857 resultados para Health status indicators.
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Large efforts are on-going within the EU to prepare the Marine Strategy Framework Directive’s (MSFD) assessment of the environmental status of the European seas. This assessment will only be as good as the indicators chosen to monitor the eleven descriptors of good environmental status (GEnS). An objective and transparent framework to determine whether chosen indicators actually support the aims of this policy is, however, not yet in place. Such frameworks are needed to ensure that the limited resources available to this assessment optimize the likelihood of achieving GEnS within collaborating states. Here, we developed a hypothesis-based protocol to evaluate whether candidate indicators meet quality criteria explicit to the MSFD, which the assessment community aspires to. Eight quality criteria are distilled from existing initiatives, and a testing and scoring protocol for each of them is presented. We exemplify its application in three worked examples, covering indicators for three GEnS descriptors (1, 5 and 6), various habitat components (seaweeds, seagrasses, benthic macrofauna and plankton), and assessment regions (Danish, Lithuanian and UK waters). We argue that this framework provides a necessary, transparent and standardized structure to support the comparison of candidate indicators, and the decision-making process leading to indicator selection. Its application could help identify potential limitations in currently available candidate metrics and, in such cases, help focus the development of more adequate indicators. Use of such standardized approaches will facilitate the sharing of knowledge gained across the MSFD parties despite context-specificity across assessment regions, and support the evidence-based management of European seas.
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The aim of the present study was to assess social inequalities in health status, health behavior and the use of health services based on education level. A population-based cross-sectional study was carried out involving 1,518 elderly residents of Campinas, São Paulo State, Brazil. Significant demographic and social differences were found between schooling strata. Elderly individuals with a higher degree of schooling are in greater proportion alcohol drinkers, physically active, have healthier diets and a lower prevalence of hypertension, diabetes, dizziness, headaches, back pain, visual impairment and denture use, and better self-rated health. But, there were no differences in the use of health services in the previous two weeks, in hospitalizations or surgeries in the previous year, nor in medicine intake over the previous three days. Among elderly people with hypertension and diabetes, there were no differences in the regular use of health services and medication. The results demonstrate social inequalities in different health indicators, along with equity in access to some health service components.
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Study objective: To assess the representativeness of survey participants by systematically comparing volunteers in a national health and sexuality survey with the Australian population in terms of self reported health status (including the SF-36) and a wide range of demographic characteristics. Design: A cross sectional sample of Australian residents were compared with demographic data from the 1996 Australian census and health data from the 1995 National Health Survey. Setting: The Australian population. Participants: A stratified random sample of adults aged 18-59 years drawn from the Australian electoral roll, a compulsory register of voters. Interviews were completed with 1784 people, representing 40% of those initially selected (58% of those for whom a valid telephone number could be located). Main results: Participants were of similar age and sex to the national population. Consistent with prior research, respondents had higher socioeconomic status, more education, were more likely to be employed, and less likely to be immigrants. The prevalence estimates, means, and variances of self reported mental and physical health measures (for example, SF-36 subscales, women's health indicators, current smoking status) were similar to population norms. Conclusions: These findings considerably strengthen inferences about the representativeness of data on health status from volunteer samples used in health and sexuality surveys.
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OBJECTIVE To analyze the association between negative self-rated health and indicators of health, wellbeing and sociodemographic variables in older adults. METHODS Cross-sectional study that used data from a population-based health survey with a probability cluster sample that was carried out in Campinas, SP, Southeastern Brazil,, in 2008 and 2009. The participants were older adults (≥ 60 years) and the dependent variable was self-rated health, categorized as: excellent, very good, good, bad and very bad. The adjusted prevalence ratios were estimated by means of Poisson multiple regression. RESULTS The highest prevalences of bad/very bad self-rated health were observed in the individuals who never attended school, in those with lower level of schooling, with monthly per capita family income lower than one minimum salary. Individuals who scored five or more in the physical health indicator also had bad self-rated health, as well as those who scored five or more in the Self-Reporting Questionnaire 20 and those who did not refer feeling happiness all the time. CONCLUSIONS The independent effects of material life conditions, physical and mental health and subjective wellbeing, observed in self-rated health, suggest that older adults can benefit by health policies supported by a global and integrative view of old age.
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OBJECTIVE To analyze the prevalence of individuals at risk of dependence and its associated factors.METHODS The study was based on data from the Catalan Health Survey, Spain conducted in 2010 and 2011. Logistic regression models from a random sample of 3,842 individuals aged ≥ 15 years were used to classify individuals according to the state of their personal autonomy. Predictive models were proposed to identify indicators that helped distinguish dependent individuals from those at risk of dependence. Variables on health status, social support, and lifestyles were considered.RESULTS We found that 18.6% of the population presented a risk of dependence, especially after age 65. Compared with this group, individuals who reported dependence (11.0%) had difficulties performing activities of daily living and had to receive support to perform them. Habits such as smoking, excessive alcohol consumption, and being sedentary were associated with a higher probability of dependence, particularly for women.CONCLUSIONS Difficulties in carrying out activities of daily living precede the onset of dependence. Preserving personal autonomy and function without receiving support appear to be a preventive factor. Adopting an active and healthy lifestyle helps reduce the risk of dependence.
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Little is known of cancer rehabilitation needs in Europe. EUROCHIP-3 organised a group of experts to propose a list of population-based indicators used for describing cancer rehabilitation across Europe. The aim of this study is to present and discuss these indicators. A EUROCHIP-3 expert panel reached agreement on two types of indicators. (a) Cancer prevalence indicators. These were proposed as a means of characterising the burden of cancer rehabilitation needs by time from diagnosis and patient health status. These indicators can be estimated from cancer registry data or by collecting data on follow-up and treatments for samples of cases archived in cancer registries. (b) Indicators of rehabilitation success. These include: return to work, quality of life, and satisfaction of specific rehabilitation needs. Studies can be performed to estimate these indicators in individual countries, but to obtain comparable data across European countries it will be necessary to administer a questionnaire to randomly selected samples of patients from population-based cancer registry databases. However, three factors complicate questionnaire studies: patients may not be aware that they have cancer; incomplete participation in surveys could lead to bias; and national confidentiality laws in some cases prohibit cancer registries from approaching patients. Although these studies are expensive and difficult to perform, but as the number of cancer survivors increases, it is important to document their needs in order to provide information on cancer control.
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Aging with quality of life does not occur equally among the racial groups of Brazilian elderly, and few studies have analyzed this issue in the states of the Brazilian Legal Amazon. The objective of this study was to investigate racial inequalities in the socioeconomic, demographic and health conditions of elderly residents of Maranhão state, Brazil. The present work is a cross-sectional study of 450 elders aged 60 years or older included in the 2008 National Household Sample Survey. The prevalence of socioeconomic, demographic, health and habit indicators and of risk factors were estimated in white, brown and black racial categories that were self-reported by the survey participants. The chi-square test was used for comparisons (a=5%). The majority of the elderly respondents identified themselves as brown (66.4%) or white (23.3%). There were significant socioeconomic, demographic, habit and lifestyle differences among the racial groups. Most of the black and brown elderly lived alone, reported lower educational levels and were in the lowest quintile for income. These respondents were also highly dependent on the Unified Health System (Sistema Único de Saúde - SUS), exhibited low rates of screening mammograms and lower physical activity levels and had a greater proportion of smokers. However, there was no difference in the prevalence of health indicators or in the proportion of elderly by gender, age, social role in the family or the urban-rural location of the household. These results indicate the presence of racial inequalities in the socioeconomic and demographic status and in the practice of healthy habits and lifestyles among elderly from Maranhão, but suggest equity in health status. The results also suggest the complexity and challenges of interlinking race with socioeconomic aspects, and the findings reinforce the need for the implementation of public policies for these population groups.
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In a series of papers (Tang, Chin and Rao, 2008; and Tang, Petrie and Rao 2006 & 2007), we have tried to improve on a mortality-based health status indicator, namely age-at-death (AAD), and its associated health inequality indicators that measure the distribution of AAD. The main contribution of these papers is to propose a frontier method to separate avoidable and unavoidable mortality risks. This has facilitated the development of a new indicator of health status, namely the Realization of Potential Life Years (RePLY). The RePLY measure is based on the concept of a “frontier country” that, by construction, has the lowest mortality risks for each age-sex group amongst all countries. The mortality rates of the frontier country are used as a proxy for the unavoidable mortality rates, and the residual between the observed mortality rates and the unavoidable mortality rates are considered as avoidable morality rates. In this approach, however, countries at different levels of development are benchmarked against the same frontier country without considering their heterogeneity. The main objective of the current paper is to control for national resources in estimating (conditional) unavoidable and avoidable mortality risks for individual countries. This allows us to construct a new indicator of health status – Realization of Conditional Potential Life Years (RCPLY). The paper presents empirical results from a dataset of life tables for 167 countries from the year 2000, compiled and updated by the World Health Organization. Measures of national average health status and health inequality based on RePLY and RCPLY are presented and compared.
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This paper engages in an interdisciplinary survey of the current state of knowledge related to the theory, determinants and consequences of occupational safety and health (OSH). First, it synthesizes the available theoretical frameworks used by economists and psychologists to understand the issues related to the optimal provision of OSH in the labour market. Second, it reviews the academic literature investigating the correlates of a comprehensive set of OSH indicators, which portray the state of OSH infrastructure (social security expenditure, prevention, regulations), inputs (chemical and physical agents, ergonomics, working time, violence) and outcomes (injuries, illnesses, absenteeism, job satisfaction) within workplaces. Third, it explores the implications of the lack of OSH in terms of the economic and social costs that are entailed. Finally, the survey identifies areas of future research interests and suggests priorities for policy initiatives that can improve the health and safety of workers.
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Health at a Glance 2013 presents the trends and influences shaping health status, services and policies in OECD countries and the BRIICS. Although indicators such as life expectancy or infant mortality suggest that things are improving overall, inequalities in wealth, education and other social indicators still have a significant impact on health status and access to health services. These health disparities may be explained by differences in living and working conditions, as well as differences that show up in the health-related lifestyle data presented here (e.g., smoking, harmful alcohol drinking, physical inactivity, and obesity).This resource was contributed by The National Documentation Centre on Drug Use.
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This second edition of Health at a Glance: Europe presents a set of key indicators of health and health systems in 35Â European countries, including the 27 European Union member states, 5 candidate countries and 3 EFTA countries. The selection of indicators is based largely on the European Community Health Indicators (ECHI) shortlist, a list of indicators that has been developed by the European Commission to guide the development and reporting of health statistics. It is complemented by additional indicators on health expenditure and quality of care, building on the OECD expertise in these areas. Contents: Introduction 12 Chapter 1. Health status 15 1.1. Life expectancy and healthy life expectancy at birth 1.2. Life expectancy and healthy life expectancy at age 65 1.3. Mortality from all causes 1.4. Mortality from heart disease and stroke 1.5. Mortality from cancer 1.6. Mortality from transport accidents 1.7. Suicide 1.8. Infant mortality 1.9. Infant health: Low birth weight 1.10. Self-reported health and disability 1.11. Incidence of selected communicable diseases 1.12. HIV/AIDS 1.13. Cancer incidence 1.14. Diabetes prevalence and incidence 1.15. Dementia prevalence 1.16. Asthma and COPD prevalence Chapter 2. Determinants of health 49 2.1. Smoking and alcohol consumption among children 2.2. Overweight and obesity among children 2.3. Fruit and vegetable consumption among children 2.4. Physical activity among children 2.5. Smoking among adults 2.6. Alcohol consumption among adults 2.7. Overweight and obesity among adults 2.8. Fruit and vegetable consumption among adults Chapter 3. Health care resources and activities 67 3.1. Medical doctors 3.2. Consultations with doctors 3.3. Nurses 3.4. Medical technologies: CT scanners and MRI units 3.5. Hospital beds 3.6. Hospital discharges 3.7. Average length of stay in hospitals 3.8. Cardiac procedures (coronary angioplasty) 3.9. Cataract surgeries 3.10. Hip and knee replacement 3.11. Pharmaceutical consumption 3.12. Unmet health care needs Chapter 4. Quality of care 93 Care for chronic conditions 4.1. Avoidable admissions: Respiratory diseases 4.2. Avoidable admissions: Uncontrolled diabetes Acute care 4.3. In-hospital mortality following acute myocardial infarction 4.4. In-hospital mortality following stroke Patient safety 4.5. Procedural or postoperative complications 4.6. Obstetric trauma Cancer care 4.7. Screening, survival and mortality for cervical cancer 4.8. Screening, survival and mortality for breast cancer 4.9. Screening, survival and mortality for colorectal cancer Care for communicable diseases 4.10. Childhood vaccination programmes 4.11. Influenza vaccination for older people Chapter 5. Health expenditure and financing 117 5.1. Coverage for health care 5.2. Health expenditure per capita 5.3. Health expenditure in relation to GDP 5.4. Health expenditure by function. 5.5. Pharmaceutical expenditure 5.6. Financing of health care 5.7. Trade in health services Bibliography 133 Annex A. Additional information on demographic and economic context 143 Most European countries have reduced tobacco consumption via public awareness campaigns, advertising bans and increased taxation. The percentage of adults who smoke daily is below 15% in Sweden and Iceland, from over 30% in 1980. At the other end of the scale, over 30% of adults in Greece smoke daily. Smoking rates continue to be high in Bulgaria, Ireland and Latvia (Figure 2.5.1). Alcohol consumption has also fallen in many European countries. Curbs on advertising, sales restrictions and taxation have all proven to be effective measures. Traditional wine-producing countries, such as France, Italy and Spain, have seen consumption per capita fall substantially since 1980. Alcohol consumption per adult rose significantly in a number of countries, including Cyprus, Finland and Ireland (Figure 2.6.1).This resource was contributed by The National Documentation Centre on Drug Use.
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A report published in 2002, Monitoring the State of the East Midlands. Sustainable Development Objectives and Targets for the East Midlands. Health Indicators, proposed a set of seven high-level health indicators for monitoring health status and health inequalities in the Region. The report also proposed a number of health improvement and health inequality reduction targets drawn from key national and regional strategy documents including Saving Lives: Our Healthier Nation and The East Midlands Integrated Regional Strategy. These relate to: - Life expectancy at birth. - Teenage pregnancy rate. - Mortality rate from circulatory disease in people aged under 75. - Mortality rate from cancer in people aged under 75. - Mortality rate from accidents in people of all ages. - Suicide rate in people of all ages. - Prevalence of cigarette smoking in people aged 16 and over. Progress towards these targets will indicate that the twin aims of the regional public health strategy Investment for Health - to improve health and to reduce health inequalities - are being achieved. This report updates these indicators with the latest available data. At the time of writing, data were available for years up to and including 2003 for most indicators. Please note that the latest data are provisional at this stage.
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BACKGROUND: Differences in morbidity and mortality between socioeconomic groups constitute one of the most consistent findings of epidemiologic research. However, research on social inequalities in health has yet to provide a comprehensive understanding of the mechanisms underlying this association. In recent analysis, we showed health behaviours, assessed longitudinally over the follow-up, to explain a major proportion of the association of socioeconomic status (SES) with mortality in the British Whitehall II study. However, whether health behaviours are equally important mediators of the SES-mortality association in different cultural settings remains unknown. In the present paper, we examine this issue in Whitehall II and another prospective European cohort, the French GAZEL study. METHODS AND FINDINGS: We included 9,771 participants from the Whitehall II study and 17,760 from the GAZEL study. Over the follow-up (mean 19.5 y in Whitehall II and 16.5 y in GAZEL), health behaviours (smoking, alcohol consumption, diet, and physical activity), were assessed longitudinally. Occupation (in the main analysis), education, and income (supplementary analysis) were the markers of SES. The socioeconomic gradient in smoking was greater (p<0.001) in Whitehall II (odds ratio [OR] = 3.68, 95% confidence interval [CI] 3.11-4.36) than in GAZEL (OR = 1.33, 95% CI 1.18-1.49); this was also true for unhealthy diet (OR = 7.42, 95% CI 5.19-10.60 in Whitehall II and OR = 1.31, 95% CI 1.15-1.49 in GAZEL, p<0.001). Socioeconomic differences in mortality were similar in the two cohorts, a hazard ratio of 1.62 (95% CI 1.28-2.05) in Whitehall II and 1.94 in GAZEL (95% CI 1.58-2.39) for lowest versus highest occupational position. Health behaviours attenuated the association of SES with mortality by 75% (95% CI 44%-149%) in Whitehall II but only by 19% (95% CI 13%-29%) in GAZEL. Analysis using education and income yielded similar results. CONCLUSIONS: Health behaviours were strong predictors of mortality in both cohorts but their association with SES was remarkably different. Thus, health behaviours are likely to be major contributors of socioeconomic differences in health only in contexts with a marked social characterisation of health behaviours. Please see later in the article for the Editors' Summary.
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OBJECTIVE: To identify which physician and patient characteristics are associated with physicians' estimation of their patient social status.DESIGN: Cross-sectional ulticentric survey. SETTING: Fourty-seven primary care private offices in Western Switzerland. PARTICIPANTS: Random sample of 2030 patients ≥ 16, who encountered a general practitioner (GP) between September 2010 and February 2011. MAIN MEASURES: PRIMARY OUTCOME: patient social status perceived by GPs, using the MacArthur Scale of Subjective Social Status, ranging from the bottom (0) to the top (10) of the social scale.Secondary outcome: Difference between GP's evaluation and patient's own evaluation of their social status. Potential patient correlates: material and social deprivation using the DiPCare-Q, health status using the EQ-5D, sources of income, and level of education. GP characteristics: opinion regarding patients' deprivation and its influence on health and care. RESULTS: To evaluate patient social status, GPs considered the material, social, and health aspects of deprivation, along with education level, and amount and type of income. GPs declaring a frequent reflexive consideration of their own prejudice towards deprived patients, gave a higher estimation of patients' social status (+1.0, p = 0.002). Choosing a less costly treatment for deprived patients was associated with a lower estimation (-0.7, p = 0.002). GP's evaluation of patient social status was 0.5 point higher than the patient's own estimate (p<0.0001). CONCLUSIONS: GPs can perceive the various dimensions of patient social status, although heterogeneously, according partly to their own characteristics. Compared to patients' own evaluation, GPs overestimate patient social status.
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Background: This paper analyses gender inequalities in health status and in social determinants of health among the elderly in Western Europe. Methods: Data came from the first wave of the “Survey of Health, Ageing and Retirement in Europe” (SHARE, 2004). For the purposes of this study a subsample of community-residing people aged 65-85 years with no paid work was selected (4218 men and 5007 women). Multiple logistic regression models separated by sex and adjusted for age and country were fitted. Results: Women were more likely to report poor health status, limitations in mobility and poor mental health. Whereas in both sexes educational attainment was associated with the three health indicators, household income was only related to poor self-rated health among women. The relationship between living arrangements and health differed by gender and was primarily associated with poor mental health. In both sexes, not living with the partner but living with other people and being the household head was related to poor mental health status (aOR=2.14; 95% CI=1.11-4.14 for men and aOR=1.75; 95% CI=1.12-2.72 for women). Additionally, women living with their partner and other(s) and those living alone were more likely to report poor mental health status (aOR=1.67; 95% CI=1.17-2.41 and aOR=1.58; 95% CI=1.26-1.97, respectively). Conclusions: Health inequalities persist among the elderly. Women have poorer health status than men and in both sexes the risk of poor health status increases among those with low educational attainment. Living arrangements are primarily associated with poor mental health status with patterns that differ by gender.