Active life expectancy from annual follow-up data with missing responses

Active life expectancy (ALE) at a given age is defined as the expected remaining years free of disability. In this study, three categories of health status are defined according to the ability to perform activities of daily living independently. Several studies have used increment-decrement life tables to estimate ALE, without error analysis, from only a baseline and one follow-up interview. The present work conducts an individual-level covariate analysis using a three-state Markov chain model for multiple follow-up data. Using a logistic link, the model estimates single-year transition probabilities among states of health, accounting for missing interviews. This approach has the advantages of smoothing subsequent estimates and increased power by using all follow-ups. We compute ALE and total life expectancy from these estimated single-year transition probabilities. Variance estimates are computed using the delta method. Data from the Iowa Established Population for the Epidemiologic Study of the Elderly are used to test the effects of smoking on ALE on all 5-year age groups past 65 years, controlling for sex and education.     

A case-control follow-up study for disease-specific mortality

Case-control studies often rely on subjects to report their own screening or exposure information: this information is often obtained from cases after the event of interest has occurred. This is problematic for mortality outcomes, because dead subjects cannot report the desired information. To avoid this problem, Weiss and Lazovich (1996, American Journal of Epidemiology 143, 319-322) proposed obtaining exposure or screening information from potential cases, i.e., subjects diagnosed with disease, at the time of disease diagnosis, and also from a referent series. The design is best viewed as a new scheme for sampling from a cohort. I review estimation of the effects of time-varying screening or exposure in cohort studies, using a new factorization. I then show how this factorization, together with ignorability assumptions, allows valid estimation from these new designs. Even when the sampling fraction of nondiseased subjects is unknown, causal risk ratios are estimable if diagnosis is rare in the cohort. I illustrate and compare conventional and new methods with data from the Health Insurance Plan study.     

Estimating impact of food choices on life expectancy: A modeling study

BackgroundInterpreting and utilizing the findings of nutritional research can be challenging to clinicians, policy makers, and even researchers. To make better decisions about diet, innovative methods that integrate best evidence are needed. We have developed a decision support model that predicts how dietary choices affect life expectancy (LE).Methods and findingsBased on meta-analyses and data from the Global Burden of Disease study (2019), we used life table methodology to estimate how LE changes with sustained changes in the intake of fruits, vegetables, whole grains, refined grains, nuts, legumes, fish, eggs, milk/dairy, red meat, processed meat, and sugar-sweetened beverages. We present estimates (with 95% uncertainty intervals [95% UIs]) for an optimized diet and a feasibility approach diet. An optimal diet had substantially higher intake than a typical diet of whole grains, legumes, fish, fruits, vegetables, and included a handful of nuts, while reducing red and processed meats, sugar-sweetened beverages, and refined grains. A feasibility approach diet was a midpoint between an optimal and a typical Western diet. A sustained change from a typical Western diet to the optimal diet from age 20 years would increase LE by more than a decade for women from the United States (10.7 [95% UI 8.4 to 12.3] years) and men (13.0 [95% UI 9.4 to 14.3] years). The largest gains would be made by eating more legumes (females: 2.2 [95% UI 1.1 to 3.4]; males: 2.5 [95% UI 1.1 to 3.9]), whole grains (females: 2.0 [95% UI 1.3 to 2.7]; males: 2.3 [95% UI 1.6 to 3.0]), and nuts (females: 1.7 [95% UI 1.5 to 2.0]; males: 2.0 [95% UI 1.7 to 2.3]), and less red meat (females: 1.6 [95% UI 1.5 to 1.8]; males: 1.9 [95% UI 1.7 to 2.1]) and processed meat (females: 1.6 [95% UI 1.5 to 1.8]; males: 1.9 [95% UI 1.7 to 2.1]). Changing from a typical diet to the optimized diet at age 60 years would increase LE by 8.0 (95% UI 6.2 to 9.3) years for women and 8.8 (95% UI 6.8 to 10.0) years for men, and 80-year-olds would gain 3.4 years (95% UI females: 2.6 to 3.8/males: 2.7 to 3.9). Change from typical to feasibility approach diet would increase LE by 6.2 (95% UI 3.5 to 8.1) years for 20-year-old women from the United States and 7.3 (95% UI 4.7 to 9.5) years for men. Using NutriGrade, the overall quality of evidence was assessed as moderate. The methodology provides population estimates under given assumptions and is not meant as individualized forecasting, with study limitations that include uncertainty for time to achieve full effects, the effect of eggs, white meat, and oils, individual variation in protective and risk factors, uncertainties for future development of medical treatments; and changes in lifestyle.ConclusionsA sustained dietary change may give substantial health gains for people of all ages both for optimized and feasible changes. Gains are predicted to be larger the earlier the dietary changes are initiated in life. The Food4HealthyLife calculator that we provide online could be useful for clinicians, policy makers, and laypeople to understand the health impact of dietary choices.

Lars Fadnes and co-workers estimate the possible benefits to life expectancy from adoption of more healthy diets.     

A predictive model for life expectancy curves

Life expectancy curves have a characteristic ominous shape that has fascinated scientists for centuries. Medawar was the first to explain this shape, specifically the steeply rising proneness of an average individual to die as a function of age, in evolutionary terms. The idea was that the "selective value" of the individual decreases as it has triggered other individuals taking its place (and carrying its genes) into existence. We demonstrate that this idea can be turned into a quantitative model. The resulting 4-parameter function reproduces well two well-known life expectancy curves from the first half of this century. Moreover, the easily interpretable parameters (3 of the 4) seem intuitively reasonable.     

A unifying framework for assessing changes in life expectancy associated with changes in mortality: the case of violent deaths

For over forty years, demographers have worked intensely to develop methods that assess a gain in life expectancy from a reduction in mortality, either hypothetical or observed. This considerable body of research was motivated by assessing the gains in life expectancy when mortality declined in a particular manner and determining the contribution of a cause of death in observed changes in life expectancy over time. As yet, there has been no framework unifying this important demographic work. In this paper, we provide a unifying framework for assessing the change in life expectancy given a change in age- and cause-specific mortality. We consider both conceptualizations of mortality change—counterfactual assessment of a hypothetical change and a retrospective assessment of an observed change. We apply our methodology to violent deaths, the leading cause of death among young adults, and show that realistic targeted reductions could have important impacts on life expectancy.     

The ethics of life expectancy

Some ethical dilemmas in health care, such as over the use of age as a criterion of patient selection, appeal to the notion of life expectancy. However, some features of this concept have not been discussed. Here I look in turn at two aspects: one positive — our expectation of further life — and the other negative — the loss of potential life brought about by death. The most common method of determining this loss, by counting only the period of time between death and some particular age, implies that those who die at ages not far from that one are regarded as losing very little potential life, while those who die at greater ages are regarded as losing none at all. This approach has methodological advantages but ethical disadvantages, in that it fails to correspond to our strong belief that anyone who dies is losing some period of life that he or she would otherwise have had. The normative role of life expectancy expressed in the 'fair innings' attitude arises from a particular historical situation: not the increase of life expectancy in modern societies, but a related narrowing in the distribution of projected life spans. Since life expectancy is really a representation of existing patterns of mortality, which in turn are determined by many influences, including the present allocation of health resources, it should not be taken as a prediction, and still less as a statement of entitlement.     

特发性急性横贯性脊髓炎临床及MRI特征分析

目的 探讨特发性急性横贯性脊髓炎（IATM）的临床特征及MRI特点,提高对其诊断准确性。方法 对41例首次发病并住院诊治的IATM患者的临床资料及MRI图像进行回顾性分析。结果 （1）临床症状：首发症状为肢体麻木无力共28例（68.29%）;截瘫12例（29.27%）,尿便障碍/失禁25例（60.98%）。查体所有患者均有感觉障碍并伴有明确的感觉平面,其中39例（95.12%）表现为痛觉减退,2例（4.88%）表现为痛觉过敏。（2）MRI特征：41例患者中38例（92.68%）MRI显示异常,表现为T2WI条片状高信号,其中颈髓11例（28.95%）,胸髓22例（57.89%）,颈胸髓1例（2.63%）,胸髓及马尾4例（10.53%）;脊髓肿胀11例（28.95%）。21例患者行增强扫描检查,13例（61.90%）轻-中度强化。结论 肢体麻木无力、尿便障碍及确切感觉平面以下的痛觉减退为IATM较主要的临床表现。临床表现与MRI特征相结合,有利于IATM的诊断。     

Work ability in midlife as a predictor of mortality and disability in later life: a 28-year prospective follow-up study

Background

Poor work ability correlates with increased morbidity and early retirement from the workforce, but the association in old age is not known. We investigated work ability in midlife among white-collar and blue-collar employees as a predictor of mortality and disability 28 years later.

Methods

A total of 5971 occupationally active people aged 44–58 years participated in the Finnish Longitudinal Study of Municipal Employees (FLAME) in 1981. Perceived work ability relative to lifetime best was categorized as excellent, moderate or poor. In 2009, the ability to perform activities of daily living was assessed among 2879 respondents (71.0% of the survivors). Mortality data were available up to July 2009.

Results

At the 28-year follow-up, 1918 of the 5971 participants had died and 1403 had some form of disability. Rates of death per 1000 person-years among white-collar men were 7.7 for those with excellent work ability, 14.7 for those with moderate work ability and 23.5 for those with poor work ability. Among blue-collar men, the corresponding rates were 15.5, 20.2 and 25.3. In women, rates ranged between 6.3 and 10.6 per 1000 person-years. The age-adjusted hazard ratios (HRs) for mortality were two to three times higher among blue-collar male employees with lower work ability than among white-collar male employees with excellent work ability in midlife (i.e., the reference group). The odds of death or disability at follow-up compared with white-collar workers with excellent work ability were highest among blue-collar employees with poor work ability in midlife (odds ratio [OR] 4.56, 95% confidence interval [CI] 2.82–7.37 for men; OR 3.37, 95% CI 2.28–4.98 for women). Among the survivors, similar but slightly lower risks of disability 28 years later were found.

Interpretation

Perceived poor work ability in midlife was associated with accelerated deterioration in health and functioning and remains evident after 28 years of follow-up.Prospective studies with a follow-up time stretching from midlife to old age have shown that lower socioeconomic status, as indicated by lower education level or occupational grade, predicts a decline in health and functioning in the working population.^1–4 This association is similar, if not more pronounced, in old age.^5–7Higher levels of work-related mental and physical strain increase the risk of early retirement and predict a decline in health and an increase in mortality among the working population.^3,8–15 However, the association between the demands of the work in conjunction with inadequate mental or physical resources (i.e., work ability)¹⁶ and health and functioning in old age has not been studied.¹⁷ Using a population-based 28-year follow-up study involving middle-aged municipal employees, we investigated whether work ability in midlife predicts the risk of death and disability during old age among white-collar and blue-collar employees.     

Prediction of the human life expectancy

We have simulated demographic changes in the human population using the Penna microscopic model, based on the simple Monte Carlo method. The results of simulations have shown that during a few generations changes in the genetic pool of a population are negligible, while improving the methods of compensation of genetic defects or genetically determined proneness to many disorders drastically affects the average life span of organisms. Age distribution and mortality of the simulated populations correspond very well to real demographic data available from different countries. Basing on the comparison of structures of real human populations and the results of simulations it is possible to predict changes in the age structure of populations in the future.     

Calculation of longevity and life expectancy in captive elephants

The concepts of longevity (longest lived) and life expectancy (typical age at death) are common demographic parameters that provide insight into a population. Defined as the longest lived individual, longevity is easily calculated but is not representative, as only one individual will live to this extreme. Longevity records for North American Asian elephants (Elephas maximus) and African elephants (Loxodonta africana) have not yet been set, as the oldest individuals (77 and 53 years, respectively) are still alive. One Asian elephant lived to 86 years in the Taipei Zoo. This is comparable to the maximum (though not typical) longevity estimated in wild populations. Calculation of life expectancy, however, must use statistics that are appropriate for the data available, the distribution of the data, and the species' biology. Using a simple arithmetic mean to describe the non‐normally distributed age at death for elephant populations underestimates life expectancy. Use of life‐table analysis to estimate median survivorship or survival analysis to estimate average survivorship are more appropriate for the species' biology and the data available, and provide more accurate estimates. Using a life‐table, the median life expectancy for female Asian elephants (Lx=0.50) is 35.9 years in North America and 41.9 years in Europe. Survival analysis estimates of average life expectancy for Asian elephants are 47.6 years in Europe and 44.8 years in North America. Survival analysis estimates for African elephants are less robust due to less data. Currently the African elephant average life expectancy estimate in North America is 33.0 years, but this is likely to increase with more data, as it has over the past 10 years. Zoo Biol 23:365–373, 2004. © 2004 Wiley‐Liss, Inc.     

Lymphangioma. A long-term follow-up study.

At the Hospital for Sick Children 177 patients with lymphangioma, exclusive of those who had intra-abdominal lesions, were seen between 1927 and 1964. Forty-nine of these (aged 8 to 41 years) were available for follow-up examination. The results of treatment by surgical excision, aspiration, incision and drainage, and radiation are reported. No cases confirmed histologically as lymphangioma underwent spontaneous regression. Two histologically unconfirmed cases underwent partial regression. Four of the 11 who were left with lymphangioma tissue at operation never had significant recurrences. Therefore, this clinical study does not clarify the possible role of spontaneous regression in lymphangioma. Extensive surgery is the treatment of choice whenever feasible, but in unilocular or bilocular cystic lymphagnioma subsequent regression can be expected after palliative treatment (aspiration, or incision and drainage). Clinical and pathological criteria should be established for differentiation between lymphangioma and primary lymphedema. Lesions involving subepithelial, subdermal, and subcutaneous or internodal networks of lymphatics will produce lymphagioma; lesions of the collecting lymphatic trunks will result in lymphedema.     

Lymphedema. A clinical review and follow-up study.

In a retrospective study of primary lymphedema, we found that the age of onset ranged from 0 to 16 years; sex did not appear to be a factor; and treatment was preventive rather than surgical. Thirteen patients were available for follow-up studies, the time interval being at least 6 years. The degree of swelling at the time of the follow-up examination was defined as mild, moderate, or severe. In patients with mild and moderate swelling the edema was not progressive and surgical treatment was not indicated. Severe swelling appeared to be associated with congenital lymphedema and with repeated infection. Pressure-support treatment can be effective in the reduction of swelling or in the prevention of further swelling, if applied constantly over a long period of time. The authors discuss the histological similarity between lymphedema and lymphagioma, and present an anatomical classification for the two entities.