897 resultados para parasitoid mortality


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BACKGROUND: Highly active antiretroviral therapy (HAART) is being scaled up in developing countries. We compared baseline characteristics and outcomes during the first year of HAART between HIV-1-infected patients in low-income and high-income settings. METHODS: 18 HAART programmes in Africa, Asia, and South America (low-income settings) and 12 HIV cohort studies from Europe and North America (high-income settings) provided data for 4810 and 22,217, respectively, treatment-naive adult patients starting HAART. All patients from high-income settings and 2725 (57%) patients from low-income settings were actively followed-up and included in survival analyses. FINDINGS: Compared with high-income countries, patients starting HAART in low-income settings had lower CD4 cell counts (median 108 cells per muL vs 234 cells per muL), were more likely to be female (51%vs 25%), and more likely to start treatment with a non-nucleoside reverse transcriptase inhibitor (NNRTI) (70%vs 23%). At 6 months, the median number of CD4 cells gained (106 cells per muL vs 103 cells per muL) and the percentage of patients reaching HIV-1 RNA levels lower than 500 copies/mL (76%vs 77%) were similar. Mortality was higher in low-income settings (124 deaths during 2236 person-years of follow-up) than in high-income settings (414 deaths during 20,532 person-years). The adjusted hazard ratio (HR) of mortality comparing low-income with high-income settings fell from 4.3 (95% CI 1.6-11.8) during the first month to 1.5 (0.7-3.0) during months 7-12. The provision of treatment free of charge in low-income settings was associated with lower mortality (adjusted HR 0.23; 95% CI 0.08-0.61). INTERPRETATION: Patients starting HAART in resource-poor settings have increased mortality rates in the first months on therapy, compared with those in developed countries. Timely diagnosis and assessment of treatment eligibility, coupled with free provision of HAART, might reduce this excess mortality.

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BACKGROUND: Patients coinfected with hepatitis C virus (HCV) and HIV experience higher mortality rates than patients infected with HIV alone. We designed a study to determine whether risks for later mortality are similar for HCV-positive and HCV-negative individuals when subjects are stratified on the basis of baseline CD4+ T-cell counts. METHODS: Antiretroviral-naive individuals, who initiated highly active antiretroviral therapy (HAART) between 1996 and 2002 were included in the study. HCV-positive and HCV-negative individuals were stratified separately by baseline CD4+ T-cell counts of 50 cell/microl increments. Cox-proportional hazards regression was used to model the effect of these strata with other variables on survival. RESULTS: CD4+ T-cell strata below 200 cells/microl, but not above, imparted an increased relative hazard (RH) of mortality for both HCV-positive and HCV-negative individuals. Among HCV-positive individuals, after adjustment for baseline age, HIV RNA levels, history of injection drug use and adherence to therapy, only CD4+ T-cell strata of <50 cells/microl (RH=4.60; 95% confidence interval [CI] 2.72-7.76) and 50-199 cells/microl (RH=2.49; 95% CI 1.63-3.81) were significantly associated with increased mortality when compared with those initiating therapy at cell counts >500 cells/microl. The same baseline CD4+ T-cell strata were found for HCV-negative individuals. CONCLUSION: In a within-groups analysis, the baseline CD4+ T-cell strata that are associated with increased RHs for mortality are the same for HCV-positive and HCV-negative individuals initiating HAART. However, a between-groups analysis reveals a higher absolute mortality risk for HCV-positive individuals.

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The NMMAPS data package contains daily mortality, air pollution, and weather data originally assembled as part of the National Morbidity,Mortality, and Air Pollution Study (NMMAPS). The data have recently been updated and are available for 108 United States cities for the years 1987--2000. The package provides tools for building versions of the full database in a structured and reproducible manner. These database derivatives may be more suitable for particular analyses. We describe how to use the package to implement a multi-city time series analysis of mortality and PM(10). In addition we demonstrate how to reproduce recent findings based on the NMMAPS data.

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In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal-normal hierarchical model. We assess the sensitivity of the results to: 1) lag structure for ozone exposure; 2) degree of adjustment for long-term trends; 3) inclusion of other pollutants in the model;4) heat waves; 5) random effects distributions; and 6) prior hyperparameters. On average across cities, we found that a 10ppb increase in summer ozone level for every day in the previous week is associated with 1.25 percent increase in CVDRESP mortality (95% posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1, and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM_10, but are robust to: 1) the adjustment for long-term trends, other gaseous pollutants (NO_2, SO_2, and CO); 2) the distributional assumptions at the second stage of the hierarchical model; and 3) the prior distributions on all unknown parameters. Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us estimation of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.

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While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS.

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We propose a method for diagnosing confounding bias under a model which links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates. We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 U.S. counties from 2000-2002. We decompose the association between PM2.5 and mortality into two components: 1) the association between “national trends” in PM2.5 and mortality; and 2) the association between “local trends,” defined as county-specificdeviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortality relative to their national trends. We find that the exposure effect estimates are different at these two spatio-temporalscales, which raises concerns about confounding bias. We believe that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.

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Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors develop Bayesian semi-parametric hierarchical models for estimating time-varying effects of pollution on mortality in multi-site time series studies. The methods are applied to the updated National Morbidity and Mortality Air Pollution Study database for the period 1987--2000, which includes data for 100 U.S. cities. At the national level, a 10 micro-gram/m3 increase in PM(10) at lag 1 is associated with a 0.15 (95% posterior interval: -0.08, 0.39),0.14 (-0.14, 0.42), 0.36 (0.11, 0.61), and 0.14 (-0.06, 0.34) percent increase in mortality for winter, spring, summer, and fall, respectively. An analysis by geographical regions finds a strong seasonal pattern in the northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.

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Prospective cohort studies have provided evidence on longer-term mortality risks of fine particulate matter (PM2.5), but due to their complexity and costs, only a few have been conducted. By linking monitoring data to the U.S. Medicare system by county of residence, we developed a retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), comprising over 20 million enrollees in the 250 largest counties during 2000-2002. We estimated log-linear regression models having as outcome the age-specific mortality rate for each county and as the main predictor, the average level for the study period 2000. Area-level covariates were used to adjust for socio-economic status and smoking. We reported results under several degrees of adjustment for spatial confounding and with stratification into by eastern, central and western counties. We estimated that a 10 µg/m3 increase in PM25 is associated with a 7.6% increase in mortality (95% CI: 4.4 to 10.8%). We found a stronger association in the eastern counties than nationally, with no evidence of an association in western counties. When adjusted for spatial confounding, the estimated log-relative risks drop by 50%. We demonstrated the feasibility of using Medicare data to establish cohorts for follow-up for effects of air pollution. Particulate matter (PM) air pollution is a global public health problem (1). In developing countries, levels of airborne particles still reach concentrations at which serious health consequences are well-documented; in developed countries, recent epidemiologic evidence shows continued adverse effects, even though particle levels have declined in the last two decades (2-6). Increased mortality associated with higher levels of PM air pollution has been of particular concern, giving an imperative for stronger protective regulations (7). Evidence on PM and health comes from studies of acute and chronic adverse effects (6). The London Fog of 1952 provides dramatic evidence of the unacceptable short-term risk of extremely high levels of PM air pollution (8-10); multi-site time-series studies of daily mortality show that far lower levels of particles are still associated with short-term risk (5)(11-13). Cohort studies provide complementary evidence on the longer-term risks of PM air pollution, indicating the extent to which exposure reduces life expectancy. The design of these studies involves follow-up of cohorts for mortality over periods of years to decades and an assessment of mortality risk in association with estimated long-term exposure to air pollution (2-4;14-17). Because of the complexity and costs of such studies, only a small number have been conducted. The most rigorously executed, including the Harvard Six Cities Study and the American Cancer Society’s (ACS) Cancer Prevention Study II, have provided generally consistent evidence for an association of long- term exposure to particulate matter air pollution with increased all-cause and cardio-respiratory mortality (2,4,14,15). Results from these studies have been used in risk assessments conducted for setting the U.S. National Ambient Air Quality Standard (NAAQS) for PM and for estimating the global burden of disease attributable to air pollution (18,19). Additional prospective cohort studies are necessary, however, to confirm associations between long-term exposure to PM and mortality, to broaden the populations studied, and to refine estimates by regions across which particle composition varies. Toward this end, we have used data from the U.S. Medicare system, which covers nearly all persons 65 years of age and older in the United States. We linked Medicare mortality data to (particulate matter less than 2.5 µm in aerodynamic diameter) air pollution monitoring data to create a new retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), consisting of 20 million persons from 250 counties and representing about 50% of the US population of elderly living in urban settings. In this paper, we report on the relationship between longer-term exposure to PM2.5 and mortality risk over the period 2000 to 2002 in the MCAPS.

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AIMS: The objective of the present study was to investigate the relationship between extremely low-frequency magnetic field (ELF-MF) exposure and mortality from several neurodegenerative conditions in Swiss railway employees. METHODS: We studied a cohort of 20,141 Swiss railway employees with 464,129 person-years of follow-up between 1972 and 2002. For each individual, cumulative exposure was calculated from on-site measurements and modelling of past exposure. We compared cause-specific mortality in highly exposed train drivers (mean exposure: 21 microT) with less exposed occupational groups (for example station masters: 1 microT). RESULTS: The hazard ratio for train drivers compared to station masters was 1.96 [95% confidence interval (CI) = 0.98-3.92] for senile dementia and 3.15 (95% CI = 0.90-11.04) for Alzheimer's disease. For every 10 microT years of cumulative exposure senile dementia mortality increased by 5.7% (95% CI = 1.3-10.4), Alzheimer's disease by 9.4% (95% CI = 2.7-16.4) and amyotrophic lateral sclerosis by 2.1% (95% CI = -6.8 to 11.7). There was no evidence for an increase in mortality from Parkinson's disease and multiple sclerosis. CONCLUSIONS: This study suggests a link between exposure to ELF-MF and Alzheimer's disease and indicates that ELF-MF might act in later stages of the disease process.

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BACKGROUND: Antiretroviral therapy (ART) decreases morbidity and mortality in HIV-infected patients but is associated with considerable adverse events (AEs). METHODS: We examined the effect of AEs to ART on mortality, treatment modifications and drop-out in the Swiss HIV Cohort Study. A cross-sectional evaluation of prevalence of 13 clinical and 11 laboratory parameters was performed in 1999 in 1,078 patients on ART. AEs were defined as abnormalities probably or certainly related to ART. A score including the number and severity of AEs was defined. The subsequent progression to death, drop-out and treatment modification due to intolerance were evaluated according to the baseline AE score and characteristics of individual AEs. RESULTS: Of the 1,078 patients, laboratory AEs were reported in 23% and clinical AEs in 45%. During a median follow up of 5.9 years, laboratory AEs were associated with higher mortality with an adjusted hazard ratio (HR) of 1.3 (95% confidence interval [CI] 1.2-1.5; P < 0.001) per score point. For clinical AEs no significant association with increased mortality was found. In contrast, an increasing score for clinical AEs (HR 1.11,95% CI 1.04-1.18; P = 0.002), but not for laboratory AEs (HR 1.07, 95% CI 0.97-1.17; P = 0.17), was associated with antiretroviral treatment modification. AEs were not associated with a higher drop-out rate. CONCLUSIONS: The burden of laboratory AEs to antiretroviral drugs is associated with a higher mortality. Physicians seem to change treatments to relieve clinical symptoms, while accepting laboratory AEs. Minimizing laboratory drug toxicity seems warranted and its influence on survival should be further evaluated.

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A rising concern exists that with the widespread use of mobile communication technologies, the incidence of brain tumours may increase. On the basis of data from the Swiss national mortality registry from 1969 to 2002, annual age-standardized brain tumour mortality rates per 100,000 person-years were calculated using the European standard population. Time trend analyses were performed by the Poisson regression for six different age groups in men and women separately. The study period was divided into two intervals: before and after 1987, when the analogue mobile technology was introduced in Switzerland. Age-standardized brain tumour mortality rates ranged between 3.7 and 6.7 for men and 2.5 and 4.4 for women per 100,000 person-years. For the whole study period, a significant increase in brain tumour mortality was observed for men and women in the older age groups (60-74 and 75+ years) but not in the younger ones in whom mobile phone use was more prevalent. Time trend analyses restricted to data from 1987 onwards revealed relatively stable brain tumour mortality rates in all age groups. For instance, the annual change in brain tumour mortality rate for the 45-59-year age group was -0.3% (95% confidence interval: -1.7; 1.1) for men and -0.4% (95% confidence interval:-2.2; 1.3) for women. We conclude that after the introduction of mobile phone technology in Switzerland, brain tumour mortality rates remained stable in all age groups. Our results suggest that mobile phone use is not a strong risk factor in the short term for mortality from brain tumours. Ecological analyses like this, however, are limited in their ability to reveal potentially small increases in risk for diseases with a long latency period.

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NcMIC4 is a Neospora caninum microneme protein that has been isolated and purified on the basis of its unique lactose-binding properties. We have shown that this protein binds to galactosyl residues of lactose; antibodies directed against NcMIC4 inhibit host cell interactions in vitro, thus making it a vaccine candidate. Because of this feature, NcMIC4 was first purified on a larger scale in its native, functionally active form using lactose-agarose affinity chromatography. Second, NcMIC4 was expressed in Escherichia coli as a histidine-tagged recombinant protein (recNcMIC4) and purified through Ni-affinity chromatography. Third, NcMIC4 cDNA was cloned into the mammalian pcDNA3.1 DNA vector and expression was confirmed upon transfection of Vero cells in vitro. For vaccination studies, we employed the murine cerebral infection model based on C57Bl/6 mice, employing experimental groups of 10 mice each. Two groups were injected intraperitoneally with purified native NcMIC4 and recNcMIC4, respectively, employing RIBI adjuvant. The third group was vaccinated intramuscularly with pcDNA-NcMIC4. Control groups included an infection control, an adjuvant control, and a pcDNA3.1 control group. Following 3 injections at 4-wk intervals, mice were challenged by i.p. inoculation of 2 x 10(6) N. caninum tachyzoites (Nc-1 isolate). During the course of parasite challenge (3 wk), mice from the 3 different test groups showed varying degrees of symptoms bearing a semblance to neosporosis, i.e., walking disorder, rounded back, apathy, and paralysis of the hind limbs. Control groups showed no symptoms at all. Most notably, vaccination with pcDNA-MIC4 proved antiprotective, with 60% of mice succumbing to infection within 3 wk, and all mice lacking a measurable anti-NcMIC4 IgG response. NcMIC4 in its native form elicited a substantial humoral IgG1 immune response and a reduction in cerebral parasite load compared to the controls, but 20% of mice succumbed to infection. Vaccination with recNcMIC4 also resulted in 20% of mice dying; however, in this group, cerebral parasite load was similar to the controls, and recNcMIC4 vaccination elicited a mixed IgG1/IgG2 response. In conclusion, vaccines based on NcMIC4, especially pcDNA-NcMIC4, render mice more susceptible to cerebral disease upon challenge with N. caninum tachyzoites.

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Due to new therapeutic modalities and modified therapeutic goals outcome of patients with acromegaly may change over time and differ by centre. We analysed treatment outcomes and mortality of our patients with acromegaly seen between 1971 and 2003.

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Moderate alcohol intake has been associated with increased life expectancy due to reduced mortality from cardiovascular disease. We prospectively examined the effects of alcohol consumption on mortality in Type 2 diabetic patients in Switzerland.