Aims: We contribute to the methodological literature on the assessment of health inequalities by applying an algorithmic approach to evaluate the capabilities of socioeconomic variables in predicting the prevalence of non-communicable diseases in a Norwegian health survey.
Methods: We use data from the seventh survey of the population based Tromsø Study (2015-2016), including 11,074 women and 10,009 men aged 40 years and above. We apply the random forest algorithm to predict four non-communicable disease outcomes (heart attack, cancer, diabetes and stroke) based on information on a number of social root causes and health behaviours.
Objectives: The aim of this study was to investigate time trends in known and undiagnosed diabetes, glycated haemoglobin (HbA1c) levels and other cardiometabolic risk factors in the general population as well as treatment target achievement among those with diabetes.
Design And Setting: Repeated cross-sectional surveys in the population-based Tromsø Study.
Methods: We used age-adjusted generalised estimating equation models to study trends in self-reported and undiagnosed (HbA1c ≥6.
Aim: There is a lack of studies on the prevalence of frailty, and the association between frailty and mortality in a Norwegian general population. Findings regarding sex differences in the association between frailty and mortality have been inconsistent. The aim of the present study was to investigate the association between the frailty phenotype and all-cause mortality in men and women in a Norwegian cohort study.
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