ObjectivesBusan is reported to have the highest mortality rate among 16 provinces in Korea, as well as considerable health inequality across its districts. This study sought to examine overall and cause-specific mortality and deprivation at the town level in Busan, thereby identifying towns and causes of deaths to be targeted for improving overall health and alleviating health inequality.MethodsStandardized mortality ratios (SMRs) for all-cause and four specific leading causes of death were calculated at the town level in Busan for the years 2005 through 2008. To construct a deprivation index, principal components and factor analysis were adopted, using 10% sample data from the 2005 census. Geographic information system (GIS) mapping techniques were applied to compare spatial distributions between the deprivation index and SMRs. We fitted the Gaussian conditional autoregressive model (CAR) to estimate the relative risks of mortality by deprivation level, controlling for both the heterogeneity effect and spatial autocorrelation.ResultsThe SMRs of towns in Busan averaged 100.3, ranging from 70.7 to 139.8. In old inner cities and towns reclaimed for replaced households, the deprivation index and SMRs were relatively high. CAR modeling showed that gaps in SMRs for heart disease, cerebrovascular disease, and physical injury were particularly high.ConclusionsOur findings indicate that more deprived towns are likely to have higher mortality, in particular from cardiovascular disease and physical injury. To improve overall health status and address health inequality, such deprived towns should be targeted.
threshold probability of 20% to predict cardiovascular diseases in a population based cohort of 6224 Iranians aged 30e74 years with 10-year follow-up. Results dividing NB by incidence resulted 17% and PAF shows 43% decrease in incidence, but NBF shows just 8% advantage for treatment according to the model. Conclusion NBF seems to be a challengeable issue in policy making using risk functions. Background Social status is associated with cardiovascular disease (CVD) prevalence and incidence. We aimed to study relationships between i) socioeconomic position (SEP) and common CVD biomarkers; cholesterol, LDL/HDL, ApoB/ApoA1 and adiponectin ii) SEP and CVD mortality in a Swedish-population-based sample, and to assess if these associations changed with age. Design A longitudinal cohort study of men born 1920-24 with clinical measurements, blood samples, questionnaire data and register-based information on SEP and cause of death. Methods Of 2322 men that participated in an investigation at age 50, 1221 attended a reinvestigation at age 70. SEP was measured as occupational class and educational level. Linear regression (adjusted for age, body mass index and physical activity) was used to study associations between SEP and CVD biomarkers. CVD mortality over 36 year's follow-up was analysed by Cox regression. Results At age 50: We found significant inverse associations of education and occupational group with mean cholesterol levels, whereas LDL/HDL ratio was associated with education only. These were statistically significant after adjustment for covariates. No significant associations were found between either measure of SEP and ApoB/ApoA1 ratio. At age 70: No significant associations were found between either measurement of SEP and any biomarker studied. Men classified as highest educated and non-manual had decreased risk for CVD mortality during follow-up. Conclusions Associations of SEP with cholesterol levels and LDL/ HDL ratio that exist at age 50, are no longer found in the same group of men at age 70. We found no significant association between SEP and adiponectin levels at age 70. Introduction Government spending on public goods (eg, education) and social assistance (eg, cash transfers) provides plausible investments in the social determinants of health. Among rich nations, countries with higher social spending and lower income inequality show longer life expectancies. However, studies of both factors have been limited by bias from residual confounding and reverse causation. Methods This study examined data from the National Longitudinal Mortality Study on 431 637 adults aged 30e74 in 48 USA states followed for 11 years. State per capita social spending (total, welfare, education, health) and income inequality (Gini coefficient) were explored as predictors of individual mortality (all-cause, cardiovascular, cancer) using linear probability models. To reduce bias, models P1-190 P1-191
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