Thursday, January 14, 2010

EPIDEMIOLOGY OF HEART FAILURE

Studies of the epidemiology of heart failure have been complicated by the lack of universal agreement on a definition of heart failure, which is primarily a clinical diagnosis. National and international comparisons have therefore been difficult, and mortality data, postmortem studies, and hospital admission rates are not easily translated into incidence and prevalence.

Several different systems have been used in large population studies, with the use of scores for clinical features determined from history and examination, and in most cases chest radiography, to define heart failure.

The Task Force on Heart Failure of the European Society of Cardiology has recently published guidelines on the diagnosis of heart failure, which require the presence of symptoms and objective evidence of cardiac dysfunction. Reversibility of symptoms on appropriate treatment is also desirable.

Echocardiography is recommended as the most practicable way of assessing cardiac function, and this investigation has been used in more recent studies. In the Framingham heart study a cohort of 5209 subjects has been assessed biennially since 1948, with a further cohort (their offspring) added in 1971. This uniquely large dataset has been used to determine the incidence and prevalence of heart failure, defined with consistent clinical and radiographic criteria.

Several recent British studies of the epidemiology of heart failure and left ventricular dysfunction have been conducted, including a study of the incidence of heart failure in one west London district (Hillingdon heart failure study) and large prevalence studies in Glasgow (north Glasgow MONICA study) and the West Midlands ECHOES (echocardiographic heart of England screening) study. It is important to note that epidemiological studies of heart failure have used different levels of ejection fraction to define systolic dysfunction.

The Glasgow study, for example, used an ejection fraction of 30% as their criteria, whereas most other epidemiological surveys have used levels of 40-45%. Indeed, prevalence of heart failure seems similar in many different surveys, despite variation in the levels of ejection fraction, and this observation is not entirely explained.


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Energy Intake

Analysis of the dietary factors associated with obesity is confounded by the difficulties in assessing food intake and eating behaviour. Dietary surveys are increasingly beset by the problem of underreporting, probably related to the increased awareness of nutrition issues and concern over body weight, which leads individuals to consciously or sub-consciously mis-report their food intake.

In 1986 Prentice et al. demonstrated that obese women under-reported energy intake relative to energy needs by a mean of 3.5MJ/day, while among lean women the two measures agreed to within 0.14 MJ/day (Prentice et al., 1986). This observation has been repeatedly reconfirmed, although it is now recognized that there is a spectrum of mis-reporting of food intake across the population, the nature of which is not easily predicted on the basis of individual phenotype or demographic statistics. Others may alter their dietary habits during periods of food recording, usually leading to a record of undereating (Goris et al., 2000)

Analysis of the dietary determinants of obesity is also confounded by the problems of post-hoc changes in consumption in response to increasing body weight. This makes it difficult to draw quantitative conclusions from crosssectional or even prospective studies of food intake and body weight. Nonetheless increasingly refined recording tools and statistical analysis are seeking to understand more about the broader context of eating behaviour with targeted questions about the location and social context of eating episodes and using factor analysis to identify types of dietary patterns, which may inform future strategies to prevent and treat obesity (Whichelow and Prevost, 1996).

Instead much of our understanding of the relationship between dietary factors and the risk of obesity comes from experimental studies in the laboratory or highly controlled intervention studies in the community. These may not truly mimic eating behaviour in a naturalistic setting, but they provide useful insights into the response to imposed dietary manipulations under standardized conditions.
(Written By: Susan A. Jebb and Jeremy Krebs in The Book Of Obesity And Diabets)


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ENERGY DENSITY

Energy density is a critical component in the regulation of human appetite and plays an important role in determining total energy intake. In one of the most robust experimental studies Stubbs et al showed that lean, young healthy men, allowed to eat ad libitum, consumed significantly more energy as the fat content of the food was increased (Stubbs et al., 1995a, b). Careful measurements of fat balance over one week in a whole body indirect calorimeter showed that body fat decreased by 0.86 ± 0.61 kg on the 20 per cent fat diet, while increasing by 0.39 ± 0.59 kg and 2.24 ± 0.94 kg on the 40 and 60 per cent fat diets respectively. These studies provided no evidence of any physiological compensation or cognitive ‘learning’ associated with sustained consumption of foods of differing fat content even after a week or more of sustained over-consumption.

Importantly, when the energy density of the food was equalized, through careful experimental manipulation of the recipes, the high fat hyperphagia was abolished (Stubbs et al., 1996) (Figure 3.1). This strongly suggests that excess energy was consumed by a process of ‘passive over-consumption’, in which changes in food quality, not quantity, were the driving force beyond the disruption in the previously accurate regulation of body weight. This phenomenon implies that the bulk of food consumed is an important determinant of energy intake.


In the ‘real world’ energy-dense diets are frequently high in fat, since fat (37 kJ/g) contains more than twice as much energy gram-for-gram as protein (17 kJ/g) or carbohydrate (16 kJ/g). Many low-fat foods, especially dairy products, contain substantially less energy than their full-fat equivalent, allowing consumers to maintain the bulk of food in the diet, while constraining energy intake. However, recent advances in food technology have resulted in some food ranges that are low in fat but where the energy content is similar to traditional equivalents. These foods, such as biscuits, cakes and desserts often contain large quantities of added sugars and might be expected to lead to similar passive over-consumption as high-fat foods of similar energy density.

Foods served in most ‘fast-food’ chains such as burger and chicken outlets are characterized by a particularly high energy density. These foods are frequently high in fat and have a low water content. A recent analysis has shown that the energy density of foods offered in a selection of these outlets has an energy density of over 1000 kJ/100 g relative to the typical energy density of the diet of a woman in the UK of 670 kJ/100 g (Prentice and Jebb, 2003). This implies that for regular consumers the total quantity of food which can be consumed without exceeding energy needs must be constrained to accommodate this increase in the energy density of the diet in regular consumers. The high energy density of these foods provides a plausible biological explanation for the epidemiological associations between ‘fast-food’ consumption and obesity. However this is likely to be compounded by large portion size and specific marketing strategies to encourage further consumption of these products.

(Written By: Susan A. Jebb and Jeremy Krebs in The Book Of Obesity And Diabets)

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Educational Inequalities In Cardiovascular Disease

Educational inequalities in cardiovascular disease are evident in many countries, especially those in northern Europe. Cardiovascular disease also strongly contributes to overall health inequalities in these countries due to its relatively high prevalence. However, much less is known about biological mechanisms accounting for these inequalities. The metabolic syndrome is one potential factor behind educational and other socioeconomic inequalities in cardiovascular disease.

The metabolic syndrome is a metabolic state characterized by many classical risk factors of cardiovascular disease, i.e. abdominal obesity, low high-density lipoprotein (HDL) cholesterol, elevated triglycerides, hyperinsulinaemia, and hyperglycaemia. The causes of the metabolic syndrome are not yet well understood. In addition to behavioural factors, such as diet and physical activity, previous research indicates a strong genetic influence. It has also been suggested that undernutrition during fetal life and early childhood may cause permanent changes in human metabolism and thus affect the development of the metabolic syndrome in later life.6 Thus, the metabolic syndrome may mediate the effect of early material resources on later cardiovascular disease risk.

Inequalities in the prevalence of the metabolic syndrome by occupational status or education have been examined by three previous studies. In the Whitehall II Study with a large sample of British civil servants, a clear negative association was found between occupational status and the prevalence of the metabolic syndrome. Among men, the prevalence of the metabolic syndrome decreased across the six categories of the occupational scale, but among women a higher prevalence was found only in the three lowest categories. In a follow-up study in the UK, negative, but statistically insignificant, associations were found between the metabolic syndrome and socioeconomic class in childhood or in adulthood.

However, the sample size was smaller than in the Whitehall II study, which may explain the statistically insignificant results. In a study of Swedish women, an inverse gradient in the prevalence of the metabolic syndrome was found across categories of education.9 In this study, the age-adjusted prevalence of the metabolic syndrome was 2.6 times higher among women with basic education compared with women who had college or university level education. Adjustment for other risk factors only slightly decreased the occupational gradient in the Whitehall II study and the educational gradient in the Swedish study.

The social gradient in the metabolic syndrome could help explain socioeconomic inequalities in coronary heart disease (CHD). If so, then factors that cause the metabolic syndrome may also be important in the formation of social inequalities in CHD risk. Further, the metabolic syndrome may offer a simple screening tool to find sub-groups and individuals at high risk for CHD. If educational variation is found in the metabolic syndrome, then interventions to prevent and treat metabolic abnormalities, especially in people with low social position, may help to narrow socioeconomic inequalities in CHD. In this study, we examined educational disparities in the metabolic syndrome in a cohort of Finnish middle-aged men and women.

Education is a good indicator of social position in epidemiological studies because it precedes other indicators, such as occupational based social position or income, is comparable between men and women, does not usually change in adulthood, and shapes health behaviours through attitudes, values, and knowledge. First, we investigated whether there were educational differences in the prevalence of the metabolic syndrome and whether adjusting for other risk factors attenuated these differences. Second, we investigated whether the educational differences in the prevalence of the metabolic syndrome at baseline explained educational inequalities in CHD incidence.


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Diagnosis of Diabetes



Diabetes once diagnosed is for life. The perseverance and self discipline needed over a lifetime can often tax even the most robust of people to the limit. Those caring for them also require perseverance and an understanding of humanity combined with a cautious optimism, to guide those with diabetes through the peaks and troughs of their lives.

Definition of Diabetes
Diabetes occurs either because of a lack of insulin or because of the presence of factors that oppose the action of insulin. The result of insufficient action of insulin is an increase in blood glucose concentration (hyperglycaemia). Many other metabolic abnormalities occur, notably an increase in ketone bodies in the blood when there is a severe lack of insulin.

Diagnosis of diabetes

The diagnosis of diabetes must always be established by a blood glucose measurement made in an accredited laboratory.

Glucose tolerance test
The glucose tolerance test is not normally needed in routine clinical practice, and then only if uncertainty exists in younger patients, or to establish an exact diagnosis in pregnancy. For reliable results, glucose tolerance tests should be performed in the morning after an overnight fast, with the patient sitting quietly and not smoking; it is also important that the patient should have normal meals for the previous three days and should not have been dieting.

False results may also occur if the patient has been ill recently or has had prolonged bed rest. Blood glucose concentrations are measured fasting and then one and two hours after a drink of 75 g of glucose in 250-350 ml water (in children 1•75 g/kg to a maximum of 75 g), preferably flavoured, for example, with pure lemon juice. Urine tests should be performed before the glucose drink and at one and two hours. Interpretation of blood glucose values according to WHO criteria is shown in the table.

Adapted from Diabetes Care 1997;20:1183-119


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Coronary Artery Disease And Its Risk Factors



Coronary heart disease is the commonest cause of heart failure in Western countries. In the studies of left ventricular dysfunction (SOLVD) coronary artery disease accounted for almost 75% of the cases of chronic heart failure in male white patients, although in the Framingham heart study, coronary heart disease accounted for only 46% of cases of heart failure inmen and 27% of chronic heart failure cases in women. Coronary artery disease and hypertension (either alone or in combination) were implicated as the cause in over 90% of cases of heart failure in the Framingham study. Recent studies that have allocated aetiology on the basis of non invasive investigations—such as the Hillingdon heart failure study—have identified coronary artery disease as the primary aetiology in 36% of cases of heart failure. In the Hillingdon study, however, researchers were not able to identify the primary aetiology in 34% of cases; this methodological failing has been addressed in the current Bromley heart failure study, which uses coronary angiography as well as historical and non invasive findings.

Coronary risk factors, such as smoking and diabetes mellitus, are also risk markers of the development of heart failure. Smoking is an independent and strong risk factor for the development of heart failure in men, although the findings in women are less consistent. In the prevention arm of SOLVD diabetes was an independent risk factor (about twofold) for mortality, the development of heart failure, and admission to hospital for heart failure, whereas in the Framingham study diabetes and left ventricular hypertrophy were the most significant risk markers of the development of heart failure. Body weight and a high ratio of total cholesterol concentration to high density lipoprotein cholesterol concentration are also independent risk factors for heart failure. Clearly, these risk factors may increase the risks of heart failure through their effects on coronary artery disease, although diabetes alone may induce important structural and functional changes in the myocardium, which further increase the risk of heart failure.
(Source: ABC of Heart Failure)

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Controlling STI (Sexually Transmitted Infections)



The approach to controlling STIs and the emphasis placed on different components will depend on the local pattern and distribution of STIs in the community and whether one is working in a setting that is resource rich or resource poor.
However, the same general principles will apply. Prevention can be aimed at uninfected people in the community to prevent them from acquiring infection (primary prevention) or at infected people to prevent the onward transmission of the infection to their sexual partners (secondary prevention).

Although effective primary prevention can theoretically reduce the prevalence of viral and bacterial STIs, secondary prevention is much more effective at reducing the prevalence of bacterial STIs, which all are curable with antibiotics. In fact, the population prevalence of a bacterial STI can be reduced entirely through effective secondary prevention activities without any reduction in risky sexual behavior occurring.

Countries that combine primary and secondary prevention approaches, at the individual and population levels, have managed substantially to reduce the burden of infection in their population. Effective implementation of prevention programmes requires strong political leadership and genuine commitment, without which the most well designed and appropriate programmes are likely to founder. Countries such as Thailand, Brazil, Uganda, and Senegal have seen a dramatic impact on their rates of STIs and HIV, which has been facilitated greatly by political support at the highest level.

Interventions that reduce the rate of STI can be aimed at the entire community or targeted at specific groups who are at high risk of, or are particularly vulnerable to, infection. One to one prevention interventions can take place in clinic settings, such as:

Primary Prevention

Primary prevention interventions aim to keep people uninfected. These approaches are obviously not mutually exclusive. Individual behaviour change probably will be best sustained in a community that is broadly supportive. In addition, the broader cultural mores of the community will influence greatly the feasibility of delivering education in that community and will also affect how people respond to it.
• Behavioural interventions are aimed at enhancing knowledge, skills, and attitudes to help people protect themselves against infection (for example, health promotion to decrease partner change and increase condom use)
• Structural interventions are aimed at broader societal and economic issues that drive the spread of STIs
• Biomedical interventions include condoms, vaccines, vaginal microbicides, or male circumcision to prevent the acquisition of infection

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CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Chronic obstructive pulmonary disease (COPD) is a growing health problem in women. The major causative agent behind the disease is smoking, but there are few longitudinal studies concerning women’s health problems in this field.
In 1968, a population study of women in Göteborg, Sweden, was initiated; engaging 1462 women aged 38–60, representative of the female population of Göteborg. Subsequently, four follow-up examinations have been performed, the latest in
2000-2001, i.e. 32 years after the initial examination.

Lung function was measured as peak expiratory flow (PEF) by a peak flow meter in 1968-1969 and as PEF, vital capacity (VC) and forced expiratory volume in one second (FEV1) at the 2000-2001 examination. A 12-year follow-up study on lung function has previously been presented from this population, in which reduced PEF increased the risk of cardiovascular disease (CVD) and death twelve years later, independent of the presence of risk factors for CVD.

In this paper, we present data concerning lung function, airway symptoms and health status in those women who were 38 years old at the initial examination and 70 years old at the 32-year follow up in 2000-2001. As there are only a few longitudinal studies concerning women’s health problems in this field and epidemiological studies of lung function impairment in women and risk factors in a long-term perspective are scarce we aimed to assess the possible association between selected risk factors among women and lung function, health status as well as airway symptoms in a 32-year perspective.

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CHILDHOOD SOCIOECONOMIC STATUS AND THE METABOLIC SYNDROME

Research on social inequalities has demonstrated a clear relationship between socioeconomic status (SES) and health. Individuals of lower SES experience higher rates of all-cause morbidity and mortality than do more advantaged individuals. This relationship is not attributable solely to poor health among the most deprived individuals, but reflects a gradient of health risk extending across the full distribution of socioeconomic position existing within populations (Adler et al., 1994).

Despite the recognition of this well-established association, the underlying mechanisms by which social inequalities impair health are not well understood. Measures of socioeconomic status indicate one’s position within a broader society, and this position might affect health in various ways. Social position can determine both the availability of preventative and health promoting resources, as well as the amount of one’s exposure to health-damaging environments (Lynch & Kaplan, 2000). SES may also influence health through its association with psychological and lifestyle-associated risk factors, such as smoking prevalence and subjective distress due to more frequent stressful life events (Anderson & Armstead, 1995). In the latter regard, chronic stressors associated with social position may conceivably alter neuroendocrine functioning, resulting in later health consequences (Brunner et al., 1997).


In the following sections, several aspects of SES and health will be reviewed. First, a conceptual basis for SES research will be presented, along with information concerning the relationship of SES to cardiovascular health. The metabolic syndrome and its association with adult SES will then be discussed. Next, research on childhood SES as a predictor of adult CHD will be presented. Finally, a review of the current study will be provided, and implications of the findings will be discussed.

A conceptualization of socioeconomic status itself is necessary when attempting to explain the relationship between socioeconomic status and health. Socioeconomic status (SES) has been defined as one’s relative “position” in society, as reflected in access to or the accumulation of material resources or prestige (Lynch & Kaplan, 2000). Measures of socioeconomic position indicate particular structural locations within society (Lynch & Kaplan, 2000) and attempt to quantify an individual’s probability of success, i.e. “life chances”. Current conceptualization of socioeconomic status relies heavily on the Marxist, Weberian and Functionalist sociological traditions, as summarized by Lynch & Kaplan (2000).

A discussion of socioeconomic status must first address the concept of social class. Social class refers to groups defined by interdependent economic and legal relationships, based on an individual’s position within the economy (Krieger, Williams & Moss, 1997). Relationships between classes co-define each other, and are determined by a society’s connections through production, consumption and distribution of goods (Krieger et al., 1997). Conceptualizing class as a social relationship emphasizes how members of different social classes advance their economic and social well-being, and how the well-being of one class is linked to the deprivation of another (Krieger et al., 1997). Measures of social class attempt to capture these economic interactions among people, rather than identify the personal characteristics that determine an individual’s position within a hierarchy.
Each sociological tradition approaches social class in a slightly different way. The Marxian definition of social class reflects stratification in relation to the means of production in society. A social class is a group within a society that is relatively similar in political, economic, educational, occupational, and prestige status (Lynch & Kaplan, 2000).

According to the Weberian tradition, one's class position yields certain probabilities (or life-chances) of success. Society is stratified by class, status, and political power, and a lack of resources (i.e. goods, skills) places certain groups at a competitive disadvantage. The functionalist approach to stratification suggests that complex societies require stratification into sectors that are more or less valuable to social maintenance and progress. This position maintains that social inequality is necessitated by the need to preferentially reward, by money and power, individuals best qualified to occupy the positions of highest responsibility (Lynch & Kaplan, 2000). The sociological schools of thought described here maintain that macrosocial processes determine the socioeconomic prospects of individuals, with prevailing political and economic conditions generating hierarchies of social position.

Ignored here, however, are individual attributes, such as cognitive abilities and dimensions of personality, that covary with indices of socioeconomic status (Tomlinson-Keasey & Little, 1990). Although such individual characteristics are affected by a wide range of variables, including macrosocial factors, both personality and intelligence also have genetic bases and are influenced by idiosyncratic developmental experiences unrelated to social class (i.e. “nonshared” environmental effects). Individual differences in educational attainment, occupation, and earnings are themselves moderately heritable, and there is significant genetic covariation of SES and intelligence (Lichtenstein & Pedersen, 1997; Rowe, Vesterdal & Rodgers, 1998). Therefore, it is likely that relative socioeconomic position results from a complex interplay of the political and economic structures described in sociological thought, along with individuals’ intellectual and personality characteristics.

Although the concept of socioeconomic status has built upon these sociological traditions of social class, it is important to differentiate these two terms. Whereas social class refers strictly to social groups arising from interdependent economic relationships (i.e. “working class”, “managerial class”), current measures of SES aim to quantify an individual’s life chances of success in a social hierarchy by including both resource-based (material resources and assets) and prestige-based measures (rank or status in a hierarchy) (Krieger et al, 1997).

For example, epidemiological research in England and many other countries draws upon social class data based on the Registrar-General’s grouping of occupations, and categorizes individuals’ structural location within the economy (Marmot, Kogevinas & Elston, 1987). Because social class in this sense is conceptualized as an ordinal variable, it cannot provide a meaningful measure of distance between adjacent occupational categories, and is therefore less precise. Measures of socioeconomic status, based on composites of resource-based and prestige-based measures at an individual, household, or childhood level, in contrast, provide a more continuous measure of one’s standing in a social structure (Krieger, et al., 1997)

Most commonly, SES is assessed at the level of the individual, although household and neighborhood-level indicators are also used. The SES indicators described here are related, but not fully overlapping, and they may affect health through disparate pathways (Gallo & Matthews, 2000). Yet interestingly, disparities in health outcomes persist independently of the measure of socioeconomic status used.

The most widely reported measures of SES are educational attainment, occupational status, income, or some combination of these measures. In studying social inequalities, each measure may be seen to have both advantages and disadvantages. Individual or family income is commonly used to index SES, and can be quantified continuously or divided into categories. Income measures at any one point in time predict various health outcomes, and mortality is strongly and inversely associated with income (Kreiger et al., 1997).

Limitations of using income alone as an indicator of SES include income’s imperfect correlation with accumulated wealth and insensitivity to fluctuations in life circumstances over time. Level of education is an important marker of socioeconomic position in that it provides information about the likelihood of future success, and is also frequently an indicator of prestige.

Potential limitations of using education as a sole measure of SES include variation in the “value” of differing educational experiences defining putatively similar levels of attainment, or in how particular educational accomplishments are rewarded in a given society or economic circumstance (Lynch & Kaplan, 2000). Occupational status is useful in reflecting the prestige, income level and educational requirements associated with various positions in the economic structure, as well as in providing information about job characteristics (such as environmental and working conditions), decision-making latitude, and psychological demands of the job (Lynch & Kaplan, 2000).

Various measures of occupation categorize job types in order to reflect a particular occupational hierarchy, such as Rose and Marmot’s (1981) Occupational Grade and the Registrar General’s Classification (Szreter, 1984). One difficulty of using occupational status as a marker of social position, though, is that of quantifying change in occupational status over the life course (Krieger et al., 1997).

In sum, the intellectual traditions of Marx, Weber and the Functionalists provide a framework for research into social inequalities, and describe structural positions within society that can be measured in several ways. Interest in the associations between socioeconomic position and health has increased in recent years (Lynch & Kaplan, 2000). Most notably, the Whitehall study of mortality (Marmot et al., 1991) demonstrated a clear SES-health gradient among occupational grades of British Civil Servants. This gradient has been shown in U.S. studies as well, using both years of education (Kitagawa & Hauser, 1973) and income (Pappas, Queen, Hadden & Fisher, 1993). Advances in this body of research continue to spur efforts toward the understanding and measurement of socioeconomic variables in relation to health.

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Causes Of Heart Failure

These are some cause of Heart Failure:
a) Coronary artery disease:
•Myocardial infarction
•Ischaemia
b) Hypertension
c) Cardiomyopathy
•Dilated (congestive)
•Hypertrophic/obstructive
•Restrictive—for example, amyloidosis, sarcoidosis, haemochromatosis
•Obliterative
d) Valvar and congenital heart disease
•Mitral valve disease
•Aortic valve disease
•Atrial septal defect, ventricular septal defect

e) Arrhythmias
•Tachycardia
•Bradycardia (complete heart block, the sick sinus syndrome)
•Loss of atrial transport—for example, atrial fibrillation
f) Alcohol and drugs
•Alcohol
•Cardiac depressant drugs (â blockers, calcium antagonists)
g) “High output” failure
•Anaemia, thyrotoxicosis, arteriovenous fistulae, Paget’s disease
h) Pericardial disease
•Constrictive pericarditis
•Pericardial effusion
i) Primary right heart failure
•Pulmonary hypertension—for example, pulmonary embolism, cor

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