Risk Factors for Human Infection with Puumala Virus, Southwestern Germany

Risk factors are available bank vole habitat, abundant vole food supply, high human population density, and warmer climate

Outbreak of leptospirosis among triathlon participants in Germany, 2006

<p>Abstract</p> <p>Background</p> <p>In August 2006, a case of leptospirosis occurred in an athlete after a triathlon held around Heidelberg and in the Neckar river. In order to study a possible outbreak and to determine risk factors for infection an epidemiological investigation was performed.</p> <p>Methods</p> <p>Participants of the triathlon were contacted by e-mail and were asked to fill out a standardized questionnaire. In addition, they were asked to supply a serum sample for laboratory diagnosis of leptospirosis. A confirmed case patient was defined as a clinical case (i.e. fever and at least one additional symptom suggestive for leptospirosis) with at least two of the following tests positive: ELISA IgM, latex agglutination testing, or microscopic agglutination testing. Rainfall and temperature records were obtained.</p> <p>Results</p> <p>A total of 142 of 507 triathletes were contacted; among these, five confirmed leptospirosis cases were found. Open wounds were identified as the only significant risk factor for illness (p = 0.02). Heavy rains that preceded the swimming event likely increased leptospiral contamination of the Neckar River.</p> <p>Discussion</p> <p>This is the first outbreak of leptospirosis related to a competitive sports event in Germany. Among people with contact to freshwater, the risk of contracting leptospirosis should be considered by health care providers also in temperate countries, particularly in the summer after heavy rains.</p

Vitamin C supplement use may protect against gallstones: an observational study on a randomly selected population

<p>Abstract</p> <p>Background</p> <p>Animal experiments have shown a protective effect of vitamin C on the formation of gallstones. Few data in humans suggest an association between reduced vitamin C intake and increased prevalence of gallstone disease. The aim of this study was to assess the possible association of regular vitamin C supplementation with gallstone prevalence.</p> <p>Methods</p> <p>An observational, population-based study of 2129 subjects aged 18-65 years randomly selected from the general population in southern Germany was conducted. Abdominal ultrasound examination, completion of a standardized questionnaire, compilation of anthropometric data and blood tests were used. Data were collected in November and December 2002. Data analysis was conducted between December 2005 and January 2006.</p> <p>Results</p> <p>Prevalence of gallstones in the study population was 7.8% (167/2129). Subjects reporting vitamin C supplementation showed a prevalence of 4.7% (11/232), whereas in subjects not reporting regular vitamin C supplementation, the prevalence was 8.2% (156/1897). Female gender, hereditary predisposition, increasing age and body-mass index (BMI) were associated with increased prevalence of gallstones. Logistic regression with backward elimination adjusted for these factors showed reduced gallstone prevalence for vitamin C supplementation (odds ratio, OR 0.34; 95% confidence interval, CI 0.14 to 0.81; P = 0.01), increased physical activity (OR 0.62; 95% CI, 0.42 to 0.94; P = 0.02), and higher total cholesterol (OR 0.65; 95% CI, 0.52 to 0.79; P < 0.001).</p> <p>Conclusion</p> <p>Regular vitamin C supplementation and, to a lesser extent, increased physical activity and total cholesterol levels are associated with a reduced prevalence of gallstones. Regular vitamin C supplementation might exert a protective effect on the development of gallstones.</p

Overweight, physical activity, tobacco and alcohol consumption in a cross-sectional random sample of German adults

BACKGROUND: There is a current paucity of data on the health behaviour of non-selected populations in Central Europe. Data on health behaviour were collected as part of the EMIL study which investigated the prevalence of infection with Echinococcus multilocularis and other medical conditions in an urban German population. METHODS: Participating in the present study were 2,187 adults (1,138 females [52.0%]; 1,049 males [48.0%], age: 18–65 years) taken from a sample of 4,000 persons randomly chosen from an urban population. Data on health behaviour like physical activity, tobacco and alcohol consumption were obtained by means of a questionnaire, documentation of anthropometric data, abdominal ultrasound and blood specimens for assessment of chemical parameters. RESULTS: The overall rate of participation was 62.8%. Of these, 50.3% of the adults were overweight or obese. The proportion of active tobacco smokers stood at 30.1%. Of those surveyed 38.9% did not participate in any physical activity. Less than 2 hours of leisure time physical activity per week was associated with female sex, higher BMI (Body Mass Index), smoking and no alcohol consumption. Participants consumed on average 12 grams of alcohol per day. Total cholesterol was in 62.0% (>5.2 mmol/l) and triglycerides were elevated in 20.5% (≥ 2.3 mmol/l) of subjects studied. Hepatic steatosis was identified in 27.4% of subjects and showed an association with male sex, higher BMI, higher age, higher total blood cholesterol, lower HDL, higher triglycerides and higher ALT. CONCLUSION: This random sample of German urban adults was characterised by a high prevalence of overweight and obesity. This and the pattern of alcohol consumption, smoking and physical activity can be considered to put this group at high risk for associated morbidity and underscore the urgent need for preventive measures aimed at reducing the significantly increased health risk

Helicobacter pylori-Specific Immune Responses of Children: Implications for Future Vaccination Strategy

We analyzed the specific anti-Helicobacter pylori immunoglobulin G (IgG) antibody profile for a sample of 824 asymptomatic schoolchildren in southern Germany (mean age, 10.7 ± 0.65 years) with an H. pylori-specific IgG enzyme-linked immunosorbent assay and Western blot analysis. The prevalence of infection was 19.8% (95% confidence interval, 17.1 to 22.7%). The immunoresponses were characterized predominantly by antibodies against low-molecular-mass antigens of 14 and 29 kDa, with a significant difference between children of German and Turkish nationalities (P = 0.0012 and P < 0.0001, respectively)

Influenza pandemic intervention planning using : pharmaceutical and non- pharmaceutical interventions-1

<p><b>Copyright information:</b></p><p>Taken from "Influenza pandemic intervention planning using : pharmaceutical and non- pharmaceutical interventions"</p><p>http://www.biomedcentral.com/1471-2334/7/76</p><p>BMC Infectious Diseases 2007;7():76-76.</p><p>Published online 13 Jul 2007</p><p>PMCID:PMC1939851.</p><p></p>eter values are based on the standard configuration [15] with = 2.5, except those listed at the end of this legend and indicated by superscripts. ranges from 0% (no antivirals available, dashed curves) to 10% (antivirals available for 10% of the population) in steps of 1% (from left to right). The dashed curve shows the epidemic without intervention. Grey dotted lines represent the scenario where antivirals are available for the whole population. Bars at the bottom of each graph indicate the period when antiviral treatment begins (model input) until stockpiles are used up (model output). : Antivirals are available from day 0 . : Antivirals become available after three weeks. The epidemic curves depart from the grey dotted line when antivirals are exhausted.:Parameter modifications are given in the following and terms in italics refer to terms in the user interface. output: = cumulative proportion of the population infected, and = cumulative proportion of outpatients in the population. : : 0%, yielding = 87%, = 29% for both, A and B. : : 10%, yielding = 82%, = 27% for both, A and B. : : 100%, yielding = 72%, = 24% for both, A and B. : : 0–80. : : 21–80

Influenza pandemic intervention planning using : pharmaceutical and non- pharmaceutical interventions-4

<p><b>Copyright information:</b></p><p>Taken from "Influenza pandemic intervention planning using : pharmaceutical and non- pharmaceutical interventions"</p><p>http://www.biomedcentral.com/1471-2334/7/76</p><p>BMC Infectious Diseases 2007;7():76-76.</p><p>Published online 13 Jul 2007</p><p>PMCID:PMC1939851.</p><p></p>ts (inset), originating from the uncertainty in four parameters (right panel). Parameter values are based on the standard configuration [15] with = 2.5, except those listed at the end of this legend and indicated by superscripts. The sensitivity analysis extends the scenario shown in Figure 5, where antivirals are available for 10% of the population and are distributed from day zero, and where contact reduction measures, including the isolation of cases, are initiated three weeks after the introduction of infection (scenario "day 21"). : parameter values for each realization are sampled independently from normal distributions as shown (means given in bold, 99% of the values lie within the range specified by dotted lines, except which is truncated). : basic reproduction number, : cumulative infectivity during the first half of the symptomatic period, : relative infectivity of asymptomatic cases, : antiviral treatment reduces infectivity by a factor of 1-. For each parameter, an increase of the value aggravates the epidemic. : from a hundred random realizations, we selected the two most extreme epidemics, and eight epidemics homogeneously placed between them. The epidemic with = 20800 is caused by parameter values drawn from the left tail of the corresponding distributions, and the epidemic with = 5000 is caused by parameter values drawn from the right tail of the corresponding distributions (see right panel). The epidemic curves show a plateau or a second wave when antiviral stockpiles are exhausted while the proportion of susceptibles is still large enough to allow for further propagation of infectives (thin curves in black); for optimistic parameter combinations (e.g. small ), the available stockpiles last over the whole period of the intervention and the epidemic curve proceeds without a plateau (bold curves in grey). : distribution of cumulative number of outpatients obtained from 1,000 random realizations.:Parameter modifications are given in the following and terms in italics refer to terms in the user interface. : : 10%. : 100%, for both, and . : : 20%. : day 21–360. : : 10%, : 20%, : 30%. : day 21–360

Influenza pandemic intervention planning using : pharmaceutical and non- pharmaceutical interventions-0

<p><b>Copyright information:</b></p><p>Taken from "Influenza pandemic intervention planning using : pharmaceutical and non- pharmaceutical interventions"</p><p>http://www.biomedcentral.com/1471-2334/7/76</p><p>BMC Infectious Diseases 2007;7():76-76.</p><p>Published online 13 Jul 2007</p><p>PMCID:PMC1939851.</p><p></p>o operation. Parameter values are based on the standard configuration [15] with = 2.5, except those listed at the end of this legend and indicated by superscripts. The dashed curve shows the epidemic without intervention. Antivirals are available for 5% of the population(black lines), compared to scenarios of full coverage(grey lines). The shaded areas under the curves represent the amounts of antivirals distributed and are identical for both scenarios. They are shown between onset of intervention and exhaustion. If antivirals are available at the beginning of the epidemic ("Intervention from day 0") they last for 45 days. Antivirals last only for a shorter period, if coming into operation in later phases of the epidemic ("Intervention from day 30"). :Parameter modifications are given in the following and terms in italics refer to terms in the user interface. output: = cumulative proportion of the population infected, and = cumulative proportion of outpatients in the population. : Yielding = 87%, = 29%. : : 5%, : 100% for both, and , yielding = 84%, = 28% for scenarios, "day 0" and "day 30". : : 100%, : 100% for both, and , yielding = 72%, = 24% for "day 0" and = 74%, = 25% for "day 30". : : 5%, : 100%, : 0–80 for both, and . : : 5%, : 100%, : 30–80 for both, and