Letters
3. Liang Y, Liu R, Du S, Qiu C. Trends in incidence of hypertension in Chinese adults, 1991-2009: the China Health and Nutrition Survey. Int J Cardiol. 2014;175 (1):96-101. 4. Zhai F, Yang X. Report of National Nutrition and Health Survey of China Residents in 2002: Part 2: Diet and Nutrition Intake. Beijing, China: People's Health Press; 2006. 5. World Health Organization, Food and Agriculture Organization of the United Nations, European Food Safety Authority. Towards a harmonised total diet study approach: a guidance document. http://www.who.int/foodsafety /publications/tds_guidance/en/. Accessed January, 12, 2016. 6. Anderson CA, Appel LJ, Okuda N, et al. Dietary sources of sodium in China, Japan, the United Kingdom, and the United States, women and men aged 40 to 59 years: the INTERMAP study. J Am Diet Assoc. 2010;110(5):736-745.
US Trends for Diabetes Prevalence Among Adults To the Editor Dr Menke and colleagues1 provided a comprehensive study of diabetes prevalence and trends in the United States from 1988 to 2012. The prevalence of diabetes was high at 14.3%. In particular, the highest prevalence rates (>20%) were seen among participants who were non-Hispanic black, nonHispanic Asian, and Hispanic. These data could be a cause for great concern. However, this study defined undiagnosed diabetes by any of 3 glycemic markers (ie, hemoglobin A1C, fasting plasma glucose [FPG], and 2-hour plasma glucose [2-hour PG] from the oral glucose tolerance test). The authors suggested that this allows for full accounting of diabetes. This is 1 of at least 2 studies2 reporting diabetes prevalence in this way in recent years. Each of the 3 measures of glycemia identifies separate but overlapping groups of individuals with diabetes.3 When diabetes is so defined, diabetes prevalence will be higher. Most previous studies have used only 1 or 2 measures of glycemia. We argue that there is little evidence supporting the value of using all 3 measures. Combining FPG and 2-hour PG to define diabetes is logical because they represent different pathophysiological processes of diabetes with respect to the relative contributions of insulin secretory defects and hepatic and peripheral insulin resistance. However, there is limited understanding of how hemoglobin A1C contributes independently to the estimate of diabetes prevalence. Furthermore, given the modest repeatability of the FPG and 2-hour PG tests on different days,4 which results in a significant proportion of those identified as having diabetes on 1 day as not having diabetes on a subsequent day, the estimated prevalence of diabetes using these glycemic markers alone will already be somewhat inflated. Hemoglobin A1C is a more practical test to use to define diabetes, but a more expensive means of assessing prevalence. Diabetes prevalence should not be estimated using any of the 3 tests until there is improved understanding of how the approach relates to more traditional and better understood methods.
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Corresponding Author: Dianna J. Magliano, PhD, Baker IDI Heart and Diabetes Institute, 99 Commercial Rd, Melbourne, Victoria, Australia 3004 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10): 1021-1029. 2. Xu Y, Wang L, He J, et al; 2010 China Noncommunicable Disease Surveillance Group. Prevalence and control of diabetes in Chinese adults. JAMA. 2013;310 (9):948-959. 3. Engelgau MM, Thompson TJ, Herman WH, et al. Comparison of fasting and 2-hour glucose and HbA1c levels for diagnosing diabetes: diagnostic criteria and performance revisited. Diabetes Care. 1997;20(5):785-791.
COMMENT & RESPONSE
Dianna J. Magliano, PhD Paul Zimmet, PhD, FRACP Jonathan Shaw, FRACP
Author Affiliations: Baker IDI Heart and Diabetes Institute, Melbourne, Australia.
4. Mooy JM, Grootenhuis PA, de Vries H, et al. Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia. 1996;39(3):298-305.
In Reply Dr Magliano and colleagues raise concern regarding our use of all 3 glycemic markers (hemoglobin A1c, FPG, and 2-hour PG) in defining undiagnosed diabetes, and of the lack of repeat testing in the National Health and Nutrition Examination Survey (NHANES). We agree that FPG and 2-hour PG may represent complementary pathophysiological processes of diabetes and using both may be logical when defining diabetes. However, hemoglobin A1c is a valid diagnostic marker that is commonly used in clinical practice and has less day-to-day variability than the other markers.1 All 3 markers are associated with diabetes complications.2 Based on this evidence, the American Diabetes Association (ADA) revised its diagnostic criteria in 2010 recommending the addition of hemoglobin A1c as a diagnostic test for diabetes,2 and the World Health Organization (WHO) made a similar revision to its diagnostic criteria in 2011.3 Magliano and colleagues state that few previous studies used all 3 measures, but this is expected because the ADA and WHO guidelines were only recently revised and prior studies would not be expected to have used hemoglobin A1c when defining diabetes. We agree that a diagnosis based on the 3 markers may not be perfectly aligned because individuals may be positive for some but not all markers; thus, using all 3 markers will result in a higher prevalence rate than using any 1 or 2 markers. However, the ADA does not state a preference for any specific marker because any 1 marker is sufficient to diagnose diabetes regardless of the results of the other 2 markers. In addition, Magliano and colleagues state there is limited understanding of how adding hemoglobin A1c contributes independently to diabetes prevalence. A previous 20052006 NHANES study found that 5.1% of the US population had either FPG or 2-hour PG above the diabetes cut point, whereas an additional 0.3% had hemoglobin A1c above the diabetes cut point. This suggests that the addition of hemoglobin A1c has a relatively small effect on estimates of undiagnosed diabetes.4 The marker that increased prevalence the most was 2-hour PG; (Reprinted) JAMA February 16, 2016 Volume 315, Number 7
Copyright 2016 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Umea University Library User on 02/16/2016
705
Letters
in our study, we used a definition based on only hemoglobin A1c and FPG and a second definition that additionally included 2-hour PG, which allows readers to compare different definitions. We agree with Magliano and colleagues that the lack of a retest for a positive marker is a limitation of the study. As we discussed, a positive marker should be retested to confirm a diagnosis of diabetes; however, the NHANES does not retest elevated markers.5 The major goal of our study was to examine trends in diabetes prevalence over time and by demographic factors. It is possible that different markers contributed differently to undiagnosed diabetes by demographic group because we did not investigate the relative contributions of each marker. However, trends were similar and the results of comparisons between different demographic groups were similar regardless of definition used; therefore, we believe it is unlikely that including hemoglobin A1c in the definition of undiagnosed diabetes substantially affected the comparisons.
tors have a 3.6 times higher cost of therapy than patients without inhibitors.2 An analysis of antihemophilic factor use by national drug codes or medication reimbursement codes should provide the opportunity to understand characteristics of the patients driving these high costs. In addition, disease severity was not noted; patients with severe hemophilia would have a much greater effect on medication costs. Populations can be stratified based on quantity of health resource use and other comorbidities. Application of predictive models and stratification can lead to targeted therapy to deliver the right medication to the right person at the right time and at the right price. In the current era of precision medicine, hemophilia seems to be an ideal target.
Andy Menke, PhD Sarah Casagrande, PhD Catherine C. Cowie, PhD
Corresponding Author: James H. Ruble, PharmD, JD, University of Utah College of Pharmacy, 30 S 2000 E, Salt Lake City, UT 84112 ([email protected] .edu).
Author Affiliations: Social & Scientific Systems, Silver Spring, Maryland (Menke, Casagrande); National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland (Cowie). Corresponding Author: Andy Menke, PhD, Social & Scientific Systems Inc, 8757 Georgia Ave, Silver Spring, MD 20910 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32 (7):1327-1334. 2. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(suppl 1):S62-S69. 3. World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. http://www.who.int/diabetes/publications/diagnosis_diabetes2011/en/. Accessed November 23, 2015. 4. Cowie CC, Rust KF, Byrd-Holt DD, et al. Prevalence of diabetes and high risk for diabetes using A1C criteria in the US population in 1988-2006. Diabetes Care. 2010;33(3):562-568. 5. Selvin E, Crainiceanu CM, Brancati FL, Coresh J. Short-term variability in measures of glycemia and implications for the classification of diabetes. Arch Intern Med. 2007;167(14):1545-1551.
Pharmacy Expenditures for Children With Serious Chronic Illness To the Editor Ms Swenson and colleagues1 reported findings from a retrospective analysis of the California Children’s Services paid claims data set for children with chronic illness. Their data illustrate the need for clinical, economic, and quality outcomes in patients with hemophilia. The authors seemed surprised by the fact that 0.4% of the cohort received 1 class of medications (antihemophilic factor) and accounted for 40.9% of total pharmacy expenditures. Numerous elements may be contributing to, and possibly skewing, these data. There is no mention of whether patients had inhibitors present. Patients with hemophilia and inhibi706
James H. Ruble, PharmD, JD Diana I. Brixner, RPh, PhD Author Affiliations: Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City.
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Swenson SM, Chamberlain LJ, Sanders LM, Sundaram V, Wise PH. Outpatient pharmacy expenditures for children with serious chronic illness in California, 2010-2012. JAMA. 2015;314(4):405-407. 2. Guh S, Grosse SD, McAlister S, Kessler CM, Soucie JM. Health care expenditures for Medicaid-covered males with haemophilia in the United States, 2008. Haemophilia. 2012;18(2):276-283.
In Reply We agree with Drs Ruble and Brixner that not all patients with hemophilia are the same. These patients represent a wide range of severity, and the cost skewing within the group reflects that. Hemophilia is not unique in this—most serious pediatric conditions demonstrate a range in severity. Ruble and Brixner accurately describe the reasons for this variation, including presence of inhibitors, and note that patients with inhibitors have higher costs of therapy.1 We do not dispute these facts. Our Research Letter was not meant to be a profile of patients with hemophilia in California, but an analysis of overall outpatient pharmacy expenditures by children with serious chronic illness. Regardless of the etiology of cost drivers within the condition, we maintain that it is surprising that 1 condition representing 0.4% of claims was responsible for nearly 41% of all outpatient pharmacy spending. Furthermore, it is the between-state variation in cost, not the within-state variation in disease, that was a second surprising issue. California spends more than twice as much per child than North Carolina and exceeded the mean cost by 4 times in 10 other states. The variation found within patients with hemophilia, including the presence of inhibitors, does offer many opportunities for precision medicine, first and foremost improving the quality of life for patients, as well as reducing costs for the state. Our hope is that our research will highlight both the need for such research and development, and in the meantime prompt
JAMA February 16, 2016 Volume 315, Number 7 (Reprinted)
Copyright 2016 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Umea University Library User on 02/16/2016
jama.com
3. Liang Y, Liu R, Du S, Qiu C. Trends in incidence of hypertension in Chinese adults, 1991-2009: the China Health and Nutrition Survey. Int J Cardiol. 2014;175 (1):96-101. 4. Zhai F, Yang X. Report of National Nutrition and Health Survey of China Residents in 2002: Part 2: Diet and Nutrition Intake. Beijing, China: People's Health Press; 2006. 5. World Health Organization, Food and Agriculture Organization of the United Nations, European Food Safety Authority. Towards a harmonised total diet study approach: a guidance document. http://www.who.int/foodsafety /publications/tds_guidance/en/. Accessed January, 12, 2016. 6. Anderson CA, Appel LJ, Okuda N, et al. Dietary sources of sodium in China, Japan, the United Kingdom, and the United States, women and men aged 40 to 59 years: the INTERMAP study. J Am Diet Assoc. 2010;110(5):736-745.
US Trends for Diabetes Prevalence Among Adults To the Editor Dr Menke and colleagues1 provided a comprehensive study of diabetes prevalence and trends in the United States from 1988 to 2012. The prevalence of diabetes was high at 14.3%. In particular, the highest prevalence rates (>20%) were seen among participants who were non-Hispanic black, nonHispanic Asian, and Hispanic. These data could be a cause for great concern. However, this study defined undiagnosed diabetes by any of 3 glycemic markers (ie, hemoglobin A1C, fasting plasma glucose [FPG], and 2-hour plasma glucose [2-hour PG] from the oral glucose tolerance test). The authors suggested that this allows for full accounting of diabetes. This is 1 of at least 2 studies2 reporting diabetes prevalence in this way in recent years. Each of the 3 measures of glycemia identifies separate but overlapping groups of individuals with diabetes.3 When diabetes is so defined, diabetes prevalence will be higher. Most previous studies have used only 1 or 2 measures of glycemia. We argue that there is little evidence supporting the value of using all 3 measures. Combining FPG and 2-hour PG to define diabetes is logical because they represent different pathophysiological processes of diabetes with respect to the relative contributions of insulin secretory defects and hepatic and peripheral insulin resistance. However, there is limited understanding of how hemoglobin A1C contributes independently to the estimate of diabetes prevalence. Furthermore, given the modest repeatability of the FPG and 2-hour PG tests on different days,4 which results in a significant proportion of those identified as having diabetes on 1 day as not having diabetes on a subsequent day, the estimated prevalence of diabetes using these glycemic markers alone will already be somewhat inflated. Hemoglobin A1C is a more practical test to use to define diabetes, but a more expensive means of assessing prevalence. Diabetes prevalence should not be estimated using any of the 3 tests until there is improved understanding of how the approach relates to more traditional and better understood methods.
jama.com
Corresponding Author: Dianna J. Magliano, PhD, Baker IDI Heart and Diabetes Institute, 99 Commercial Rd, Melbourne, Victoria, Australia 3004 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10): 1021-1029. 2. Xu Y, Wang L, He J, et al; 2010 China Noncommunicable Disease Surveillance Group. Prevalence and control of diabetes in Chinese adults. JAMA. 2013;310 (9):948-959. 3. Engelgau MM, Thompson TJ, Herman WH, et al. Comparison of fasting and 2-hour glucose and HbA1c levels for diagnosing diabetes: diagnostic criteria and performance revisited. Diabetes Care. 1997;20(5):785-791.
COMMENT & RESPONSE
Dianna J. Magliano, PhD Paul Zimmet, PhD, FRACP Jonathan Shaw, FRACP
Author Affiliations: Baker IDI Heart and Diabetes Institute, Melbourne, Australia.
4. Mooy JM, Grootenhuis PA, de Vries H, et al. Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia. 1996;39(3):298-305.
In Reply Dr Magliano and colleagues raise concern regarding our use of all 3 glycemic markers (hemoglobin A1c, FPG, and 2-hour PG) in defining undiagnosed diabetes, and of the lack of repeat testing in the National Health and Nutrition Examination Survey (NHANES). We agree that FPG and 2-hour PG may represent complementary pathophysiological processes of diabetes and using both may be logical when defining diabetes. However, hemoglobin A1c is a valid diagnostic marker that is commonly used in clinical practice and has less day-to-day variability than the other markers.1 All 3 markers are associated with diabetes complications.2 Based on this evidence, the American Diabetes Association (ADA) revised its diagnostic criteria in 2010 recommending the addition of hemoglobin A1c as a diagnostic test for diabetes,2 and the World Health Organization (WHO) made a similar revision to its diagnostic criteria in 2011.3 Magliano and colleagues state that few previous studies used all 3 measures, but this is expected because the ADA and WHO guidelines were only recently revised and prior studies would not be expected to have used hemoglobin A1c when defining diabetes. We agree that a diagnosis based on the 3 markers may not be perfectly aligned because individuals may be positive for some but not all markers; thus, using all 3 markers will result in a higher prevalence rate than using any 1 or 2 markers. However, the ADA does not state a preference for any specific marker because any 1 marker is sufficient to diagnose diabetes regardless of the results of the other 2 markers. In addition, Magliano and colleagues state there is limited understanding of how adding hemoglobin A1c contributes independently to diabetes prevalence. A previous 20052006 NHANES study found that 5.1% of the US population had either FPG or 2-hour PG above the diabetes cut point, whereas an additional 0.3% had hemoglobin A1c above the diabetes cut point. This suggests that the addition of hemoglobin A1c has a relatively small effect on estimates of undiagnosed diabetes.4 The marker that increased prevalence the most was 2-hour PG; (Reprinted) JAMA February 16, 2016 Volume 315, Number 7
Copyright 2016 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Umea University Library User on 02/16/2016
705
Letters
in our study, we used a definition based on only hemoglobin A1c and FPG and a second definition that additionally included 2-hour PG, which allows readers to compare different definitions. We agree with Magliano and colleagues that the lack of a retest for a positive marker is a limitation of the study. As we discussed, a positive marker should be retested to confirm a diagnosis of diabetes; however, the NHANES does not retest elevated markers.5 The major goal of our study was to examine trends in diabetes prevalence over time and by demographic factors. It is possible that different markers contributed differently to undiagnosed diabetes by demographic group because we did not investigate the relative contributions of each marker. However, trends were similar and the results of comparisons between different demographic groups were similar regardless of definition used; therefore, we believe it is unlikely that including hemoglobin A1c in the definition of undiagnosed diabetes substantially affected the comparisons.
tors have a 3.6 times higher cost of therapy than patients without inhibitors.2 An analysis of antihemophilic factor use by national drug codes or medication reimbursement codes should provide the opportunity to understand characteristics of the patients driving these high costs. In addition, disease severity was not noted; patients with severe hemophilia would have a much greater effect on medication costs. Populations can be stratified based on quantity of health resource use and other comorbidities. Application of predictive models and stratification can lead to targeted therapy to deliver the right medication to the right person at the right time and at the right price. In the current era of precision medicine, hemophilia seems to be an ideal target.
Andy Menke, PhD Sarah Casagrande, PhD Catherine C. Cowie, PhD
Corresponding Author: James H. Ruble, PharmD, JD, University of Utah College of Pharmacy, 30 S 2000 E, Salt Lake City, UT 84112 ([email protected] .edu).
Author Affiliations: Social & Scientific Systems, Silver Spring, Maryland (Menke, Casagrande); National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland (Cowie). Corresponding Author: Andy Menke, PhD, Social & Scientific Systems Inc, 8757 Georgia Ave, Silver Spring, MD 20910 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32 (7):1327-1334. 2. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(suppl 1):S62-S69. 3. World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. http://www.who.int/diabetes/publications/diagnosis_diabetes2011/en/. Accessed November 23, 2015. 4. Cowie CC, Rust KF, Byrd-Holt DD, et al. Prevalence of diabetes and high risk for diabetes using A1C criteria in the US population in 1988-2006. Diabetes Care. 2010;33(3):562-568. 5. Selvin E, Crainiceanu CM, Brancati FL, Coresh J. Short-term variability in measures of glycemia and implications for the classification of diabetes. Arch Intern Med. 2007;167(14):1545-1551.
Pharmacy Expenditures for Children With Serious Chronic Illness To the Editor Ms Swenson and colleagues1 reported findings from a retrospective analysis of the California Children’s Services paid claims data set for children with chronic illness. Their data illustrate the need for clinical, economic, and quality outcomes in patients with hemophilia. The authors seemed surprised by the fact that 0.4% of the cohort received 1 class of medications (antihemophilic factor) and accounted for 40.9% of total pharmacy expenditures. Numerous elements may be contributing to, and possibly skewing, these data. There is no mention of whether patients had inhibitors present. Patients with hemophilia and inhibi706
James H. Ruble, PharmD, JD Diana I. Brixner, RPh, PhD Author Affiliations: Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City.
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Swenson SM, Chamberlain LJ, Sanders LM, Sundaram V, Wise PH. Outpatient pharmacy expenditures for children with serious chronic illness in California, 2010-2012. JAMA. 2015;314(4):405-407. 2. Guh S, Grosse SD, McAlister S, Kessler CM, Soucie JM. Health care expenditures for Medicaid-covered males with haemophilia in the United States, 2008. Haemophilia. 2012;18(2):276-283.
In Reply We agree with Drs Ruble and Brixner that not all patients with hemophilia are the same. These patients represent a wide range of severity, and the cost skewing within the group reflects that. Hemophilia is not unique in this—most serious pediatric conditions demonstrate a range in severity. Ruble and Brixner accurately describe the reasons for this variation, including presence of inhibitors, and note that patients with inhibitors have higher costs of therapy.1 We do not dispute these facts. Our Research Letter was not meant to be a profile of patients with hemophilia in California, but an analysis of overall outpatient pharmacy expenditures by children with serious chronic illness. Regardless of the etiology of cost drivers within the condition, we maintain that it is surprising that 1 condition representing 0.4% of claims was responsible for nearly 41% of all outpatient pharmacy spending. Furthermore, it is the between-state variation in cost, not the within-state variation in disease, that was a second surprising issue. California spends more than twice as much per child than North Carolina and exceeded the mean cost by 4 times in 10 other states. The variation found within patients with hemophilia, including the presence of inhibitors, does offer many opportunities for precision medicine, first and foremost improving the quality of life for patients, as well as reducing costs for the state. Our hope is that our research will highlight both the need for such research and development, and in the meantime prompt
JAMA February 16, 2016 Volume 315, Number 7 (Reprinted)
Copyright 2016 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Umea University Library User on 02/16/2016
jama.com
