Littérature scientifique sur le sujet « Cause-Of-Death modelling »

AbstractThe analysis of causal mortality provides rich insight into changes in mortality trends that are hidden in population-level data. Therefore, we develop and apply a multinomial logistic framework to model causal mortality. We use internationally classified cause-of-death categories and data obtained from the World Health Organization. Inherent dependence amongst the competing causes is accounted for in the framework, which also allows us to investigate the effects of improvements in, or the elimination of, cause-specific mortality. This has applications to scenario-based forecasting often used to assess the impact of changes in mortality. The multinomial model is shown to be more conservative than commonly used approaches based on the force of mortality. We use the model to demonstrate the impact of cause-elimination on aggregate mortality using residual life expectancy and apply the model to a French case study.

Hepatitis B carriers who acquired the infection perinatally die from hepatocellular carcinoma (HCC) and cirrhosis at high rates. Published cohort studies are largely limited to males and are too small to estimate the age-specific risk of death. We therefore used routinely collected Hong Kong data to estimate the risks. Deaths were partitioned between carriers and non-carriers, then current life table calculations determined life expectancy and probability of dying from HCC or cirrhosis. HCC is the dominant cause of death for male carriers in middle adulthood with a lifetime risk of 27% for HCC compared to 4% for females. Predicted life expectancy is 72 years for male carriers, compared to 79 years for non-carriers. Female carriers have a life expectancy of 81 years and non-carriers 83 years. This model probably applies to all southern Chinese populations and emigrants with similar life history, and other populations that acquired infection early in life.

TB is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent. Decreasing the length of time for TB treatment is an important step towards the goal of reducing mortality. Mechanistic in silico modelling can provide us with the tools to explore gaps in our knowledge, with the opportunity to model the complicated within-host dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases – much can be learned in infection research from these advances.

Objective To investigate the association between estimated GFR (eGFR) and all-cause mortality, including the contribution of temporal eGFR changes, in well-characterised community-based patients with type 2 diabetes. Design Longitudinal observational study. Methods Participants from the Fremantle Diabetes Study Phase 1 were assessed between 1993 and 1996 and followed until end-December 2012. Cox proportional hazards modelling was used to assess the relationship between baseline eGFR category (Stage 1–5) and all-cause death, and between eGFR trajectories assigned by semiparametric group-based modelling (GBM) and all-cause death in patients with five post-baseline annual eGFR measurements. Results In the full cohort (1296 patients; mean±s.d. age 64.1±11.3years, 48.6% males), 738 (56.9%) died during 12.9±6.1years of follow-up. There was a U-shaped relationship between all-cause death and eGFR category. With Stage 3 (45–59mL/min/1.73m2) as reference, the strongest association was for eGFR ≥90mL/min/1.73m2 (hazard ratio (95% CI) 2.01 (1.52–2.66); P<0.001). GBM identified four linear trajectories (‘low’, ‘medium’, ‘high’, ‘high/declining’) in 532 patients with serial eGFR measurements. With medium trajectory as reference, eGFR trajectory displaced baseline eGFR category as an independent predictor of death, with low and high/declining trajectories associated with more than double the risk (2.03 (1.30–3.18) and 2.24 (1.31–3.83) respectively, P≤0.003) and associated median reductions in survival of 6.5 and 8.7years respectively. Conclusion There is a nonlinear relationship between eGFR and death in type 2 diabetes, which is at least partially explained by a sub-group of patients with an initially high but then rapidly declining eGFR.

In this retrospective study of a randomised trial of simtuzumab in idiopathic pulmonary fibrosis (IPF), prodromal decline in forced vital capacity (FVC) was significantly associated with increased risk of mortality, respiratory and all-cause hospitalisations, and categorical disease progression. Predictive modelling of progression-free survival event risk was used to assess the effect of population enrichment for patients at risk of rapid progression of IPF; C-index values were 0.64 (death), 0.69 (disease progression), and 0.72 (adjudicated respiratory hospitalisation) and 0.76 (all-cause hospitalisation). Predictive modelling may be a useful tool for improving efficiency of clinical trials with categorical end points.

ABSTRACTActuarial practice as regards mortality analysis and projection is changing rapidly. This paper provides a short introduction to some of the limitations and risks in using trends in cause of death as a means for projecting future mortality rates. It also covers recent developments in analysing the mortality of smaller populations, including survival models and “piggyback” models.

A traffic accident, a traffic collision or crash occurs when a vehicle collides with another vehicle, pedestrian, animal, road barriers, or any stationary obstruction such as a tree or a utility pole. Traffic collisions may result in injury, death, vehicle damage and possession damage. Motor vehicle collisions cause death and disability as well as a financial burden. Traffic accidents cause many losses especially of human life, property damages, and loss of resources. Indeed, even in strife influenced countries such as Afghanistan, Libya, Pakistan, and Yemen, road traffic remains the most common cause of fatal injuries, causing between two and eight times more fatalities than war and lawful mediation. The World Health Organization (WHO) 2013 assessed the traffic casualty rate in the Eastern Mediterranean Region (EMR) to be the second most elevated rate universally after the African Region and extending a few other countries in the region. The aim of this paper was to enrich the global highway safety knowledge by revealing the catastrophic impact of traffic accidents on the economy of the societies and the safety of the common worldwide.

Au cours des deux derniers siècles, l’espérance de vie tout autour du globe a connu un accroissement considérable. Si la tendance sur le long terme est plutôt régulière, l’amélioration de la longévité peut être décomposée sur le court-terme en plusieurs phases, que l’on peut relier le plus souvent aux progrès médicaux et à la diminution de causes de mortalité particulières. L’année 2020 marque un tournant du fait de l’ampleur de la pandémie Covid-19 et de ses conséquences. Ses effets directs et indirects sur l’économie et les systèmes de santé se manifestent également au travers des autres causes majeures de décès. Pour comprendre et anticiper les risques liés à la mortalité, il devient de plus en plus nécessaire pour les acteurs de la réassurance de raisonner et modéliser en termes de causes de décès. Ce type de modélisation pose néanmoins des défis spécifiques, issues de la nature multivariée des modèles, dont la complexité dépasse celle des outils classiques de l’actuaire. Nous proposons dans cette thèse plusieurs axes pour étendre la modélisation de la mortalité à un cadre multivarié. Ces axes sont abordés sous forme d’articles de recherche. La première étude porte sur des aspects techniques des distributions multivariées au sein de modèles linéaires généralisés. Lorsque les variables explicatives sont catégorielles, nous proposons de nouveaux estimateurs pour les distributions multinomiale, multinomiale négative et de Dirichlet sous forme de formules fermées, qui permettent notamment un gain considérable en temps de calcul. Ces estimateurs sont utilisés dans la seconde étude pour proposer une nouvelle méthode d’estimation des paramètres de modèles de mortalité. Cette méthode prolonge le cadre existant pour la mortalité toute cause, et permet de traiter toutes les problématiques de modélisation de mortalité en une seule étape, en particulier par cause de décès. Le troisième axe porte sur les projections de mortalité. Nous étudions des réseaux de neurones spécifiquement adaptés aux séries temporelles. Nous montrons par des exemples concrets auxquels peut faire face l’actuaire que ces modèles sont suffisamment flexibles et robustes, offrant une alternative crédible aux modèles classiques
Over the past two centuries, life expectancy around the globe has increased considerably. While the long-term trend is fairly regular, the improvement in longevity can be broken down into several phases in the short term, which can most often be linked to medical progress and the reduction in specific causes of mortality. The year 2020 marks a turning point due to the scale of the Covid-19 pandemic and its consequences. Its direct and indirect effects on the economy and healthcare systems will also be felt through other major causes of death. To understand and anticipate mortality-related risks, it is becoming increasingly necessary for reinsurance players to reason and model in terms of causes of death. However, this type of modeling poses specific challenges. By its very nature, it involves multivariate models, whose complexity exceeds that of conventional actuary tools. In this thesis, we propose several avenues for extending mortality modeling to a multivariate framework. These are presented in the form of research articles. The first study deals with technical aspects of multivariate distributions within generalized linear models. When the explanatory variables are categorical, we propose new estimators for the multinomial, negative multinomial and Dirichlet distributions in the form of closed formulas, which notably enable considerable savings in computation time. These estimators are used in the second study to propose a new method for estimating the parameters of mortality models. This method extends the existing framework for all-cause mortality, and enables all mortality modeling issues to be addressed in a single step, particularly by cause-of-death. The third axis concerns mortality forecasts. We study neural networks specifically adapted to time series. Based on concrete use cases, we show that these models are sufficiently flexible and robust to offer a credible alternative to conventional models

Malaria is the leading cause of death in many countries. Numerous studies have been carried out to introduce prevention mechanisms; but most methods employed are limited to mathematical modelling and analysis. Predicting the occurrence of malaria incidence and understanding the dynamics of transmission still remain two key challenges. In this paper, we have utilised two different computational techniques to address these issues. We have used machine learning methods and developed models for predicting the likelihood of malaria outbreak using the incidence data against climatic factors. The success of machine learning depends on the availability of reliable large data sets; but in most cases it is not possible to have reliable and complete data. Also, machine learning does not provide a good understanding of the transmission mechanism. Hence, we have used agent-based modelling approach to simulate the malaria dynamics using some parameters. The model developed has potential to emulate real scenarios for showing impact of the incubation period on malaria dynamics. Moreover, the model can also assist hospitals, public health officials and policy makers with near-real evidence on how malaria infection invading population so as to strategies for feasible intervention.

The World Health Organization has estimated that chronic obstructive pulmonary disease (COPD) is the fourth most common cause of death worldwide. Due to the economic and social extent of the problem, patient engagement must be comprised as a new resource for the achievement of higher health outcomes and lower costs. As many key processes involved in the COPD patient engagement consist of communication and education processes, modelling those processes in a whole framework, including actors and content needs, is a requirement. The main scope of this chapter is contributing to design such a framework within the Italian Health System. Final recommendations suggest to create a synergy among patient engagement and a set of legal tools, namely informed consent, integrated care and advance care planning. The synergy is based on the fact that both patient engagement and those legal tools have a common deep root in the universal principle of patient autonomy.

Abstract A biological assay, or bioassay, is an experiment to study the reaction of a living organism to some material, usually a pharmaceutical preparation. When the only response recorded is presence or absence of the reaction, it is called a quantal response. Usually, the material to be studied is applied at different dose levels in an inert diluent and the response observed. Comparison may be made to a negative control, i.e. inert material, or to a positive control, usually a competing pharmaceutical preparation. In many such assays, the material may cause irreversible change, such as death, so that the subjects can only be used once. One of the very early quantal response assays, published in 1926, concerned a study of the effect of insulin on mice, as measured by whether or not they had convulsions. The data are given in Table 10.1. We are interested in modelling how the proportion of mice with convulsions varies with the dose of insulin and with the type of preparation.

Injuries and degenerative disease of the skeletal and articular systems are the most common reasons for undergoing orthopaedic surgery. Those diseases are often associated with pain, which is experienced by the patient long before the surgical procedure. Strong stress reaction is the main source of adjustment disorders of patients undergoing surgical treatment. Orthopaedic surgery, like any other surgery, upsets body's homeostasis. The results of the surgery are not completely predictable, but are always closely related to life and health. Patients' strong stress reaction is also connected with anaesthesia (emergence from anaesthesia), being worried of complications caused by central neuraxial anaesthesia – the fear of paresis or death. The factor which influences worse adaptation process is the patients' post-surgery mood. Right after the surgery, patients feel worse than before it, they are weak, move less freely, they are anxious about their consciousness being dimmed due to medicine intake and pain. The expectations concerning the ways of controlling the dynamics of the pain one experiences are crucial. According to the researchers, in the central nervous system there exist neural circuits that may cause physiological reactions according to one's expectations, and due to this fact the pain one experiences may become stronger or alleviated depending on one's expectations. The lack of positive pain-reducing experience may lead to the learned helplessness or no sense of one's control over pain, both of which make the pain stronger. The pain-influencing factors include cognitive processes and emotions. The role of attention processes, one's cognitive appraisal and one's attitude towards pain has been emphasised, as well as the pain-modelling influence of emotions, all of which emphasise the complexity of one's pain experience. Patients, when asked to point out the factors that hinder effective pain therapy, indicate frustration caused by the lack of information, numerous worries concerning the treatment and the stereotypical image of pain. Relieving tension influences the patient's mood positively, whilst stress influences it in a negative way. The stress one experiences and one's emotions lower one's pain threshold, which leads to greater pain experience and thus makes the healing process last longer. The quality of pre- and post-operational care is thus crucial, as it influences the level of the experienced stress. The pain components influence one another, there occur interactions of biological, psychological and situational factors, which makes it advisable to personalise one's pain treatment. The need of an interdisciplinary approach towards a person, especially to their health, has been recently emphasised. Pain is a biopsychosocial occurrence, which makes pain therapy an interdisciplinary problem. This chapter discusses the following issues: 1) The characteristics of pain in conditions that require surgical treatment,2) Surgery-related stress reaction, 3) Psychological factors which influence how one feels pain, 4) The consequences of pain in people's functioning, and 5) Postoperative pain, the assessment of pain level and its relieving.

Injuries and degenerative disease of the skeletal and articular systems are the most common reasons for undergoing orthopaedic surgery. Those diseases are often associated with pain, which is experienced by the patient long before the surgical procedure. Strong stress reaction is the main source of adjustment disorders of patients undergoing surgical treatment. Orthopaedic surgery, like any other surgery, upsets body's homeostasis. The results of the surgery are not completely predictable, but are always closely related to life and health. Patients' strong stress reaction is also connected with anaesthesia (emergence from anaesthesia), being worried of complications caused by central neuraxial anaesthesia – the fear of paresis or death. The factor which influences worse adaptation process is the patients' post-surgery mood. Right after the surgery, patients feel worse than before it, they are weak, move less freely, they are anxious about their consciousness being dimmed due to medicine intake and pain. The expectations concerning the ways of controlling the dynamics of the pain one experiences are crucial. According to the researchers, in the central nervous system there exist neural circuits that may cause physiological reactions according to one's expectations, and due to this fact the pain one experiences may become stronger or alleviated depending on one's expectations. The lack of positive pain-reducing experience may lead to the learned helplessness or no sense of one's control over pain, both of which make the pain stronger. The pain-influencing factors include cognitive processes and emotions. The role of attention processes, one's cognitive appraisal and one's attitude towards pain has been emphasised, as well as the pain-modelling influence of emotions, all of which emphasise the complexity of one's pain experience. Patients, when asked to point out the factors that hinder effective pain therapy, indicate frustration caused by the lack of information, numerous worries concerning the treatment and the stereotypical image of pain. Relieving tension influences the patient's mood positively, whilst stress influences it in a negative way. The stress one experiences and one's emotions lower one's pain threshold, which leads to greater pain experience and thus makes the healing process last longer. The quality of pre- and post-operational care is thus crucial, as it influences the level of the experienced stress. The pain components influence one another, there occur interactions of biological, psychological and situational factors, which makes it advisable to personalise one's pain treatment. The need of an interdisciplinary approach towards a person, especially to their health, has been recently emphasised. Pain is a biopsychosocial occurrence, which makes pain therapy an interdisciplinary problem. This chapter discusses the following issues: 1) The characteristics of pain in conditions that require surgical treatment,2) Surgery-related stress reaction, 3) Psychological factors which influence how one feels pain, 4) The consequences of pain in people's functioning, and 5) Postoperative pain, the assessment of pain level and its relieving.

Abstract Cardiovascular disease is a leading cause of death in economically developed countries, despite significant improvements to monitoring and treatment technologies. Early diagnosis for the purposes of targeting interventions is critical, as advanced stages of the disease are frequently fatal. One complication of advanced cardiovascular disease is aortic dissection, which is a catastrophic failure of the endothelial wall. Computational modelling can be used to create 3D models with clinical data, and the clinical blood and pressure waveforms’ model can be used to form boundary conditions for assessing wall shear stress (WSS) along the aorta to predict the development of cardiovascular disease. In this study, we present patient-specific data for a rare case of severe Type A aortic dissection, which was fatally simulated using the k-omega with shear stress transport (SST), turbulent and non-Newtonian assumption. The investigated data for the patient was based on CT scan images taken nine months apart. Both cases were compared regarding pressure waveforms at the ascending and descending aorta and the aortic arch. The results for the before-dissection situation show that the pressure waveform at the ascending aorta is higher, and the systolic pressure is lagging at the descending aorta. For the after-dissection situation, we observe the same outcome; however, the amplitude for the waveform (systolic pressure) at the ascending aorta increased for the false lumen by 25% compared to the true lumen by 3%; also, the waveform peak (systolic) is leading by 0.01s.

Objective: This review aims to identify and categorise demographic methods used in modelling multiple causes of death. The assumption that each death is caused by exactly one disease is debatable, as other possible diseases or causes may be associated with the main cause. Hence, the multiple causes of death approach is essential for understanding mortality. Therefore, through this study, we will carry out a Scoping Review of the existing literature on the topic of MCOD. Inclusion criteria: This review considers literature pertaining to methods for the analysis and utilization of multiple cause of death data. Papers that discuss the methods used as well as the strengths and limitations of multiple cause of death approach will be considered for this study. Methods: Preliminary searches were conducted in July 2022 and focussed on concepts of multiple cause of death mortality and multiple causes of death. Searches were conducted in PubMed, Web of Science, and Scopus and was conducted in English, French, Spanish and Portuguese. There were no time constraints on the studies to be included in this review. Articles were initially screened by title and abstract and then reviewed by full text by three independent reviewers. Two reviewers extracted the data from the eligible articles. Results: A total of 769 papers were reviewed at the abstract and title level. Of these, 124 were screened for full-text eligibility. A total of 53 articles were included in the final analysis. Among the articles included, 31 were articles from the United States, 14 were from Europe and 8 were from other countries. The papers were categorized as methodological (33) papers, data assessment papers (19), papers discussing socioeconomic differences in mortality (13) and mixed method papers (11). Conclusions: There are many different types of methodologies and procedures used to analyse multiple cause of death statistics. All papers included in this study used descriptive methods (mostly frequency tables and cross-tabulations) to analyze multiple cause of death data, and almost half of them use visualizations to model the results. One of the most common limitations cited among the articles is the comparability of the statistics. Accurate data and analysis of vital statistics require resources, and many countries do not have the to report high-quality statistics. This could explain why most of the papers selected for this study focused on data from developed countries.

Background Lung cancer is the number one cause of cancer death worldwide.(1) It is the fifth most commonly diagnosed cancer in Australia (12,741 cases diagnosed in 2018) and the leading cause of cancer death.(2) The number of years of potential life lost to lung cancer in Australia is estimated to be 58,450, similar to that of colorectal and breast cancer combined.(3) While tobacco control strategies are most effective for disease prevention in the general population, early detection via low dose computed tomography (LDCT) screening in high-risk populations is a viable option for detecting asymptomatic disease in current (13%) and former (24%) Australian smokers.(4) The purpose of this Evidence Check review is to identify and analyse existing and emerging evidence for LDCT lung cancer screening in high-risk individuals to guide future program and policy planning. Evidence Check questions This review aimed to address the following questions: 1. What is the evidence for the effectiveness of lung cancer screening for higher-risk individuals? 2. What is the evidence of potential harms from lung cancer screening for higher-risk individuals? 3. What are the main components of recent major lung cancer screening programs or trials? 4. What is the cost-effectiveness of lung cancer screening programs (include studies of cost–utility)? Summary of methods The authors searched the peer-reviewed literature across three databases (MEDLINE, PsycINFO and Embase) for existing systematic reviews and original studies published between 1 January 2009 and 8 August 2019. Fifteen systematic reviews (of which 8 were contemporary) and 64 original publications met the inclusion criteria set across the four questions. Key findings Question 1: What is the evidence for the effectiveness of lung cancer screening for higher-risk individuals? There is sufficient evidence from systematic reviews and meta-analyses of combined (pooled) data from screening trials (of high-risk individuals) to indicate that LDCT examination is clinically effective in reducing lung cancer mortality. In 2011, the landmark National Lung Cancer Screening Trial (NLST, a large-scale randomised controlled trial [RCT] conducted in the US) reported a 20% (95% CI 6.8% – 26.7%; P=0.004) relative reduction in mortality among long-term heavy smokers over three rounds of annual screening. High-risk eligibility criteria was defined as people aged 55–74 years with a smoking history of ≥30 pack-years (years in which a smoker has consumed 20-plus cigarettes each day) and, for former smokers, ≥30 pack-years and have quit within the past 15 years.(5) All-cause mortality was reduced by 6.7% (95% CI, 1.2% – 13.6%; P=0.02). Initial data from the second landmark RCT, the NEderlands-Leuvens Longkanker Screenings ONderzoek (known as the NELSON trial), have found an even greater reduction of 26% (95% CI, 9% – 41%) in lung cancer mortality, with full trial results yet to be published.(6, 7) Pooled analyses, including several smaller-scale European LDCT screening trials insufficiently powered in their own right, collectively demonstrate a statistically significant reduction in lung cancer mortality (RR 0.82, 95% CI 0.73–0.91).(8) Despite the reduction in all-cause mortality found in the NLST, pooled analyses of seven trials found no statistically significant difference in all-cause mortality (RR 0.95, 95% CI 0.90–1.00).(8) However, cancer-specific mortality is currently the most relevant outcome in cancer screening trials. These seven trials demonstrated a significantly greater proportion of early stage cancers in LDCT groups compared with controls (RR 2.08, 95% CI 1.43–3.03). Thus, when considering results across mortality outcomes and early stage cancers diagnosed, LDCT screening is considered to be clinically effective. Question 2: What is the evidence of potential harms from lung cancer screening for higher-risk individuals? The harms of LDCT lung cancer screening include false positive tests and the consequences of unnecessary invasive follow-up procedures for conditions that are eventually diagnosed as benign. While LDCT screening leads to an increased frequency of invasive procedures, it does not result in greater mortality soon after an invasive procedure (in trial settings when compared with the control arm).(8) Overdiagnosis, exposure to radiation, psychological distress and an impact on quality of life are other known harms. Systematic review evidence indicates the benefits of LDCT screening are likely to outweigh the harms. The potential harms are likely to be reduced as refinements are made to LDCT screening protocols through: i) the application of risk predication models (e.g. the PLCOm2012), which enable a more accurate selection of the high-risk population through the use of specific criteria (beyond age and smoking history); ii) the use of nodule management algorithms (e.g. Lung-RADS, PanCan), which assist in the diagnostic evaluation of screen-detected nodules and cancers (e.g. more precise volumetric assessment of nodules); and, iii) more judicious selection of patients for invasive procedures. Recent evidence suggests a positive LDCT result may transiently increase psychological distress but does not have long-term adverse effects on psychological distress or health-related quality of life (HRQoL). With regards to smoking cessation, there is no evidence to suggest screening participation invokes a false sense of assurance in smokers, nor a reduction in motivation to quit. The NELSON and Danish trials found no difference in smoking cessation rates between LDCT screening and control groups. Higher net cessation rates, compared with general population, suggest those who participate in screening trials may already be motivated to quit. Question 3: What are the main components of recent major lung cancer screening programs or trials? There are no systematic reviews that capture the main components of recent major lung cancer screening trials and programs. We extracted evidence from original studies and clinical guidance documents and organised this into key groups to form a concise set of components for potential implementation of a national lung cancer screening program in Australia: 1. Identifying the high-risk population: recruitment, eligibility, selection and referral 2. Educating the public, people at high risk and healthcare providers; this includes creating awareness of lung cancer, the benefits and harms of LDCT screening, and shared decision-making 3. Components necessary for health services to deliver a screening program: a. Planning phase: e.g. human resources to coordinate the program, electronic data systems that integrate medical records information and link to an established national registry b. Implementation phase: e.g. human and technological resources required to conduct LDCT examinations, interpretation of reports and communication of results to participants c. Monitoring and evaluation phase: e.g. monitoring outcomes across patients, radiological reporting, compliance with established standards and a quality assurance program 4. Data reporting and research, e.g. audit and feedback to multidisciplinary teams, reporting outcomes to enhance international research into LDCT screening 5. Incorporation of smoking cessation interventions, e.g. specific programs designed for LDCT screening or referral to existing community or hospital-based services that deliver cessation interventions. Most original studies are single-institution evaluations that contain descriptive data about the processes required to establish and implement a high-risk population-based screening program. Across all studies there is a consistent message as to the challenges and complexities of establishing LDCT screening programs to attract people at high risk who will receive the greatest benefits from participation. With regards to smoking cessation, evidence from one systematic review indicates the optimal strategy for incorporating smoking cessation interventions into a LDCT screening program is unclear. There is widespread agreement that LDCT screening attendance presents a ‘teachable moment’ for cessation advice, especially among those people who receive a positive scan result. Smoking cessation is an area of significant research investment; for instance, eight US-based clinical trials are now underway that aim to address how best to design and deliver cessation programs within large-scale LDCT screening programs.(9) Question 4: What is the cost-effectiveness of lung cancer screening programs (include studies of cost–utility)? Assessing the value or cost-effectiveness of LDCT screening involves a complex interplay of factors including data on effectiveness and costs, and institutional context. A key input is data about the effectiveness of potential and current screening programs with respect to case detection, and the likely outcomes of treating those cases sooner (in the presence of LDCT screening) as opposed to later (in the absence of LDCT screening). Evidence about the cost-effectiveness of LDCT screening programs has been summarised in two systematic reviews. We identified a further 13 studies—five modelling studies, one discrete choice experiment and seven articles—that used a variety of methods to assess cost-effectiveness. Three modelling studies indicated LDCT screening was cost-effective in the settings of the US and Europe. Two studies—one from Australia and one from New Zealand—reported LDCT screening would not be cost-effective using NLST-like protocols. We anticipate that, following the full publication of the NELSON trial, cost-effectiveness studies will likely be updated with new data that reduce uncertainty about factors that influence modelling outcomes, including the findings of indeterminate nodules. Gaps in the evidence There is a large and accessible body of evidence as to the effectiveness (Q1) and harms (Q2) of LDCT screening for lung cancer. Nevertheless, there are significant gaps in the evidence about the program components that are required to implement an effective LDCT screening program (Q3). Questions about LDCT screening acceptability and feasibility were not explicitly included in the scope. However, as the evidence is based primarily on US programs and UK pilot studies, the relevance to the local setting requires careful consideration. The Queensland Lung Cancer Screening Study provides feasibility data about clinical aspects of LDCT screening but little about program design. The International Lung Screening Trial is still in the recruitment phase and findings are not yet available for inclusion in this Evidence Check. The Australian Population Based Screening Framework was developed to “inform decision-makers on the key issues to be considered when assessing potential screening programs in Australia”.(10) As the Framework is specific to population-based, rather than high-risk, screening programs, there is a lack of clarity about transferability of criteria. However, the Framework criteria do stipulate that a screening program must be acceptable to “important subgroups such as target participants who are from culturally and linguistically diverse backgrounds, Aboriginal and Torres Strait Islander people, people from disadvantaged groups and people with a disability”.(10) An extensive search of the literature highlighted that there is very little information about the acceptability of LDCT screening to these population groups in Australia. Yet they are part of the high-risk population.(10) There are also considerable gaps in the evidence about the cost-effectiveness of LDCT screening in different settings, including Australia. The evidence base in this area is rapidly evolving and is likely to include new data from the NELSON trial and incorporate data about the costs of targeted- and immuno-therapies as these treatments become more widely available in Australia.