Predictors of Insulin Regimens and Impact on Outcomes in Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study

Predictors of Insulin Regimens and Impact on Outcomes in Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study Carolyn A. Paris, MD, MPH, Giuseppina Imperatore, MD, PhD, Georgeanna Klingensmith, MD, Diana Petitti, MD, MPH, Beatriz Rodriguez, MD, MPH, PhD, Andrea M. Anderson, MS, I. David Schwartz, MD, Debra A. Standiford, RN, MSN, CNP, and Catherine Pihoker, MD Objectives To describe the insulin regimens used to treat type 1 diabetes mellitus (T1DM) in youth in the United States, to explore factors related to insulin regimen, and to describe the associations between insulin regimen and clinical outcomes, particularly glycemic control. Study design A total of 2743 subjects participated in the SEARCH for Diabetes in Youth study, an observational population-based study of youth diagnosed with T1DM, conducted at 6 centers. Data collected during a study visit included clinical and sociodemographic information, body mass index, laboratory measures, and insulin regimen. Results Sociodemographic characteristics were associated with insulin regimen. Insulin pump therapy was more frequently used by older youth, females, non-Hispanic whites, and families with higher income and education (P = .02 for females, P < .001 for others). Insulin pump use was associated with the lowest hemoglobin A1C levels in all age groups. A1C levels were >7.5% in >70% of adolescents, regardless of regimen. Conclusions Youth using insulin pumps had the lowest A1C; A1C was unacceptably high in adolescents. There is a need to more fully assess and understand factors associated with insulin regimens recommended by providers and the influence of race/ethnicity, education, and socioeconomic status on these treatment recommendations and to develop more effective treatment strategies, particularly for adolescents. (J Pediatr 2009;155:183-9). See editorial, p 161

T

he importance of optimal glycemic control in the management of type 1 diabetes mellitus (T1DM) has been firmly established in the Diabetes Control and Complications Trial (DCCT).1,2 Although the DCCT documented the importance of intensive diabetes management in lowering A1C levels, treatment components, including optimal insulin regimens, that are essential to improved clinical outcomes remain unclear.3 Insulin regimens are believed to affect metabolic outcomes, but this relationship is complex. In observational studies in children, insulin pump use has been associated with lower A1C, fewer episodes of severe hypoglycemia and seizures,4-6 as well as improved quality of life.7 However, in longitudinal observational studies such as the international multicenter study from the Hvidore Study Group, the efficacy of one particular insulin regimen over others remains unclear. No associations were found between the frequency of insulin dosing or the use of insulin pump therapy with A1C values.3,8 In addition, persistent, unexplained center differences exist within the Hvidore Study Group.9,10 In a short-term randomized control trial of insulin pump versus multiple daily injection (MDI) therapy (using insulin glargine as basal insulin), insulin pump use was associated with a sharp reduction in A1C levels, but, for those using MDI, there was no significant change in A1C.11 On the other hand, Bolli et al12 reported that A1C and frequency of acute complications were similar between insulin pump users and patients on MDI with insulin glargine, although the cost of therapy was 4 times higher for insulin pump users. Greater resources are typically available in the research setting than in practice; this raises concern about the generalizability of these studies’ better outcomes to other/nonresearch settings. The American Diabetes Association (ADA), in recognition of the DCCT results, recently published the age-specific A1C goals for children, along with From the University of Washington (C. Paris, C. Pihoker), recommendations that encourage health care providers to consider using an

ADA DCCT DM FCP MDI T1DM

American Diabetes Association Diabetes Control and Complications Trial Diabetes mellitus Fasting C-peptide Multiple daily injection Type 1 diabetes mellitus

Seattle, WA; Division of Diabetes Translation, NCCDPHP, Centers for Disease Control and Prevention (G.I.), Atlanta, GA; Barbara Davis Center for Childhood Diabetes, University of Colorado at Denver and Health Sciences Center (G.K.), Denver, CO; Kaiser Permanente Southern California (D.P.), Pasadena, CA; Pacific Health Research Institute (B.R.), Honolulu, HI; Wake Forest University (A.A.), Winston-Salem, NC; University of South Carolina (I.S.), Columbia, SC; Children’s Hospital Medical Center (D.S.), Cincinnati, OH Funding and conflict of interest information available at www.jpeds.com (Appendix). 0022-3476/$ - see front matter. Copyright Ó 2009 Mosby Inc. All rights reserved. 10.1016/j.jpeds.2009.01.063

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intensive insulin regimen (eg, basal-bolus therapy) for most pediatric patients.13 The ADA recommends the following target A1C by age group: <6 years, 7.5%-8.5%; 6 to 12 years, #8.0%; 13 to 18 years, #7.5%; $ 19 years, #7.0%. There are limited data on the relationship between insulin regimens and these glycemic control goals or how often children achieve glycemic goals. Moreover, little is known about the distribution of insulin regimens across racial/ethnic groups or socioeconomic strata. The objectives of this report are to describe the insulin regimens used to treat T1DM in a large cohort of pediatric patients in the United States, to explore factors related to insulin regimen, and to describe the associations between insulin regimen and clinical outcomes, particularly A1C levels.

Methods Data for this analysis are from the SEARCH for Diabetes in Youth study, a multicenter population-based observational study of physician-diagnosed nongestational diabetes mellitus (DM) in youth aged less than 20 years at DM diagnosis. Detailed study methods have been described previously; the study was approved by the appropriate institutional review boards.14 In brief, SEARCH began conducting populationbased ascertainment of children <20 years old with DM in 2001 and continues through the present. The identified children included all prevalent cases in that year with new (incident) cases identified in subsequent years. Children are identified in geographically defined populations in Colorado, Ohio, South Carolina, and Washington; among health plan enrollees in California (Kaiser Permanente Southern California excluding San Diego) and Hawaii (HMSA, MedQuest, Kaiser Permanente Hawaii); and coordinated by the Colorado center, among 4 American Indian populations in Arizona and New Mexico and among participants in the Pima Indian NIH longitudinal study. Cases are considered valid if they were diagnosed with DM by a health care provider. Following Health Insurance Portability and Accountability Act compliant procedures, youth with DM were identified and asked to complete a survey that collected information on current age, age at diagnosis, treatment history, and self-reported race/ethnicity. The type of DM (type 1 or type 2) was based on health care providers’ reports to the SEARCH study or was abstracted from medical records. All participants who completed this survey (except those with known secondary DM) were invited to participate in an in-person study visit while metabolically stable (no episode of diabetic ketoacidosis during the previous month). At this visit, additional information was collected on demographics, duration of DM, household income, highest level of education of either parent/guardian, insurance status, and type of provider delivering DM care (eg, pediatric endocrinologist, adult endocrinologist, family practitioner). Additionally, blood was withdrawn to measure A1C, and fasting C-peptide (FCP), and an examination was performed to 184

Vol. 155, No. 2 measure height and weight. These height and weight measurements were compared with US National Center for Health Statistics standards, and body mass index (BMI) was calculated and expressed as a normalized standard deviation score (or Z) score with US National Center for Health Statistics standards. Self-reported information obtained at the time of the study visit used for these analyses included history of insulin use, number of daily injections, types of insulin; mode of insulin delivery (e.g., insulin pump, syringes, insulin pen devices); and frequency of self blood glucose monitoring. Measures of acute clinical complications included self-reports of severe hypoglycemia (seizure, treatment with glucagon, or needing outside assistance), hospital admission, or an emergency department visit that occurred during the 6 months before the study visit. Of youth with a provider diagnosis of T1DM (n = 9220), 4591 had an in-person study visit by October 30, 2007. Of those, we excluded individuals with a DM duration of less than 1 year (n = 1417), those not reporting insulin use or having missing data on insulin regimen (n = 47), and those not having A1C measured at the study visit (n = 384). The final study sample included 2743 youth with T1DM. Insulin regimen was classified into 5 categories: (1) basalbolus with continuous subcutaneous infusion (insulin pump), (2) basal-bolus with glargine plus rapid-acting insulin (lispro insulin or insulin aspart), (3) MDI ($3 injections) with glargine plus more than/or other than rapid-acting insulin type (may include glargine plus either regular- or intermediate-acting plus rapid-acting insulin), (4) MDI ($3 injections) with any insulin types excluding basal insulin (glargine), (5) 1 to 2 injections per day, excluding insulin glargine. These categories represented basal-bolus regimens (regimens 1 and 2), modified basal-bolus regimens (regimen 3), typical multiple daily injections (regimen 4), or what had been considered standard therapy at the initiation of the DCCT (regimen 5). Insulins detemir and glulisine were not in clinical use in the SEARCH population during the data collection period. Statistical Analysis Analyses were conducted with SAS version 9.1 (SAS Institute, Cary, North Carolina). Descriptive statistics were reported to enumerate the population of children and adolescents under study, and percentages were used to summarize categorical variables. Continuous variables were summarized with means and standard deviations (SDs). General summary statistics were presented and then stratified by insulin regimen. Associations between insulin regimen and patient characteristics and outcomes were analyzed with c2 statistics or analysis of variance as appropriate. Multiple linear regression and Poisson regression analyses were used to examine the independent association of insulin regimen with the short-term clinical outcomes. To examine potential differences in prescribing practices by region, a regimen by clinical center interaction was also tested within the multiple linear regression setting for the A1C outcome. Clinical center refers Paris et al

ORIGINAL ARTICLES

August 2009 to the site at which the participant data were collected, not to where clinical care was provided. Logistic regression was used to examine the independent associations of patient characteristics on use of a 1 to 2 injections per day insulin regimen (regimen category 5) compared with all other regimens and to examine associations with A1C and frequency of meeting ADA goals of A1C levels. Additional analyses of variance were run to explore associations of regimen, age, and frequency of blood glucose testing on A1C. To explore the potential association between residual beta-cell function and insulin regimen and A1C, mean fasting C-peptide (FCP) was compared among insulin regimens, and also included in the logistic regression, along with other clinical and socioeconomic variables. We consider results significant if P < .05.

Results Age, disease duration, and sociodemographic characteristics are shown for the 2743 youth in the study population (Table I). Health Care Provider for Diabetes Care Most participants received their DM treatment from a pediatric endocrinologist (75.6%) or from a nurse practitioner or physician assistant (12.4%), who was most often part of a multidisciplinary team with a pediatric endocrinologist, thus not a distinctly separate set of providers from pediatric endocrinologists (Table I). The remaining 12.0% of the participants received care from adult endocrinologists (5.3%), generalists (4.3%), and other health care providers (2.4%). Analysis of A1C and acute complication outcomes among participants cared for by pediatric endocrinologists compared with all other provider types did not reveal significant differences; therefore further associations between health care provider type and insulin regimens were not explored. Insulin Regimens Almost half of participants (46.8%) were managed with basal-bolus therapy (regimens 1 and 2), including 22.0% on insulin pump therapy (regimen 1) (Table I). More than one quarter of participants (27.0%) managed their DM with 1 to 2 injections daily of intermediate and short- or rapid-acting insulin (regimen 5). The remainder of the participants (26.2%) used MDI with or without long-acting analog insulin (regimens 3 and 4). Factors Associated with Insulin Regimen Younger age and shorter mean duration of DM were associated with higher frequency of using 1 to 2 injections daily (regimen 5) (P < .001). For participants aged <6 years, 36.6% were managed with 1 to 2 injections per day compared with 27.0% overall. Higher mean FCP was associated with higher frequency of using regimen 4 than regimens 1 to 3 (P < .001). The mean FCP for insulin pump users was significantly lower than the FCP for participants on regimens 4 or 5.

Insulin pump use was most common in the oldest age group (P < .001) and was more common among non-Hispanic whites; among youth from families with higher household income, higher parental education, and private insurance (P < .001 for all); and among females (P = .02). Of the non-Hispanic white children, 52.7% were treated with basal-bolus therapy (regimens 1 and 2), and 19.1% of black children and 27.8% of Native American children used basal-bolus therapy. BMI Z-score was similar in all regimens except for regimen 3, which had the lowest BMI z-score (P < .001). BMI z-score did not differ significantly between participants in insulin regimens 1 and 2 (more intensive basal-bolus therapy) and those using insulin regimens 4 and 5 (less intensive therapy). The distribution of insulin regimens varied across study centers (Table I). In unadjusted models of A1C, there was a significant interaction between center and insulin regimen and A1C (P = .001). A1C remained significantly different among study centers after adjustment for sociodemographic variables (sex, race/ethnicity, household/family income, parental education, insurance, and age at visit), but less so (P = .04). After including FCP, DM duration, and frequency of blood glucose testing, the A1C difference between centers was no longer significant (P = .18). Associated Clinical Outcomes Adjusted and unadjusted associations between insulin regimen and selected clinical outcomes appear in Table II. In the unadjusted analysis, A1C was lowest in the group using an insulin pump, and this association persisted after adjustment for sociodemographic and clinical factors. Frequency of hypoglycemia was not significantly different between regimens. After adjustment for possible confounders, there was no significant difference in the frequency of visits to ED between insulin regimen groups. However, participants who did not use an insulin pump were significantly more likely to be hospitalized than those treated with a pump. As shown in Figure 1, we explored the associations between A1C, age group, and insulin regimen, both before and after adjusting for demographic and clinical characteristics. The frequency of participants achieving an A1C in the ADA target range, by age group, is shown in Figure 2. Potential Modifiers The frequency of self blood glucose monitoring was associated with A1C, with those who checked their blood glucose infrequently (#2 times daily) showing higher A1C levels than those who checked more often ($4 times daily) regardless of insulin regimen, as shown in Table III (available at www.jpeds.com). The way in which participants administered their insulin (insulin pen devices or syringes) was not significantly associated with A1C level (data not shown).

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Table I. Characteristics of youth with type 1 diabetes stratified by insulin regimens Regimen

Age at visit (yr) Age at diagnosis (yr) Duration of diabetes (yr) BMI Z-score FCP A1C Total Sex Male Female Race Asian/Pacific Islander Black Hispanic Multiple Native American White Household income <$25K $25-49K $50-74K $75K + DK/Refused Maximum parental education Less than high school graduate High school graduate Some college through associates degree Bachelors degree or more Insurance Private Medicaid/Medicare Other None Center A B C D E F Type of provider Pediatric endocrinologist Adult endocrinologist Generalist NP/PA Other

Totals

1 Pump

2 MDI: Glargine/ Rapid

3 MDI:Glargine/ Rapid+ other

4 MDI: No Glargine

5 One-Two Injections/ No Glargine

13.2  4.5 7.8  4.2 5.0  3.9 0.63  0.88 0.34  0.43 8.5  1.5 2743 (100.0%)

14.0 (4.2) 7.6 (4.0) 6.0 (3.9) 0.65 (0.79) 0.28 (0.27) 8.0 (1.1) 22.0%

14.0 (4.4) 8.7 (4.4) 4.9 (4.0) 0.60 (0.88) 0.33 (0.34) 8.5 (1.6) 24.8%

12.3 (4.6) 6.6 (4.3) 5.3 (4.0) 0.45 (0.86) 0.31 (0.46) 8.9 (1.6) 10.5%

13.0 (4.4) 7.9 (4.3) 4.7 (3.8) 0.72 (0.94) 0.43 (0.66) 8.6 (1.6) 15.7%

12.1 (4.3) 7.3 (4.0) 4.4 (3.6) 0.65 (0.92) 0.37 (0.44) 8.6 (1.7) 27.0%

1371 (50.0%) 1372 (50.0%)

20.2% 23.8%

23.% 26.%

10.9% 10.1%

17.0% 14.5%

28.5% 25.5%

49 (1.8%) 188 (6.9%) 326 (11.9%) 101 (3.7%) 18 (0.7%) 2058 (75.1%)

6.1% 5.3% 12.3% 8.9% 0.0% 26.3%

38.9% 13.8% 17.2% 29.7% 27.8% 26.4%

6.1% 5.3% 8.0% 15.8% 11.1% 11.2%

20.4% 29.3% 31.3% 18.8% 22.2% 11.7%

28.6% 46.3% 31.3% 26.7% 38.9% 24.4%

351 (12.8%) 581 (21.2%) 565 (20.6%) 1041 (38.0%) 199 (7.3%)

8.0% 13.9% 24.8% 30.7% 17.6%

22.2% 24.3% 25.3% 26.2% 22.1%

7.4% 11.7% 9.2% 11.1% 12.1%

21.9% 18.4% 17.0% 10.5% 19.6%

40.5% 31.7% 23.7% 21.4% 28.6%

P value <.0001 <.0001 <.0001 .0012 <.0001 <.0001 .0208 <.0001

<.0001

<.0001 112 (4.1%)

2.7%

9.8%

8.0%

30.4%

49.1%

446 (16.3%) 918 (33.6%)

13.4% 18.5%

25.3% 26.1%

6.7% 11.3%

20.6% 17.3%

33.9% 26.7%

1256 (46.0%)

29.5%

25.2%

11.4%

11.3%

22.7%

2193 (80.1%) 455 (16.6%) 43 (1.6%) 46 (1.7%)

25.2% 7.7% 27.9% 6.5%

25.0% 24.4% 18.6% 26.1%

10.8% 8.4% 9.3% 15.2%

14.6% 20.2% 23.3% 17.4%

24.3% 39.3% 20.9% 34.8%

270 (9.8%) 577 (21.0%) 829 (30.2%) 371 (13.5%) 638 (23.3%) 58 (2.1%)

32.2% 31.0% 22.9% 12.7% 14.6% 13.8%

22.2% 23.2% 14.1% 24.8% 38.1% 60.3%

1.9% 0.4% 29.6% 3.5% 2.8% 6.9%

27.8% 7.8% 9.3% 40.4% 12.2% 10.3%

15.9% 37.6% 24.1% 18.6% 32.3% 8.6%

2073 (75.6%) 144 (5.3%) 119 (4.3%) 340 (12.4%) 66 (2.4%)

21.6% 23.6% 12.6% 25.6% 31.8%

21.9% 45.1% 40.3% 28.2% 27.3%

12.2% 4.9% 10.1% 3.5% 6.1%

16.4% 14.6% 23.5% 8.8% 18.2%

28.0% 11.8% 13.4% 33.8% 16.7%

<.0001

<.0001

<.0001

DK, Don’t know; NP/PA, Nurse practitioner/physician assistant.

Discussion In this racially and ethnically diverse cohort of youth with T1DM, we found that basal-bolus therapy (as insulin pump or glargine plus rapid-acting insulin) was used in less than half of all participants, with more than one fourth receiving 1 to 2 injections daily. Even when adjusted for income and education, children of minority groups, especially AfricanAmerican participants, were less likely to be treated with basal-bolus therapy than were non-Hispanic whites. Not surprisingly, insulin pump use was most common by the oldest 186

age group, by non-Hispanic white youth, and by children of families with higher household income and higher parental education. The use of an insulin pump was associated with lower A1C levels and fewer hospitalizations without an increase in hypoglycemic episodes or BMI z-score. As observed in other studies, increasing age was associated with a higher mean A1C and a decreased likelihood of attaining A1C in the target range regardless of insulin regimen.4,5,15-17 Our results suggest that there is an urgent need for better DM management tools and strategies that would lead to improvements in DM care, and especially self-care for adolescents, to more often achieve A1C levels within the target range.13 Paris et al

ORIGINAL ARTICLES

August 2009

Table II. A1C and adverse clinical outcomes in last 6 months according to insulin regimen Insulin Regimen

Outcomes A1C, mean % (SD) Unadjusted Adjusted*,† Hypoglycemic Episodes Percentage $ 1 Rate Ratio*,z ER visits Percentage $ 1 Rate Ratio*,z Hospitalizations Percentage $ 1 Rate Ratio*,z

4 MDI: No Glargine

5 One -Two Injections No Glargine

P value

8.9 (1.6) 9.7 (0.1)

8.6 (1.6) 9.4 (0.1)

8.6 (1.7) 9.3 (0.1)

<.0001 <.0001

13.7 1.13 (0.31)

12.5 1.70 (0.56)

11.4 0.69 (0.22)

13.5 1.12 (0.3)

.30 .14

14.2 1 (Referent)

22.6 1.36 (0.19)

19.9 1.24 (0.23)

25.6 1.46 (0.24)

22.6 1.08 (0.16)

<.0001 .06

3.2 1 (Referent)

8.2 2.49 (0.75)

10.5 6.25 (2.30)

7.0 2.32 (0.80)

7.8 1.84 (0.59)

.0003 <.0001

2 MDI: Glargine/ Rapid

3 MDI: Glargine/ Rapid+ other

8.5 (1.6) 9.5 (0.1)

10.3 1 (Referent)

1 Pump 8.0 (1.2) 9.0 (0.1)

*Regression analyses adjusted for sex, race, center, household income, parental education, insurance, age at visit, duration of DM, FCP, and number of blood sugar checks per day. †Linear regression-estimates reported are least squares means (SE). zPoisson regression-estimates reported are rate ratios (SE).

Clinical measures were associated with the insulin regimen prescribed. We found an association between FCP and insulin regimen. Shorter duration of DM and higher FCP were observed in those using less intensive insulin regimens. For participants with residual endogenous insulin, less intensive regimens can be effective and adequate to control hyperglycemia; this treatment/approach may contribute to the relatively small differences observed between insulin regimens and outcomes. To decrease the potential impact of residual insulin production, we report data excluding participants with disease duration < 12 months. We performed additional analyses excluding participants with a FCP >1.0 ng/mL (the 5th percentile from the NHANES data for youth ages 13-17)18,19 and excluding participants with a FCP > 0.225 ng/mL (from the DCCT trial, this FCP value correlates with a stimulated FCP of 0.6 ng/mL, used as a measure of clinically significant FCP)20 (Lachin J, McGee P, personal communication, 2008). There were no statistically significant changes observed in Tables I and II when these additional FCP criteria were applied. There was no association between increased BMI and basal-bolus insulin regimen, as had been observed in earlier studies where more intensive regimens, such as insulin pumps, were associated with weight gain and higher BMI.1 Children using regimen 3 (a combination of basal and intermediate-, rapid-, and short-acting insulins) had a significantly lower BMI z-score. This relatively creative regimen is often used to reduce or eliminate the need for lunch-time or school-time insulin administration, often occurring when school staff and parents are unable to assist a child or when older children are unwilling to administer insulin at school. This group overall had the poorest glycemic control. Unfortunately in this study we did not collect data on diabetes management at school, such as whether insulin/carbohydrate or set carbohydrate counts are used at lunch, but doing so may provide insight into improving management particularly in this group.

The Hvidore Study Group, a multicenter observational study, has consistently reported unexplained center differences in A1C and generally reported no difference in A1C by insulin regimen. In the multicenter study reported here, adjustment for sociodemographic characteristics alone did not eliminate the center differences, but additional adjustment for clinical diabetes features, including DM duration, FCP and frequency of glucose monitoring resulted in no significant difference in A1C by center. These findings suggest that sociodemographic and clinical differences, including preferred insulin regimens, frequency of blood glucose monitoring, and patient mean FCP, may explain center differences in A1C outcomes. Longitudinal data from SEARCH centers will be needed to confirm these findings. The strengths of this study include a large and unique U.S. cohort that is diverse according to race, ethnicity, and geographical location. Further, the inclusion of participants from both academic and non-academic practices makes these findings more generalizable. The large sample size and the extensive demographic and clinical data available allowed differences by center and insulin regimen to be studied in detail. The findings of both clinical and sociodemographic associations with insulin regimens require further exploration, especially because insulin regimens were found to affect the important clinical outcome of measurement of A1C. There are several limitations to this study. Although the participants were drawn from a population-based study, only 49.8% of the registered children participated in a study visit and completed study questionnaires. Using data from all 6 centers, SEARCH investigators compared race/ethnicity distribution and mean age between all participants and nonparticipants, including those with type 2 diabetes.21 This analysis showed that older youth were much less likely to participate than those who were younger (youth aged 18 to 19 years at study invitation were 3 times less likely to participate than those who were <10 years). African-Americans were less likely to participate than youth of other races/ethnicities, but

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A 9.5

A1C, mean %

9 8.5

Age Groups <6 yr

8

6-12 yr >12 yr

7.5 7 6.5

% meeting ADA target A1C

100 Regimen 1. Insulin pump 2. Glargine+rapid-acting insulin 3. Glargine+two or more insulins 4. Multiple injections w/o glargine 5. 2 or fewer insulin injections

80 70

2

3

4

Age Groups

60

<6 yr

50

6-12 yr

40

>12 yr

30 20 10 0

1

Regimen 1. Insulin pump 2. Glargine+rapid-acting insulin 3. Glargine+two or more insulins 4. Multiple injections w/o glargine 5. 2 or fewer insulin injections

90

1

2

3

4

5

Insulin Regimen

5

Insulin Regimen

B

10

A1C, mean %

9.5 9

Age Groups

8.5

< 6 yr 6-12 yr

8

Figure 2. Proportion of participants attaining ADA target A1C by age group and insulin regimen. Overall, age group and regimen are both significantly associated with meeting the target A1C level (P < .0001 for both), with younger age groups having a higher probability of meeting the target level and regimen 1 having a higher, and regimen 3 having a lower, probability of meeting the target level as compared with all other regimens.

> 12 yr

7.5 7 6.5

1

2

3

4

5

Insulin Regimen

Figure 1. Mean A1C by age group and insulin regimen, A, unadjusted and B, adjusted for sex, race/ethnicity, income, education, insurance, center, DM duration, FCP, and frequency of glucose monitoring. The following significant comparisons were noted: in the unadjusted model, the oldest age group is higher than the other age groups (P < .0001) for all regimens; regimen 1 is lower than all other regimens (P < .0001 for all); and regimen 3 is higher than 1 and 2 (P < .0001) and 4 (P = .0068). In the adjusted model, age group >12 is higher than 6-12 (P = .0426); regimen 3 is higher than 1 (P = .0011), 4 (P = .0439) and 5 (P = .0088).

other race/ethnic groups participated at the same or higher rates as non-Hispanic whites. Because nonresponse differences in insulin regimen could not be considered, we cannot be certain the insulin regimens of participants and nonparticipants are similar. Although we did not observe differences by provider type, the vast majority of participants received care by a pediatric endocrinologist or multidisciplinary team, including nonphysician providers. There are many factors associated with choice of provider; specialized teams may include expertise and extra resources (such as mental health professionals and social work and interpreter services) that lead to them caring for patients with less metabolic stability. We were not able to examine these factors and their impact on insulin regimen and outcomes. These are cross-sectional, observational data from a single study visit; it is not a randomized controlled trial comparing different insulin regimens. Many factors are taken into consideration when prescribing or selecting an insulin treatment 188

regimen including, but not limited to, perceived need for a change and clinician/patient/family preferences. Many of the collected measures were self-reported, which may result in overestimations of the actual frequency of insulin administration and blood glucose testing. Thus the expected bias is that our findings are conservative and the true differences between outcomes in insulin regimens 2 through 5 would actually be greater. In collecting the data, the frequency of glucose testing was limited to 3 categories. Many children and adolescents now test much more frequently, so additional categories describing frequency of glucose testing may yield even greater differences between testing frequency and outcomes. Lastly, because the predominance of care was provided by pediatric endocrinologists and associated providers, we could not assess the relationship between provider type, insulin regimen, and outcome. Overall, the best results were observed among participants using insulin pumps, as well as those with more frequent blood glucose monitoring. These data suggest that children with DM may have better long-term outcomes if treated with more intensive insulin regimens and encouraged to monitor blood glucose levels more frequently. This more intensive management would require more financial resources and more equitable distribution of these resources in order to benefit all children with DM. There is a critical need to develop more effective management strategies for adolescents, because their poor glycemic control decreases the chances of a healthy, complication-free adult life. Also, there is a need to more fully assess and understand factors related to insulin regimen use and the influence of age, race/ethnicity, education, and socioeconomic status on treatment options, hopefully leading to improvements in glycemic control for all children with DM. n Paris et al

ORIGINAL ARTICLES

August 2009 Acknowledgments available at www.jpeds.com. 10. Submitted for publication July 24, 2008; last revision received Dec 2, 2008; accepted Jan 21, 2009. Reprint requests: Carolyn A. Paris, MD, MPH, Children’s Hospital & Regional Medical Center, 4800 Sand Point Way NE, B-5518, Seattle, Washington 98105-0371. E-mail: [email protected].

11.

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Predictors of Insulin Regimens and Impact on Outcomes in Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study

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Table III. Unadjusted A1C% by insulin regimen and number of daily blood glucose checks Regimen Overall Blood glucose checks Overall* 0-2 times/day 3 times/day $4 times/day

1

2

3

4

5

N

A1C % (SD)

N

A1C % (SD)

N

A1C % (SD)

N

A1C % (SD)

N

A1C % (SD)

N

A1C % (SD)

2744 284 363 2063

8.5 (1.5) 9.5 (2.1) 9.0 (1.6) 8.2 (1.3)

604 40 62 501

8.0 (1.1) 8.6 (1.3) 8.7 (0.9) 7.9 (1.1)

681 56 70 545

8.5 (1.6) 9.6 (2.1) 9.3 (1.6) 8.3 (1.4)

287 38 42 204

8.9 (1.6) 10.4 (2.1) 9.3 (1.7) 8.6 (1.2)

432 35 63 325

8.6 (1.6) 9.2 (2.1) 8.9 (1.7) 8.4 (1.4)

740 115 126 488

8.6 (1.7) 9.6 (2.3) 9.1 (1.7) 8.3 (1.3)

*The Overall Ns do not equal the sum of their components due to missing data in the reporting of blood glucose checks.

Acknowledgments The SEARCH for Diabetes in Youth study is indebted to the many youth, their families, and their health care providers, whose participation made this study possible. The writing group for this manuscript wishes to acknowledge the contributions of the following individuals to the SEARCH for Diabetes in Youth Study: California: Jean M. Lawrence, ScD, MPH, MSSA, Ann K. Kershnar, MD, Kristi Reynolds, PhD, MPH, and Marlene Y. Gonzalez, MPH, for Kaiser Permanente Southern California; David J. Pettitt, MD, for the Sansum Diabetes Research Institute; and Diana B. Petitti, MD, MPH, for the University of Southern California Colorado: Dana Dabelea, MD, PhD, Richard F. Hamman, MD, DrPH, Lisa Testaverde, MS, for the Department of Preventive Medicine and Biometrics, University of Colorado Denver, Georgeanna J. Klingensmith, MD, and Marian J. Rewers, MD, PhD, for the Barbara Davis Center for Childhood Diabetes, Stephen Daniels, MD, PhD, Department of Pediatrics and Children’s Hospital, Clifford A. Bloch, MD, for the Pediatric Endocrine Associates, Jonathan Krakoff, MD, and Peter H. Bennett, MD, FRCP, for the NIDDK Pima Indian Study, Joquetta A. DeGroat, BA, for the Navajo Area Indian Health Prevention Program, Teresa Coons, PhD, for the St. Mary’s Hospital Grand Junction Hawaii: Beatriz L. Rodriguez, MD, PhD, Beth Waitzfelder, PhD, Wilfred Fujimoto, MD, J. David Curb, MD, Fiona Kennedy, RN, Greg Uramoto, MD, Sorrell Waxman, MD, Teresa Hillier, MD, Richard Chung, MD, for the Pacific Health Research Institute Ohio: Lawrence M. Dolan, MD, Michael Seid, PhD, Nancy Crimmins, MD, Debra A. Standiford, MSN, CNP, for the Cincinnati Children’s Hospital Medical Center South Carolina: Elizabeth J. Mayer-Davis, PhD, Joan Thomas MS, RD, for the University of North Carolina, Chapel Hill, Angela D. Liese, PhD, MPH, Robert McKeown, PhD, Robert R. Moran, PhD, Deborah Truell, RN, CDE, Gladys Gaillard-McBride, RN, CFNP, Deborah Lawler, MT (ASCP), Malaka Jackson, MD, for the University of South

Appendix SEARCH for Diabetes in Youth is funded by the Centers for Disease Control and Prevention (PA number 00097) and General Clinical Research Centers (Medical University of South Carolina [M01 RR01070]; Cincinnati Children’s Hospital [M01 RR08084]; Children’s Hospital and Regional Medical Center and the University of Washington School of 189.e1

Carolina, Lynne Hartel, MA, Yaw Appiagyei-Dankah, MD, Lyndon Key, MD, for the Medical University of South Carolina, Sheree Mejia, RN, James Amrhein, MD, Kent Reifschneider, MD, for Greenville Hospital Systems, Pam Clark, MD, for McLeod Pediatric Subspecialists, Mark Parker, MD, for Pediatric Endocrinology & Diabetes Specialists, Charlotte, NC, Pediatric Endocrinology at the Medical College of Georgia, I. David Schwartz, MD Washington: Catherine Pihoker, MD, Lisa Gilliam, MD, PhD, Irl Hirsch, MD, Lenna L. Liu, MD, MPH, Carolyn Paris, MD, MPH, Dimitri Christakis, MD, MPH, for the University of Washington, Beth Loots, MPH, MSW, Joyce Yi, PhD, Stacey Bryant, RN, Michelle Sadler-Greever, RN, CDE, Rebecca O’Connor, RN, Ellen Braun-Kelly, BS, Amber Sexton, BS, and Corinne Shubin, BA, for the Seattle Children’s Hospital and Regional Medical Center, and Carla Greenbaum, MD, for Benaroya Research Institute Centers for Disease Control and Prevention: Giuseppina Imperatore, MD, PhD, Desmond E. Williams, MD, PhD, Michael M. Engelgau, MD, Henry S. Kahn, MD, K. M Venkat Narayan, MD, MPH, Bernice Moore, MBA National Institute of Diabetes and Digestive and Kidney Diseases, NIH: Barbara Linder, MD, PhD Central Laboratory (University of Washington): Santica M. Marcovina, PhD, ScD, Vinod P. Gaur, PhD, Kathy Gadbois Coordinating Center (Wake Forest University School of Medicine): Ronny Bell, PhD, MS, Ralph D’Agostino, Jr., PhD, Douglas Case, PhD, Timothy Morgan, PhD, Michelle J. Naughton, PhD, Susan Vestal, BS, Gena Hargis, MPH, Andrea Anderson, MS, Cralen Davis, MS, Jeanette Andrews, MS, Jennifer Beyer, MS Site Contract Numbers: Kaiser Permanente Southern California (U01 DP000246), University of Colorado Health Sciences Center (U01 DP000247), Pacific Health Research Institute (U01 DP000245), Children’s Hospital Medical Center (Cincinnati) (U01 DP000248), University of North Carolina at Chapel Hill (U01 DP000254), University of Washington School of Medicine (U01 DP000244), Wake Forest University School of Medicine (U01 DP000250)

Medicine [M01RR00037 and M01RR001271]; Colorado Pediatric General Clinical Research Center [M01 RR00069] and is supported by the National Institute of Diabetes and Digestive and Kidney Diseases. The authors declare no conflicts of interest. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention. Paris et al