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Revision: 1864 Author: marisademeglio Date: 2007-02-09 04:07:01 -0800 (Fri, 09 Feb 2007) Log Message: - Bugfix for my last bugfix Modified Paths: - trunk/Langpacks/tools/AmisLangpackTemplate.rc Modified: trunk/Langpacks/tools/AmisLangpackTemplate.rc - trunk/Langpacks/tools/AmisLangpackTemplate.rc 2007-02-09 12:06:36 UTC (rev 1863) trunk/Langpacks/tools/AmisLangpackTemplate.rc 2007-02-09 12:07:01 UTC (rev 1864) @@ -1,4 +1,4 @@ -// Microsoft Visual C generated resource script. +// Microsoft Visual C generated resource script. // #include 'resource.h' @@ -359,7 +359,7 @@ IDDFIND DIALOGEX 0, 0, 181, 55 STYLE DSSETFONT DSMODALFRAME WSPOPUP WSCAPTION WSSYSMENU -CAPTION '$$' +CAPTION '$$' FONT 8, 'MS Sans Serif', 0, 0, 0x0 BEGIN DEFPUSHBUTTON '$$',IDOK,124,7,50,14 This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.

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We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence—defined as fasting plasma glucose of 70 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs—in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. We also calculated the posterior probability of meeting the global diabetes target if post-2000 trends continue. We used data from 751 studies including 4 372 000 adults from 146 of the 200 countries we make estimates for. Global age-standardised diabetes prevalence increased from 43% (95% credible interval 24–70) in 1980 to 90% (72–111) in 2014 in men, and from 50% (29–79) to 79% (64–97) in women. The number of adults with diabetes in the world increased from 108 million in 1980 to 422 million in 2014 (285% due to the rise in prevalence, 397% due to population growth and ageing, and 318% due to interaction of these two factors). Age-standardised adult diabetes prevalence in 2014 was lowest in northwestern Europe, and highest in Polynesia and Micronesia, at nearly 25%, followed by Melanesia and the Middle East and north Africa.

Between 1980 and 2014 there was little change in age-standardised diabetes prevalence in adult women in continental western Europe, although crude prevalence rose because of ageing of the population. By contrast, age-standardised adult prevalence rose by 15 percentage points in men and women in Polynesia and Micronesia. In 2014, American Samoa had the highest national prevalence of diabetes (30% in both sexes), with age-standardised adult prevalence also higher than 25% in some other islands in Polynesia and Micronesia. If post-2000 trends continue, the probability of meeting the global target of halting the rise in the prevalence of diabetes by 2025 at the 2010 level worldwide is lower than 1% for men and is 1% for women. Only nine countries for men and 29 countries for women, mostly in western Europe, have a 50% or higher probability of meeting the global target. A few studies have reported diabetes trends in one or a few countries.

A previous study reported diabetes prevalence trends to 2008 as a secondary outcome, which was estimated from mean fasting plasma glucose. This study was done before the global target on diabetes was agreed, hence there are no recent data. The International Diabetes Federation periodically reports diabetes prevalence but does not analyse trends, uses some sources that are only based on self-reported diabetes, and does not fully account for differences in diabetes definitions in different data sources. We estimated trends in diabetes prevalence from 1980 to 2014, in 200 countries and territories organised into 21 regions, mostly on the basis of geography and national income. The exception was a region consisting of high-income English-speaking countries because cardiometabolic risk factors, especially body-mass index (BMI), an important risk factor for diabetes, have similar trends in these countries, which can be distinct from other countries in their geographical region.

As the primary outcome, diabetes was defined as fasting plasma glucose of 70 mmol/L or higher, history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs. This definition of diabetes is used in the Global Monitoring Framework for NCDs. Our study had three stages, each described in detail below and in the ). First, we identified, accessed, and reanalysed population-based health-examination surveys that had measured at least one diabetes biomarker. We then converted diabetes prevalence in sources that had defined diabetes through 2hOGTT or HbA 1c or used a cutoff other than 70 mmol/L for fasting plasma glucose, to a corresponding prevalence based on the primary outcome as defined above. Finally, we applied a statistical model to the pooled data to estimate trends for all countries and years. Data sources.

and presented in the, was the primary outcome (prevalence of fasting plasma glucose ≥70 mmol/L or history of diabetes diagnosis or use of diabetes drugs), and the main independent variable was a prevalence based on the definitions in at least one study that did not report the primary outcome but had some form of data on diabetes and glycaemia. The coefficients of these regressions were estimated from data sources with individual-level data, which could be used to calculate prevalence with both definitions.

Details of conversion (or cross-walking) regressions, and their specification and coefficients, are presented in the. Datapoints based on fewer than 25 people were excluded. All regressions included terms for age, sex, country, income (natural logarithm of per-capita gross domestic product adjusted for purchasing power and inflation), and the year of study. When we used more than 400 datapoints to estimate the regression coefficients, the regressions also included regional random effects.

Finally, we included interaction terms in the regressions if the interaction terms provided a better fit to the data as determined by the Bayesian Information Criterion. Statistical analysis. In summary, we used a hierarchical probit model in which diabetes levels and trends in countries were nested in regional levels and trends, which were in turn nested in those of super-regions and worldwide. In this structure, estimates of diabetes levels and trends for each country and year were informed by the country's own data, if available, and by data from other years in the same country and in other countries, especially those in the same region, with data for similar time periods. The hierarchical structure borrows information to a greater degree when data are non-existent or weakly informative (eg, because they have a small sample size or are not national), and to a lesser extent in data-rich countries and regions. The model accounted for the fact that prevalence in subnational and community studies might systematically differ from nationally representative surveys, and also tends to have larger variation relative to the true values than national studies do.

These features were implemented by including data-driven fixed-effect and random-effect terms for subnational and community data. The fixed effects adjust for systematic differences between subnational or community studies and national studies.

The random effects allow national data to have larger influence on the estimates than subnational or community data with similar sample sizes. The model also accounted for rural–urban differences in diabetes prevalence, through the use of data-driven fixed effects for rural-only and urban-only studies. These rural and urban effects were weighted by the difference between study-level and country-level urbanisation.

The statistical model also included country-level covariates that help predict diabetes prevalence, including average number of years of education, proportion of national population living in urban areas, a summary measure of availability of different food types for human consumption, and age-standardised adult mean BMI. We report the posterior probability that an estimated increase or decrease represents a truly increasing or decreasing trend. Posterior probability would be 050 in a country or region in which an increase is statistically indistinguishable from a decrease; a larger posterior probability indicates more certainty in a change in prevalence. Additionally, we calculated the posterior probability of meeting the global target of no increase in diabetes prevalence if post-2000 trends continue. All analyses were done separately by sex.

We used the WHO standard population for age standardisation. We used data from 751 population-based measurement surveys and studies, which included 4 372 000 participants aged 18 years or older.

The studies covered 146 (73%) of the 200 countries and territories for which estimates were made. These 146 countries contained 90% of the world's adult population in 2014.

Regionally, the average number of data sources per country ranged from less than one in central Africa to 24 in the high-income Asia Pacific region. 21 (39%) of the 54 countries without data were in sub-Saharan Africa, 11 (20%) in the Caribbean, seven (13%) in central Europe, four (7%) in central Asia, and the remaining 11 (20%) in other regions. Nearly a third of surveys and studies (242) were from years before 2000, and the other two-thirds (509) for 2000 and later. Age-standardised diabetes prevalence in women in 2014 was lowest in northwestern and southwestern Europe, which each had a prevalence of less than 5%. The lowest prevalence in adult men was also in northwestern Europe, at 58% (95% CrI 36–87).

Crude adult prevalence in northwestern Europe was 59% (38–86) for women and 79% (51–115) for men in 2014. At the other extreme, age-standardised diabetes prevalence was higher than 20% in adult men and women in Polynesia and Micronesia, and around 15% in Melanesia and in the Middle East and north Africa.

Over the 35 years of analysis, there was almost no change in age-standardised diabetes prevalence in women in northwestern and southwestern Europe, and only a small non-significant increase in central and eastern Europe. Adult men in northwestern Europe also had a smaller rise in prevalence than did other regions. By contrast, age-standardised prevalence in Polynesia and Micronesia rose by 150 (95% CrI 55–259) percentage points in adult men (posterior probability 0999) and by 149 (45–262) percentage points in adult women (posterior probability 0998). Crude adult prevalence increased more than age-standardised prevalence in regions that had substantial ageing—eg, in high-income regions. In 1980, age-standardised adult diabetes prevalence was lower than 3% in men in 32 countries and in women in 23 countries (, ).

In the same year, age-standardised prevalence was higher than 12% in adult men and women in a few islands in Polynesia and Micronesia and women in Kuwait, reaching 25% in men and women in Nauru. By 2014, women in only one country had an age-standardised adult prevalence lower than 3% and women in only nine countries had one lower than 4%, with the lowest prevalence estimated in some countries in northwestern Europe such as Switzerland, Austria, Denmark, Belgium, and the Netherlands.

In the same year, age-standardised prevalence in adult men was higher than 4% in every country ; the lowest estimated prevalences were in the same northwestern European countries as those for women and in a few countries in east Africa and southeast Asia. At the other extreme, age-standardised adult diabetes prevalence in 2014 was 31% (95% CrI 19–44) in men and 33% (21–47) in women in American Samoa, and was also higher than 25% in men and women in some other islands in Polynesia and Micronesia (,; ). No country had a statistically significant decrease in diabetes prevalence from 1980 to 2014 , although the relative increase over these 35 years was lower than 20% in nine countries for men, mostly in northwestern Europe, and in 39 countries for women. Over the same period, age-standardised adult prevalence of diabetes at least doubled for men in 120 countries and for women in 87 countries, with a posterior probability of 0887 or higher.

The largest absolute increases in age-standardised adult prevalence were in Oceania, exceeding 15 percentage points in some countries, followed by the Middle East and north Africa. Worldwide, if post-2000 trends continue, the probability of meeting the global diabetes target for men is lower than 1%; for women it is 1%. Only nine countries, mostly in northwestern Europe, had a 50% or higher probability of meeting the global target for men, as did 29 countries for women. Rather, if post-2000 trends continue, age-standardised prevalence of diabetes in 2025 will be 128% (95% CrI 83–196) in men and 104% (71–151) in women. The number of adults with diabetes will surpass 700 million. The number of adults with diabetes in the world increased from 108 million in 1980, to 422 million in 2014.

East Asia and south Asia had the largest rises of absolute numbers, and had the largest number of people with diabetes in 2014: 106 million and 86 million, respectively. 397% (n=1248 million) of the rise in the number of people with diabetes was due to population growth and ageing, 285% (n=897 million) due to the rise in age-specific prevalences, and the remaining 318% (n=999 million) due to the interaction of the two—ie, an older and larger population with higher age-specific prevalences. Half of adults with diabetes in 2014 lived in five countries: China, India, the USA, Brazil, and Indonesia. These countries also accounted for one half of the world's adult population in 2014. Although the top three countries on this list remained unchanged from 1980 to 2014, the global share of adults with diabetes who live in China and India increased, by contrast with the USA, where the share decreased. The changes in the share of adults with diabetes from India and the USA might be partly because of the changes in their shares. However, the share of the adult population of China remained virtually unchanged, while its share of the adult population with diabetes increased.

Low-income and middle-income countries, including Indonesia, Pakistan, Mexico, and Egypt, replaced European countries, including Germany, Ukraine, Italy, and the UK, on the list of the top ten countries with most adults with diabetes. This rise in prevalence has been compounded by population growth and ageing, nearly quadrupling the number of adults with diabetes over these 35 years. The burden of diabetes, both in terms of prevalence and number of adults with diabetes, increased at a greater rate in low-income and middle-income countries than in high-income countries. The highest national prevalences—generally those in Oceania, and the Middle East and north Africa—are now five to ten times greater than the lowest prevalences, which are in some western European countries. but there were differences between our estimates and those from the IDF in some countries and regions. In particular, we estimated a higher age-standardised prevalence of diabetes in most countries in the Middle East and north Africa than the IDF.

The IDF does not estimate trends, hence we could not compare trend estimates. Our finding that diabetes prevalence was low in much of Asia and sub-Saharan Africa in the 1980s and 1990s is consistent with other studies that found low prevalences in these regions in those decades. At the moment, information on the proportion of people with, or at risk of, diabetes who are diagnosed and receive treatment is limited to a few countries.

Consistent information on diagnosis and treatment coverage will become increasingly important as universal health coverage becomes a central theme of global health efforts, and should be a focus of future analyses. Finally, in addition to total caloric intake and adiposity, dietary composition and physical activity might affect diabetes risk and contribute to differences in regional trends. The strengths of our study include its scope of making consistent and comparable estimates of trends in diabetes prevalence and of the probabilities of meeting the global diabetes target. We used an unprecedented amount of population-based data, which came from countries in which 90% of the global adult population lives. We used only data from studies that had measured a diabetes biomarker to avoid bias in self-reported data. Data were analysed according to a common protocol, and the characteristics and quality of data sources were rigorously verified through repeated checks by Collaborating Group members from each country.

We pooled data using a statistical model that took into account the epidemiological features of diabetes, including non-linear time trends and age associations, and used all available data while giving more weight to national data than to subnational and community sources. Despite our extensive efforts to identify and access worldwide population-based data, some countries had no or few data sources, especially those in sub-Saharan Africa, the Caribbean, central Asia, and central Europe.

Estimates for these countries relied mostly or entirely on the statistical model, which shares information across countries and regions through its hierarchy and through predictive covariates. The absence or scarcity of data is reflected in wider uncertainty intervals of our estimates for these countries and regions (,; ). Diabetes was reported using a definition other than our primary outcome in some data sources, either because fasting plasma glucose was not measured or because individual-level data could not be accessed. To overcome this issue, we systematically used the reported metrics to estimate our primary outcome; the cross-walking regressions used for this purpose had good predictive accuracy. The share of studies that used a portable device (instead of laboratory analysis) for measuring diabetes biomarkers has increased over time.

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We do not expect the rise in the use of portable devices to affect the estimated levels and trends because their higher use in population-based research is partly due to increasing similarity between their measurements and those in laboratory-based tests. Age-standardised adult diabetes prevalence would be 100% (95% CrI 80–125) for men and 88% (72–107) for women, worldwide, if we applied the cross-walking regression (similar to those in ) to convert our estimates to prevalence of diabetes defined as fasting plasma glucose of 70 mmol/L or higher, or 2hOGTT of 111 mmol/L or higher, or history of diagnosis with diabetes or use of insulin or oral hypoglycaemic drugs.

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Finally, the survey data did not separate type 1 and type 2 diabetes because distinguishing between these disorders is difficult in adults.