986 resultados para Monica Project Populations


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Background. From the mid-1980s to mid-1990s, the WHO MONICA Project monitored coronary events and classic risk factors for coronary heart disease (CHD) in 38 populations from 21 countries. We assessed the extent to which changes in these risk factors explain the variation in the trends in coronary-event rates, across the populations. Methods. In men and women aged 35-64 years, non-fatal myocardial infarction and coronary deaths were registered continuously to assess trends in rates of coronary events. We carried out population surveys to estimate trends in risk factors. Trends in event rates were regressed on trends in risk score and in individual risk factors. Findings. Smoking rates decreased in most male populations but trends were mixed in women; mean blood pressures and cholesterol concentrations decreased, body-mass index increased, and overall risk scores and coronary-event rates decreased. The model of trends in 10-year coronary-event rates against risk scores and single risk factors showed a poor fit, but this was improved with a 4-year time lag for coronary events. The explanatory power of the analyses was limited by imprecision of the estimates and homogeneity of trends in the study populations. Interpretation. Changes in the classic risk factors seem to partly explain the variation in population trends in CHD. Residual variance is attributable to difficulties in measurement and analysis, including time lag, and to factors that were not included, such as medical interventions. The results support prevention policies based on the classic risk factors but suggest potential for prevention beyond these.

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The association between adiposity measures and dyslipidemia has seldom been assessed in a multipopulational setting. 27 populations from Europe, Australia, New Zealand and Canada (WHO MONICA project) using health surveys conducted between 1990 and 1997 in adults aged 35-64 years (n = 40,480). Dyslipidemia was defined as the total/HDL cholesterol ratio >6 (men) and >5 (women). Overall prevalence of dyslipidemia was 25% in men and 23% in women. Logistic regression showed that dyslipidemia was strongly associated with body mass index (BMI) in men and with waist circumference (WC) in women, after adjusting for region, age and smoking. Among normal-weight men and women (BMI<25 kg/m(2)), an increase in the odds for being dyslipidemic was observed between lowest and highest WC quartiles (OR = 3.6, p < 0.001). Among obese men (BMI ≥ 30), the corresponding increase was smaller (OR = 1.2, p = 0.036). A similar weakening was observed among women. Classification tree analysis was performed to assign subjects into classes of risk for dyslipidemia. BMI thresholds (25.4 and 29.2 kg/m(2)) in men and WC thresholds (81.7 and 92.6 cm) in women came out at first stages. High WC (>84.8 cm) in normal-weight men, menopause in women and regular smoking further defined subgroups at increased risk. standard categories of BMI and WC, or their combinations, do not lead to optimal risk stratification for dyslipidemia in middle-age adults. Sex-specific adaptations are necessary, in particular by taking into account abdominal obesity in normal-weight men, post-menopausal age in women and regular smoking in both sexes.

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Background. The World Health Organization (WHO) MONICA Project was established to determine how trends in event rates for coronary heart disease (CHD) and, optionally, stroke were related to trends in classic coronary risk factors. Risk factors were therefore monitored over ten years across 38 populations from 21 countries in four continents (overall period covered: 1979-1996). Methods. A standard protocol was applied across participating centres, in at least two, and usually three, independent surveys conducted on random samples of the study populations, well separated within the 10-year study period. Results. Smoking rates decreased in most male populations (35-64 years) but in females the majority showed increases. Systolic blood pressure showed decreasing trends in the majority of centres in both sexes. Mean levels of cholesterol generally showed downward trends, which, although the changes were small, had large effects on risk. There was a trend of increasing body mass index (BMI) with half the female populations and two-thirds of the male populations showing a significant increase. Conclusions. It is feasible to monitor the classic CHD risk factors in diverse populations through repeated surveys over a decade. In general, the risk factor trends are downwards in most populations but in particular, an increase in smoking in women in many populations and increasing BMI, especially in men, are worrying findings with significant public health implications.

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Individuals from the same population share a number of contextual circumstances that may condition a common level of blood pressure over and above individual characteristics. Understanding this population effect is relevant for both etiologic research and prevention strategies. Using multilevel regression analyses, the authors quantified the extent to which individual differences in systolic blood pressure (SBP) could be attributed to the population level. They also investigated possible cross-level interactions between the population in which a person lived and pharmacological (antihypertensive medication) and nonpharmacological (body mass index) effects on individual SBP. They analyzed data on 23,796 men and 24,986 women aged 35-64 years from 39 worldwide Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) study populations participating in the final survey of this World Health Organization project (1989-1997). SBP was positively associated with low educational achievement, high body mass index, and use of antihypertensive medication and, for women, was negatively associated with smoking. About 7-8% of all SBP differences between subjects were attributed to the population level. However, this population effect was particularly strong (i.e., 20%) in antihypertensive medication users and overweight women. This empirical evidence of a population effect on individual SBP emphasizes the importance of developing population-wide strategies to reduce individual risk of hypertension.

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Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.

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Background: The aim of this article was to investigate the size and possible causes of the reported excess in coronary events on Mondays. Methods: We conducted a metaanalysis of data from the World Health Organization (WHO) MONICA Project, which monitored trends and determinants in cardiovascular disease. The MONICA Project was undertaken in 21 countries from 1980 to 1995. Results: We found a small overall excess rate of coronary events on Mondays. In a population experiencing 100 events per week, we estimate there would be approximately I more event on Monday than on any other day. Hierarchical logistic regression showed that the Monday excess was greater in centers with less thorough data collection procedures. Conclusions: The excess of coronary events on Mondays is probably an artifact resulting from events with uncertain dates being coded as taking place on Mondays.

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Introduction: In the World Health Organization (WHO) MONICA (multinational MONItoring of trends and determinants in CArdiovascular disease) Project considerable effort was made to obtain basic data on non-respondents to community based surveys of cardiovascular risk factors. The first purpose of this paper is to examine differences in socio-economic and health profiles among respondents and non-respondents. The second purpose is to investigate the effect of non-response on estimates of trends. Methods:Socio-economic and health profile between respondents and non-respondents in the WHO MONICA Project final survey were compared. The potential effect of non-response on the trend estimates between the initial survey and final survey approximately ten years later was investigated using both MONICA data and hypothetical data. Results: In most of the populations, non-respondents were more likely to be single, less well educated, and had poorer lifestyles and health profiles than respondents. As an example of the consequences, temporal trends in prevalence of daily smokers are shown to be overestimated in most populations if they were based only on data from respondents. Conclusions: The socio-economic and health profiles of respondents and non-respondents differed fairly consistently across 27 populations. Hence, the estimators of population trends based on respondent data are likely to be biased. Declining response rates therefore pose a threat to the accuracy of estimates of risk factor trends in many countries.

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Study objective: To investigate the association between cold periods and coronary events, and the extent to which climate, sex, age, and previous cardiac history increase risk during cold weather. Design: A hierarchical analyses of populations from the World Health Organisation's MONICA project. Setting: Twenty four populations from the WHO's MONICA project, a 21 country register made between 1980 and 1995. Patients: People aged 35 - 64 years who had a coronary event. Main results: Daily rates of coronary events were correlated with the average temperature over the current and previous three days. In cold periods, coronary event rates increased more in populations living in warm climates than in populations living in cold climates, where the increases were slight. The increase was greater in women than in men, especially in warm climates. On average, the odds for women having an event in the cold periods were 1.07 higher than the odds for men (95% posterior interval: 1.03 to 1.11). The effects of cold periods were similar in those with and without a history of a previous myocardial infarction. Conclusions: Rates of coronary events increased during comparatively cold periods, especially in warm climates. The smaller increases in colder climates suggest that some events in warmer climates are preventable. It is suggested that people living in warm climates, particularly women, should keep warm on cold days.

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Aims Classical risk factors do not fully explain international differences in risk of coronary heart disease (CHD). We therefore measured thrombotic and inflammatory markers in a substudy of the WHO MONICA project and correlated these with CHD event rates.

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BACKGROUND: Tumor necrosis factor-alpha (TNF-alpha) and interleukin-1beta (IL-1beta), produced by endotoxin-activated Kupffer cells, play a key role in the pathogenesis of alcoholic liver cirrhosis (ALC). Alleles TNFA -238A, IL1B -31T and variant IL1RN*2 of repeat polymorphism in the gene encoding the IL-1 receptor antagonist increase production of TNF-alpha and IL-1beta, respectively. Alleles CD14 -159T, TLR4 c.896G and TLR4 c.1196T modify activation of Kupffer cells by endotoxin. We confirmed the published associations between these common variants and genetic predisposition to ALC by means of a large case-control association study conducted on two Central European populations. METHODS: The study population comprised a Czech sample of 198 ALC patients and 370 controls (MONICA project), and a German sample of 173 ALC patients and 331 controls (KORA-Augsburg), and 109 heavy drinkers without liver disease. RESULTS: Single locus analysis revealed no significant difference between patients and controls in all tested loci. Diplotype [IL1RN 2/ 2; IL1B -31T+] was associated with increased risk of ALC in the pilot study, but not in the validation samples. CONCLUSIONS: Although cytokine mediated immune reactions play a role in the pathogenesis of ALC, hereditary susceptibility caused by variants in the corresponding genes is low in Central European populations.

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In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

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The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.

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We present two methods of estimating the trend, seasonality and noise in time series of coronary heart disease events. In contrast to previous work we use a non-linear trend, allow multiple seasonal components, and carefully examine the residuals from the fitted model. We show the importance of estimating these three aspects of the observed data to aid insight of the underlying process, although our major focus is on the seasonal components. For one method we allow the seasonal effects to vary over time and show how this helps the understanding of the association between coronary heart disease and varying temperature patterns. Copyright (C) 2004 John Wiley Sons, Ltd.