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We evaluated the impact of reimbursement for non-face-to-face chronic care management (NFFCCM) on healthcare utilization among Medicare beneficiaries with type 2 diabetes in Louisiana.We implemented group-based trajectory balancing and propensity score matching to obtain comparable treatment (with NFFCCM) and control (without NFFCCM) groups at baseline. Patients with diabetes with Medicare as their primary payer at baseline were extracted using electronic health records of 3 health systems from Research Action for Health Network, a Clinical Research Network. The study period is from 2013 to early 2020. Our outcomes include general healthcare utilization (outpatient, emergency department, and inpatient encounters) and health utilization related to diabetic complications. We tested each of these outcomes according to multiple treatment definitions and different subgroups.Receiving any NFFCCM was associated with an increase in outpatient visits of 657 (95% confidence interval [CI] 626-687; P.001) per 1000 patients per month, a decrease in inpatient admissions of 5 (95% CI 2-7; P.001) per 1000 patients per month, and a decrease in emergency department visits of 4 (95% CI 1-7; P = .005) per 1000 patients per month after 24-month follow-up from initial NFFCCM encounter. Both complex and noncomplex NFFCCM significantly increased visits to outpatient services and inpatient admissions per month. Receiving NFFCCM has a dose-response association with increasing outpatient visits per month.Patients with diabetes in Louisiana who received NFFCCM had more low-cost primary healthcare and less high-cost healthcare utilization in general. The cost savings of NFFCCM in diabetes management could be further explored in the future.

ObjectiveTo examine patient/caregiver preference for key attributes of treatments for spinal muscular atrophy (SMA).BackgroundIn the rapidly evolving SMA treatment landscape, it is critically important to understand how attributes of potential treatments may impact patient/caregiver choices.Design/methodsA discrete choice experiment survey was developed based on qualitative interviews. Patients with SMA (≥ 18 years) and caregivers of patients were recruited through a U.S. patient organization. Respondents made choices in each of 12 sets of hypothetical treatments. The relative importance of five treatment characteristics was compared (measured by regression coefficients [RC] of conditional logit models): (1) improvement or stabilization of motor function, (2) improvement or stabilization of breathing function, (3) indication for all ages or pediatric patients only, (4) route of administration [repeated intrathecal (IT) injections, one-time intravenous (IV) infusion, daily oral delivery] and (5) potential harm (mild, moderate, serious/life threatening).ResultsPatient ages ranged from less than 1 to 67 years (n = 101, 65 self-reported and 36 caregiver-reported) and 64 were female. Total SMA subtypes included: type 1 (n = 21), type 2 (n = 48), type 3 (n = 29), other (n = 3). Prior spinal surgery was reported in 47 patients. Nusinersen and onasemnogene abeparvovec-xioi use were reported in 59 and 10 patients, respectively. Improvement in motor and breathing function was highly valued [RC: 0.65, 95% confidence interval (CI): 0.47–0.83 and RC: 0.79, 95% CI: 0.60–0.98, respectively]. Oral medication and one-time infusion were strongly preferred over repeated IT injections (RC: 0.80, 95% CI: 0.60–0.98 and RC: 0.51, 95% CI: 0.30–0.73, respectively). Patients least preferred an age-restricted label/approved use (≤ 2 years of age) (RC: − 1.28, 95% CI: − 1.47 to − 1.09). Cross-attributes trade-off decision suggested a lower willingness for a high-risk therapy despite additional efficacy gain. For some patients, there may be willingness to trade off additional gains in efficacy for a change in route of administration from repeated intrathecal administration to oral medication.ConclusionsImprovements in motor/breathing function, broad indication, oral or one-time infusion, and minimal risk were preferred treatment attributes. Treatment decisions should be made in clinical context and be tailored to patient needs.

Introduction Increased utilization of electronic health records (EHR) has enriched databases for creating risk models. We used machine learning techniques to develop an EHR-based risk model locally fitted to patients with type 2 diabetes mellitus (T2DM) for predicting cardiovascular disease. Methods This retrospective observational study was conducted within Ochsner Health, Louisiana, USA, between 2013–2017. Data analysis included 6245 patients who had two outpatient diagnoses of T2DM recorded on separate days or a diagnosis recorded during an inpatient encounter. Baseline clinical data were limited to 180 days before the index diagnosis. Cardiovascular outcomes were coronary heart disease (CHD), heart failure and stroke. Machine learning approaches were used to select predictor variables into Cox proportional hazards models for each outcome. Locally fit equations were compared to “generalized” risk equations (RECODe, AS-CVD, QRISK3) using model discrimination and calibration. Results Among factors identified in the Ochsner (n = 11), RECODe (n = 14), AS-CVD (n = 15) and QRISK3 (n = 23), only age was common to all four risk equations. The Ochsner model had high internal discrimination for CHD (C-statistics 0.85) and better discrimination than RECODe (C-statistics 0.45), the QRISK3 (C-statistics 0.72) and AS-CVD (C-statistics 0.54). Conclusions The Ochsner model overestimated 5-year CHD risk, but had relatively higher calibration than the other models in CHD. Risk equations fitted for local populations improved cardiovascular risk stratification for patients with T2DM. Application of machine learning simplified the models compared to “generalized” risk equations. Supplementary Information The online version contains supplementary material available at 10.1007/s13300-021-01096-w.

IntroductionThe prevalence of diabetes self-management education and support (DSME/S) use among patients with newly diagnosed type 2 diabetes mellitus (T2DM) and patients with insulin prescription has not been evaluated. It is also unclear what demographic, behavioral, and clinical factors associated with use of DSME/S.Research design and methodsThis retrospective analysis was based on electronic health records from the Research Action for Health Network (2013–2019). Patients with newly diagnosed T2DM were identified as 35–94 year-olds diagnosed with T2DM≥1 year after the first recorded office visit. Patients with insulin were identified by the first insulin prescription records. DSME/S (Healthcare Common Procedure Coding System G0108 and G0109) codes that occurred from 2 months before the ‘new diagnosis date’ or first insulin prescription date through 1 year after were defined as use of DSME/S. Age-matched controls (non-users) were identified from the Electronic Health Records (EHR). The date of first DSME/S record was selected as the index date. Logistic regression was used to estimate the associations between patient factors and use of DSME/S.ResultsThe prevalence of DSME/S use was 6.5% (8909/137 629) among patients with newly diagnosed T2DM and 32.7% (13,152/40,212) among patients with diabetes taking insulin. Multivariable analysis found that among patients with newly diagnosed T2DM, black and male patients were less likely to use DSME/S, while in patients with insulin, they were more likely to use the service compared with white and female counterparts, respectively. Among patients taking insulin, those with private insurance or self-pay status were significantly less likely, while those with Medicaid were more likely to use the service compared with their Medicare counterparts. A strong positive association was found between HbA1c, obesity, and DSME/S use in both cohorts, while hypertension was negatively associated with DSME/S in both cohorts.ConclusionWe showed a low rate of DSME/S use in Louisiana, especially in patients with newly diagnosed T2DM. Our findings demonstrated heterogeneity in factors influencing DSME/S use between patients with newly diagnosed T2D and patients with insulin.

BACKGROUND: Electronic health records (EHRs) and claims records are widely used in defining type 2 diabetes mellitus (T2DM) complications across different types of health care encounters. OBJECTIVE: This study investigates whether using different EHR encounter types to define diabetes complications may lead to different results when examining associations between diabetes complications and their risk factors in patients with T2DM. RESEARCH DESIGN: The study cohort of 64,855 adult patients with T2DM was created from EHR data from the Research Action for Health Network (REACHnet), using the Surveillance Prevention, and Management of Diabetes Mellitus (SUPREME-DM) definitions. Incidence of coronary heart disease (CHD) and stroke events were identified using International Classification of Diseases (ICD)-9/10 codes and grouped by encounter types: (1) inpatient (IP) or emergency department (ED) type, or (2) any health care encounter type. Cox proportional hazards regression was used to estimate associations between diabetes complications (ie, CHD and stroke) and risk factors (ie, low-density lipoprotein cholesterol and hemoglobin A1c). RESULTS: The incidence rates of CHD and stroke in all health care settings were more than twice the incidence rates of CHD and stroke in IP/ED settings. The age-adjusted and multivariable-adjusted hazard ratios for incident CHD and stroke across different levels of low-density lipoprotein cholesterol and hemoglobin A1c were similar between IP/ED and all settings. CONCLUSION: While there are large variations in incidence rates of CHD and stroke as absolute risks, the associations between both CHD and stroke and their respective risk factors measured by hazard ratios as relative risks are similar, regardless of alternative definitions.

This study aimed to systematically review cost-effectiveness studies of newer antidiabetic medications. The PubMed/MEDLINE, EMBASE, CINAHL Plus, Cochrane Library–NHS Economic Evaluation Database (Wiley), Cochrane Library–Health Technology Assessment Database (Wiley), Cochrane Library–Database of Abstracts of Reviews of Effects (Wiley), and the Cost-Effectiveness Analysis Registry databases (from 1 January 2000 to 1 June 2018) were searched. The search strategies included the Medical Subject Heading (MeSH) term ‘economics’, and the MeSH entry terms ‘cost’, ‘cost effectiveness’, ‘value’, and ‘cost utility’, as well as all names for GLP-1 receptor agonists, DPP-4 inhibitors, and SGLT2 inhibitors. Inclusion criteria included (1) cost-effectiveness studies of the newer antidiabetic medications, including sodium-glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and dipeptidyl peptidase-4 (DPP-4) inhibitors; and (2) full-text publications in English. Two reviewers independently screened the titles, abstracts, and full-text articles to select studies for data extraction. Discrepancies were resolved by discussion and consensus. The quality of reporting cost-effectiveness analyses was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guideline. Among 85 studies selected, 82 clearly stated the types of diabetes model used (e.g. CORE model), and 70 studied used validated diabetes models. Seventy-four (87%) studies were funded by pharmaceutical companies, and 72 (85%) studies were conducted from a payer’s perspective. Seventy-six (89%) studies presented were of good quality (20–24 CHEERS items), and nine were of moderate quality (14–19 items). Thirty studies compared newer antidiabetic medications with insulin, 3 studies compared newer antidiabetic medications with thiazolidinediones (TZDs), 15 studies compared newer antidiabetic medications with sulfonylureas, 40 studies compared new antidiabetic medications with alternative newer antidiabetic medication, and 9 studies compared other antidiabetic agents that were not included above. Newer antidiabetic medications were reported to be cost-effective in 26 of 30 (87%) studies compared with insulin, and 13 of 15 (87%) studies compared with sulfonylureas. Most economic evaluations of antidiabetic medications have good reporting quality and use validated diabetes models. The newer antidiabetic medications in most of the reviewed studies were found to be cost effective, compared with insulin, TZDs, and sulfonylureas.