Reducing socio-economic inequalities in all-cause mortality: a counterfactual mediation approach

Background: Socio-economic inequalities in mortality are well established, yet the contribution of intermediate risk factors that may underlie these relationships remains unclear. We evaluated the role of multiple modifiable intermediate risk factors underlying socio-economic-associated mortality and quantified the potential impact of reducing early all-cause mortality by hypothetically altering socio-economic risk factors.Methods: Data were from seven cohort studies participating in the LIFEPATH Consortium (total n = 179 090). Using both socio-economic position (SEP) (based on occupation) and education, we estimated the natural direct effect on all-cause mortality and the natural indirect effect via the joint mediating role of smoking, alcohol intake, dietary patterns, physical activity, body mass index, hypertension, diabetes and coronary artery disease. Hazard ratios (HRs) were estimated, using counterfactual natural effect models under different hypothetical actions of either lower or higher SEP or education.Results: Lower SEP and education were associated with an increase in all-cause mortality within an average follow-up time of 17.5 years. Mortality was reduced via modelled hypothetical actions of increasing SEP or education. Through higher education, the HR was 0.85 [95% confidence interval (CI) 0.84, 0.86] for women and 0.71 (95% CI 0.70, 0.74) for men, compared with lower education. In addition, 34% and 38% of the effect was jointly mediated for women and men, respectively. The benefits from altering SEP were slightly more modest.Conclusions: These observational findings support policies to reduce mortality both through improving socio-economic circumstances and increasing education, and by altering intermediaries, such as lifestyle behaviours and morbidities.</p

Reducing socioeconomic inequalities in all-cause mortality: a counterfactual mediation approach

Background: Socioeconomicinequalities inmortality arewell established, yet the contribution of intermediate risk factors that may underlie these relationships remains unclear.We evaluated the role of multiple modifiable intermediate risk factors underlyingsocioeconomicassociated-mortality and quantifiedthe potentialimpact of reducing early all-cause mortality by hypothetically altering socioeconomic risk factors. Methods: Data were fromsevencohort studies participating in the LIFEPATH consortium (total n=179,090). Using bothsocioeconomic position (SEP) (based on occupation) and education, we estimated thenaturaldirect effect on all-cause mortality, and thenatural indirect effect via the joint mediatingrole of smoking, alcohol intake, dietary patterns, physical activity, body mass index,hypertension, diabetes, and coronary artery disease.Hazard ratios(HR)were estimated, using counterfactual natural effect modelsunder different hypothetical actions of either lower or higher SEP or education. Results: Lower SEP and educationwereassociated with anincreaseinall-cause mortalitywithin an average follow up time of 17.5 years.Mortality wasreducedviamodelled hypothetical actions of increasing SEP oreducation. Through higher educationtheHR was0.85(95% confidence interval (CI) 0.84, 0.86) for women and 0.71(95% CI 0.70, 0.74)for men,compared to lower education. In addition, 34% and 38% of the effect was jointlymediatedfor womenand men, respectively. The benefits from alteringSEP were slightly more modest.Conclusions: Theseobservational findings supportpoliciesto reducemortalityboththrough improving socioeconomic circumstances and increasing education,andby altering intermediaries, such as lifestyle behaviours and morbidities

Smoking, secondhand smoke, and cotinine levels in a subset of EPIC cohort.

BACKGROUND: Several countries are discussing new legislation regarding the ban on smoking in public places, based on the growing evidence of the hazards of secondhand smoke (SHS) exposure. The objective of the present study is to quantitatively assess the relationship between smoking, SHS, and serum cotinine levels in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. METHODS: From a study on lung cancer in the EPIC cohort, questionnaire information on smoking was collected at enrolment, and cotinine was measured in serum. Three statistical models were applied by using samples available in a cross-section design: (i) cotinine levels by categories combining smoking and SHS (n = 859); (ii) the effect of hours of passive smoking exposure in nonsmokers only (n = 107); (iii) the effect of the number of cigarettes consumed per day in current smokers only (n = 832). All models were adjusted for country, sex, age, and body mass index. RESULTS: Among nonsmokers, passive smokers presented significant differences in cotinine compared with nonexposed, with a marked (but not significant) difference among former-smokers. A one hour per day increment of SHS gave rise to a significant 2.58 nmol/L (0.45 ng/mL) increase in mean serum cotinine (P &lt; 0.001). In current smokers, a one cigarette per day increment gave rise to a significant 22.44 nmol/L (3.95 ng/mL) increase in cotinine mean (P &lt; 0.001). CONCLUSIONS: There is clear evidence that not only tobacco smoking but also involuntary exposure increases cotinine levels. IMPACT: This study strengthens the evidence for the benefits of a smoking ban in public places

A structural equation modelling approach to explore the role of B vitamins and immune markers in lung cancer risk

The one-carbon metabolism (OCM) is considered key in maintaining DNA integrity and regulating gene expression, and may be involved in the process of carcinogenesis. Several B-vitamins and amino acids have been implicated in lung cancer risk, via the OCM directly as well as immune system activation. However it is unclear whether these factors act independently or through complex mechanisms. The current study applies structural equations modelling (SEM) to further disentangle the mechanisms involved in lung carcinogenesis. SEM allows simultaneous estimation of linear relations where a variable can be the outcome in one equation and the predictor in another, as well as allowing estimation using latent variables (factors estimated by correlation matrix). A large number of biomarkers have been analysed from 891 lung cancer cases and 1,747 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Four putative mechanisms in the OCM and immunity were investigated in relation to lung cancer risk: methionine-homocysteine metabolism, folate cycle, transsulfuration, and mechanisms involved in inflammation and immune activation, all adjusted for tobacco exposure. The hypothesized SEM model confirmed a direct and protective effect for factors representing methionine-homocysteine metabolism (p = 0.020) and immune activation (p = 0.021), and an indirect protective effect of folate cycle (p = 0.019), after adjustment for tobacco smoking. In conclusion, our results show that in the investigation of the involvement of the OCM, the folate cycle and immune system in lung carcinogenesis, it is important to consider complex pathways (by applying SEM) rather than the effects of single vitamins or nutrients (e.g. using traditional multiple regression). In our study SEM were able to suggest a greater role of the methionine-homocysteine metabolism and immune activation over other potential mechanisms