Trends in Glycemic among Youth with Diabetes: The SEARCH for Diabetes in Youth Study

<b>OBJECTIVES: </b>To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. <p><b>RESEARCH DESIGN AND METHODS: </b>The study included 6,492 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods 2002-2007, 2008-2013 and 2014-2019, diabetes durations of 1-4, 5-9, and 10+ years, and age groups 1-9, 10-14, 15-19, 20-24, 25+ years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A_1c (HbA_1c) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen and diabetes duration, overall and stratified for each duration and age group.</p> <p><b>RESULTS: </b>Adjusted mean HbA_1c for the 2014-2019 cohort of YYA with type 1 diabetes was 8.8%±0.04%. YYA with type 1 diabetes in the 10-14, 15-19, and 20-24 age groups from the 2014-2019 cohort had worse glycemic control than the 2002-2007 cohort. Race/ethnicity, household income and treatment regimen predicted differences in glycemic control in 2014-2019 type 1 diabetes participants. Adjusted mean HbA1c was 8.6%±0.12% for 2014-2019 YYA with type 2 diabetes. Participants age 25+ with type 2 diabetes had worse glycemic control relative to the 2008-2013 cohort. Only treatment regimen was associated with differences in glycemic control in type 2 diabetes participants.</p> <p><b>CONCLUSIONS: </b>Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.</p> <br>

Trends in Glycemic among Youth with Diabetes: The SEARCH for Diabetes in Youth Study

<b>OBJECTIVES: </b>To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. <p><b>RESEARCH DESIGN AND METHODS: </b>The study included 6,492 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods 2002-2007, 2008-2013 and 2014-2019, diabetes durations of 1-4, 5-9, and 10+ years, and age groups 1-9, 10-14, 15-19, 20-24, 25+ years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A_1c (HbA_1c) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen and diabetes duration, overall and stratified for each duration and age group.</p> <p><b>RESULTS: </b>Adjusted mean HbA_1c for the 2014-2019 cohort of YYA with type 1 diabetes was 8.8%±0.04%. YYA with type 1 diabetes in the 10-14, 15-19, and 20-24 age groups from the 2014-2019 cohort had worse glycemic control than the 2002-2007 cohort. Race/ethnicity, household income and treatment regimen predicted differences in glycemic control in 2014-2019 type 1 diabetes participants. Adjusted mean HbA1c was 8.6%±0.12% for 2014-2019 YYA with type 2 diabetes. Participants age 25+ with type 2 diabetes had worse glycemic control relative to the 2008-2013 cohort. Only treatment regimen was associated with differences in glycemic control in type 2 diabetes participants.</p> <p><b>CONCLUSIONS: </b>Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.</p> <br>

Trends in Glycemic Control Among Youth and Young Adults With Diabetes: The SEARCH for Diabetes in Youth Study

OBJECTIVES To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. RESEARCH DESIGN AND METHODS The study included 6,369 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods of 2002–2007, 2008–2013, and 2014–2019, diabetes durations of 1–4, 5–9, and ≥10 years, and age groups of 1–9, 10–14, 15–19, 20–24, and ≥25 years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A1c (HbA1c) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen, and diabetes duration, overall and stratified for each diabetes duration and age group. RESULTS Adjusted mean HbA1c for the 2014–2019 cohort of YYA with type 1 diabetes was 8.8 ± 0.04%. YYA with type 1 diabetes in the 10–14-, 15–19-, and 20–24-year-old age groups from the 2014–2019 cohort had worse glycemic control than the 2002–2007 cohort. Race/ethnicity, household income, and treatment regimen predicted differences in glycemic control in participants with type 1 diabetes from the 2014–2019 cohort. Adjusted mean HbA1c was 8.6 ± 0.12% for 2014–2019 YYA with type 2 diabetes. Participants aged ≥25 years with type 2 diabetes had worse glycemic control relative to the 2008–2013 cohort. Only treatment regimen was associated with differences in glycemic control in participants with type 2 diabetes. CONCLUSIONS Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.

Prevalence and Predictors of Household Food Insecurity and Supplemental Nutrition Assistance Program Use in Youth and Young Adults With Diabetes: The SEARCH for Diabetes in Youth Study

<a>OBJECTIVES: To assess the prevalence of household food insecurity (HFI) and Supplemental Nutrition Assistance Program (SNAP) participation in youth and young adults (YYA) with diabetes overall, by type, and sociodemographic characteristics.</a> <p>RESEARCH DESIGN AND METHODS: The study included participants with youth-onset type 1 diabetes and type 2 diabetes from the SEARCH for Diabetes in Youth study. HFI was assessed using the 18-item U.S. Household Food Security Survey Module (HFSSM) administered from 2016-2019; ³3 affirmations on the HFSSM were considered indicative of HFI. Participants were asked about SNAP participation. Chi-square tests were used to assess whether the prevalence of HFI and SNAP participation differed by diabetes type. Multivariable logistic regression models were used to examine differences in HFI by participant characteristics. </p> <p>RESULTS: Of 2561 respondents (age range 10-35 years; 79.6% ≤ 25 years), 2177 had type 1 diabetes (mean age 21.0 years, 71.8% non-Hispanic white, 11.8% non-Hispanic black, 13.3% Hispanic, 3.1% other) and 384 had type 2 diabetes (mean age 24.7 years, 18.8% non-Hispanic white, 45.8% non-Hispanic black, 23.7% Hispanic, 18.7% other). The overall prevalence of HFI was 19.7% (95% CI 18.1, 21.2). HFI was more prevalent in type 2 diabetes than type 1 diabetes (30.7% vs. 17.7%, p< 0.01). In multivariable regression models, YYA on Medicaid/Medicare or without insurance, with lower parental education, and with lower household income had greater odds of experiencing HFI. SNAP participation was 14.1% (95% CI 12.7, 15.5) with higher participation among those with type 2 diabetes compared to type 1 diabetes (34.8% vs. 10.7%; p<0.001).</p> <p>CONCLUSIONS: Almost 1 in 3 YYA with type 2 diabetes and more than 1 in 6 with type 1 diabetes reported HFI in the past year, a significantly higher prevalence than the general U.S. population. </p>

Prevalence and Predictors of Household Food Insecurity and Supplemental Nutrition Assistance Program Use in Youth and Young Adults With Diabetes: The SEARCH for Diabetes in Youth Study

<a>OBJECTIVES: To assess the prevalence of household food insecurity (HFI) and Supplemental Nutrition Assistance Program (SNAP) participation in youth and young adults (YYA) with diabetes overall, by type, and sociodemographic characteristics.</a> <p>RESEARCH DESIGN AND METHODS: The study included participants with youth-onset type 1 diabetes and type 2 diabetes from the SEARCH for Diabetes in Youth study. HFI was assessed using the 18-item U.S. Household Food Security Survey Module (HFSSM) administered from 2016-2019; ³3 affirmations on the HFSSM were considered indicative of HFI. Participants were asked about SNAP participation. Chi-square tests were used to assess whether the prevalence of HFI and SNAP participation differed by diabetes type. Multivariable logistic regression models were used to examine differences in HFI by participant characteristics. </p> <p>RESULTS: Of 2561 respondents (age range 10-35 years; 79.6% ≤ 25 years), 2177 had type 1 diabetes (mean age 21.0 years, 71.8% non-Hispanic white, 11.8% non-Hispanic black, 13.3% Hispanic, 3.1% other) and 384 had type 2 diabetes (mean age 24.7 years, 18.8% non-Hispanic white, 45.8% non-Hispanic black, 23.7% Hispanic, 18.7% other). The overall prevalence of HFI was 19.7% (95% CI 18.1, 21.2). HFI was more prevalent in type 2 diabetes than type 1 diabetes (30.7% vs. 17.7%, p< 0.01). In multivariable regression models, YYA on Medicaid/Medicare or without insurance, with lower parental education, and with lower household income had greater odds of experiencing HFI. SNAP participation was 14.1% (95% CI 12.7, 15.5) with higher participation among those with type 2 diabetes compared to type 1 diabetes (34.8% vs. 10.7%; p<0.001).</p> <p>CONCLUSIONS: Almost 1 in 3 YYA with type 2 diabetes and more than 1 in 6 with type 1 diabetes reported HFI in the past year, a significantly higher prevalence than the general U.S. population. </p>

The Impact of Racial and Ethnic Health Disparities in Diabetes Management on Clinical Outcomes: A Reinforcement Learning Analysis of Health Inequity Among Youth and Young Adults in the SEARCH for Diabetes in Youth Study

<a><b>Objective: </b></a>To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario whereby non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. <p><b>Research Design and Methods:</b> Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). <a>In the White versus non-White subgroups, propensity scores model estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use.</a> An analysis based on policy evaluation technique in reinforcement learning estimated the effect of each treatment regimen on hemoglobin A1c (HbA1c) and diabetes complications for non-White YYA. </p> <p><b>Results: </b>The study included n=978 YYA. The sample was 47.5% female and77.5% non-Hispanic White, with mean age 12.8±2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference=0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95%CI: -0.45%, -0.21%) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining approximately 35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (p<0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations.</p> <p><b>Conclusions: </b>Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race/ethnicity-based health inequity.</p>

The Impact of Racial and Ethnic Health Disparities in Diabetes Management on Clinical Outcomes: A Reinforcement Learning Analysis of Health Inequity Among Youth and Young Adults in the SEARCH for Diabetes in Youth Study

<a><b>Objective: </b></a>To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario whereby non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. <p><b>Research Design and Methods:</b> Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). <a>In the White versus non-White subgroups, propensity scores model estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use.</a> An analysis based on policy evaluation technique in reinforcement learning estimated the effect of each treatment regimen on hemoglobin A1c (HbA1c) and diabetes complications for non-White YYA. </p> <p><b>Results: </b>The study included n=978 YYA. The sample was 47.5% female and77.5% non-Hispanic White, with mean age 12.8±2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference=0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95%CI: -0.45%, -0.21%) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining approximately 35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (p<0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations.</p> <p><b>Conclusions: </b>Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race/ethnicity-based health inequity.</p>

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study

Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospital, Seattle, WA, and Children’s Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. Results Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. Conclusions Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.