Heather M. Devlin

Variation in Prevalence of Gestational Diabetes Mellitus Among Hospital Discharges for Obstetric Delivery Across 23 States in the United States

OBJECTIVE To examine variability in diagnosed gestational diabetes mellitus (GDM) prevalence at delivery by race/ethnicity and state. RESEARCH DESIGN AND METHODS We used data from the Healthcare Cost and Utilization Project State Inpatient Databases for 23 states of the United States with available race/ethnicity data for 2008 to examine age-adjusted and race-adjusted rates of GDM by state. We used multilevel analysis to examine factors that explain the variability in GDM between states. RESULTS Age-adjusted and race-adjusted GDM rates (per 100 deliveries) varied widely between states, ranging from 3.47 in Utah to 7.15 in Rhode Island. Eighty-six percent of the variability in GDM between states was explained as follows: 14.7% by age; 11.8% by race/ethnicity; 5.9% by insurance; and 2.9% by interaction between race/ethnicity and insurance at the individual level; 17.6% by hospital level factors; 27.4% by the proportion of obese women in the state; 4.3% by the proportion of Hispanic women aged 15–44 years in the state; and 1.5% by the proportion of white non-Hispanic women aged 15–44 years in the state. CONCLUSIONS Our results suggest that GDM rates differ by state, with this variation attributable to differences in obesity at the population level (or “at the state level”), age, race/ethnicity, hospital, and insurance.

Trends in Gestational Diabetes Among Hospital Deliveries in 19 U.S. States, 2000-2010.

INTRODUCTION Diabetes is one of the most common and fastest-growing comorbidities of pregnancy. Temporal trends in gestational diabetes mellitus (GDM) have not been examined at the state level. This study examines GDM prevalence trends overall and by age, state, and region for 19 states, and by race/ethnicity for 12 states. Sub-analysis assesses trends among GDM deliveries by insurance type and comorbid hypertension in pregnancy. METHODS Using the Agency for Healthcare Research and Qualitys National and State Inpatient Databases, deliveries were identified using diagnosis-related group codes for GDM and comorbidities using ICD-9-CM diagnosis codes among all community hospitals. General linear regression with a log-link and binomial distribution was used in 2014 to assess annual change in GDM prevalence from 2000 through 2010. RESULTS The age-standardized prevalence of GDM increased from 3.71 in 2000 to 5.77 per 100 deliveries in 2010 (relative increase, 56%). From 2000 through 2010, GDM deliveries increased significantly in all states (p<0.01), with relative increases ranging from 36% to 88%. GDM among deliveries in 12 states reporting race and ethnicity increased among all groups (p<0.01), with the highest relative increase in Hispanics (66%). Among GDM deliveries in 19 states, those with pre-pregnancy hypertension increased significantly from 2.5% to 4.1% (relative increase, 64%). The burden of GDM delivery payment shifted from private insurers (absolute decrease of 13.5 percentage points) to Medicaid/Medicare (13.2-percentage point increase). CONCLUSIONS Results suggest that GDM deliveries are increasing. The highest rates of increase are among Hispanics and among GDM deliveries complicated by pre-pregnancy hypertension.

Seeking Best Practices: A Conceptual Framework for Planning and Improving Evidence-Based Practices

How can we encourage ongoing development, refinement, and evaluation of practices to identify and build an evidence base for best practices? On the basis of a review of the literature and expert input, we worked iteratively to create a framework with 2 interrelated components. The first — public health impact — consists of 5 elements: effectiveness, reach, feasibility, sustainability, and transferability. The second — quality of evidence — consists of 4 levels, ranging from weak to rigorous. At the intersection of public health impact and quality of evidence, a continuum of evidence-based practice emerges, representing the ongoing development of knowledge across 4 stages: emerging, promising, leading, and best. This conceptual framework brings together important aspects of impact and quality to provide a common lexicon and criteria for assessing and strengthening public health practice. We hope this work will invite and advance dialogue among public health practitioners and decision makers to build and strengthen a diverse evidence base for public health programs and strategies.

Effect of lifestyle interventions on glucose regulation among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis.

This study systematically assessed the effectiveness of lifestyle interventions on glycemic indicators among adults (⩾18years) without IGT or diabetes. Randomized controlled trials using physical activity (PA), diet (D), or their combined strategies (PA+D) with follow-up ⩾12months were systematically searched from multiple electronic-databases between inception and May 4, 2016. Outcome measures included fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), fasting insulin (FI), homeostasis model assessment-estimated insulin resistance (HOMA-IR), and bodyweight. Included studies were divided into low-range (FPG <5.5mmol/L or HbA1c <5.5%) and high-range (FPG ⩾5.5mmol/L or HbA1c ⩾5.5%) groups according to baseline glycemic levels. Seventy-nine studies met inclusion criteria. Random-effect models demonstrated that compared with usual care, lifestyle interventions achieved significant reductions in FPG (-0.14mmol/L [95%CI, -0.19, -0.10]), HbA1c (-0.06% [-0.09, -0.03]), FI (%change: -15.18% [-20.01, -10.35]), HOMA-IR (%change: -22.82% [-29.14, -16.51]), and bodyweight (%change: -3.99% [-4.69, -3.29]). The same effect sizes in FPG reduction (0.07) appeared among both low-range and high-range groups. Similar effects were observed among all groups regardless of lengths of follow-up. D and PA+D interventions had larger effects on glucose reduction than PA alone. Lifestyle interventions significantly improved FPG, HbA1c, FI, HOMA-IR, and bodyweight among adults without IGT or diabetes, and might reduce progression of hyperglycemia to type 2 diabetes mellitus.

The importance of natural experiments in diabetes prevention and control and the need for better health policy research.

Diabetes has steadily increased in prevalence, becoming one of the nation’s most challenging public health threats (1). Prevalence among adults is now more than 10%, and diabetes is the leading cause of nontraumatic lower-extremity amputation, end-stage kidney disease, and blindness; it more than doubles the risk of heart disease, stroke, and disability (1,2). Strong clinical trial evidence indicates that much of the illness caused by diabetes is preventable, further positioning diabetes as a public health priority (3,4) and stimulating a national emphasis on the quality of diabetes care and self-management (5–7). Although many such efforts have been successful, leading to better care, risk factor control, and reduced risk of complications, new challenges have arisen. The increases in obesity and in diabetes incidence demand that health systems and communities apply primary prevention strategies at the population level while simultaneously tackling the pervasive geographic and socioeconomic disparities in diabetes prevalence, care, and complications that remain (8,9). Compared to the long list of clinical best practices to prevent diabetes complications, the evidence base is thin for population- and policy-level approaches to improve health behaviors, access to and delivery of care and preventive services, and the healthful attributes of communities. This imbalance of evidence calls for a new platform of public health research for diabetes. We contend that the imbalance can be corrected by a greater emphasis on natural experiments: rigorously designed quasi-experimental studies to investigate the health effects of naturally occurring population- and policy-level approaches emanating from health systems, communities, business organizations, and governments. The gaps in evidence for naturally occurring population- and policy-level approaches have not resulted from a lack of such approaches. Numerous large-scale initiatives and health-related services to reduce the risk and consequences of diabetes are taking place. Employers, health plans, health systems, and communities regularly embark on screening and wellness programs and quality-improvement programs for entire populations; state and local governments have proposed or implemented policies such as taxes on unhealthful foods, vouchers for lifestyle and community programs, or restrictions on the way social services can be used. To remain competitive in a nation where large employers and government are the dominant purchasers of health insurance, health plans frequently develop new reimbursement and benefit designs that influence patterns of services provided to large populations. Finally, national and state legislatures adopt laws that fundamentally affect the access to and delivery, quality, and costs of care and preventive services for people at risk for or diagnosed with diabetes. By 2014, features of the Affordable Care Act of 2010 are likely to change access to services and quality of care, particularly for people who were previously uninsured. The gaps in evidence for naturally occurring population- and policy-level approaches have resulted from a lack of rigorous health policy research: the objective, critical examination and evaluation of the benefits and drawbacks of such approaches. Health policy studies have typically lacked control conditions, which has limited the ability to distinguish between policy effects and secular trends and gauge true effectiveness (10). Randomized controlled trials establish causality and quantify efficacy under ideal conditions but are often impractical for the study of health policies in a complex world. Instead of seeking more rigorous nonrandomized alternatives, health policy research has frequently settled for cross-sectional or noncontrolled alternatives that lead to ambiguous or misleading conclusions. Responding to both the need and opportunity for better health policy research for diabetes, the Centers for Disease Control and Prevention (CDC) and the National Institute of Diabetes and Digestive and Kidney Diseases has initiated a multicenter research network: Natural Experiments in Translation for Diabetes, or NEXT-D. The mission of NEXT-D is to examine the effectiveness of population-level health policies on diabetes prevention, control, and inequalities through rigorous health policy research. A collaborative approach was chosen because it facilitates multisite studies and the use of common measurements and indicators. Collaboration will also enhance the design, analysis, and dissemination of translational research. The ultimate goal of the collaboration is to provide stakeholders with a clear understanding of best practices that can be implemented by employers, health plans, health systems, communities, legislatures, or governments to prevent and control diabetes. NEXT-D studies are also intended to inform the priorities of the CDC-funded Diabetes Prevention and Control Programs (DPCPs) in 58 state and territorial health departments (1). DPCPs bring together diverse stakeholders to implement population-based interventions to improve diabetes risk factors, control, and disparities and to drive state and territorial progress toward national public health objectives. Innovative DPCP strategies that are similar across states could become candidates for NEXT-D evaluations. Conversely, several components of the NEXT-D portfolio of natural experiments may have important implications for DPCPs, including diabetes care quality improvement, access to self-management education, access to lifestyle-based diabetes prevention programs, and healthy food environments in communities. Articles in this Preventing Chronic Disease collection describe the NEXT-D natural experiments now under way — their rationale and importance, their design, and their intended effects. The objective of this collection is to share expertise and methods for addressing the complexity of real-world data. We hope to stimulate others to embark on and publish studies on natural experiments. The NEXT-D studies have several attributes that will enhance their effect on diabetes health research and policies. First, interventions are being implemented naturally (ie, not for research purposes), and they take place among health systems, insurers, employers, the private sector, communities, and government agencies, each of which reaches a large population. As a result, study investigators do not use their own research funds for implementation, and interventions have high external generalizability. Second, the NEXT-D studies span several major public health themes, including the design of health care benefits, clinic–community partnerships, adoption of health information technology, and employer-based initiatives to screen and prevent diabetes. Third, the studies use longitudinal, controlled study designs involving diverse populations and rigorous analytic methods that aim to distinguish between policy effects and underlying trends. Fourth, through close partnerships with the organizations that implement these interventions in real-world settings, the NEXT-D studies will help to eliminate barriers to sustaining and disseminating approaches that are found to be effective at preventing and improving care for people who have diabetes. Fifth, by working in partnership with private sector and public policy decision makers, NEXT-D research teams can identify and analyze outcome indicators that are most informative (ie, provide actionable evidence) to those decision makers. Finally, the studies encompass primary and secondary prevention and complementary, nonredundant approaches. This new platform of public health research for diabetes — natural experiments — will fill the gaps in evidence for population- and policy-level approaches, correct the imbalance in the evidence base between clinical best practices and population- and policy-level approaches, and ultimately help to reduce the burden of diabetes.

Trends in Pre-Pregnancy Diabetes Among Deliveries in 19 U.S. States, 2000−2010

BACKGROUND Trends in state-level prevalence of pre-pregnancy diabetes mellitus (PDM; i.e., type 1 or type 2 diabetes diagnosed before pregnancy) among delivery hospitalizations are needed to inform healthcare delivery planning and prevention programs. PURPOSE To examine PDM trends overall, by age group, race/ethnicity, primary payer, and with comorbidities such as pre-eclampsia and pre-pregnancy hypertension, and to report changes in prevalence over 11 years. METHODS In 2014, State Inpatient Databases from the Agency for Healthcare Research and Quality were analyzed to identify deliveries with PDM and comorbidities using diagnosis-related group codes and ICD-9-CM codes. General linear regression with a log-link and binomial distribution was used to assess the annual change. RESULTS Between 2000 and 2010, PDM deliveries increased significantly in all age groups, all race/ethnicity groups, and in all states examined (p<0.01). The age-standardized prevalence of PDM increased from 0.65 per 100 deliveries in 2000 to 0.89 per 100 deliveries in 2010, with a relative change of 37% (p<0.01). Although PDM rates were highest in the South, some of the largest relative increases occurred in five Western states (≥69%). Non-Hispanic blacks had the highest PDM rates and the highest absolute increase (0.26 per 100 deliveries). From 2000 to 2010, the proportion of PDM deliveries with pre-pregnancy hypertension increased significantly (p<0.01) from 7.4% to 14.1%. CONCLUSIONS PDM deliveries are increasing overall and particularly among those with PDM who have hypertension. Effective diabetes prevention and control strategies for women of childbearing age may help protect their health and that of their newborns.

Trends and Disparities Among Diabetes-Complicated Births in Minnesota, 1993–2003

We used Minnesota birth certificate data from 1993-2003 to test 2 hypotheses: rates of diabetes-complicated pregnancy are increasing, and disparities between more and less socially advantaged groups are widening. Significant increases occurred in rates (per 1000 live births) of prepregnancy and gestational diabetes mellitus (from 2.6 to 4.9 and 25.6 to 34.8, respectively). Increases were significant in all demographic groups except gestational diabetes among American Indian mothers, and disparities worsened among all groups. Targeted interventions and surveillance improvements are needed.

Effect of lifestyle interventions on cardiovascular risk factors among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis

Structured lifestyle interventions can reduce diabetes incidence and cardiovascular disease (CVD) risk among persons with impaired glucose tolerance (IGT), but it is unclear whether they should be implemented among persons without IGT. We conducted a systematic review and meta-analyses to assess the effectiveness of lifestyle interventions on CVD risk among adults without IGT or diabetes. We systematically searched MEDLINE, EMBASE, CINAHL, Web of Science, the Cochrane Library, and PsychInfo databases, from inception to May 4, 2016. We selected randomized controlled trials of lifestyle interventions, involving physical activity (PA), dietary (D), or combined strategies (PA+D) with follow-up duration ≥12 months. We excluded all studies that included individuals with IGT, confirmed by 2-hours oral glucose tolerance test (75g), but included all other studies recruiting populations with different glycemic levels. We stratified studies by baseline glycemic levels: (1) low-range group with mean fasting plasma glucose (FPG) <5.5mmol/L or glycated hemoglobin (A1C) <5.5%, and (2) high-range group with FPG ≥5.5mmol/L or A1C ≥5.5%, and synthesized data using random-effects models. Primary outcomes in this review included systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Totally 79 studies met inclusion criteria. Compared to usual care (UC), lifestyle interventions achieved significant improvements in SBP (-2.16mmHg[95%CI, -2.93, -1.39]), DBP (-1.83mmHg[-2.34, -1.31]), TC (-0.10mmol/L[-0.15, -0.05]), LDL-C (-0.09mmol/L[-0.13, -0.04]), HDL-C (0.03mmol/L[0.01, 0.04]), and TG (-0.08mmol/L[-0.14, -0.03]). Similar effects were observed among both low-and high-range study groups except for TC and TG. Similar effects also appeared in SBP and DBP categories regardless of follow-up duration. PA+D interventions had larger improvement effects on CVD risk factors than PA alone interventions. In adults without IGT or diabetes, lifestyle interventions resulted in significant improvements in SBP, DBP, TC, LDL-C, HDL-C, and TG, and might further reduce CVD risk.

The State of Evaluation Research on Food Policies to Reduce Obesity and Diabetes Among Adults in the United States, 2000–2011

Introduction Improvements in diet can prevent obesity and type 2 diabetes. Although policy changes provide a foundation for improvement at the population level, evidence for the effectiveness of such changes is slim. This study summarizes the literature on recent efforts in the United States to change food-related policies to prevent obesity and diabetes among adults. Methods We conducted a systematic review of evidence of the impact of food policies. Websites of government, academic, and nonprofit organizations were scanned to generate a typology of food-related policies, which we classified into 18 categories. A key-word search and a search of policy reports identified empirical evaluation studies of these categories. Analyses were limited to strategies with 10 or more reports. Of 422 articles identified, 94 met these criteria. Using publication date, study design, study quality, and dietary outcomes assessed, we evaluated the strength of evidence for each strategy in 3 assessment categories: time period, quality, and study design. Results Five strategies yielded 10 or more reports. Only 2 of the 5 strategies, menu labeling and taxes on unhealthy foods, had 50% or more studies with positive findings in at least 2 of 3 assessment categories. Most studies used methods that were rated medium quality. Although the number of published studies increased over 11 years, study quality did not show any clear trend nor did it vary by strategy. Conclusion Researchers and policy makers can improve the quality and rigor of policy evaluations to synthesize existing evidence and develop better methods for gleaning policy guidance from the ample but imperfect data available.

Utilization of E-Health Services Among U.S. Adults With Diabetes

Electronic health (e-health) services have become increasingly important as a method to improve access to health care, including online renewal of prescription medications and making appointments. Healthy People 2020 includes objectives related to e-health to improve population health outcomes and to reduce health disparities, particularly to improve shared decision-making processes between patients and health care professionals (1). E-health services have been encouraged as part of the strategy to improve diabetes care and prevention (2,3). The adoption of these services among health care professionals has improved the quality and efficiency of care (4). However, e-health services may be underutilized among patients with diabetes. While research related to e-health services has been conducted in clinical and managed care settings (5), there are no national population estimates or trends for the utilization of e-health …