Educational differences in mortality and hospitalisation for cardiovascular diseases

Comparing methods for estimating causal treatment effects of administrative health data: A plasmode simulation study

Estimating the causal effects of health policy interventions is crucial for policymaking but is challenging when using real-world administrative health care data due to a lack of methodological guidance. To help fill this gap, we conducted a plasmode simulation using such data from a recent policy initiative launched in a deprived urban area in Germany. Our aim was to evaluate and compare the following methods for estimating causal effects: propensity score matching, inverse probability of treatment weighting, and entropy balancing, all combined with difference-in-differences analysis, augmented inverse probability weighting, and targeted maximum likelihood estimation. Additionally, we estimated nuisance parameters using regression models and an ensemble learner called superlearner. We focused on treatment effects related to the number of physician visits, total health care cost, and hospitalization. While each approach has its strengths and weaknesses, our results demonstrate that the superlearner generally worked well for handling nuisance terms in large covariate sets when combined with doubly robust estimation methods to estimate the causal contrast of interest. In contrast, regression-based nuisance parameter estimation worked best in small covariate sets when combined with singly robust methods.

Education Level and Self-Reported Cardiovascular Disease in Norway-The Tromsø Study, 1994-2016

BACKGROUND

Cardiovascular disease (CVD) is a leading source of morbidity and mortality, and research has shown education level to be a risk factor for the disease. The aim of this study was to investigate the association between education level and self-reported CVD in Tromsø, Norway.

METHODS

This prospective cohort study included 12,400 participants enrolled in the fourth and seventh surveys of the Tromsø Study (Tromsø4 and Tromsø7) in 1994-1995 and 2015-2016, respectively. Logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

For every 1-level increase in education, the age-adjusted risk of self-reported CVD decreased by 9% (OR = 0.91, 95% CI: 0.87-0.96), but after adjustment for covariates, the association was weaker (OR = 0.96, 95% CI: 0.92-1.01). The association was stronger for women (OR = 0.86, 95% CI: 0.79-0.94) than men (OR = 0.91, 95% CI: 0.86-0.97) in age-adjusted models. After adjustment for covariates, the associations for women and men were similarly weak (women: OR = 0.95, 95% CI: 0.87-1.04; men: OR = 0.97, 95% CI: 0.91-1.03). In age-adjusted-models, higher education level was associated with a lower risk of self-reported heart attack (OR = 0.90, 95% CI: 0.84-0.96), but not stroke (OR = 0.97, 95% CI: 0.90-1.05) or angina (OR = 0.98, 95% CI: 0.90-1.07). There were no clear associations observed in the multivariable models for CVD components (heart attack: OR = 0.97, 95% CI: 0.91-1.05; stroke: OR = 1.01, 95% CI: 0.93-1.09; angina: OR = 1.04, 95% CI: 0.95-1.14).

CONCLUSIONS

Norwegian adults with a higher education level were at lower risk of self-reported CVD. The association was present in both genders, with a lower risk observed in women than men. After accounting for lifestyle factors, there was no clear association between education level and self-reported CVD, likely due to covariates acting as mediators.