Building Toward a Population-Based Approach to Diabetes Screening and Prevention for US Adults

Derivation and Validation of D-RISK: An Electronic Health Record-Driven Risk Score to Detect Undiagnosed Dysglycemia in Clinical Practice

OBJECTIVE

We derive and validate D-RISK, an electronic health record (EHR)-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (prediabetes plus diabetes) in clinical practice.

RESEARCH DESIGN AND METHODS

We used retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backward selection was used to predict dysglycemia (HbA1c ≥5.7%) using diabetes risk factors consistently captured in structured EHR data. Model coefficients were converted to a points-based risk score. We report discrimination, sensitivity, and specificity and compare D-RISK to the American Diabetes Association (ADA) risk test and the ADA and United States Preventive Services Task Force (USPSTF) screening guidelines.

RESULTS

The derivation cohort included 11,387 patients (mean age 48 years; 65% female; 42% Hispanic; 32% non-Hispanic Black; mean BMI 32; 29% with hypertension). D-RISK included age, race, BMI, hypertension, and random glucose. The area under curve (AUC) for the risk score was 0.75 (95% CI 0.74-0.76). In the validation screening study (n = 519), the AUC was 0.71 (95% CI 0.66-0.75) which was better than the ADA and USPSTF diabetes screening guidelines (AUC = 0.52 and AUC = 0.58, respectively; P < 0.001 for both). Discrimination was similar to the ADA risk test (AUC = 0.67) using patient-reported data to supplement EHR data, although D-RISK was more sensitive (75% vs. 61%) at the recommended screening thresholds.

CONCLUSIONS

Designed for use in EHR, D-RISK performs better than commonly used screening guidelines and risk scores and may help detect undiagnosed cases of dysglycemia in clinical practice.

Implementation and Evaluation of an Automated Text Message-Based Diabetes Prevention Program for Adults With Pre-diabetes

BACKGROUND

Despite the efficacy of diabetes prevention programs, only an estimated 5% of people with pre-diabetes actually participate. Mobile health (mHealth) holds promise to engage patients with pre-diabetes into lifestyle modification programs by decreasing the referral burden, centralizing remote enrollment, removing the physical requirement of a brick-and-mortar location, lowering operating costs through automation, and reducing time and transportation barriers.

METHODS

Non-randomized implementation study enrolling patients with pre-diabetes from a large health care organization. Patients were exposed to a text message-based program combining live human coaching guidance and support with automated scheduled, interactive, data-driven, and on-demand messages. The primary analysis examined predicted weight outcomes at 6 and 12 months. Secondary outcomes included predicted changes in HbA1c and minutes of exercise at 6 and 12 months.

RESULTS

Of the 163 participants included in the primary analysis, participants had a mean predicted weight loss of 5.5% at six months (P < .001) and of 4.3% at 12 months (P < .001). We observed a decrease in predicted HbA1c from 6.1 at baseline to 5.8 at 6 and 12 months (P < .001). Activity minutes were statistically similar from a baseline of 155.5 minutes to 146.0 minutes (P = .567) and 142.1 minutes (P = .522) at 6 and 12 months, respectively, for the overall cohort.

CONCLUSIONS

In this real-world implementation of the myAgileLife Diabetes Prevention Program among patients with pre-diabetes, we observed significant decreases in weight and HbA1c at 6 and 12 months. mHealth may represent an effective and easily scalable potential solution to deliver impactful diabetes prevention curricula to large numbers of patients.

Interactions Between Gastroesophageal Reflux Disease and Diabetes Mellitus: A Systematic Review of Pathophysiological Insights and Clinical Management Strategies

This systematic review elucidates the complex interplay between gastroesophageal reflux disease (GERD) and diabetes mellitus, integrating findings from various studies to highlight pathophysiological connections and effective clinical management strategies. Our examination reveals that mechanisms such as delayed gastric emptying and autonomic neuropathy significantly contribute to the exacerbation of GERD symptoms in diabetic patients, influencing clinical outcomes and treatment efficacy. The review underscores the necessity of multidisciplinary approaches in treating these comorbid conditions and advocates for therapeutic strategies that simultaneously address GERD and diabetes, such as the use of prokinetic agents and tailored surgical interventions like laparoscopic Roux-en-Y gastric bypass. This synthesis advances our understanding and proposes a foundation for future research and clinical practice, aiming to improve the quality of life and treatment outcomes for affected patients. This work contributes significantly to gastroenterology and endocrinology, providing a comprehensive resource for clinicians and researchers alike.

Metformin prescription for U.S. veterans with prediabetes, 2010-2019

Affecting an estimated 88 million Americans, prediabetes increases the risk for developing type 2 diabetes mellitus (T2DM), and independently, cardiovascular disease, retinopathy, nephropathy, and neuropathy. Nevertheless, little is known about the use of metformin for diabetes prevention among patients in the Veterans Health Administration, the largest integrated healthcare system in the U.S. This is a retrospective observational cohort study of the proportion of Veterans with incident prediabetes who were prescribed metformin at the Veterans Health Administration from October 2010 to September 2019. Among 1,059,605 Veterans with incident prediabetes, 12,009 (1.1%) were prescribed metformin during an average 3.4 years of observation after diagnosis. Metformin prescribing was marginally higher (1.6%) among those with body mass index (BMI) ≥35 kg/m2, age <60 years, HbA1c≥6.0%, or those with a history of gestational diabetes, all subgroups at a higher risk for progression to T2DM. In a multivariable model, metformin was more likely to be prescribed for those with BMI ≥35 kg/m2 incidence rate ratio [IRR] 2.6 [95% confidence intervals (CI): 2.1-3.3], female sex IRR, 2.4 [95% CI: 1.8-3.3], HbA1c≥6% IRR, 1.93 [95% CI: 1.5-2.4], age <60 years IRR, 1.7 [95% CI: 1.3-2.3], hypertriglyceridemia IRR, 1.5 [95% CI: 1.2-1.9], hypertension IRR, 1.5 [95% CI: 1.1-2.1], Major Depressive Disorder IRR, 1.5 [95% CI: 1.1-2.0], or schizophrenia IRR, 2.1 [95% CI: 1.2-3.8]. Over 20% of Veterans with prediabetes attended a comprehensive structured lifestyle modification clinic or program. Among Veterans with prediabetes, metformin was prescribed to 1.1% overall, a proportion that marginally increased to 1.6% in the subset of individuals at highest risk for progression to T2DM.

Improving screening of prediabetes and undiagnosed diabetes

BACKGROUND

Type II diabetes mellitus is a chronic medical condition affecting societies worldwide. The duration of hyperglycemia is a strong predictor of adverse outcomes and imposes immense clinical and public health concerns. The best way to prevent complications and reduce the economic burden is by capturing asymptomatic individuals early in the disease process.

LOCAL PROBLEM

Patients at a large urban academic medical center were not consistently identified as having a high risk of hyperglycemia.

METHODS

The project used a pretest-posttest design. Retrospective data on new-onset hyperglycemia incidence were compared for all individuals seeking primary care services 6 weeks before and after the intervention.

INTERVENTION

Patients without a known hyperglycemia history were provided the screening tool to determine risk status. Additional screening measures were implemented for patients identified as high risk on the initial screening.

RESULTS

A total of 52 (61.6%) of the 84 individuals who met inclusion criteria during the intervention period were diagnosed with new-onset chronic hyperglycemia. In contrast, 20 (22.5%) of the 89 individuals identified during the retrospective period resulted in a statistically significant difference ( p < .001) in the frequency and accuracy of patients diagnosed with hyperglycemia between groups.

CONCLUSION

A diabetes risk assessment tool is quick and reliable in capturing high-risk individuals who would benefit from additional screening measures.

The impact of the National Health Program on diabetes incidence among working-age men with prediabetes: A regression discontinuity analysis of a nation-wide database in Japan

AIM

This study aimed to investigate the impact of the National Health Program in Japan ("Specific Health Check-ups and Specific Health Guidance") on diabetes prevention among working-age men with prediabetes.

METHODS

This study used a regression discontinuity design, based on the program's criterion that the program starts at age 40 or older and that the intervention is provided only to prediabetic individuals with abdominal obesity, to assess the impact of the program on the diabetes incidence in a total of 49,848 men with prediabetes, aged 37-42 years.

RESULTS

The National Health Program in which interventions were provided for individuals aged 40 years or over with both prediabetes and abdominal obesity was associated with a decrease in diabetes incidence rate equivalent to 10.1 reduction/1000 person-years. The relative risk was 0.75. However, among those without abdominal obesity and not subjected to the intervention, there was no significant change in the diabetes incidence at age 40.

CONCLUSIONS

The National Health Program in Japan was associated with a decrease in the incidence of diabetes among working-age men with prediabetes and abdominal obesity and may have a meaningful impact among working-age men.

Machine learning for diabetes clinical decision support: a review

Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.

The type 2 diabetes 'modern preventable pandemic' and replicable lessons from the COVID-19 crisis

To frame the substantial prevalence of type 2 diabetes (T2D) as a ‘Modern Preventable Pandemic’ (MPP) and present certain replicable policy lessons from the COVID-19 crisis to address it. A literature and policy review was performed to analyze data about the COVID-19 and T2D pandemics to establish their multi-factorial health, social, and economic impacts. With the global prevalence of T2D tripling in the last two decades, T2D has become an MPP largely due to modifiable human behaviors. Certain successful elements of the response to the COVID-19 pandemic provide important lessons that can be adapted for the growing T2D MPP. With proper education and access to resources, it is possible to mitigate the T2D MPP through focused government policies as illustrated by many of the lessons of the COVID-19 pandemic response. Without such government intervention, the T2D MPP will continue to grow at an unsustainable pace with enormous health, social and economic implications. Immediate action is necessary. The scale of the T2D pandemic warrants a robust response in health policy as outlined through eight coordinated efforts; the lessons of the COVID-19 crisis should be studied and applied to the T2D MPP.

Costing a population health management approach for participant recruitment to a diabetes prevention study

Limited research has reported the economic feasibility-from both a research and practice perspective-of efforts to recruit and enroll an intended audience in evidence-based approaches for disease prevention. We aimed to retrospectively assess and estimate the costs of a population health management (PHM) approach to identify, engage, and enroll patients in a Type 1 Hybrid Effectiveness-Implementation (HEI), diabetes-prevention trial. We used activity-based costing to estimate the recruitment costs of a PHM approach integrated within an HEI trial. We took the perspective of a healthcare system that may adopt, and possibly sustain, the strategy in the typical practice. We also estimated replication costs based on how the strategy could be applied in healthcare systems interested in referring patients to a local diabetes prevention program from a payer perspective. The total recruitment and enrollment costs were $360,424 to accrue 599 participants over approximately 15 months. The average cost per screened and enrolled participant was $263 and $620, respectively. Translating to the typical settings, total recruitment costs for replication were estimated as $193,971 (range: $43,827-$210,721). Sensitivity and scenario analysis results indicated replication costs would be approximately $283-$444 per patient enrolled if glucose testing was necessary, based on the Medicare-covered services. From a private payer perspective, and without glucose testing, per-participant assessed costs were estimated at $31. A PHM approach can be used to accrue a large number of participants in a short period of time for an HEI trial, at a comparable cost per participant.

Treatment of Patients with Prediabetes in a Primary Care Setting 2011-2018: an Observational Study

BACKGROUND

Over one third of American adults are at high risk for developing diabetes, which can be delayed or prevented using interventions such as medical nutrition therapy (MNT) or metformin. Physicians' self-reported rates of prediabetes treatment are improving, but patterns of actual referral, prescription, and MNT visits are unknown.

OBJECTIVE

To characterize treatment of prediabetes in primary care.

DESIGN

We conducted a retrospective cohort study using electronic health record data. We described patterns of treatment and used multivariable logistic regression to evaluate the association of patient factors and PCP-specific treatment rate with patient treatment.

PATIENTS

We included overweight or obese outpatients who had a first prediabetes-range hemoglobin A1c (HbA1c) during 2011-2018 and had primary care provider (PCP) follow-up within a year.

MAIN MEASURES

We collected patient characteristics and the following treatments: metformin prescription; referral to MNT, diabetes education, endocrinology, or bariatric medicine; and MNT visit. We did not capture within-visit physician counseling.

KEY RESULTS

Of 16,713 outpatients with prediabetes, 20.4% received treatment, including metformin prescriptions (7.8%) and MNT referrals (11.3%), but only 7.4% of referred patients completed a MNT visit. The strongest predictor of treatment was the patient's PCP's treatment rate. Some PCPs never treated prediabetes, but two treated more than half of their patients; 62% had no patients complete a MNT visit. Being younger or female and having higher body mass index or HbA1c were also positively associated with treatment. Compared to white patients, black patients were more likely to receive MNT referral and less likely to receive metformin.

CONCLUSIONS

Almost 80% of patients with new prediabetes never received treatment, and those who did receive referrals had very poor visit completion. Treatment rates appear to reflect provider rather than patient preferences.

Mobile Delivery of the Diabetes Prevention Program in People With Prediabetes: Randomized Controlled Trial

BACKGROUND

The Centers for Disease Control and Prevention (CDC) diabetes prevention program (DPP) has formed the foundation for Type 2 Diabetes Mellitus (T2DM) prevention efforts and lifestyle change modifications in multiple care settings. To our knowledge, no randomized controlled trial has verified the efficacy of a fully mobile version of CDC's diabetes prevention program (DPP).

OBJECTIVE

This study aimed to investigate the long-term weight loss and glycemic efficacy of a mobile-delivered DPP compared with a control group receiving usual medical care.

METHODS

Adults with prediabetes (N=202) were recruited from a clinic and randomized to either a mobile-delivered, coach-guided DPP (Noom) or a control group that received regular medical care including a paper-based DPP curriculum and no formal intervention. The intervention group learned how to use the Noom program, how to interact with their coach, and the importance of maintaining motivation. They had access to an interactive coach-to-participant interface and group messaging, daily challenges for behavior change, DPP-based education articles, food logging, and automated feedback. Primary outcomes included changes in weight and hemoglobin A_1c (HbA_1c) levels at 6 and 12 months, respectively. Exploratory secondary outcomes included program engagement as a predictor of changes in weight and HbA_1c levels.

RESULTS

A total of 202 participants were recruited and randomized into the intervention (n=101) or control group (n=99). In the intention-to-treat (ITT) analyses, changes in the participants' weight and BMI were significantly different at 6 months between the intervention and control groups, but there was no difference in HbA_1c levels (mean difference 0.004%, SE 0.05; P=.94). Weight and BMI were lower in the intervention group by -2.64 kg (SE 0.71; P<.001) and -0.99 kg/m2 (SE 0.29; P=.001), respectively. These differences persisted at 12 months. However, in the analyses that did not involve ITT, program completers achieved a significant weight loss of 5.6% (SE 0.81; P<.001) at 6 months, maintaining 4.7% (SE 0.88; P<.001) of their weight loss at 12 months. The control group lost -0.15% at 6 months (SE 0.64; P=.85) and gained 0.33% (SE 0.70; P=.63) at 12 months. Those randomized to the intervention group who did not start the program had no meaningful weight or HbA_1c level change, similar to the control group. At 1 year, the intervention group showed a 0.23% reduction in HbA_1c levels; those who completed the intervention showed a 0.28% reduction. Those assigned to the control group had a 0.16% reduction in HbA_1c levels.

CONCLUSIONS

This novel mobile-delivered DPP achieved significant weight loss reductions for up to 1 year compared with usual care. This type of intervention reduces the risk of overt diabetes without the added barriers of in-person interventions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03865342; https://clinicaltrials.gov/ct2/show/NCT03865342.

Health Insurance Is Associated with Decreased Odds for Undiagnosed Prediabetes and Type 2 Diabetes in American Adults

Over a third of adults in the United States have prediabetes, and many of those with prediabetes will progress to type 2 diabetes within 3-5 years. Health insurance status may factor into a proper diagnosis of prediabetes and diabetes. This study sought to determine the associations between health insurance and undiagnosed prediabetes and diabetes in a national sample of American adults. Publicly available data from 13,029 adults aged 18-64 years from the 2005-2016 waves of the National Health and Nutrition Examination Survey were analyzed. Health insurance type (Medicaid, Private, Other, None) was self-reported. Prediabetes and diabetes status were assessed with measures of self-report, glycohemoglobin, fasting plasma glucose, and two-hour glucose. Covariate-adjusted logistic models were used for the analyses. Overall, 5976 (45.8%) participants had undiagnosed prediabetes, while 897 (6.8%) had undiagnosed diabetes. Having health insurance was associated with decreased odds ratios for undiagnosed prediabetes: 0.87 (95% confidence interval (CI: 0.79, 0.95)) for private insurance, 0.84 (CI: 0.73, 0.95) for other insurance, and 0.78 (CI: 0.67, 0.90) for Medicaid. Moreover, having private health insurance was associated with 0.82 (CI: 0.67, 0.99) decreased odds for undiagnosed diabetes. Health insurance coverage and screening opportunities for uninsured individuals may reduce prediabetes and diabetes misclassifications.

Multilevel Variation in Diabetes Screening Within an Integrated Health System

OBJECTIVE

Variation in diabetes screening in clinical practice is poorly described. We examined the interplay of patient, provider, and clinic factors explaining variation in diabetes screening within an integrated health care system in the U.S.

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study of primary care patients aged 18-64 years with two or more outpatient visits between 2010 and 2015 and no diagnosis of diabetes according to electronic health record (EHR) data. Hierarchical three-level models were used to evaluate multilevel variation in screening at the patient, provider, and clinic levels across 12 clinics. Diabetes screening was defined by a resulted gold standard screening test.

RESULTS

Of 56,818 patients, 70% completed diabetes screening with a nearly twofold variation across clinics (51-92%; P < 0.001). Of those meeting American Diabetes Association (ADA) (69%) and U.S. Preventive Services Task Force (USPSTF) (36%) screening criteria, three-quarters were screened with a nearly twofold variation across clinics (ADA 53-92%; USPSTF 49-93%). The yield of ADA and USPSTF screening was similar for diabetes (11% vs. 9%) and prediabetes (38% vs. 36%). Nearly 70% of patients not eligible for guideline-based screening were also tested. The USPSTF guideline missed more cases of diabetes (6% vs. 3%) and prediabetes (26% vs. 19%) than the ADA guideline. After adjustment for patient, provider, and clinic factors and accounting for clustering, twofold variation in screening by provider and clinic remained (median odds ratio 1.97; intraclass correlation 0.13).

CONCLUSIONS

Screening practices vary widely and are only partially explained by patient, provider, and clinic factors available in the EHR. Clinical decision support and system-level interventions are needed to optimize screening practices.

Current Perspectives on the Impact of the National Diabetes Prevention Program: Building on Successes and Overcoming Challenges

To address the public health and economic burden of type 2 diabetes, the Centers for Disease Control and Prevention (CDC) began dissemination of the National Diabetes Prevention Program (NDPP) in the United States in 2010. Based on the intensive lifestyle intervention from a large efficacy trial, the NDPP aims to reduce incidence through lifestyle change and weight loss. This narrative review summarizes evidence on reach, effectiveness, and sustainability of the NDPP, while highlighting opportunities to overcome challenges in these areas. Major successes include reaching hundreds of thousands of at-risk individuals across the nation, with notable effectiveness upon full participation and widespread insurance coverage. Yet, more work is needed to ensure greater public health impact, particularly among priority populations at heightened risk who also experience disparities in program outcomes. Preliminary evidence suggests a number of strategies may improve reach and effectiveness of the NDPP, often with more rigorous study needed prior to widespread uptake. Updating the NDPP to better match the current evidence-base may also be important, such as directly targeting glycemia with a patient-centered approach and promoting metformin as an adjunct or second-line treatment. Finally, revisiting pay-for-performance reimbursement models may be critical to sustainability by ensuring adequate availability of suppliers and ultimately reducing diabetes prevalence.

The Reach of an Urban Hospital System-Based Diabetes Prevention Program: Patient Engagement and Weight Loss Characteristics

PURPOSE

The purpose of this study was to identify patient and program delivery characteristics associated with engagement and weight loss in a Diabetes Prevention Program (DPP) implemented in an urban hospital system.

METHODS

Patient and program delivery data were collected between July 2015 and December 2017. DPP eligibility was determined based on age, body mass index (BMI), and hemoglobin A1C data via the electronic health record. Engagement was measured at 3 levels: ≤3 sessions, 4 to 8 sessions, and ≥9 sessions. Weight was measured at each DPP session.

RESULTS

Among the eligible patients (N = 31 524), referrals and engagement were lower in men than women, in Spanish speakers than English speakers, in younger (18-34 years) and middle-aged (35-54 years) than older adults, and in patients receiving Medicaid than other patients. Referral and engagement were higher in patients with higher BMIs and those prescribed ≥5 medications. Current smokers were less frequently engaged. Prior health care provider contact was associated with higher engagement. Overall, 28% of DPP participants achieved ≥5% weight loss; younger and middle-aged patients and those who gained weight in the prior 2 years were less likely to lose weight.

CONCLUSION

This assessment identified characteristics of patients with lower levels of referral and engagement. The DPP staff may need to increase outreach to address barriers to referral and during all points of engagement among men, younger patients, and Spanish speakers. Future research is needed to increase understanding with regard to why referrals and engagement are lower among these groups.