The influence of parents' and partner's education on own health behaviours

The link between educational attainment and multiple health behaviours has been explained in various ways. This paper provides new insights into the social patterning in health behaviours by investigating the influence of parents' and partners' educational attainments on a composite indicator that integrates the four commonly studied lifestyle behaviours (smoking, alcohol, physical activity and BMI). Two key outcome indicators of interests were created to reflect both ends of the "healthy - unhealthy spectrum". Data was drawn from The Tromsø Study, conducted in 2015/16 (N = 21,083, aged 40-93 years). We controlled for two indicators of early life human capital and one personality trait variable. Partners' education attainments are relatively more important for avoiding unhealthy behaviour than choosing healthy behaviour; on the contrary, parents' education is more important for healthy behaviour. Heterogeneity by sex and age was also evident. The influences of partner's education on widening the socioeconomic contrasts in health behaviours were much stronger in the younger (40-59 years) age group. In conclusion, our results support the hypothesis that own health behaviour is affected by the educational attainments of our 'nearest and dearest' (i.e. spouse, mother, and father), net of own education. This study facilitates a better understanding of education-health behaviours nexus from a life course perspective and supports the importance of family-based interventions to improve healthy behaviours.

Lifestyle changes in middle age and risk of cancer: evidence from the European Prospective Investigation into Cancer and Nutrition

In this study, we aimed to provide novel evidence on the impact of changing lifestyle habits on cancer risk. In the EPIC cohort, 295,865 middle-aged participants returned a lifestyle questionnaire at baseline and during follow-up. At both timepoints, we calculated a healthy lifestyle index (HLI) score based on cigarette smoking, alcohol consumption, body mass index and physical activity. HLI ranged from 0 (most unfavourable) to 16 (most favourable). We estimated the association between HLI change and risk of lifestyle-related cancers-including cancer of the breast, lung, colorectum, stomach, liver, cervix, oesophagus, bladder, and others-using Cox regression models. We reported hazard ratios (HR) with 95% confidence intervals (CI). Median time between the two questionnaires was 5.7 years, median age at follow-up questionnaire was 59 years. After the follow-up questionnaire, we observed 14,933 lifestyle-related cancers over a median follow-up of 7.8 years. Each unit increase in the HLI score was associated with 4% lower risk of lifestyle-related cancers (HR 0.96; 95%CI 0.95-0.97). Among participants in the top HLI third at baseline (HLI > 11), those in the bottom third at follow-up (HLI ≤ 9) had 21% higher risk of lifestyle-related cancers (HR 1.21; 95%CI 1.07-1.37) than those remaining in the top third. Among participants in the bottom HLI third at baseline, those in the top third at follow-up had 25% lower risk of lifestyle-related cancers (HR 0.75; 95%CI 0.65-0.86) than those remaining in the bottom third. These results indicate that lifestyle changes in middle age may have a significant impact on cancer risk.

The relative importance of education and health behaviour for health and wellbeing

BACKGROUND

Indicators of socioeconomic position (SEP) and health behaviours (HB) are widely used predictors of health variations. Their relative importance is hard to establish, because HB takes a mediating role in the link between SEP and health. We aim to provide new knowledge on how SEP and HB are related to health and wellbeing.

METHODS

The analysis considered 14,713 Norwegians aged 40-63. Separate regressions were performed using two outcomes for health-related quality of life (EQ-5D-5 L; EQ-VAS), and one for subjective wellbeing (Satisfaction with Life Scale). As predictors, we used educational attainment and a composite measure of HB - both categorized into four levels. We adjusted for differences in childhood financial circumstances, sex and age. We estimated the percentage share of each predictor in total explained variation, and the relative contributions of HB in the education-health association.

RESULTS

The reference case model, excluding HB, suggests consistent stepwise education gradients in health-related quality of life. The gap between the lowest and highest education was 0.042 on the EQ-5D-5 L, and 0.062 on the EQ-VAS. When including HB, the education effects were much attenuated, making HB take the lion share of the explained health variance. HB contributes 29% of the education-health gradient when health is measured by EQ-5D-5 L, and 40% when measured by EQ-VAS. For subjective wellbeing, we observed a strong HB-gradient, but no education gradient.

CONCLUSION

In the institutional context of a rich egalitarian country, variations in health and wellbeing are to a larger extent explained by health behaviours than educational attainment.

Changes in health behaviours in adults at-risk of chronic disease: primary outcomes from the My health for life program

Background

Chronic disease is the leading cause of premature death globally, and many of these deaths are preventable by modifying some key behavioural and metabolic risk factors. This study examines changes in health behaviours among men and women at risk of diabetes or cardiovascular disease (CVD) who participated in a 6-month lifestyle intervention called the My health for life program.

Methods

The My health for life program is a Queensland Government-funded multi-component program designed to reduce chronic disease risk factors amongst at-risk adults in Queensland, Australia. The intervention comprises six sessions over a 6-month period, delivered by a trained facilitator or telephone health coach. The analysis presented in this paper stems from 9,372 participants who participated in the program between July 2017 and December 2019. Primary outcomes included fruit and vegetable intake, consumption of sugar-sweetened drinks and take-away, alcohol consumption, tobacco smoking, and physical activity. Variables were summed to form a single Healthy Lifestyle Index (HLI) ranging from 0 to 13, with higher scores denoting healthier behaviours. Longitudinal associations between lifestyle indices, program characteristics and socio-demographic characteristics were assessed using Gaussian Generalized Estimating Equations (GEE) models with an identity link and robust standard errors.

Results

Improvements in HLI scores were noted between baseline (Md = 8.8; IQR = 7.0, 10.0) and 26-weeks (Md = 10.0; IQR = 9.0, 11.0) which corresponded with increases in fruit and vegetable consumption and decreases in takeaway frequency (p < .001 for all) but not risky alcohol intake. Modelling showed higher average HLI among those aged 45 or older (β = 1.00, 95% CI = 0.90, 1.10, p < .001) with vocational educational qualifications (certificate/diploma: β = 0.32, 95% CI = 0.14, 0.50, p < .001; bachelor/post-graduate degree β = 0.79, 95% CI = 0.61, 0.98, p < .001) while being male, Aboriginal or Torres Strait Islander background, or not currently working conferred lower average HLI scores (p < .001 for all).

Conclusions

While participants showed improvements in dietary indicators, changes in alcohol consumption and physical activity were less amenable to the program. Additional research is needed to help understand the multi-level barriers and facilitators of behaviour change in this context to further tailor the intervention for priority groups.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14056-1.

Healthy lifestyle index and risk of pancreatic cancer in the Women's Health Initiative

PURPOSE

Lifestyle factors such as smoking, alcohol, body weight, physical activity, and diet quality have been associated with the risk of pancreatic cancer. However, studies of their combined association in women are limited.

METHODS

Data on smoking habits, alcohol intake, diet composition, recreational physical activity, body weight, and waist circumference, obtained at recruitment for 136,945 postmenopausal women (aged 50-79 years) participating in the Women's Health Initiative study, were categorized separately, with higher scores for each variable assigned to the categories representing healthier behaviors. The combined healthy lifestyle index (HLI) score, created by summing the scores for each risk factor, was grouped into quartiles. We used multivariable-adjusted Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) for pancreatic cancer risk in association with the HLI.

RESULTS

Over an average follow-up period of approximately 16.0 years, 1,119 incident cases of pancreatic cancer were ascertained. Compared to women in the lowest HLI quartile, those in the upper quartiles (qt) had a reduced risk of pancreatic cancer (multivariable-adjusted HRqt3rd 0.83, 95% CI 0.74-0.99; and HRqt4th 0.74, 95% CI 0.62-0.88, respectively, p trend = 0.001). Use of waist circumference instead of BMI in the HLI score yielded similar results. Among women who were either non-diabetic or non-smokers, high HLI was also associated with reduced risk (HRqt4th 0.78, 95% CI 0.65-0.85 and HRqt4th 0.80, 95% CI 0.66-0.97, respectively). Stratification by BMI categories (18.5- < 25.0, 25.0- < 30.0 and > 30.0 kg/m2) showed similar results in all groups.

CONCLUSIONS

Our findings suggest that in postmenopausal women, a healthy lifestyle is associated with reduced risk of pancreatic cancer.

Management of hypertension and multiple risk factors to enhance cardiovascular health - a feasibility study in Singapore polyclinics

Background

High blood pressure (BP) is a leading contributor to cardiovascular mortality globally. There is scarcity of information on effective health systems interventions to lower BP and reduce cardiovascular risk in Southeast Asian countries. We conducted a pilot exploratory trial on 100 adults aged 40 years or older with uncontrolled hypertension to optimize the design for a structured multi-component intervention in primary care clinics for management of hypertension.

Methods

Two clinics were involved, each enrolling 50 participants, with one as the intervention clinic and the other as the control (usual care). The intervention comprised the following four components: 1) an algorithm-driven intervention using a fixed-dose combination (FDC) antihypertensive treatment and lipid lowering medication for high risk individuals, 2) subsidized FDC antihypertensive medication; 3) motivational conversation (MC) for high risk individuals; and 4) telephone follow-ups of all individuals. The process outcomes were intervention fidelity measures. The outcomes of change in parameters of interest were healthy lifestyle index (composite score of body mass index, physical activity, dietary habit, dietary quality and smoking), adherence to antihypertensive medications, and systolic and diastolic BP from baseline to follow-up at 3 months.

Results

Greater than 90 % fidelity was achieved for 3 of the 4 intervention components. Although not designed for conclusive results, the healthy lifestyle score increased by 0.16 (±0.68) with the intervention and decreased by 0.18 (±0.75) with usual care (p = 0.02). Adherence to anti-hypertensive medications at follow-up was 95.3 % in the intervention group compared to 83.8 % for usual care (p = 0.01). Systolic and diastolic BP decreased in both intervention and control groups, although statistical significance between groups was not achieved. Hypertensive individuals rated all intervention components ‘highly favorable’ on a Likert scale.

Conclusions

Our findings indicate that the proposed, structured multi-component approach for management of hypertension is feasible for implementation in primary care clinics in Singapore, with some changes to the protocol. The observed improvement in the healthy lifestyle index and adherence to anti-hypertensive medications is promising. A large scale, adequately powered trial would be informative to assess intervention effectiveness on BP and cardiovascular risk reduction.

Trial registration

This trial has been registered at ClinicalTrials.gov. ClinicalTrials.gov number NCT02330224. Registered on 28 December 2014.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1491-6) contains supplementary material, which is available to authorized users.