An Interrupted Time Series Analysis to Determine the Effect of an Electronic Health Record-Based Intervention on Appropriate Screening for Type 2 Diabetes in Urban Primary Care Clinics in New York City

Improving Diabetes Screening in the Primary Care Clinic

BACKGROUND

In our suburban primary care clinic, the average rate of screening for diabetes among eligible patients was only 51%, similar to national screening data. We conducted a quality improvement project to increase this rate.

METHODS

During the 6-month preintervention phase, we collected baseline data on the percentage of eligible patients screened per week (percentage of patients with hemoglobin A1c checked in the prior 3 years out of patients eligible for screening who completed a visit during the week). We then implemented a two-phase intervention. In phase 1 (approximately 8 months), we generated an electronic health record (EHR) report to identify eligible patients and pended laboratory orders for physicians to sign. In phase 2 (approximately 3 months), we replaced the phase 1 intervention with an EHR clinical decision support tool that automatically identifies eligible patients. We compared screening rates in the preintervention vs. intervention period. For phase 1, we also assessed laboratory completion rates and the laboratory results. We surveyed physicians regarding intervention acceptability and satisfaction at 3, 6, 9, and 12 months during the intervention period.

RESULTS

The weekly percentage of patients screened increased from an average of 51% in the preintervention phase to 65% in the intervention phase (p < 0.001). During phase 1, most patients underwent laboratory blood testing as recommended (83% within 3 months), and results were consistent with prediabetes in 23% and with diabetes in 4%. Overall, most physicians believed that the intervention appropriately identified patients due for screening and was helpful (100% of respondents agreed at 9 months vs. 71% at 3 months).

CONCLUSION

We successfully implemented a systematic screening intervention involving a manual workflow and EHR tool and improved diabetes screening rates in our clinic.

Screening for Prediabetes and Diabetes in a National Network of Federally Qualified Health Centers: An Observational Study

BACKGROUND

In 2021, the U.S. Preventive Services Task Force (USPSTF) recommended screening for prediabetes and diabetes among adults aged 35-70 years with overweight or obesity. Studying dysglycemia screening in federally qualified health centers (FQHCs) that serve vulnerable patient populations is needed to understand health equity implications of this recommendation.

OBJECTIVE

To investigate screening practices among FQHC patients who would be eligible according to the 2021 USPSTF recommendation.

DESIGN

Retrospective cohort study analyzing electronic health records from a national network of 282 FQHC sites.

PARTICIPANTS

We included 183,329 patients without prior evidence of prediabetes or diabetes, who had ≥ 1 office visit from 2018-2020.

MAIN MEASURES

Screening eligibility was based on age and measured body mass index (BMI). The primary outcome, screening completion, was ascertained using hemoglobin A1c or fasting plasma glucose results from 2018-2020.

KEY RESULTS

Among 89,543 patients who would be eligible according to the 2021 USPSTF recommendation, 53,263 (59.5%) were screened. Those who completed screening had higher BMI values than patients who did not (33.0 ± 6.7 kg/m2 vs. 31.9 ± 6.2 kg/m2, p < 0.001). Adults aged 50-64 years had greater odds of screening completion relative to younger patients (OR 1.13, 95% CI: 1.10-1.17). Patients from racial and ethnic minority groups, as well as those without health insurance, were more likely to complete screening than White patients and insured patients, respectively. Clinical risk factors for diabetes were also associated with dysglycemia screening. Among patients who completed screening, 23,588 (44.3%) had values consistent with prediabetes or diabetes.

CONCLUSIONS

Over half of FQHC patients who would be eligible according to the 2021 USPSTF recommendation were screened. Screening completion was higher among middle-aged patients, those with greater BMI values, as well as vulnerable groups with a high risk of developing diabetes. Future research should examine adoption of the 2021 USPSTF screening recommendation and its impact on health equity.

Clinical performance and health equity implications of the American Diabetes Association's 2023 screening recommendation for prediabetes and diabetes

Introduction

The American Diabetes Association (ADA) recommends screening for prediabetes and diabetes (dysglycemia) starting at age 35, or younger than 35 years among adults with overweight or obesity and other risk factors. Diabetes risk differs by sex, race, and ethnicity, but performance of the recommendation in these sociodemographic subgroups is unknown.

Methods

Nationally representative data from the National Health and Nutrition Examination Surveys (2015-March 2020) were analyzed from 5,287 nonpregnant US adults without diagnosed diabetes. Screening eligibility was based on age, measured body mass index, and the presence of diabetes risk factors. Dysglycemia was defined by fasting plasma glucose ≥100mg/dL (≥5.6 mmol/L) or haemoglobin A1c ≥5.7% (≥39mmol/mol). The sensitivity, specificity, and predictive values of the ADA screening criteria were examined by sex, race, and ethnicity.

Results

An estimated 83.1% (95% CI=81.2-84.7) of US adults were eligible for screening according to the 2023 ADA recommendation. Overall, ADA's screening criteria exhibited high sensitivity [95.0% (95% CI=92.7-96.6)] and low specificity [27.1% (95% CI=24.5-29.9)], which did not differ by race or ethnicity. Sensitivity was higher among women [97.8% (95% CI=96.6-98.6)] than men [92.4% (95% CI=88.3-95.1)]. Racial and ethnic differences in sensitivity and specificity among men were statistically significant (P=0.04 and P=0.02, respectively). Among women, guideline performance did not differ by race and ethnicity.

Discussion

The ADA screening criteria exhibited high sensitivity for all groups and was marginally higher in women than men. Racial and ethnic differences in guideline performance among men were small and unlikely to have a significant impact on health equity. Future research could examine adoption of this recommendation in practice and examine its effects on treatment and clinical outcomes by sex, race, and ethnicity.

Use of Real-World Data in Population Science to Improve the Prevention and Care of Diabetes-Related Outcomes

The past decade of population research for diabetes has seen a dramatic proliferation of the use of real-world data (RWD) and real-world evidence (RWE) generation from non-research settings, including both health and non-health sources, to influence decisions related to optimal diabetes care. A common attribute of these new data is that they were not collected for research purposes yet have the potential to enrich the information around the characteristics of individuals, risk factors, interventions, and health effects. This has expanded the role of subdisciplines like comparative effectiveness research and precision medicine, new quasi-experimental study designs, new research platforms like distributed data networks, and new analytic approaches for clinical prediction of prognosis or treatment response. The result of these developments is a greater potential to progress diabetes treatment and prevention through the increasing range of populations, interventions, outcomes, and settings that can be efficiently examined. However, this proliferation also carries an increased threat of bias and misleading findings. The level of evidence that may be derived from RWD is ultimately a function of the data quality and the rigorous application of study design and analysis. This report reviews the current landscape and applications of RWD in clinical effectiveness and population health research for diabetes and summarizes opportunities and best practices in the conduct, reporting, and dissemination of RWD to optimize its value and limit its drawbacks.

Changes in Opioid Prescriptions and Potential Misuse and Substance Use Disorders Among Childhood Cancer Survivors Following the 2016 Opioid Prescribing Guideline

Importance

The Centers for Disease Control and Prevention (CDC) released an opioid-prescribing guideline in March 2016. Little is known about the guideline's potential effects on childhood cancer survivors, a population at high risk for pain.

Objective

To examine changes in opioid prescriptions and potential misuse/substance use disorders (SUD) among childhood cancer survivors and peers without cancer following the guideline release.

Design, Setting, and Participants

In this cohort study using the MarketScan Commercial Claims and Encounters Database, 8969 survivors who completed treatment for hematologic, central nervous system, bone, or gonadal cancers (aged ≤21 years at diagnosis) from 2009 to 2018 and 44 845 age-matched, sex-matched, and region-matched individuals without cancer were identified. With data aggregated based on the quarter-year of survivors' treatment completion, interrupted time series analyses were conducted in this cohort study to estimate the immediate (level) change and change in time trend (trend change) for each outcome after the guideline release, accounting for autocorrelation. Data were analyzed from September 2021 to April 2022.

Exposures

Release of the CDC opioid-prescribing guideline.

Main Outcomes and Measures

Outcomes included any opioid prescription and any indicator for potential misuse/SUD within 1 year following completion of treatment.

Results

This study included 8969 childhood cancer survivors (mean [SD] age, 13.7 [6.2] years old; 3814 [42.5%] female patients) and 44 845 peers without cancer (mean [SD] age, 13.7 [6.2] years old; 19 070 [42.5%] female patients). Before the guideline release, the opioid prescription rate (21.1% vs 7.2%) and rate of potential misuse/SUD (5.6% vs 1.9%) were higher among survivors than peers without cancer. After the guideline release, the trend in opioid prescription rate declined among survivors (trend change, -1.1 percentage points [ppt]; P < .001; 95% CI, -1.5 to -0.7). Survivors also experienced an immediate level decrease (-2.1 ppt; P = .04; 95% CI, -4.2 to -0.1) and a decreasing trend (trend change, -0.4 ppt; P = .009; 95% CI, -0.6 to -0.1) in rate of potential misuse/SUD. Peers without cancer experienced decreasing trends in opioid prescription rate (trend change, -0.3 ppt; P < .001; 95% CI, -0.5 to -0.1) and rate of potential misuse/SUD (trend change, -0.1 ppt; P = .03; 95% CI, -0.1 to -0.01). By 2 years after the guideline release, relative reductions in opioid prescription rate and rate of potential misuse/SUD among survivors were 36.7% and 65.4%, respectively, with peers without cancer experiencing smaller reductions (15.9% and 29.9%).

Conclusions and Relevance

In this cohort study, the opioid prescription rate and rate of potential misuse/SUD declined among both survivors and peers without cancer following the CDC guideline release, with survivors experiencing greater reductions. More research is needed to understand the guideline's potential effects on access to opioids required for pain control among childhood cancer survivors.

An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis

Interrupted time series (ITS) design is commonly used to evaluate the impact of interventions in healthcare settings. Segmented regression (SR) is the most commonly used statistical method and has been shown to be useful in practical applications involving ITS designs. Nevertheless, SR is prone to aggregation bias, which leads to imprecision and loss of power to detect clinically meaningful differences. The objective of this article is to present a weighted SR method, where variability across patients within the healthcare facility and across time points is incorporated through weights. We present the methodological framework, provide optimal weights associated with data at each time point and discuss relevant statistical inference. We conduct extensive simulations to evaluate performance of our method and provide comparative analysis with the traditional SR using established performance criteria such as bias, mean square error and statistical power. Illustrations using real data is also provided. In most simulation scenarios considered, the weighted SR method produced estimators that are uniformly more precise and relatively less biased compared to the traditional SR. The weighted approach also associated with higher statistical power in the scenarios considered. The performance difference is much larger for data with high variability across patients within healthcare facilities. The weighted method proposed here allows us to account for the heterogeneity in the patient population, leading to increased accuracy and power across all scenarios. We recommend researchers to carefully design their studies and determine their sample size by incorporating heterogeneity in the patient population.

Evaluating Natural Experiments that Impact the Diabetes Epidemic: an Introduction to the NEXT-D3 Network

PURPOSE OF REVIEW

Diabetes is an ongoing public health issue in the USA, and, despite progress, recent reports suggest acute and chronic diabetes complications are increasing.

RECENT FINDINGS

The Natural Experiments for Translation in Diabetes 3.0 (NEXT-D3) Network is a 5-year research collaboration involving six academic centers (Harvard University, Northwestern University, Oregon Health & Science University, Tulane University, University of California Los Angeles, and University of California San Francisco) and two funding agencies (Centers for Disease Control and Prevention and National Institutes of Health) to address the gaps leading to persisting diabetes burdens. The network builds on previously funded networks, expanding to include type 2 diabetes (T2D) prevention and an emphasis on health equity. NEXT-D3 researchers use rigorous natural experiment study designs to evaluate impacts of naturally occurring programs and policies, with a focus on diabetes-related outcomes. NEXT-D3 projects address whether and to what extent federal or state legislative policies and health plan innovations affect T2D risk and diabetes treatment and outcomes in the USA; real-world effects of increased access to health insurance under the Affordable Care Act; and the effectiveness of interventions that reduce barriers to medication access (e.g., decreased or eliminated cost sharing for cardiometabolic medications and new medications such as SGLT-2 inhibitors for Medicaid patients). Overarching goals include (1) expanding generalizable knowledge about policies and programs to manage or prevent T2D and educate decision-makers and organizations and (2) generating evidence to guide the development of health equity goals to reduce disparities in T2D-related risk factors, treatment, and complications.

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

Objectives

Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.

Material and Methods

We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.

Results

The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.

Conclusion

We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.

Registration

PROSPERO (CRD42020203716)

Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review

Background

Disease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated.

Objective

This review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings.

Methods

A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted.

Results

The search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9%), clinical decision support (88/241, 36.5%), telehealth (88/241, 36.5%), and multiple technologies (154/241, 63.9%). DHIs were predominantly used for tertiary prevention (131/241, 54.4%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4%). DHI users were providers (205/241, 85.1%), patients (111/241, 46.1%), or multiple types (89/241, 36.9%). Reported outcomes were primarily clinical (179/241, 70.1%), and statistically significant improvements were common (192/241, 79.7%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low.

Conclusions

Preventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored.

Association Between State Policies on Improving Opioid Prescribing in 2 States and Opioid Overdose Rates Among Reproductive-aged Women

BACKGROUND

The opioid overdose epidemic has been declared a public health emergency. Women are more likely than men to be prescribed opioid medications. Some states have adopted policies to improve opioid prescribing, including prescription drug monitoring programs (PDMPs) and pain clinic laws.

OBJECTIVE

Among reproductive-aged women, we examined the association of mandatory use laws for PDMPs in Kentucky (concurrent with a pain clinic law) and New York with overdose involving prescription opioids or heroin and opioid use disorder (OUD).

STUDY DESIGN, SUBJECTS, AND OUTCOME MEASURES

We conducted interrupted time series analyses estimating outcome changes after policy implementation in Kentucky and New York, compared with geographically close states without these policies (comparison states), using 2010-2014 State Inpatient and State Emergency Department Databases. Outcomes included rates of inpatient discharges and emergency department visits for overdoses involving prescription opioids or heroin and OUD among reproductive-aged women.

RESULTS

Relative to comparison states, following Kentucky's policy change, we found an immediate postpolicy decrease and a decreasing trend in the rate of overdoses involving prescription opioids, an immediate postpolicy increase in the rate of overdoses involving heroin, and a decreasing trend in the OUD rate (P<0.01); New York's policy change was not associated with the assessed outcomes.

CONCLUSIONS

PDMPs and pain clinic laws, such as those implemented in Kentucky, may be promising strategies to reduce the adverse impacts of high-risk opioid prescribing among reproductive-aged women. As states continue efforts to improve inappropriate opioid prescribing, similar strategies as those adopted in Kentucky merit consideration.

Impact of DRGs-based inpatient service management on the performance of regional inpatient services in Shanghai, China: an interrupted time series study, 2013-2019

Background

The asymmetry of information brings difficulty for government to manage public hospitals. Therefore, Jiading District of Shanghai has been establishing DRGs-based inpatient service management system (ISMS) to effectively compare the output of different hospitals through DRGs, reward desired hospital performance and enhance inpatient service capacity. However, the impact of the implementation of DRGs-based inpatient service management (ISM) policy in Jiading district is still unknow. We therefore conducted this study to evaluate the impact of DRGs-based ISM policy on the performance of inpatient service since its implementation in Jiading District, Shanghai, China in 2017.

Methods

Using an interrupted time series design, we analyzed quarterly data of seven DRGs-based performance measures from the ISMS which covered all five public hospitals in Jiading District from 2013 to 2019. We utilized the segmented linear regression model to assess the change of level and trend of performance indicators before and after ISM policy. Dickey–Fuller test was used to examine the stationary of the data. Durbin-Watson test was performed to check the series autocorrelation of indicators.

Results

Significant changes in the following indicators were observed since the implementation of ISM policy. The case-mix index (CMI) level decreased by 0.103 (P < 0.05), the trend increased by 0.008 (P < 0.05). The total weight level decreased by 3719.05 (P < 0.05), and the trend increased by 250.13 (P < 0.05). The time efficiency index (TEI) level increased by 0.12 (P < 0.05), and the trend decreased by 0.01 (P < 0.05). The cost efficiency index (CEI) level increased by 0.31 (P < 0.05), and the trend decreased by 0.02 (P < 0.05). No significant difference was found in the change of DRGs number, inpatient mortality of low-risk group cases (IMLRG) and inpatient mortality of medium-to-low risk group cases (IMMLRG).

Conclusions

Findings highlight the role of ISM policy in improving the capacity and efficiency of regional inpatient service. Three prerequisites, including a good information system, high-quality EMR data, and a management team, are needed for other countries to implement their own ISM policy to help government manage public hospitals and improve the performance of regional inpatient service.

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.

The DREAM Initiative: study protocol for a randomized controlled trial testing an integrated electronic health record and community health worker intervention to promote weight loss among South Asian patients at risk for diabetes

BACKGROUND

Electronic health record (EHR)-based interventions that use registries and alerts can improve chronic disease care in primary care settings. Community health worker (CHW) interventions also have been shown to improve chronic disease outcomes, especially in minority communities. Despite their potential, these two approaches have not been tested together, including in small primary care practice (PCP) settings. This paper presents the protocol of Diabetes Research, Education, and Action for Minorities (DREAM) Initiative, a 5-year randomized controlled trial integrating both EHR and CHW approaches into a network of PCPs in New York City (NYC) in order to support weight loss efforts among South Asian patients at risk for diabetes.

METHODS/DESIGN

The DREAM Initiative was funded by the National Institute of Diabetes and Digestive and Kidney Diseases (National Institutes of Health). A total of 480 individuals at risk for type 2 diabetes will be enrolled into the intervention group, and an equal number will be included in a matched control group. The EHR intervention components include the provision of technical assistance to participating PCPs regarding prediabetes-related registry reports, alerts, and order sets. The CHW intervention components entail group education sessions on diabetes prevention, including weight loss and nutrition. A mixed-methods approach will be used to evaluate the feasibility, adoption, and impact (≥ 5% weight loss) of the integrated study components. Additionally, a cost effectiveness analysis will be conducted using outcomes, implementation costs, and healthcare claims data to determine the incremental cost per person achieving 5% weight loss.

DISCUSSION

This study will be the first to test the efficacy of an integrated EHR-CHW intervention within an underserved, minority population and in a practical setting via a network of small PCPs in NYC. The study's implementation is enhanced through cross-sector partnerships, including the local health department, a healthcare payer, and EHR vendors. Through use of a software platform, the study will also systematically track and monitor CHW referrals to social service organizations. Study findings, including those resulting from cost-effectiveness analyses, will have important implications for translating similar strategies to other minority communities in sustainable ways.

TRIAL REGISTRATION

This study protocol has been approved and is made available on ClinicalTrials.gov by NCT03188094 as of 15 June 2017.

Reach and Use of Diabetes Prevention Services in the United States, 2016-2017

IMPORTANCE

Coordinated efforts by national organizations in the United States to implement evidence-based lifestyle modification programs are under way to reduce type 2 diabetes (hereinafter referred to as diabetes) and cardiovascular risks.

OBJECTIVE

To provide a status report on the reach and use of diabetes prevention services nationally.

DESIGN, SETTING, AND PARTICIPANTS

This nationally representative, population-based cross-sectional analysis of 2016 and 2017 National Health Interview Survey data was conducted from August 3, 2017, through November 15, 2018. Nonpregnant, noninstitutionalized, civilian respondents 18 years or older at high risk for diabetes, defined as those with no self-reported diabetes diagnosis but with diagnosed prediabetes or an elevated American Diabetes Association (ADA) risk score (>5), were included in the analysis. Analyses were conducted for adults with (and in sensitivity analyses, for those without) elevated body mass index.

MAIN OUTCOMES AND MEASURES

Absolute numbers and proportions of adults at high risk with elevated body mass index receiving advice about diet, physical activity guidance, referral to weight loss programs, referral to diabetes prevention programs, or any of these, and those affirming engagement in each (or any) activity in the past year were estimated. To identify where gaps exist, a prevention continuum diagram plotted existing vs desired goal achievement. Variation in risk-reducing activities by age, sex, race/ethnicity, educational attainment, insurance status, history of gestational diabetes mellitus, hypertension, or body mass index was also examined.

RESULTS

This analysis included 50 912 respondents (representing 223.0 million adults nationally) 18 years or older (mean [SE] age, 46.1 [0.2] years; 48.1% [0.3%] male) with complete data and no self-reported diabetes diagnosis by their health care professional. Of the represented population, 36.0% (80.0 million) had either a physician diagnosis of prediabetes (17.9 million), an elevated ADA risk score (73.3 million), or both (11.3 million). Among those with diagnosed prediabetes, 73.5% (95% CI, 71.6%-75.3%) reported receiving advice and/or referrals for diabetes risk reduction from their health care professional, and, of those, 35.0% (95% CI, 30.5%-39.8%) to 75.8% (95% CI, 73.2%-78.3%) reported engaging in the respective activity or program in the past year. Half of adults with elevated ADA risk scores but no diagnosed prediabetes (50.6%; 95% CI, 49.5%-51.8%) reported receiving risk-reduction advice and/or referral, of whom 33.5% (95% CI, 30.1%-37.0%) to 75.2% (95% CI, 73.4%-76.9%) reported engaging in activities and/or programs. Participation in diabetes prevention programs was exceedingly low. Advice from a health care professional, age range from 45 to 64 years, higher educational attainment, health insurance status, gestational diabetes mellitus, hypertension, and obesity were associated with higher engagement in risk-reducing activities and/or programs.

CONCLUSIONS AND RELEVANCE

Among adults at high risk for diabetes, major gaps in receiving advice and/or referrals and engaging in diabetes risk-reduction activities and/or programs were noted. These results suggest that risk perception, health care professional referral and communication, and insurance coverage may be key levers to increase risk-reducing behaviors in US adults. These findings provide a benchmark from which to monitor future program availability and coverage, identification of prediabetes, and referral to and retention in programs.

Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network

PURPOSE OF REVIEW

To advance our understanding of the impacts of policies and programs aimed at improving detection, engagement, prevention, and clinical diabetes management in the USA, we synthesized findings from a network of studies that used natural experiments to evaluate diabetes health policies and programs.

FINDINGS

Studies from the Natural EXperiments for Translation in Diabetes (NEXT-D) network used rigorous longitudinal quasi-experimental study designs (e.g., interrupted time series) and analytical methods (e.g., difference-in-differences) to augment causal inference. Investigators partnered with health system stakeholders to evaluate whether glucose testing rates changed from before-to-after clinic interventions (e.g., integrating electronic screening decision prompts in New York City) or employer programs (e.g., targeted messaging and waiving copayments for at-risk employees). Other studies examined participation and behavior change in low- (e.g., wellness coaching) or high-intensity lifestyle modification programs (e.g., diabetes prevention program-like interventions) offered by payers or employers. Lastly, studies assessed how employer health insurance benefits impacted healthcare utilization, adherence, and outcomes among people with diabetes. NEXT-D demonstrated that low-intensity interventions to facilitate glucose testing and enhance engagement in lifestyle modification were associated with small improvements in weight but large improvements in screening and testing when supported by electronic health record-based decision-support. Regarding high-intensity diabetes prevention program-like lifestyle programs offered by payers or employers, enrollment was modest and led to weight loss and marginally lower short-term health expenditures. Health plans that incentivize patient behaviors were associated with increases in medication adherence. Meanwhile, shifting patients to high-deductible health plans was associated with no change in medication use and preventive screenings, but patients with diabetes delayed accessing healthcare for acute complications (e.g., cellulitis). Findings were more pronounced among lower-income patients, who experienced increased rates and acuity of emergency department visits for diabetes complications and other high-severity conditions. Findings from NEXT-D studies provide informative data that can guide programs and policies to facilitate detection, prevention, and treatment of diabetes in practice.

Retrospective review of the management of acute infections and the indications for antibiotic prescription in primary care in northern Thailand

INTRODUCTION

Antibiotic use in low-income and middle-income countries continues to rise despite the knowledge that antibiotic overuse can lead to antimicrobial resistance. There is a paucity of detailed data on the use of antibiotics in primary care in low-resource settings.

OBJECTIVE

To describe the presentation of acute infections and the indications for antibiotic prescription.

DESIGN

A 2-year retrospective review of routinely collected data.

SETTING

All 32 primary care units in one district in northern Thailand.

PARTICIPANTS

Patients attending primary care with a history of fever, documented temperature, International Statistical Classification of Diseases 10 code for infection or prescribed a systemic antibiotic. Patients attending after the initiation of a study on C-reactive protein testing in four centres were excluded.

OUTCOME MEASURES

The proportion of patients prescribed an antibiotic and the frequency of clinical presentations.

RESULTS

762 868 patients attended the health centres, of whom 103 196 met the inclusion criteria, 5966 were excluded resulting in 97 230 attendances consisting of 83 661 illness episodes.46.9% (39 242) of the patients were prescribed an antibiotic during their illness. Indications for antibiotic prescription in the multivariable logistic regression analysis included male sex (adjusted OR (aOR) 1.21 (95% CI 1.16 to 1.28), p<0.001), adults (aOR 1.77 (95% CI 1.57 to 2), p<0.001) and a temperature >37.5°C (aOR 1.24 (95% CI 1.03 to 1.48), p=0.020). 77.9% of the presentations were for respiratory-related problems, of which 98.6% were upper respiratory tract infections. The leading infection diagnoses were common cold (50%), acute pharyngitis (18.9%) and acute tonsillitis (5%) which were prescribed antibiotics in 10.5%, 88.7% and 87.1% of cases, respectively. Amoxicillin was the most commonly prescribed antibiotic.

CONCLUSIONS

Nearly half of the patients received an antibiotic, the majority of whom had a respiratory infection. The results can be used to plan interventions to improve the rational use of antibiotics. Further studies in private facilities, pharmacies and dental clinics are required.

Introductory Overview of the Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) Network: Examining the Impact of US Health Policies and Practices to Prevent Diabetes and Its Complications

PURPOSE OF REVIEW

Diabetes incidence is rising among vulnerable population subgroups including minorities and individuals with limited education. Many diabetes-related programs and public policies are unevaluated while others are analyzed with research designs highly susceptible to bias which can result in flawed conclusions. The Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) Network includes eight research centers and three funding agencies using rigorous methods to evaluate natural experiments in health policy and program delivery.

RECENT FINDINGS

NEXT-D2 research studies use quasi-experimental methods to assess three major areas as they relate to diabetes: health insurance expansion; healthcare financing and payment models; and innovations in care coordination. The studies will report on preventive processes, achievement of diabetes care goals, and incidence of complications. Some studies assess healthcare utilization while others focus on patient-reported outcomes. NEXT-D2 examines the effect of public and private policies on diabetes care and prevention at a critical time, given ongoing and rapid shifts in the US health policy landscape.