McCoy, Rozalina G.

OBJECTIVETo characterize contemporary trends in glucagon fill rates and expenditures in a nationwide cohort of adults with diabetes overall and by key demographic and clinical characteristics.RESEARCH DESIGN AND METHODSIn this retrospective cohort study, we examined 1) glucagon fill rates per 1,000 person-years and 2) patient out-of-pocket and health plan costs per filled glucagon dose among adults with diabetes included in OptumLabs Data Warehouse between 1 January 2011 and 31 March 2021.RESULTSThe study population comprised 2,814,464 adults with diabetes with a mean age of 62.8 (SD 13.2) years. The overall glucagon fill rate decreased from 2.91 to 2.28 per 1,000 person-years (−22%) over the study period. In groups at high risk for severe hypoglycemia, glucagon fill rates increased from 22.46 to 36.76 per 1,000 person-years (64%) among patients with type 1 diabetes, 11.64 to 16.63 per 1,000 person-years (43%) among those treated with short-acting insulin, and 16.08 to 20.12 per 1,000 person-years (25%) among those with a history of severe hypoglycemia. White patients, women, individuals with high income, and commercially insured patients had higher glucagon fill rates compared with minority patients, males, individuals with low income, and Medicare Advantage patients, respectively. Total cost per dosing unit increased from $157.97 to $275.32 (74%) among commercial insurance beneficiaries and from $150.37 to $293.57 (95%) among Medicare Advantage beneficiaries.CONCLUSIONSGlucagon fill rates are concerningly low and declined between 2011 and 2021 but increased in appropriate subgroups with type 1 diabetes, using short-acting insulin, or with a history of severe hypoglycemia. Fill rates were disproportionately low among minority patients and individuals with low income.

Abstract Objective To emulate the GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) trial using real world data before its publication. GRADE directly compared second line glucose lowering drugs for their ability to lower glycated hemoglobin A 1c (HbA 1c ). Design Observational study. Setting OptumLabs® Data Warehouse (OLDW), a nationwide claims database in the US, 25 January 2010 to 30 June 2019. Participants Adults with type 2 diabetes and HbA 1c 6.8-8.5% while using metformin monotherapy, identified according to the GRADE trial specifications, who also used glimepiride, liraglutide, sitagliptin, or insulin glargine. Main outcome measures The primary outcome was time to HbA 1c ≥7.0%. Secondary outcomes were time to HbA 1c >7.5%, incident microvascular complications, incident macrovascular complications, adverse events, all cause hospital admissions, and all cause mortality. Propensity scores were estimated using the gradient boosting machine method, and inverse propensity score weighting was used to emulate randomization of the treatment groups, which were then compared using Cox proportional hazards regression. Results 8252 people were identified (19.7% of adults starting the study drugs in OLDW) who met eligibility criteria for the GRADE trial (glimepiride arm=4318, liraglutide arm=690, sitagliptin arm=2993, glargine arm=251). The glargine arm was excluded from analyses owing to small sample size. Median times to HbA 1c ≥7.0% were 442 days (95% confidence interval 394 to 480 days) for glimepiride, 764 (741 to not calculable) days for liraglutide, and 427 (380 to 483) days for sitagliptin. Liraglutide was associated with lower risk of reaching HbA 1c ≥7.0% compared with glimepiride (hazard ratio 0.57, 95% confidence interval 0.43 to 0.75) and sitagliptin (0.55, 0.41 to 0.73). Results were consistent for the secondary outcome of time to HbA 1c >7.5%. No significant differences were observed among treatment groups for the remaining secondary outcomes. Conclusions In this emulation of the GRADE trial, liraglutide was statistically significantly more effective at maintaining glycemic control than glimepiride or sitagliptin when added to metformin monotherapy. Generating timely evidence on medical treatments using real world data as a complement to prospective trials is of value.

OBJECTIVE To characterize trends in clinical complexity, treatment burden, health care use, and diabetes-related outcomes among adults with diabetes. RESEARCH DESIGN AND METHODS We used a nationwide claims database to identify enrollees in commercial and Medicare Advantage plans who met claims criteria for diabetes between 1 January 2006 and 31 March 2019 and to quantify annual trends in clinical complexity (e.g., active health conditions), treatment burden (e.g., medications), health care use (e.g., ambulatory, emergency department [ED], and hospital visits), and diabetes-related outcomes (e.g., hemoglobin A1c [HbA1c] levels) between 2006 and 2018. RESULTS Among 1,470,799 commercially insured patients, the proportion with ≥10 active health conditions increased from 33.3% (95% CI 33.1–33.4) in 2006 to 38.9% (38.8–39.1) in 2018 (P = 0.001) and the proportion taking three or more glucose-lowering medications increased from 11.6% (11.5–11.7) to 23.1% (22.9–23.2) (P = 0.007). The proportion with HbA1c ≥8.0% (≥64 mmol/mol) increased from 28.0% (27.7–28.3) in 2006 to 30.5% (30.2–30.7) in 2015, decreasing to 27.8% (27.5–28.0) in 2018 (overall trend P = 0.04). Number of ambulatory visits per patient per year decreased from 6.86 (6.84–6.88) to 6.19 (6.17–6.21), (P = 0.001) while ED visits increased from 0.26 (0.257–0.263) to 0.29 (0.287–0.293) (P = 0.001). Among 1,311,903 Medicare Advantage enrollees, the proportion with ≥10 active conditions increased from 51.6% (51.2–52.0) to 65.1% (65.0–65.2) (P < 0.001); the proportion taking three or more glucose-lowering medications was stable at 16.6% (16.3–16.9) and 18.1% (18.0–18.2) (P = 0.98), and the proportion with HbA1c ≥8.0% increased from 17.4% (16.7–18.1) to 18.6% (18.4–18.7) (P = 0.008). Ambulatory visits per patient per year remained stable at 8.01 (7.96–8.06) and 8.17 (8.16–8.19) (P = 0.23), but ED visits increased from 0.41 (0.40–0.42) to 0.66 (0.66–0.66) (P < 0.001). CONCLUSIONS Among patients with diabetes, clinical complexity and treatment burden have increased over time. ED utilization has also increased, and patients may be using ED services for low-acuity conditions.

Introduction Acute kidney injury (AKI) affects nearly 20% of all hospitalised patients and is associated with poor outcomes. Long-term complications can be partially attributed to gaps in kidney-focused care and education during transitions. Building capacity across the healthcare spectrum by engaging a broad network of multidisciplinary providers to facilitate optimal follow-up care represents an important mechanism to address this existing care gap. Key participants include nephrologists and primary care providers and in-depth study of each specialty’s approach to post-AKI care is essential to optimise care processes and healthcare delivery for AKI survivors. Methods and analysis This explanatory sequential mixed-methods study uses survey and interview methodology to assess nephrologist and primary care provider recommendations for post-AKI care, including KAMPS (kidney function assessment, awareness and education, medication review, blood pressure monitoring and sick day education) elements of follow-up, the role of multispecialty collaboration, and views on care process-specific and patient-specific factors influencing healthcare delivery. Nephrologists and primary care providers will be surveyed to assess recommendations and clinical decision-making in the context of post-AKI care. Descriptive statistics and the Pearson’s χ 2 or Fisher’s exact test will be used to compare results between groups. This will be followed by semistructured interviews to gather rich, qualitative data that explains and/or connects results from the quantitative survey. Both deductive analysis and inductive analysis will occur to identify and compare themes. Ethics and dissemination This study has been reviewed and deemed exempt by the Institutional Review Board at Mayo Clinic (IRB 20–0 08 793). The study was deemed exempt due to the sole use of survey and interview methodology. Results will be disseminated in presentations and manuscript form through peer-reviewed publication.

OBJECTIVE To examine factors associated with emergency department (ED) transport after hypoglycemia treated by emergency medical services (EMS) and assess the impact of ED transport on severe hypoglycemia recurrence. RESEARCH DESIGN AND METHODS We retrospectively analyzed electronic health records of a multistate advanced life support EMS provider and an integrated healthcare delivery system serving an overlapping geographic area in the upper Midwest. For adults with diabetes treated by EMS for hypoglycemia between 2013 and 2019, we examined rates of ED transport, factors associated with it, and its impact on rates of recurrent hypoglycemia requiring EMS, ED, or hospital care within 3, 7, and 30 days. RESULTS We identified 1,977 hypoglycemia-related EMS encounters among 1,028 adults with diabetes (mean age 63.5 years [SD 17.7], 55.2% male, 87.4% non-Hispanic White, 42.4% rural residents, and 25.6% with type 1 diabetes), of which 46.4% resulted in ED transport (31.1% of calls by patients with type 1 diabetes and 58.0% of calls by patients with type 2 diabetes). Odds of ED transport were lower in patients with type 1 diabetes (odds ratio [OR] 0.44 [95% CI 0.31–0.62] vs. type 2 diabetes) and higher in patients with prior ED visits (OR 1.38 [95% CI 1.03–1.85]). Within 3, 7, and 30 days, transported patients experienced recurrent severe hypoglycemia 2.8, 5.2, and 10.6% of the time, respectively, compared with 7.4, 11.2, and 22.8% of the time among nontransported patients (all P < 0.001). This corresponds to OR 0.58 (95% CI 0.42–0.80) for recurrent severe hypoglycemia within 30 days for transported versus nontransported patients. When subset by diabetes type, odds of recurrent severe hypoglycemia among transported patients were 0.64 (95% CI 0.43–0.96) and 0.42 (95% CI 0.24–0.75) in type 1 and type 2 diabetes, respectively. CONCLUSIONS Transported patients experienced recurrent hypoglycemia requiring medical attention approximately half as often as nontransported patients, reinforcing the importance of engaging patients in follow-up to prevent recurrent events.

Introduction Diabetes is one of the most common serious chronic health conditions in the USA. People living with diabetes face multiple barriers to optimal diabetes care, including gaps in access to medical care and self-management education, diabetes distress, and high burden of treatment. Community paramedics (CPs) are uniquely positioned to support multidisciplinary care for patients with diabetes by delivering focused diabetes self-management education and support and bridging the gaps between patients and the clinical and community resources they need to live well with their disease. Methods and analysis We will conduct a pragmatic single-arm prospective trial of a CP-led Diabetes Rescue, Engagement and Management (D-REM) programme that seeks to reduce diabetes distress. We will enrol 70 adults (≥18 years) with diabetes who have haemoglobin A1c (HbA1c)≥9.0%, experienced an emergency department (ED) visit or hospitalisation for any cause within the prior 6 months, and reside in areas with available CP support in Southeast Minnesota (Olmsted, Freeborn and Mower counties) and Northwest Wisconsin (Barron, Rusk and Dunn counties). Participants will be identified using Mayo Clinic electronic health records, contacted for consent and enrolled into the D-REM programme. Visit frequency will be individualised for each patient, but will be an average of four CP visits over the course of approximately 1 month. Outcomes will be change in diabetes distress (primary outcome), confidence in diabetes self-management, health-related quality of life, self-reported hypoglycaemia and hyperglycaemia, HbA1c, ED visits and hospitalisations. Outcomes will be assessed on enrolment, programme completion and 3 months after programme completion. Ethics and dissemination The study was approved by Mayo Clinic Institutional Review Board. Findings will be disseminated through peer-reviewed publications and presentations. If demonstrated to be successful, this model of care can be implemented across diverse settings and populations to support patients living with diabetes. Trial registration number NCT04385758 .

Objective We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States. Design We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports. Interventions Issuance of stay-at-home orders. Primary and secondary outcome measures Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility. Results Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%–6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties. Conclusion Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.