Prediction of survival in 70-year olds (original) (raw)
Related papers
Aging Clinical and Experimental Research, 1996
The main objective of this study was to analyze how the identification of determinants of survival is dependent on age at the time of data collection and on the length of the predictive period. The study is part of a gerontological and geriatric population investigation in Göteborg (Gothenburg), Sweden, called H70, which is a longitudinal study based on a large random sample of men and women born in 1901/1902 in Göteborg. They were first examined at the age of 70, and have then been reexamined at the ages of 75 and 79. Twelve variables from different areas were selected for use in the present study. As physical health has proven to be highly correlated to survival, the sample was split into two subgroups, one consisting of elderly with few health problems and the other of less healthy elderly. All analyses were performed on each gender separately, and on the whole samples of men and women, respectively, as well as on each health subgroup. The results show that the statistically significant determinants of survival differ substantially between the two subgroups. In addition, age at examination and length of predictive period proved to be very important in identifying determinants of survival. Lung capacity, measured by peak flow, was the only variable, among those selected for this study, that had predictive power for both genders, in both health groups and at each of the three observation ages.
Aging Clinical and Experimental Research, 2015
Background and aims While predictors of survival in older people have been examined in depth in a large number of studies, a literature search revealed no cross-national comparative prospective cohort studies on this issue. This study investigated survival and its predictors from age 75 to 85 among three local Nordic populations using survival data on national cohorts as background information. Methods The data were derived from national registers and from samples of 75-year old living in Denmark, Sweden, and Finland. The subjects were invited to take part in interviews and examinations focusing on different domains of health, functional capacity, and physical and social activities. Results The proportion of survivors to age 75 was markedly smaller among the Finnish men and women than Danish or Swedish subjects. In the local population no marked differences in survival from age 75 to 85 were observed between the groups of men, while women survived longer than men and longer in Göteborg than in Glostrup or Jyväskylä. Univariate models revealed 12 predictors of survival. In the multivariate models, the significant predictors among men related to physical fitness, whereas among women they pertained to social activities and morbidity. Conclusions Despite great differences in the proportions of survivors to age 75, and excepting the survival advantage of women, only minor differences were present in the subjects' further survival to age 85. In the univariate analyses, many of the factors predictive of survival from age 75 to 85 were the same in the examined populations, whereas in the multivariate analyses differences between the sexes emerged.
How to live until 90 – Factors predicting survival in 75-year-olds from the general population
Healthy Aging Research, 2014
Background: The objective of the study was to explore potential predictors common in a clinical context, for survival to age 90 among 75-year-olds from Sweden's general population. We performed a prospective community-based cohort study with 15-year follow-up among 75-year-olds from a defined geographical area. Methods: Of 1,100 inhabitants born in 1922 and living in Västerås in 1997, 618 were invited to participate in a cardiovascular health survey, and 432 individuals accepted participation. Among them, 380 subjects (61% of those originally invited; 191 men and 189 women) had complete records for all examined variables. Variables were categorized into 4 groups: 1) Previous or present disease; 2. Exercise test variables; 3. Conventional risk factors; 4. Other potential risk factors. Through Area Under the Receiver Operating Characteristic (AUROC) curves, strong predictors for survival until age 90 (AUROC≥0.60) in men and women were selected. Results: The strongest individual predictors for reaching the age of 90 were metabolic equivalents and systolic blood pressure (BP) rise during exercise test, QTc interval in resting electrocardiogram (ECG) and peak expiratory flow (PEF) in men, and, for women, white blood cell (WBC) counts and systolic blood pressure BP rise during exercise. The strongest independent predictor in multivariable models were metabolic equivalents in men and WBC counts in women (explained variability 22 and 6%). Conclusions: High exercise capacity in men and low WBC in women were the strongest independent predictors of reaching age 90 among the clinical predictors at 75. The strongest modifiable predictor was exercise capacity in men, which can be improved by physical training.
Prediction of survival: a comparison between two subjective health measures in an elderly population
Social Science & Medicine, 2004
A large amount of evidence shows that the subjective evaluation of health is a predictor of survival in many different populations. Subjective health (SH) is measured using different types of measures such as a general evaluation of health or a comparative evaluation of health. The aim of this study was to compare the prediction of survival by two measures of SH (a general measure and an age-related measure) and evaluate the association with other variables in an elderly population.
Life history and risk of death after 50: a survival analysis for Europe
In this study we investigated the impact of events from an individual’s past on the risk of death for Europeans aged 50 and older, controlling for other relevant variables. Our analysis was based on the data from retrospective biographic interviews, regular longitudinal interviews, and end-of-life interviews from the Survey of Health, Ageing and Retirement in Europe. In particular, we captured retrospectively self-reported health in childhood; periods of poverty, hunger, and poor health experienced in the past; and the history of health care, including regular dental care, blood tests, and blood pressure measurements. This information, along with age, gender, current subjective and objective health, and other socio-demographic characteristics, enables assessment of the risk of death. We applied the proportional hazard model to explain the risk of death. The survival analysis shows that events experienced in the past signifi cantly affect risk of death for Europeans aged 50 and older, controlling for other relevant variables.
BMC Geriatr, 2010
Background: Prediction of long-term survival in healthy adults requires recognition of features that serve as early indicators of successful aging. The aims of this study were to identify predictors of long-term survival in older women and to develop a multivariable model based upon longitudinal data from the Study of Osteoporotic Fractures (SOF). Methods: We considered only the youngest subjects (n = 4,097) enrolled in the SOF cohort (65 to 69 years of age) and excluded older SOF subjects more likely to exhibit a “frail” phenotype. A total of 377 phenotypic measures were screened to determine which were of most value for prediction of long-term (19-year) survival. Prognostic capacity of individual predictors, and combinations of predictors, was evaluated using a cross-validation criterion with prediction accuracy assessed according to time-specific AUC statistics. Results: Visual contrast sensitivity score was among the top 5 individual predictors relative to all 377 variables evaluated (mean AUC = 0.570). A 13-variable model with strong predictive performance was generated using a forward search strategy (mean AUC = 0.673). Variables within this model included a measure of physical function, smoking and diabetes status, self-reported health, contrast sensitivity, and functional status indices reflecting cumulative number of daily living impairments (HR ≥ 0.879 or RH ≤ 1.131; P < 0.001). We evaluated this model and show that it predicts long-term survival among subjects assigned differing causes of death (e.g., cancer, cardiovascular disease; P < 0.01). For an average follow-up time of 20 years, output from the model was associated with multiple outcomes among survivors, such as tests of cognitive function, geriatric depression, number of daily living impairments and grip strength (P < 0.03). Conclusions: The multivariate model we developed characterizes a “healthy aging” phenotype based upon an integration of measures that together reflect multiple dimensions of an aging adult (65-69 years of age). Age-sensitive components of this model may be of value as biomarkers in human studies that evaluate anti-aging interventions. Our methodology could be applied to data from other longitudinal cohorts to generalize these findings, identify additional predictors of long-term survival, and to further develop the “healthy aging” concept.
Predictors of Mortality Among the Elderly
1999
Financial support from the National Institute on Aging through a grant to the NBER is gratefully acknowledged. The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research.
Predictors of mortality for the oldest old. A 4-year follow-up of community-based elderly in Sweden
Archives of Gerontology and Geriatrics, 1992
The aim of this study is to investigate predictors for mortality in two age groups, 75-84 years old and 85 years and older. Interviews were carried out on 161 community-based persons aged 75-84 and 260 persons aged 85 and over. Predictors for mortality 4 years later were analyzed using logistic regression. Different models to predict mortality were found for the two age groups. Gender and IADL (Instrumental Activities of Daily Living) were found to be significant for the older group while ADL (Primary Activities of Daily Living), mobility and life satisfaction were significant for the younger group. Eliminating variables based on the nurse's evaluations did not change the model for the 85+ age group; in the 75-84 age group the nurse's assessment of ADL was replaced by a self -reported 1ADL index. Results confirm the heterogeneity of the elderly population and the importance of ADL and subjective measures for predicting mortality.
Age and Ageing, 2013
Background: information about the predictors of mortality among the oldest-old is limited. Also possible gender differences are poorly known. Objective: to examine the predictors of mortality among individuals aged 90 and older, focusing on differences between men and women. We also analysed gender differences in survival at different levels of mobility and activities in daily living (ADL). Design: this 9-year follow-up study is part of the Vitality 90+ study, a population-based study of people aged 90 and older. Subjects: all inhabitants aged 90 and older in the area of Tampere, Finland were contacted, irrespective of health or 468 K. Tiainen et al.