[Skip to Navigation]
Sign In
Figure 1.  Study Design and Sample Recruitment
Study Design and Sample Recruitment

A, Study design of the SEARCH cohort study. B, Flowchart depicting participants in this report, including reasons for exclusion. The final sample included 1313 youths with type 1 diabetes. BV indicates baseline visit.

Figure 2.  Trajectories of Hemoglobin A1c in 1313 Patients With Type 1 Diabetes in the SEARCH for Diabetes in Youth Study
Trajectories of Hemoglobin A1c in 1313 Patients With Type 1 Diabetes in the SEARCH for Diabetes in Youth Study

Group-based trajectory modeling identified 3 distinct hemoglobin A1c trajectories over a mean type 1 diabetes duration of 108 months. To convert hemoglobin A1c to proportion of total hemoglobin, multiply by 0.01.

Table 1.  Baseline Characteristics of 1313 Participants With Type 1 Diabetes by Hemoglobin A1c Trajectory Group
Baseline Characteristics of 1313 Participants With Type 1 Diabetes by Hemoglobin A1c Trajectory Group
Table 2.  Association of Black and Hispanic Race/Ethnicity, Compared With Non-Hispanic White Race/Ethnicity, With Hemoglobin A1c Trajectory Groups in 1011 Patients
Association of Black and Hispanic Race/Ethnicity, Compared With Non-Hispanic White Race/Ethnicity, With Hemoglobin A1c Trajectory Groups in 1011 Patients
Table 3.  Association of Nonwhite Race/Ethnicity, Compared With Non-Hispanic White Race/Ethnicity, With Hemoglobin A1c Trajectory Group, Stratified by Sex and Age at Diagnosisa
Association of Nonwhite Race/Ethnicity, Compared With Non-Hispanic White Race/Ethnicity, With Hemoglobin A1c Trajectory Group, Stratified by Sex and Age at Diagnosisa
1.
Diabetes Control and Complications Trial Research Group.  Effect of intensive diabetes treatment on the development and progression of long-term complications in adolescents with insulin-dependent diabetes mellitus: Diabetes Control and Complications Trial.  J Pediatr. 1994;125(2):177-188. doi:10.1016/S0022-3476(94)70190-3PubMedGoogle ScholarCrossref
2.
Nathan  DM, Genuth  S, Lachin  J,  et al; Diabetes Control and Complications Trial Research Group.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.  N Engl J Med. 1993;329(14):977-986. doi:10.1056/NEJM199309303291401PubMedGoogle ScholarCrossref
3.
White  NH, Cleary  PA, Dahms  W, Goldstein  D, Malone  J, Tamborlane  WV; Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group.  Beneficial effects of intensive therapy of diabetes during adolescence: outcomes after the conclusion of the Diabetes Control and Complications Trial (DCCT).  J Pediatr. 2001;139(6):804-812. doi:10.1067/mpd.2001.118887PubMedGoogle ScholarCrossref
4.
Pyatak  EA, Sequeira  PA, Whittemore  R, Vigen  CP, Peters  AL, Weigensberg  MJ.  Challenges contributing to disrupted transition from paediatric to adult diabetes care in young adults with type 1 diabetes.  Diabet Med. 2014;31(12):1615-1624. doi:10.1111/dme.12485PubMedGoogle ScholarCrossref
5.
Helgeson  VS, Snyder  PR, Seltman  H, Escobar  O, Becker  D, Siminerio  L.  Brief report: trajectories of glycemic control over early to middle adolescence.  J Pediatr Psychol. 2010;35(10):1161-1167. doi:10.1093/jpepsy/jsq011PubMedGoogle ScholarCrossref
6.
Rohan  JM, Rausch  JR, Pendley  JS,  et al.  Identification and prediction of group-based glycemic control trajectories during the transition to adolescence.  Health Psychol. 2014;33(10):1143-1152. doi:10.1037/hea0000025PubMedGoogle ScholarCrossref
7.
Moran  A, Jacobs  DR  Jr, Steinberger  J,  et al.  Insulin resistance during puberty: results from clamp studies in 357 children.  Diabetes. 1999;48(10):2039-2044. doi:10.2337/diabetes.48.10.2039PubMedGoogle ScholarCrossref
8.
Szadkowska  A, Pietrzak  I, Zmysłowska  A, Wyka  K, Bodalski  J.  Insulin resistance in newly diagnosed type 1 diabetic children and adolescents [in Polish].  Med Wieku Rozwoj. 2003;7(2):181-191.PubMedGoogle Scholar
9.
Moore  SM, Hackworth  NJ, Hamilton  VE, Northam  EP, Cameron  FJ.  Adolescents with type 1 diabetes: parental perceptions of child health and family functioning and their relationship to adolescent metabolic control.  Health Qual Life Outcomes. 2013;11:50. doi:10.1186/1477-7525-11-50PubMedGoogle ScholarCrossref
10.
Pyatak  EA, Sequeira  P, Peters  AL, Montoya  L, Weigensberg  MJ.  Disclosure of psychosocial stressors affecting diabetes care among uninsured young adults with type 1 diabetes.  Diabet Med. 2013;30(9):1140-1144. doi:10.1111/dme.12248PubMedGoogle ScholarCrossref
11.
Helgeson  VS, Reynolds  KA, Snyder  PR,  et al.  Characterizing the transition from paediatric to adult care among emerging adults with type 1 diabetes.  Diabet Med. 2013;30(5):610-615. doi:10.1111/dme.12067PubMedGoogle ScholarCrossref
12.
Hynes  L, Byrne  M, Dinneen  SF, McGuire  BE, O’Donnell  M, Mc Sharry  J.  Barriers and facilitators associated with attendance at hospital diabetes clinics among young adults (15-30 years) with type 1 diabetes mellitus: a systematic review.  Pediatr Diabetes. 2016;17(7):509-518. doi:10.1111/pedi.12198PubMedGoogle ScholarCrossref
13.
Lawrence  JM, Standiford  DA, Loots  B,  et al; SEARCH for Diabetes in Youth Study.  Prevalence and correlates of depressed mood among youth with diabetes: the SEARCH for Diabetes in Youth study.  Pediatrics. 2006;117(4):1348-1358. doi:10.1542/peds.2005-1398PubMedGoogle ScholarCrossref
14.
Dabelea  D, Stafford  JM, Mayer-Davis  EJ,  et al; SEARCH for Diabetes in Youth Research Group.  Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood.  JAMA. 2017;317(8):825-835.PubMedGoogle ScholarCrossref
15.
Marcovecchio  ML, Heywood  JJ, Dalton  RN, Dunger  DB.  The contribution of glycemic control to impaired growth during puberty in young people with type 1 diabetes and microalbuminuria.  Pediatr Diabetes. 2014;15(4):303-308. doi:10.1111/pedi.12090PubMedGoogle ScholarCrossref
16.
Stadler  M, Peric  S, Strohner-Kaestenbauer  H,  et al.  Mortality and incidence of renal replacement therapy in people with type 1 diabetes mellitus—a three decade long prospective observational study in the Lainz T1DM cohort.  J Clin Endocrinol Metab. 2014;99(12):4523-4530. doi:10.1210/jc.2014-2701PubMedGoogle ScholarCrossref
17.
Prince  CT, Becker  DJ, Costacou  T, Miller  RG, Orchard  TJ.  Changes in glycaemic control and risk of coronary artery disease in type 1 diabetes mellitus: findings from the Pittsburgh Epidemiology of Diabetes Complications Study (EDC).  Diabetologia. 2007;50(11):2280-2288. doi:10.1007/s00125-007-0797-7PubMedGoogle ScholarCrossref
18.
Chalew  SA, Gomez  R, Butler  A,  et al.  Predictors of glycemic control in children with type 1 diabetes: the importance of race.  J Diabetes Complications. 2000;14(2):71-77. doi:10.1016/S1056-8727(00)00072-6PubMedGoogle ScholarCrossref
19.
Petitti  DB, Klingensmith  GJ, Bell  RA,  et al; SEARCH for Diabetes in Youth Study Group.  Glycemic control in youth with diabetes: the SEARCH for Diabetes in Youth study.  J Pediatr. 2009;155(5):668-72.e1, 3. doi:10.1016/j.jpeds.2009.05.025PubMedGoogle ScholarCrossref
20.
Redondo  MJ, Libman  I, Cheng  P,  et al; Pediatric Diabetes Consortium.  Racial/ethnic minority youth with recent-onset type 1 diabetes have poor prognostic factors.  Diabetes Care. 2018;41(5):1017-1024. doi:10.2337/dc17-2335PubMedGoogle ScholarCrossref
21.
Barnard  KD, Skinner  TC, Peveler  R.  The prevalence of co-morbid depression in adults with type 1 diabetes: systematic literature review.  Diabet Med. 2006;23(4):445-448. doi:10.1111/j.1464-5491.2006.01814.xPubMedGoogle Scholar
22.
Hood  KK, Beavers  DP, Yi-Frazier  J,  et al.  Psychosocial burden and glycemic control during the first 6 years of diabetes: results from the SEARCH for Diabetes in Youth study.  J Adolesc Health. 2014;55(4):498-504. doi:10.1016/j.jadohealth.2014.03.011PubMedGoogle Scholar
23.
Varni  JW, Burwinkle  TM, Jacobs  JR, Gottschalk  M, Kaufman  F, Jones  KL.  The PedsQL in type 1 and type 2 diabetes: reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales and type 1 Diabetes Module.  Diabetes Care. 2003;26(3):631-637. doi:10.2337/diacare.26.3.631PubMedGoogle Scholar
24.
Naughton  MJ, Ruggiero  AM, Lawrence  JM,  et al; SEARCH for Diabetes in Youth Study Group.  Health-related quality of life of children and adolescents with type 1 or type 2 diabetes mellitus: SEARCH for Diabetes in Youth study.  Arch Pediatr Adolesc Med. 2008;162(7):649-657. doi:10.1001/archpedi.162.7.649PubMedGoogle Scholar
25.
Mayer-Davis  EJ, Beyer  J, Bell  RA,  et al; SEARCH for Diabetes in Youth Study Group.  Diabetes in African American youth: prevalence, incidence, and clinical characteristics: the SEARCH for Diabetes in Youth study.  Diabetes Care. 2009;32(suppl 2):S112-S122. doi:10.2337/dc09-S203PubMedGoogle Scholar
26.
Gallegos-Macias  AR, Macias  SR, Kaufman  E, Skipper  B, Kalishman  N.  Relationship between glycemic control, ethnicity and socioeconomic status in Hispanic and white non-Hispanic youths with type 1 diabetes mellitus.  Pediatr Diabetes. 2003;4(1):19-23. doi:10.1034/j.1399-5448.2003.00020.xPubMedGoogle Scholar
27.
Paris  CA, Imperatore  G, Klingensmith  G,  et al; SEARCH for Diabetes in Youth Study Group.  Predictors of insulin regimens and impact on outcomes in youth with type 1 diabetes: the SEARCH for Diabetes in Youth study.  J Pediatr. 2009;155(2):183-9.e1. doi:10.1016/j.jpeds.2009.01.063PubMedGoogle Scholar
28.
Willi  SM, Miller  KM, DiMeglio  LA,  et al; T1D Exchange Clinic Network.  Racial-ethnic disparities in management and outcomes among children with type 1 diabetes.  Pediatrics. 2015;135(3):424-434. doi:10.1542/peds.2014-1774PubMedGoogle Scholar
29.
Auslander  WF, Thompson  S, Dreitzer  D, White  NH, Santiago  JV.  Disparity in glycemic control and adherence between African-American and Caucasian youths with diabetes: family and community contexts.  Diabetes Care. 1997;20(10):1569-1575. doi:10.2337/diacare.20.10.1569PubMedGoogle Scholar
30.
Walker  AF, Schatz  DA, Johnson  C, Silverstein  JH, Rohrs  HJ.  Disparities in social support systems for youths with type 1 diabetes.  Clin Diabetes. 2015;33(2):62-69. doi:10.2337/diaclin.33.2.62PubMedGoogle Scholar
31.
Clarke  ABM, Daneman  D, Curtis  JR, Mahmud  FH.  Impact of neighbourhood-level inequity on paediatric diabetes care.  Diabet Med. 2017;34(6):794-799. doi:10.1111/dme.13326PubMedGoogle Scholar
32.
Flores  G; Committee On Pediatric Research.  Technical report—racial and ethnic disparities in the health and health care of children.  Pediatrics. 2010;125(4):e979-e1020. doi:10.1542/peds.2010-0188PubMedGoogle Scholar
33.
Nelson  A.  Unequal treatment: confronting racial and ethnic disparities in health care.  J Natl Med Assoc. 2002;94(8):666-668.PubMedGoogle Scholar
34.
Schwandt  A, Hermann  JM, Rosenbauer  J,  et al; DPV Initiative.  Longitudinal trajectories of metabolic control from childhood to young adulthood in type 1 diabetes from a large German/Austrian registry: a group-based modeling approach.  Diabetes Care. 2017;40(3):309-316. doi:10.2337/dc16-1625PubMedGoogle Scholar
35.
SEARCH Study Group.  SEARCH for Diabetes in Youth: a multicenter study of the prevalence, incidence and classification of diabetes mellitus in youth.  Control Clin Trials. 2004;25(5):458-471. doi:10.1016/j.cct.2004.08.002PubMedGoogle Scholar
36.
Hamman  RF, Bell  RA, Dabelea  D,  et al; SEARCH for Diabetes in Youth Study Group.  The SEARCH for Diabetes in Youth study: rationale, findings, and future directions.  Diabetes Care. 2014;37(12):3336-3344. doi:10.2337/dc14-0574PubMedGoogle Scholar
37.
Kuczmarski  RJ, Ogden  CL, Grummer-Strawn  LM,  et al.  CDC growth charts: United States.  Adv Data. 2000;(314):1-27.PubMedGoogle Scholar
38.
Ingram  DD, Parker  JD, Schenker  N,  et al.  United States Census 2000 population with bridged race categories.  Vital Health Stat 2. 2003;(135):1-55.PubMedGoogle Scholar
39.
Mayer-Davis  EJ, Bell  RA, Dabelea  D,  et al; SEARCH for Diabetes in Youth Study Group.  The many faces of diabetes in American youth: type 1 and type 2 diabetes in five race and ethnic populations: the SEARCH for Diabetes in Youth study.  Diabetes Care. 2009;32(suppl 2):S99-S101. doi:10.2337/dc09-S201PubMedGoogle Scholar
40.
Jones  BL, Nagin  D, Roeder  K.  A SAS procedure based on mixture models for estimating developmental trajectories.  Sociol Methods Res. 2001;29:374-393. doi:10.1177/0049124101029003005Google Scholar
41.
Nagin  DS, Odgers  CL.  Group-based trajectory modeling in clinical research.  Annu Rev Clin Psychol. 2010;6:109-138. doi:10.1146/annurev.clinpsy.121208.131413PubMedGoogle Scholar
42.
Nagin  DS, Odgers  CL.  Group-based trajectory modeling (nearly) two decades later.  J Quant Criminol. 2010;26(4):445-453. doi:10.1007/s10940-010-9113-7PubMedGoogle Scholar
43.
Song  M, Willett  WC, Hu  FB,  et al.  Trajectory of body shape across the lifespan and cancer risk.  Int J Cancer. 2016;138(10):2383-2395. doi:10.1002/ijc.29981PubMedGoogle Scholar
44.
Nagin  DS.  Analyzing developmental trajectories: a semiparametric, group-based approach.  Psychol Methods. 1999;4(2):139-157. doi:10.1037/1082-989X.4.2.139Google Scholar
45.
Currie  C, Zanotti  C, Morgan  A,  et al, eds.  Social Determinants of Health and Well-Being Among Young People. Health Behaviour in School-aged Children (HBSC) Study: International Report From the 2009-2010 Survey. Copenhagen, Denmark: WHO Regional Office for Europe; 2012.
46.
Secrest  AM, Costacou  T, Gutelius  B, Miller  RG, Songer  TJ, Orchard  TJ.  Associations between socioeconomic status and major complications in type 1 diabetes: the Pittsburgh Epidemiology of Diabetes Complication (EDC) Study.  Ann Epidemiol. 2011;21(5):374-381. doi:10.1016/j.annepidem.2011.02.007PubMedGoogle Scholar
47.
Rewers  A, Chase  HP, Mackenzie  T,  et al.  Predictors of acute complications in children with type 1 diabetes.  JAMA. 2002;287(19):2511-2518. doi:10.1001/jama.287.19.2511PubMedGoogle Scholar
48.
Nathan  DM; DCCT/EDIC Research Group.  The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview.  Diabetes Care. 2014;37(1):9-16. doi:10.2337/dc13-2112PubMedGoogle Scholar
49.
Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group.  Intensive diabetes treatment and cardiovascular outcomes in type 1 diabetes: the DCCT/EDIC study 30-year follow-up.  Diabetes Care. 2016;39(5):686-693. doi:10.2337/dc15-1990PubMedGoogle Scholar
50.
Jaacks  LM, Oza-Frank  R, D’Agostino  R  Jr,  et al.  Migration status in relation to clinical characteristics and barriers to care among youth with diabetes in the US.  J Immigr Minor Health. 2012;14(6):949-958. doi:10.1007/s10903-012-9617-3PubMedGoogle Scholar
51.
Hardeman  RR, Medina  EM, Kozhimannil  KB.  Race vs burden in understanding health equity.  JAMA. 2017;317(20):2133. doi:10.1001/jama.2017.4616PubMedGoogle Scholar
52.
Valenzuela  JM, Seid  M, Waitzfelder  B,  et al; SEARCH for Diabetes in Youth Study Group.  Prevalence of and disparities in barriers to care experienced by youth with type 1 diabetes.  J Pediatr. 2014;164(6):1369-75.e1. doi:10.1016/j.jpeds.2014.01.035PubMedGoogle Scholar
53.
Harris  MI.  Racial and ethnic differences in health care access and health outcomes for adults with type 2 diabetes.  Diabetes Care. 2001;24(3):454-459. doi:10.2337/diacare.24.3.454PubMedGoogle Scholar
54.
Sparud-Lundin  C, Öhrn  I, Danielson  E, Forsander  G.  Glycaemic control and diabetes care utilization in young adults with type 1 diabetes.  Diabet Med. 2008;25(8):968-973. doi:10.1111/j.1464-5491.2008.02521.xPubMedGoogle Scholar
55.
Karatekin  C, Ahluwalia  R.  Effects of adverse childhood experiences, stress, and social support on the health of college students  [published online December 5, 2016].  J Interpers Violence. doi:10.1177/0886260516681880PubMedGoogle Scholar
56.
Hall  WJ, Chapman  MV, Lee  KM,  et al.  Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review.  Am J Public Health. 2015;105(12):e60-e76. doi:10.2105/AJPH.2015.302903PubMedGoogle Scholar
57.
Sabin  JA, Greenwald  AG.  The influence of implicit bias on treatment recommendations for 4 common pediatric conditions: pain, urinary tract infection, attention deficit hyperactivity disorder, and asthma.  Am J Public Health. 2012;102(5):988-995. doi:10.2105/AJPH.2011.300621PubMedGoogle Scholar
58.
Sabin  JA, Rivara  FP, Greenwald  AG.  Physician implicit attitudes and stereotypes about race and quality of medical care.  Med Care. 2008;46(7):678-685. doi:10.1097/MLR.0b013e3181653d58PubMedGoogle Scholar
59.
Peek  ME, Wagner  J, Tang  H, Baker  DC, Chin  MH.  Self-reported racial discrimination in health care and diabetes outcomes.  Med Care. 2011;49(7):618-625. doi:10.1097/MLR.0b013e318215d925PubMedGoogle Scholar
60.
Ryan  AM, Gee  GC, Griffith  D.  The effects of perceived discrimination on diabetes management.  J Health Care Poor Underserved. 2008;19(1):149-163. doi:10.1353/hpu.2008.0005PubMedGoogle Scholar
61.
Reitblat  L, Whittemore  R, Weinzimer  SA, Tamborlane  WV, Sadler  LS.  Life with type 1 diabetes: views of Hispanic adolescents and their clinicians.  Diabetes Educ. 2016;42(4):408-417. doi:10.1177/0145721716647489PubMedGoogle Scholar
62.
Hunter  CM.  Understanding diabetes and the role of psychology in its prevention and treatment.  Am Psychol. 2016;71(7):515-525. doi:10.1037/a0040344PubMedGoogle Scholar
63.
Jaacks  LM, Liu  W, Ji  L, Mayer-Davis  EJ.  Type 1 diabetes stigma in China: a call to end the devaluation of individuals living with a manageable chronic disease.  Diabetes Res Clin Pract. 2015;107(2):306-307. doi:10.1016/j.diabres.2014.12.002PubMedGoogle Scholar
64.
Steptoe  A, Marmot  M.  Burden of psychosocial adversity and vulnerability in middle age: associations with biobehavioral risk factors and quality of life.  Psychosom Med. 2003;65(6):1029-1037. doi:10.1097/01.PSY.0000097347.57237.2DPubMedGoogle Scholar
65.
Adler  NE, Newman  K. Inequality in education, income, and occupation exacerbates the gaps between the health “haves” and “have-nots.” In: Bemelmans-Videc  M-L, Rist  RC, Vedung  EO, eds.  Carrots, Sticks and Sermons: Policy Instruments and Their Evaluation. Piscataway, NJ: Transaction Publishers; 2002:249-274.
66.
McEwen  BS.  Stress, adaptation, and disease: allostasis and allostatic load.  Ann N Y Acad Sci. 1998;840(1):33-44. doi:10.1111/j.1749-6632.1998.tb09546.xPubMedGoogle Scholar
67.
Herman  WH, Cohen  RM.  Racial and ethnic differences in the relationship between HbA1c and blood glucose: implications for the diagnosis of diabetes.  J Clin Endocrinol Metab. 2012;97(4):1067-1072. doi:10.1210/jc.2011-1894PubMedGoogle Scholar
68.
Selvin  E, Sacks  DB.  Variability in the relationship of hemoglobin A1c and average glucose concentrations: how much does race matter?  Ann Intern Med. 2017;167(2):131-132. doi:10.7326/M17-1231PubMedGoogle Scholar
69.
Bergenstal  RM, Gal  RL, Connor  CG,  et al; T1D Exchange Racial Differences Study Group.  Racial differences in the relationship of glucose concentrations and hemoglobin A1c levels.  Ann Intern Med. 2017;167(2):95-102. doi:10.7326/M16-2596PubMedGoogle Scholar
70.
Gail  MH, Wieand  S, Piantadosi  S.  Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates.  Biometrika. 1984;71(3):431-444. doi:10.1093/biomet/71.3.431Google Scholar
71.
Hauck  WW, Neuhaus  JM, Kalbfleisch  JD, Anderson  S.  A consequence of omitted covariates when estimating odds ratios.  J Clin Epidemiol. 1991;44(1):77-81. doi:10.1016/0895-4356(91)90203-LPubMedGoogle Scholar
72.
Dabelea  D, Mayer-Davis  EJ, Saydah  S,  et al; SEARCH for Diabetes in Youth Study.  Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009.  JAMA. 2014;311(17):1778-1786. doi:10.1001/jama.2014.3201PubMedGoogle Scholar
73.
Akesson  K, Hanberger  L, Samuelsson  U.  The influence of age, gender, insulin dose, BMI, and blood pressure on metabolic control in young patients with type 1 diabetes.  Pediatr Diabetes. 2015;16(8):581-586. doi:10.1111/pedi.12219PubMedGoogle Scholar
Original Investigation
Diabetes and Endocrinology
September 7, 2018

Association of Race and Ethnicity With Glycemic Control and Hemoglobin A1c Levels in Youth With Type 1 Diabetes

Author Affiliations
  • 1Department of Nutrition, University of North Carolina at Chapel Hill
  • 2American Heart Association, Dallas, Texas
  • 3School of Nursing, University of North Carolina at Chapel Hill
  • 4Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
  • 5Department of Epidemiology, Colorado School of Public Health, Aurora
  • 6Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia
  • 7Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena
  • 8Department of Epidemiology and Biostatistics, University of South Carolina, Columbia
  • 9Department of Pediatrics, University of Washington, Seattle
  • 10Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
  • 11Department of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 12Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
  • 13Department of Medicine, University of North Carolina at Chapel Hill
JAMA Netw Open. 2018;1(5):e181851. doi:10.1001/jamanetworkopen.2018.1851
Key Points español 中文 (chinese)

Question  Is there evidence for racial/ethnic health inequity with respect to longitudinal patterns of glycemic control among youth with type 1 diabetes?

Findings  In a longitudinal cohort study of 1313 youths (aged <20 years) with type 1 diabetes, patients with black race or Hispanic ethnicity were at higher risk of being in the highest and most rapidly increasing hemoglobin A1c trajectory group over 9 years after diabetes diagnosis when compared with non-Hispanic white patients. These associations persisted only among male patients and those with diagnosis at age 9 years or younger.

Meaning  There is health inequity with regard to glycemic control, particularly among young nonwhite male patients and nonwhite youth diagnosed earlier in life.

Abstract

Importance  Health disparities in the clinical presentation and outcomes among youth with type 1 diabetes exist. Long-term glycemic control patterns in racially/ethnically diverse youth are not well described.

Objectives  To model common trajectories of hemoglobin A1c (HbA1c) among youth with type 1 diabetes and test how trajectory group membership varies by race/ethnicity.

Design, Setting, and Participants  Longitudinal cohort study conducted in 5 US locations. The analysis included data from 1313 youths (aged <20 years) newly diagnosed in 2002 through 2005 with type 1 diabetes in the SEARCH for Diabetes in Youth study (mean [SD] age at diabetes onset, 8.9 [4.2] years) who had 3 or more HbA1c study measures during 6.1 to 13.3 years of follow-up. Data were analyzed in 2017.

Exposures  Self-reported race/ethnicity.

Main Outcomes and Measures  Hemoglobin A1c trajectories identified through group-based trajectory modeling over a mean (SD) of 9.0 (1.4) years of diabetes duration. Multinomial models studied the association of race/ethnicity with HbA1c trajectory group membership, adjusting for demographic characteristics, clinical factors, and socioeconomic position.

Results  The final study sample of 1313 patients was 49.3% female (647 patients) with mean (SD) age 9.7 (4.3) years and mean (SD) disease duration of 9.2 (6.3) months at baseline. The racial/ethnic composition was 77.0% non-Hispanic white (1011 patients), 10.7% Hispanic (140 patients), 9.8% non-Hispanic black (128 patients), and 2.6% other race/ethnicity (34 patients). Three HbA1c trajectories were identified: group 1, low baseline and mild increases (50.7% [666 patients]); group 2, moderate baseline and moderate increases (41.7% [548 patients]); and group 3, moderate baseline and major increases (7.5% [99 patients]). Group 3 was composed of 47.5% nonwhite youths (47 patients). Non-Hispanic black youth had 7.98 higher unadjusted odds (95% CI, 4.42-14.38) than non-Hispanic white youth of being in the highest HbA1c trajectory group relative to the lowest HbA1c trajectory group; the association remained significant after full adjustment (adjusted odds ratio of non-Hispanic black race in group 3 vs group 1, 4.54; 95% CI, 2.08-9.89). Hispanic youth had 3.29 higher unadjusted odds (95% CI, 1.78-6.08) than non-Hispanic white youth of being in the highest HbA1c trajectory group relative to the lowest HbA1c trajectory group; the association remained significant after adjustment (adjusted odds ratio of Hispanic ethnicity in group 3 vs group 1, 2.24; 95% CI, 1.02-4.92). In stratified analyses, the adjusted odds of nonwhite membership in the highest HbA1c trajectory remained significant among male patients and youth diagnosed at age 9 years or younger, but not female patients and youth who were older than 9 years when they were diagnosed (P for interaction = .04 [sex] and .02 [age at diagnosis]).

Conclusions and Relevance  There are racial/ethnic differences in long-term glycemic control among youth with type 1 diabetes, particularly among nonwhite male patients and nonwhite youth diagnosed earlier in life.

Introduction

Type 1 diabetes (T1D) treatment is centered around the improvement and maintenance of tight glycemic control, as assessed by levels of hemoglobin A1c (HbA1c), to prevent acute and chronic diabetes-related complications.1-3 Glycemic control can vary considerably from diabetes onset through adolescence,4-6 where fluctuations are known to occur during puberty3,4,7-12 and during early adulthood. Poorer glycemic control during early adulthood or from childhood to young adulthood has been attributed to a lack of continuity in diabetes-related clinical care4,11,12 as well as changes in self-care as children and adolescents with T1D grow into adulthood.9,10,13 However, glycemic control in youth and young adults with T1D is critical, as a higher average HbA1c level in this period of development is associated with impaired growth as well as diabetic complications.14-17

In cross-sectional studies of adolescents and young adults, glycemic control differs by racial and ethnic subgroups.18 African American, American Indian, Hispanic, and Asian or Pacific Islander youth with T1D are more likely to have higher HbA1c levels compared with non-Hispanic white youth.19 In longitudinal studies, nonwhite youth with T1D have increased markers of poor prognosis at diagnosis and 3 years following diagnosis, including higher HbA1c levels, more frequent diabetic ketoacidosis, and severe hypoglycemia.20 A constellation of sociodemographic factors related to race/ethnicity and glycemic control have been proposed, ranging from family dynamics, depressive symptoms, and quality of life13,21-25 to diabetes regimen.26-28 The role of socioeconomic position as a mediator of racial/ethnic associations remains controversial.28-31 Additionally, health care–specific factors such as disparities in health literacy, diabetes-related knowledge, or access to health care are known to contribute to pediatric health disparity but have not been well explored in T1D.32,33

Latent class trajectory modeling has been used to identify subgroups who share a similar trajectory of HbA1c over time.34 Few studies have examined whether racial/ethnic disparities in glycemic control persist over time from childhood into young adulthood among individuals with T1D. Our objective was to first visualize major trajectories of glycemic control from childhood into young adulthood using all data from youth of all racial and ethnic groups and to then characterize specific associations between race/ethnicity and distinct longitudinal patterns of glycemic control. Our hypothesis was that non-Hispanic black and Hispanic youth would be more likely than non-Hispanic white youth to have unfavorable trajectory patterns representing poor glycemic control and that this association may be mediated by clinical factors such as diabetes regimen26-28 and by socioeconomic position.29-31

Methods
Study Population

The SEARCH for Diabetes in Youth study began in 2000 with an overarching objective to describe the incidence and prevalence of childhood diabetes among the 5 major racial and ethnic groups in the United States.35 Individuals with diabetes diagnosed before age 20 years were identified from a population-based incidence registry network at 5 US sites (South Carolina; Cincinnati, Ohio, and surrounding counties; Colorado with southwestern Native American sites; Seattle, Washington, and surrounding counties; and Kaiser Permanente, southern California).36 Patients were newly diagnosed with T1D in 2002 through 2005. Patients who could be contacted were asked to complete a short survey and recruited for a baseline visit. If they completed the first visit, they were asked to return for visits at 12, 24, and 60 months to measure risk factors for diabetes complications (Figure 1A). A subset of participants who were aged 10 years and older and had at least 5 years of diabetes duration were recruited for a follow-up cohort visit between 2012 and 2015. The subset of youth who were included in the SEARCH cohort visit were not significantly different from all other youth diagnosed between the years of 2002 and 2008 in terms of average age at diabetes onset, distribution of sex or race and ethnicity, or clinical measures.14

Inclusion criteria for these analyses consisted of youth diagnosed with T1D between 2002 and 2005. Type 1 diabetes was based on the clinical diagnosis made by a physician or other health care professional at onset and was collected from these health care professionals or abstracted from medical records. Youth with a clinical diagnosis of type 1a, type 1b, or type 1 diabetes were included. Youth who had fewer than 3 measures of HbA1c from research visits during 6.1 to 13.3 years of follow-up were excluded (n = 618). Excluded individuals were not different with regard to HbA1c measures using available data from the study baseline and the cohort visit. The final study sample included 1313 youths with T1D (Figure 1B). The study was approved by institutional review boards with jurisdiction; the parent, the participant, or both provided written consent or assent for all participants (consent of ≥1 parent or legal guardian was required for participants aged <18 years). The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Research Visits

Trained personnel administered questionnaires; measured height, weight, and blood pressure; and obtained blood samples. Body mass index was defined as weight (kilograms) divided by height (meters squared) and converted to a z score.37 A blood draw occurred after an 8-hour overnight fast, and medications, including short-acting insulin, were withheld the morning of the visit.

Laboratory Measures

Blood samples were obtained under conditions of metabolic stability, defined as no episodes of diabetic ketoacidosis in the preceding month and the absence of fever and acute infections. They were processed locally and shipped within 24 hours to the central laboratory (Northwest Lipid Metabolism and Diabetes Research Laboratories). Hemoglobin A1c was measured by a dedicated ion exchange high-performance liquid chromatography instrument (TOSOH Bioscience).

Other Measures

Self-reported race and ethnicity were collected based on questions modeled after the 2000 US Census38 and categorized as non-Hispanic white, non-Hispanic black, Hispanic, and other (Asian, Native American, Pacific Islander, other, and unknown). Although the US Census accommodates reporting of multiple races, the SEARCH study did not have sufficient participant numbers to allow evaluation of separate categories of reported multiple-race groups39 and used the National Center for Health Statistics plurality approach, in which data from a study designed to address multiple-race reporting was used to determine which single-race category should be assigned for specific combinations of multiple races reported.38

Insulin regimen was based on mode of insulin delivery, classified as pumps, long-acting with rapid-acting insulin injections with 3 or more injections per day, and any other form of multiple daily injections. Insulin dose was reported as units per kilogram of body weight. Frequency of self-monitoring of blood glucose was self-reported and categorized as less than 1 time per day, 1 to 3 times per day, and 4 or more times per day. Health insurance type was classified as none, private, Medicaid, or other. Parental education was based on the highest educational level attained by either parent and classified as less than high school degree, high school graduate, some college through associate’s degree, and bachelor’s degree or more. Household structure was classified as 2 parent, single parent, or other. Receipt of diabetes care was based on reported number of visits with prespecified diabetes health care professionals, including pediatric endocrinologists, adult diabetologists, and nurse diabetes educators, in the previous 6 months and classified based on the distribution: 0 to 1 visit, 2 to 3 visits, 4 to 5 visits, and 6 or more visits. Receipt of nondiabetes care was based on reported number of visits with prespecified nondiabetes health care professionals (pediatrician, family practice physician, general practice physician, internist, nurse practitioner or physician assistant, traditional healer, dietician, optometrist or ophthalmologist, and psychiatrist, psychologist, or mental health counselor) in the previous 6 months and classified as 0 to 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or more visits. Satisfaction with diabetes care was based on the response to the question, “How would you rate your diabetes care overall?” (possible responses were excellent, good, fair, poor, and not applicable).

Statistical Analysis

We used group-based trajectory modeling to identify trajectories of HbA1c among youth with T1D using duration of diabetes (months) as the time scale via the PROC TRAJ macro of SAS statistical software version 9.4 (SAS Institute Inc), which fits a semiparametric (discrete mixture) model for longitudinal data using the maximum-likelihood method.40-44 Trajectory analysis uses all available data for a participant and is robust to data that are missing at random. Details about trajectory analysis have been described elsewhere.43,44 The optimal number of groups was determined based on Bayesian information criterion and having at least 5% of the sample in the smallest trajectory group. We named the trajectories based on the baseline HbA1c value (from the initial research visit) and shape of the trajectory over the follow-up visits. We then calculated the posterior predicted probability for each participant of being a member of each trajectory group given his or her observed HbA1c pattern. Participants were assigned to the trajectory group for which they had the greatest posterior probability for group membership. Multinomial regression was used to assess the association of race/ethnicity (non-Hispanic white vs non-Hispanic black vs Hispanic) with HbA1c trajectory group membership. Youths who reported Asian or Pacific Islander, Native American, other, and unknown race/ethnicity (n = 34) were excluded from multinomial modeling. Non-Hispanic white was designated as the referent group.

All covariates were measured at baseline. Model 1 was unadjusted. Model 2 was adjusted for demographic factors (sex, age at diagnosis, and clinic site). Model 3 was additionally adjusted for clinical variables (body mass index z score, insulin regimen, insulin dose, and frequency of self-monitoring of blood glucose). Model 4 was further adjusted for socioeconomic position (highest parental education, household structure, and health insurance type).

Given previous findings of health inequity,45 we tested for sex- and age-related subgroups who may be particularly vulnerable to the effects of heath inequity. Modification of race/ethnicity effects by age and sex was tested by adding an interaction term (race/ethnicity × sex and race/ethnicity × age at diagnosis, respectively) to model 4. The nature of the modification was explored in models stratified by sex and the median age of diagnosis (9 years old). Because of limited sample size, for stratified analyses, race/ethnicity was categorized into non-Hispanic white and other (defined as non-Hispanic black, Hispanic, Asian or Pacific Islander, Native American, other, and unknown).

All analyses were completed in SAS software in 2017. Statistical significance was based on a 2-sided P value of .05. Descriptive analyses used the mean and standard deviation or median and interquartile range (IQR) for nonnormal distributions and for continuous variables and frequencies to describe categorical variables. The means and frequencies of demographic and clinical characteristics were compared using χ2 test for categorical variables and analysis of variance or Kruskal-Wallis test for continuous variables.

Results

The sample of 1313 youths with T1D was 49.3% female (647 patients); 77.0% were non-Hispanic white (1011 patients); 10.7%, Hispanic (140 patients); 9.8%, non-Hispanic black (128 patients); and 2.6%, other race/ethnicity (34 patients) (Table 1). At the baseline visit, the mean (SD) age was 9.7 (4.3) years and the mean (SD) diabetes duration was 9.2 (6.3) months. Group-based trajectory modeling identified 3 distinct HbA1c trajectories over a mean (SD) follow-up of 108 (16) months (9.0 [1.4] years) of diabetes duration: group 1, low baseline and mild increases (50.7% [666 patients]); group 2, moderate baseline and moderate increases (41.7% [548 patients]); and group 3, moderate baseline and major increases (7.5% [99 patients]) (Figure 2).

The prevalence of black and Hispanic youth was the highest in group 3 and the lowest in group 1 (non-Hispanic black patients made up 5.1% of group 1, 12.6% of group 2, and 25.3% of group 3; Hispanic patients made up 8.4% of group 1, 12.2% of group 2, and 17.2% of group 3). For non-Hispanic black patients, the difference between group 1 and group 2 was 7.5% (95% CI, 4.2%-10.7%; P < .001); between group 1 and group 3, 20.2% (95% CI, 11.4%-28.9%; P < .001); and between group 2 and group 3, 12.6% (95% CI, 3.7%-21.7%; P = .001). For Hispanic patients, the difference between group 1 and group 2 was 3.8% (95% CI, 0.4%-7.3%; P = .03); between group 1 and group 3, 8.8% (95% CI, 1.0%-16.5%; P = .006); and between group 2 and group 3, 5.0% (95% CI, 3.0%-12.9%; P = .18). Group 3 was composed of 47.5% nonwhite youths (47 patients) (Table 1). Table 2 depicts the odds ratios (ORs) for non-Hispanic black and Hispanic vs non-Hispanic white race/ethnicity and HbA1c trajectory group in a series of sequentially adjusted models. Non-Hispanic black youth had 7.98 higher odds than non-Hispanic white youth of being in the highest HbA1c trajectory group relative to the lowest HbA1c trajectory group (unadjusted OR of non-Hispanic black race in group 3 vs group 1, 7.98; 95% CI, 4.42-14.38). After adjustment for baseline demographic characteristics, clinical factors, and socioeconomic position, non-Hispanic black youth had 4.54 times higher odds than non-Hispanic white youth of being in the highest HbA1c trajectory group relative to the lowest HbA1c trajectory group (adjusted OR [aOR] of non-Hispanic black race in group 3 vs group 1, 4.54; 95% CI, 2.08-9.89). Hispanic youth had 3.29 higher unadjusted odds than non-Hispanic white youth of being in the highest HbA1c trajectory group relative to the lowest HbA1c trajectory group (unadjusted OR of Hispanic ethnicity in group 3 vs group 1, 3.29; 95% CI, 1.78-6.08). Adjustment for baseline demographic characteristics, clinical factors, and socioeconomic position did not fully attenuate the association (aOR of Hispanic ethnicity in group 3 vs group 1, 2.24; 95% CI, 1.02-4.92). Adjustment for clinical variables diminished statistical significance associated with the moderate HbA1c trajectory (aOR of Hispanic ethnicity in group 2 vs group 1, 1.43; 95% CI, 0.90-2.27 vs unadjusted OR, 1.71; 95% CI, 1.17-2.49).

The association of race/ethnicity and HbA1c trajectory was modified by sex (P for interaction = .04) (Table 3). Nonwhite male patients had significantly elevated odds of membership in the highest HbA1c trajectory group (OR of group 3 vs group 1, 5.34; 95% CI, 2.16-13.2) and moderate HbA1c trajectory group (OR of group 2 vs group 1, 2.06; 95% CI, 1.18-3.57) relative to non-Hispanic white male patients. The associations were not significant in female patients (aOR of group 3 vs group 1, 1.48; 95% CI, 0.65-3.39 and aOR of group 2 vs group 1, 1.00; 95% CI, 0.61-1.64). The association of race/ethnicity and HbA1c trajectory was also modified by age at diagnosis (P for interaction = .02) (Table 3). Nonwhite youths diagnosed at or younger than 9 years had significantly elevated odds of membership in the highest HbA1c trajectory group (aOR of group 3 vs group 1, 5.37; 95% CI, 1.91-15.1) and the moderate HbA1c trajectory group (aOR of group 2 vs group 1, 2.04; 95% CI, 1.23-3.37). The association was not significant in youth who were diagnosed when they were older than 9 years (aOR of group 3 vs group 1, 1.65; 95% CI, 0.77-3.51 and aOR of group 2 vs group 1, 0.96; 95% CI, 0.55-1.65).

Discussion

In a large, population-based multiethnic cohort of youth with T1D, we found 3 distinct HbA1c trajectories that deteriorated over a mean (SD) follow-up of 9.0 (1.4) years (range, 6.1-13.3 years) following diabetes diagnosis, reinforcing that early youth and the transition to adulthood are high-risk periods for worsening glycemic control.3,7,8 Black race and Hispanic ethnicity were associated with membership in the highest and most rapidly increasing (worsening) HbA1c trajectory group.

We tested the association of race/ethnicity with HbA1c trajectory by adjusting for other variables, including clinical factors and socioeconomic position. For example, prescribing practices may vary based on race/ethnicity27 and insulin pump use is known to be higher in white youth than non-Hispanic black or Hispanic youth.28 Lower socioeconomic position has been proposed as a major mediator of the association of race/ethnicity with health outcomes,29-31 including T1D complications, due to poorer self-management among persons whose socioeconomic conditions are less favorable.20,46 Despite adjustment for these known risk factors, black race remained significantly associated with HbA1c trajectory. Similarly, adjustment for demographic characteristics, clinical variables, and socioeconomic position did not fully attenuate the association of Hispanic ethnicity with the highest HbA1c trajectory, where the OR remained significantly elevated, suggesting remaining impact of inequity in this group. Evidence of disparity in glycemic control trajectory that exists particularly among nonwhite male patients and nonwhite youth with diabetes diagnosis at an early age (≤9 years) is consistent with previously reported patterns in acute glycemic complications that are more common among the youngest patients and male patients of all ages.47

An important finding of the trajectory analysis was that the highest HbA1c trajectory subgroup also showed the highest mean HbA1c level at baseline, which occurred at a mean (SD) of 9.8 (6.3) months following diagnosis. This suggests that glycemic control obtained in the first year following diagnosis may confer information about longitudinal trends over time. Furthermore, the magnitude of racial/ethnic inequity over the longitudinal data are striking. Group 3 diverged over the follow-up period to give vastly different mean HbA1c measures at the cohort visit that may translate to significant increases in the risk for complications of diabetes based on evidence from the Diabetes Control and Complications Trial (DCCT) and the Epidemiology of Diabetes Interventions and Complications Study (EDIC).1-3,48 Disparity in glycemic control across trajectory groups in the present analyses even exceeds differences reported across groups of the DCCT/EDIC trial (which compared a median HbA1c of 7% of total hemoglobin in the intensive insulin treatment group with a median HbA1c of 9% of total hemoglobin in the conventional group), suggesting that those risk estimates may be conservative for youth who additionally face a longer period of disease-related exposures.49

Previous studies have shown that the migration status of parents is associated with glycemic control among youth with T1D.50 To address potential differences, we examined a subset of the sample with data on parental nativity (ie, US born vs foreign born) and found no significant differences across HbA1c trajectory groups. Adjustment for parental nativity did not attenuate the association of black race or Hispanic ethnicity with the moderate or highest HbA1c trajectory group, although the analysis is limited by small sample size (data not shown). Differences in youth and parental nativity status likely warrant future study in adequately powered samples.

Given the complexity of the study of race and health outcomes in the United States, in which health risks associated with race/ethnicity are not inherent but instead may signal underlying inequalities,51 we posit that our results may reflect health inequity in T1D operating at multiple levels. The social determinants of health operating outside of the health care system, including aspects of the physical environment, food security, social integration, barriers to health care,52 and complex patterns in health care utilization,53,54 may create race-based groups of individuals for whom glycemic control is challenged by inconsistencies in the availability of resources or support for T1D management. In general, adverse childhood experiences among nonwhite youth have been shown to result in a myriad of psychological and medical sequelae later in life.55

There may also be modifiable aspects within the health care system, including racial/ethnic differences in the interpersonal dynamics of interactions between patients or parents and health care professionals that occur in pediatric clinical settings, extending from implicit bias and microaggressions to stereotyping, prejudice, and macroaggressions.56 Nonwhite youth and families report overtly weakened patient–health care professional communication and decreased participatory decision making.32,33 Implicit bias, the unconscious attitudes that unintentionally influence behavior, may affect health care professionals’ medical management decisions56 and perceptions about black, Hispanic, and young people of color in terms of disease experience57 and patient compliance.58 Higher levels of perceived bias or discrimination have been linked to worse diabetes care.59,60 The direct effect of implicit bias on HbA1c has not been well studied in pediatric diabetes. Finally, while social stigma associated with T1D is known,61-63 it may be more pronounced in specific communities where health literacy and resources are lacking or where T1D is significantly less common than type 2 diabetes. Nonwhite youth may struggle with misunderstanding and stigma that act as chronic stressors that indirectly affect glycemic control via psychosocial or behavioral effects,64,65 resulting in impaired self-care strategies or maladaptive coping behaviors that damage health.66

Limitations

A limitation of the study is that the observed inequity after adjustment for other factors may reflect racial and ethnic differences in the validity of HbA1c as a measure of average glycemia owing to racial differences in the glycation of hemoglobin or other factors affecting red blood cell turnover.67-69 However, the between-race differences that have been reported are small (0.4 percentage point in HbA1c69) relative to the differences in the present study, where the mean (SD) HbA1c of group 3 was 12.2% (1.5%) of total hemoglobin at the last visit, roughly 2.2% higher than group 2 and 4.4% higher than group 1 at that time. Combining individuals of many races, ethnicities, and cultures into single categories for analysis may result in residual confounding and underemphasize within-group heterogeneity. We are careful to avoid implying that all nonwhite youth have poor control; in our data, nearly a quarter of nonwhite youth had an HbA1c at or below 7.4% of total hemoglobin at the cohort visit (data not shown). Several of the variables measured at baseline may change over time, including health insurance status. Adjustment variables may provide information for future work that will delve into what drives the inequities. For example, measures of socioeconomic position may be improved by including other measures such as the ability to pay for medication, heath literacy, housing security, or food security. We did not control for diet and physical activity in these analyses. A larger sample may identify additional trajectories that capture the experience of smaller subpopulations, such as individuals who initially have low HbA1c that deteriorates later in the course of T1D. The outcome of trajectory group necessitated the use of logistic regression modeling, which may overestimate effect estimates, particularly when the outcome is common.70,71 Finally, there were relatively small numbers of participants across groups in the analyses stratified by sex and age at diagnosis. Larger studies are needed to further explore interactions and identify nonwhite youth who are at the highest risk for poor glycemic control over time. Finally, associations of data-driven trajectory models should be confirmed with future analyses that quantify and compare differences in longitudinal HbA1c across racial/ethnic groups.

However, the study has several strengths, including the large, well-characterized, multiethnic cohort;72 the extended follow-up period; and the use of an analytic approach to characterize multiple common HbA1c trajectories and understand associated individual characteristics from an extensive collection of covariates.

Conclusions

Compared with non-Hispanic white youth with T1D, non-Hispanic black youth, Hispanic youth, and youth with other racial/ethnic backgrounds who are male and diagnosed earlier in life are more likely to show rapid deterioration in glycemic control within 9 years of T1D diagnosis. The findings of this study can be used to inform future research on the identification of factors that contribute to and reinforce racial and ethnic disparity among youth with T1D, particularly nonwhite male patients and nonwhite youth diagnosed earlier in life.

Back to top
Article Information

Accepted for Publication: June 19, 2018.

Published: September 7, 2018. doi:10.1001/jamanetworkopen.2018.1851

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Kahkoska AR et al. JAMA Network Open.

Corresponding Author: Anna R. Kahkoska, BS, Department of Nutrition, University of North Carolina at Chapel Hill, 245 Rosenau, 135 Dauer Dr, Chapel Hill, NC 27599 (anna_kahkoska@med.unc.edu).

Author Contributions: Ms Kahkoska had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Kahkoska, Shay, Crandell, Dabelea, Pihoker, Wagenknecht, Mayer-Davis.

Acquisition, analysis, or interpretation of data: Shay, Crandell, Dabelea, Imperatore, Lawrence, Liese, Pihoker, Reboussin, Agarwal, Tooze, Zhong, Mayer-Davis.

Drafting of the manuscript: Kahkoska, Liese, Pihoker.

Critical revision of the manuscript for important intellectual content: Shay, Crandell, Dabelea, Imperatore, Lawrence, Pihoker, Reboussin, Agarwal, Tooze, Wagenknecht, Zhong, Mayer-Davis.

Statistical analysis: Kahkoska, Shay, Crandell, Imperatore, Reboussin.

Obtained funding: Dabelea, Lawrence, Liese, Pihoker, Wagenknecht.

Administrative, technical, or material support: Wagenknecht.

Supervision: Shay, Crandell, Agarwal, Wagenknecht, Mayer-Davis.

Conflict of Interest Disclosures: Dr Crandell reported grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Lawrence reported grants from the National Institute of Diabetes and Digestive and Kidney Diseases during the conduct of the study. Dr Tooze reported grants from the NIH during the conduct of the study. Dr Wagenknecht reported grants from the NIH during the conduct of the study. Dr Zhong reported other support from Sanofi US outside the submitted work. No other disclosures were reported.

Funding/Support: The SEARCH for Diabetes in Youth Cohort study (1UC4DK108173-01) is funded by the NIH and National Institute of Diabetes and Digestive and Kidney Diseases and is supported by the Centers for Disease Control and Prevention. Site Contract Numbers: Kaiser Permanente Southern California (U48/CCU919219, U01 DP000246, and U18DP002714), University of Colorado Denver (U48/CCU819241-3, U01 DP000247, and U18DP000247-06A1), Children’s Hospital Medical Center (Cincinnati) (U48/CCU519239, U01 DP000248, and 1U18DP002709), University of North Carolina at Chapel Hill (U48/CCU419249, U01 DP000254, and U18DP002708), University of Washington School of Medicine (U58/CCU019235-4, U01 DP000244, and U18DP002710-01), Wake Forest University School of Medicine (U48/CCU919219, U01 DP000250, and 200-2010-35171). Ms Kahkoska is supported by funding from the National Institute of Diabetes and Digestive and Kidney Diseases of the NIH (grant F30DK113728).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the National Institute of Diabetes and Digestive and Kidney Diseases.

Additional Contributions: The SEARCH for Diabetes in Youth study is indebted to the youth, their families, and their health care professionals, whose participation made this study possible. We acknowledge the involvement of the South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina, Seattle Children’s Hospital and the University of Washington, University of Colorado Pediatric Clinical and Translational Research Center, the Barbara Davis Center at the University of Colorado at Denver, the University of Cincinnati, and the Children with Medical Handicaps program managed by the Ohio Department of Health. Rumay Alexander, EdD, Chief Diversity Officer and Associate Vice Chancellor of the University of North Carolina at Chapel Hill, provided review and guidance on the topic of racial and ethnic health disparities. She was not compensated for her contribution.

References
1.
Diabetes Control and Complications Trial Research Group.  Effect of intensive diabetes treatment on the development and progression of long-term complications in adolescents with insulin-dependent diabetes mellitus: Diabetes Control and Complications Trial.  J Pediatr. 1994;125(2):177-188. doi:10.1016/S0022-3476(94)70190-3PubMedGoogle ScholarCrossref
2.
Nathan  DM, Genuth  S, Lachin  J,  et al; Diabetes Control and Complications Trial Research Group.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.  N Engl J Med. 1993;329(14):977-986. doi:10.1056/NEJM199309303291401PubMedGoogle ScholarCrossref
3.
White  NH, Cleary  PA, Dahms  W, Goldstein  D, Malone  J, Tamborlane  WV; Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group.  Beneficial effects of intensive therapy of diabetes during adolescence: outcomes after the conclusion of the Diabetes Control and Complications Trial (DCCT).  J Pediatr. 2001;139(6):804-812. doi:10.1067/mpd.2001.118887PubMedGoogle ScholarCrossref
4.
Pyatak  EA, Sequeira  PA, Whittemore  R, Vigen  CP, Peters  AL, Weigensberg  MJ.  Challenges contributing to disrupted transition from paediatric to adult diabetes care in young adults with type 1 diabetes.  Diabet Med. 2014;31(12):1615-1624. doi:10.1111/dme.12485PubMedGoogle ScholarCrossref
5.
Helgeson  VS, Snyder  PR, Seltman  H, Escobar  O, Becker  D, Siminerio  L.  Brief report: trajectories of glycemic control over early to middle adolescence.  J Pediatr Psychol. 2010;35(10):1161-1167. doi:10.1093/jpepsy/jsq011PubMedGoogle ScholarCrossref
6.
Rohan  JM, Rausch  JR, Pendley  JS,  et al.  Identification and prediction of group-based glycemic control trajectories during the transition to adolescence.  Health Psychol. 2014;33(10):1143-1152. doi:10.1037/hea0000025PubMedGoogle ScholarCrossref
7.
Moran  A, Jacobs  DR  Jr, Steinberger  J,  et al.  Insulin resistance during puberty: results from clamp studies in 357 children.  Diabetes. 1999;48(10):2039-2044. doi:10.2337/diabetes.48.10.2039PubMedGoogle ScholarCrossref
8.
Szadkowska  A, Pietrzak  I, Zmysłowska  A, Wyka  K, Bodalski  J.  Insulin resistance in newly diagnosed type 1 diabetic children and adolescents [in Polish].  Med Wieku Rozwoj. 2003;7(2):181-191.PubMedGoogle Scholar
9.
Moore  SM, Hackworth  NJ, Hamilton  VE, Northam  EP, Cameron  FJ.  Adolescents with type 1 diabetes: parental perceptions of child health and family functioning and their relationship to adolescent metabolic control.  Health Qual Life Outcomes. 2013;11:50. doi:10.1186/1477-7525-11-50PubMedGoogle ScholarCrossref
10.
Pyatak  EA, Sequeira  P, Peters  AL, Montoya  L, Weigensberg  MJ.  Disclosure of psychosocial stressors affecting diabetes care among uninsured young adults with type 1 diabetes.  Diabet Med. 2013;30(9):1140-1144. doi:10.1111/dme.12248PubMedGoogle ScholarCrossref
11.
Helgeson  VS, Reynolds  KA, Snyder  PR,  et al.  Characterizing the transition from paediatric to adult care among emerging adults with type 1 diabetes.  Diabet Med. 2013;30(5):610-615. doi:10.1111/dme.12067PubMedGoogle ScholarCrossref
12.
Hynes  L, Byrne  M, Dinneen  SF, McGuire  BE, O’Donnell  M, Mc Sharry  J.  Barriers and facilitators associated with attendance at hospital diabetes clinics among young adults (15-30 years) with type 1 diabetes mellitus: a systematic review.  Pediatr Diabetes. 2016;17(7):509-518. doi:10.1111/pedi.12198PubMedGoogle ScholarCrossref
13.
Lawrence  JM, Standiford  DA, Loots  B,  et al; SEARCH for Diabetes in Youth Study.  Prevalence and correlates of depressed mood among youth with diabetes: the SEARCH for Diabetes in Youth study.  Pediatrics. 2006;117(4):1348-1358. doi:10.1542/peds.2005-1398PubMedGoogle ScholarCrossref
14.
Dabelea  D, Stafford  JM, Mayer-Davis  EJ,  et al; SEARCH for Diabetes in Youth Research Group.  Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood.  JAMA. 2017;317(8):825-835.PubMedGoogle ScholarCrossref
15.
Marcovecchio  ML, Heywood  JJ, Dalton  RN, Dunger  DB.  The contribution of glycemic control to impaired growth during puberty in young people with type 1 diabetes and microalbuminuria.  Pediatr Diabetes. 2014;15(4):303-308. doi:10.1111/pedi.12090PubMedGoogle ScholarCrossref
16.
Stadler  M, Peric  S, Strohner-Kaestenbauer  H,  et al.  Mortality and incidence of renal replacement therapy in people with type 1 diabetes mellitus—a three decade long prospective observational study in the Lainz T1DM cohort.  J Clin Endocrinol Metab. 2014;99(12):4523-4530. doi:10.1210/jc.2014-2701PubMedGoogle ScholarCrossref
17.
Prince  CT, Becker  DJ, Costacou  T, Miller  RG, Orchard  TJ.  Changes in glycaemic control and risk of coronary artery disease in type 1 diabetes mellitus: findings from the Pittsburgh Epidemiology of Diabetes Complications Study (EDC).  Diabetologia. 2007;50(11):2280-2288. doi:10.1007/s00125-007-0797-7PubMedGoogle ScholarCrossref
18.
Chalew  SA, Gomez  R, Butler  A,  et al.  Predictors of glycemic control in children with type 1 diabetes: the importance of race.  J Diabetes Complications. 2000;14(2):71-77. doi:10.1016/S1056-8727(00)00072-6PubMedGoogle ScholarCrossref
19.
Petitti  DB, Klingensmith  GJ, Bell  RA,  et al; SEARCH for Diabetes in Youth Study Group.  Glycemic control in youth with diabetes: the SEARCH for Diabetes in Youth study.  J Pediatr. 2009;155(5):668-72.e1, 3. doi:10.1016/j.jpeds.2009.05.025PubMedGoogle ScholarCrossref
20.
Redondo  MJ, Libman  I, Cheng  P,  et al; Pediatric Diabetes Consortium.  Racial/ethnic minority youth with recent-onset type 1 diabetes have poor prognostic factors.  Diabetes Care. 2018;41(5):1017-1024. doi:10.2337/dc17-2335PubMedGoogle ScholarCrossref
21.
Barnard  KD, Skinner  TC, Peveler  R.  The prevalence of co-morbid depression in adults with type 1 diabetes: systematic literature review.  Diabet Med. 2006;23(4):445-448. doi:10.1111/j.1464-5491.2006.01814.xPubMedGoogle Scholar
22.
Hood  KK, Beavers  DP, Yi-Frazier  J,  et al.  Psychosocial burden and glycemic control during the first 6 years of diabetes: results from the SEARCH for Diabetes in Youth study.  J Adolesc Health. 2014;55(4):498-504. doi:10.1016/j.jadohealth.2014.03.011PubMedGoogle Scholar
23.
Varni  JW, Burwinkle  TM, Jacobs  JR, Gottschalk  M, Kaufman  F, Jones  KL.  The PedsQL in type 1 and type 2 diabetes: reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales and type 1 Diabetes Module.  Diabetes Care. 2003;26(3):631-637. doi:10.2337/diacare.26.3.631PubMedGoogle Scholar
24.
Naughton  MJ, Ruggiero  AM, Lawrence  JM,  et al; SEARCH for Diabetes in Youth Study Group.  Health-related quality of life of children and adolescents with type 1 or type 2 diabetes mellitus: SEARCH for Diabetes in Youth study.  Arch Pediatr Adolesc Med. 2008;162(7):649-657. doi:10.1001/archpedi.162.7.649PubMedGoogle Scholar
25.
Mayer-Davis  EJ, Beyer  J, Bell  RA,  et al; SEARCH for Diabetes in Youth Study Group.  Diabetes in African American youth: prevalence, incidence, and clinical characteristics: the SEARCH for Diabetes in Youth study.  Diabetes Care. 2009;32(suppl 2):S112-S122. doi:10.2337/dc09-S203PubMedGoogle Scholar
26.
Gallegos-Macias  AR, Macias  SR, Kaufman  E, Skipper  B, Kalishman  N.  Relationship between glycemic control, ethnicity and socioeconomic status in Hispanic and white non-Hispanic youths with type 1 diabetes mellitus.  Pediatr Diabetes. 2003;4(1):19-23. doi:10.1034/j.1399-5448.2003.00020.xPubMedGoogle Scholar
27.
Paris  CA, Imperatore  G, Klingensmith  G,  et al; SEARCH for Diabetes in Youth Study Group.  Predictors of insulin regimens and impact on outcomes in youth with type 1 diabetes: the SEARCH for Diabetes in Youth study.  J Pediatr. 2009;155(2):183-9.e1. doi:10.1016/j.jpeds.2009.01.063PubMedGoogle Scholar
28.
Willi  SM, Miller  KM, DiMeglio  LA,  et al; T1D Exchange Clinic Network.  Racial-ethnic disparities in management and outcomes among children with type 1 diabetes.  Pediatrics. 2015;135(3):424-434. doi:10.1542/peds.2014-1774PubMedGoogle Scholar
29.
Auslander  WF, Thompson  S, Dreitzer  D, White  NH, Santiago  JV.  Disparity in glycemic control and adherence between African-American and Caucasian youths with diabetes: family and community contexts.  Diabetes Care. 1997;20(10):1569-1575. doi:10.2337/diacare.20.10.1569PubMedGoogle Scholar
30.
Walker  AF, Schatz  DA, Johnson  C, Silverstein  JH, Rohrs  HJ.  Disparities in social support systems for youths with type 1 diabetes.  Clin Diabetes. 2015;33(2):62-69. doi:10.2337/diaclin.33.2.62PubMedGoogle Scholar
31.
Clarke  ABM, Daneman  D, Curtis  JR, Mahmud  FH.  Impact of neighbourhood-level inequity on paediatric diabetes care.  Diabet Med. 2017;34(6):794-799. doi:10.1111/dme.13326PubMedGoogle Scholar
32.
Flores  G; Committee On Pediatric Research.  Technical report—racial and ethnic disparities in the health and health care of children.  Pediatrics. 2010;125(4):e979-e1020. doi:10.1542/peds.2010-0188PubMedGoogle Scholar
33.
Nelson  A.  Unequal treatment: confronting racial and ethnic disparities in health care.  J Natl Med Assoc. 2002;94(8):666-668.PubMedGoogle Scholar
34.
Schwandt  A, Hermann  JM, Rosenbauer  J,  et al; DPV Initiative.  Longitudinal trajectories of metabolic control from childhood to young adulthood in type 1 diabetes from a large German/Austrian registry: a group-based modeling approach.  Diabetes Care. 2017;40(3):309-316. doi:10.2337/dc16-1625PubMedGoogle Scholar
35.
SEARCH Study Group.  SEARCH for Diabetes in Youth: a multicenter study of the prevalence, incidence and classification of diabetes mellitus in youth.  Control Clin Trials. 2004;25(5):458-471. doi:10.1016/j.cct.2004.08.002PubMedGoogle Scholar
36.
Hamman  RF, Bell  RA, Dabelea  D,  et al; SEARCH for Diabetes in Youth Study Group.  The SEARCH for Diabetes in Youth study: rationale, findings, and future directions.  Diabetes Care. 2014;37(12):3336-3344. doi:10.2337/dc14-0574PubMedGoogle Scholar
37.
Kuczmarski  RJ, Ogden  CL, Grummer-Strawn  LM,  et al.  CDC growth charts: United States.  Adv Data. 2000;(314):1-27.PubMedGoogle Scholar
38.
Ingram  DD, Parker  JD, Schenker  N,  et al.  United States Census 2000 population with bridged race categories.  Vital Health Stat 2. 2003;(135):1-55.PubMedGoogle Scholar
39.
Mayer-Davis  EJ, Bell  RA, Dabelea  D,  et al; SEARCH for Diabetes in Youth Study Group.  The many faces of diabetes in American youth: type 1 and type 2 diabetes in five race and ethnic populations: the SEARCH for Diabetes in Youth study.  Diabetes Care. 2009;32(suppl 2):S99-S101. doi:10.2337/dc09-S201PubMedGoogle Scholar
40.
Jones  BL, Nagin  D, Roeder  K.  A SAS procedure based on mixture models for estimating developmental trajectories.  Sociol Methods Res. 2001;29:374-393. doi:10.1177/0049124101029003005Google Scholar
41.
Nagin  DS, Odgers  CL.  Group-based trajectory modeling in clinical research.  Annu Rev Clin Psychol. 2010;6:109-138. doi:10.1146/annurev.clinpsy.121208.131413PubMedGoogle Scholar
42.
Nagin  DS, Odgers  CL.  Group-based trajectory modeling (nearly) two decades later.  J Quant Criminol. 2010;26(4):445-453. doi:10.1007/s10940-010-9113-7PubMedGoogle Scholar
43.
Song  M, Willett  WC, Hu  FB,  et al.  Trajectory of body shape across the lifespan and cancer risk.  Int J Cancer. 2016;138(10):2383-2395. doi:10.1002/ijc.29981PubMedGoogle Scholar
44.
Nagin  DS.  Analyzing developmental trajectories: a semiparametric, group-based approach.  Psychol Methods. 1999;4(2):139-157. doi:10.1037/1082-989X.4.2.139Google Scholar
45.
Currie  C, Zanotti  C, Morgan  A,  et al, eds.  Social Determinants of Health and Well-Being Among Young People. Health Behaviour in School-aged Children (HBSC) Study: International Report From the 2009-2010 Survey. Copenhagen, Denmark: WHO Regional Office for Europe; 2012.
46.
Secrest  AM, Costacou  T, Gutelius  B, Miller  RG, Songer  TJ, Orchard  TJ.  Associations between socioeconomic status and major complications in type 1 diabetes: the Pittsburgh Epidemiology of Diabetes Complication (EDC) Study.  Ann Epidemiol. 2011;21(5):374-381. doi:10.1016/j.annepidem.2011.02.007PubMedGoogle Scholar
47.
Rewers  A, Chase  HP, Mackenzie  T,  et al.  Predictors of acute complications in children with type 1 diabetes.  JAMA. 2002;287(19):2511-2518. doi:10.1001/jama.287.19.2511PubMedGoogle Scholar
48.
Nathan  DM; DCCT/EDIC Research Group.  The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview.  Diabetes Care. 2014;37(1):9-16. doi:10.2337/dc13-2112PubMedGoogle Scholar
49.
Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group.  Intensive diabetes treatment and cardiovascular outcomes in type 1 diabetes: the DCCT/EDIC study 30-year follow-up.  Diabetes Care. 2016;39(5):686-693. doi:10.2337/dc15-1990PubMedGoogle Scholar
50.
Jaacks  LM, Oza-Frank  R, D’Agostino  R  Jr,  et al.  Migration status in relation to clinical characteristics and barriers to care among youth with diabetes in the US.  J Immigr Minor Health. 2012;14(6):949-958. doi:10.1007/s10903-012-9617-3PubMedGoogle Scholar
51.
Hardeman  RR, Medina  EM, Kozhimannil  KB.  Race vs burden in understanding health equity.  JAMA. 2017;317(20):2133. doi:10.1001/jama.2017.4616PubMedGoogle Scholar
52.
Valenzuela  JM, Seid  M, Waitzfelder  B,  et al; SEARCH for Diabetes in Youth Study Group.  Prevalence of and disparities in barriers to care experienced by youth with type 1 diabetes.  J Pediatr. 2014;164(6):1369-75.e1. doi:10.1016/j.jpeds.2014.01.035PubMedGoogle Scholar
53.
Harris  MI.  Racial and ethnic differences in health care access and health outcomes for adults with type 2 diabetes.  Diabetes Care. 2001;24(3):454-459. doi:10.2337/diacare.24.3.454PubMedGoogle Scholar
54.
Sparud-Lundin  C, Öhrn  I, Danielson  E, Forsander  G.  Glycaemic control and diabetes care utilization in young adults with type 1 diabetes.  Diabet Med. 2008;25(8):968-973. doi:10.1111/j.1464-5491.2008.02521.xPubMedGoogle Scholar
55.
Karatekin  C, Ahluwalia  R.  Effects of adverse childhood experiences, stress, and social support on the health of college students  [published online December 5, 2016].  J Interpers Violence. doi:10.1177/0886260516681880PubMedGoogle Scholar
56.
Hall  WJ, Chapman  MV, Lee  KM,  et al.  Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review.  Am J Public Health. 2015;105(12):e60-e76. doi:10.2105/AJPH.2015.302903PubMedGoogle Scholar
57.
Sabin  JA, Greenwald  AG.  The influence of implicit bias on treatment recommendations for 4 common pediatric conditions: pain, urinary tract infection, attention deficit hyperactivity disorder, and asthma.  Am J Public Health. 2012;102(5):988-995. doi:10.2105/AJPH.2011.300621PubMedGoogle Scholar
58.
Sabin  JA, Rivara  FP, Greenwald  AG.  Physician implicit attitudes and stereotypes about race and quality of medical care.  Med Care. 2008;46(7):678-685. doi:10.1097/MLR.0b013e3181653d58PubMedGoogle Scholar
59.
Peek  ME, Wagner  J, Tang  H, Baker  DC, Chin  MH.  Self-reported racial discrimination in health care and diabetes outcomes.  Med Care. 2011;49(7):618-625. doi:10.1097/MLR.0b013e318215d925PubMedGoogle Scholar
60.
Ryan  AM, Gee  GC, Griffith  D.  The effects of perceived discrimination on diabetes management.  J Health Care Poor Underserved. 2008;19(1):149-163. doi:10.1353/hpu.2008.0005PubMedGoogle Scholar
61.
Reitblat  L, Whittemore  R, Weinzimer  SA, Tamborlane  WV, Sadler  LS.  Life with type 1 diabetes: views of Hispanic adolescents and their clinicians.  Diabetes Educ. 2016;42(4):408-417. doi:10.1177/0145721716647489PubMedGoogle Scholar
62.
Hunter  CM.  Understanding diabetes and the role of psychology in its prevention and treatment.  Am Psychol. 2016;71(7):515-525. doi:10.1037/a0040344PubMedGoogle Scholar
63.
Jaacks  LM, Liu  W, Ji  L, Mayer-Davis  EJ.  Type 1 diabetes stigma in China: a call to end the devaluation of individuals living with a manageable chronic disease.  Diabetes Res Clin Pract. 2015;107(2):306-307. doi:10.1016/j.diabres.2014.12.002PubMedGoogle Scholar
64.
Steptoe  A, Marmot  M.  Burden of psychosocial adversity and vulnerability in middle age: associations with biobehavioral risk factors and quality of life.  Psychosom Med. 2003;65(6):1029-1037. doi:10.1097/01.PSY.0000097347.57237.2DPubMedGoogle Scholar
65.
Adler  NE, Newman  K. Inequality in education, income, and occupation exacerbates the gaps between the health “haves” and “have-nots.” In: Bemelmans-Videc  M-L, Rist  RC, Vedung  EO, eds.  Carrots, Sticks and Sermons: Policy Instruments and Their Evaluation. Piscataway, NJ: Transaction Publishers; 2002:249-274.
66.
McEwen  BS.  Stress, adaptation, and disease: allostasis and allostatic load.  Ann N Y Acad Sci. 1998;840(1):33-44. doi:10.1111/j.1749-6632.1998.tb09546.xPubMedGoogle Scholar
67.
Herman  WH, Cohen  RM.  Racial and ethnic differences in the relationship between HbA1c and blood glucose: implications for the diagnosis of diabetes.  J Clin Endocrinol Metab. 2012;97(4):1067-1072. doi:10.1210/jc.2011-1894PubMedGoogle Scholar
68.
Selvin  E, Sacks  DB.  Variability in the relationship of hemoglobin A1c and average glucose concentrations: how much does race matter?  Ann Intern Med. 2017;167(2):131-132. doi:10.7326/M17-1231PubMedGoogle Scholar
69.
Bergenstal  RM, Gal  RL, Connor  CG,  et al; T1D Exchange Racial Differences Study Group.  Racial differences in the relationship of glucose concentrations and hemoglobin A1c levels.  Ann Intern Med. 2017;167(2):95-102. doi:10.7326/M16-2596PubMedGoogle Scholar
70.
Gail  MH, Wieand  S, Piantadosi  S.  Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates.  Biometrika. 1984;71(3):431-444. doi:10.1093/biomet/71.3.431Google Scholar
71.
Hauck  WW, Neuhaus  JM, Kalbfleisch  JD, Anderson  S.  A consequence of omitted covariates when estimating odds ratios.  J Clin Epidemiol. 1991;44(1):77-81. doi:10.1016/0895-4356(91)90203-LPubMedGoogle Scholar
72.
Dabelea  D, Mayer-Davis  EJ, Saydah  S,  et al; SEARCH for Diabetes in Youth Study.  Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009.  JAMA. 2014;311(17):1778-1786. doi:10.1001/jama.2014.3201PubMedGoogle Scholar
73.
Akesson  K, Hanberger  L, Samuelsson  U.  The influence of age, gender, insulin dose, BMI, and blood pressure on metabolic control in young patients with type 1 diabetes.  Pediatr Diabetes. 2015;16(8):581-586. doi:10.1111/pedi.12219PubMedGoogle Scholar
×