The effect of life expectancy on fertility, saving, schooling and economic growth: theory and evidence

We construct a simple growth model where agents with uncertain survival choose schooling time, life-cycle consumption and the number of children. We show that rising longevity reduces fertility but raises saving, schooling time and the growth rate at a diminishing rate. Cross-section analyses using data from 76 countries support these propositions: life expectancy has a significant positive effect on the saving rate, secondary school enrollment and growth but a significant negative effect on fertility. Through sensitivity analyses, the effect on the saving rate is inconclusive, while the effects on the other variables are robust and consistent. These estimated effects are decreasing in life expectancy. Copyright The editors of the Scandinavian Journal of Economics 2005.

Life expectancy for community-based patients with heart failure from time of diagnosis

AIMS: Heart failure has been demonstrated in previous studies to have a dismal prognosis. However, the modern-day prognosis of patients with new onset heart failure diagnosed in the community managed within a disease management programme is not known. The purpose of this study is to report on prognosis of patients presenting with new onset heart failure in the community who are subsequently followed in a disease management program.

METHODS AND RESULTS: A review of patients referred to a rapid access heart failure diagnostic clinic between 2002 and 2012 was undertaken. Details of diagnosis, demographics, medical history, medications, investigations and mortality data were analysed. A total of 733 patients were seen in Rapid Access Clinic for potential new diagnosis of incident of heart failure. 38.9% (n=285) were diagnosed with heart failure, 40.7% (n=116) with HF-REF and 59.3% (n=169) with HF-PEF. There were 84 (29.5%) deaths in the group of patients diagnosed with heart failure; 41 deaths (35.3%) occurred in patients with HF-REF and 43 deaths (25.4%) occurred in patients with HF-PEF. In patients with heart failure, 52.4% (n=44) died from cardiovascular causes. 63.8% of HF patients were alive after 5 years resulting on average in a month per year loss of life expectancy over that period compared with aged matched simulated population.

CONCLUSIONS: In this community-based cohort, the prognosis of heart failure was better than reported in previous studies. This is likely due to the impact of prompt diagnosis, the improvement in therapies and care within a disease management structure.

STOPPFrail (Screening Tool of Older Persons Prescriptions in Frail Adults with Limited Life Expectancy): Consensus Validation

Background: To validate STOPPFrail, a list of explicit criteria for potentially inappropriate medications (PIMs) in frailer older adults with limited life expectancy. A Delphi consensus survey of an expert panel (n = 17) comprising specialists in geriatric medicine, clinical pharmacology, palliative care, psychiatry of old age, clinical pharmacy and general practice.
Methods: STOPPFrail criteria was initially created by the authors based on clinical
experience and appraisal of the available literature. Criteria were organised according to physiological system. Each criterion was accompanied by an explanation. Panellists ranked their agreement with each criterion on a 5-point Likert scale and invited to provide written feedback. Criteria with a median Likert response of 4/5 (agree/strongly agree) and a 25th centile of ≥4 were included in the final criteria.
Results: Three Delphi rounds were required. All panellists completed all rounds. Thirty criteria were proposed for inclusion; 26 were accepted. No new criteria were added. The first two criteria suggest deprescribing medications with no indication or where compliance is poor. The remaining 24 criteria include lipid-lowering therapies, alpha-blockers for hypertension, anti-platelets, neuroleptics, proton pump inhibitors, H-2 receptor antagonists, anti-spasmodics, theophylline, leukotriene antagonists, calcium supplements, bone anti-resorptive therapy, selective oestrogen receptor modulators, non-steroidal antiinflammatories, corticosteroids, 5-alpha reductase inhibitors, alpha-1 selective blockers, muscarinic antagonists, oral diabetic agents, ACE-inhibitors, angiotensin receptor blockers, systemic oestrogens, multivitamins, nutritional supplements and prophylactic antibiotics. Anticoagulants and anti-depressants were excluded. Despite incorporation of panellists’ suggestions, memantine and acetyl-cholinesterase inhibitors remained inconclusive.
Conclusion: STOPPFrail comprises 26 criteria, which have been judged by broad consensus, to be potentially inappropriate in frailer older patients with limited life expectancy. STOPPFrail may assist in deprescribing medications in these patients.

Life expectancy: recent trends in older ages

Over the last 30 years there has been an upward trend in life expectancy at older ages in England. Figures 1 and 2 show life expectancy in England at ages 65, 75, 85 and 95 from 1981 to 2014. The data points shaded red in Figures 1 and 2 indicate where life expectancy in that year was lower than in the previous year, showing that there is some fluctuation in life expectancy at these age groups, although the overall trend has been upwards. Male life expectancy was lower in 2012 than 2011 at ages 85 and 95, and at ages 65 and 75 it was the same in both years. There were no further falls in 2013. This flattening of the recent trend has not continued in 2014, which saw a rise in male life expectancy at all four ages. Male life expectancy increased by 0.3 years at age 65 and 0.2 years at ages 75, 85 and 95. For females, life expectancy at all four ages was lower in 2012 than 2011. At age 65, that was the first fall since 1995 and at age 75 the first fall since 2003. At ages 85 and 95, there have been frequent occasions when life expectancy in a year was lower than in the previous year. Between 2012 and 2013, there were no further falls in life expectancy at any of these ages. Between 2013 and 2014, there was an increase in female life expectancy at all four ages. Female life expectancy increased by 0.3 years at age 65 and by 0.2 years at ages 75, 85 and 95.  

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015 : a systematic analysis for the Global Burden of Disease Study 2015

Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015 : a systematic analysis for the Global Burden of Disease Study 2015

Life expectancy of type 1 diabetic patients during 1997-2010: a national Australian registry-based cohort study

AIMS/ HYPOTHESIS:
There is limited information about the impact of type 1 diabetes on life expectancy in a contemporary population. We examined the life expectancy of type 1 diabetic patients and explored the contribution of mortality at different ages and of different causes of death to years of life lost (YLL) compared with the general population.

METHODS:
We derived mortality rates of Australians with type 1 diabetes listed on the National Diabetes Services Scheme (NDSS) between 1997 and 2010 (n = 85,547) by linking the NDSS to the National Death Index. The Chiang method was used to estimate life expectancy and Arriaga's method was used to estimate the contributions of age-specific and cause-specific mortality to the YLL.

RESULTS:
A total of 5,981 deaths were identified during the 902,136 person-years of follow up. Type 1 diabetic patients had an estimated life expectancy at birth of 68.6 years (95% CI 68.1, 69.1), which was 12.2 years (95% CI 11.8, 12.7) less than that in the general population. The improvement in life expectancy at birth in 2004-2010 compared with 1997-2003 was similar for both type 1 diabetic patients (men, 1.9 years [95% CI 0.4, 3.3]; women, 1.5 years [95% CI 0.0, 3.2]) and the general population (men, 2.2 years; women, 1.4 years). Deaths at age <60 years accounted for 60% of the YLL from type 1 diabetes for men and 45% for women. The major contribution to YLL was mortality from endocrine and metabolic disease at age 10-39 years (men, 39-59%; women, 35-50%) and from circulatory disease at age ≥40 years (men, 43-75%; women, 34-75%).

CONCLUSIONS/ INTERPRETATION:
Data from 1997 to 2010 showed that Australian type 1 diabetic patients had an estimated loss in life expectancy at birth of 12.2 years compared with the general population.

Obesity in adulthood and its consequences for life expectancy: a life-table analysis

BACKGROUND: Overweight and obesity in adulthood are linked to an increased risk for death and disease. Their potential effect on life expectancy and premature death has not yet been described. OBJECTIVE: To analyze reductions in life expectancy and increases in premature death associated with overweight and obesity at 40 years of age. DESIGN: Prospective cohort study. SETTING: The Framingham Heart Study with follow-up from 1948 to 1990. PARTICIPANTS: 3457 Framingham Heart Study participants who were 30 to 49 years of age at baseline. MEASUREMENTS: Mortality rates specific for age and body mass index group (normal weight, overweight, or obese at baseline) were derived within sex and smoking status strata. Life expectancy and the probability of death before 70 years of age were analyzed by using life tables. RESULTS: Large decreases in life expectancy were associated with overweight and obesity. Forty-year-old female nonsmokers lost 3.3 years and 40-year-old male nonsmokers lost 3.1 years of life expectancy because of overweight. Forty-year-old female nonsmokers lost 7.1 years and 40-year-old male nonsmokers lost 5.8 years because of obesity. Obese female smokers lost 7.2 years and obese male smokers lost 6.7 years of life expectancy compared with normal-weight smokers. Obese female smokers lost 13.3 years and obese male smokers lost 13.7 years compared with normal-weight nonsmokers. Body mass index at ages 30 to 49 years predicted mortality after ages 50 to 69 years, even after adjustment for body mass index at age 50 to 69 years. CONCLUSIONS: Obesity and overweight in adulthood are associated with large decreases in life expectancy and increases in early mortality. These decreases are similar to those seen with smoking. Obesity in adulthood is a powerful predictor of death at older ages. Because of the increasing prevalence of obesity, more efficient prevention and treatment should become high priorities in public health.

Living healthier for longer: comparative effects of three heart-healthy behaviors on life expectancy with and without cardiovascular disease

BACKGROUND: Non-smoking, having a normal weight and increased levels of physical activity are perhaps the three key factors for preventing cardiovascular disease (CVD). However, the relative effects of these factors on healthy longevity have not been well described. We aimed to calculate and compare the effects of non-smoking, normal weight and physical activity in middle-aged populations on life expectancy with and without cardiovascular disease.

METHODS: Using multi-state life tables and data from the Framingham Heart Study (n = 4634) we calculated the effects of three heart healthy behaviours among populations aged 50 years and over on life expectancy with and without cardiovascular disease. For the life table calculations, we used hazard ratios for 3 transitions (No CVD to CVD, no CVD to death, and CVD to death) by health behaviour category, and adjusted for age, sex, and potential confounders.

RESULTS: High levels of physical activity, never smoking (men), and normal weight were each associated with 20-40% lower risks of developing CVD as compared to low physical activity, current smoking and obesity, respectively. Never smoking and high levels of physical activity reduced the risks of dying in those with and without a history of CVD, but normal weight did not. Never-smoking was associated with the largest gains in total life expectancy (4.3 years, men, 4.1 years, women) and CVD-free life expectancy (3.8 and 3.4 years, respectively). High levels of physical activity and normal weight were associated with lesser gains in total life expectancy (3.5 years, men and 3.4 years, women, and 1.3 years, men and 1.0 year women, respectively), and slightly lesser gains in CVD-free life expectancy (3.0 years, men and 3.1 years, women, and 3.1 years men and 2.9 years women, respectively). Normal weight was the only behaviour associated with a reduction in the number of years lived with CVD (1.8 years, men and 1.9 years, women).

CONCLUSIONS: Achieving high levels of physical activity, normal weight, and never smoking, are effective ways to prevent cardiovascular disease and to extend total life expectancy and the number of years lived free of CVD. Increasing the prevalence of normal weight could further reduce the time spent with CVD in the population.

Obesity in older adults and life expectancy with and without cardiovascular disease

BACKGROUND: The prevalence of overweight and obesity is increasing globally and is an established risk factor for cardiovascular disease (CVD). Our objective was to evaluate the impact of overweight and obesity on life expectancy and years lived with and without CVD in older adults.

METHODS: The study included 6636 individuals (3750 women) aged 55 years and older from the population-based Rotterdam Study. We developed multistate life tables by using prevalence, incidence rate and hazard ratios (HR) for three transitions (free-of-CVD-to-CVD, free-of-CVD-to-death and CVD-to-death), stratifying by the categories of body mass index (BMI) at baseline and adjusting for confounders.

RESULTS: During 12 years of follow-up, we observed 1035 incident CVD events and 1902 overall deaths. Obesity was associated with an increased risk of CVD among men (HR 1.57 (95% confidence interval (CI) 1.17, 2.11)) and women (HR 1.49 (95% CI 1.19, 1.86)), compared with normal weight individuals. Overweight and obesity were not associated with mortality in men and women without CVD. Among men with CVD, obesity compared with normal weight, was associated with a lower risk of mortality (HR 0.67 (95% CI 0.49, 0.90)). Overweight and obesity did not influence total life expectancy. However, obesity was associated with 2.6 fewer years (95% CI -4.8, -0.4) lived free from CVD in men and 1.9 (95% CI -3.3, -0.9) in women. Moreover, men and women with obesity lived 2.9 (95% CI 1.1, 4.8) and 1.7 (95% CI 0.6, 2.8) more years suffering from CVD compared with normal weight counterparts.

CONCLUSIONS: Obesity had no effect on total life expectancy in older individuals, but increased the risk of having CVD earlier in life and consequently extended the number of years lived with CVD. Owing to increasing prevalence of obesity and improved treatment of CVD, we might expect more individuals living with CVD and for a longer period of time.

Burden of diabetes in Australia: life expectancy and disability-free life expectancy in adults with diabetes.

AIMS/HYPOTHESIS: The aim of this work was to estimate the life expectancy (LE) and disability-free life expectancy (DFLE) for adults with and without diabetes. METHODS: The Chiang method and the adapted Sullivan method were used to estimate LE and DFLE by age and sex. Mortality data in 2011 were available from the National Diabetes Services Scheme for diabetes and from standard national mortality datasets for the general population. Data on prevalence of disability and severe or profound core activity limitation were derived from the 2012 Australian Survey of Disability, Ageing and Carers (SDAC). The definitions of disability used in the SDAC followed the International Classification of Functioning, Disability and Health. Data on diabetes prevalence were derived from the Australian Diabetes, Obesity and Lifestyle study. RESULTS: The estimated LE and DFLE (with 95% uncertainty interval [UI]) at age 50 years were 30.2 (30.0, 30.4) and 12.7 (11.5, 13.7) years, respectively, for men with diabetes, and the estimates were 33.9 (33.6, 34.1) and 13.1 (12.3, 13.9) years, respectively, for women with diabetes. The estimated loss of LE associated with diabetes at age 50 years was 3.2 (3.0, 3.4) years for men and 3.1 (2.9, 3.4) years for women, as compared with their counterparts without diabetes. The corresponding estimated loss of DFLE was 8.2 (6.7, 9.7) years for men and 9.1 (7.9, 10.4) years for women. Women with diabetes spent a greater number of absolute years and a greater proportion of their life with disability as compared with men with diabetes and women without diabetes. The gains in LE and DFLE across the whole population at age 50 years after hypothetically eliminating diagnosed diabetes were 0.6 (0.5, 0.6) years and 1.8 (1.0, 2.8) years. CONCLUSIONS/INTERPRETATION: In adults, diabetes results in a modest reduction in LE and a substantial reduction in DFLE. Efforts to identify the specific causes of disability and effective interventions are needed.

What Is a Firm’s Life Expectancy? Empirical Evidence in the Context of Portuguese Companies

It is a fact that the uncertainty about a firm’s future has to be measured and incorporated into a company’s valuation throughout the explicit analysis period – in the continuing or terminal value within valuation models. One of the concerns that can influence the continuing value of enterprises, which is not explicitly considered in traditional valuation models, is a firm’s average life expectancy. Although the literature has studied the life cycle of a firm, there is still a considerable lack of references on this topic. If we ignore the period during which a company has the ability to produce future cash flows, the valuations can fall into irreversible errors, leading to results markedly different from market values. This paper aims to provide a contribution in this area. Its main objective is to construct a mortality table for non-listed Portuguese enterprises, showing that the use of a terminal value through a mathematical expression of perpetuity of free cash flows is not adequate. We provide the use of an appropriate coefficient to perceive the number of years in which the company will continue to operate until its theoretical extinction. If well addressed regarding valuation models, this issue can be used to reduce or even to eliminate one of the main problems that cause distortions in contemporary enterprise valuation models: the premise of an enterprise’s unlimited existence in time. Besides studying the companies involved in it, from their existence to their demise, our study intends to push knowledge forward by providing a consistent life and mortality expectancy table for each age of the company, presenting models with an explicitly and different survival rate for each year. Moreover, we show that, after reaching a certain age, firms can reinvent their business, acquiring maturity and consequently postponing their mortality through an additional life period.

Examination of social demographic status, disease status, health behaviours and quality of life in older Australians using the theory of planned behaviour model

The increase of life expectancy worldwide during the last three decades has increased age-related disability leading to the risk of loss of quality of life. How to improve quality of life including physical health and mental health for older people and optimize their life potential has become an important health issue. This study used the Theory of Planned Behaviour Model to examine factors influencing health behaviours, and the relationship with quality of life. A cross-sectional mailed survey of 1300 Australians over 50 years was conducted at the beginning of 2009, with 730 completed questionnaires returned (response rate 63%). Preliminary analysis reveals that physiological changes of old age, especially increasing waist circumference and co morbidity was closely related to health status, especially worse physical health summary score. Physical activity was the least adherent behaviour among the respondents compared to eating healthy food and taking medication regularly as prescribed. Increasing number of older people living alone with co morbidity of disease may be the barriers that influence their attitude and self control toward physical activity. A multidisciplinary and integrated approach including hospital and non hospital care is required to provide appropriate services and facilities toward older people.

Prognostic modelling options for remaining useful life estimation by industry

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

The impact of temperature on years of life lost in Brisbane, Australia

Temperature is an important determinant of health. A better knowledge of how temperature affects population health is important not only to the scientific community, but also to the decision-makers who develop and implement early warning systems and intervention strategies to mitigate the health effects of extreme temperatures. The temperature–health relationship is also of growing interest as climate change is projected to shift the overall temperature distribution higher. Previous studies have examined the relative risks of temperature-related mortality, but the absolute measure of years of life lost is also useful as it combines the number of deaths with life expectancy. Here we use years of life lost to provide a novel measure of the impact of temperature on mortality in Brisbane, Australia. We also project the future temperature-related years of life lost attributable to climate change. We show that the association between temperature and years of life lost is U-shaped, with increased years of life lost for cold and hot temperatures. The temperature-related years of life lost will worsen greatly if future climate change goes beyond a 2 �C increase and without any adaptation to higher temperatures. This study highlights that public health adaptation to climate change is necessary.