- Email: [email protected]
Global control of diabetes: information for action
Comment
Global control of diabetes: information for action On Dec 20, 2006, the UN General Assembly passed resolution 61/225, in recognition that diabetes “poses severe risks for families, Member States and the entire world”.1 In The Lancet, the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group2 presents evidence that strongly supports this resolution, and reinforces the need to strengthen worldwide diabetes surveillance. Unlike the communicable disease influenza3 no worldwide surveillance network exists for diabetes, a non-communicable disease. Instead, the Collaborating Group has assembled available data (that meet appropriate quality standards) from population-based health surveys and epidemiological studies. The data were adjusted for different diabetes case definitions and glycaemic metrics, then a hierarchical Bayesian model was applied to fill in missing data for each of 199 countries and 29 years (1980–2008) by borrowing information from similar but more data-dense strata. The Bayesian model also adjusted for variation in study population coverage (national, subnational, or local community), so the data could be appropriately weighted. The result is an estimate of trends in dysglycaemia and diabetes globally, regionally, and nationally, covering the whole timecourse of the pandemic. Readers may ask how trends can be estimated for 92 countries with no data at all, and for the first decade of the pandemic (the 1980s), for which information is sparse. We can be reassured since the Collaborating Group tested the validity of their model by deliberately removing data for some countries and years, and applied the model to this restricted dataset. The model adequately predicted values for the withheld data (ie, out-of-sample prediction), which confirms fitness for purpose. The findings themselves are stark. Worldwide, mean fasting plasma glucose concentrations have risen on average by 0·08 mmol/L per decade (standardised for age), which corresponds to an increase in diabetes prevalence of about 7% per decade (from 8·3% in 1980 to 9·8% in 2008 among men, and from 7·5% to 9·2% among women). Regionally the epidemic has grown most rapidly in Oceania (both sexes) and south Asia (women), whereas east Asia has had substantially slower (women) or even negative growth rates (men; figure). The study lacks sufficient power to estimate www.thelancet.com Vol 378 July 2, 2011
change in the epidemic growth rates from one decade to the next—information that would be of great value in predicting the future course of the pandemic. The Collaborating Group estimates that about 70% of the global increase in the number of people living with diabetes from 1980 to 2008 can be attributed to population growth and ageing, confirming the old saying that demography explains two-thirds of everything. However, rising adiposity rates have also fuelled the epidemic; according to the authors’ estimate, they account for much of the remaining 30% of the increase, albeit with substantial inter-regional variation. This variation is indicative not only of regional differences in the dynamics of the obesity epidemic,4 but also of the complex relation between body fat and diabetes, which is affected by body fat distribution, duration of exposure, amount of physical activity, dietary pattern, alcohol consumption, adult weight gain and weight cycling, and fetal and infant nutrition.5 Although the investigators do not comment about the quality of diabetes control, data from other studies suggest that less than half of diabetes cases are diagnosed globally, and, of this subset, less than half have glycated
Published Online June 25, 2011 DOI:10.1016/S01406736(11)60604-1 See Articles page 31
Men Women
East and southeast Asia Central and eastern Europe Sub-Saharan Africa Central Asia, Middle East, and north Africa Central and Andean Latin America South Asia Southern and tropical Latin America Western high income Asia-Pacific high income Oceania –10
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Figure: Percentage growth in age-standardised diabetes prevalence, 1980–2008, by region Data from reference 2; percentage change calculated by fitting linear model to all 29 annual age-standardised (WHO World Population) prevalence values from 1980 to 2008 for each region; diabetes defined by current American Diabetes Association definition.
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Comment
haemoglobin (HbA1c) concentrations below 7% or 53 mmol/mol (indicative of tight control).6 Furthermore, this glycocentric view of control is too narrow; blood pressure and lipid concentrations, kidney function, and absolute cardiovascular risk are equally important biomarkers for people living with diabetes, and might be even less well controlled.7 In an editorial in February, 2011, The Lancet warned against paralysis by analysis.8 Although it is true that governments sometimes call for more research as a delaying tactic, it is equally true that to motivate and sustain change is difficult without data. Provided that surveillance is regarded as information for action, with effective linkage to accountability mechanisms, surveillance can make a valuable, and cost-effective, contribution to the global and national response to the non-communicable disease crisis.9 Country-level survey-based surveillance for dysglycaemia and diabetes should use HbA1c as the test of choice, because it does not necessitate a fasting blood sample, is readily calibrated against an international reference standard, and is accepted by WHO as a diagnostic test for diabetes (cutoff 6·5% or 48 mmol/mol).10 By use of surveys that link HbA1c measurements to self-reported doctor-diagnosed diabetes, the prevalence of prediabetic states can be monitored, as can the ratio of undiagnosed to diagnosed diabetes and the quality of glycaemic control. If body-mass index (or waist circumference), blood pressure, blood lipids, and microalbuminuria are also measured, metabolic control, chronic kidney disease, and absolute cardiovascular risk can all be monitored. Furthermore, surveys can be extended to include nested cohort studies of participants with prediabetes and diabetes, which will also allow progression of microvascular complications and outcomes of treatment to be monitored.11 Countries at all income levels have long experience with health surveys; however, in those with welldeveloped health information systems, reliance on surveys for chronic disease surveillance is no longer necessary. Instead, data linkage (with unique identifiers of patients or probabilistic matching) can integrate primary care with hospital and specialist clinic data, including laboratory test results and prescriptions, enabling diabetes incidence, age at onset, progression,
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clinical outcomes, and relative survival to be monitored along with prevalence.12 Worldwide, however, the urgent need is to strengthen basic surveillance of dysglycaemia and diabetes, including standardised frameworks, case definitions, survey methods, tools, and reporting protocols. The forthcoming high-level meeting of the UN General Assembly on the Prevention and Control of Non-communicable Diseases (New York, Sept 19–20, 2011),13 provides a welcome opportunity to strengthen global commitment to non-communicable disease surveillance. Martin Tobias Health and Disability Intelligence, Ministry of Health, Wellington 6011, New Zealand [email protected] I declare that I have no conflicts of interest. 1
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UN General Assembly. Resolution 61/225 World Diabetes Day. Jan 18, 2007. http://www.worlddiabetesday.org/files/docs/U4D/UN_Resolution.pdf (accessed April 7, 2011). Danaei G, Finucane MM, Lu Y, et al, on behalf of the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Glucose). National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet 2011; published online June 25. DOI:10.1016/S0140-6736(11)60679-X. WHO. Global alert and response: influenza. 2011. www.who.int/csr/ disease/influenza (accessed April 7, 2011). Finucane MM, Stevens GA, Cowan MJ, et al for the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Body Mass Index). National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet 2011; 377: 557–67. Webber J. Changing epidemiology of obesity—implications for diabetes. In: Barnett A, Kumar S, eds. Obesity and diabetes. 2nd edn. Oxford: John Wiley and Sons, 2009: 1–12. Barr E, Magliano D, Zimmet P, et al. AUSDIAB 2005: The Australian Diabetes, Obesity and Lifestyle Study. 2006. http://www.diabetes.com.au/ pdf/AUSDIAB_Report_Final.pdf (accessed April 7, 2011). Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med 2008; 358: 580–91. The Lancet. An epidemic of risk factors for cardiovascular disease. Lancet 2011; 377: 527. WHO. Global status report on noncommunicable diseases 2010. April, 2011. http://www.who.int/nmh/publications/ncd_report2010/en (accessed April 7, 2011). WHO. Use of glycated haemoglobin (HbA1C) in the diagnosis of diabetes mellitus. 2011. http://www.who.int/diabetes/publications/reporthba1c_2011.pdf (accessed April 7, 2011). Tobias MI. Diabetes surveillance: population-based estimates and projections for New Zealand, 2001–2011. September, 2007. http://www.moh.govt.nz/ moh.nsf/indexmh/diabetes-suveillance-population-estimatesprojections-2001-2011 (accessed April 7, 2011). Lipscombe LL, Hux JE. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population-based study. Lancet 2007; 369: 750–56. Beaglehole R, Bonita R, Horton R, et al. Priority actions for the non-communicable disease crisis. Lancet 2011; 377: 1438–47.
www.thelancet.com Vol 378 July 2, 2011
Global control of diabetes: information for action On Dec 20, 2006, the UN General Assembly passed resolution 61/225, in recognition that diabetes “poses severe risks for families, Member States and the entire world”.1 In The Lancet, the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group2 presents evidence that strongly supports this resolution, and reinforces the need to strengthen worldwide diabetes surveillance. Unlike the communicable disease influenza3 no worldwide surveillance network exists for diabetes, a non-communicable disease. Instead, the Collaborating Group has assembled available data (that meet appropriate quality standards) from population-based health surveys and epidemiological studies. The data were adjusted for different diabetes case definitions and glycaemic metrics, then a hierarchical Bayesian model was applied to fill in missing data for each of 199 countries and 29 years (1980–2008) by borrowing information from similar but more data-dense strata. The Bayesian model also adjusted for variation in study population coverage (national, subnational, or local community), so the data could be appropriately weighted. The result is an estimate of trends in dysglycaemia and diabetes globally, regionally, and nationally, covering the whole timecourse of the pandemic. Readers may ask how trends can be estimated for 92 countries with no data at all, and for the first decade of the pandemic (the 1980s), for which information is sparse. We can be reassured since the Collaborating Group tested the validity of their model by deliberately removing data for some countries and years, and applied the model to this restricted dataset. The model adequately predicted values for the withheld data (ie, out-of-sample prediction), which confirms fitness for purpose. The findings themselves are stark. Worldwide, mean fasting plasma glucose concentrations have risen on average by 0·08 mmol/L per decade (standardised for age), which corresponds to an increase in diabetes prevalence of about 7% per decade (from 8·3% in 1980 to 9·8% in 2008 among men, and from 7·5% to 9·2% among women). Regionally the epidemic has grown most rapidly in Oceania (both sexes) and south Asia (women), whereas east Asia has had substantially slower (women) or even negative growth rates (men; figure). The study lacks sufficient power to estimate www.thelancet.com Vol 378 July 2, 2011
change in the epidemic growth rates from one decade to the next—information that would be of great value in predicting the future course of the pandemic. The Collaborating Group estimates that about 70% of the global increase in the number of people living with diabetes from 1980 to 2008 can be attributed to population growth and ageing, confirming the old saying that demography explains two-thirds of everything. However, rising adiposity rates have also fuelled the epidemic; according to the authors’ estimate, they account for much of the remaining 30% of the increase, albeit with substantial inter-regional variation. This variation is indicative not only of regional differences in the dynamics of the obesity epidemic,4 but also of the complex relation between body fat and diabetes, which is affected by body fat distribution, duration of exposure, amount of physical activity, dietary pattern, alcohol consumption, adult weight gain and weight cycling, and fetal and infant nutrition.5 Although the investigators do not comment about the quality of diabetes control, data from other studies suggest that less than half of diabetes cases are diagnosed globally, and, of this subset, less than half have glycated
Published Online June 25, 2011 DOI:10.1016/S01406736(11)60604-1 See Articles page 31
Men Women
East and southeast Asia Central and eastern Europe Sub-Saharan Africa Central Asia, Middle East, and north Africa Central and Andean Latin America South Asia Southern and tropical Latin America Western high income Asia-Pacific high income Oceania –10
0
10
20
30
40 50 60 70 Percentage change
80
90
100
110
Figure: Percentage growth in age-standardised diabetes prevalence, 1980–2008, by region Data from reference 2; percentage change calculated by fitting linear model to all 29 annual age-standardised (WHO World Population) prevalence values from 1980 to 2008 for each region; diabetes defined by current American Diabetes Association definition.
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Comment
haemoglobin (HbA1c) concentrations below 7% or 53 mmol/mol (indicative of tight control).6 Furthermore, this glycocentric view of control is too narrow; blood pressure and lipid concentrations, kidney function, and absolute cardiovascular risk are equally important biomarkers for people living with diabetes, and might be even less well controlled.7 In an editorial in February, 2011, The Lancet warned against paralysis by analysis.8 Although it is true that governments sometimes call for more research as a delaying tactic, it is equally true that to motivate and sustain change is difficult without data. Provided that surveillance is regarded as information for action, with effective linkage to accountability mechanisms, surveillance can make a valuable, and cost-effective, contribution to the global and national response to the non-communicable disease crisis.9 Country-level survey-based surveillance for dysglycaemia and diabetes should use HbA1c as the test of choice, because it does not necessitate a fasting blood sample, is readily calibrated against an international reference standard, and is accepted by WHO as a diagnostic test for diabetes (cutoff 6·5% or 48 mmol/mol).10 By use of surveys that link HbA1c measurements to self-reported doctor-diagnosed diabetes, the prevalence of prediabetic states can be monitored, as can the ratio of undiagnosed to diagnosed diabetes and the quality of glycaemic control. If body-mass index (or waist circumference), blood pressure, blood lipids, and microalbuminuria are also measured, metabolic control, chronic kidney disease, and absolute cardiovascular risk can all be monitored. Furthermore, surveys can be extended to include nested cohort studies of participants with prediabetes and diabetes, which will also allow progression of microvascular complications and outcomes of treatment to be monitored.11 Countries at all income levels have long experience with health surveys; however, in those with welldeveloped health information systems, reliance on surveys for chronic disease surveillance is no longer necessary. Instead, data linkage (with unique identifiers of patients or probabilistic matching) can integrate primary care with hospital and specialist clinic data, including laboratory test results and prescriptions, enabling diabetes incidence, age at onset, progression,
4
clinical outcomes, and relative survival to be monitored along with prevalence.12 Worldwide, however, the urgent need is to strengthen basic surveillance of dysglycaemia and diabetes, including standardised frameworks, case definitions, survey methods, tools, and reporting protocols. The forthcoming high-level meeting of the UN General Assembly on the Prevention and Control of Non-communicable Diseases (New York, Sept 19–20, 2011),13 provides a welcome opportunity to strengthen global commitment to non-communicable disease surveillance. Martin Tobias Health and Disability Intelligence, Ministry of Health, Wellington 6011, New Zealand [email protected] I declare that I have no conflicts of interest. 1
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UN General Assembly. Resolution 61/225 World Diabetes Day. Jan 18, 2007. http://www.worlddiabetesday.org/files/docs/U4D/UN_Resolution.pdf (accessed April 7, 2011). Danaei G, Finucane MM, Lu Y, et al, on behalf of the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Glucose). National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet 2011; published online June 25. DOI:10.1016/S0140-6736(11)60679-X. WHO. Global alert and response: influenza. 2011. www.who.int/csr/ disease/influenza (accessed April 7, 2011). Finucane MM, Stevens GA, Cowan MJ, et al for the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Body Mass Index). National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet 2011; 377: 557–67. Webber J. Changing epidemiology of obesity—implications for diabetes. In: Barnett A, Kumar S, eds. Obesity and diabetes. 2nd edn. Oxford: John Wiley and Sons, 2009: 1–12. Barr E, Magliano D, Zimmet P, et al. AUSDIAB 2005: The Australian Diabetes, Obesity and Lifestyle Study. 2006. http://www.diabetes.com.au/ pdf/AUSDIAB_Report_Final.pdf (accessed April 7, 2011). Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med 2008; 358: 580–91. The Lancet. An epidemic of risk factors for cardiovascular disease. Lancet 2011; 377: 527. WHO. Global status report on noncommunicable diseases 2010. April, 2011. http://www.who.int/nmh/publications/ncd_report2010/en (accessed April 7, 2011). WHO. Use of glycated haemoglobin (HbA1C) in the diagnosis of diabetes mellitus. 2011. http://www.who.int/diabetes/publications/reporthba1c_2011.pdf (accessed April 7, 2011). Tobias MI. Diabetes surveillance: population-based estimates and projections for New Zealand, 2001–2011. September, 2007. http://www.moh.govt.nz/ moh.nsf/indexmh/diabetes-suveillance-population-estimatesprojections-2001-2011 (accessed April 7, 2011). Lipscombe LL, Hux JE. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population-based study. Lancet 2007; 369: 750–56. Beaglehole R, Bonita R, Horton R, et al. Priority actions for the non-communicable disease crisis. Lancet 2011; 377: 1438–47.
www.thelancet.com Vol 378 July 2, 2011