Integrated Seismic Risk Assessment in Nepal

As Nepal is at high risk of earthquakes, the district-wide (VDC/Municipality level) study has been performed for vulnerability assessment of seismic-hazard, and the hazard-risk study is incorporated with social conditions as it has become a crucial issue in recent years. There is an interrelationship between hazards, physical risk, and the social characteristics of populations which are significant for policy-makers and individuals. Mapping the spatial variability of average annual loss (seismic risk) and social vulnerability discretely does not reflect the true nature of parameters contributing to the earthquake risk, so when the integrated risk is mapped, such combined spatial distribution becomes more evident. The purpose of this paper is to compute the risk analysis from the exposure model of the country using OpenQuake and then integrate the results with socio-economic parameters. The methodology of seismic-risk assessment and the way of combining the results of the physical risk and socio-economic data to develop an integrated vulnerability score of the regions has been described. This study considers all 75 districts and corresponding VDC/Municipalities using the available census. The combined vulnerability score has been developed and presented by integrating earthquake risk and social vulnerability aspects of the country and represented in form of the map produced using ArcGIS 10. The knowledge and information of the relationship between earthquake hazards and the demographic characteristics of the population in the vulnerable area are imperative to mitigate the local impact of earthquakes. Therefore, we utilize social vulnerability study as part of a comprehensive risk management framework to recuperate and recover from natural disasters.

Procedural Risk in Congenital Cardiac Catheterization (PREDIC 3 T)

Background Advancements in the field, including novel procedures and multiple interventions, require an updated approach to accurately assess patient risk. This study aims to modernize patient hemodynamic and procedural risk classification through the creation of risk assessment tools to be used in congenital cardiac catheterization. Methods and Results Data were collected for all cases performed at sites participating in the C3PO (Congenital Cardiac Catheterization Project on Outcomes) multicenter registry. Between January 2014 and December 2017, 23 119 cases were recorded in 13 participating institutions, of which 88% of patients were <18 years of age and 25% <1 year of age; a high‐severity adverse event occurred in 1193 (5.2%). Case types were defined by procedure(s) performed and grouped on the basis of association with the outcome, high‐severity adverse event. Thirty‐four unique case types were determined and stratified into 6 risk categories. Six hemodynamic indicator variables were empirically assessed, and a novel hemodynamic vulnerability score was determined by the frequency of high‐severity adverse events. In a multivariable model, case‐type risk category (odds ratios for category: 0=0.46, 1=1.00, 2=1.40, 3=2.68, 4=3.64, and 5=5.25; all P ≤0.005) and hemodynamic vulnerability score (odds ratio for score: 0=1.00, 1=1.27, 2=1.89, and ≥3=2.03; all P ≤0.006) remained independent predictors of patient risk. Conclusions These case‐type risk categories and the weighted hemodynamic vulnerability score both serve as independent predictors of patient risk for high‐severity adverse events. This contemporary procedure‐type risk metric and weighted hemodynamic vulnerability score will improve our understanding of patient and procedural outcomes.

Stratification of the risk of developing severe or lethal Covid-19 using a new score from a large Italian population: a population-based cohort study

ObjectivesTo develop a population-based risk stratification model (COVID-19 Vulnerability Score) for predicting severe/fatal clinical manifestations of SARS-CoV-2 infection, using the multiple source information provided by the healthcare utilisation databases of the Italian National Health Service.DesignRetrospective observational cohort study.SettingPopulation-based study using the healthcare utilisation database from five Italian regions.ParticipantsBeneficiaries of the National Health Service, aged 18–79 years, who had the residentship in the five participating regions. Residents in a nursing home were not included. The model was built from the 7 655 502 residents of Lombardy region.Main outcome measureThe score included gender, age and 29 conditions/diseases selected from a list of 61 conditions which independently predicted the primary outcome, that is, severe (intensive care unit admission) or fatal manifestation of COVID-19 experienced during the first epidemic wave (until June 2020). The score performance was validated by applying the model to several validation sets, that is, Lombardy population (second epidemic wave), and the other four Italian regions (entire 2020) for a total of about 15.4 million individuals and 7031 outcomes. Predictive performance was assessed by discrimination (areas under the receiver operating characteristic curve) and calibration (plot of observed vs predicted outcomes).ResultsWe observed a clear positive trend towards increasing outcome incidence as the score increased. The areas under the receiver operating characteristic curve of the COVID-19 Vulnerability Score ranged from 0.85 to 0.88, which compared favourably with the areas of generic scores such as the Charlson Comorbidity Score (0.60). A remarkable performance of the score on the calibration of observed and predicted outcome probability was also observed.ConclusionsA score based on data used for public health management accurately predicted the occurrence of severe/fatal manifestations of COVID-19. Use of this score may help health decision-makers to more accurately identify high-risk citizens who need early preventive or treatment interventions.

Methodology for Assessing the Vulnerability of Participants in Regional Food Markets

The vulnerability of regional food markets is difficult to study due to the lack of a unified approach to analysing the weaknesses of agricultural enterprises, which is necessary for investigating their response to external and internal changes. The study aims to solve this problem by developing an adaptable method for assessing the vulnerability of organisations that produce, process and sell agricultural products in regional food markets. For this purpose, we applied hierarchical mechanisms of integrated assessment for combining various indicators of agricultural enterprises into a single vulnerability score. The present research examines the alcohol market: while this particular sector is represented by a small number of producers, these organisations are usually the largest taxpayers in the agri-food industry, especially in areas of risky farming. As an example, we show how the vulnerability of Permalko JSC (a large producer of alcoholic beverages) has changed because of the COVID-19 pandemic. This company not only satisfies the needs of Perm oblast’s market, but also exports its production to many Russian regions, as well as to near and far abroad. As a result, we propose a new methodology, represented by a set of mathematical formulas, and define its variables. This versatile approach to vulnerability assessment can be adapted for any agri-food enterprise by specifying the parameters. The model is implemented in the dekon software package, which is a web application. The cloud service will provide unified access to all agricultural enterprises.

Geospatial indicators of exposure, sensitivity, and adaptive capacity to assess neighbourhood variation in vulnerability to climate change-related health hazards

Background Although the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community’s exposures, pre-existing sensitivities, and adaptive capacities in response to a hazard’s impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity. Methods A systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped. Results Two hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16–49%) across all hazards. Distinct spatial patterns were observed – for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region. Conclusions The creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.

Vulnerability to Climate Change and Socio-Economic Factors: A Comparison of Selected Districts of Punjab

This study analyzes ten districts of the province Punjab of Pakistan to investigate and compare the vulnerability of selected districts. Total Three sub-groups (socio-economic variables, adaptive capacity, bio-physical variables) are generated by using the data from Pakistan Social & Living Standard Measurement Survey (PSLM) and Pakistan Meteorological Department of the years 2014-15, to calculate total vulnerability. Using primary variables at the district level, this study determines each district’s rural and urban areas' total vulnerability score. The results show that few districts, e.g., Rawalpindi has 0.74 total vulnerability score out of 1, are highly vulnerable compared to other districts despite having a better socio-economic situation. On the other hand, few districts, like Multan, have a low vulnerability to climate change and socio-economic factors. Keywords: CO2, socio-economic, bio-physical, environment, Vulnerability. JEL Classification Codes: Q3, O13, P28.

Modification of the Priority Risk Index: Adapting to Emergency Management Accreditation Program standards for institutes of higher learning hazard mitigation plans

The Priority Risk Index is increasingly used as a methodology for quantifying jurisdictional risk for hazard mitigation planning purposes, and it can evolve to meet specific community needs. The index incorporates probability, impact, spatial extent, warning time, and duration when assessing each hazard, but it does not explicitly integrate a vulnerability and consequence analysis into its final scoring. To address this gap, a new index was developed—the Enhanced Priority Risk Index (EPRI). The new index adds a sixth category, vulnerability, calculated from a vulnerability and consequence analysis of the impacts on seven sectors identified in Standard 4.1.2 of the Emergency Management Accreditation Program (EMAP). To obtain a vulnerability score, impacts are ranked by sector from low (1) to very high (4), then a weighting factor is applied to each sector. The vulnerability score is added to the EPRI and provides risk levels based on the number of exploitable weaknesses and countermeasures identified within a specific jurisdiction. The vulnerability score and resulting EPRI are scalable and can be applied across jurisdictions, providing a transferable methodology that improves the hazard identification and risk assessment process and provides an approach for meeting EMAP accreditation standards.

Spatio-temporal Assessment of Landscape Ecological Risk and Associated Drivers: A Case Study of Delhi

Urbanization is considered as the key driver for land use and land cover (LULC) changes across the globe and Delhi is no exception to this phenomenon. The population of Delhi has almost doubled from 8.4 million in 1991 to 16.3 million in 2011. Correspondingly, the built-up area has also increased from 336.82 to 598.22 km 2 during 1999–2018. This urban expansion has led to emergence of serious ecological risk and fragmentation of the landscape. In this context, it is imperative to analyse the level of risks induced by such urban expansion and its underlying associations with other factors. This article quantifies the LULC changes in Delhi during 1999–2018 using Landsat 5 (TM) and Landsat 8 (OLI) data. A spatio-temporal sprawl induced risk assessment index has been developed by combining landscape fragmentation score and land use land cover vulnerability score. The landscape fragmentation score was based on four landscape metrics, whereas the vulnerability score was computed from LULC data. The article also assesses the association between risk areas and economic activities, environmental and infrastructural amenities that are considered key drivers of urban expansion in Delhi. To estimate spatio-temporal variability between risk areas and key drivers, ordinary least square regression and geographical weighted regression (GWR) were used. The GWR results reveal that sprawl-induced ecological risk in Delhi is strongly associated with economic activity, infrastructural accessibility and environmental amenities. This spatial empirical assessment also shows that urban growth incentives or services such as roads, metro rail, schools and hospitals can also create pressure on the landscape if local authorities arbitrarily provide these services across space without considering the associated risks.