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Logistic regression studies which assess landslide susceptibility are widely available in the literature. However, a global review of these studies to synthesise and compare the results does not exist. There are currently no guidelines for the selection of covariates to be used in logistic regression analysis, and as such, the covariates selected vary widely between studies. An inventory of significant covariates associated with landsliding produced from the full set of such studies globally would be a useful aid to the selection of covariates in future logistic regression studies. Thus, studies using logistic regression for landslide susceptibility estimation published in the literature were collated, and a database was created of the significant factors affecting the generation of landslides. The database records the paper the data were taken from, the year of publication, the approximate longitude and latitude of the study area, the trigger method (where appropriate) and the most dominant type of landslides occurring in the study area. The significant and non-significant (at the 95 % confidence level) covariates were recorded, as well as their coefficient, statistical significance and unit of measurement. The most common statistically significant covariate used in landslide logistic regression was slope, followed by aspect. The significant covariates related to landsliding varied for earthquake-induced landslides compared to rainfall-induced landslides, and between landslide type. More importantly, the full range of covariates used was identified along with their frequencies of inclusion. The analysis showed that there needs to be more clarity and consistency in the methodology for selecting covariates for logistic regression analysis and in the metrics included when presenting the results. Several recommendations for future studies were given.

Natural hazards are natural processes of the complex Earth system and may interact and affect each other. Often a single hazard can trigger a subsequent, different hazard, such as earthquakes triggering landslides. The effect of such cascading hazards has received relatively little attention in the literature. The majority of previous research has focused on single hazards in isolation, and even multi-hazard risk assessment currently does not account for the interaction between hazards, therefore ignoring potential amplification effects. Global earthquake-and-landslide fatality data were used to model cascading events to explore relationships between the number of fatalities during single and cascading events and covariates. A multivariate statistical approach was used to model the relationship between earthquake fatalities and several covariates. The covariates included earthquake magnitude, gross domestic product, slope, poverty, health, access to cities, exposed population to earthquake shaking, building strength and whether a landslide was triggered or not. Multivariate regression analysis showed the numbers of earthquake fatalities are significantly affected by whether a subsequent landslide is triggered or not.

Quantitative modelling of landslide hazard, as opposed to landslide susceptibility, as a function of the earthquake trigger is vital in understanding and assessing future potential exposure to landsliding. Logistic regression analysis is a method commonly used to assess susceptibility to landsliding; however, estimating probability of landslide hazard as a result of an earthquake trigger is rarely undertaken. This paper utilises a very detailed landslide inventory map and a comprehensive dataset on peak ground acceleration for the 1994 M w 6.7 Northridge earthquake event to fit a landslide hazard logistic regression model. The model demonstrates a high success rate for estimating probability of landslides as a result of earthquake shaking. Seven earthquake magnitude scenarios were simulated using the Open Source Seismic Hazard Analysis (OpenSHA) application to simulate peak ground acceleration, a covariate of landsliding, for each event. The exposure of assets such as population, housing and roads to high levels of shaking and high probabilities of landsliding was estimated for each scenario. There has been urban development in the Northridge region since 1994, leading to an increase in prospective exposure of assets to the earthquake and landslide hazards in the event of a potential future earthquake. As the earthquake scenario magnitude increases, the impact from earthquake shaking initially increases then quickly levels out, but potential losses from landslides increase at a rapid rate. The modelling approach, as well as the specific model, developed in this paper can be used to estimate landslide probabilities as a result of an earthquake event for any scenario where the peak ground acceleration variable is available.

(1) Background: Nanomedicine has recently emerged as a new area of research, particularly to fight cancer. In this field, we were interested in the vectorization of pepstatin A, a peptide which does not cross cell membranes, but which is a potent inhibitor of cathepsin D, an aspartic protease particularly overexpressed in breast cancer. (2) Methods: We studied two kinds of nanoparticles. For pepstatin A delivery, mesoporous silica nanoparticles with large pores (LPMSNs) and hollow organosilica nanoparticles (HOSNPs) obtained through the sol–gel procedure were used. The nanoparticles were loaded with pepstatin A, and then the nanoparticles were incubated with cancer cells. (3) Results: LPMSNs were monodisperse with 100 nm diameter. HOSNPs were more polydisperse with diameters below 100 nm. Good loading capacities were obtained for both types of nanoparticles. The nanoparticles were endocytosed in cancer cells, and HOSNPs led to the best results for cancer cell killing. (4) Conclusions: Mesoporous silica-based nanoparticles with large pores or cavities are promising for nanomedicine applications with peptides.

Infections due to antibiotic-resistant bacteria are threatening modern health care. However, estimating their incidence, complications, and attributable mortality is challenging. We aimed to estimate the burden of infections caused by antibiotic-resistant bacteria of public health concern in countries of the EU and European Economic Area (EEA) in 2015, measured in number of cases, attributable deaths, and disability-adjusted life-years (DALYs). We estimated the incidence of infections with 16 antibiotic resistance–bacterium combinations from European Antimicrobial Resistance Surveillance Network (EARS-Net) 2015 data that was country-corrected for population coverage. We multiplied the number of bloodstream infections (BSIs) by a conversion factor derived from the European Centre for Disease Prevention and Control point prevalence survey of health-care-associated infections in European acute care hospitals in 2011–12 to estimate the number of non-BSIs. We developed disease outcome models for five types of infection on the basis of systematic reviews of the literature. From EARS-Net data collected between Jan 1, 2015, and Dec 31, 2015, we estimated 671 689 (95% uncertainty interval [UI] 583 148–763 966) infections with antibiotic-resistant bacteria, of which 63·5% (426 277 of 671 689) were associated with health care. These infections accounted for an estimated 33 110 (28 480–38 430) attributable deaths and 874 541 (768 837–989 068) DALYs. The burden for the EU and EEA was highest in infants (aged <1 year) and people aged 65 years or older, had increased since 2007, and was highest in Italy and Greece. Our results present the health burden of five types of infection with antibiotic-resistant bacteria expressed, for the first time, in DALYs. The estimated burden of infections with antibiotic-resistant bacteria in the EU and EEA is substantial compared with that of other infectious diseases, and has increased since 2007. Our burden estimates provide useful information for public health decision-makers prioritising interventions for infectious diseases. European Centre for Disease Prevention and Control.

Reducing disaster risk is critical to securing the ambitions of the Sustainable Development Goals (SDGs), and natural hazard scientists make a key contribution to achieving this aim. Understanding Earth processes and dynamics underpins hazard analysis, which (alongside analysis of other disaster risk drivers) informs the actions required to manage and reduce disaster risk. Here we suggest how natural hazard research scientists can better contribute to the planning and development of sustainable and resilient communities through improved engagement in disaster risk reduction (DRR). Building on existing good practice, this perspective piece aims to provoke discussion in the natural hazard science community about how we can strengthen our engagement in DRR. We set out seven recommendations for enhancing the integration of natural hazard science into DRR: (i) characterise multi-hazard environments; (ii) prioritise effective, positive, long-term partnerships; (iii) understand and listen to your stakeholders; (iv) embed cultural understanding into natural hazard research; (v) ensure improved and equitable access to hazard information; (vi) champion people-centred DRR (leaving no one behind); and (vii) improve links between DRR and sustainable development. We then proceed to synthesise key actions that natural hazard scientists and research funders should consider taking to improve education, training, and research design and to strengthen institutional, financial, and policy actions. We suggest that these actions should help to strengthen the effective application of natural hazard science to reduce disaster risk. By recognising and taking steps to address the issues raised in these recommendations, we propose that the natural hazard science community can more effectively contribute to the inter-/transdisciplinary, integrated work required to improve DRR.

With the Internet of Things (IoT) becoming increasingly prevalent in people’s homes, new threats to residents are emerging such as the cyber-physical attack, i.e. a cyber-attack with physical consequences. In this study, we aimed to gain insights into how people experience and respond to cyber-physical attacks to their IoT devices. We conducted a naturalistic field experiment and provided 9 Dutch and 7 UK households, totalling 18 and 13 participants respectively, with a number of smart devices for use in their home. After a period of adaptation, simulated attacks were conducted, leading to events of varying noticeability (e.g., the light going on or off once or several times). After informing people simulated attacks had occurred, the attacks were repeated one more time. User experiences were collected through interviews and analysed with thematic analyses. Four relevant themes were identified, namely (1) the awareness of and concern about privacy and security risks was rather low, (2) the simulated attacks made little impression on the participants, (3) the participants had difficulties with correctly recognizing simulated attacks, and (4) when informed about simulated attacks taking place; participants noticed more simulated attacks and presented decision rules for them (but still were not able to identify and distinguish them well—see Theme 3). The findings emphasise the need for training interventions and an intrusion detection system to increase detection of cyber-physical attacks.

Information about the prevalence of Staphylococcus aureus resistance to antimicrobial drugs has mainly been obtained from invasive strains, although the commensal microbiota is thought to be an important reservoir of resistance. We aimed to compare the prevalence of nasal S aureus carriage and antibiotic resistance, including meticillin-resistant S aureus (MRSA), in healthy patients across nine European countries. In this cross-sectional study, nasal swabs were obtained from 32 206 patients recruited by family doctors participating in existing nationwide family doctor networks in Austria, Belgium, Croatia, France, Hungary, Spain, Sweden, the Netherlands, and the UK. Eligible patients were aged 4 years or older (≥18 years in the UK) and presented with a non-infectious disorder. Swabs were sent to national microbiological laboratories for identification and isolation of S aureus. Antibiotic resistance testing was done at one central microbiological laboratory. We established the genotypic structure of the isolated MRSA strains with the spa typing method. S aureus was isolated from 6956 (21·6%) of 32 206 patients swabbed. The adjusted S aureus prevalence for patients older than 18 years ranged from 12·1% (Hungary) to 29·4% (Sweden). Except for penicillin, the highest recorded resistance rate was to azithromycin (from 1·6% in Sweden to 16·9% in France). In total, 91 MRSA strains were isolated, and the highest MRSA prevalence was reported in Belgium (2·1%). 53 different spa types were detected—the most prevalent were t002 (n=9) and t008 (n=8). The prevalence of S aureus nasal carriage differed across the nine European countries assessed, even after correction for age, sex, and family doctor. Generally, the prevalence of resistance, including that of MRSA, was low. The MRSA strains recorded showed genotypic heterogeneity, both within and between countries. European Commission, 7th Framework Programme(grant agreement 223083).

Antimicrobial resistance (AMR) has become a global public health concern which threatens the effective treatment of bacterial infections. Resistant Staphylococcus aureus (including MRSA) increasingly appears in individuals with no healthcare associated risks. Our study assessed risk factors for nasal carriage of resistant S. aureus in a multinational, healthy, community-based population, including ecological exposure to antibiotics. Data were collected in eight European countries (Austria, Belgium, Croatia, France, Hungary, the Netherlands, Spain and Sweden). Commensal AMR patterns were assessed by collecting 28,929 nasal swabs from healthy persons (aged 4+). Ecological exposure to antibiotics was operationalized as systemic antibiotic treatment patterns, extracted from electronic medical records of primary care practices in which the participants were listed (10-27 per country). A multilevel analysis related AMR in nasal commensal S. aureus to antibiotic exposure and other risk factors (e.g. age and profession). Of the 6,093 S. aureus isolates, 77% showed resistance to at least one antibiotic. 7.1% exhibited multidrug resistance (defined as resistance to 3 or more antibiotic classes), and we found 78 cases MRSA (1.3%). A large variation in antibiotic exposure was found between and within countries. Younger age and a higher proportion of penicillin prescriptions in a practice were associated with higher odds for carriage of a resistant S. aureus. Also, we found higher multidrug resistance rates in participants working in healthcare or nurseries. This study indicates that in a population with no recent antibiotic use, the prescription behavior of the general practitioner affects the odds for carriage of a resistant S. aureus, highlighting the need for cautious prescribing in primary care. Finally, since variation in AMR could partly be explained on a national level, policy initiatives to decrease AMR should be encouraged at the national level within Europe.