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Introduction: Hypothermia is a well‐recognised finding in trauma patients, which can occur even in warmer climates. It is an independent predictor of increased morbidity and mortality. It is associated with pre‐hospital intubation, although the reasons for this are likely to be multifactorial. Core temperature drop after induction of anaesthesia is a well‐known phenomenon in the context of elective surgery, and the mechanisms of this are well established. Methods: We conducted a prospective observational study to examine the behaviour of core temperature in patients undergoing pre‐hospital anaesthesia for traumatic injuries. Results: Between 2017 and 2021 data were collected on 48 patients. The data from 40 of these were included in the final analysis. Discussion: Our data do not show a decrease in the core temperatures of patients who receive pre‐hospital anaesthesia, unlike patients who are anaesthetised without pre‐warming, in operating theatres. The lack of a change could relate to patient, anaesthetic or environmental factors. [ABSTRACT FROM AUTHOR]

Parametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both of these can be realized by using flexible parametric relative survival modeling. The overall aim of this article is to introduce and contrast various approaches for applying constraints on the long-term disease-related (excess) mortality including cure models and evaluate the consequent implications for extrapolation. We describe flexible parametric relative survival modeling approaches. We then introduce various options for constraining the long-term excess mortality and compare the performance of each method in simulated data. These methods include fitting a standard flexible parametric relative survival model, enforcing statistical cure, and forcing the long-term excess mortality to converge to a constant. We simulate various scenarios, including where statistical cure is reasonable and where the long-term excess mortality persists. The compared approaches showed similar survival fits within the follow-up period. However, when extrapolating the all-cause survival beyond trial follow-up, there is variation depending on the assumption made about the long-term excess mortality. Altering the time point from which the excess mortality is constrained enables further flexibility. The various constraints can lead to applying explicit assumptions when extrapolating, which could lead to more plausible survival extrapolations. The inclusion of general population mortality directly into the model-building process, which is possible for all considered approaches, should be adopted more widely in survival extrapolation in health technology assessment. • Extrapolation of all-cause survival from randomized controlled trial data is often required in the context of economic evaluation of novel interventions. External information can be used to guide long-term survival trends when extrapolating all-cause survival. This article investigates relative survival modeling approaches to incorporate general population mortality rates into long-term extrapolation. In doing so, we expand on existing approaches by clearly detailing how to apply various constraints on the long-term disease-specific mortality in this modeling framework. • Flexible parametric survival models can apply various extrapolation approaches in the framework. We compare models by imposing constraints on parameter estimation and applying those constraints at specific points of follow-up, including beyond the range of the data. We compare these methods in scenarios where cure is simulated to be reasonable and in cases where it is not. The result shows that different assumptions on the excess hazards can result in different extrapolations of all-cause survival. The various constraints can lead to more plausible survival extrapolation depending on the disease or treatment characteristics. • The approaches for all-cause survival extrapolation outlined offer a suite of possibilities to clearly describe an approach to extrapolation that automatically incorporates general population mortality rates while making explicit choices on how long disease-related mortality will affect the cohort. The choice of approach can be dictated by the clinical context in the decision-making process. [ABSTRACT FROM AUTHOR]

Citizens' attitudes towards their national healthcare are important indicators of satisfaction and of political perspectives. In this article we summarise individual and national level patterns in healthcare evaluations across Europe. An innovative feature of our analysis is that we demonstrate that assessing healthcare evaluations in relative terms (relative to citizens' views about the performance of national institutions in other domains), offers new insights about individual and national level variations in attitudes. Thus, we introduce an indicator of relative attitudes towards healthcare and contrast it to an absolute measure in a cross-national analysis. We use a larger dataset than previous studies of healthcare evaluations including countries from all regions of Europe and spanning eight rounds of the European Social Survey (2002–2017, N = 342,000). We find that Europeans' healthcare evaluations are multidimensional, with different patterns sometimes operating at an absolute and a relative level. When comparing countries, for instance, several nations in Southern and Eastern Europe compare poorly to other nations in their absolute ratings of healthcare but compare favourably if assessed in relative terms. Likewise, using a relative measure, most Scandinavian countries compare less favourably to other countries, but score positively when evaluations are measured in absolute terms. [ABSTRACT FROM AUTHOR]

Background: Completeness of recording for cancer stage at diagnosis is often historically poor in cancer registries, making it challenging to provide long-term stage-specific survival estimates. Stage-specific survival differences are driven by differences in short-term prognosis, meaning estimated survival metrics using period analysis are unlikely to be sensitive to imputed historical stage data.Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER) Program for lung, colon and breast cancer. To represent missing data patterns in less complete registry data, we artificially inflated the proportion of missing stage information conditional on stage at diagnosis and calendar year of diagnosis. Period analysis was applied and missing stage at diagnosis information was imputed under four different conditions to emulate extreme imputed stage distributions.Results: We fit a flexible parametric model for each cancer stage on the excess hazard scale and the differences in stage-specific marginal relative survival were assessed. Estimates were also obtained from non-parametric approaches for validation. There was little difference between the 10-year stage-specific marginal relative survival estimates, regardless of the assumed historical stage distribution.Conclusions: When conducting a period analysis, multiple imputation can be used to obtain stage-specific long-term estimates of relative survival, even when the historical stage information is largely incomplete. [ABSTRACT FROM AUTHOR]

Merkel cell polyomavirus (MCPyV) is the only human polyomavirus currently known to cause human cancer. MCPyV is believed to be an etiological factor in at least 80% of cases of the rare but aggressive skin malignancy Merkel cell carcinoma (MCC). In these MCPyV+ MCC tumors, clonal integration of the viral genome results in the continued expression of two viral proteins: the viral small T antigen (ST) and a truncated form of the viral large T antigen. The oncogenic potential of MCPyV and the functional properties of the viral T antigens that contribute to neoplasia are becoming increasingly well-characterized with the recent development of model systems that recapitulate the biology of MCPyV+ MCC. In this review, we summarize our understanding of MCPyV and its role in MCC, followed by the current state of both in vitro and in vivo model systems used to study MCPyV and its contribution to carcinogenesis. We also highlight the remaining challenges within the field and the major considerations related to the ongoing development of in vitro and in vivo models of MCPyV+ MCC. [ABSTRACT FROM AUTHOR]

Human papillomaviruses (HPVs) cause a substantial amount of human disease from benign disease such as warts to malignant cancers including cervical carcinoma, head and neck cancer, and non-melanoma skin cancer. Our ability to model HPV-induced malignant disease has been impeded by species specific barriers and pre-clinical animal models have been challenging to develop. The recent discovery of a murine papillomavirus, MmuPV1, that infects laboratory mice and causes the same range of malignancies caused by HPVs provides the papillomavirus field the opportunity to test mechanistic hypotheses in a genetically manipulatable laboratory animal species in the context of natural infections. The E6 and E7 proteins encoded by high-risk HPVs, which are the HPV genotypes associated with human cancers, are multifunctional proteins that contribute to HPV-induced cancers in multiple ways. In this review, we describe the known activities of the MmuPV1-encoded E6 and E7 proteins and how those activities relate to the activities of HPV E6 and E7 oncoproteins encoded by mucosal and cutaneous high-risk HPV genotypes. [ABSTRACT FROM AUTHOR]

The Clinical Practice Research Datalink (CPRD) is a widely used data resource, representative in demographic profile, with accurate death recordings but it is unclear if mortality rates within CPRD GOLD are similar to rates in the general population. Rates may additionally be affected by selection bias caused by the requirement that a cohort have a minimum lookback window, i.e. observation time prior to start of at-risk follow-up. Standardised Mortality Ratios (SMRs) were calculated incorporating published population reference rates from the Office for National Statistics (ONS), using Poisson regression with rates in CPRD GOLD contrasted to ONS rates, stratified by age, calendar year and sex. An overall SMR was estimated along with SMRs presented for cohorts with different lookback windows (1, 2, 5, 10 years). SMRs were stratified by calendar year, length of follow-up and age group. Mortality rates in a random sample of 1 million CPRD GOLD patients were slightly lower than the national population [SMR = 0.980 95% confidence interval (CI) (0.973, 0.987)]. Cohorts with observational lookback had SMRs below one [1 year of lookback; SMR = 0.905 (0.898, 0.912), 2 years; SMR = 0.881 (0.874, 0.888), 5 years; SMR = 0.849 (0.841, 0.857), 10 years; SMR = 0.837 (0.827, 0.847)]. Mortality rates in the first two years after patient entry into CPRD were higher than the general population, while SMRs dropped below one thereafter. Mortality rates in CPRD, using simple entry requirements, are similar to rates seen in the English population. The requirement of at least a single year of lookback results in lower mortality rates compared to national estimates. [ABSTRACT FROM AUTHOR]

Background: A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on.Methods: We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented.Results: We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research.Conclusions: We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research. [ABSTRACT FROM AUTHOR]

As a part of the drive to reduce carbon dioxide emissions, embodied energy figures are under consideration for construction materials. One aspect that needs to be included is the loss of embodied energy associated with corrosion and degradation throughout the life of a structure. This paper presents a review of corrosion and protection methods from the point of view of whole-life embodied carbon. This paper presents the examples that provide the lowest embodied carbon option for different environments for steel and reinforced concrete. [ABSTRACT FROM AUTHOR]

Background: When quantifying the probability of survival in cancer patients using cancer registration data, it is common to estimate marginal relative survival, which under assumptions can be interpreted as marginal net survival. Net survival is a hypothetical construct giving the probability of being alive if it was only possible to die of the cancer under study, enabling comparisons between populations with differential mortality rates due to causes other the cancer under study. Marginal relative survival can be estimated non-parametrically (Pohar Perme estimator) or in a modeling framework. In a modeling framework, even when just interested in marginal relative survival it is necessary to model covariates that affect the expected mortality rates (e.g. age, sex and calendar year). The marginal relative survival function is then obtained through regression standardization. Given that these covariates will generally have non-proportional effects, the model can become complex before other exposure variables are even considered.Methods: We propose a flexible parametric model incorporating restricted cubic splines that directly estimates marginal relative survival and thus removes the need to model covariates that affect the expected mortality rates. In order to do this the likelihood needs to incorporate the marginal expected mortality rates at each event time taking account of informative censoring. In addition time-dependent weights are incorporated into the likelihood. An approximation is proposed through splitting the time scale into intervals, which enables the marginal relative survival model to be fitted using standard software. Additional weights can be incorporated when standardizing to an external reference population.Results: The methods are illustrated using national cancer registry data. In addition, a simulation study is performed to compare different estimators; a non-parametric approach, regression-standardization and the new marginal relative model. The simulations study shows the new approach is unbiased and has good relative precision compared to the non-parametric estimator.Conclusion: The approach enables estimation of standardized marginal relative survival without the need to model covariates that affect expected mortality rates and thus reduces the chance of model misspecification. [ABSTRACT FROM AUTHOR]

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population‐level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user‐friendly Stata software with accompanying example code. [ABSTRACT FROM AUTHOR]

Objective: Topical sinonasal rinse therapies may alter the local microbiome and improve disease control in chronic rhinosinusitis (CRS). The objective of this study was to examine microbiome changes in post‐surgical CRS patients when rinsing with commercially available products containing xylitol or Lactococcus lactis. Methods: A crossover‐type protocol with a washout period was designed. Swab samples from anterior ethmoid cavities of CRS patients were collected prospectively at baseline. Subjects were provided packets containing either L. lactis W136 or xylitol in non‐blinded fashion and instructed to add it to their rinse bottles daily for 28 days, after which another swab was taken. A saline wash‐out period was completed and a third swab taken. A final 28‐day regimen of the opposite product was followed by a final swab. DNA extraction and sequencing of the 16S rRNA gene allowed for global microbiome analysis. Results: We enrolled 25 subjects with CRS and 10 controls resulting in 70 adequate samples. Increased detection of Lactococcus was observed after use of L. lactis. No significant trends in alpha or beta diversity as a result of treatment were observed. SNOT‐22 score did not change significantly following treatment with xylitol, L. lactis, or saline. Conclusion: We did not detect any major clinical or microbiome‐level effect due to treatment with two topical rinse products. Further research is needed to elucidate their clinical utility and possible probiotic effect. Level of Evidence: 3. [ABSTRACT FROM AUTHOR]

Background: This paper investigates the difference in survival of melanoma patients across stage and sex by utilising net survival measures. Metrics are presented at both the individual and population level.Methods: Flexible parametric models were fitted to estimate life-expectancy metrics to be applied to a group of 104,938 subjects with a melanoma skin cancer diagnosis from 2000 to 2017. Period analysis was used for better predictions for newly diagnosed patients, and missing-stage information was imputed for 9918 patients. Female relative survival was assigned to male subjects to demonstrate the survival discrepancies experienced between sexes.Results: At the age of 60, males diagnosed at the regional stage lose an average of 4.99 years of life compared to the general population, and females lose 4.79 years, demonstrating the sex variation in expected mortality. In 2017, males contributed 3545 more life years lost than females, and a potential 1931 life years could be preserved if sex differences in survival were eliminated.Conclusions: This study demonstrates the survival differences across population subgroups as a result of a melanoma cancer diagnosis. Females experience better prognosis across age and stage at diagnosis; however, further investigation is necessary to better understand the mechanisms behind this difference. [ABSTRACT FROM AUTHOR]

High entropy alloys (HEA) contain multiple principal alloying elements, and possess unique properties due to the high configurational entropy and lattice strain in the system. Ferromagnetic FeCoNi-based HEAs exhibit dramatic changes in crystal structure and the type of magnetism expressed when adding non-magnetic elements such as Al, Cr, Ga, Ti, etc. Interestingly, Alnico permanent magnets also contain multiple principal alloying elements, such as Fe, Co, Ni, and Al, along with other minor additions. This well-studied system is similar in concept to high entropy alloys (HEAs). In this paper, we investigate the hard magnetic properties of FeCoNiAl-based HEAs with additions of Cu/Ti. The addition of Cu/Ti to an equimolar FeCoNiAl alloy is effective at enhancing coercivity, due to spinodal decomposition, but at the expense of saturation magnetization. By varying the ratio of Fe and Co, however, with respect to the other alloying elements, the saturation magnetization is increased, while generally retaining or improving the coercivity. In particular, the Fe2CoNiAlCu0.4Ti0.4 HEA shows promising hard magnetic properties as an isotropic cast magnet, with an HC of 1,078 Oe and (BH)max of 2.06 MGOe, slightly better than the performance of isotropic cast Alnico 2 magnets. The thermal stability is also sufficient for use at elevated temperatures over 200 °C. There was also an interesting increase in high temperature coercivity observed at temperatures from ∼650-800 °C, where these alloys often exhibited higher coercivity than that measured at RT. [ABSTRACT FROM AUTHOR]

Background: In population-based cancer survival studies, the most common measure to compare population groups is age-standardized marginal relative survival, which under assumptions can be interpreted as marginal net survival; the probability of surviving if it was not possible to die of causes other than the cancer under study (if the age distribution was that of a common reference population). The hypothetical nature of this definition has led to confusion and incorrect interpretation. For any measure to be fair in terms of comparing cancer survival, then differences between population groups should depend only on differences in excess mortality rates due to the cancer and not differences in other-cause mortality rates or differences in the age distribution.Methods: We propose using crude probabilities of death and all-cause survival which incorporate reference expected mortality rates. This makes it possible to obtain marginal crude probabilities and all-cause probability of death that only differ between population groups due to excess mortality rate differences. Choices have to be made regarding what reference mortality rates to use and what age distribution to standardize to.Results: We illustrate the method and some potential choices using data from England for men diagnosed with melanoma. Various marginal measures are presented and compared.Conclusions: The new measures help enhance understanding of cancer survival and are a complement to the more commonly used measures. [ABSTRACT FROM AUTHOR]

In this paper we present empirical results that show that detailed occupations have distinctive patterns of association with voluntary participation. We draw upon data from four secondary survey datasets from the UK (coverage 1972–2012). Occupations are shown to link to volunteering in a wide range of scenarios and in individual, household, and longitudinal contexts. We argue that these linkages provide insight into social inequalities in volunteering, and that they can help us to understand the relative influence of "circumstance" and "habits" in enabling or inhibiting voluntary participation. [ABSTRACT FROM AUTHOR]

A novel coronavirus (CoV), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in late 2019 in Wuhan, China and has since spread as a global pandemic. Safe and effective vaccines are thus urgently needed to reduce the significant morbidity and mortality of Coronavirus Disease 2019 (COVID-19) disease and ease the major economic impact. There has been an unprecedented rapid response by vaccine developers with now over one hundred vaccine candidates in development and at least six having reached clinical trials. However, a major challenge during rapid development is to avoid safety issues both by thoughtful vaccine design and by thorough evaluation in a timely manner. A syndrome of "disease enhancement" has been reported in the past for a few viral vaccines where those immunized suffered increased severity or death when they later encountered the virus or were found to have an increased frequency of infection. Animal models allowed scientists to determine the underlying mechanism for the former in the case of Respiratory syncytial virus (RSV) vaccine and have been utilized to design and screen new RSV vaccine candidates. Because some Middle East respiratory syndrome (MERS) and SARS-CoV-1 vaccines have shown evidence of disease enhancement in some animal models, this is a particular concern for SARS-CoV-2 vaccines. To address this challenge, the Coalition for Epidemic Preparedness Innovations (CEPI) and the Brighton Collaboration (BC) Safety Platform for Emergency vACcines (SPEAC) convened a scientific working meeting on March 12 and 13, 2020 of experts in the field of vaccine immunology and coronaviruses to consider what vaccine designs could reduce safety concerns and how animal models and immunological assessments in early clinical trials can help to assess the risk. This report summarizes the evidence presented and provides considerations for safety assessment of COVID-19 vaccine candidates in accelerated vaccine development. [ABSTRACT FROM AUTHOR]

Animal models of viral pathogenesis are essential tools in human disease research. Human papillomaviruses (HPVs) are a significant public health issue due to their widespread sexual transmission and oncogenic potential. Infection-based models of papillomavirus pathogenesis have been complicated by their strict species and tissue specificity. In this GEM review, we discuss the discovery of a murine papillomavirus, MmuPV1, and how its experimental use represents a major advancement in models of papillomavirus-induced pathogenesis/carcinogenesis, and their transmission. [ABSTRACT FROM AUTHOR]

Background: Cancer survival statistics are typically reported by using measures discounting the impact of other-cause mortality, such as net survival. This is a hypothetical measure and is interpreted as excluding the possibility of cancer patients dying from other causes. Crude probability of death partitions the all-cause probability of death into deaths from cancer and other causes.Methods: The National Cancer Registration and Analysis Service is the single cancer registry for England. In 2006-2015, 1,590,477 malignant tumours were diagnosed for breast, colorectal, lung, melanoma and prostate cancer in adults. We used a relative survival framework, with a period approach, providing estimates for up to 10-year survival. Mortality was partitioned into deaths due to cancer or other causes. Unconditional and conditional (on surviving 1-years and 5-years) crude probability of death were estimated for the five cancers.Results: Elderly patients who survived for a longer period before dying were more likely to die from other causes of death (except for lung cancer). For younger patients, deaths were almost entirely due to the cancer.Conclusion: There are different measures of survival, each with their own strengths and limitations. Careful choices of survival measures are needed for specific scenarios to maximise the understanding of the data. [ABSTRACT FROM AUTHOR]

The article discusses the omnivores, distinction, and the measurement of social position in context with their Class, status and lifestyle. Topics include evaluation of empirical premises about lifestyles and the framework of multivariate statistical models; lack of connection between omnivorousness and status consciousness is compelling evidence against a Bourdieusian homology, and development individuals lifestyle orientations, and socio-economic wealth with resources at young age.

The 'microclass' approach advocated by Grusky, Weeden and colleagues emphasises fine-grained occupational differences and their relevance to social reproduction and social mobility. Using recent developments in historical occupational classifications, we adopted a microclass approach to the analysis of intergenerational social mobility using linked census data for Norway and the USA in the late 19th and early 20th century (1850–1910). We describe a procedure that offers an operationalisation of microclass units for these datasets, and show how its application enables us to disentangle different forms of immobility which would not be distinguished in other approaches. Results suggest that microclass immobility is an important part of social reproduction in both Norway and the United States during the era of industrialisation. Both countries reveal a similar balance between 'big class' and 'microclass' immobility patterns. In Norway, the relative importance of microclasses in social reproduction regimes, when compared to the role of 'big class' structures, seems to decline very slightly over the course of industrialisation; but in the USA the relative importance of microclasses seems if anything to increase over the period. [ABSTRACT FROM AUTHOR]

In this work, we investigate the effects of substituting Ni/Al for Cr on the thermomagnetic and magnetocaloric properties of FeCoNiCrAl-type high entropy alloys (HEAs). Ni and Al appear to prefer the BCC phase, and increases in the Al composition appear to stabilize the BCC phase. In contrast to Al, Ni content yields an increase in the FCC phase fraction, resulting in a drop off in magnetization. The phase transformation from BCC to FCC was intensified at annealing temperatures of 800 °C and higher due to increased diffusion rates and the resulting spinodal decomposition. A magnetic phase transition around 150 K was found in the FeCoNi1.5Cr0.5Al annealed alloy potentially corresponding to the FCC phase, and a very broad magnetic phase transition was observed in the annealed FeCoNiCrAl alloy, resulting in a high refrigerant capacity of RCFWHM = 242.6 J⋅kg-1 near room temperature. A peak magnetic entropy change of −ΔSM = 0.674 J⋅kg-1⋅K-1 was also obtained at applied fields of ∼70 kOe at 290 K in the FeCoNiCrAl HEA. These magnetocaloric values are comparable to Fe-based metallic glasses such as Fe-Tm-B-Nb and Fe-Zr-B-Co alloys, with a similar transition near room temperature. [ABSTRACT FROM AUTHOR]

Impressed anodic current techniques are used widely to accelerate corrosion of steel embedded in concrete in the laboratory. This paper investigates the relationship between applied current density, degree of corrosion, type of corrosion and ultimate strength of corroded beams. Twenty beams were divided into four groups with a 0.5, 1, 2.5 or 5% degree of corrosion. Current densities of 0.25, 0.5, 1.0 or 2.5 mA/cm2 were applied to each group, respectively. The beams were flexurally tested and the reinforcing steel bars were retrieved, cleaned, weighed and the surface examined. The results demonstrated that higher current densities and degree of corrosion caused more localised corrosion. The applied current density has little effect on the degree of corrosion and on ultimate strength of the same degree of corroded beam. Based on this work, a current density of 1 mA/cm2 is recommended for the artificial acceleration of corrosion for steel embedded in concrete. [ABSTRACT FROM AUTHOR]

Multistate models are increasingly being used to model complex disease profiles. By modelling transitions between disease states, accounting for competing events at each transition, we can gain a much richer understanding of patient trajectories and how risk factors impact over the entire disease pathway. In this article, we concentrate on parametric multistate models, both Markov and semi-Markov, and develop a flexible framework where each transition can be specified by a variety of parametric models including exponential, Weibull, Gompertz, Royston-Parmar proportional hazards models or log-logistic, log-normal, generalised gamma accelerated failure time models, possibly sharing parameters across transitions. We also extend the framework to allow time-dependent effects. We then use an efficient and generalisable simulation method to calculate transition probabilities from any fitted multistate model, and show how it facilitates the simple calculation of clinically useful measures, such as expected length of stay in each state, and differences and ratios of proportion within each state as a function of time, for specific covariate patterns. We illustrate our methods using a dataset of patients with primary breast cancer. User-friendly Stata software is provided. [ABSTRACT FROM AUTHOR]

Preclinical infection model systems are extremely valuable tools to aid in our understanding of Human Papillomavirus (HPV) biology, disease progression, prevention, and treatments. In this context, rodent papillomaviruses and their respective infection models are useful tools but remain underutilized resources in the field of papillomavirus biology. Two rodent papillomaviruses, MnPV1, which infects the Mastomys species of multimammate rats, and MmuPV1, which infects laboratory mice, are currently the most studied rodent PVs. Both of these viruses cause malignancy in the skin and can provide attractive infection models to study the lesser understood cutaneous papillomaviruses that have been frequently associated with HPV-related skin cancers. Of these, MmuPV1 is the first reported rodent papillomavirus that can naturally infect the laboratory strain of mice. MmuPV1 is an attractive model virus to study papillomavirus pathogenesis because of the ubiquitous availability of lab mice and the fact that this mouse species is genetically modifiable. In this review, we have summarized the knowledge we have gained about PV biology from the study of rodent papillomaviruses and point out the remaining gaps that can provide new research opportunities. [ABSTRACT FROM AUTHOR]

Human papillomaviruses (HPVs) are double-stranded DNA (dsDNA) tumor viruses that are causally associated with human cancers of the anogenital tract, skin, and oral cavity. Despite the availability of prophylactic vaccines, HPVs remain a major global health issue due to inadequate vaccine availability and vaccination coverage. The HPV life cycle is established and completed in the terminally differentiating stratified epithelia, and decades of research using in vitro organotypic raft cultures and in vivo genetically engineered mouse models have contributed to our understanding of the interactions between HPVs and the epithelium. More recently, important and emerging roles for the underlying stroma, or microenvironment, during the HPV life cycle and HPV-induced disease have become clear. This review discusses the current understanding of the bidirectional communication and relationship between HPV-infected epithelia and the surrounding microenvironment. As is the case with other human cancers, evidence suggests that the stroma functions as a significant partner in tumorigenesis and helps facilitate the oncogenic potential of HPVs in the stratified epithelium. [ABSTRACT FROM AUTHOR]

Background: Life expectancy is a simple measure of assessing health differences between two or more populations but current life expectancy calculations are not reliable for small populations. A potential solution to this is to borrow strength from larger populations from the same source, but this has not formally been investigated. Methods: Using data on 451,222 individuals from the Clinical Practice Research Datalink on the presence/absence of intellectual disability and type 2 diabetes mellitus, we compared stratified and combined flexible parametric models, and Chiang's methods, for calculating life expectancy. Confidence intervals were calculated using the Delta method, Chiang's adjusted life table approach and bootstrapping. Results: The flexible parametric models allowed calculation of life expectancy by exact age and beyond traditional life expectancy age thresholds. The combined model that fit age interaction effects as a spline term provided less bias and greater statistical precision for small covariate subgroups by borrowing strength from the larger subgroups. However, careful consideration of the distribution of events in the smallest group was needed. Conclusions: Life expectancy is a simple measure to compare health differences between populations. The use of combined flexible parametric methods to calculate life expectancy in small samples has shown promising results by allowing life expectancy to be modelled by exact age, greater statistical precision, less bias and prediction of different covariate patterns without stratification. We recommend further investigation of their application for both policymakers and researchers. [ABSTRACT FROM AUTHOR]

Competing risks arise with time-to-event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause-specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a function of follow-up time, accounting for the fact that some individuals may have a competing event. When modelling the cause-specific CIF, the most common model is a semi-parametric proportional subhazards model. In this paper, we propose the use of flexible parametric survival models to directly model the cause-specific CIF where the effect of follow-up time is modelled using restricted cubic splines. The models provide smooth estimates of the cause-specific CIF with the important advantage that the approach is easily extended to model time-dependent effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and introducing time-dependent weights. Various link functions are available that allow modelling on different scales and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct a simulation study to evaluate how well the spline functions approximate subhazard functions with complex shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry showing excellent agreement between parametric estimates of the cause-specific CIF and those obtained from a semi-parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link functions and/or including time-dependent effects. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Competing risks occur in survival analysis when an individual is at risk of more than one type of event and one event’s occurrence precludes another’s. The cause-specific cumulative incidence function (CIF) is a measure of interest with competing-risks data. It gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event occurs first. The user-written command stcompet calculates nonparametric estimates of the cause-specific CIF, and the official Stata command stcrreg fits the Fine and Gray (1999, Journal of the American Statistical Association 94: 496–509) model for competing-risks data. Geskus (2011, Biometrics 67: 39–49) has recently shown that standard software can estimate some of the key measures in competing risks by restructuring the data and incorporating weights. This has a number of advantages because any tools developed for standard survival analysis can then be used to analyze competing-risks data. In this article, I describe the stcrprep command, which restructures the data and calculates the appropriate weights. After one uses stcrprep, a number of standard Stata survival analysis commands can then be used to analyze competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF, and stcox will fit the Fine and Gray (1999) proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, stcrprep opens up new opportunities for competing-risk models. I illustrate this by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF. [ABSTRACT FROM AUTHOR]

Epidermal growth factor receptor (EGFR) has fundamental roles in normal physiology and cancer, making it a rational target for cancer therapy. Surprisingly, however, inhibitors that target canonical, ligand-stimulated EGFR signaling have proven to be largely ineffective in treating many EGFR-dependent cancers. Recent evidence indicates that both intrinsic and therapy-induced cellular stress triggers robust, noncanonical pathways of ligand-independent EGFR trafficking and signaling, which provides cancer cells with a survival advantage and resistance to therapeutics. Here, we review the mechanistic regulation of noncanonical EGFR trafficking and signaling, and the pathological and therapeutic stresses that activate it. We also discuss the implications of this pathway in clinical treatment of EGFR-overexpressing cancers. [ABSTRACT FROM AUTHOR]

The dual function of a carbon fibre reinforced polymer (CFRP) rod working as the near-surface mounted (NSM) strengthening and impressed current cathodic protection (ICCP) anode for corroded reinforced concrete structures has been proposed and researched. In this paper, a CFRP rod was used for both flexural strengthening of pre-corroded reinforced concrete beams and in a dual functional capacity as an ICCP anode. After a period of ICCP operation at high current density, the beams were subjected to flexural testing to determine the load–deflection relationships. The potential decays of the steel met recognised ICCP standards, and the CFRP remained effective in strengthening the corroded reinforced concrete beams. The bonding at the CFRP rod anode and concrete interface was improved by using a combination of geopolymer and epoxy resin; therefore, the ultimate strength of a dual function CFRP rod with combination of bonding medium (geopolymer and epoxy) increased significantly. [ABSTRACT FROM AUTHOR]

Background: Age-standardized net survival provides an important population-based summary of cancer survival that appropriately accounts for differences in other-cause mortality rates and standardizes the population age distribution to allow fair comparisons. Recently, there has been debate over the most appropriate method for estimating this quantity, with the traditional Ederer II approach being shown to have potential bias. Methods: We compare lifetable-based estimates (Ederer II), a new unbiased method based on inverse probability of censoring weights (Pohar Perme) and model-based estimates. We make the comparison in a simulation setting; generating scenarios where we would expect to see a large theoretical bias. Results: Our simulations demonstrate that even in relatively extreme scenarios there is negligible bias in age-standardized net survival when using the age-standardized Ederer II method, modelling with continuous age or using the Pohar Perme method. However, both the Ederer II and modelling approaches have some advantages over the Pohar Perme method in terms of greater precision, particularly for longer-term follow-up (10 and 15 years). Conclusions: Our results show that, when age-standardizing, concern over bias with the traditional methods is unfounded. We have also shown advantages in using the more traditional and modelling methods. [ABSTRACT FROM AUTHOR]

The background is given as to how statistical analysis is used by quantitative social scientists. Developing statistical analyses requires substantial effort, yet there are important limitations in current practice. This has motivated the authors to create a more systematic and effective methodology with supporting tools. The approach to modelling quantitative data analysis in the social sciences is presented. Analysis scripts are treated abstractly as mathematical functions and concretely as web services. This allows individual scripts to be combined into high-level workflows. A comprehensive set of tools allows workflows to be defined, automatically validated and verified, and automatically implemented. The workflows expose opportunities for parallel execution, can define support for proper fault handling, and can be realised by non-technical users. Services, workflows and datasets can also be readily shared. The approach is illustrated with a realistic case study that analyses occupational position in relation to health. [ABSTRACT FROM AUTHOR]

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. [ABSTRACT FROM AUTHOR]

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert ( Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for using survey data to understand the composition of elites, through analysing the pool of potential members. An occupational-based measure of ‘potential power elite’ (PPE) is created and compared with other measures of occupational advantage. It is argued that this measure can be utilised to explore if the processes causing certain social groups to be under-represented in elite positions are around selection or the population recruited from. We provide analysis of elite positions in the UK and Sweden, demonstrating differences in terms of the potential pool of elite members and the occupational histories of people of those employed in roles associated with elite recruitment. We argue that understanding the composition of the PPE provides a more nuanced analysis of the processes of meritocracy in accessing positions of power and social influence. [ABSTRACT FROM PUBLISHER]

Background: When interested in studying the effect of a treatment (or other exposure) on a time-to-event outcome, the most popular approach is to estimate survival probabilities using the Kaplan-Meier estimator. In the presence of confounding, regression models are fitted, and results are often summarised as hazard ratios. However, despite their broad use, hazard ratios are frequently misinterpreted as relative risks instead of relative rates.Methods: We discuss measures for summarising the analysis from a regression model that overcome some of the limitations associated with hazard ratios. Such measures are the standardised survival probabilities for treated and untreated: survival probabilities if everyone in the population received treatment and if everyone did not. The difference between treatment arms can be calculated to provide a measure for the treatment effect.Results: Using publicly available data on breast cancer, we demonstrated the usefulness of standardised survival probabilities for comparing the experience between treated and untreated after adjusting for confounding. We also showed that additional important research questions can be addressed by standardising among subgroups of the total population.Discussion: Standardised survival probabilities are a useful way to report the treatment effect while adjusting for confounding and have an informative interpretation in terms of risk. [ABSTRACT FROM AUTHOR]

The electrochemical noise method (ENM) has previously been employed to monitor the corrosion of steel reinforcement in concrete. The development of solid-state Ag/AgCl-based probes and dedicated monitoring technology (ProCoMeter) now offers a wider range of ENM configurations. The present study involves the laboratory investigation of three mortar samples containing steel bars and varying additions of chloride, with a view to future field application. ENM could be used to provide corrosion information on reinforcement without the need to provide direct electrical connections to the steel and without the risk or inducing or increasing corrosion. In addition to half-cell potentials, measurements were made using ENM in three different probe configurations over a total test period of 90 days. The samples were then broken open and the bars extracted and cleaned. A comparison was then made between the calculated metal thickness loss obtained from the Rn values and the actual metal thickness loss. The results showed that each configuration was able to order the results in the expected manner, with the simple single substrate (SSS) arrangement providing the best correlation with direct measurements. The study is ongoing with the intention of measurements being obtained in situ on existing reinforced concrete structures. [ABSTRACT FROM AUTHOR]

Simple Summary: IQ motif-containing GTPase-activating protein 1 (IQGAP1) is a signal scaffolding protein that regulates a range of cellular activities by facilitating signal transduction in cells. IQGAP1 is involved in many cancer-related activities, such as proliferation, apoptosis, migration, invasion and metastases. In this article, we review the different pathways regulated by IQGAP1 during cancer development, and the role of IQGAP1 in different types of cancer, including cancers of the head and neck, breast, pancreas, liver, colorectal, stomach, and ovary. We also discuss IQGAP1′s regulation of the immune system, which is of importance to cancer progression. This review highlights the significant roles of IQGAP1 in cancer and provides a rationale for pursuing IQGAP1 as a drug target for developing novel cancer therapies. Scaffolding proteins can play important roles in cell signaling transduction. IQ motif-containing GTPase-activating protein 1 (IQGAP1) influences many cellular activities by scaffolding multiple key signaling pathways, including ones involved in carcinogenesis. Two decades of studies provide evidence that IQGAP1 plays an essential role in promoting cancer development. IQGAP1 is overexpressed in many types of cancer, and its overexpression in cancer is associated with lower survival of the cancer patient. Here, we provide a comprehensive review of the literature regarding the oncogenic roles of IQGAP1. We start by describing the major cancer-related signaling pathways scaffolded by IQGAP1 and their associated cellular activities. We then describe clinical and molecular evidence for the contribution of IQGAP1 in different types of cancers. In the end, we review recent evidence implicating IQGAP1 in tumor-related immune responses. Given the critical role of IQGAP1 in carcinoma development, anti-tumor therapies targeting IQGAP1 or its associated signaling pathways could be beneficial for patients with many types of cancer. [ABSTRACT FROM AUTHOR]

• Large-grained FeGa alloys exhibited discontinuous yielding. • Despite catastrophic failure, alloy exhibited strains up to 5%. • Strain banding observed during mechanical testing using Digital Image Correlation. • Observed strain is due to twinning induced plasticity. ga1 Fe 100−x Ga x alloys require highly textured samples for optimal magnetostrictive performance, which is often accomplished through directional solidification. In this study, we measure the mechanical properties of commercial Fe 81.6 Ga 18.4 oligocrystals using sub-sized tensile specimens and digital image correlation (DIC). While many samples fractured around 360 MPa (the literature yield/tensile stress), most had discontinuous yielding as low as 186 MPa. Many of the samples also exhibited surprisingly large tensile strains, up to 5%, with substantial specimen-to-specimen variability. In all specimens, DIC showed high strain bands that develop at very defined angles, in agreement with electron back-scattered diffraction, which revealed significant deformation twinning. [ABSTRACT FROM AUTHOR]

Murine papillomavirus, MmuPV1, causes natural infections in laboratory mice that can progress to squamous cell carcinoma (SCC) making it a useful preclinical model to study the role of papillomaviruses in cancer. Papillomavirus can infect cells within hair follicles, which contain multiple epithelial progenitor cell populations, including Lgr5+ progenitors, and transgenic mice expressing human papillomavirus oncogenes develop tumors derived from Lgr5 progenitors. We therefore tested the hypothesis that Lgr5+ progenitors contribute to neoplastic lesions arising in skins infected with MmuPV1 by performing lineage tracing experiments. Ears of 6–8-week-old Lgr5-eGFP-IRES-CreERT2/Rosa26LSLtdTomato mice were treated topically with 4-OH Tamoxifen to label Lgr5+ progenitor cells and their progeny with tdTomato and, 72 h later, infected with MmuPV1. Four months post-infection, tissue at the infection site was harvested for histopathological analysis and immunofluorescence to determine the percentage of tdTomato+ cells within the epithelial lesions caused by MmuPV1. Squamous cell dysplasia showed a low percentage of tdTomato+ cells (7%), indicating that it arises primarily from non-Lgr5 progenitor cells. In contrast, cutaneous SCC (cSCC) was substantially more positive for tdTomato+ cells (42%), indicating that cSCCs preferentially arise from Lgr5+ progenitors. Biomarker analyses of dysplasia vs. cSCC revealed further differences consistent with cSCC arising from LGR5+ progenitor cells. [ABSTRACT FROM AUTHOR]

Background: Multi-state models are being increasingly used to capture complex disease pathways. The convenient formula of the exponential multi-state model can facilitate a quick and accessible understanding of the data. However, assuming time constant transition rates is not always plausible. On the other hand, obtaining predictions from a fitted model with time-dependent transitions can be challenging. One proposed solution is to utilise a general simulation algorithm to calculate predictions from a fitted multi-state model.Methods: Predictions obtained from an exponential multi-state model were compared to those obtained from two different parametric models and to non-parametric Aalen-Johansen estimates. The first comparative approach fitted a multi-state model with transition-specific distributions, chosen separately based on the Akaike Information Criterion. The second approach was a Royston-Parmar multi-state model with 4 degrees of freedom, which was chosen as a reference model flexible enough to capture complex hazard shapes. All quantities were obtained analytically for the exponential and Aalen-Johansen approaches. The transition rates for the two comparative approaches were also obtained analytically, while all other quantities were obtained from the fitted models via a general simulation algorithm. Metrics investigated were: transition probabilities, attributable mortality (AM), population attributable fraction (PAF) and expected length of stay. This work was performed on previously analysed hospital acquired infection (HAI) data. By definition, a HAI takes three days to develop and therefore selected metrics were also predicted from time 3 (delayed entry).Results: Despite clear deviations from the constant transition rates assumption, the empirical estimates of the transition probabilities were approximated reasonably well by the exponential model. However, functions of the transition probabilities, e.g. AM and PAF, were not well approximated and the comparative models offered considerable improvements for these metrics. They also provided consistent predictions with the empirical estimates in the case of delayed entry time, unlike the exponential model.Conclusion: We conclude that methods and software are readily available for obtaining predictions from multi-state models that do not assume constant transition rates. The multistate package in Stata facilitates a range of predictions with confidence intervals, which can provide a more comprehensive understanding of the data. User-friendly code is provided. [ABSTRACT FROM AUTHOR]

A commonly reported measure when interested in the survival of cancer patients is relative survival. Relative survival circumvents issues with inaccurate cause of death information by incorporating the expected mortality rates of cancer individuals from population lifetables of the general population. A summary of the cancer population prognosis can be obtained using the marginal relative survival. To explore differences between exposure groups, such as socioeconomic groups, the difference in marginal relative survival between exposed and unexposed can be obtained and under assumptions is interpreted as the average causal effect of exposure to survival. In a modeling context, this is usually estimated by applying regression standardization as the average of the individual‐specific estimates after fitting a relative survival model. Regression standardization yields an estimator that consistently estimates the causal effect under standard causal inference assumptions and if the relative survival model is correctly specified. We extend inverse probability weighting (IPW) and doubly robust standardization methods in the relative survival framework as additional valuable tools for obtaining average causal effects when correct model specification might not hold for the relative survival model. IPW yields an unbiased estimate of the average causal effect if a correctly specified model has been fitted for the exposure (propensity score) whereas doubly robust standardization requires that at least one of the propensity score model or the relative survival model is correctly specified. An example using data on melanoma is provided and a simulation study is conducted to investigate how sensitive are the methods to model misspecification, including different ways for obtaining standard errors. [ABSTRACT FROM AUTHOR]

A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up; hence, sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a hazard ratio (HR) to 1 does not necessarily estimate loss of individual-level treatment effect accurately because of HR selection bias. A simulation study was designed to explore the behavior of marginal HRs under a waning conditional (individual-level) treatment effect and demonstrate bias in forcing a marginal HR to 1 when the estimand is "survival difference with individual-level waning". Data were simulated under 4 parameter combinations (varying prognostic strength of heterogeneity and treatment effect). Time-varying marginal HRs were estimated in scenarios where the true conditional HR attenuated to 1. Restricted mean survival time differences, estimated having constrained the marginal HR to 1, were compared with true values to assess bias induced by marginal constraints. Under loss of conditional treatment effect, the marginal HR took a value >1 because of covariate imbalances. Constraining this value to 1 lead to restricted mean survival time difference bias of up to 0.8 years (57% increase). Inflation of effect size estimates also increased with the magnitude of initial protective treatment effect. Important differences exist between survival extrapolations assuming marginal versus conditional treatment effect waning. When a marginal HR is constrained to 1 to assess efficacy under individual-level treatment effect waning, the survival benefits associated with the new treatment will be overestimated, and incremental cost-effectiveness ratios will be underestimated. • Noncollapsibility of odds/hazard ratios (HRs) has been widely discussed in causal inference literature. Discussion of the impact in randomized controlled trial extrapolations is scarcer. This article explores the importance of understanding the difference between marginal and conditional HRs, specifically in the context of an economic evaluation/health technology assessment using randomized controlled trial data, carrying out an extrapolation assuming treatment effect waning. • If the individual-level treatment effect truly wanes after a period of protective treatment effect, a marginal (eg, unadjusted) HR > 1 from this point is to be expected. This reflects covariate imbalances between arms induced by the treatment's effect and demonstrates the difficulty in interpreting a HR that is dependent on both participant-level hazards and trial-level covariate distributions (and how these shift over time). • Forcing a marginal HR to 1 from the end of trial follow-up is not equivalent to individual-level treatment effect loss (inducing bias in restricted mean survival time difference of up to 0.8 years, increasing with treatment strength and prognostic power of marginal covariates). To most accurately assess sensitivity to treatment effect waning, constraints on the HR should be applied to an estimate that is conditional on all feasible prognostic factors. [ABSTRACT FROM AUTHOR]

Background: The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making. Methods and findings: This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only—as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI. Conclusions: In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study. Using hospital records of 34 million adults admitted to hospitals in England, Marlous Hall and team examine the long-term risk of major health outcomes following myocardial infarction. Author summary: Why was this study done?: Myocardial infarction (MI; heart attack) can have major long-term impact on individuals and result in a wide range of further health conditions. Existing studies have focussed on determining the short-term risk of a second heart attack, stroke, or major bleeding, but research describing the long-term risk of major health outcomes for specific age, sex, and deprivation groups was lacking. Nationally representative and robust information of a wide range of long-term health outcomes following a heart attack is critical for the development of treatment recommendations, which take account of an individuals' specific risk. What did the researchers do and find?: From the population of 56 million adults in England, we analysed hospital records for 34 million adults admitted to hospital (constituting 145 million admission records) to investigate the long-term health outcomes following a heart attack compared with individuals without a heart attack. Of 433,361 individuals with a heart attack, up to a third developed heart failure or renal failure, 7% had further heart attacks, and 38% died within the 9-year study period. Heart failure, atrial fibrillation, stroke, peripheral arterial disease, severe bleeding, renal failure, diabetes, and depression occurred more frequently for people who had a heart attack compared with those who did not, but the risk of cancer was lower overall and the risk of dementia did not differ overall. What do these findings mean?: Efforts should be made to prevent or limit the development of long-term health outcomes that follow a heart attack—the likelihood of which differ depending on the age, sex, and deprivation of an individual. These findings are based on the full population of adults admitted to hospital in England, address limitations of previous studies, and can be used to inform preventative strategies tailored to specific individuals surviving a heart attack. The study was limited to hospitalisation data only—therefore, some diagnoses made outside of hospital may have been missed. [ABSTRACT FROM AUTHOR]

When analyzing cohort data, we rarely consider potential simultaneous effects of multiple time-scales on estimated survival proportions and hazard ratios. Instead, often, only one time-scale, such as time-on-study or attained age, is taken as the main time-scale and the other time-scale(s) included in the model as time-fixed covariate(s). In this simulation study, we investigate the potential bias in estimated survival proportions and hazard ratios by not modeling multiple time-scales. We simulate data with two time-scales, under various scenarios, and a binary covariate as the exposure of interest. Flexible parametric models with one time-scale were fitted to the simulated data, including the exposure variable of interest and the other time-scale as a fixed covariate. For many of our simulated scenarios, the models with one time-scale showed small bias for the survival proportions. Bias in the log hazard ratios (for the covariate of interest) was observed when there were non-proportional hazards on the second time-scale. If one is modeling only one time-scale it is advisable to include non-proportional hazards and interactions with the time-scale. Exploration of approaches to modeling multiple time-scales is outside the scope of this study. [ABSTRACT FROM AUTHOR]