Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

We investigated the relation of overweight and obesity with cancer in a population-based cohort of more than 145 000 Austrian adults over an average of 9.9 years. Incident cancers (n ¼ 6241) were identified through the state cancer registry. Using Cox proportional-hazards models adjusted for smoking and occupation, increases in relative body weight in men were associated with colon cancer (hazard rate (HR) ratio 2.48; 95% confidence interval (CI): 1.15, 5.39 for body mass index (BMI) X35 kg m À2 ) and pancreatic cancer (HR 2.34, 95% CI: 1.17, 4.66 for BMI430 kg m À2 ) compared to participants with normal weight (BMI 18.5 -24.9 kg m À2 ). In women, there was a weak positive association between increasing BMI and all cancers combined, and strong associations with non-Hodgkin's lymphomas (HR 2.86, 95% CI: 1.49, 5.49 for BMIX30 kg m À2 ) and cancers of the uterine corpus (HR 3.93, 95% CI: 2.35, 6.56 for BMIX35 kg m À2 ). Incidence of breast cancer was positively associated with high BMI only after age 65 years. These findings provide further evidence that overweight is associated with the incidence of several types of cancer.

These findings provide further evidence that elevated blood glucose is associated with the incidence of several types of cancer in men and women.

,6 and the VHM&PP Study Group BACKGROUND: The role of serum uric acid (SUA) as an independent risk factor for cardiovascular disease (CVD) remains controversial, and little is known about its prognostic importance for mortality from congestive heart failure (CHF) and stroke. Few large-scale epidemiologic studies with sufficient follow-up have addressed the association of SUA and CVD mortality in apparently healthy men across a wide age range.

BACKGROUND: Blood lipid levels as part of the metabolic syndrome are thought to be linked to cancer risk. Few epidemiological studies have addressed the association between serum triglyceride (STG) concentrations and cancer risk. METHODS: Serum triglyceride concentrations were collected in a health investigation (1988 -2003). The analyses included 156 153 subjects (71 693 men and 84 460 women), with 5079 incident cancers in men and 4738 cancers in women, and an average of 10.6 years of follow-up. All malignancies were ascertained from the population cancer registry. Multivariate Cox proportional hazard models stratified by age and sex were used to determine adjusted cancer risk estimates and 95% confidence interval (95% CI). RESULTS: In men and women combined, higher STG concentrations were associated with increased risk of lung (4th vs 1st quartile: HR, 1.94; 95% CI, 1.47 -2.54), rectal (HR, 1.56; 95% CI, 1.00 -2.44), and thyroid cancer (HR, 1.96; 95% CI, 1.00 -3.84). Serum triglyceride concentrations were inversely associated with non-Hodgkin's lymphoma. In men, STG concentrations were inversely associated with prostate cancer and positively with renal cancer. In women, STG concentrations were positively associated with gynaecological cancers. Stratification by BMI revealed a higher risk of gynaecological cancers in overweight than in normal weight women. No other associations were found. CONCLUSIONS: Our findings support the hypothesis that STG concentrations are involved in the pathogenesis of lung, rectal, thyroid, prostate, and gynaecological cancers.

Consider a case-control study in which prevalent cases of a given disease define the index series and members of the base population without the disease are sampled to provide the referent series. Information on a set of explanatory variables (eg, genotypes) is collected at great cost for cases and controls. The objective of the study is to evaluate the relationship between case status and the explanatory variables. Subsequently, an investigator notes that the prevalence of a second disease was measured for the members of the index and referent series. The investigator wishes to make efficient use of the available data by assessing the relationship between this second disease and the set of explanatory variables. In this paper, we discuss 2 analytic approaches that might be used to assess associations between the explanatory variables and an outcome other than the original disease. One is through the inclusion of a design variable for original disease status as a covariate; and, the second is through weighted logistic regression using the inverse of the sampling fractions as the weights. The latter approach allows the investigator to derive an estimate of association between the explanatory variables and the second disease without adjustment for the first disease. Weighted logistic regression methods are readily implemented using available statistical packages.

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