEP (female: OR=16.58, male: OR=12.79). Furthermore, we discovered a smaller, but additional and independent effect of regional socioeconomic deprivation on GDD, with a higher odds for children from a more deprived school district (female: OR=1.35, male: OR=1.20). However, rurality did not show a significant link to GDD in preschool children beyond family SEP and regional deprivation.

CONCLUSION

Family SEP and regional deprivation are risk factors for child development and of particular interest to promote health of children in early childhood and over the life course.

A method for identifying critical time windows of maternal air pollution exposures associated with low birth weight in offspring using massive geographic data

Associations between maternal exposures to air pollutants and low birth weight (LBW) in offspring varied when different exposure windows were considered. Methods used in previous studies lacked flexibility in delineating exposure windows and did not consider time periods before conception, which may restrict the discoveries of critical exposure windows. This study introduces a novel method to identify critical windows of maternal air pollution exposures associated with LBW in offspring using massive georeferenced data. Through a case-control study based on birth data (94,106 LBW cases and 376,424 controls) and air quality monitoring data (367 chemicals) in Texas during 1996-2008, this study used the average ambient concentration measured by the monitoring site closest to the residence location of a mother during a time window as the maternal exposure to a specific chemical during that exposure window. Binary logistic regression was utilized to estimate air pollutant-LBW associations in different exposure windows. The odds ratios (ORs) were adjusted for child's sex, gestational weeks, maternal age, race/ethnicity, and education. The adjusted ORs were plotted against the exposure window series of different sizes for each chemical, aiming at interactively visualizing and exploring the critical exposure windows across multiple temporal scales. This study identifies ten chemicals and seventeen corresponding critical exposure windows where strong air pollutant-LBW associations are detected. The ten identified chemicals are benzaldehyde, sum of Photochemical Assessment Monitoring Stations (PAMS) target compounds, n-undecane, m-tolualdehyde, organic carbon fraction 2 (OC2), ethylene dibromide, valeraldehyde, propionaldehyde, 4-methyl-1-pentene, and zirconium. Nine critical exposure windows involving six chemicals start more than five months prior to conception, seven windows involving five chemicals commence in the second and/or third trimester of pregnancy, and the remaining one window is located in other time periods. The novel method reveals a number of critical time windows of maternal exposure to ten chemicals that are positively associated with LBW in offspring. These ten chemicals were identified as LBW risk factors for the first time. Additional studies with more data are needed to validate the results in the future.

Spatial-temporal distribution of biogas production from agricultural waste per capita in rural China and its correlation with ground temperature

Biogas production from agricultural waste has played a key role in recent years in reducing the environmental pollution risk of agricultural waste and alleviating energy shortage in rural China. However, few studies have focused on the effect of ground temperature on biogas production from agricultural waste from a quantitative point of view and the solution to increasing the temperature of biogas digesters in the cold season to improve biogas production from agricultural waste in rural areas at a provincial scale. Here, we calculated the spatial-temporal distribution of biogas production from agricultural waste per capita in rural China from 2001 to 2016 and agricultural waste per capita in rural China in 2016. Furthermore, we analyzed the correlation between biogas production from agricultural waste per capita in rural China and ground temperature. Finally, we proposed a solution to increase the temperature of biogas digesters in rural China in the cold season after analyzing its energy utilization. Results showed that the biogas production from agricultural waste per capita in rural China was correlated with the average and maximum ground temperature in January, February, March, November, and December from 2001 to 2016. The results also indicated that the suggested heating mode of biogas production from agricultural waste in rural China differed greatly among provinces. The provinces with high agricultural waste per capita in rural areas and low averaged biogas production from agricultural waste per capita are Inner Mongolia, Heilongjiang, and Xinjiang, which needed the suggested heating mode of biogas production from agricultural waste for increasing the temperature of biogas digesters in rural China in the cold season. Our findings provide a reference for biogas production from agricultural waste, greenhouse gas emission reduction, and the spatial layout of energy utilization in rural China.

Evaluating the importance of urban green spaces: a spatial analysis of citizens' perceptions in Thessaloniki

In Greek cities, urban green spaces are scarce and well below acceptable standards. However, policy makers and planners are not prioritizing long-term planning strategies for urban green and do not attempt to engage citizens in relevant decision-making and urban planning processes. In this context, a web-based public survey was conducted in the city of Thessaloniki (Greece) during the COVID-19 pandemic, aiming to identify citizens' attitudes, satisfaction levels, actual behaviour and future expectations about urban green spaces (UGS). It also aimed to measure the effect of COVID-19 (mobility) restrictions on UGS visitation. All these issues were explored through a spatial lens, by developing measurable and mappable results suitable for future urban planning decisions. According to these results, citizens tend to report a very low satisfaction level about the current state of UGS (in terms of their adequacy and quality), and they tend to travel a great distance to reach an urban park (about 2 km on average). Moreover, the results indicate that spatial differences are very significant in terms of UGS availability and accessibility. Another important outcome of this study is that, unlike in other cities, the frequency of visiting green spaces in Thessaloniki did not increase during the pandemic. On the contrary, a slight downward trend was observed, maybe due to the combined effect of restriction measures and the lack of proximity/availability of UGS to local population groups. The maps produced in this study may thus facilitate well-informed planning decisions related to the development of new green projects.

Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted "U-shaped" characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted "N" shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China's carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east-west direction) changes greatly, while the latitude of the gravity center (north-south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.

Identifying Spatial Patterns and Ecosystem Service Delivery of Nature-Based Solutions

Compared to technical infrastructure, nature-based solutions, NBS, strive to work with nature and to move beyond business-as-usual practices in order to address societal challenges such as flood risks. This research aims to spatially identify possible NBS areas and evaluate the areas capacity to provide selected ecosystem services, ES, for the Lahn river landscape in Germany. The research follows the functional landscape approach using hydromorphological landscape units, HLU, based on specific biophysical spatial criteria, such as slope, to then identify locations which may be considered suitable for NBS. The current ES delivery of these possible NBS areas is then evaluated. The three ES assessed are carbon storage, nutrient retention and recreation. We then undertake a geospatial comparison analysis to show the spatial relationships and patterns that emerge in regards to the ES configuration of the distinct NBS apt areas. Results show the HLU method serves to delineate and identify areas where NBS may exist or be implemented. The data depicts a distinct spatial pattern for each possible NBS space and complementary ES delivery. This explorative method is a useful spatial approach that can support NBS implementation and serve to investigate the multiple benefits NBS provide. The use of ecosystem services to compare and understand NBS is a viable prospect that must, however, be cautiously, locally and scientifically approached. Noticeable limitations regarding ES assessment remain, as available methods are often insufficiently inclusive of natural ecosystem processes and functions. Further research should assess a broader spectrum of NBS and their delivery of ES.

Assessment of environmentally sensitive areas to desertification in the Blue Nile Basin driven by the MEDALUS-GEE framework

Assessing environmentally sensitive areas (ESA) to desertification and understanding their primary drivers are necessary for applying targeted management practices to combat land degradation at the basin scale. We have developed the MEditerranean Desertification And Land Use framework in the Google Earth Engine cloud platform (MEDALUS-GEE) to map and assess the ESA index at 300 m grids in the Blue Nile Basin (BNB). The ESA index was derived from elaborating 19 key indicators representing soil, climate, vegetation, and management through the geometric mean of their sensitivity scores. The results showed that 43.4%, 28.8%, and 70.4% of the entire BNB, Upper BNB, and Lower BNB, respectively, are highly susceptible to desertification, indicating appropriate land and water management measures should be urgently implemented. Our findings also showed that the main land degradation drivers are moderate to intensive cultivation across the BNB, high slope gradient and water erosion in the Upper BNB, and low soil organic matter and vegetation cover in the Lower BNB. The study presented an integrated monitoring and assessment framework for understanding desertification processes to help achieve land-related sustainable development goals.

Intra-urban variation in tuberculosis and community socioeconomic deprivation in Lisbon metropolitan area: a Bayesian approach

Background

Multidrug resistant tuberculosis (MDR-TB) is a recognized threat to global efforts to TB control and remains a priority of the National Tuberculosis Programs. Additionally, social determinants and socioeconomic deprivation have since long been associated with worse health and perceived as important risk factors for TB. This study aimed to analyze the spatial distribution of non-MDR-TB and MDR-TB across parishes of the Lisbon metropolitan area of Portugal and to estimate the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.

Methods

In this study, we used hierarchical Bayesian spatial models to analyze the spatial distribution of notification of non-MDR-TB and MDR-TB cases for the period from 2000 to 2016 across 127 parishes of the seven municipalities of the Lisbon metropolitan area (Almada, Amadora, Lisboa, Loures, Odivelas, Oeiras, Sintra), using the Portuguese TB Surveillance System (SVIG-TB). In order to characterise the populations, we used the European Deprivation Index for Portugal (EDI-PT) as an indicator of poverty and estimated the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.

Results

The notification rates per 10,000 population of non-MDR TB ranged from 18.95 to 217.49 notifications and that of MDR TB ranged from 0.83 to 3.70. We identified 54 high-risk areas for non-MDR-TB and 13 high-risk areas for MDR-TB. Parishes in the third [relative risk (RR) = 1.281, 95% credible interval (CrI): 1.021–1.606], fourth (RR = 1.786, 95% CrI: 1.420–2.241) and fifth (RR = 1.935, 95% CrI: 1.536–2.438) quintile of socioeconomic deprivation presented higher non-MDR-TB notifications rates. Parishes in the fourth (RR = 2.246, 95% CrI: 1.374–3.684) and fifth (RR = 1.828, 95% CrI: 1.049–3.155) quintile of socioeconomic deprivation also presented higher MDR-TB notifications rates.

Conclusions

We demonstrated significant heterogeneity in the spatial distribution of both non-MDR-TB and MDR-TB at the parish level and we found that socioeconomically disadvantaged parishes are disproportionally affected by both non-MDR-TB and MDR-TB. Our findings suggest that the emergence of MDR-TB and transmission are specific from each location and often different from the non-MDR-TB settings. We identified priority areas for intervention for a more efficient plan of control and prevention of non-MDR-TB and MDR-TB.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00949-1.

State of biodiversity documentation in the Philippines: Metadata gaps, taxonomic biases, and spatial biases in the DNA barcode data of animal and plant taxa in the context of species occurrence data

Anthropogenic changes in the natural environment have led to alarming rates of biodiversity loss, resulting in a more urgent need for conservation. Although there is an increasing cognizance of the importance of incorporating biodiversity data into conservation, the accuracy of the inferences generated from these records can be highly impacted by gaps and biases in the data. Because of the Philippines' status as a biodiversity hotspot, the assessment of potential gaps and biases in biodiversity documentation in the country can be a critical step in the identification of priority research areas for conservation applications. In this study, we systematically assessed biodiversity data on animal and plant taxa found in the Philippines by examining the extent of metadata gaps, taxonomic biases, and spatial biases in DNA barcode data while using species occurrence data as a backdrop of the 'Philippines' biodiversity. These barcode and species occurrence datasets were obtained from public databases, namely: GenBank, Barcode of Life Data System and Global Biodiversity Information Facility. We found that much of the barcode data had missing information on either records and publishing, geolocation, or taxonomic metadata, which consequently, can limit the usability of barcode data for further analyses. We also observed that the amount of barcode data can be directly associated with the amount of species occurrence data available for a particular taxonomic group and location-highlighting the potential sampling biases in the barcode data. While the majority of barcode data came from foreign institutions, there has been an increase in local efforts in recent decades. However, much of the contribution to biodiversity documentation only come from institutions based in Luzon.

Improving urban sustainability and resilience with the optimal arrangement of water-energy-food related practices

Water-, energy-, and food (WEF) related practices, such as low impact development (LID), residential solar panels, and rooftop urban agriculture, have been applied to improve urban sustainability and resilience under climate change and urbanization. However, most practices require space. This requirement may result in competition for land. In addition, not all newly built practices benefit the environment from the life cycle perspective. Therefore, this study aims to develop a systematic WEF-related practice planning method to improve urban sustainability and resilience in a limited space. The core method is a multi-objective optimization model that considers the performance and environmental impacts of the selected practices. The assessment was conducted in a densely populated area in Taipei, the capital city of Taiwan, to describe the planning processes and demonstrate the feasibility of the methods. In the Taipei case, five goals were defined: the supply of WEF, the sponge city development target, and the greenhouse gas reduction target. The optimal results of the multi-objective optimization model indicated the closeness of the optimal implementation of WEF-related practices to achieving the goals. The results showed that the optimal arrangement of WEF-related practices could provide water supply benefits and was favorable for developing a sponge city. According to the sensitivities, to achieve urban sustainability and resilience, the priorities in order of importance are as follows: establish a rainwater harvesting system for buildings, encourage the implementation of rooftop photovoltaic systems, and improve the materials and processes used solar panel and bioretention cell production. The systematic planning method provides a quantitative assessment and delivers practical cross-sectoral integrated strategies for decision-making.

Is climate change threatening or beneficial to the habitat distribution of global pangolin species? Evidence from species distribution modeling

Global climate change caused by fossil energy consumption is strongly threatening the species diversity of mammals. In particular, changes in temperature and precipitation have affected the habitat of pangolins. Thus, we employed the MaxEnt modeling approach to simulate the potential habitat distribution of pangolins under the current climate and future climate change scenarios during 2081-2100. The habitats of the two Phataginus pangolins were mainly affected by temperature and precipitation. Conversely, geomorphological factors mainly affected the habitat of pangolins in the genus Smutsia. Under the SSP5-8.5 scenario, the habitat of Smutsia gigantea increased by 460.8 Mha, while that of Smutsia temminckii decreased by 89.4 Mha. Temperature and altitude affected the habitat of Manis crassicaudata, while vegetation coverage affected the habitat of Manis javanica. Moreover, human activities threatened the habitat of pangolins in Africa and India. However, labor transfer in southern China weakened the negative effects of human activities on the survival of pangolins in rural regions. Due to the lack of uniform intergovernmental schemes regarding global pangolin protection, the illegal pangolin trade threatens pangolin species worldwide, especially in Africa. From current to future scenarios, climate change increased the habitats of Manis crassicaudata, Manis javanica, Smutsia gigantea and Phataginus tetradactyla, while the habitats of Manis pentadactyla and Smutsia temminckii were threatened. Moreover, the total habitat area of the pantropical distribution zone in the Southern Hemisphere (26°S-33°S) decreased, mainly due to the extensive reduction in Smutsia temminckii habitat. The habitat of the pantropical zone in the Northern Hemisphere (19°N-28°N) basically remained unchanged. Increases in the habitat of the tropical distribution zone (11°S-17°N) were dominated by habitat gains for Smutsia gigantea. These findings provide scientific evidence to support global pangolin protection.

Carbonate bedrock control of soil Cd background in Southwestern China: Its extent and influencing factors based on spatial analysis

Previous studies have found that Cadmium (Cd) may be condensed during the processes of the weathering and soil formation over carbonate bedrock, which is a typical phenomenon in the southwest region of China. However, the extent of the high value background and the geographical factors influencing its spatial distribution are still unknown. This study collected data from the Multi-Purpose Regional Geochemical Survey (MPRGS) and the Regional Geochemistry-National Reconnaissance Program (RGNR) to investigate the extent and its controlling factors based on spatial analysis. It was found that the RGNR is an ideal data source for regional Cd background studies. Spatially, the high Cd area (Cd > 1.5 m/kg with reference of the GB15618-2018 soil environmental quality in China, 68.9 × 10³ km²) coincide well with the carbonate distribution. In the total area (68.9 × 10³ km²) with high Cd concentration, 89.8% is related to carbonates. The composition of parent rock, climate conditions, and landuse are controlling factors of the enrichment. The high Cd background effect of limestone is higher than that of dolomite and their effects decease with increasing detritus component they contain. Warm and humid climate in forest land helps to enrich Cd during weathering. Soil Cd concentration related to carbonate background can be predicted by major element concentrations, which is useful for differentiating Cd background from human pollution in the future soil pollution monitoring. Modelling by artificial neural network (ANN) is recommended rather than tradition multiple linear methods (LM) as the latter may have the effect of collinearity.

Spatial variation and association between maternal chemical fertilizer exposure and preterm birth in a rural area in Northern China

Whether maternal macro-environmental chemical fertilizer exposure has an association with the risk of preterm birth remains unclear. This study aimed to investigate the association between maternal exposure to chemical fertilizer during pregnancy and the risk of preterm birth. A total of 13,861 births, including 402 preterm birth from 313 villages in Pingding County, Shanxi Province, China, were analyzed to explore spatial variations of preterm birth risks at the village level. Spatial variations of preterm birth were visualized and tested with Disease Mapping, Moran's I and G* Statistic. The spatial zero-inflated negative binomial model was used to evaluate the incidence rate ratio (IRR) of preterm birth in different chemical fertilizer consumption levels at the village level. A population-based case-control study was conducted including 153 preterm births cases and 204 controls at the household level. The two-level logistic regression model was performed to estimate the association between risks of preterm birth and maternal exposure to chemical fertilizer after adjusting confounding factors. The findings indicated a remarkably clustering effect in relative risks of preterm birth and identified a high-risk clustering region of preterm birth from the south-central to the southwest and a low-risk clustering region in the northern Pingding county. The results of the spatial zero-inflated negative binomial model showed that the risk of preterm birth in the villages with chemical fertilizer consumption≥100 tons was 2.82 (95% CI: 1.50-5.57) times higher than those with <50 tons. Maternal exposure to chemical fertilizer ≥100 tons at village level was associated with an increased risk of preterm birth (aOR 2.14, 95%CI: 1.18-3.96), compared with exposure <50 tons after adjusting for confounding variables. The findings suggests that chemical fertilizers deserve more attention as a potential risk factor of preterm birth.

Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan

AIMS

We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern.

METHODS

A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted.

RESULTS

The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal.

CONCLUSIONS

We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.

Spatio-temporal analysis of leprosy risks in a municipality in the state of Mato Grosso-Brazilian Amazon: results from the leprosy post-exposure prophylaxis program in Brazil

BACKGROUND

Leprosy post-exposure prophylaxis (LPEP) with single dose rifampicin (SDR) can be integrated into different leprosy control program set-ups once contact tracing has been established. We analyzed the spatio-temporal changes in the distribution of index cases (IC) and co-prevalent cases among contacts of leprosy patients (CP) over the course of the LPEP program in one of the four study areas in Brazil, namely the municipality of Alta Floresta, state of Mato Grosso, in the Brazilian Amazon basin.

METHODS

Leprosy cases were mapped, and socioeconomic indicators were evaluated to explain the leprosy distribution of all leprosy cases diagnosed in the period 2016-2018. Data were obtained on new leprosy cases [Notifiable diseases information system (Sinan)], contacts traced by the LPEP program, and socioeconomic variables [Brazilian Institute of Geography and Statistics (IBGE)]. Kernel, SCAN, factor analysis and spatial regression were applied to analyze changes.

RESULTS

Overall, the new case detection rate (NCDR) was 20/10 000 inhabitants or 304 new cases, of which 55 were CP cases among the 2076 examined contacts. Changes over time were observed in the geographic distribution of cases. The highest concentration of cases was observed in the northeast of the study area, including one significant cluster (Relative risk = 2.24; population 27 427, P-value < 0.001) in an area characterized by different indicators associated with poverty as identified through spatial regression (Coefficient 3.34, P-value = 0.01).

CONCLUSIONS

The disease distribution was partly explained by poverty indicators. LPEP influences the spatial dynamic of the disease and results highlighted the relevance of systematic contact surveillance for leprosy elimination.

Expressing carbon storage in economic terms: The case of the upper Omo Gibe Basin in Ethiopia

Terrestrial carbon storage is important for planning decisions regarding climate change. Therefore, modelling the spatial distribution of carbon storage and valuation can help restore the sustainability of the ecosystems. This study aimed at showing the spatial and temporal variations in carbon storage and valuation in the upper Omo Gibe Basin. Land use/cover and carbon pool data based on field data collection and laboratory analyses supported by GIS and remote sensing were used. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) software was used for modelling carbon storage. The Global voluntary carbon market price and Tropical Economics of Ecosystems and Biodiversity (TEEB) data were used for describing carbon storage in economic terms. ANOVA was carried out to detect significant differences in carbon stock correlation with parameters. The results show that the annual carbon stock declined by 0.37 t/ha and the carbon market declined from USD 25.04 billion in 1988 to USD 24.01 billion in 2018. The highest loss of carbon storage and valuation was found in forest land followed by grazing land. Moreover, carbon stock was positively correlated with NDVI and habitat quality (p < 0.05). Slopes did not affect carbon stock (p > 0.05). This study helps promote and enhance carbon trading.

Social Support and Linkage to HIV Care Following Routine HIV Testing in a Ugandan Refugee Settlement

We aimed to identify factors associated with linkage to care for individuals newly diagnosed with HIV in a refugee settlement. This study was conducted from October 2018 through January 2020 in Nakivale Refugee Settlement in Uganda. We conducted a cross-sectional survey among individuals accessing routine HIV testing services. The survey included questions on demographic factors, physical and mental health conditions, social support, and HIV-related stigma. We collected GPS coordinates of the homes of individuals newly diagnosed with HIV. Associations with linkage to care were assessed using bivariate and multivariable analyses. Linkage to care was defined as clinic attendance within 90 days of a positive HIV test, not including the day of testing. Network analysis was used to estimate the travel distance between participants' homes and HIV clinic and to spatially characterize participants living with HIV and their levels of social support. Of 219 participants diagnosed with HIV (out of 5,568 participants screened), 74.4% linked to HIV care. Those who reported higher social support had higher odds of linking to care compared with those who reported lower social support. On spatial analysis, lower levels of social support were most prevalent in Nakivale Refugee Settlement itself, with more robust social support southeast and west of the study area. Social support is a salient correlate of linkage to care for individuals living in refugee settlements and could be the focus of an intervention for improving uptake of HIV care services.