Individualised Life Tables

A life table is a table which shows, at each age, the probability that a person in a given population will die before their next birthday. It can be used to calculate life expectancy and healthy life expectancy for people of different ages. In this work, using longitudinal datasets and panel data methods, we produce life tables for different subgroups of the population, defined according to cohabitation status, employment and other factors. As a first step, we estimate the dynamics of factors which are of particular importance in people’s lives: health, labour market participation, cohabitation and mortality. The significance of these variables is twofold: they determine the well-being of individuals, but the variables also determine the resources available to the individuals in times of ill health. Using the British Household Panel Survey, we analyse the extent to which these variables are influenced by one another, and by exogenous factors such as education and ethnicity. Estimating a system of probit models using simulation techniques, we are able to distinguish the effects of the exogenous and endogenous variables from state dependence and unobserved heterogeneity. We also correct for attrition and the initial conditions problem. We estimate time trends in mortality, health and other dependent variables to investigate whether a compression of morbidity has occurred in the recent past. Finally, the parameter estimates are used to simulate life tables for various sub-groups in the population and compare measures of life expectancy and healthy life expectancy for different groups.     

Computer-assisted predictive formulas expressing survival probability and life expectancy in US adults, men and women, 2001

The National Center for Health Statistics (NCHS) reported the United States life tables, 2001 for US total, male and female populations on the basis of 2001 mortality statistics, the 2000 decennial census and the data from the Medicare program [E. Arias, United State life tables, 2001, Natl. Vital Stat. Rep. 52 (2004) 1-40]. The life tables show life expectancy, survival and death rate at each year between birth and 100 years of age. In this study formulas expressing survival probability and life expectancy in US adults, men and women are constructed from the data of the NCHS. A model of the 'probacent'-probability equation previously published by the author is employed in the study. Analysis of the formula-predicted values and the NCHS-reported data indicates that the formulas are accurate and reliable with a close agreement. The formula representing a generalized lognormal distribution might be useful for biomedical investigation, and epidemiological and demographic studies in US adults, men and women.     

Expected Lifetime with and without Cataract among Older Adults in China

Despite its curability by a simple and low-cost surgical procedure, cataract remains the leading cause??accounting for almost one-half??of all blindness in the world. The problem is particularly severe in developing countries, and with the ageing of populations and increasing life expectancy, the burden of cataract disease is expected to increase substantially in the coming years. Using a demographic approach, this study examined the prevalence of cataract and estimated lifetime with and without cataract among older adults in China, a rapidly ageing country with the world??s largest population. Data came from the 2002 Chinese Longitudinal Healthy Longevity Survey and life tables in the same year. Sullivan??s method was used. The results indicated that overall, 5.7% percent of Chinese aged 65 and over reported having cataract that is diagnosed by an ophthalmologist. Cataract increases with age, affects women more than men, and is higher in urban than rural areas. At age 65, men and women can expect on average 0.7 years (4.7%) and 1.2 years (7.3%) of remaining life with cataract, respectively. The corresponding figures for urban and rural older adults are 1.7 (9.8%) and 0.6 (4.1%) years. The prevalence rates and life expectancy with cataract were likely underestimated given the low levels of eye examinations to detect eye diseases among the Chinese, particularly in rural areas. We discussed preventive strategies to minimize the risk and delay the onset of cataract. Policy measures pertaining to availability and accessibility of cataract surgeries are further discussed.     

How Long Do We Expect to Live? A Review of the Evidence

How long people expect to live sets an important context for longevity risk in retirement planning and may contribute to the acceptability of policies to raise pension age. However, there have been few studies representative of a national population on subjective longevity. This paper reviews the available evidence. It finds that despite some studies reporting that subjective longevity is close to average measures in population life tables the prevailing tendency is to underestimate lifespans. Men generally reflect that they have lower actual longevity than women on average, but men are more optimistic, so that women are more likely to underestimate their likely longevity. People may take account of some mortality risk factors in estimating their own longevity, but appear not to understand the true extent of risk. There is little data on how people think about longevity or why they choose a particular estimate of their own likely lifespan. Thus, international evidence suggests potential for longevity risk in individual retirement planning and raises significant questions about the policy implications which can only become more acute in ageing populations.     

An experimental approach to medical decision problems

An attempt was made to develop a model for the formulation of clinical decision-making. The model was constructed in the context of Bayesian statistical decision theory and applied experimentally to clinical problems. In the model, physicians diagnose any of four possible pancreatic diseases using four special clinical tests and select one of five available treatments. The effectiveness of each treatment was measured by the expected gain which was expressed by a linear combination of the expected gains in undesirability and utility for life expectancy. The mean expected gain for each treatment was calculated using the posterior probability distribution. The treatment with the maximum mean expected gain was selected as an optimal treatment from the set of admissible treatments. Satisfactory coincidence between the treatment recommended from the model and that employed effectively in clinical care was verified. In conclusion, utilization of personal probability and utility was found to be useful in clinical decision-making problems.     

Proteomics of hosts and pathogens in cystic fibrosis

Cystic fibrosis (CF) is a congenital disease that results in great morbidity and mortality mainly in the Caucasian population. Although CF is a monogenic disease caused by mutation in the CF conductance transmembrane regulator (CFTR) gene, most of the related mortality can be attributed to infection mediated by opportunistic bacterial and fungal pathogens. Over the past decade, advancements in the field of proteomics have helped to gain insight into the repertoire of host and pathogen proteins involved in CF pathophysiology. This review provides an overview of the contributions of proteomic studies in advancing our knowledge of the biology of CF and disease progression associated with pathogen infection and host defense responses.     

Modelling PM2.5 impact indicators in Europe: Health effects and legal compliance

Long-term exposure to fine particulate matter (PM_2.5) has been shown to have significant negative impacts on human health. It is estimated that current levels of air pollution shorten the statistical life expectancy of European citizens by several months. The GAINS integrated assessment model calculates shortening of life expectancy from population exposure to PM_2.5 using epidemiologically-derived health impact functions. In addition, GAINS estimates PM_2.5 concentrations at 1875 air quality monitoring stations located in diverse environments ranging from remote background locations to busy street canyons. In this article, different approaches to dealing with the PM_2.5 pollution problem are compared. We assess for the present and future the attainment of EU and WHO air quality standards for PM_2.5 and estimate the loss of life expectancy under different policy scenarios developed for the ongoing revision of the EU Air Quality Legislation.     

A causal probabilistic network for optimal treatment of bacterialinfections

The fatality rate associated with severe bacterial infections is about 30 percent and appropriate antibiotic treatment reduces it by half. Unfortunately, about a third of antibiotic treatments prescribed by physicians are inappropriate. We have built a causal probabilistic network (CPN) for treatment of severe bacterial infections. The net is based on modules, each module representing a site of infection. The general configuration of a module is as follows: Major distribution factors define groups of patients, each of them with a definite prevalence of infection caused by a given pathogen. Minor distribution factors multiply the likelihood of one pathogen, without changing much of the prevalence of infection. Infection caused by a pathogen causes local and generalized signs and symptoms. Antibiotic treatment is appropriate if it matches the susceptibility of the pathogens in vitro and appropriate treatment is associated with a gain in life expectancy. This is balanced against the cost of the drug, side effects, and ecological damage, to reach the most cost effective treatment. The net was constructed in such a way that the data for the conditional probability tables will be available, even if it meant sometimes giving up on fine modeling details. For data, we used large databases collected in the last 10 years (1990-2000) and data from the literature. The CPN was a convenient way to combine data from databases collected at different locations and times with published information. Although the net is based on detailed and large databases, its calibration to new sites requires data that is available in most modern hospitals     

MedDietScore: a computer program that evaluates the adherence to the Mediterranean dietary pattern and its relation to cardiovascular disease risk

Mediterranean dietary traditions have long been associated with lower mortality rates and better health status. The Mediterranean dietary pattern has become customary to be represented in the form of a pyramid, the base of which refers to foods which are suggested to be consumed most frequently and the top of the pyramid to those foods consumed rarely. Recently, an index (diet score) that estimates the adherence level to Mediterranean diet was developed and associated with cardiovascular disease risk and biomarkers. In this work we present a computer program that can easily calculate this diet score, as well as its association with cardiovascular disease risk. The application of this software to the general public might be a useful tool for the reduction of the disease burden related with nutritional habits at population level.     

Biomarkers for cardiovascular risk assessment in autoimmune diseases

Autoimmune diseases, such as antiphospholipid syndrome, systemic lupus erythematosus, and rheumatoid arthritis, are characterized by a high prevalence of cardiovascular (CV) disease (CVD), which constitutes the leading causes of morbidity and mortality among such patients. Although such effects are partly explained by a higher prevalence of traditional CV risk factors, many studies indicate that such factors do not fully explain the enhanced CV risk in these patients. In addition, risk stratification algorithms based upon traditional CV risk factors are not as predictive in autoimmune diseases as in the general population. For these reasons, the timely and accurate assessment of CV risk in these high-risk populations still remains an unmet clinical need. An enhanced contribution of different inflammatory components of the immune response, as well as autoimmune elements (e.g. autoantibodies, autoantigens, and cellular response), has been proposed to underlie the incremental CV risk observed in these populations. Recent advances in proteomic tools have contributed to the discovery of proteins involved in CVDs, including some that may be suitable to be used as biological markers. In this review we summarize the main markers in the field of CVDs associated with autoimmunity, as well as the recent advances in proteomic technology and their application for biomarker discovery in autoimmune disease.     

How did national life expectation related to school years in developing countries—An approach using panel data mining

Background

The purpose of the study was to probe into the changes in life expectancy associated with schooling years found by the Organization for Economic Co-operation and Development (OECD).

Methods

The study was based on the OECD database from the period 2000 to 2006. The data of thirty countries were constructed to allow comparisons over time and across these countries. Panel data analysis was used to estimate the relationship of national education, as defined as school years, with life expectancy. The control factors considered were numbers of practicing physicians, practicing nurses, hospital beds, and GDP.

Results

We used fixed effects of both country and time through linear regression, the coefficient of school years in relation to life expectancy was statistically significant but negative. This finding is not in accord with the hypothesis that investing in human capital through education stimulates better health outcomes.

Conclusion

Within developing countries, educational attainment is no longer keeping the same pace with life expectancy as before. Therefore, we suggest that an effective education policy should cover diverse topics, for example, balancing economic growth and mental hygiene, to improve national life expectancy.     

The Old-Age Healthy Dependency Ratio in Europe

The aim of this study is to answer the question of whether improvements in the health of the elderly in European countries could compensate for population ageing on the supply side of the labour market. We propose a state-of-health-specific (additive) decomposition of the old-age dependency ratio into an old-age healthy dependency ratio and an old-age unhealthy dependency ratio in order to participate in a discussion of the significance of changes in population health to compensate for the ageing of the labour force. Applying the proposed indicators to the Eurostat's population projection for the years 2010-2050, and assuming there will be equal improvements in life expectancy and healthy life expectancy at birth, we discuss various scenarios concerning future of the European labour force. While improvements in population health are anticipated during the years 2010-2050, the growth in the number of elderly people in Europe may be expected to lead to a rise in both healthy and unhealthy dependency ratios. The healthy dependency ratio is, however, projected to make up the greater part of the old-age dependency ratio. In the European countries in 2006, the value of the old-age dependency ratio was 25. But in the year 2050, with a positive migration balance over the years 2010-2050, there would be 18 elderly people in poor health plus 34 in good health per 100 people in the current working age range of 15-64. In the scenarios developed in this study, we demonstrate that improvements in health and progress in preventing disability will not, by themselves, compensate for the ageing of the workforce. However, coupled with a positive migration balance, at the level and with the age structure assumed in the Eurostat's population projections, these developments could ease the effect of population ageing on the supply side of the European labour market.     

SIMULATING DEMOGRAPHY AND HUMAN DEVELOPMENT DYNAMICS

A deterministic/stochastic model in which the demographic and the well-being subsystems of a country are involved and related is presented as a way to approach human development. The demographic subsystem is a side-by-side, single-gender, age-structured population dynamic model. The well-being subsystem states the dynamics of the United Nations Hybrid Human Development Index. The model has been validated in the case of Spain and Belgium. Some simulations have been performed with the model for the case of Spain in the 2009–2020 period to determine strategies and scenarios that could increase the life expectancy at birth per gender.     

Artificial intelligence models to stratify cardiovascular risk in incident hemodialysis patients

End stage renal disease condition increases the risk of cardiovascular disease. The mortality rates among hemodialysis patients are 20% higher than the general population, thus in recent years the preservation of the cardiovascular system has become a major point of focus for nephrology care in patients. Cardiovascular events jeopardize the life of a dialysis patient and must therefore be prevented. The aim of this study is to develop forecast models that can predict the cardiovascular outcome of incident hemodialysis (HD) patients. Data relating to the treatment methods and the physiological condition of patients was collected during the first 18 months of renal replacement therapy and then used to predict the insurgence of cardiovascular events within a 6-month time window. Information regarding 4246 incident hemodialysis patients was collected. A Lasso logistic regression model and a random forest model were developed and used for predictive comparison. Every forecast model was tested on 20% of the data and a 5-fold cross validation approach was used to validate the random forest model. Random forest showed higher performance with AUC of the ROC curve and sensitivity higher than 70% in both the temporal windows models, proving that random forests are able to exploit non-linear patterns retrieved in the feature space. Out of bag estimates of variable importance and regression coefficients were used to gain insight into the models implemented. We found out that malnutrition and an inflammatory condition strongly influence cardiovascular outcome in incident HD patients. Indeed the most important variables in the model were blood test variables such as the total protein content, percentage value of albumin, total protein content, creatinine and C reactive protein. Age of patients and weight loss in the first six months of renal replacement therapy were also highly involved in the prediction. A greater understanding of the mechanisms involved in the insurgence of cardiovascular events in dialysis patients can ensure physicians to intervene in the appropriate manner when a high-risk cardiovascular condition is identified.     

KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network

In recent times, Chronic Kidney Disease (CKD) has affected more than 10% of the population worldwide and millions of people die every year. So, early-stage detection of CKD could be beneficial for increasing the life expectancy of suffering patients and reducing the treatment cost. It is required to build such a multimedia driven model which can help to diagnose the disease efficiently with higher accuracy before leading to worse conditions. Various techniques related to conventional machine learning models have been used by researchers in the past time without involvement of multimodal data-driven learning. This research paper offers a novel deep learning framework for chronic kidney disease classification using stacked autoencoder model utilizing multimedia data with a softmax classifier. The stacked autoencoder helps to extract the useful features from the dataset and then a softmax classifier is used to predict the final class. It has experimented on UCI dataset which contains early stages of 400 CKD patients with 25 attributes, which is a binary classification problem. Precision, recall, specificity and F1-score were used as evaluation metrics for the assessment of the proposed network. It was observed that this multimodal model outperformed the other conventional classifiers used for chronic kidney disease with a classification accuracy of 100%.

     

A multimodal conversational coach for active ageing based on sentient computing and m-health

As life expectancy increases, it has become more necessary to find ways to support healthy ageing. A number of active ageing initiatives are being developed nowadays to foster healthy habits in the population. This paper presents our contribution to these initiatives in the form of a multimodal conversational coach that acts as a coach for physical activities. The agent can be developed as an Android app running on smartphones and coupled with cheap widely available sport sensors in order to provide meaningful coaching. It can be employed to prepare exercise sessions, provide feedback during the sessions, and discuss the results after the exercise. It incorporates an affective component that informs dynamic user models to produce adaptive interaction strategies.     

Sharing a life with Harvey: Exploring the acceptance of and relationship-building with a social robot

Social robots will become ubiquitous in our everyday environments. These robots could potentially extend life expectancy, and improve the health and quality of life of an aging population. A long-term explorative study has been conducted by installing a social robot for health promotion in elderly people’s own homes. Content analysis of interviews provided an in-depth understanding of the factors that influence the acceptance of and relationship-building with social robots in domestic environments. The permanent presence of a robot in users’ own homes yields the vital challenges social robots encounter to be successfully accepted by their users. These vital acceptance challenges are unlikely to be revealed in one-day laboratory human-robot interaction studies or even in multiple observations of short interactions between humans and robots.     

Images of Ageing in Society: A Literature Review

Rapid population ageing triggered by a low fertility rate and a rise in life expectancy is being experienced worldwide, which has raised many formidable social, political and economic challenges. It becomes increasingly important to gain an in-depth understanding into prevailing attitudes towards older people and ageing in society. This article has systematically reviewed the literature on images of ageing. In addition to statistically analysing the characteristics of the reviewed literature, findings of the reviewed articles have been reported using the main themes identified. The review found that older characters, especially older female characters, are underrepresented compared with census figures in both Western and Asian media. Although mixed images are presented, negative ones tend to predominate. Compared with older people, the young and middle-aged people especially those who have less contact with older people tend to have simpler, vaguer and more negative stereotypes for the older population. Through long exposure to the stereotypes held by the public towards their group, older members of the stereotype-target groups may internalise those perceptions, especially for those who are relatively weak in filtering information. In addition, negative self-image and images of ageing held by older people have been found to be detrimental to their physical, mental, behavioural and social functioning. Based on the findings of this review, recommendations are made for research, policy, practice and education so as to construct new images of ageing in the society in which everyone look forward to entering into old age and the older group remain healthy, well-groomed, outgoing, capable, and active members of the community.     

Rules and reason in the forced retirement of commercial airline pilots at age 60

Abstract

In this salute to Tom Singleton, a logic to discuss a mandatory retirement age is suggested. Mandatory retirement in airline pilots is examined as a framework for the discussion. In the thirty-five years since the age 60 mandatory retirement age for commercial airline pilots was adopted by the Federal Aviation Administration, many changes have occurred in life expectancy, mortality and morbidity rates, economic conditions, and in the nature of flying itself that impact upon that decision. It is suggested that the policymakers look at the following three general areas as criteria when adjusting the age of mandatory retirement: actual job performance, information processing capacity and physical capacity. Information processing and physical capacity provide information about the necessary, but not sufficient conditions that must be met in order to fly an aircraft. Actual job performance is the final measure of a pilot's ability to fly at any age.     

A diagnostic reasoning and optimal treatment model for bacterial infections with fuzzy information

This study proposes an optimization model for optimal treatment of bacterial infections. Using an influence diagram as the knowledge and decision model, we can conduct two kinds of reasoning simultaneously: diagnostic reasoning and treatment planning. The input information of the reasoning system are conditional probability distributions of the network model, the costs of the candidate antibiotic treatments, the expected effects of the treatments, and extra constraints regarding belief propagation. Since the prevalence of the pathogens and infections are determined by many site-by-site factors, which are not compliant with conventional approaches for approximate reasoning, we introduce fuzzy information. The output results of the reasoning model are the likelihood of a bacterial infection, the most likely pathogen(s), the suggestion of optimal treatment, the gain of life expectancy for the patient related to the optimal treatment, the probability of coverage associated with the antibiotic treatment, and the cost-effect analysis of the treatment prescribed.