Socioeconomic inequality and mortality:a regional Danish cohort study

BACKGROUND: Socioeconomic inequalities in mortality pose a serious impediment to enhance public health even in highly developed welfare states. This study aimed to improve the understanding of socioeconomic disparities in all-cause mortality by using a comprehensive approach including a range of behavioural, psychological, material and social determinants in the analysis. METHODS: Data from The North Denmark Region Health Survey 2007 among residents in Northern Jutland, Denmark, were linked with data from nationwide administrative registries to obtain information on death in a 5.8-year follow-up period (1(st)February 2007- 31(st)December 2012). Socioeconomic position was assessed using educational status as a proxy. The study population was assigned to one of five groups according to highest achieved educational level. The sample size was 8,837 after participants with missing values or aged below 30 years were excluded. Cox regression models were used to assess the risk of death from all causes according to educational level, with a step-wise inclusion of explanatory covariates. RESULTS: Participants’ mean age at baseline was 54.1 years (SD 12.6); 3,999 were men (45.3%). In the follow-up period, 395 died (4.5%). With adjustment for age and gender, the risk of all-cause mortality was significantly higher in the two least-educated levels (HR = 1.5, 95%, CI = 1.2-1.8 and HR = 3.7, 95% CI = 2.4-5.9, respectively) compared to the middle educational level. After adjustment for the effect of subjective and objective health, similar results were obtained (HR = 1.4, 95% CI = 1.1-1.7 and HR = 3.5, 95% CI = 2.0-6.3, respectively). Further adjustment for the effect of behavioural, psychological, material and social determinants also failed to eliminate inequalities found among groups, the risk remaining significantly higher for the least educated levels (HR = 1.4, 95% CI = 1.1-1.9 and HR = 4.0, 95% CI = 2.3-6.8, respectively). In comparison with the middle level, the two highest educated levels remained statistically insignificant throughout the entire analysis. CONCLUSION: Socioeconomic inequality influenced mortality substantially even when adjusted for a range of determinants that might explain the association. Further studies are needed to understand this important relationship. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12889-015-1813-3) contains supplementary material, which is available to authorized users

Bottom-Up Policy Risk Assessment

Otorepec P, Martin-Olmedo P, Bolivar J, et al. Bottom-Up Policy Risk Assessment. In: Guliš G, Mekel O, Ádám B, Cori L, eds. Assessment of Population Health Risks of Policies. New York, NY: Springer New York; 2014: 131-198.The top-down risk assessment that fits the classical HIA method and its application on policy was in depth presented in the previous chapter. Many public health experts find large policies difficult to assess as for their impact on health. People knowing health outcome and its societal burden well may find it easier to find proper policies starting from the bottom line—from health outcome. The use of complex causal process diagrams for analyzing health impacts of policy interventions was already described. The RAPID guidance based on bottom-up approach might be helpful to act more efficiently in reducing prevalence of health outcomes by identification and selection of proper policies for structural intervention. The health outcome was taken as a starting point and assessment through levels of risk factors and determinants of health lead to identification of policies needed to reduce burden of health outcome. The present chapter illustrates through eight case studies conducted in seven different countries a bottom-up policy risk assessment model aiming to describe casual pathways upstream, from a single health outcome (effect) up through risk factors and health determinants leading to a list of policies. The purpose of this approach is to emphasize that results in health outcomes are related to many different policies at the same time, bringing up the need to adopt proactive inter-sectoral negotiations and putting health in the agenda of non-health sectors. Special interest was put on identifying the type of information needed for the description of the full-chain, possible discrepancies in defining health outcomes, risk factors or health determinants, the availability of data, the designation of possible scenarios related to political options, and the characterization of uncertainties