Spatial Pattern Of Risk Research Articles

Modeling soil erosion using RUSLE and GIS at watershed level in the upper beles, Ethiopia

Soil erosion by water often described as the most worst form of land degradation with serious environmental and socioeconomic ramification, which accelerated by human-induced activities and impacts agricultural productivity, water resources sustainability, and ecological conservations. Despite, the significant amount of research on the topic, location-specific soil erosion studies are still limited in Ethiopia, particularly in the study region. Therefore, this study investigates the spatial pattern of soil erosion risk and map the annual soil loss rate in the Upper Beles watershed of the Blue Nile Basin, Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) model, coupled with geographical information system (GIS), has been adopted for soil loss estimation. The respective model factors were derived from rainfall, soil, land use/cover, and digital elevation model data. The raster layers of rainfall erosivity, soil erodibility, topographic, cover management, and conservation practices were processed and multiplied together in the GIS platform. The order of magnitude of the estimated soil loss was within the range of zero in bare areas and watercourses to well over 50 t ha−1 yr−1 in degraded slope and steep mountain areas. The resulting average annual soil loss was founded to be 13.2 t ha-1 yr-1, which exceeded the soil loss tolerances of Ethiopia's northern highlands. Based on soil erosion risk's spatial distribution, 58.2% of the watershed has suffered soil loss less than 5 t ha−1 yr−1. Result shows that high soil erosion prevails at steep slopes and mountainous areas with no vegetation covers and extensively cultivated areas. The RUSLE and GIS-based approach provides a reliable estimation of soil loss that helps identify the priority area for effective planning and implementation of sustainable soil management practices to reduce soil erosion, particularly for the sustainability of the Grand Ethiopian Renesance Dam (GERD) located at the downstream.

Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies

BackgroundMost epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR) the omnipresent risk indicator for small areas epidemiologic studies.MethodsTo deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age–space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates.ResultsThree case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of considering age–space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered.ConclusionOur age–space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas.

Patterns of livestock predation risk by large carnivores in India’s Eastern and Western Ghats

Large scale spatial patterns of livestock predation risk from multiple co-predators are fundamental to applied conservation planning. Here, we examined important ecological, social, and landscape attributes explaining spatial patterns of human-carnivore interactions. We used a systematic grid-based framework, across an area of 14,200 km2 of sixteen Forest Divisions at the human-wildlife interface encompassing Protected Areas, Reserved Forest and Fringe Areas at the human-wildlife interface in the Eastern and Western Ghats, India. The data was collected on livestock depredation incidents from the tiger (Panthera tigris), leopard (Panthera pardus), and dhole (Cuon alpinus) for the past five years, through semi-structured interviews (n = 1460) of local communities. We examined socio-ecological (i.e. livestock abundance and forest dependency) and landscape attributes (i.e. forest cover, climates and topographic) influencing the depredation events from each carnivore species. We found that livestock predation risk by the tiger, leopard and dhole was driven by the size of livestock species, the dependency of local people on the forest, topography, proximity to water body and the forest boundary, precipitation, and forest cover. Risk of predation from leopard and dhole exhibited high spatial overlap, and predation by leopards was higher than dhole and tiger. Livestock predation by leopard and dhole was frequent in open areas of Reserved Forest and buffer zones, while that from tiger occurred in densely forested core regions of Protected Areas (PAs). Our predictive risk maps (ca. 22,525 km2) showed species-specific predation patterns, reflected ecological differences among large carnivores with regards to their habitat and spatial partitioning for domestic prey. Our predictive predation risk map and factors associated with livestock predations provides powerful visual guidance and tools for PA managers in developing multi-species conflict mitigation strategies. We recommend diversifying local economic livelihoods and benefit-sharing options for local communities to minimize their forest dependency.

Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques

Droughts are recurring events in Australia and cause a severe effect on agricultural and water resources. However, the studies about agricultural drought risk mapping are very limited in Australia. Therefore, a comprehensive agricultural drought risk assessment approach that incorporates all the risk components with their influencing criteria is essential to generate detailed drought risk information for operational drought management. A comprehensive agricultural drought risk assessment approach was prepared in this work incorporating all components of risk (hazard, vulnerability, exposure, and mitigation capacity) with their relevant criteria using geospatial techniques. The prepared approach is then applied to identify the spatial pattern of agricultural drought risk for Northern New South Wales region of Australia. A total of 16 relevant criteria under each risk component were considered, and fuzzy logic aided geospatial techniques were used to prepare vulnerability, exposure, hazard, and mitigation capacity indices. These indices were then incorporated to quantify agricultural drought risk comprehensively in the study area. The outputs depicted that about 19.2% and 41.7% areas are under very-high and moderate to high risk to agricultural droughts, respectively. The efficiency of the results is successfully evaluated using a drought inventory map. The generated spatial drought risk information produced by this study can assist relevant authorities in formulating proactive agricultural drought mitigation strategies.

Air Pollution, PFOA, and Preeclampsia in the C8 Studies

Background/Aim: Preeclampsia is a type of pregnancy-induced hypertension that also includes proteinuria. Traffic-related particulate air pollution has been linked to preeclampsia in some previous studies. The C8 Study of perfluorooctanoate (PFOA) and preeclampsia did not consider potential confounding by air pollution. We assessed the association between distance to road (DTR), a marker of traffic related air pollution, and preeclampsia in an area with elevated exposure to PFOA from drinking water contaminated by chemical plant releases.Methods: We calculated distance to nearest major road (class A1, A2, or A3) for 5,960 pregnancies occurring between 1990 and 2006 in twelve Ohio and West Virginia counties that were included in previous publications from the C8 Studies. Generalized additive models (GAM) included smoothing for maternal location at birth to assess spatial patterns of preeclampsia risk. We adjusted for estimated annual serum PFOA exposure, exposure year, maternal age at time of pregnancy, parity, education, and smoking status. We then assessed the contribution of DTR to preeclampsia risk with a spatial GAM and a logistic regression without location.Results: For our adjusted model with PFOA exposure, the odds ratio (OR) for preeclampsia was 1.01 (95% confidence interval (CI): 0.92, 1.12) per interquartile shift in log-transformed DTR in meters. The ORs for preeclampsia analyses with and without adjustment for DTR were both 1.11 (95% CI: 0.99, 1.25) per interquartile shift in log-transformed PFOA. Residual spatial patterns in preeclampsia risk were present after PFOA adjustment and did not change with inclusion of DTR. Conclusion: No associations between DTR and preeclampsia were observed in this large, population-based study with elevated PFOA exposure. We did not find any evidence to suggest DTR confounded the relationship observed with estimated serum PFOA levels. Residual spatial patterns of preeclampsia may be due to other unmeasured confounders or limited adjustment for socioeconomic status.

Influence of Climate Variability on Soybean Yield in MATOPIBA, Brazil

The objective of this study was to analyze the influence of large-scale atmospheric–oceanic mechanisms (El Niño–Southern Oscillation—ENSO and the inter-hemispheric thermal gradient of the Tropical Atlantic) on the spatial–temporal variability of soy yield in MATOPIBA. The following, available in the literature, were used: (i) daily meteorological data from 1980 to 2013 (Xavier et al., 2016); (ii) (chemical, physical, and hydric) properties of the predominant soil class in the area of interest, available at the World Inventory of Soil Emission Potentials platform; (iii) genetic coefficients of soybean cultivar with Relative Maturity Group adapted to the conditions of the region. The simulations were performed using the CROPGRO-Soybean culture model of the Decision Support System for Agrotechnology Transfer (DSSAT) system, considering sowing dates between the months of October and December of 33 agricultural years, as well as for three meteorological scenarios (climatology, favorable-wet, and unfavorable-dry). Results showed that the different climate scenarios can alter the spatial patterns of agricultural risk. In the favorable-wet scenario, there was a greater probability of an increase in yield and a greater favorable window for sowing soybean, while in the unfavorable-dry scenario these values were lower. However, considering the unfavorable-dry scenario, in some areas the reduction in yield losses will depend on the chosen planting date.

Area deprivation is inversely associated with measles incidence: A spatiotemporal analysis in Germany

Abstract Background Although measles is endemic in all WHO European regions, very few studies have directly analysed socioeconomic inequalities in disease incidence of measles. We examined the spatiotemporal association between socioeconomic deprivation and measles incidence considering relevant demographic and geographical factors at district level. Methods We conducted a longitudinal small-area analysis using nationally representative data of 401 districts from 2001 to 2017. We used Bayesian spatiotemporal regression models to assess the potential effects of area deprivation on measles incidence, adjusted for relevant demographic (district population size, sex, age, and proportion of non-nationals) and geographical factors (north-south-west-east effect) as well as spatial and temporal effects. We computed risk ratios (RR) for deprivation quintiles (Q1 - Q5), and district-specific adjusted relative risks (ARR) to assess the area-level risk profile of measles in Germany. Results The risk of measles infection in areas with lowest deprivation quintile (Q1) was 1.58 times higher (95%-credible interval [CrI] 1.32-2.00) than in those with highest deprivation (Q5). Areas with medium-low (Q2), medium (Q3), and medium-high deprivation (Q4) had higher adjusted risks of measles relative to areas with highest deprivation (Q5) (RR: 1.23 (0.99-1.51), 1.05 (0.87-1.26), and 1.23 (1.05-1.43), respectively). We identified 22 areas at high risk and 56 at medium-high risk for measles infections, with highest area-level risks in south-western Germany. Conclusions Socioeconomic deprivation in Germany is inversely associated with measles incidence, with elevated risk for measles infections in areas with higher socioeconomic status. Our findings contribute to current global and national debates on measles elimination strategies, and demonstrate the importance of spatial modelling techniques in identifying socioeconomic inequalities and spatial risk patterns of measles for public health actions. Key messages Socioeconomic deprivation is inversely associated with measles incidence in Germany, with higher risk of infection in areas with highest socioeconomic status. The social, spatial, and temporal patterns of elevated risk of measles infection require targeted public health action and policy to address the complexity underlying measles epidemiology.

A stratified generalized additive model and permutation test for temporal heterogeneity of smoothed bivariate spatial effects

Generalized additive models (GAMs) with bivariate smoothers are frequently used to map geographic disease risks in epidemiology studies. A challenge in identifying health disparities has been the lack of intuitive and computationally feasible methods to assess whether the pattern of spatial effects varies over time. In this research, we accommodate time-stratified smoothers into the GAM framework to estimate time-specific spatial risk patterns while borrowing information from confounding effects across time. A backfitting algorithm for model estimation is proposed along with a permutation testing framework for assessing temporal heterogeneity of geospatial risk patterns across two or more time points. Simulation studies show that our proposed permuted mean squared difference (PMSD) test performs well with respect to type I error and power in various settings when compared with existing methods. The proposed model and PMSD test are used geospatial risk patterns of patent ductus arteriosus (PDA) in the state of Massachusetts over 2003-2009. We show that there is variation over time in spatial patterns of PDA risk, adjusting for other known risk factors, suggesting the presence of potential time-varying and space-related risk factors other than the adjusted ones.

Heatwave-induced human health risk assessment in megacities based on heat stress-social vulnerability-human exposure framework

Assessing heatwave-induced human health risk is of critical importance in order to mitigate hazards caused by extreme environmental events. Air temperature or land surface temperature in previous studies was often used to characterize the severity of heatwaves, and human perception of the thermal environment was neglected as a key component in the heatwave-induced risk assessment. In order to redress this issue, in this study we applied the Universal Thermal Climate Index (UTCI) to represent human thermal comfort perception and embedded the measure within an assessment framework of heat stress-social vulnerability-human exposure. The heatwave-induced human health risk was then evaluated in Wuhan City, China across 177 blocks covering the entire city area and local risk governance measures were also explored based on risk zoning. The results showed that spatial patterns of heatwave-induced human health risk followed a decreasing trend from the city center towards the surrounding areas, with the average risk of the main urban area being 1.6 times that beyond the metropolitan development area. Through the heatwave-induced human health risk zoning, about 73.45% of the 177 blocks in Wuhan City demonstrated a positive relationship between heat stress and human exposure, and both were opposite with social vulnerability. Multiple linear regression between UTCI and the proportion of greenspace, water body and construction land indicated that, more blue or green infrastructure should be integrated within the urban fabric to help mitigate heat stress particularly in the main urban area, while in the metropolitan development area construction land dominating heat stress should be strictly regulated. Furthermore, protecting vulnerable groups such as left-behind children and elderly people should be a priority in rural areas that were generally associated with higher levels of social vulnerability. This study proposed a new heatwave-induced human health risk framework with a local evidence in Wuhan City, and further emphasized that risk zoning could be used as a basic yet important approach to facilitating more effective urban planning guidelines for risk governance.

Hotspots of livestock depredation by pumas and jaguars in Brazil: a biome‐scale analysis

AbstractRetaliatory killing, motivated by depredation of domestic animals by large carnivores, has had a significant impact on wild predators’ populations. In Brazil, pumas and jaguars are the most persecuted species, so it is important to analyse the spatial patterns of depredation risk of livestock. We aimed to identify the hotspots of depredation risk by these two felids in Brazil, as well as the most important variables that determine the risk at the biome scale. We generated spatial models of livestock depredation using a database of depredation records, anthropogenic, topographic and vegetation variables, and ecological niche models. We used six algorithms to generate spatial risk models of depredation and selected those with the best performance, for inclusion in a consensus model, for each biome. Finally, we overlapped the areas of high depredation risk by both felids, so that areas considered as hotspots are those in which there is a high risk that livestock will be preyed by pumas or jaguars. Approximately 17% of the area of the biomes analysed for puma had high depredation risk, and 18% in the case of jaguar. The hotspots encompassed 6.5% of the included biomes’ area. The variables associated with the high depredation risk were different between species and biomes. Depredation risk by pumas was primarily associated with anthropogenic variables, whereas a combination of anthropogenic variables and vegetation types increased depredation risk by jaguar. Biome‐scale analysis, coupled with the availability of reliable data and the implementation of statistically robust methods, provides information specifically on the areas of highest risk and the conditions that contribute to it.

Malaria risk cartography in the municipality of Maxixe: Case of the Chambone Neighborhood

This article focuses on the assessment of malaria risk spatialization, based on data from residential areas by type of breeding sites in the Chambone neighborhood (Maxixe, Mozambique). For the operationalization of the objectives, we opted, in addition to the bibliographic review, for a quali-quantitative approach, for accompanied observations and surveys. The results reveal that the areas at high risk of malaria in Chambone are the extreme northeast, center-eastern, southern and inland southwest-central. These high-risk areas, according to the reclassification given by kernel density, are of higher concentration of breeding sites and dwellings. Given the diversity in the spatial distribution pattern of risk and the factors with conditioning factors, taking into account the type of planning for all areas of the Chambone neighborhood, it was feasible to develop plans adjusted to local interests to modify the current spatial pattern of malaria risk distribution.

Assessing Urban Risk to Extreme Heat in China

Many cities are experiencing persistent risk in China due to frequent extreme weather events. Some extreme weather events, such as extreme heat hazard, have seriously threatened human health and socio-economic development in cities. There is an urgent need to measure the degree of extreme heat risk and identify cites with the highest levels of extreme heat risk. In this study, we presented a risk assessment framework of extreme heat and considered risk as a combination of hazard, exposure, and vulnerability. Based on these three dimensions, we selected relevant variables from historical meteorological data (1960–2016) and socioeconomic statistics in 2016, establishing an indicator system of extreme heat risk evaluation. Finally, we developed an extreme heat risk index to quantify the levels of extreme heat risk of 296 prefecture-level cities in China and revealed the spatial pattern of extreme heat risk in China in 2016 and their dominant factors. The results show that (1) cities with high levels of extreme heat hazard are mainly located in the south of China, especially in the southeast of China; (2) the spatial distribution of the extreme heat risk index shows obvious agglomeration characteristics; (3) the spatial distribution of the extreme heat risk is still mostly controlled by natural geographical conditions such as climate and topography; (4) among the four types of hazard-dominated, exposure-dominated, vulnerability-dominated, and low risk cities, the number of vulnerability-dominated cities is the largest. The results of this study can provide support for the risk management of extreme heat disasters and the formation of targeted countermeasures in China.

Global-scale drought risk assessment for agricultural systems

Abstract. Droughts continue to affect ecosystems, communities and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk for both irrigated and rainfed agricultural systems at the global scale. Composite hazard indicators were calculated for irrigated and rainfed systems separately using different drought indices based on historical climate conditions (1980–2016). Exposure was analyzed for irrigated and non-irrigated crops. Vulnerability was assessed through a socioecological-system (SES) perspective, using socioecological susceptibility and lack of coping-capacity indicators that were weighted by drought experts from around the world. The analysis shows that drought risk of rainfed and irrigated agricultural systems displays a heterogeneous pattern at the global level, with higher risk for southeastern Europe as well as northern and southern Africa. By providing information on the drivers and spatial patterns of drought risk in all dimensions of hazard, exposure and vulnerability, the presented analysis can support the identification of tailored measures to reduce drought risk and increase the resilience of agricultural systems.

Recent evolution of spatial and temporal patterns of burnt areas and fire weather risk in the Iberian Peninsula

The weather is an important driver of the wildfire incidence because it strongly influences fuel availability and flammability. In the Mediterranean climate, the spatial and temporal patterns of fire weather present high variability, which help to understand the variable fire regimes. To assess this link in the Iberian Peninsula and the role of fire weather on fire incidence variability, this work identified pyro-regions, characterized the fire regimes in these regions and compared them with the spatial and seasonal distribution of several components of the Canadian Forest Fire Weather Index (FWI) System. A cluster analysis was performed on monthly normalized burnt area (NBA) series for the 1980 – 2015 period in each administrative division of the Iberian Peninsula (Portuguese districts and Spanish provinces), which revealed four pyro-regions (Northwest, North, Southwest and East) as well as significant spatial and temporal variability of NBA patterns. A separate analysis for 1980 – 1997 and 1998 – 2015 revealed noteworthy changes in the border of pyro-regions caused by changes in NBA seasonal patterns in some administrative regions. The analysis of the fire weather risk distribution for 1980 – 2017 included the assessment of extreme fire weather days, defined as days with Daily Severity Rating (DSR) above the 95th percentile, for the summer fire season, and as days with Drought Code above the 95th percentile, for the winter-spring fire season. The distribution of the monthly extreme fire weather days is highly correlated with the NBA seasonal variability and explains the differences between seasonal wildfire characteristics and the recent changes in the border administrative regions. The analysis of the fire weather patterns for large wildfires disclosed that these events were linked with extreme DSR days, especially in the NW pyro-region. These findings highlight the strong link between climate variability and fire regimes in the Iberian Peninsula, and can therefore help assess the impacts of climate change and to project future burnt area patterns.

Spatial risk modelling of behavioural hotspots: Risk-aware path planning for autonomous vehicles

Abstract Autonomous vehicles (AVs) are expected to considerably improve road safety. That said, accident risk will continue to inflict societal costs. The ability to manage and measure these risks is fundamental to ensure societal acceptance and public adoption of AVs. In particular, the ability to quantitatively compare the safety of AVs relative to human drivers is crucial. Managing risk exposures through driving operational design domains (ODD) will also become prevalent. Ultimately, the deployment of AVs will hinge on the premise that they are safer than humans. In this paper, we posit a methodology to quantitatively evaluate AV risks and minimise their risk exposure once they are publically available. Two contributions are offered. First, we provide a proactive means of evaluating AV risks based on driving behaviour and safety-critical events. This offers statistically meaningful comparisons between humans and AVs given the limitation of current historical data. Second, we propose a novel risk-aware path planning methodology for AVs based on telematics behavioural data. Driving data from a cohort of young human drivers over roughly 270,000 km in Ireland is used to demonstrate the posited methodology. An unsupervised geostatistical tool called Kernel Density Estimation (KDE) is used to identify “behavioural hotspots” and the risk exposure at each edge or road segment is modelled. The results are incorporated into a path planning algorithm to find safe route paths for AVs, minimising risk exposures. In addition, Self-Organising Maps (SOM) are employed to identify similar risk groups and individual spatial risk patterns are considered.

Human health risk assessment of heavy metals in soil and food crops in the Pearl River Delta urban agglomeration of China

Heavy metal contamination is one of the most pressing concerns for food security and human health. Understanding the risk to human health posed by soil pollution that enters the food chain is paramount. Here, we systematically assessed potential risks due to heavy metal (Cd, Cr, Pb, Hg, and As) contamination in soil and crops (2241 pairs of soil and crop (78 species) samples) in the Pearl River Delta urban agglomeration of China. Cadmium was the most common pollutant among all the heavy metals identified in soil and crops. The greatest risk of Cd ingestion from leaf and root vegetable consumption was concentrated within Huizhou. The highest risks to human health from heavy metals were found in rice and maize. This is the first time that the risk to human health from heavy metal contamination in soil and crops, and their associated spatial risk pattern, in China have been assessed systematically.

Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir

Turkey is exposed to forest fires damaging thousands of hectares of forest every year. Earlier studies indicate that Turkey would be one of the most affected Mediterranean countries due to increased fire risk as a result of climate change. Therefore, early detection and suppression of forest fires are very important to minimize fire damage. The main goal of this study is to analyse and model the fire risk using remote sensing technology. For this purpose, multi-temporal remote sensing data obtained pre-fire was integrated with ancillary data in Geographic Information System (GIS) to evaluate the spatial and temporal patterns of forest fire risk in Menderes region, İzmir. First, the rapid fire risk of the study area was assessed using Land Surface Temperature (LST) changes and in-situ meteorological measurements. LST and humidity were found to be the most important indicators for fire risk. In order to generate a fire risk map in the study area, six fire risk variables, determined statistically in previous studies, were considered. A linear model that uses predetermined weights for Turkish forest ecosystems was applied. According to the model, 22% of the burned area was found to be high risk area, while 77.5% was identified as moderate high risk area. The model is validated by overlaying all 292 forest fires obtained from the NASA-FIRMS website between 2002 and 2018 on the fire risk map.

Landscape Ecological Risk Assessment of Dianchi Lake Basin Based on Land Use Change

In order to explore the impact of land use type change on landscape ecological risk, this paper uses Landsat remote sensing image of Dianchi Lake Basin in 2003-2018 as the basis data to interpret its land use type by ENVI, and combines ArcGIS and Fragstats4.2 according to landscape ecology theory. The various landscape ecological indices were calculated, and the spatial pattern and process characteristics of landscape ecological risk in Dianchi Lake Basin from 2003 to 2018 were analyzed. The results show that: (1) The landscape pattern of Dianchi Lake Basin has changed significantly in 2003-2018. (2) The landscape ecological risk level has a gradually increasing trend, that is, the proportion of the medium risk area to the high-risk area is relatively large. (3) From the perspective of spatial distribution, the overall risk distribution of Dianchi Lake Basin has an increasing trend from Dianchi Lake to Dianchi Lake.

Regional drinking water composition effects on hip fracture risk: a spatial analysis of nationwide hospital admissions in Portugal, from 2000 to 2010

Abstract Geographical variation on hip fractures (HF) may be related to the geographical variation of drinking water composition (DWC); minerals in drinking water may contribute to its fragility. We aim to investigate the effects of DWC on HF risk in Portugal (2000-2010). From National Hospital Discharge Register we selected admissions of patients aged ≥50 years, diagnosed with HF caused by low/moderate energy traumas. Water components and characteristics were selected at the municipality level. A spatial generalized additive model with a negative binomial distribution as a link function was used to estimate the association of HF with variations in DWC. There were 96,905HF (77.3% in women). The spatial pattern of HF risk was attenuated after being adjusted for water parameters. Results show an indirect association between calcium, magnesium, and iron and HF risk but no clear relation between aluminum, cadmium, fluoride, manganese, or color and HF risk. Regarding pH, the 6.7pH and 7pH interval seems to pose a lower risk. Different dose-response relationships were identified. The increase of calcium, magnesium, and iron values in DWC seems to reduce regional HF risk. Long-term exposure to water parameters, even within the regulatory limits, might increase the regional HF risk.

Modelling tick bite risk by combining random forests and count data regression models

The socio-economic and demographic changes that occurred over the past 50 years have dramatically expanded urban areas around the globe, thus bringing urban settlers in closer contact with nature. Ticks have trespassed the limits of forests and grasslands to start inhabiting green spaces within metropolitan areas. Hence, the transmission of pathogens causing tick-borne diseases is an important threat to public health. Using volunteered tick bite reports collected by two Dutch initiatives, here we present a method to model tick bite risk using human exposure and tick hazard predictors. Our method represents a step forward in risk modelling, since we combine a well-known ensemble learning method, Random Forest, with four count data models of the (zero-inflated) Poisson family. This combination allows us to better model the disproportions inherent in the volunteered tick bite reports. Unlike canonical machine learning models, our method can capture the overdispersion or zero-inflation inherent in data, thus yielding tick bite risk predictions that resemble the original signal captured by volunteers. Mapping model predictions enables a visual inspection of the spatial patterns of tick bite risk in the Netherlands. The Veluwe national park and the Utrechtse Heuvelrug forest, which are large forest-urban interfaces with several cities, are areas with high tick bite risk. This is expected, since these are popular places for recreation and tick activity is high in forests. However, our model can also predict high risk in less-intensively visited recreational areas, such as the patchy forests in the northeast of the country, the natural areas along the coastline, or some of the Frisian Islands. Our model could help public health specialists to design mitigation strategies for tick-borne diseases, and to target risky areas with awareness and prevention campaigns.