This paper studies a non-response problem in survival analysis where the occurrence of missing data in the risk factor is related to mortality. In a study to determine the influence of blood pressure on survival in the very old (85+ years), blood pressure measurements are missing in about 12.5 per cent of the sample. The available data suggest that the process that created the missing data depends jointly on survival and the unknown blood pressure, thereby distorting the relation of interest. Multiple imputation is used to impute missing blood pressure and then analyse the data under a variety of non-response models. One special modelling problem is treated in detail; the construction of a predictive model for drawing imputations if the number of variables is large. Risk estimates for these data appear robust to even large departures from the simplest non-response model, and are similar to those derived under deletion of the incomplete records.

This paper studies a non-response problem in survival analysis where the occurrence of missing data in the risk factor is related to mortality. In a study to determine the influence of blood pressure on survival in the very old (85# years), blood pressure measurements are missing in about 12)5 per cent of the sample. The available data suggest that the process that created the missing data depends jointly on survival and the unknown blood pressure, thereby distorting the relation of interest. Multiple imputation is used to impute missing blood pressure and then analyse the data under a variety of non-response models. One special modelling problem is treated in detail; the construction of a predictive model for drawing imputations if the number of variables is large. Risk estimates for these data appear robust to even large departures from the simplest non-response model, and are similar to those derived under deletion of the incomplete records.

Waist circumference and WHR, indicators of abdominal obesity, were strongly associated with colon cancer risk in men and women in this population. The association of abdominal obesity with colon cancer risk may vary depending on HRT use in postmenopausal women; however, these findings require confirmation in future studies.

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Background:The extent to which moderate overweight (body mass index [BMI], 25.0-29.9 [calculated as weight in kilograms divided by height in meters squared]) and obesity (BMI, Ն30.0) are associated with increased risk of coronary heart disease (CHD) through adverse effects on blood pressure and cholesterol levels is unclear, as is the risk of CHD that remains after these mediating effects are considered.Methods: Relative risks (RRs) of CHD associated with moderate overweight and obesity with and without adjustment for blood pressure and cholesterol concentrations were calculated by the members of a collaboration of prospective cohort studies of healthy, mainly white persons and pooled by means of random-effects models (RRs for categories of BMI in 14 cohorts and for continuous BMI in 21 cohorts; total N = 302 296).Results: A total of 18 000 CHD events occurred during follow-up. The age-, sex-, physical activity-, and smoking-adjusted RRs (95% confidence intervals) for moderate overweight and obesity compared with normal weight were 1.32 (1.24-1.40) and 1.81 (1.56-2.10), respectively. Additional adjustment for blood pressure and cholesterol levels reduced the RR to 1.17 (1.11-1.23) for moderate overweight and to 1.49 (1.32-1.67) for obesity. The RR associated with a 5-unit BMI increment was 1.29 (1.22-1.35) before and 1.16 (1.11-1.21) after adjustment for blood pressure and cholesterol levels.Conclusions: Adverse effects of overweight on blood pressure and cholesterol levels could account for about 45% of the increased risk of CHD. Even for moderate overweight, there is a significant increased risk of CHD independent of these traditional risk factors, although confounding (eg, by dietary factors) cannot be completely ruled out.

It has been suggested that noise exposure is associated with blood pressure changes and ischemic heart disease risk, but epidemiologic evidence is still limited. Furthermore, most reviews investigating these relations were not carried out in a systematic way, which makes them more prone to bias. We conducted a meta-analysis of 43 epidemiologic studies published between 1970 and 1999 that investigate the relation between noise exposure (both occupational and community) and blood pressure and/or ischemic heart disease (International Classification of Diseases, Ninth Revision, codes 410-414). We studied a wide range of effects, from blood pressure changes to a myocardial infarction. With respect to the association between noise exposure and blood pressure, small blood pressure differences were evident. Our meta-analysis showed a significant association for both occupational noise exposure and air traffic noise exposure and hypertension: We estimated relative risks per 5 dB(A) noise increase of 1.14 (1.01-1.29) and 1.26 (1.14-1.39), respectively. Air traffic noise exposure was positively associated with the consultation of a general practitioner or specialist, the use of cardiovascular medicines, and angina pectoris. In cross-sectional studies, road traffic noise exposure increases the risk of myocardial infarction and total ischemic heart disease. Although we can conclude that noise exposure can contribute to the prevalence of cardiovascular disease, the evidence for a relation between noise exposure and ischemic heart disease is still inconclusive because of the limitations in exposure characterization, adjustment for important confounders, and the occurrence of publication bias.

Our finding may support a beneficial role of higher intake of dietary fiber, especially cereal fiber, in prevention of body-weight and waist circumference gain.

BackgroundObesity is a major cause of morbidity and mortality and is associated with high medical expenditures. It has been suggested that obesity prevention could result in cost savings. The objective of this study was to estimate the annual and lifetime medical costs attributable to obesity, to compare those to similar costs attributable to smoking, and to discuss the implications for prevention.Methods and FindingsWith a simulation model, lifetime health-care costs were estimated for a cohort of obese people aged 20 y at baseline. To assess the impact of obesity, comparisons were made with similar cohorts of smokers and “healthy-living” persons (defined as nonsmokers with a body mass index between 18.5 and 25). Except for relative risk values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the effects of epidemiologic parameters and cost definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Because of differences in life expectancy, however, lifetime health expenditure was highest among healthy-living people and lowest for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and cost definitions did not alter these conclusions.ConclusionsAlthough effective obesity prevention leads to a decrease in costs of obesity-related diseases, this decrease is offset by cost increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-effective way of improving public health, but it is not a cure for increasing health expenditures.