Prevalence of Diabetes by Race and Ethnicity in the United States, 2011-2016

JAMA - Journal of the American Medical Association

Volumn 322, Issue 24, 2019, Pages 2389-2398

  Cheng, Yiling J ^a   Kanaya, Alka M ^b   Araneta, Maria Rosario G ^c   Saydah, Sharon H ^a   Kahn, Henry S ^d   Gregg, Edward W ^e   Fujimoto, Wilfred Y ^f   Imperatore, Giuseppina ^a  

^a NATIONAL CENTER FOR CHRONIC DISEASE PREVENTION AND HEALTH PROMOTION   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d EMORY UNIVERSITY   (United States)

^e IMPERIAL COLLEGE LONDON   (United Kingdom)

^f UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GLUCOSE; HEMOGLOBIN A1C;

ADULT; AFRICAN AMERICAN; AGE; ARTICLE; ASIAN AMERICAN; BODY MASS; CENTRAL AMERICAN; CONTROLLED STUDY; CROSS-SECTIONAL STUDY; CUBAN; DIABETES MELLITUS; DOMINICAN (DOMINICA); EAST ASIAN; ETHNICITY; FEMALE; GENDER; GLUCOSE BLOOD LEVEL; HEMOGLOBIN BLOOD LEVEL; HISPANIC; HUMAN; MAJOR CLINICAL STUDY; MALE; MEXICAN AMERICAN; MIDDLE AGED; PREVALENCE; PRIORITY JOURNAL; PUERTO RICAN; RACE; SELF REPORT; SOUTH AMERICAN; SOUTH ASIAN; SOUTHEAST ASIAN; UNITED STATES; ANCESTRY GROUP; EPIDEMIOLOGY; ETHNIC GROUP; ETHNOLOGY; NUTRITION; SOCIOECONOMICS;

ADULT; ASIAN AMERICANS; CONTINENTAL POPULATION GROUPS; CROSS-SECTIONAL STUDIES; DIABETES MELLITUS; ETHNIC GROUPS; FEMALE; HISPANIC AMERICANS; HUMANS; MALE; NUTRITION SURVEYS; PREVALENCE; SOCIOECONOMIC FACTORS; UNITED STATES;

EID: 85077038976     PISSN: 00987484     EISSN: 15383598     Source Type: Journal    
DOI: 10.1001/jama.2019.19365     Document Type: Article

References (28)

1. Colby SL, Ortman JM. Projections of the size and composition of the US population: 2014 to 2060: population estimates and projections. US Census Bureau website. https://census.gov/library/publications/2015/demo/p25-1143.html. Published 2015. Accessed October 23, 2019.
2. Cho NH, Shaw JE, Karuranga S, IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018; 138: 271-281. doi: 10.1016/j.diabres.2018.02.023 29496507
3. Arroyo-Johnson C, Mincey KD, Ackermann N, Milam L, Goodman MS, Colditz GA. Racial and ethnic heterogeneity in self-reported diabetes prevalence trends across Hispanic subgroups, National Health Interview Survey, 1997-2012. Prev Chronic Dis. 2016; 13:
4. Rhee EJ. Diabetes in Asians. Endocrinol Metab (Seoul). 2015; 30 (3): 263-269. doi: 10.3803/EnM.2015.30.3.263 26435131
5. Kanaya AM, Herrington D, Vittinghoff E, Understanding the high prevalence of diabetes in U.S. south Asians compared with four racial/ethnic groups: the MASALA and MESA studies. Diabetes Care. 2014; 37 (6): 1621-1628. doi: 10.2337/dc13-2656 24705613
6. Schneiderman N, Llabre M, Cowie CC, Prevalence of diabetes among Hispanics/Latinos from diverse backgrounds: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Diabetes Care. 2014; 37 (8): 2233-2239. doi: 10.2337/dc13-2939 25061138
7. 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. doi: 10.1001/jama.2015.10029 26348752
8. Centers for Disease Control and Prevention (CDC). NHANES questionnaires, datasets, and related documentation. CDC website. https://wwwn.cdc.gov/nchs/nhanes/default.aspx. Published 2018. Accessed October 23, 2019.
9. Centers for Disease Control and Prevention (CDC). NHANES response rates and CPS totals. CDC website. https://wwwn.cdc.gov/nchs/nhanes/ResponseRates.aspx. Published 2018. Accessed October 23, 2019.
10. Johnson CL, Dohrmann SM, Burt VL, Mohadjer LK. National health and nutrition examination survey: sample design, 2011-2014. Vital Health Stat 2. 2014;(162): 1-33. 25569458
11. Paulose-Ram R, Burt V, Broitman L, Ahluwalia N. Overview of Asian American data collection, release, and analysis: National Health and Nutrition Examination Survey 2011-2018. Am J Public Health. 2017; 107 (6): 916-921. doi: 10.2105/AJPH.2017.303815 28426300
12. Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013; 22 (3): 278-295. doi: 10.1177/0962280210395740 21220355
13. Centers for Disease Control and Prevention (CDC). US diabetes resources and publications. CDC website. https://www.cdc.gov/diabetes/resources-publications/index.html. Published 2019. Accessed October 23, 2019.
14. Centers for Disease Control and Prevention (CDC). NHANES Survey Methods and Analytic Guidelines, 2011-2014 and 2015-2016. CDC website. https://wwwn.cdc.gov/nchs/data/nhanes/2011-2012/analyticguidelines/analytic-guidelines-11-16.pdf. Published 2018. Accessed October 23, 2019.
15. Graubard BI, Korn EL. Predictive margins with survey data. Biometrics. 1999; 55 (2): 652-659. doi: 10.1111/j.0006-341X.1999.00652.x 11318229
16. Parr WC. A note on the jackknife, the bootstrap and the delta method estimators of bias and variance. Biometrika. 1983; 70 (3): 719-722. doi: 10.1093/biomet/70.3.719
17. Parker JD, Talih M, Malec DJ, National Center for Health Statistics data presentation standards for proportions. Vital Health Stat 2. 2017;(175): 1-22. 30248016
18. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies [published correction appears in Lancet. 2004;363(9412):902]. Lancet. 2004; 363 (9403): 157-163. doi: 10.1016/S0140-6736(03)15268-3 14726171
19. Hsu WC, Araneta MR, Kanaya AM, Chiang JL, Fujimoto W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care. 2015; 38 (1): 150-158. doi: 10.2337/dc14-2391 25538311
20. American Diabetes Association. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes-2019. Diabetes Care. 2019; 42 (suppl 1): S13-S28. doi: 10.2337/dc19-S002 30559228
21. Araneta MR, Kanaya AM, Hsu WC, Optimum BMI cut points to screen Asian Americans for type 2 diabetes. Diabetes Care. 2015; 38 (5): 814-820. doi: 10.2337/dc14-2071 25665815
22. Meijnikman AS, De Block CEM, Dirinck E, Not performing an OGTT results in significant underdiagnosis of (pre)diabetes in a high risk adult Caucasian population. Int J Obes (Lond). 2017; 41 (11): 1615-1620. doi: 10.1038/ijo.2017.165 28720876
23. Abdul-Ghani MA, Tripathy D, DeFronzo RA. Contributions of beta-cell dysfunction and insulin resistance to the pathogenesis of impaired glucose tolerance and impaired fasting glucose. Diabetes Care. 2006; 29 (5): 1130-1139. doi: 10.2337/dc05-2179 16644654
24. 1c levels: a cross-sectional analysis of 2 studies. Ann Intern Med. 2010; 152 (12): 770-777. doi: 10.7326/0003-4819-152-12-201006150-00004 20547905
25. 1c levels between African-American and non-Hispanic white adults with diabetes: a meta-analysis. Diabetes Care. 2006; 29 (9): 2130-2136. doi: 10.2337/dc05-1973 16936167
26. 1c identifies a different subgroup of individuals with type 2 diabetes compared to fasting or post-challenge glucose in Asian Indians: the CARRS and MASALA studies. Diabetes Res Clin Pract. 2019; 153: 93-102. doi: 10.1016/j.diabres.2019.05.026 31150721
27. 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. doi: 10.1001/archinte.167.14.1545 17646610
28. Selvin E, Wang D, Matsushita K, Grams ME, Coresh J. Prognostic implications of single-sample confirmatory testing for undiagnosed diabetes: a prospective cohort study. Ann Intern Med. 2018; 169 (3): 156-164. doi: 10.7326/M18-0091 29913486