Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study

Receipt of Diabetes Specialty Care and Management Services by Older Adults With Diabetes in the U.S., 2015-2019: An Analysis of Medicare Fee-for-Service Claims

OBJECTIVE

We characterized the receipt of diabetes specialty care and management services among older adults with diabetes.

RESEARCH DESIGN AND METHODS

Using a 20% random sample of fee-for-service Medicare beneficiaries aged ≥65 years, we analyzed cohorts of type 1 diabetes (T1D) or type 2 diabetes (T2D) with history of severe hypoglycemia (HoH), and all other T2D annually from 2015 to 2019. Outcomes were receipt of office-based endocrinology care, diabetes education, outpatient diabetes health services, excluding those provided in primary care, and any of the aforementioned services.

RESULTS

In the T1D cohort, receipt of endocrinology care and any service increased from 25.9% and 29.2% in 2015 to 32.7% and 37.4% in 2019, respectively. In the T2D with HoH cohort, receipt of endocrinology care and any service was 13.9% and 16.4% in 2015, with minimal increases. Age, race/ethnicity, residential setting, and income were associated with receiving care.

CONCLUSIONS

These findings suggest that many older adults may not receive specialty diabetes care and underscore health disparities.

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records

OBJECTIVE

Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence.

MATERIALS AND METHODS

We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020). T1D cases were identified using a previously validated computable phenotyping algorithm. The T1D prevalence for each ZIP Code Tabulation Area (ZCTA, 5 digits), defined as the number of T1D cases divided by the total number of residents in the corresponding ZCTA, was calculated. Population coverage for each ZCTA was measured using observed health system penetration rates (HSPR), which was calculated as the ratio of residents in the corresponding ZTCA and captured by OneFlorida+ to the overall population in the same ZCTA reported by the Census. We used a recursive partitioning algorithm to identify the minimum required observed HSPR to estimate T1D prevalence and compare our estimate with the reported T1D prevalence from the SEARCH study.

RESULTS

Observed HSPRs of 55%, 55%, and 60% were identified as the minimum thresholds for the non-Hispanic White, non-Hispanic Black, and Hispanic populations. The estimated T1D prevalence for non-Hispanic White and non-Hispanic Black were 2.87 and 2.29 per 1000 youth, which are comparable to the reference study's estimation. The estimated prevalence of T1D for Hispanics (2.76 per 1000 youth) was higher than the reference study's estimation (1.48-1.64 per 1000 youth). The standardized T1D prevalence in the overall Florida population was 2.81 per 1000 youth in 2019.

CONCLUSION

Our study provides a method to estimate T1D prevalence in children and adolescents using EHRs and reports the estimated HSPRs and prevalence of T1D for different race and ethnicity groups to facilitate EHR-based diabetes surveillance.

Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence

AIMS

To find the best-performing algorithms to distinguish type 1 and type 2 diabetes in administrative data.

METHODS

Embase and MEDLINE databases were searched from January 2000 until January 2023. Papers evaluating the performance of algorithms to define type 1 and type 2 diabetes by reporting diagnostic metrics against a range of reference standards were selected. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies.

RESULTS

Of the 24 studies meeting the eligibility criteria, 19 demonstrated a low risk of bias and low concerns about the applicability of the study population across all domains. Algorithms considering multiple diabetes diagnostic codes alone were sensitive and specific approaches to classify diabetes type (both metrics >92.1% for type 1 diabetes; >86.9% for type 2 diabetes). Among the top 10-performing algorithms to detect type 1 and type 2 diabetes, 70% and 100% featured multiple criteria, respectively. Information on insulin use was more sensitive and specific for detecting diabetes type than were criteria based on use of oral hypoglycaemic agents.

CONCLUSIONS

Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type.

Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches

OBJECTIVES

We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.

STUDY DESIGN AND SETTING

We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).

RESULTS

Approaches' accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.

CONCLUSION

Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.

Medical Costs Associated With Diabetes Complications in Medicare Beneficiaries Aged 65 Years or Older With Type 1 Diabetes

OBJECTIVE

To estimate medical costs associated with 17 diabetes complications and treatment procedures among Medicare beneficiaries aged ≥65 years with type 1 diabetes.

RESEARCH DESIGN AND METHODS

With use of the 2006-2017 100% Medicare claims database for beneficiaries enrolled in fee-for-service plans and Part D, we estimated the annual cost of 17 diabetes complications and treatment procedures. Type 1 diabetes and its complications and procedures were identified using ICD-9/ICD-10, procedure, and diagnosis-related group codes. Individuals with type 1 diabetes were followed from the year when their diabetes was initially identified in Medicare (2006-2015) until death, discontinuing plan coverage, or 31 December 2017. Fixed-effects regression was used to estimate costs in the complication occurrence year and subsequent years. The cost proportion of a complication was equal to the total cost of the complication, calculated by multiplying prevalence by the per-person cost divided by the total cost for all complications. All costs were standardized to 2017 U.S. dollars.

RESULTS

Our study included 114,879 people with type 1 diabetes with lengths of follow-up from 3 to 10 years. The costliest complications per person were kidney failure treated by transplant ($77,809 in the occurrence year and $13,556 in subsequent years), kidney failure treated by dialysis ($56,469 and $41,429), and neuropathy treated by lower-extremity amputation ($40,698 and $7,380). Sixteen percent of the total medical cost for diabetes complications was for treating congestive heart failure.

CONCLUSIONS

Costs of diabetes complications were large and varied by complications. Our results can assist in cost-effectiveness analysis of treatments and interventions for preventing or delaying diabetes complications in Medicare beneficiaries aged ≥65 years with type 1 diabetes.

Incidence Trends of New-Onset Diabetes in Children and Adolescents Before and During the COVID-19 Pandemic: Findings From Florida

This study examined the incidence trends of new-onset type 1 and type 2 diabetes in children and adolescents in Florida before and during the coronavirus disease 2019 (COVID-19) pandemic. In this observational descriptive cohort study, we used a validated computable phenotype to identify incident diabetes cases among individuals <18 years of age in the OneFlorida+ network of the national Patient-Centered Clinical Research Network between January 2017 and June 2021. We conducted an interrupted time series analysis based on the autoregressive integrated moving average model to compare changes in age-adjusted incidence rates of type 1 and type 2 diabetes before and after March 2020, when COVID-19 was declared a national health emergency in the U.S. The age-adjusted incidence rates of both type 1 and type 2 diabetes increased post-COVID-19 for children and adolescents. These results highlight the need for longitudinal cohort studies to examine how the pandemic might influence subsequent diabetes onset in young individuals.

Medical Costs Associated With Diabetes Complications in Medicare Beneficiaries Aged 65 Years or Older With Type 2 Diabetes

OBJECTIVE

To estimate medical costs associated with 17 major diabetes-related complications and treatment procedures among Medicare beneficiaries aged ≥65 years with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Claims data from 100% of Medicare beneficiaries enrolled in fee-for-service plans from 2006 to 2017 were analyzed. Records with type 2 diabetes and complications were identified using ICD-9, ICD-10, and diagnosis-related group codes. The index year was the year when a person was first identified as having diabetes with an inpatient claim or an outpatient claim plus another inpatient/outpatient claim in the 2 years following the first claim in Medicare. Included individuals were followed from index years until death, discontinuation of plan coverage, or 31 December 2017. Fixed-effects regression was used to estimate the cost in years when the complication event occurred and in subsequent years. The total cost for each complication was calculated for 2017 by multiplying the complication prevalence by the cost estimate. All costs were standardized to 2017 U.S. dollars.

RESULTS

Our study included 10,982,900 beneficiaries with type 2 diabetes. Follow-up ranged from 3 to 10 years. The three costliest complications were kidney failure treated by transplant (occurring year $79,045, subsequent years $17,303), kidney failure treated by dialysis ($54,394, $38,670), and lower-extremity amputation ($38,982, $8,084). Congestive heart failure accounted for the largest share (18%) of total complication costs.

CONCLUSIONS

Costs associated with diabetes complications were substantial. Our cost estimates provide essential information needed for conducting economic evaluation of treatment and programs to prevent and delay diabetes complications in Medicare beneficiaries.

The effects of patient out-of-pocket costs for insulin on medication adherence and health care utilization in patients with commercial insurance; 2007-2018

BACKGROUND: High out-of-pocket costs (OOPCs) for insulin can lead to cost-related nonadherence and poor outcomes, prompting payers to limit insulin OOPCs. However, data are scarce on whether insulin OOPCs at policy-relevant levels is associated with improved adherence and outcomes. OBJECTIVE: To identify associations between insulin OOPCs and insulin adherence, noninsulin antihyperglycemic (AHG) medication adherence, and diabetes-related emergency department (ED) visits and hospitalizations. METHODS: This retrospective cohort study was conducted using OptumLabs Data Warehouse, a longitudinal, real-world data asset with deidentified administrative claims and electronic health record data. Individuals with type 1 diabetes (T1D) or type 2 diabetes (T2D), insulin use on January 1 of a study year (index date: 2007-2018), continuous commercial health plan eligibility 12 months pre-index and post-index date, and at least 1 insulin claim during the 12-month follow-up period were included. Average insulin OOPCs per 30-day supply in the follow-up period was identified and categorized ($0, > $0-$20 [referent group], > $20-$35, > $35-$50, and > $50). The proportion of patients with a gap in insulin supply of 60 or more continuous days, AHG nonadherence per modified proportion of days covered less than 0.80, and a diabetes-related ED visit or hospitalization were identified and compared by insulin OOPC category vs more than $0 to $20 using pairwise chi-square tests and multivariable logistic regression. RESULTS: The study included 21,085 individuals with T1D and 72,512 with T2D. Patients with average OOPCs more than $50 were more likely to have a gap in insulin supply vs those with OOPCs more than $0 to $20, with an odds ratio (OR) of 1.14 (95% CI =1.05-1.24) and 1.38 (95% CI = 1.32-1.45) for T1D and T2D, respectively. Those with T2D and OOPCs more than $35 were also more likely to have a 60-day gap in insulin supply (OR 1.17; 95% CI = 1.11-1.23). Odds of having a diabetes-related hospitalization or ED visit did not increase with higher OOPCs; rather, associations tended to be inverse. Nonadherence to AHG medications in the T2D cohort was higher with insulin OOPCs more than $20 vs those more than $0-$20 (P < 0.05 for all). CONCLUSIONS: Individuals with T2D were more likely to have a 60-day gap in insulin supply when the OOPC was more than $35 per 30-day supply and with the OOPC more than $50 in those with T1D. These findings suggest that health plans can facilitate adherence to insulin therapy and possibly to noninsulin AHG medications by protecting patients with diabetes from experiencing high insulin OOPC. A study with a longer follow-up period is warranted to fully assess ED and hospitalization outcomes. DISCLOSURES: This study was funded by the Robert Wood Johnson Foundation, Health Data for Action Research Program. The study sponsor played no role in the design or conduct of this study. The views expressed here do not necessarily reflect the views of the Foundation.

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.

Real-world treatment escalation from metformin monotherapy in youth-onset Type 2 diabetes mellitus: A retrospective cohort study

BACKGROUND

Due to high rates of comorbidities and rapid progression, youth with Type 2 diabetes may benefit from early and aggressive treatment. However, until 2019, the only approved medications for this population were metformin and insulin.

OBJECTIVE

To investigate patterns and predictors of treatment escalation within 5 years of metformin monotherapy initiation for youth with Type 2 diabetes in clinical practice.

SUBJECTS

Commercially-insured patients with incident youth-onset (10-18 years) Type 2 diabetes initially treated with metformin only.

METHODS

Retrospective cohort study using a patient-level medical claims database with data from 2000 to 2020. Frequency and order of treatment escalation to insulin and non-insulin antihyperglycemics were determined and categorized by age at diagnosis. Cox proportional hazards regression was used to evaluate potential predictors of treatment escalation, including age, sex, race/ethnicity, comorbidities, complications, and metformin adherence (medication possession ratio ≥ 0.8).

RESULTS

The cohort included 829 (66% female; median age at diagnosis 15 years; 19% Hispanic, 17% Black) patients, with median 2.9 year follow-up after metformin initiation. One-quarter underwent treatment escalation (n = 207; 88 to insulin, 164 to non-insulin antihyperglycemic). Younger patients were more likely to have insulin prescribed prior to other antihyperglycemics. Age at diagnosis (HR 1.14, 95% CI 1.07-1.21), medication adherence (HR 4.10, 95% CI 2.96-5.67), Hispanic ethnicity (HR 1.83, 95% CI 1.28-2.61), and diabetes-related complications (HR 1.78, 95% CI 1.15-2.74) were positively associated with treatment escalation.

CONCLUSIONS

In clinical practice, treatment escalation for pediatric Type 2 diabetes differs with age. Off-label use of non-insulin antihyperglycemics occurs, most commonly among older adolescents.

Intensive Care Unit Admission, Mechanical Ventilation, and Mortality Among Patients With Type 1 Diabetes Hospitalized for COVID-19 in the U.S

OBJECTIVE

To assess whether risk of severe outcomes among patients with type 1 diabetes mellitus (T1DM) hospitalized for coronavirus disease 2019 (COVID-19) differs from that of patients without diabetes or with type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

Using the Premier Healthcare Database Special COVID-19 Release records of patients discharged after COVID-19 hospitalization from U.S. hospitals from March to November 2020 (N = 269,674 after exclusion), we estimated risk differences (RD) and risk ratios (RR) of intensive care unit admission or invasive mechanical ventilation (ICU/MV) and of death among patients with T1DM compared with patients without diabetes or with T2DM. Logistic models were adjusted for age, sex, and race or ethnicity. Models adjusted for additional demographic and clinical characteristics were used to examine whether other factors account for the associations between T1DM and severe COVID-19 outcomes.

RESULTS

Compared with patients without diabetes, T1DM was associated with a 21% higher absolute risk of ICU/MV (RD 0.21, 95% CI 0.19-0.24; RR 1.49, 95% CI 1.43-1.56) and a 5% higher absolute risk of mortality (RD 0.05, 95% CI 0.03-0.07; RR 1.40, 95% CI 1.24-1.57), with adjustment for age, sex, and race or ethnicity. Compared with T2DM, T1DM was associated with a 9% higher absolute risk of ICU/MV (RD 0.09, 95% CI 0.07-0.12; RR 1.17, 95% CI 1.12-1.22), but no difference in mortality (RD 0.00, 95% CI -0.02 to 0.02; RR 1.00, 95% CI 0.89-1.13). After adjustment for diabetic ketoacidosis (DKA) occurring before or at COVID-19 diagnosis, patients with T1DM no longer had increased risk of ICU/MV (RD 0.01, 95% CI -0.01 to 0.03) and had lower mortality (RD -0.03, 95% CI -0.05 to -0.01) in comparisons with patients with T2DM.

CONCLUSIONS

Patients with T1DM hospitalized for COVID-19 are at higher risk for severe outcomes than those without diabetes. Higher risk of ICU/MV in patients with T1DM than in patients with T2DM was largely accounted for by the presence of DKA. These findings might further guide recommendations related to diabetes management and the prevention of COVID-19.

Twenty years of pediatric diabetes surveillance: what do we know and why it matters

SEARCH for Diabetes in Youth (SEARCH) was initiated in 2000 as a multicenter study to address major gaps in the understanding of childhood diabetes in the United States. An active registry of youth diagnosed with diabetes at age <20 years since 2002 assessed prevalence, annual incidence, and trends by age, race/ethnicity, sex, and diabetes type. An observational cohort nested within the population-based registry was established to assess the natural history and risk factors for acute and chronic diabetes-related complications, as well as the quality of care and quality of life of children and adolescents with diabetes from diagnosis into young adulthood. SEARCH findings have contributed to a better understanding of the complex and heterogeneous nature of youth-onset diabetes. Continued surveillance of the burden and risk of type 1 and type 2 diabetes is important to track and monitor incidence and prevalence within the population. SEARCH reported evidence of early diabetes complications highlighting that continuing the long-term follow-up of youth with diabetes is necessary to further our understanding of its natural history and to develop the most appropriate approaches to primary, secondary, and tertiary prevention of diabetes and its complications. This review summarizes two decades of research and suggests avenues for further work.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Association of community types and features in a case-control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania

OBJECTIVES

To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions.

DESIGN

Nested case-control study within the open dynamic cohort of health system patients.

SETTING

Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA.

PARTICIPANTS AND ANALYSIS

We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types.

RESULTS

Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds.

CONCLUSIONS

Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.

Adult-onset type 1 diabetic patients with less severe clinical manifestation have less risk DR-DQ genotypes than childhood-onset patients

BACKGROUND

The aim of this study was to investigate differences in clinical features and HLA genotypes between adult-onset and childhood-onset patients with type 1 diabetes in a Chinese population.

MATERIALS AND METHODS

This study enrolled 716 Han Chinese patients with type 1 diabetes from Guangdong (258 childhood-onset and 458 adult-onset) to compare their clinical features. Of them 214 patients with classical type 1 diabetes (100 childhood-onset and 114 adult-onset) were selected for HLA DR and DQ genotyping by next-generation sequencing.

RESULTS

Adult-onset patients were characterized by longer duration of symptoms before diagnosis, lower frequency of DKA at disease onset, less frequent autoantibody positivity, higher serum C-peptide concentrations, and better glycemic control. These findings were replicated in the restricted cohort of 214 patients with classical type 1 diabetes. Compared with childhood-onset patients, adult-onset patients had a lower frequency of the DR9 haplotype, as well as lower frequency of high-risk DR3/DR4 and DR3/DR9 genotypes, but higher frequency of DR3/DR3 genotype and DR3/X, DR4/X or DR9/X (X, non-risk) genotypes.

CONCLUSIONS

Adult-onset type 1 diabetic patients with susceptible haplotypes (DR3, DR4 or DR9) were more likely to carry protective DR-DQ haplotypes than childhood-onset patients, which suggested the association between less risk DR-DQ genotypes and the less severe clinical manifestation in adult-onset patients.

The accuracy of provider diagnosed diabetes type in youth compared to an etiologic criteria in the SEARCH for Diabetes in Youth Study

BACKGROUND

Although surveillance for diabetes in youth relies on provider-assigned diabetes type from medical records, its accuracy compared to an etiologic definition is unknown.

METHODS

Using the SEARCH for Diabetes in Youth Registry, we evaluated the validity and accuracy of provider-assigned diabetes type abstracted from medical records against etiologic criteria that included the presence of diabetes autoantibodies (DAA) and insulin sensitivity. Youth who were incident for diabetes in 2002-2006, 2008, or 2012 and had complete data on key analysis variables were included (n = 4001, 85% provider diagnosed type 1). The etiologic definition for type 1 diabetes was ≥1 positive DAA titer(s) or negative DAA titers in the presence of insulin sensitivity and for type 2 diabetes was negative DAA titers in the presence of insulin resistance.

RESULTS

Provider diagnosed diabetes type correctly agreed with the etiologic definition of type for 89.9% of cases. Provider diagnosed type 1 diabetes was 96.9% sensitive, 82.8% specific, had a positive predictive value (PPV) of 97.0% and a negative predictive value (NPV) of 82.7%. Provider diagnosed type 2 diabetes was 82.8% sensitive, 96.9% specific, had a PPV and NPV of 82.7% and 97.0%, respectively.

CONCLUSION

Provider diagnosis of diabetes type agreed with etiologic criteria for 90% of the cases. While the sensitivity and PPV were high for youth with type 1 diabetes, the lower sensitivity and PPV for type 2 diabetes highlights the value of DAA testing and assessment of insulin sensitivity status to ensure estimates are not biased by misclassification.

Ondansetron use in early pregnancy and the risk of miscarriage

BACKGROUND

Ondansetron is commonly used to treat nausea and vomiting in pregnancy despite inconclusive evidence of its safety. Previous studies have reported no increase in risk of miscarriage but relied on methods that failed to account for gestational weeks at risk and non-user comparators, which may increase the potential for unmeasured confounding. Our objective was to estimate the risk of miscarriage among women prescribed ondansetron vs alternative antiemetics during the first 20 weeks of pregnancy.

METHODS

A pregnancy cohort was created using electronic health record data from a health care system in North Carolina. Women were classified as exposed to either ondansetron or comparator antiemetics (metoclopramide or promethazine) based on the first antiemetic prescription received in the first 20 weeks of gestation. Cumulative incidence of miscarriage at 20 weeks was estimated in each antiemetic group. Hazard ratios (HR) were estimated with 95% confidence intervals and measured confounding was controlled using inverse probability of treatment weights. Sensitivity analyses assessed the potential impact of exposure misclassification, latency period, and selection bias.

RESULTS

We identified 2620 eligible pregnancies with antiemetic orders; 65% had a first ondansetron order and 35% had a first comparator antiemetic order. In total, 95 women had a miscarriage. After adjustment, there was no difference in risk of miscarriage (HR 1.21, 95% CI 0.77, 1.90). Results from the per-protocol and other sensitivity analyses were similar to the main analysis.

CONCLUSIONS

We did not observe an increase in the risk of miscarriage for pregnancies exposed to ondansetron vs comparator antiemetics.

Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study

OBJECTIVE

Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification.

RESEARCH DESIGN AND METHODS

Youth (<20 years old) with potential evidence of diabetes (N = 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly.

RESULTS

The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (n = 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The Se, Sp, and PPV for type 2 diabetes using the combined method were ≥0.91.

CONCLUSIONS

An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

Sex Differences in Autoimmune Multimorbidity in Type 1 Diabetes Mellitus and the Risk of Cardiovascular and Renal Disease: A Longitudinal Study in the United States, 2001-2017

Background: Autoimmune diseases are usually more prevalent in women. The risks of cardiovascular and renal disease in those with multiple autoimmune diseases have not been fully described. Materials and Methods: Using a national database from a large health insurer in the United States (years 2001-2017) containing ∼75 million members, we calculated age- and sex-specific co-prevalence of 12 autoimmune disorders for individuals with type 1 diabetes. We then evaluated whether concomitant autoimmune diseases were associated with renal failure, ischemic stroke, and myocardial infarction. Results: Of the 179,248 people diagnosed with type 1 diabetes, 1 in 4 had a concomitant autoimmune disease (27.03%; 95% confidence interval [CI] = 26.83%-27.24%), with hypothyroidism, rheumatoid arthritis, and celiac disease being the most common. The prevalence of autoimmune disease was 1.9 times greater in female than male patients (p < 0.001). In female patients with type 1 diabetes, one in three had another autoimmune disease (35.62%; 95% CI = 35.30%-35.94%) compared with one in five male patients (19.17%; 95% CI = 18.92%-19.42%). The risk of renal failure, ischemic stroke, and myocardial infarction increased with a greater number of concomitant autoimmune diseases (p < 0.001, test for trend for both female and male patients). Patients with type 1 diabetes who had multiple sclerosis or myasthenia gravis experienced an approximate threefold increase in risk of ischemic stroke (odds ratio [OR] = 3.57, OR = 3.22, respectively). Patients with type 1 diabetes and Addison's disease had a threefold increased risk of renal failure. Conclusions: Patients with type 1 diabetes, particularly women, frequently have coexisting autoimmune diseases that are associated with higher rates of renal failure, ischemic stroke, and myocardial infarction. Additional study is warranted, as are preventive efforts in this high-risk population.

Concordance and Discordance in the Geographic Distribution of Childhood Obesity and Pediatric Type 2 Diabetes in New York City

OBJECTIVE

As rates of childhood obesity and pediatric type 2 diabetes (T2D) increase, a better understanding is needed of how these 2 conditions relate and which subgroups of children are more likely to develop diabetes with and without obesity.

METHODS

To compare hotspots of childhood obesity and pediatric T2D in New York City, we performed geospatial clustering analyses on obesity estimates obtained from surveys of school-aged children and diabetes estimates obtained from health care claims data, from 2009 to 2013. Analyses were performed at the Census tract level. We then used multivariable regression analysis to identify sociodemographic and environmental factors associated with these hotspots.

RESULTS

We identified obesity hotspots in Census tracts with a higher proportion of Black or Hispanic residents, with low median household income, or located in a food swamp. Total 51.1% of pediatric T2D hotspots overlapped with obesity hotspots. For pediatric T2D, hotspots were identified in Census tracts with a higher proportion of Black residents and a lower proportion of Hispanic residents.

CONCLUSIONS

Non-Hispanic Black neighborhoods had a higher probability of being hotspots of both childhood obesity and pediatric T2D. However, we identified a discordance between hotspots of childhood obesity and pediatric diabetes in Hispanic neighborhoods, suggesting either under-detection or under-diagnosis of diabetes, or that obesity may influence diabetes risk differently in these 2 populations. These findings warrant further investigation of the relationship between childhood obesity and pediatric diabetes among different racial and ethnic groups, and may help guide pediatric public health interventions to specific neighborhoods.

Validation of an Optimized Algorithm for Identifying Persons Living With Diagnosed HIV From New York State Medicaid Data, 2006-2014

Algorithms are regularly used to identify persons living with diagnosed human immunodeficiency virus (HIV) (PLWDH) in Medicaid data. To our knowledge, there are no published reports of an HIV algorithm from Medicaid claims codes that have been compared with an HIV surveillance system to assess its sensitivity, specificity, positive predictive value, and negative predictive value in identifying PLWDH. Therefore, our aims in this study were to 1) develop an algorithm that could identify PLWDH in New York State Medicaid data from 2006-2014 and 2) validate this algorithm using the New York State HIV surveillance system. Classification and regression tree analysis identified 16 nodes that we combined to create a case-finding algorithm with 5 criteria. This algorithm identified 86,930 presumed PLWDH, 88.0% of which were verified by matching to the surveillance system. The algorithm yielded a sensitivity of 94.5%, a specificity of 94.4%, a positive predictive value of 88.0%, and a negative predictive value of 97.6%. This validated algorithm has the potential to improve the utility of Medicaid data for assessing health outcomes and programmatic interventions.

Development and validation of algorithms to classify type 1 and 2 diabetes according to age at diagnosis using electronic health records

BACKGROUND

Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis.

METHODS

We included all people with diabetes (age at diagnosis 1.5-100 years during 2002-15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria ("combination algorithms"). We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort.

RESULTS

There were 10,196 (T1D n = 60, T2D n = 10,136) and 5101 (T1D n = 43, T2D n = 5058) people in the derivation and validation cohorts (mean age at diagnosis 22.7, 55.9 years; 53.3, 43.9% female; for T1D and T2D respectively). Algorithms using codes or prescriptions classified T1D well for age at diagnosis < 20 years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The "high sensitivity for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, or at least 1 insulin prescription within 90 days) had a sensitivity of 95.3% (95% confidence interval 84.2-99.4%; PPV 12.8%, 9.3-16.9%), while the "high PPV for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, and multiple daily injections with no other glucose-lowering medication prescription) had a PPV of 100.0% (79.4-100.0%; sensitivity 37.2%, 23.0-53.3%), and the "optimized" algorithm (ratio of type 1 to type 2 codes ≥ 4, and at least 1 insulin prescription within 90 days) had a sensitivity of 65.1% (49.1-79.0%) and PPV of 75.7% (58.8-88.2%) across all ages. Accuracy of T2D classification was high for all algorithms.

CONCLUSIONS

Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question.

Temporal trends in incidence of pediatric type 1 diabetes in Alabama: 2000-2017

OBJECTIVE

The incidence of type 1 diabetes has increased in the United States and worldwide. We hypothesized that trends in the annual incidence rates of childhood-onset type 1 diabetes in the state of Alabama would be different by race and sex.

METHODS

We performed a retrospective observational cohort study, analyzing children with type 1 diabetes (n = 3770) managed at the Children's Hospital of Alabama between 2000 and 2017. We compared crude incidence rates using negative binomial regression models and analyzed differences in annual trends of age-adjusted incidence by race and sex using joinpoint regression.

RESULTS

The crude type 1 diabetes incidence rate was estimated at 16.7 per 100 000 children <19 years of age in Alabama. Between 2000 and 2007, there was an increase in age-adjusted incidence of type 1 diabetes with an annual percent change (APC) of 10% from 2000 to 2007 and a 1.7% APC decrease from 2007 to 2017. The age-adjusted incidence for Whites and Blacks increased with an average annual percentage change (AAPC) of 4.4% and 2.8%, respectively. A nearly 11% increasing trend in age-adjusted incidence was observed for both races, though the increase plateaued in 2006 for Whites and 2010 for Blacks.

CONCLUSIONS

Following significantly increasing annual trends for both races, the age-adjusted rate remained statistically stable for Whites and decreased significantly for Blacks. Longer-sustained trend increases for Blacks resulted in type 1 diabetes incidence tripling compared to the doubling of the rate for Whites.

Outcomes, care utilization, and expenditures in adolescent pregnancy complicated by diabetes

BACKGROUND AND OBJECTIVE

Adolescence and pregestational diabetes separately increase risks of adverse pregnancy outcomes, but little is known about their combined effect. To analyze pregnancy outcomes, healthcare utilization, and expenditures in adolescent pregnancies with and without pregestational diabetes using a national claims database.

METHODS

Retrospective study using Truven Health MarketScan Commercial Claims and Encounters Database, 2011 to 2015. Females 12 to 19 years old, continuously enrolled for at least 12 months before a livebirth until 2 months after, were included. Pregestational diabetes, diabetes complications (ketoacidosis, retinopathy, neuropathy, nephropathy), comorbidities, and pregnancy outcomes (preeclampsia, preterm delivery, high birthweight, cesarean delivery) were identified using claims data algorithms. Healthcare utilization and payer expenditure were tabulated per enrollee. Multivariate logistic regressions assessed pregnancy outcomes; multivariate OLS regression assessed payer expenditures.

RESULTS

About 33 502 adolescents were included. Adolescents without diabetes had pregnancy outcomes consistent with national estimates. Adolescents with uncomplicated diabetes had increased odds of preeclampsia adjusted odds ratios 2.41 (95% confidence interval 1.93-3.02), preterm delivery 1.50 (1.21-1.87), high birthweight 1.84 (1.50-2.27), and cesarean delivery 1.81 (1.52-2.15). Diabetes with ketoacidosis and/or end-organ damage had higher odds of preeclampsia 5.62 (2.77-11.41), preterm delivery 5.81 (3.00-11.25), high birthweight 2.38 (1.08-5.24), and cesarean delivery 3.43 (1.78-6.64). Adolescents with diabetes utilized significantly more outpatient and inpatient care during pregnancy. Payer expenditures increased by 45.3% (34.8-55.9%) among adolescents with diabetes and by 82.6% (49.1-116.0%) among adolescents with diabetes complicated by ketoacidosis and/or end-organ damage.

CONCLUSION

Compared with normal adolescent pregnancies, pregestational diabetes significantly increases risks of adverse pregnancy outcomes and significantly escalates healthcare utilization and cost.

Prevalence of type 1 and type 2 diabetes among US pediatric population in the MarketScan Multi-State Database, 2002 to 2016

OBJECTIVES

To estimate the prevalence of type 1 (T1DM) and type 2 diabetes mellitus (T2DM) among U.S. Medicaid pediatric population aged <18 years 2002 to 2016 by age, sex, and race/ethnicity.

METHODS

Participants aged <18 years old from 2002 to 2016 were identified from the MarketScan Multi-State Medicaid Database. Diabetes was defined as having (a) ≥1 claims for an outpatient or inpatient diabetes diagnosis and ≥2 prescriptions for any anti-diabetes medications or (b) records of ≥2 claims for an outpatient or inpatient diabetes diagnosis that were at least 30 days apart. Annual prevalence of diabetes and 95% confidence intervals (CIs) were calculated. Age-, sex-, and race-stratified prevalence were also assessed.

RESULTS

The annual prevalence of T1DM increased from 1.29 to 2.34/1000 pediatric persons from 2002 to 2016. The prevalence of T2DM rose from 0.70 in 2002 to 2.76/1000 in 2011, but then dropped to 2.12/1000 pediatric persons in 2016 in the Medicaid population. Prevalence of both T1DM and T2DM increased with age. While the prevalence of T1DM was similar in both sexes, and was most prevalent in Whites, prevalence of T2DM was higher in girls and was most prevalent in Blacks.

CONCLUSIONS

While the annual prevalence of T1DM in pediatric persons enrolled in Medicaid increased continuously from 2002 to 2016, the annual prevalence of T2DM increased from 2002 to 2011, with a subsequent decrease in 2016, possibly because of the increase of relatively healthier participants with the expanded eligibility through the ACA between 2011 and 2016.

Interruptions In Private Health Insurance And Outcomes In Adults With Type 1 Diabetes: A Longitudinal Study

Type 1 diabetes mellitus, which often originates during childhood, is a lifelong disease that requires intensive daily medical management. Because health care services are critical to patients with this disease, we investigated the frequency of interruptions in private health insurance, and the outcomes associated with them, for working-age adults with type 1 diabetes in the United States in the period 2001-15. We designed a longitudinal study with a nested self-controlled case series, using the Clinformatics Data Mart Database. The study sample consisted of 168,612 adults ages 19-64 with type 1 diabetes who had 2.6 mean years of insurance coverage overall. Of these adults, 24.3 percent experienced an interruption in coverage. For each interruption, there was a 3.6 percent relative increase in glycated hemoglobin. The use of acute care services was fivefold greater after an interruption in health insurance compared to before the interruption and remained elevated when stratified by age, sex, or diabetic complications. An interruption was associated with lower perceived health status and lower satisfaction with life. We conclude that interruptions in private health insurance are common among adults with type 1 diabetes and have serious consequences for their well-being.

Private Insurance Coverage for Diabetes Before and After Enactment of the Preexisting Condition Mandate of the Affordable Care Act, 2005-2016

OBJECTIVES

To examine private insurance coverage for persons with diabetes before and after enactment of the preexisting condition mandate of the Affordable Care Act (ACA) in the United States.

METHODS

We conducted a nationwide study in adults aged 20 to 59 years with private health insurance with the Clinformatics Data Mart Database (2005-2016). We used fixed-effects negative binomial regression to evaluate differences in pre-post mandate trends.

RESULTS

There was a 4% decline in prevalence rates of type 1 diabetes in adults with private health insurance before the mandate and an 11% increase afterward (P < .001). Coverage increased to the greatest extent (-6% before, +20% after) in those aged 50 to 59 years (P < .001). For type 2 diabetes, there was a significant decline in prevalence before the mandate, which increased afterward in those aged 40 to 49 years (-4% before, 3% after; P = .031) and 50 to 59 years (-6% before, 15% after; P < .001).

CONCLUSIONS

Adults with diabetes may have benefited in obtaining private health insurance after implementation of the preexisting condition mandate of the ACA. Public Health Implications. Efforts to limit enforcement of these protections are likely to contribute to setbacks in access to care.

Development and validation of algorithms to identify newly diagnosed type 1 and type 2 diabetes in pediatric population using electronic medical records and claims data

PURPOSE

To develop and validate algorithms to classify diabetes type in newly diagnosed pediatric patients with DM.

METHOD

Data from the United States Department of Defense health system were used to identify patients aged 10 to 18 years with incident DM. Two independent sets of 200 children were randomly sampled for algorithm development and validation. Algorithms were developed based on clinical insight, published literature, and quantitative approaches. The actual DM type was ascertained via chart review. Finally, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated.

RESULTS

Among the 400 patients, mean age was 14.2 (±2.5 years), and 50% were female. The best performing algorithms were based on data available in claims. They consisted of several logical expressions based on one predictor or more, which classified patients by use of glucose-lowering drugs or testing, DM ICD-9 diagnosis codes, and comorbidities. The best performing T2DM and T1DM algorithms achieved 90% and 98% sensitivity, 95% and 95% specificity, 87% and 98% PPV, and 96% and 96% NPV, respectively.

CONCLUSIONS

Our results suggest that claims algorithms can accurately identify newly diagnosed T1DM and T2DM pediatric patients, which can facilitate large database studies in children with T1DM and T2DM. However, external validation in other data sources is needed.

Validity of ICD-10-CM codes for determination of diabetes type for persons with youth-onset type 1 and type 2 diabetes

OBJECTIVE

Diagnosis codes might be used for diabetes surveillance if they accurately distinguish diabetes type. We assessed the validity of International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) among health plan members with youth-onset (diagnosis age <20 years) diabetes.

RESEARCH DESIGN AND METHODS

Diabetes case identification and abstraction of diabetes type was done as part of the SEARCH for Diabetes in Youth Study. The gold standard for diabetes type is the physician-assigned diabetes type documented in patients' medical records. Using all healthcare encounters with ICD-10-CM codes for diabetes, we summarized codes within each encounter and determined diabetes type using percent of encounters classified as T2DM. We chose 50% as the threshold from a receiver operating characteristic curve because this threshold yielded the largest Youden's index. Persons with ≥50% T2DM-coded encounters were classified as having T2DM. Otherwise, persons were classified as having T1DM. We calculated sensitivity, specificity, positive and negative predictive values, and accuracy overall and by demographic characteristics.

RESULTS

According to the gold standard, 1911 persons had T1DM and 652 persons had T2DM (mean age (SD): 19.1 (6.5) years). We obtained 90.6% (95% CI 88.4% to 92.9%) sensitivity, 96.3% (95% CI 95.4% to 97.1%) specificity, 89.3% (95% CI 86.9% to 91.6%) positive predictive value, 96.8% (95% CI 96.0% to 97.6%) negative predictive value, and 94.8% (95% CI 94.0% to 95.7%) accuracy for discriminating T2DM from T1DM.

CONCLUSIONS

ICD-10-CM codes can accurately classify diabetes type for persons with youth-onset diabetes, showing promise for rapid, cost-efficient diabetes surveillance.

Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data

PURPOSE

Algorithms using information from electronic health records to identify adults with type 1 diabetes have not been well studied. Such algorithms would have applications in pharmacoepidemiology, drug safety research, clinical trials, surveillance, and quality improvement. Our main objectives were to determine the positive predictive value for identifying type 1 diabetes in adults using a published algorithm (developed by Klompas et al) and to compare it to a simple requirement that the majority of diabetes diagnosis codes be type 1.

METHODS

We applied the Klompas algorithm and the diagnosis code criterion to a cohort of 66 690 adult Kaiser Permanente Colorado members with diabetes. We reviewed 220 charts of those identified as having type 1 diabetes and calculated positive predictive values.

RESULTS

The Klompas algorithm identified 3286 (4.9% of 66 690) adults with diabetes as having type 1 diabetes. Based on chart reviews, the overall positive predictive value was 94.5%. The requirement that the majority of diabetes diagnosis codes be type 1 identified 3000 (4.5%) as having type 1 diabetes and had a positive predictive value of 96.4%. However, the algorithm criterion involving dispensing of urine acetone test strips performed poorly, with a positive predictive value of 20.0%.

CONCLUSIONS

Data from electronic health records can be used to accurately identify adults with type 1 diabetes. When identifying adults with type 1 diabetes, we recommend either a modified version of the Klompas algorithm without the urine acetone test strips criterion or the requirement that the majority of diabetes diagnosis codes be type 1 codes.

A plateau in new onset type 1 diabetes: Incidence of pediatric diabetes in the United States Military Health System

OBJECTIVES

To describe the incidence and prevalence of type 1 diabetes among pediatric dependents of the US Department of Defense.

METHODS

The Military Health System (MHS) data repository was used to identify pediatric patients (≤17 years of age) with type 1 diabetes from January 1, 2007 to December 31, 2012. Annual incidence, annual prevalence and adjusted incidence were calculated and stratified by sex, age group, and region of residence.

RESULTS

Within a 6-year study period from 2007 to 2012, 5616 pediatric patients with type 1 diabetes were identified; 57% male, mean (SD) age of 10.9 (4.2) years. Annual type 1 diabetes incidence (per 100 000 persons) over the 5-year time period ranged from 20.7/100 000 to 21.3/100 000. Incidence for each year was highest in the 10 to 14 years age group and ranged from 30.9/100 000 in 2008 to 35.2/100 000 in 2011. Annual type 1 diabetes prevalence (per 1000 persons) remained stable throughout the study period at 1.5/1000. Adjusted incidence for males was significantly higher compared to females (21.0/100 000 vs 18.1/100 000; P = .001). During the study period, annual incidence remained steady (test for trend, P = .984).

CONCLUSIONS

The incidence of type 1 diabetes among children appears to plateau during the study period, suggesting a steady state of type 1 diabetes within this pediatric population. The MHS provides an accurate and up to date look at incidence of type 1 diabetes and may reflect broader trends of incidence of pediatric disease for the United States as a whole.

Emerging Approaches in Surveillance of Type 1 Diabetes

PURPOSE OF REVIEW

Surveillance of type 1 diabetes provides an opportunity to address public health needs, inform etiological research, and plan health care services. We present issues in type 1 diabetes surveillance, review previous and current methods, and present new initiatives.

RECENT FINDINGS

Few diabetes surveillance systems distinguish between type 1 and type 2 diabetes. Most worldwide efforts have focused on registries and ages < 15 years, resulting in limited information among adults. Recently, surveillance includes use of electronic health information and national health surveys. However, distinguishing by diabetes type remains a challenge. Enhancing and improving surveillance of type 1 diabetes across all age groups could include validating questions for use in national health surveys. In addition, validated algorithms for classifying diabetes type in electronic health records could further improve surveillance efforts and close current gaps in our understanding of the epidemiology of type 1 diabetes.

Longitudinal study of short-term corticosteroid use by working-age adults with diabetes mellitus: Risks and mitigating factors

BACKGROUND

This study assessed the frequency of short-term oral corticosteroid use in adults with diabetes, examined the incidence of fractures, venous thromboembolism (VTE), and hospitalization for sepsis after corticosteroid use, and evaluated whether preventative medications mitigated adverse events.

METHODS

A longitudinal study (2012-14) was conducted of 1 548 945 adults (aged 18-64 years) who received healthcare coverage through a large national health insurer. Incidence rate ratios (IRR) were calculated using conditional Poisson regression.

RESULTS

Short-term oral corticosteroids were used by 23.9%, 20.8%, and 20.9% of adults with type 2 diabetes, type 1 diabetes, and no diabetes, respectively, during the 3-year period (P < 0.001). Baseline risks of fracture, VTE, and sepsis were greater for individuals with than without diabetes (P < 0.001). The combined effect of having diabetes and using corticosteroids was greater than the sum of the individual effects (synergy indices of 1.17, 1.23, 1.30 for fracture, VTE, and sepsis, respectively). The IRR for VTE in the 5-30 days after corticosteroid use was 3.62 (95% confidence interval [CI] 2.41-5.45). Fractures increased in the 5-30 days after corticosteroid use (IRR 2.06; 95% CI 1.52, 2.80), but concomitant use of ergocalciferol mitigated this risk (IRR 1.13; 95% CI 0.12, 11.07). The risk of hospitalization for sepsis was elevated with corticosteroid use (IRR 3.79; 95% CI 2.05, 7.01), but was mitigated by the concomitant use of statins.

CONCLUSIONS

Short-term oral corticosteroid use is common in adults with diabetes and is associated with an elevated, but low, risk of adverse events. The findings suggest that preventative medications may mitigate risk.

Incidence and prevalence trends of youth-onset type 2 diabetes in a cohort of Canadian youth: 2002-2013

OBJECTIVE

Youth-onset type 2 diabetes is an emerging disease. We estimated incidence and prevalence trends of youth-onset type 2 diabetes between 2002 and 2013 in the Canadian province of British Columbia.

METHODS

This population-based cohort study used a validated diabetes case-finding definition and algorithm to differentiate type 2 from type 1 diabetes to identify youth <20 years with type 2 diabetes within linked population-based administrative data. Age-standardized incidence and prevalence were calculated. JoinPoint regression and double exponential smooth modeling were used.

RESULTS

From 2002/2003 to 2012/2013, the incidence of youth-onset type 2 diabetes increased from 3.45 (95% confidence interval, CI: 2.43, 4.80) to 5.16 (95% CI: 3.86, 6.78)/100 000. The annual percent change (APC) in incidence was 3.74 (95% CI: 1.61, 5.92; P = 0.003) overall, while it was 5.94 (95% CI: 1.84, 10.20; P = 0.009) and 0.53 (95% CI: -5.04, 6.43; P = 0.837) in females and males, respectively. The prevalence increased from 0.009% (95% CI: 0.007, 0.011) in 2002/2003 to 0.021% (95% CI: 0.018, 0.024) in 2012/2013 with an APC of 7.89 (95% CI: 6.41, 9.40; P < 0.0001). In females, it increased from 0.012% (95% CI: 0.009, 0.015) to 0.027% (95% CI: 0.023, 0.032) and in males from 0.007% (95% CI: 0.005, 0.009) to 0.015% (95% CI: 0.012, 0.019). By 2030, we forecast a prevalence of 0.046% (95% CI: 0.043, 0.048).

CONCLUSIONS

Youth-onset type 2 diabetes is increasing with higher rates in females vs males. If these rates continue, in 2030, the number of cases will increase by 5-fold. These data are needed to set priorities for diabetes prevention in youth.

Patient and prescriber characteristics among patients with type 2 diabetes mellitus continuing or discontinuing sulfonylureas following insulin initiation: data from a large commercial database

OBJECTIVE

To describe patient and provider characteristics for patients with type 2 diabetes (T2DM) initiating basal insulin and describe basal insulin's impact on sulfonylurea (SU) discontinuation.

METHODS

A retrospective cohort study was conducted using the HealthCore Integrated Research Database. Patients had ≥12 months of continuous coverage prior to initiating insulin, and were utilizing at least one anti-hyperglycemic drug at the time of insulin initiation. Predictors for SU discontinuation were evaluated utilizing Cox proportional hazards models.

RESULTS

Among the 74,334 individuals aged ≥18 years with T2DM who initiated basal insulin from 2006-2015, 30% were taking metformin (MET) and SU when initiating insulin. Among the 22,418 MET/SU patients, 31% discontinued SU within 3 months of insulin initiation and, by 12 months, 55% had discontinued SU. Sulfonylurea discontinuation was similar among many patient and provider characteristics, while being modestly positively associated (p < .05; HRs <1.5) with female gender, more co-morbidities, cardiac revascularization, chronic liver disease, hospitalizations with a T2DM diagnosis, and hypoglycemia prior to insulin initiation. SU discontinuation was modestly inversely associated with receiving an insulin prescription from an endocrinologist (HR = 0.90, 95% CI = 0.85-0.95).

CONCLUSIONS

Roughly half of commercially-insured T2DM patients discontinued SU within 1 year after insulin initiation, and SU discontinuation was not strongly associated with a range of patient and provider characteristics.

Identifying Geographic Disparities in Diabetes Prevalence Among Adults and Children Using Emergency Claims Data

Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income (P = 0.001), whereas adult type 2 diabetes was associated with lower income (P < 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes (P < 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents (P < 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods.

Incidence of type 1 diabetes in China, 2010-13: population based study

OBJECTIVE

To estimate the incidence of type 1 diabetes in all age groups in China during 2010-13.

DESIGN

Population based, registry study using data from multiple independent sources.

SETTING

National registration system in all 505 hospitals providing diabetes care, and communities of patients with diabetes in 13 areas across China, covering more than 133 million person years at risk, approximately 10% of the whole population.

PARTICIPANTS

5018 people of all ages with newly diagnosed type 1 diabetes and resident in the study areas from 1 January 2010 to 31 December 2013.

MAIN OUTCOME MEASURES

Incidence of type 1 diabetes per 100 000 person years by age, sex, and study area. Type 1 diabetes was doctor diagnosed and further validated by onsite follow-up. Completeness of case ascertainment was assessed using the capture mark recapture method.

RESULTS

5018 cases of newly diagnosed type 1 diabetes were ascertained: 1239 participants were aged <15 years, 1799 were aged 15-29 years, and 1980 were aged ≥30 years. The proportion of new onset cases in participants aged ≥20 years was 65.3%. The estimated incidence of type 1 diabetes per 100 000 persons years for all ages in China was 1.01 (95% confidence interval 0.18 to 1.84). Incidence per 100 000 persons years by age group was 1.93 (0.83 to 3.03) for 0-14 years, 1.28 (0.45 to 2.11) for 15-29 years, and 0.69 (0.00 to 1.51) for ≥30 years, with a peak in age group 10-14 years. The incidence in under 15s was positively correlated with latitude (r=0.88, P<0.001), although this association was not observed in age groups 15-29 years or ≥30 years.

CONCLUSION

Most cases of new onset type 1 diabetes in China occurred among adults. The incidence of type 1 diabetes in Chinese children was among the lowest reported in the study.

Fluctuations in the incidence of type 1 diabetes in the United States from 2001 to 2015: a longitudinal study

BACKGROUND

While the United States has the largest number of children with type 1 diabetes mellitus, less is known regarding adult-onset disease. The present study utilizes nationwide data to compare the incidence of type 1 diabetes in youth (0-19 years) to that of adults (20-64 years).

METHODS

In this longitudinal study, the Clinformatics® Data Mart Database was used, which contains information from 61 million commercially insured Americans (years 2001-2015). Incidence rates and exact Poisson 95% confidence intervals were calculated by age group, sex, census division, and year of diagnosis. Changes in rates over time were assessed by negative binomial regression.

RESULTS

Overall, there were 32,476 individuals who developed type 1 diabetes in the cohort. The incidence rate was greatest in youth aged 10-14 years (45.5 cases/100,000 person-years); however, because adulthood spans over a longer period than childhood, there was a greater number of new cases in adults than in youth (n = 19,174 adults; n = 13,302 youth). Predominance in males was evident by age 10 and persisted throughout adulthood. The male to female incidence rate ratio was 1.32 (95% CI 1.30-1.35). The incidence rate of type 1 diabetes in youth increased by 1.9% annually from 2001 to 2015 (95% CI 1.1-2.7%; P < 0.001), but there was variation across regions. The greatest increases were in the East South Central (3.8%/year; 95% CI 2.0-5.6%; P < 0.001) and Mountain divisions (3.1%/year; 95% CI 1.6-4.6%; P < 0.001). There were also increases in the East North Central (2.7%/year; P = 0.010), South Atlantic (2.4%/year; P < 0.001), and West North Central divisions (2.4%/year; P < 0.001). In adults, however, the incidence decreased from 2001 to 2015 (-1.3%/year; 95% CI -2.3% to -0.4%; P = 0.007). Greater percentages of cases were diagnosed in January, July, and August for both youth and adults. The number of new cases of type 1 diabetes (ages 0-64 years) in the United States is estimated at 64,000 annually (27,000 cases in youth and 37,000 cases in adults).

CONCLUSIONS

There are more new cases of type 1 diabetes occurring annually in the United States than previously recognized. The increase in incidence rates in youth, but not adults, suggests that the precipitating factors of youth-onset disease may differ from those of adult-onset disease.

Evaluating the Use of Electronic Health Records for Type 2 Diabetes Surveillance in 2 California Counties, 2010-2014

OBJECTIVES

Electronic health records (EHRs) and electronic laboratory records (ELRs) are increasingly seen as a rich source of data for performing public health surveillance activities and monitoring community health status. Their potential for surveillance of chronic illness, however, may be underused. Our objectives were to (1) evaluate the use of EHRs and ELRs for diabetes surveillance in 2 California counties and (2) examine disparities in diabetes prevalence by geography, income, and race/ethnicity.

METHODS

We obtained data on a clinical diagnosis of diabetes and hemoglobin A1c (HbA1c) test results for adult members of Kaiser Permanente Northern California living in Contra Costa County or Solano County at any time during 2010-2014. We evaluated the validity of using HbA1c test results to determine diabetes prevalence, using clinical diagnoses as a gold standard. We estimated disparities in diabetes prevalence by combining HbA1c test results with US Census data on income, race, and ethnicity.

RESULTS

When compared with a clinical diagnosis of diabetes, data on a patient's 5-year maximum HbA1c value ≥6.5% yielded the best combination of sensitivity (87.4%) and specificity (99.2%). The prevalence of 5-year maximum HbA1c ≥6.5% decreased with increasing median family income and increased with greater proportions of residents who were either non-Hispanic black or Hispanic.

CONCLUSIONS

Timely diabetes surveillance data from ELRs can be used to document disparities, target interventions, and evaluate changes in population health. ELR data may be easier to access than a patient's entire EHR, but outcome metric validation with diabetes diagnoses would need to be ongoing. Future research should validate ELR and EHR data across multiple providers.

Validity of hospital discharge records to identify pregestational diabetes in an Italian population

AIMS

In recent years, the prevalence of pregestational diabetes (PGDM) and the concern about the possibility of adverse pregnancy outcomes in affected women have been increasing. Routinely collected health data represent a timely and cost-efficient approach in PGDM epidemiological research. This study aims to evaluate the reliability of hospital discharge (HD) coding to identify a population-based cohort of pregnant women with PGDM and to assess trends in prevalence in two provinces of Northern Italy.

METHODS

We selected all deliveries occurred in the period 1997-2010 with ICD-9-CM codes for PGDM in HD record and we matched up to 5 controls from mothers without diabetes. We used Diabetes Registers (DRs) as the gold standard for validation analysis.

RESULTS

We selected 3800 women, 653 with diabetes and 3147 without diabetes. The agreement between HD records and DRs was 90.7%, with K=0.58. We detected 350 false positives and only 1 false negative. Sensitivity was 99.3%, specificity 90.0%, positive predictive value 46.4% and negative predictive value 99.9%. Of the false positives, 48.6% had gestational diabetes and 2.3% impaired glucose tolerance. After the validation process, PGDM prevalence decreased from 4.4 to 2.0 per 1000 deliveries.

CONCLUSIONS

Our results show that HD facilitate detection of almost all PGDM cases, but they also include a large number of false positives, mainly due to gestational diabetes. This misclassification causes a large overestimation of PGMD prevalence. Our findings require accuracy evaluation of ICD-9-CM codes, before they can be widely applied to epidemiological research and public health surveillance related to PGDM.

The Burden of Obesity, Elevated Blood Pressure, and Diabetes in Uninsured and Underinsured Adolescents

BACKGROUND

Obesity, elevated blood pressure (BP), and diabetes mellitus are rising among the general U.S. adolescent population, but prevalence estimates are not available for uninsured or Medicaid populations.

METHODS

This retrospective epidemiological study extracted 155,139 electronic medical records collected between 1998 and 2012 on patients aged 10-19 years, from a clinical population predominantly uninsured or insured by Medicaid. Age, sex, race, height, weight, BP, and insurance type were captured at first clinic visit. Classifications included obesity (≥95th body mass index percentile), elevated BP (≥90th percentile), and diabetes mellitus (ICD-9-250.xx).

RESULTS

Among the 26,696 patients with complete data at first clinic visit, 24.4% were classified as obese and 39.5% had elevated BP. In logistic regression analyses, odds of obesity were significantly higher among uninsured versus commercially insured patients (odds ratio [OR]: 1.1 [95% confidence interval: 1.0-1.2]) and girls (OR: 1.3 [1.2-1.4]), but lower among older adolescents (for 15-17 years, OR: 0.7 [0.6-0.7]; for 18-19 years, OR: 0.7 [0.7-0.8]). Odds of elevated BP were significantly higher among Medicaid (OR: 1.1 [1.0-1.2]) and uninsured (OR: 1.2 [1.1-1.4]) versus commercially insured patients, but lower among African American versus White youth (OR: 0.9 [0.8-0.9]). Prevalence of type 1 diabetes was 1.46 per 1000 and prevalence of type 2 diabetes was 1.68 per 1000, with both occurring more often in girls versus boys and in Whites versus African Americans.

CONCLUSION

In this low-income clinical population, prevalence of obesity and elevated BP were higher than national estimates. The provision of preventive healthcare to all Medicaid and uninsured youth should remain a priority.

Incidence of type 1 diabetes in age groups above 15 years: facts, hypothesis and prospects for future epidemiologic research

Although onset of type 1 diabetes can occur in adulthood, epidemiological data are scarce, limiting our potential to identify unknown determinants of the disease. Paucity of registries expanding the recruitment of incident cases up to adulthood, atypical clinical features of type 1 diabetes at onset, misclassification of type 1 as type 2 diabetes and little use of markers of β-cell autoimmunity represents major obstacles in studying the risk of type 1 diabetes in adults. New strategies in study design, data collection and analyses may overcome these problems in the future. Population-based surveys and registries including adulthood; use of etiological rather than clinical criteria to define type 1 diabetes; availability of electronic health records as prescription data sources to avoid missing data; and application of proper statistical methods will be instrumental to gain better insight on the epidemiology and natural history of the disease.

An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

OBJECTIVE

To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data.

MATERIALS AND METHODS

Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified.

RESULTS

We developed a stepwise surveillance approach using billing code-based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with "other" type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods.

CONCLUSION

EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.

The SEARCH for Diabetes in Youth study: rationale, findings, and future directions

The SEARCH for Diabetes in Youth (SEARCH) study was initiated in 2000, with funding from the Centers for Disease Control and Prevention and support from the National Institute of Diabetes and Digestive and Kidney Diseases, to address major knowledge gaps in the understanding of childhood diabetes. SEARCH is being conducted at five sites across the U.S. and represents the largest, most diverse study of diabetes among U.S. youth. An active registry of youth diagnosed with diabetes at age <20 years allows the assessment of prevalence (in 2001 and 2009), annual incidence (since 2002), and trends by age, race/ethnicity, sex, and diabetes type. Prevalence increased significantly from 2001 to 2009 for both type 1 and type 2 diabetes in most age, sex, and race/ethnic groups. SEARCH has also established a longitudinal cohort to assess the natural history and risk factors for acute and chronic diabetes-related complications as well as the quality of care and quality of life of persons with diabetes from diagnosis into young adulthood. Many youth with diabetes, particularly those from low-resourced racial/ethnic minority populations, are not meeting recommended guidelines for diabetes care. Markers of micro- and macrovascular complications are evident in youth with either diabetes type, highlighting the seriousness of diabetes in this contemporary cohort. This review summarizes the study methods, describes key registry and cohort findings and their clinical and public health implications, and discusses future directions.