The Experts below are selected from a list of 38590389 Experts worldwide ranked by ideXlab platform
Christopher J L Murray - One of the best experts on this subject based on the ideXlab platform.
-
Global patterns of healthy life expectancy in the year 2002
BMC Public Health, 2004Co-Authors: Colin D Mathers, Kim Moesgaard Iburg, Joshua A Salomon, Ajay Tandon, Somnath Chatterji, Bedirhan Ustün, Christopher J L MurrayAbstract:Background Healthy life expectancy – sometimes called health-adjusted life expectancy (HALE) – is a form of health expectancy indicator that extends measures of life expectancy to account for the distribution of health states in the population. The World Health Organization reports on healthy life expectancy for 192 WHO Member States. This paper describes variation in average levels of population health across these countries and by sex for the year 2002. Methods Mortality was analysed for 192 countries and disability from 135 causes assessed for 17 regions of the world. Health surveys in 61 countries were analyzed using new methods to improve the comparability of self-report data. Results Healthy life expectancy at birth ranged from 40 years for males in Africa to over 70 years for females in developed countries in 2002. The equivalent "lost" healthy years ranged from 15% of total life expectancy at birth in Africa to 8–9% in developed countries. Conclusion People living in poor countries not only face lower life expectancies than those in richer countries but also live a higher proportion of their lives in poor health.
-
defining and measuring health inequality an approach based on the distribution of health expectancy
Bulletin of The World Health Organization, 2000Co-Authors: Emmanuela Gakidou, Christopher J L Murray, Julio FrenkAbstract:This paper proposes an approach to conceptualizing and operationalizing the measurement of health inequality, defined as differences in health across individuals in the population. We propose that health is an intrinsic component of well-being and thus we should be concerned with inequality in health, whether or not it is correlated with inequality in other dimensions of well-being. In the measurement of health inequality, the complete range of fatal and non-fatal health outcomes should be incorporated. This notion is operationalized through the concept of healthy lifespan. Individual health expectancy is preferable, as a measurement, to individual healthy lifespan, since health expectancy excludes those differences in healthy lifespan that are simply due to chance. In other words, the quantity of interest for studying health inequality is the distribution of health expectancy across individuals in the population. The inequality of the distribution of health expectancy can be summarized by measures of individual/mean differences (differences between the individual and the mean of the population) or inter-individual differences. The exact form of the measure to summarize inequality depends on three normative choices. A firmer understanding of people’s views on these normative choices will provide a basis for deliberating on a standard WHO measure of health inequality.
Md Tahmidul Islam Molla - One of the best experts on this subject based on the ideXlab platform.
-
Estimating healthy life expectancies using longitudinal survey data; methods and techniques in population health measures
Vital and health statistics. Series 2 Data evaluation and methods research, 2008Co-Authors: Jennifer H Madans, Md Tahmidul Islam MollaAbstract:Objective-Summary measures of population health are statistics that combine mortality and morbidity to represent overall population health in a single index. Such measures include healthy life expectancy, also called disability-free life expectancy and active life expectancy. Healthy life expectancy can be calculated using cross-sectional or longitudinal survey data. This report presents a comprehensive discussion of a method for calculating healthy life expectancy using data from longitudinal surveys. Methods-Healthy life expectancies are calculated using the multistate life table model. Expected life in various states of health is estimated using data from the Second Longitudinal Study of Aging and the Medicare Current Beneficiary Survey to illustrate the calculation of the statistics and the discussion of data and methodology related issues. Results-The study shows that estimating summary measures of population health using longitudinal survey data provides the opportunity of using incidence rather than prevalence rates. Health measures estimated based on incidence reflect the most recent health status of the population. Models that use longitudinal survey data measure transitions from good to poor health as well as poor to good health. That is, the models account for recovery from morbidity or illness. Longitudinal survey data canalsobeusedtocalculate healthy or active life expectancies by initial health states.
Miriam Isola - One of the best experts on this subject based on the ideXlab platform.
-
empowering the aging with mobile health a mhealth framework for supporting sustainable healthy lifestyle behavior
Current Problems in Cardiology, 2019Co-Authors: Anthony Faiola, Elizabeth Lerner Papautsky, Miriam IsolaAbstract:Abstract Healthcare providers are shifting to a value-based model that acknowledges the importance of a healthy lifestyle for managing chronic disease and mental health. This approach empowers patients to adopt and/or sustain healthy lifestyle choices through the use of innovative technologies—providing beneficial ways of delivering health literacy, self-monitoring, and patient–provider collaboration. Such pathways have the potential to enable healthy lifestyle management for a growing U.S. cohort—the “baby boomer” generation (BBG)—who are at risk for developing heart disease, stroke, arthritis, high cholesterol, and diabetes, etc. In this paper, we argue for a new mHealthy lifestyle management (MLM) model that uses mobile health technology as a means to engage BBG consumers in ways that establish their role in self-care and decision-making, as well as patient–provider collaboration that can significantly impact sustainable healthy lifestyle behaviors. By merging the domains of health informatics and human factors psychology, MLM addresses the complex challenges associated with patient–provider collaborative work, while offering a healthcare framework to BBGs in their quest to self-manage a physical and/or mental healthy lifestyle. A MLM use-case highlights the challenges and solutions for team-based clinical counseling. Finally, recommendations for future MLM tools are outlined that support patient access to personal health eTools, information, and services.
Jennifer H Madans - One of the best experts on this subject based on the ideXlab platform.
-
Estimating healthy life expectancies using longitudinal survey data; methods and techniques in population health measures
Vital and health statistics. Series 2 Data evaluation and methods research, 2008Co-Authors: Jennifer H Madans, Md Tahmidul Islam MollaAbstract:Objective-Summary measures of population health are statistics that combine mortality and morbidity to represent overall population health in a single index. Such measures include healthy life expectancy, also called disability-free life expectancy and active life expectancy. Healthy life expectancy can be calculated using cross-sectional or longitudinal survey data. This report presents a comprehensive discussion of a method for calculating healthy life expectancy using data from longitudinal surveys. Methods-Healthy life expectancies are calculated using the multistate life table model. Expected life in various states of health is estimated using data from the Second Longitudinal Study of Aging and the Medicare Current Beneficiary Survey to illustrate the calculation of the statistics and the discussion of data and methodology related issues. Results-The study shows that estimating summary measures of population health using longitudinal survey data provides the opportunity of using incidence rather than prevalence rates. Health measures estimated based on incidence reflect the most recent health status of the population. Models that use longitudinal survey data measure transitions from good to poor health as well as poor to good health. That is, the models account for recovery from morbidity or illness. Longitudinal survey data canalsobeusedtocalculate healthy or active life expectancies by initial health states.
Yuhua Christine Sun - One of the best experts on this subject based on the ideXlab platform.
-
health concern food choice motives and attitudes toward healthy eating the mediating role of food choice motives
Appetite, 2008Co-Authors: Yuhua Christine SunAbstract:Abstract This study addresses how various health concerns might influence not only consumers’ food choice motives but also consumers’ subsequent attitudes toward healthy eating. This study expects that those consumers with greater health concerns would have different food choice motives and better attitudes toward healthy eating. A self-completion questionnaire was used to gather information. Participants, a random sample of 500 undergraduate students from a national university in Taipei, Taiwan, provided a total of 456 usable questionnaires, representing a valid response rate of 91%. The average age of the respondents at the time of the survey was 21 years and 63% of respondents were females. The relationship between health concern and healthy eating attitudes was confirmed. The relationship between health concern of developing diseases and attitudes toward healthy eating was fully mediated by food choice motives. However, the relationship between calorie consumption health concern and healthy eating attitudes was only partially mediated by food choice motives. Implications of these findings are discussed.
