Abbreviationswhich includes all abbreviations and acronyms used in the Factbook, with their expansions. Acronyms An acronym is an abbreviation coined from the initial letter of each successive word in a term or phrase.
One estimate, based on data our analysis of new data shows is an undercount, predicted in globally an estimated 2. Below we provide trends data from repeated surveys conducted over the — period over 40 countries with the same methods to suggest possibly 2 or more billion are already overweight or obese today.
The implications of these trends for health, quality of life, productivity, and health care costs are staggering. The burden is greater for much of Asia, Latin America, the Middle East, and Africa due to differences in fat patterning and body composition and the cardiometabolic effects of body mass index BMI at levels far below standard BMI overweight cutoffs of 25 14 These results are seen in India in relation to the prevalence of diabetes and impaired fasting glucose and in China in the prevalence of hypertension and diabetes.
This paper documents the changes in global obesity, describes what countries are doing, with a focus on the potential options low and middle income countries are considering. It provides a comprehensive examination of the state of the art of current knowledge on the diet-related dimension of the changes in the low and middle income world.
However, there is little detailed information on longitudinal trends in low- and middle-income countries aside from Brazil, China, India, and Mexico 17 The general impression has been that in higher-income countries we often find greater obesity rates in rural areas and among the poor—the reverse of what is seen in lower-income countries.
However, new evidence suggests that these patterns are changing, and the increasing rate of obesity among the poor has important implications for the distribution of health inequalities In the past three decades the age-standardized mean BMI, the most widely used metric for defining overweight and obesity has increased by 0.
The major gaps in this literature relate to lack of data and superficial examinations of patterns and trends without sufficient attention to the extant literature and the dynamics of change rather than simplistic cross-sectional perspectives. For example, in recent papers, Subramanium and colleagues, using just one wave of data and ignoring dynamics point out that the rich are far more likely than the poor to be obese This is a very different conclusion from Jones-Smith and colleagues, who use similar data, but longitudinal analysis 20 Meta-regression was used to examine the associations between GDP and disproportionate increases in overweight prevalence by SES with additional testing for modification by country-level income inequality.
In 27 of 37 countries, higher SES vs. GDP was positively related to a faster increase in overweight prevalence among the lower wealth groups.
Among countries with a higher GDP, lower income inequality was associated with faster overweight growth among the poor. Another limitation is the focus on women of childbearing age and preschoolers.
This reflects the availability of data from multiple countries which have relied on the Demographic and Health Surveys, which focus on women of childbearing age and their children. A few studies, in particular some national surveys in Mexico and Brazil and a few large-scale longitudinal studies, including the China Health and Nutrition Survey, the Indonesia Family Life Survey, and the Mexico Family Life Survey, cover all age and gender groups Using inclusive data, one sees quite different gender-specific patterns of change and differentials by socioeconomic status.
According to the limited research and data available, men with higher socioeconomic status SES have higher rates of overweight and obesity than do lower SES men While we know obesity prevalence appears to be rising across all low- and middle-income countries, it is not clear what urban-rural difference may exist.
Here we use some recent data we published in other form and rearrange for this review 19 These data provided repeated nationally representative cross-sectional surveys that includerural andurbantotal adult women 18—49 years old from 42 countries in Asia, the Middle East, Africa East, West, central, and southernand Latin America.
The absolute and relative change in the prevalence of overweight and obesity for women in these countries and the regions are shown in Supplementary Table 1. Using population weights, we estimate that 19 percent of rural women and However, there are regional differences, with rural women in Latin America, the Middle East, and North Africa having much higher increases in prevalence compared to their urban counterparts.
However, the relative annual change in weighted prevalence is higher for rural 3. In other words, women in rural areas are quickly catching-up to their urban counterparts.
Supplemental Figure 1 shows the statistics for obesity prevalence, and the results are consistent. The higher relative annual rates of change for obesity compared to overweight suggest that obesity in particular is changing very quickly.An acronym is an abbreviation coined from the initial letter of each successive word in a term or phrase.
In general, an acronym made up solely from the first letter of the major words in the expanded form is rendered in all capital letters (NATO from North Atlantic Treaty Organization; an exception would be ASEAN for Association of Southeast Asian Nations). They deny the seriousness of low body weight and have a morbid fear of the standards of female beauty imposed by modern industrial society or Prevalence of Eating Disorders in Non-Western.
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