The 2020 national seismic hazard model for the United Kingdom

We present updated seismic hazard maps for the United Kingdom (UK) intended for use with the National Annex for the revised edition of Eurocode 8. The last national maps for the UK were produced by Musson and Sargeant (Eurocode 8 seismic hazard zoning maps for the UK. British Geological Survey Report CR/07/125, United Kingdom, 2007). The updated model uses an up-to-date earthquake catalogue for the British Isles, for which the completeness periods have been reassessed, and a modified source model. The hazard model also incorporates some advances in ground motion modelling since 2007, including host-to-target adjustments for the ground motion models selected in the logic tree. For the first time, the new maps are provided for not only peak ground acceleration (PGA) but also spectral acceleration at 0.2 s (SA0.2s) and 1.0 s for 5% damping on rock (time-averaged shear wave velocity for the top 30 m Vs30 ≥ 800 m/s) and four return periods, including 475 and 2475 years. The hazard in most of the UK is generally low and increases slightly in North Wales, the England–Wales border region, and western Scotland. A similar spatial variation is observed for PGA and SA0.2s but the effects are more pronounced for SA0.2s. Hazard curves, uniform hazard spectra, and disaggregation analysis are calculated for selected sites. The new hazard maps are compared with the previous 2007 national maps and the 2013 European hazard maps (Woessner et al. in Bull Earthq Eng 13:3553–3596, 2015). There is a slight increase in PGA from the 2007 maps to this work; whereas the hazard in the updated maps is lower than indicated by the European maps.

2021 US National Seismic Hazard Model for the State of Hawaii

The 2021 US National Seismic Hazard Model (NSHM) for the State of Hawaii updates the previous two-decade-old assessment by incorporating new data and modeling techniques to improve the underlying ground shaking forecasts of tectonic-fault, tectonic-flexure, volcanic, and caldera collapse earthquakes. Two earthquake ground shaking hazard forecasts (public policy and research) are produced that differ in how they account for declustered catalogs. The earthquake source model is based on (1) declustered earthquake catalogs smoothed with adaptive methods, (2) earthquake rate forecasts based on three temporally varying 60-year time periods, (3) maximum magnitude criteria that extend to larger earthquakes than previously considered, (4) a separate Kīlauea-specific seismogenic caldera collapse model that accounts for clustered event behavior observed during the 2018 eruption, and (5) fault ruptures that consider historical seismicity, GPS-based strain rates, and a new Quaternary fault database. Two new Hawaii-specific ground motion models (GMMs) and five additional global models consistent with Hawaii shaking data are used to forecast ground shaking at 23 spectral periods and peak parameters. Site effects are calculated using western US and Hawaii specific empirical equations and provide shaking forecasts for 8 site classes. For most sites the new analysis results in similar spectral accelerations as those in the 2001 NSHM, with a few exceptions caused mostly by GMM changes. Ground motions are the highest in the southern portion of the Island of Hawai’i due to high rates of forecasted earthquakes on décollement faults. Shaking decays to the northwest where lower earthquake rates result from flexure of the tectonic plate. Large epistemic uncertainties in source characterizations and GMMs lead to an overall high uncertainty (more than a factor of 3) in ground shaking at Honolulu and Hilo. The new shaking model indicates significant chances of slight or greater damaging ground motions across most of the island chain.

Nonlinear discrete-time hazard models for women's entry into marriage

We propose a hazard model for entry into marriage, based on a bell-shaped function to model the dependence on age. We demonstrate near-aliasing in an extension that estimates the support of the hazard and mitigate this via re-parameterization. Our proposed model parameterizes the maximum hazard and corresponding age, thereby facilitating more general models where these features depend on covariates. For data on women's marriages from the Living in Ireland Surveys 1994–2001, this approach captures a reduced propensity to marry over successive cohorts and an increasing delay in the timing of marriage with increasing education.

Development and Validation of Multicenter Study on Novel Artificial Intelligence Based Cardiovascular Risk Score (AICVD)

Cardiovascular diseases (CVD) are one of the most prevalent diseases in India amounting for nearly 30% of total deaths. A dearth of research on CVD risk scores in Indian population, limited performance of conventional risk scores and inability to reproduce the initial accuracies in randomized clinical trials – has led to this study on large-scale patient data. The objective is to develop an Artificial Intelligence based Risk Score (AICVD) to predict CVD Event (e.g. Acute MI / ACS) in next 10 years and compare the model with the Framingham Heart Risk Score (FHRS) and QRisk3.Our study included 31,599 participants aged 18-91 years from 2009 - 2018 in six Apollo Hospitals in India. A multi-step risk factors selection process using Spearman correlation coefficient and propensity score matching yielded 21 risk factors. A Deep Learning Hazard Model was built on risk factors to predict event occurrence (classification) and time to event (hazard model) using multi-layered neural network. Further, the model was validated with independent retrospective cohorts of participants from India and the Netherlands and compared with FHRS and QRisk3.The Deep Learning Hazard model had a good performance (AUC 0.853). Validation and comparative results showed AUCs between 0.84 to 0.92 with better Positive Likelihood Ratio (AICVD-6.16 to FHRS–2.24 and QRisk3–1.16) and Accuracy (AICVD– 80.15% to FHRS 59.71% and QRisk3 51.57%). In the Netherlands cohort, AICVD also outperformed the Framingham Heart Risk Model (AUC - 0.737 vs 0.707).This study concludes that the novel AI based CVD risk score has a higher predictive performance for cardiac events than conventional risk scores.

Analysis of Survival Data with Competing Risks in ADRD (Alzheimer's Disease and Related Dementias) Research

Competing risk is an event that precludes the occurrence of the primary event of interest. For example, when studying risk factors associated with dementia, death before the onset of dementia serve as a competing event. A subject who dies is no longer at risk of dementia. This issue play more important role in ADRD research given the elderly population. Conventional methods for survival analysis assume independent censoring and ignore the competing events. However, there are some challenge issues using those conventional methods in the presence of competing risks. First, no one-to-one link between hazard function and cumulative incidence function (CIF), and Kaplan-Meier approach overestimates the cumulative incidence of the event of interest. Second, the effect of covariates on hazard rate cannot be directly linked to the effect of cumulative incidence (the risk). We will discuss two types of analyses in the presence of competing risk: Cause-specific hazard model and Fine-Gray subdistribution hazard model. Cause-specific hazard model directly quantify the cause-specific hazard among subjects who are at risk of developing the event of interest, while Fine-Gray subdistribution hazard model directly model the effects of covariates on the cumulative incidence function. The type of research questions (Association vs. Prediction) may guide the choice of different statistical approaches. We will illustrate those two competing risk analyses using the large national dataset from National Alzheimer’s Coordinating Center (NACC). We will analyze the association between baseline diabetes status and the incidence of dementia, in which death before the onset of dementia is a competing event.

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit

Background Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI. Methods Patients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine–Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes. Results A total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137–1.875), 1.532 (1.197–1.961), and 2.232 (1.795–2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results. Conclusions The trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.

Sex Difference in All-Cause and Infection-Specific Mortality Over 10 Years Post Hip Fracture

Men die at a twice higher rate than women in the first two years after fracture and also experience higher infection-related mortality. Most research has only looked at differences in short-term mortality after hip fracture. The objective was to determine if cumulative incidence of all-cause mortality and infection-specific mortality is higher in men compared to women over ten years. Data came from Baltimore Hip Studies7th cohort. Women were frequency-matched (1:1) to men on timing of fracture to ensure equal numbers of men and women. The association of sex and all-cause mortality was analyzed using Cox proportional hazard model and a cause-specific hazard model for infection-specific mortality. Both models controlled for age, cognition, comorbidity, depressive symptoms, BMI, and pre-fracture ADL limitations. Complete-case sample size was 300 (men=145, women=155). By the end of ten years from the date of admission for a hip fracture, there were 237 (men=132, women=105) all-cause deaths and 38 (men=25, women=13) infection-specific deaths. Men had significantly higher all-cause mortality risk [73.7% vs 59.3%; HR=2.31(2.02-2.59)] and infection-specific mortality [17.2% vs 8.3%; HR=4.43(2.07-9.51)] compared to women. In addition to sex, older age, cognition, and comorbidities were associated with all-cause mortality whereas only BMI was associated with infection-specific mortality in adjusted models. Men had a higher risk of mortality over 10 years compared to women, specifically two-fold higher risk of infection-specific mortality compared to all-cause mortality. Findings imply that interventions to prevent/treat infection, tailored by sex, may be needed to narrow significant differences in long-term mortality rates between men and women.

Introduction of Two-Dose Mumps-Containing Vaccine into Routine Immunization Schedule in Quzhou, China, Using Cox-Proportional Hazard Model

Mumps is a vaccine-preventable disease caused by the mumps virus, but the incidence of mumps has increased among the children who were vaccinated with one-dose measles-mumps-rubella (MMR) in recent years. In this study, we analyzed the influence of different doses of mumps-containing vaccine (MuCV) against mumps using Cox-proportional hazard model. We collected 909 mumps cases of children who were born from 2006 to 2010 and vaccinated with different doses of MuCV in Quzhou during 2006-2018, which were all clinically diagnosed. Kaplan-Meier survival methods and Cox-proportional hazard model were used to estimate the hazard probabilities. Kaplan–Meier curves showed that the cumulative hazard of male and female has no difference; lower hazards were detected among those who were vaccinated with two-dose MuCV, born in 2006, and infected after supplementary immunization activities (SIA). Cox-proportional hazard regression suggested that onset after SIA, born in 2006, and vaccinated with two-dose MuCV were protective factors against infection even after adjusting for potential confounding effects. Our study showed that it was necessary to revise the diagnostic criteria of mumps and identify RT-PCR as the standard for mumps diagnosis in China. We suggested that routine immunization schedule should introduce two doses of MMR and prevaccination screening should be performed before booster immunization in vaccinated populations.

Combined principal component analysis and proportional hazard model for optimizing condition-based maintenance

Large companies are investing heavily in digitalization to be more competitive and economically viable. Hence, physical assets and maintenance operations have been digitally transformed to transmit a high volume of data, e.g., condition monitoring data. Such high-volume data can be useful to optimize maintenance operations and minimize maintenance and replacement costs. A tool to optimize maintenance using condition monitoring data is the Proportional hazard model (PHM). However, it is challenging to implement PHM for industrial complex systems that generate big data. Therefore, machine learning algorithms shall support PHM method to handle such a high volume of data. Thus, the purpose of this paper is to explore how to support PHM with Principal Component Analysis (PCA) to maintenance optimization of complex industrial systems. A case study of hydraulic power unit was purposefully selected to apply and validate the proposed analytical approach. The results show that PCA supported PHM optimizes and extends the preventive maintenance interval by 79.27% which might lead to maintenance cost reductions. This model enables PHM to handle complex systems where big data is collected.