Predicting self-reported health: the CORDIS study
Introduction
Self-reported health (SRH) is a simple but valid proxy measure for health status [1] since it consistently predicts mortality even after adjustment for physical ill health at baseline [2]. It is most commonly measured with a single question with a few response options like “very good”, “good”, “fair”, “poor”, and “very poor” with a dose–response association commonly demonstrated in predicting all-cause mortality [3], [4], [5], [6].
The independent prediction of SRH for mortality suggests that there are subtle preclinical ill feelings (i.e. poor SRH) that appear on the path from good health to medically defined morbidity and mortality. There have been few attempts to determine the factors important in predicting subsequent self-reported health in population studies of men or women. These studies focused on socioeconomic risk factors and have shown that over follow-up periods from 3 to 10 years, that living in a poverty area [7], low social-economic class [8], [9], poor work conditions [10], lower employment grade [11], and higher job insecurity [12] predict a worsening of SRH. It is important however to determine if there are modifiable risk factors which predict poor SRH. If there are such risk factors, then such findings might be used as an argument beyond simply increasing longevity, in favor of interventions. We are unaware however of previous prospective studies exploring the predictive value of the established total mortality risk markers for subsequent poor self-rated health.
In the following study, we determine the predictive value of behavioral and biomedical risk factors for self-evaluated health 7.7–11.5 years later in 2,962 male industrial workers.
Section snippets
Study population
An effort was made in 1985 to identify all Jewish male industrial employees of 21 plants throughout Israel. A total of 5,547 employees of furniture, electronic, textile, food, tire, and metal manufacturing plants were located and offered free of charge screening examinations for selected risk factors for CVD. Nearly 70% were blue-collar workers. A total of 3,816 (68.8%) of all eligible employees responded and were entered between 1985 and 1987 into the Cardiovascular Occupational Risk Factor
Selection
Those contacted were similar to those who completed the questionnaire (Table 1). They did however have statistically significantly lower systolic blood pressures, a lower proportion with a history of stroke or myocardial infarction (CVD), less diabetics, and less chronic medications, but significantly more married individuals (Table 1).
Validation and comparison of the visual analogue scale to the conventional four-point SRH scale
The first step in the data analysis was to validate the analogue SRH scale. This was done before using the SRH as an endpoint in the study. There were 76 deaths
Discussion
We have demonstrated for the first time in a single model that increasing age, current smoking, higher systolic blood pressure measurements, use of chronic medications, diagnosis of diabetes mellitus, low educational status, and lack of regular leisure sports activity predict a lower self-evaluated health 7.7–11.5 years later. Since good self-rated health is an important goal, our findings might be used as an argument beyond the benefit of increased longevity in favor of interventions such as
References (34)
- et al.
Self-rated health status as a health measure: the predictive value of self-reported health status on the use of physician services and on mortality in the working-age population
J. Clin. Epidemiol.
(1997) - et al.
Social class and self-rated health: can the gradient be explained by differences in life style or work environment?
Soc. Sci. Med.
(2000) - et al.
Healthy volunteer effect in industrial workers
J. Clin. Epidemiol.
(1999) - et al.
The Tromso Study: predictors of self-evaluated health—Has society adopted the expanded health concept?
Soc. Sci. Med.
(1991) - et al.
Effects of age, hypertension history, and neuroticism on health perceptions
Exp. Gerontol.
(1986) - et al.
Health-related quality of life by disease and socio-economic group in the general population in Sweden
Health Policy
(2001) - et al.
Inequalities in self-rated health: explanations from different stages of life
Lancet
(1998) - et al.
Do risk factors and health behaviours contribute to self-ratings of health?
Soc. Sci. Med.
(1999) - et al.
What factors predict student self-rated physical health
J. Adolesc.
(1998) - et al.
Self-reported health status and mortality in a multiethnic US cohort
Am. J. Epidemiol.
(1999)
Health perceptions and survival—do global evaluations of health-status really predict mortality
J. Gerontol.
Self-rated health, longevity, and chronic diseases in elderly men. The Zutphen Study
Am. J. Epidemiol.
Perceived health and mortality: a nine-year follow-up of the human population laboratory cohort
Am. J. Epidemiol.
Perceived health status and morbidity and mortality: evidence from the Kuopio Ischaemic Heart Disease Risk Factor Study
Int. J. Epidemiol.
Predicting changes in perceived health status
Am. J. Public Health
Social differences in health: life-cycle effects between ages 23 and 33 in the 1958 British birth cohort
Am. J. Public Health
Work environment and changes in self-rated health: a five-year follow-up study
Stress Med.
Cited by (47)
How reliable are self-assessments using mobile technology in healthcare? The effects of technology identity and self-efficacy
2019, Computers in Human BehaviorCitation Excerpt :Given that smartphones are widely used by the public across the world they are becoming part of the individual's identity (Carter & Grover, 2015). Therefore, in this paper we utilized MTI theory and self-efficacy to study and address one of the contemporary issues identified in both literature and businesses (Bardage et al., 2005; Bowring et al., 2012; Froom et al., 2004; Gorber et al., 2007; Kaplan & Baron-Epel, 2003; Shmueli, 2003; Shmueli et al., 2008; Taylor et al., 2006). By extension, utilizing smartphones to provide healthcare services would require patients to interact with technology with the absence of physicians and nurses, therefore the patient's tend to precisely measure and report their physical wellness status (such as weight, height, blood pressure and so forth) becomes very crucial.
Estimation and development of 10- and 20-year cardiovascular mortality risk models in an industrial male workers database
2017, Preventive MedicineCitation Excerpt :The CORDIS questionnaires constituted the basis for population characterization and the identification of risk factors. Approximately 900 variables were collected for each participant, including workplace variables (Froom et al., 2004). The data obtained from the CORDIS cohort participants in both phases was merged in 2007, and updated in 2012 with mortality data obtained from the National Death Registry of the Israel Ministry of the Interior and the Central Bureau of Statistics.
Hard Work Makes It Hard to Sleep: Work Characteristics Link to Multidimensional Sleep Health Phenotypes
2024, Journal of Business and PsychologySport Participation and Subjective Outcomes of Health in Middle-Aged Men: A Scoping Review
2022, American Journal of Men's HealthWork–Family Balance Self-Efficacy and Work–Family Balance During the Pandemic: A Longitudinal Study of Working Informal Caregivers of Older Adults
2022, Journal of Occupational Health Psychology