BACKGROUND: Guidelines for prevention of cardiovascular disease (CVD) include calculation of total risk. A new risk model based on updated Norwegian data is needed, as the European SCORE function overestimates the risk of fatal CVD in Norway. NORRISK for 10-year CVD mortality is presented. It includes gender, age and smoking and levels of systolic blood pressure and serumtotal cholesterol. MATERIAL AND METHODS: NORRISK is based on national age- and sex specific mortality rates from Statistics Norway (1999-2003), mean levels of risk factors from Norwegian Health Surveys (2000-03) and relative risks from mortality follow-up of Norwegian Cardiovascular Screenings (1985-2002). The model is adjusted to the mortality level in the period 1999-2003 and is compared with the SCORE model. RESULTS: 10-year risk estimates calculated from NORRISK fall between SCORE high- and low-risk estimates and increase strongly with age. Very few persons below 50 years of age have a 10-year risk above 5% (European limit for high risk). More than half of men aged 60 years have estimated risks above this limit, while only 7% of 60-year-old women exceed the limit. Even if the risk limit is reduced to 1% for younger age groups, very few women below 50 years of age have risks above the limit. INTERPRETATION: NORRISK is more adapted to the current situation in Norway than the SCORE model and may be a useful and relevant tool in Norwegian clinical practice.
People with a lower socioeconomic position have a higher the prevalence of most self-rated health problems. In this article we ask whether this may be attributed to self-rated health not reflecting actual health, understood as mortality, in different socioeconomic groups.
For the study we used data from the Nord-Trøndelag Health Study 1984-86 (HUNT1), in which the county's entire adult population aged 20 years and above were invited to participate. The association between self-rated health and mortality in different occupational classes and income groups was analysed. The analysis corrected for age, chronic disease, functional impairment and lifestyle factors.
The association between self-rated health and mortality was of the same order of magnitude for the occupational classes and income groups, but persons without work/income and with poor self-rated health stood out. Compared with persons in the highest socioeconomic class, unemployed men had a hazard ratio for death that was three times higher in the follow-up period. For women with no income, the ratio was twice as high. INTERPRETATION Self-rated health and mortality largely conform to the different socioeconomic strata. This supports the perception that socioeconomic differences in health are a reality and represent a significant challenge nationally. Our results also increase the credibility of findings from other studies that use self-reported health in surveys to measure differences and identify the mechanisms that create them.
Comment In: Tidsskr Nor Laegeforen. 2015 Mar 10;135(5):41225761015
BACKGROUND: A prominent theme in current health research is whether large income inequality in a society in itself has negative consequences for population health, in addition to the effects of individual risk factors. The present study investigates whether mortality in Norway during the 1990s was higher in geographical regions with particularly skewed income distributions. MATERIAL AND METHODS: Register data for all inhabitants aged 25-66 in Norway in 1992 were used (N = approx. 2.2 millions), including information about deaths 1993-99. Norwegian municipalities were grouped into 23 regions. Gini coefficients indicating the degree of inequality in the income distribution were calculated for each region. Deaths 1993-99 were analysed by means of multiple logistic regression. RESULTS: Odds ratios for deaths 1993-99 were strongly influenced by well-known individual risk factors such as sex, age, marital status, educational level, personal income, and disability. In addition, odds ratios for death were significantly associated with the regions' gini coefficients when adjustments were made for average income level in the regions. This effect of income inequality was pronounced for people with low education, but almost absent among those with higher education. Moreover, the income inequality effect was to some extent driven by special circumstances in Oslo, Norway's capital city, with its high mortality among the less educated and a particularly non-egalitarian income distribution. However, the pattern of higher mortality associated with higher levels of income inequality among the less educated was also observed in the 22 regions outside Oslo. INTERPRETATION: We found a discernible, although not very strong, association between regional income inequality and mortality levels among the less educated. Several interpretations for this statistical tendency could be proposed; the social processes generating this tendency should be clarified further.