How do combinations of unhealthy behaviors relate to attitudinal factors and subjective health among the adult population in the Netherlands?

BACKGROUND: Health behaviours like smoking, nutrition, alcohol consumption and physical activity (SNAP) are often studied separately, while combinations can be particularly harmful. This study aims to contribute to a better understanding of lifestyle choices by studying the prevalence of (combinations of) unhealthy SNAP behaviours in relation to attitudinal factors (time orientation, risk attitude) and subjective health (self-rated health, life expectancy) among the adult Dutch population. METHODS: In total 1006 respondents, representative of the Dutch adult population (18-75 years) in terms of sex, age, and education, were drawn from a panel in 2016. They completed an online questionnaire. Groups comparisons and logistic regression analyses (crude and adjusted) were applied to analyse (combinations of) SNAP behaviours in relation to time orientation (using the Consideration of Future Consequences scale comprising Immediate (CFC-I) and Future (CFC-F) scales) and risk attitude (Health-Risk Attitude Scale; HRAS-6), as well as subjective health (visual analogue scale and subjective life expectancy). RESULTS: In the analyses, 989 respondents (51% men, average 52 years, 22% low, 48% middle, and 30% high educated) were included. About 8% of respondents engaged in four unhealthy SNAP behaviours and 18% in none. Self-rated health varied from 5.5 to 7.6 in these groups, whilst subjective life expectancy ranged between 73.7 and 85.5 years. Logistic regression analyses, adjusted for socio-demographic variables, showed that smoking, excessive drinking and combining two or more unhealthy SNAP behaviours were significantly associated with CFC-I scores, whi

Mixed evidence for the compression of morbidity hypothesis for smoking elimination

__Background:__ There is debate around the composition of life years gained from smoking elimination. The aim of this study was to conduct a systematic review of the literature to synthesize existing evidence on the effect of smoking status on health expectancy and to examine whether smoking elimination leads to compression of morbidity. __Methods:__ Five databases were systematically searched for peer-reviewed articles. Studies that presented quantitative estimates of health expectancy for smokers and non-/never-smokers were eligible for inclusion. Studies were searched, selected and reviewed by two reviewers who extracted the relevant data and assessed the risk of bias of the included articles independently. __Results:__ The search identified 2491 unique records, whereof 20 articles were eligible for inclusion (including 26 cohorts). The indicators used to measure health included disability/activity limitations (n¼9), health-related quality of life (EQ-5D) (n¼2), weighted disabilities (n¼1), self-rated health (n¼9), chronic diseases (n¼6), cardiovascular diseases (n¼4) and cognitive impairment (n¼1). Available evidence showed consistently that non-/never-smokers experience more healthy life years throughout their lives than smokers. Findings were inconsistent on the effect of smoking on the absolute number of unhealthy life years. Findings concerning the time proportionally spent unhealthy were less heterogeneous: nearly all included articles reported that non-/never-smokers experience relatively less unhealthy life years (e.g. relative compression of morbidity). __Conclusions:__ Support for the relative compression of morbidity due to smoking elimination was evident. Further research is needed into the absolute compression of morbidity hypothesis since current evidence is mixed, and methodology of studies needs to be harmonized

The Healthy Aging Index analyzed over 15 years in the general population

The Healthy Aging Index (HAI), an index of physiological aging, has been demonstrated to predicts mortality, morbidity and disability. We studied the longitudinal development of the HAI to identify aging trajectories and evaluated the role of baseline sociodemographic characteristics and lifestyle factors of the trajectories. Four measurements with intervals of 5 years were included from the Doetinchem Cohort Study. The HAI reflects levels of systolic blood pressure, non-fasting plasma glucose levels, global cognitive functioning, plasma creatinine levels and lung functioning. The HAI score ranges from 0 to 10: higher scores indicate a better health profile. Latent class mixture modelling was used to model within-person change and to identify aging trajectories. Area under the curve was calculated per trajectory to estimate total healthy years. In total, 2324 women and 2013 men were included. One HAI trajectory was identified for women, and two trajectories for men, labelled ‘gradual’ aging and ‘early’ aging. Men who were medium/high educated, below 36 years at baseline, complied with guidelines on physical activity and were not obese in any round were associated with increased odds to ‘gradual’ aging of 1.46, 1.93, 1.26 and 1.76, respectively. Between 30 and 70 years of age, men in the ‘early’ aging trajectory had the least healthy years, followed by women, and ‘gradual’ aging men. This study emphasizes that ‘physiological aging’ is not only an issue of older ages. Between 30 and 70 years of age, ‘early’ aging men and women had approximately five healthy years less compared to ‘gradual’ aging men. Lifestyle factors seem to play an important role in optimal aging

Correction to: How do combinations of unhealthy behaviors relate to attitudinal factors and subjective health among the adult population in the Netherlands? (BMC Public Health, (2020), 20, 1, (441), 10.1186/s12889-020-8429-y)

It was highlighted that in the original article [1] the graphs in Fig. 1 were duplicated. This Correction article shows the correct Fig. 1. The original article has been updated. Author details 1Erasmus University Rotterdam, Erasmus School of Health Policy & Management, P.O. Box 1738, 3000, DR, Rotterdam, the Netherlands. 2Erasmus University Rotterdam, Erasmus School of Economics, Rotterdam, The Netherlands