- Email: [email protected]
Challenges of monitoring global diabetes prevalence
Correspondence
Challenges of monitoring global diabetes prevalence
Switzerland; Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, USA (KH); Department of Global Health and Population and Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA (GD) 1
Peter Bennett and colleagues 1 state in their letter that it is unclear whether analyses of diabetes trends by the NCD Risk Factor Collaboration (NCD-RisC) accounted for whether glucose was measured in whole blood versus plasma. NCD-RisC’s approach to handling data sources that had measured glucose in whole blood instead of plasma are described in our global trends papers—in the methods section of the Article by Danaei and colleagues2 and in table 2 of the appendix of the 2016 Article by NCD-RisC. 3 Specifically, following recommendations by Sacks and colleagues in 2011,4 measurements of fasting and postprandial glucose in venous whole blood and of fasting glucose in capillary whole blood were multiplied by 1·11 to convert them to equivalent plasma glucose concentrations before diabetes prevalence was calculated; a few studies in which postprandial glucose was measured in capillary whole blood were excluded because the association with venous plasma glucose is highly variable.5 This protocol was applied to individual-level glucose data, including to those from WHO STEPS surveys. ME reports a charitable grant from Youth Health Programme of AstraZeneca and personal fees from Thirdbridge, both outside the submitted work. All other authors declare no competing interests.
*Majid Ezzati, Bin Zhou, Leanne Riley, Gretchen A Stevens, Kaveh Hajifathalian, Goodarz Danaei, on behalf of the NCD Risk Factor Collaboration [email protected] MRC-PHE Centre for Environment and Health and Department of Epidemiology and Biostatistics, School of Public Health (ME, BZ), WHO Collaborating Centre on NCD Surveillance and Epidemiology (ME), Imperial College London, London W2 1PG, UK; Surveillance and Populationbased Prevention Unit, Prevention of Noncommunicable Diseases Department (LR) and Department of Information, Evidence, and Research (GAS), World Health Organization, Geneva,
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Bennett PH, Magliano DJ, Alberti KG, Zimmet P. Liberating non-communicable disease data. Lancet Diabetes Endocrinol 2016; 4: 815–16. 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 countryyears and 2·7 million participants. Lancet 2011; 378: 31–40. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 2016; 387: 1513–30. Sacks DB, Arnold M, Bakris GL, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2011; 57: e1–e47. Carstensen B, Lindström J, Sundvall J, Borch-Johnsen K, Tuomilehto J, DPSS Study Group. Measurement of blood glucose: comparison between different types of specimens. Ann Clin Biochem 2008; 45: 140–48.
Authors’ reply Our original letter was a follow-up to the Comment by Justine Davies and colleagues on the need and importance of liberating access to data, such as those from the WHO STEPS surveys, for further research on diabetes and NCDs.1 Our letter was intended to emphasise the importance of ensuring the validity of analyses using such data. The analysis of the WHO STEPS surveys from Oceania that we cited is an example of the potential problems that can occur by incorrect application of diagnostic cutpoints, resulting in reported diabetes prevalences that are almost double what they would have been if correct cutpoints were used: Fiji (2011) 29.6% versus 15·6%; Samoa (2013) 45·8% versus 24·3%; and Tonga (2012) 34·4% versus 19·0%.2 We thank Majid Ezzati and colleagues for emphasising how the NCD Risk Factor Collaboration converted the glucose measurements made in capillary and venous whole blood to obtain the equivalent venous
plasma concentrations for their analyses.3 The recommendations that they cite,4 with which we agree, also state that: (1) when glucose is used to establish the diagnosis of diabetes (or for screening) it should be measured in venous plasma in an accredited laboratory; and (2) imprecision of the results, coupled with the substantial differences between portable glucose meters, precludes their use from the diagnosis of diabetes and limits their use for screening. These STEPS studies 2 also violated both these recommendations. Many other specific issues are associated with measuring and monitoring the diabetes epidemic,5 but the major point of our letter was and remains that when multisource data are used, great care is needed to assure their comparability and validity if they are to be incorporated into large datasets for further research. Flawed data can lead to flawed or erroneous conclusions. PHB is a member of The Lancet Commission on Diabetes and DJM is a member of The Lancet Diabetes & Endocrinology International Advisory Board. All other authors declare no competing interests.
*Peter H Bennett, Dianna J Magliano, K George Alberti, Paul Zimmet [email protected] National Institutes of Health, Phoenix, AZ 85014, USA (PHB); Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia (DJM); Imperial College, St Mary’s Campus, London, UK (KGA); Department of Medicine, Monash University, Melbourne, VIC, Australia (PZ) 1
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3
4
5
Davies J, Yudkin JS, Atun R. Liberating data: the crucial weapon in the fight against NCDs. Lancet Diabetes Endocrinol 2016; 4: 197–98. Taylor R, Zimmet P, Naseri T, et al. Erroneous inflation of diabetes prevalence: are there global implications? J Diabetes 2016; 8: 766–769. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 2016; 387: 1513–30. Sacks DB, Arnold M, Bakris GL, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2011; 57: e1–e47. Zimmet P, Alberti KG, Magliano DJ, Bennett PH. Diabetes mellitus statistics on prevalence and mortality: facts and fallacies. Nat Rev Endocrinol 2016; 12: 616–22.
www.thelancet.com/diabetes-endocrinology Vol 5 March 2017
Challenges of monitoring global diabetes prevalence
Switzerland; Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, USA (KH); Department of Global Health and Population and Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA (GD) 1
Peter Bennett and colleagues 1 state in their letter that it is unclear whether analyses of diabetes trends by the NCD Risk Factor Collaboration (NCD-RisC) accounted for whether glucose was measured in whole blood versus plasma. NCD-RisC’s approach to handling data sources that had measured glucose in whole blood instead of plasma are described in our global trends papers—in the methods section of the Article by Danaei and colleagues2 and in table 2 of the appendix of the 2016 Article by NCD-RisC. 3 Specifically, following recommendations by Sacks and colleagues in 2011,4 measurements of fasting and postprandial glucose in venous whole blood and of fasting glucose in capillary whole blood were multiplied by 1·11 to convert them to equivalent plasma glucose concentrations before diabetes prevalence was calculated; a few studies in which postprandial glucose was measured in capillary whole blood were excluded because the association with venous plasma glucose is highly variable.5 This protocol was applied to individual-level glucose data, including to those from WHO STEPS surveys. ME reports a charitable grant from Youth Health Programme of AstraZeneca and personal fees from Thirdbridge, both outside the submitted work. All other authors declare no competing interests.
*Majid Ezzati, Bin Zhou, Leanne Riley, Gretchen A Stevens, Kaveh Hajifathalian, Goodarz Danaei, on behalf of the NCD Risk Factor Collaboration [email protected] MRC-PHE Centre for Environment and Health and Department of Epidemiology and Biostatistics, School of Public Health (ME, BZ), WHO Collaborating Centre on NCD Surveillance and Epidemiology (ME), Imperial College London, London W2 1PG, UK; Surveillance and Populationbased Prevention Unit, Prevention of Noncommunicable Diseases Department (LR) and Department of Information, Evidence, and Research (GAS), World Health Organization, Geneva,
162
2
3
4
5
Bennett PH, Magliano DJ, Alberti KG, Zimmet P. Liberating non-communicable disease data. Lancet Diabetes Endocrinol 2016; 4: 815–16. 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 countryyears and 2·7 million participants. Lancet 2011; 378: 31–40. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 2016; 387: 1513–30. Sacks DB, Arnold M, Bakris GL, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2011; 57: e1–e47. Carstensen B, Lindström J, Sundvall J, Borch-Johnsen K, Tuomilehto J, DPSS Study Group. Measurement of blood glucose: comparison between different types of specimens. Ann Clin Biochem 2008; 45: 140–48.
Authors’ reply Our original letter was a follow-up to the Comment by Justine Davies and colleagues on the need and importance of liberating access to data, such as those from the WHO STEPS surveys, for further research on diabetes and NCDs.1 Our letter was intended to emphasise the importance of ensuring the validity of analyses using such data. The analysis of the WHO STEPS surveys from Oceania that we cited is an example of the potential problems that can occur by incorrect application of diagnostic cutpoints, resulting in reported diabetes prevalences that are almost double what they would have been if correct cutpoints were used: Fiji (2011) 29.6% versus 15·6%; Samoa (2013) 45·8% versus 24·3%; and Tonga (2012) 34·4% versus 19·0%.2 We thank Majid Ezzati and colleagues for emphasising how the NCD Risk Factor Collaboration converted the glucose measurements made in capillary and venous whole blood to obtain the equivalent venous
plasma concentrations for their analyses.3 The recommendations that they cite,4 with which we agree, also state that: (1) when glucose is used to establish the diagnosis of diabetes (or for screening) it should be measured in venous plasma in an accredited laboratory; and (2) imprecision of the results, coupled with the substantial differences between portable glucose meters, precludes their use from the diagnosis of diabetes and limits their use for screening. These STEPS studies 2 also violated both these recommendations. Many other specific issues are associated with measuring and monitoring the diabetes epidemic,5 but the major point of our letter was and remains that when multisource data are used, great care is needed to assure their comparability and validity if they are to be incorporated into large datasets for further research. Flawed data can lead to flawed or erroneous conclusions. PHB is a member of The Lancet Commission on Diabetes and DJM is a member of The Lancet Diabetes & Endocrinology International Advisory Board. All other authors declare no competing interests.
*Peter H Bennett, Dianna J Magliano, K George Alberti, Paul Zimmet [email protected] National Institutes of Health, Phoenix, AZ 85014, USA (PHB); Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia (DJM); Imperial College, St Mary’s Campus, London, UK (KGA); Department of Medicine, Monash University, Melbourne, VIC, Australia (PZ) 1
2
3
4
5
Davies J, Yudkin JS, Atun R. Liberating data: the crucial weapon in the fight against NCDs. Lancet Diabetes Endocrinol 2016; 4: 197–98. Taylor R, Zimmet P, Naseri T, et al. Erroneous inflation of diabetes prevalence: are there global implications? J Diabetes 2016; 8: 766–769. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 2016; 387: 1513–30. Sacks DB, Arnold M, Bakris GL, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2011; 57: e1–e47. Zimmet P, Alberti KG, Magliano DJ, Bennett PH. Diabetes mellitus statistics on prevalence and mortality: facts and fallacies. Nat Rev Endocrinol 2016; 12: 616–22.
www.thelancet.com/diabetes-endocrinology Vol 5 March 2017