Cost Offset of Dapagliflozin in the US Medicare Population with Cardio-Kidney Metabolic Syndrome.

INTRODUCTION: Cardiovascular-kidney-metabolic (CKM) syndrome is highly prevalent in the US Medicare population and is projected to increase further. Sodium-glucose co-transporter 2 inhibitors have indications in chronic kidney disease (CKD), heart failure (HF), and type 2 diabetes (T2D), providing protective efficacy across conditions within CKM syndrome. The objective of this study was to develop a model to extrapolate key outcomes observed in pivotal clinical trials to the US Medicare population, and to assess the potential direct cost offsets associated with dapagliflozin therapy. METHODS: All US 2022 Medicare beneficiaries (≥ 65 years of age) eligible to receive dapagliflozin were estimated according to drug label indication and Medicare enrollment and claims data. Incidence of key outcomes from the dapagliflozin clinical program were modelled over a 4-year time horizon based on patient-level data with CKD, HF, and T2D. Published cost data of relevant clinical outcomes were used to calculate direct medical care cost-offset associated with treatment with dapagliflozin. RESULTS: In a population of 13.1 million patients with CKM syndrome, treatment with dapagliflozin in addition to historical standard of care (hSoC) versus hSoC alone led to fewer incidents of HF-related events (hospitalization for HF, 613,545; urgent HF visit, 98,896), renal events (kidney failure, 285,041; ≥ 50% sustained decline in kidney function, 375,137), and 450,355 fewer deaths (of which 225,346 and 13,206 incidences of cardiovascular and renal death were avoided). In total this led to medical care cost offsets of $99.3 billion versus treatment with hSoC only (dapagliflozin plus hSoC, $310.3 billion; hSoC, $211.0 billion). CONCLUSION: By extrapolating data from trials across multiple indications within CKM syndrome, this broader perspective shows that considerable medical care cost offsets may result through attenuated incidence of clinical events in CKD, T2D, and HF populations if treated with dapagliflozin in addition to hSoC over a 4-year time horizon. Graphical abstract available for this article.

Cause-Specific Health Care Costs Following Hospitalization for Heart Failure and Cost Offset With SGLT2i Therapy.

BACKGROUND: Little is known regarding differences in cause-specific costs between heart failure (HF) with ejection fraction (EF) ≤40% vs >40%, and potential cost implications of sodium glucose co-transporter 2 inhibitor (SGLT2i) therapy. OBJECTIVES: This study sought to compare cause-specific health care costs following hospitalization for HF with EF ≤40% vs >40% and estimate the cost offset with implementation of SGLT2i therapy. METHODS: This study examined Medicare beneficiaries hospitalized for HF in the Get With The Guidelines-Heart Failure registry from 2016 to 2020. Mean per-patient total (excluding drug costs) and cause-specific costs from discharge through 1-year follow-up were calculated and compared between EF ≤40% vs >40%. Next, risk reductions on total all-cause and HF hospitalizations were estimated in a trial-level meta-analysis of 5 pivotal trials of SGLT2is in HF. Finally, these relative treatment effects were applied to Medicare beneficiaries eligible for SGLT2i therapy to estimate the projected cost offset with implementation of SGLT2i, excluding drug costs. RESULTS: Among 146,003 patients, 50,598 (34.7%) had EF ≤40% and 95,405 (65.3%) had EF >40%. Mean total cost through 1 year was $40,557. Total costs were similar between EF groups overall but were higher for EF ≤40% among patients surviving the 1-year follow-up period. Patients with EF >40% had higher costs caused by non-HF and noncardiovascular hospitalizations, and skilled nursing facilities (all P < 0.001). Trial-level meta-analysis of the 5 SGLT2i clinical trials estimated 11% (rate ratio: 0.89; 95% CI: 0.84-0.93; P < 0.001) and 29% (rate ratio: 0.71; 95% CI: 0.66-0.76; P < 0.001) relative reductions in rates of total all-cause and HF hospitalizations, respectively, regardless of EF. Reductions in all-cause and HF hospitalizations were projected to reduce annual costs of readmission by $2,451 to $2,668 per patient with EF ≤40% and $1,439 to $2,410 per patient with EF >40%. CONCLUSIONS: In this large cohort of older U.S. adults hospitalized for HF, cause-specific costs of care differed among patients with EF ≤40% vs >40%. SGLT2i significantly reduced the rate of HF and all-cause hospitalizations irrespective of EF in clinical trials, and implementation of SGLT2i therapy in clinical practice is projected to reduce costs by $1,439 to $2,668 per patient over the 1 year post-discharge, excluding drug costs.

Medical Therapy Before, During and After Hospitalization in Medicare Beneficiaries With Heart Failure and Diabetes: Get With The Guidelines - Heart Failure Registry.

BACKGROUND: Patients hospitalized with heart failure (HF) and diabetes mellitus (DM) are at risk for worsening clinical status. Little is known about the frequency of therapeutic changes during hospitalization. We characterized the use of medical therapies before, during and after hospitalization in patients with HF and DM. METHODS: We identified Medicare beneficiaries in Get With The Guidelines-Heart Failure (GWTG-HF) hospitalized between July 2014 and September 2019 with Part D prescription coverage. We evaluated trends in the use of 7 classes of antihyperglycemic therapies (metformin, sulfonylureas, GLP-1RA, SGLT2-inhibitors, DPP-4 inhibitors, thiazolidinediones, and insulins) and 4 classes of HF therapies (evidence-based ß-blockers, ACEi or ARB, MRA, and ARNI). Medication fills were assessed at 6 and 3 months before hospitalization, at hospital discharge and at 3 months post-discharge. RESULTS: Among 35,165 Medicare beneficiaries, the median age was 77 years, 54% were women, and 76% were white; 11,660 (33%) had HFrEF (LVEF ≤ 40%), 3700 (11%) had HFmrEF (LVEF 41%-49%), and 19,805 (56%) had HFpEF (LVEF ≥ 50%). Overall, insulin was the most commonly prescribed antihyperglycemic after HF hospitalization (n = 12,919, 37%), followed by metformin (n = 7460, 21%) and sulfonylureas (n = 7030, 20%). GLP-1RA (n = 700, 2.0%) and SGLT2i (n = 287, 1.0%) use was low and did not improve over time. In patients with HFrEF, evidence-based beta-blocker, RASi, MRA, and ARNI fills during the 6 months preceding HF hospitalization were 63%, 62%, 19%, and 4%, respectively. Fills initially declined prior to hospitalization, but then rose from 3 months before hospitalization to discharge (beta-blocker: 56%-82%; RASi: 51%-57%, MRA: 15%-28%, ARNI: 3%-6%, triple therapy: 8%-20%; P < 0.01 for all). Prescription rates 3 months after hospitalization were similar to those at hospital discharge. CONCLUSIONS: In-hospital optimization of medical therapy in patients with HF and DM is common in participating hospitals of a large US quality improvement registry.

Cardiorenal disease in the United States: Future health care burden and potential impact of novel therapies.

BACKGROUND: Currently, concerted efforts to identify, prevent, and treat type 2 diabetes mellitus (T2DM), heart failure (HF), and chronic kidney disease (CKD) comorbidities are lacking at the institutional level, with emphasis placed on individual specialties. An integrated approach to tackle T2DM, HF, and CKD within the context of cardiorenal disease has the potential to improve outcomes and reduce costs at the system level. OBJECTIVE: To synthesize published evidence describing the burden of those diagnosed with T2DM, HF, and CKD in the United States as individual discrete chronic conditions, in order to evaluate the potential economic impact of novel therapies in this population. METHODS: We developed a compartmental Markov model with an annual time cycle to model an evolving prevalent US patient population with T2DM, HF, or CKD over the period 2021-2030 (either in isolation or combined). The model was used to explore the potential impact of novel therapies such as sodium-glucose cotransporter 2 inhibitors on future disease burden, by extrapolating the results of relevant clinical trials to representative patient populations. RESULTS: The model estimates that total prevalence across all disease states will have increased by 28% in 2030. Cumulatively, the direct health care cost of cardiorenal disease between 2021 and 2030 is estimated at $4.8 trillion. However, treatment with dapagliflozin has the potential to reduce disease prevalence by 8.0% and estimated cumulative service delivery costs by 3.6% by 2030. CONCLUSIONS: Considering a holistic approach when managing patients with cardiorenal disease offers an opportunity to reduce the disease burden over the next 10 years in the US population. DISCLOSURES: This work was funded by AstraZeneca, which provided support for data analysis. McEwan, Morgan, and Boyce are employees of Health Economics and Outcomes Research Ltd., Cardiff, UK, which received fees from AstraZeneca in relation to this study. Song and Huang are employees of AstraZeneca. Bergenheim is an employee of AstraZeneca and holds AstraZeneca stocks/stock options. Green has no conflicts of interest to declare.

Projected Clinical Benefits of Implementation of SGLT-2 Inhibitors Among Medicare Beneficiaries Hospitalized for Heart Failure.

BACKGROUND: The sodium-glucose cotransporter-2 (SGLT-2) inhibitors form the latest pillar in the management of heart failure with reduced ejection fraction (HFrEF) and appear to be effective across a range of patient profiles. There is increasing interest in initiating SGLT-2 inhibitors during hospitalization, yet little is known about the putative benefits of this implementation strategy. METHODS: We evaluated Medicare beneficiaries with HFrEF (≤ 40%) hospitalized at 228 sites in the Get With The Guidelines-Heart Failure (GWTG-HF) registry in 2016 who had linked claims data for ≥ 1 year postdischarge. We identified those eligible for dapagliflozin under the latest U.S. Food and Drug Administration label (excluding estimated glomerular filtration rates < 25 mL/min per 1.73 m2, dialysis and type 1 diabetes). We evaluated 1-year outcomes overall and among key subgroups (age ≥ 75 years, gender, race, hospital region, kidney function, diabetes status, triple therapy). We then projected the potential benefits of implementation of dapagliflozin based on the risk reductions observed in the Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure (DAPA-HF) trial. RESULTS: Among 7523 patients hospitalized for HFrEF, 6576 (87%) would be candidates for dapagliflozin (mean age 79 ± 8 years, 39% women, 11% Black). Among eligible candidates, discharge use of ß-blockers, ACEi/ARB, MRA, ARNI, and triple therapy (ACEi/ARB/ARNI+ß-blocker+MRA) was recorded in 88%, 64%, 29%, 3%, and 20%, respectively. Among treatment-eligible patients, the 1-year incidence (95% CI) of mortality was 37% (36-38%) and of HF readmission was 33% (32-34%), and each exceeded 25% across all key subgroups. Among 1333 beneficiaries eligible for dapagliflozin who were already on triple therapy, the 1-year incidence of mortality was 26% (24%-29%) and the 1-year readmission due to HF was 30% (27%-32%). Applying the relative risk reductions observed in DAPA-HF, absolute risk reductions with complete implementation of dapagliflozin among treatment-eligible Medicare beneficiaries are projected to be 5% (1%-9%) for mortality and 9% (5%-12%) for HF readmission by 1 year. The projected number of Medicare beneficiaries who would need to be treated for 1 year to prevent 1 death is 19 (11-114), and 12 (8-21) would need to be treated to prevent 1 readmission due to HF. CONCLUSIONS: Medicare beneficiaries with HFrEF who are eligible for dapagliflozin after hospitalization due to HF, including those well-treated with other disease-modifying therapies, face high risks of mortality and HF readmission by 1 year. If the benefits of reductions in death and hospitalizations due to HF observed in clinical trials can be fully realized, the absolute benefits of implementation of SGLT-2 inhibitors among treatment-eligible candidates are anticipated to be substantial in this high-risk postdischarge setting.

Development and Validation of the Warfarin-Aspirin Bleeding Assessment Tool (WA-BAT) in Children.

Bleeding assessment tools (BATs) aim to screen and estimate bleeding risk in patients with inherited bleeding disorders. However, the use of BAT as a standardized measure for comparing bleeding in patients on long-term thromboprophylaxis has not yet been validated. We developed a self-administrable BAT to assess bleeding in patients undergoing long-term thromboprophylaxis with aspirin or warfarin. Eligible participants were invited to complete the warfarin-aspirin -BAT (WA-BAT) online. The WA-BAT was readministered a number of weeks later to determine intrarater reliability. The WA-BAT showed substantial intrarater reliability and assesses major and minor bleeding associated with long-term warfarin or aspirin use.

Impact of dosing frequency (once daily or twice daily) on patient adherence to oral targeted therapies for hematologic malignancies: a retrospective cohort study among managed care enrollees.

PURPOSE: Existing studies evaluating patient adherence to oral targeted therapies such as tyrosine kinase inhibitors focus on small populations with single malignancies. This study evaluated patterns of use of oral agents in a larger population across multiple hematologic malignancies. METHODS: Adult patients diagnosed with a hematologic malignancy and prescribed oral targeted therapy between 2011 and 2016 (N = 18,976) were identified from the MarketScan Commercial Claims and Encounters, and Medicare Supplemental databases. Eligible patients were enrolled in monthly prescription plans 6 months before and 12 months after the index date (date of first prescription claim; n = 2442). Multivariable logistic regressions were used to determine predictors of adherence using the medication possession ratio (MPR) and persistence through prescription refill gaps. RESULTS: The overall median adherence was 0.9 (MPR ≥ 80%) and was comparable between once-daily (QD) and twice-daily (BID) groups. Overall, 59% of patients were persistent at 12 months. Patients on QD and BID products did not have any significant differences in adherence (fixed-interval MPR, odds ratio 0.94; 95% confidence interval (CI), 0.75-1.18) or persistence (odds ratio 0.93; 95% CI, 0.75-1.17) 12 months from index. Significant predictors of adherence and persistence included patient age, total inpatient admissions, number of adverse events, and total hospital visits. CONCLUSION: Patient-specific clinical factors, rather than regimen-specific factors, were the main predictors of oral targeted therapy adherence and persistence. Adherence to oral targeted therapies appears to be similar for patients on QD and BID regimens in the real-world setting.

Association of obesity with healthcare resource utilization and costs in a commercial population.

OBJECTIVE: To examine the association of obesity with healthcare resource utilization (HRU) and costs among commercially insured individuals. METHODS: This retrospective observational cohort study used administrative claims from 1 January 2007 to 1 December 2013. The ICD-9-CM status codes (V85 hierarchy) from 2008 to 2012 classified body mass index (BMI) into the World Health Organizations' BMI categories. The date of first observed BMI code was defined as the index date and continuous eligibility for one year pre- and post- index date was ensured. Post-index claims determined individuals' HRU and costs. Sampling weights developed using the entropy balance method and National Health and Nutrition Examination Survey data ensured representation of the US adult commercially insured population. Baseline characteristics were described across BMI classes and associations between BMI categories, and outcomes were examined using multivariable regression. RESULTS: The cohort included 9651 individuals with BMI V85 codes. After weighting, the BMI distribution was: normal (31.1%), overweight (33.4%), obese class I (22.0%), obese class II (8.1%) and obese class III (5.4%). Increasing BMI was associated with greater prevalence of cardiometabolic conditions, including hypertension, type 2 diabetes and metabolic syndrome. The use of antihypertensives, antihyperlipidemics, antidiabetics, analgesics and antidepressants rose with increasing BMI. Greater BMI level was associated with increased inpatient, emergency department and outpatient utilization, and higher total healthcare, medical and pharmacy costs. CONCLUSIONS: Increasing BMI was associated with higher prevalence of cardiometabolic conditions and higher HRU and costs. There is an urgent need to address the epidemic of obesity and its clinical and economic impacts.

Positive predictive value between medical-chart body-mass-index category and obesity versus codes in a claims-data warehouse.

OBJECTIVE: To evaluate the positive predictive value of claims-based V85 codes for identifying individuals with varying degrees of BMI relative to their measured BMI obtained from medical record abstraction. METHODS: This was a retrospective validation study utilizing administrative claims and medical chart data from 1 January 2009 to 31 August 2015. Randomly selected samples of patients enrolled in a Medicare Advantage Prescription Drug (MAPD) or commercial health plan and with a V85 claim were identified. The claims-based BMI category (underweight, normal weight, overweight, obese class I-III) was determined via corresponding V85 codes and compared to the BMI category derived from chart abstracted height, weight and/or BMI. The positive predictive values (PPVs) of the claims-based BMI categories were calculated with the corresponding 95% confidence intervals (CIs). RESULTS: The overall PPVs (95% CIs) in the MAPD and commercial samples were 90.3% (86.3%-94.4%) and 91.1% (87.3%-94.9%), respectively. In each BMI category, the PPVs (95% CIs) for the MAPD and commercial samples, respectively, were: underweight, 71.0% (55.0%-87.0%) and 75.9% (60.3%-91.4%); normal, 93.8% (85.4%-100%) and 87.8% (77.8%-97.8%); overweight, 97.4% (92.5%-100%) and 93.5% (84.9%-100%); obese class I, 96.9 (90.9%-100%) and 97.2% (91.9%-100%); obese class II, 97.0% (91.1%-100%) and 93.0% (85.4%-100%); and obese class III, 85.0% (73.3%-96.1%) and 97.1% (91.4%-100%). CONCLUSIONS: BMI categories derived from administrative claims, when available, can be used successfully particularly in the context of obesity research.

Impact of Obesity on Work Productivity in Different US Occupations: Analysis of the National Health and Wellness Survey 2014 to 2015.

OBJECTIVE: The aim of this study was to quantify the relationship between workers' body mass index and work productivity within various occupations. METHODS: Data from two administrations (2014 and 2015) of the United States (US) National Health and Wellness Survey, an Internet-based survey administered to an adult sample of the US population, were used for this study (n = 59,772). Occupation was based on the US Department of Labor's 2010 Standardized Occupation Codes. Outcomes included work productivity impairment and indirect costs of missed work time. RESULTS: Obesity had the greatest impact on work productivity in Construction, followed by Arts and Hospitality occupations. Outcomes varied across occupations; multivariable analyses found significant differences in work productivity impairment and indirect costs between normal weight and at least one obesity class. CONCLUSION: Obesity differentially impacted productivity and costs, depending upon occupation.

Association of obesity with healthcare utilization and costs in a Medicare population.

OBJECTIVES: To examine the association of obesity with healthcare resource utilization and costs in a Medicare population. METHODS: This study was a retrospective cohort study using Humana Medicare Advantage (MA) claims data. Body mass index (BMI) was assessed using ICD-9-CM status codes (V85 hierarchy) that have been validated in the data source to classify patients into BMI categories: normal (N), overweight (Ow), obese class I (ObI), obese class II (ObII), and obese class III (ObIII). Healthcare resource utilization (HRU) and costs were determined based on claims data. Descriptive statistics were used to examine baseline characteristics and HRU across BMI classes. Multivariable analysis was used to examine the association between BMI class and outcome measures. RESULTS: Among the 172,866 patients aged ≥65 years that were identified, BMI distribution was: N, 21%; Ow 37%; ObI, 24%, ObII, 10%; and ObIII, 9%. Inpatient, emergency department and outpatient utilization increased with greater BMI level, and greater BMI level was associated with higher total healthcare, medical and pharmacy costs. Greater prevalence of several cardiometabolic conditions, total medication use, and use of specific medication classes was observed with increasing BMI class. CONCLUSIONS: Greater BMI was associated with greater HRU and costs and observed increase in prevalence of cardiometabolic conditions. These results reflect an urgent need to address the epidemic of obesity and the resulting excessive clinical and economic burden on the healthcare system.

A comparison of entropy balance and probability weighting methods to generalize observational cohorts to a population: a simulation and empirical example.

PURPOSE: We compared methods to control bias and confounding in observational studies including inverse probability weighting (IPW) and stabilized IPW (sIPW). These methods often require iteration and post-calibration to achieve covariate balance. In comparison, entropy balance (EB) optimizes covariate balance a priori by calibrating weights using the target's moments as constraints. METHODS: We measured covariate balance empirically and by simulation by using absolute standardized mean difference (ASMD), absolute bias (AB), and root mean square error (RMSE), investigating two scenarios: the size of the observed (exposed) cohort exceeds the target (unexposed) cohort and vice versa. The empirical application weighted a commercial health plan cohort to a nationally representative National Health and Nutrition Examination Survey target on the same covariates and compared average total health care cost estimates across methods. RESULTS: Entropy balance alone achieved balance (ASMD ≤ 0.10) on all covariates in simulation and empirically. In simulation scenario I, EB achieved the lowest AB and RMSE (13.64, 31.19) compared with IPW (263.05, 263.99) and sIPW (319.91, 320.71). In scenario II, EB outperformed IPW and sIPW with smaller AB and RMSE. In scenarios I and II, EB achieved the lowest mean estimate difference from the simulated population outcome ($490.05, $487.62) compared with IPW and sIPW, respectively. Empirically, only EB differed from the unweighted mean cost indicating IPW, and sIPW weighting was ineffective. CONCLUSION: Entropy balance demonstrated the bias-variance tradeoff achieving higher estimate accuracy, yet lower estimate precision, compared with IPW methods. EB weighting required no post-processing and effectively mitigated observed bias and confounding. Copyright © 2016 John Wiley & Sons, Ltd.

Prevalence and healthcare costs of obesity-related comorbidities: evidence from an electronic medical records system in the United States.

OBJECTIVE: This study estimated the economic burden of obesity-related comorbidities (ORCs) in the US, at both the person and population levels. METHODS: The Geisinger Health System provided electronic medical records and claims between January 2004 and May 2013 for a sample of 153,561 adults (50% males and 97% white). Adults with < 2 years of data, who were underweight (body mass index (BMI) < 18.5 kg/m(2)), or had diseases causing major weight change (e.g., malignancy) during the study period (i.e., continuous enrollment in health plans) were excluded. A total of 21 chronic conditions, with established association with obesity in the literature, were identified by diagnosis codes and/or lab test results. The total healthcare costs were measured in each year. The association between annual costs and ORCs was assessed by a regression, which jointly considered all the ORCs. The per-person incremental costs of a single comorbidity, without any of the other ORCs, were calculated. The population-level economic burden was the product of each ORC's incremental costs and the annual prevalence of the ORC among 100,000 individuals. The prevalence of ORCs was stratified by obesity status to estimate the economic burden among 100,000 individuals with obesity and among those without. RESULTS: This study identified 56,895 adults (mean age = 47 years; mean BMI = 29.6 kg/m(2)). The annual prevalence of ORCs ranged from 0.5% for pulmonary embolism (PE) to 41.8% for dyslipidemia. The per-person annual incremental costs of a single ORC ranged from $120 for angina to $1665 for PE. Hypertensive diseases (HTND), dyslipidemia, and osteoarthritis were the three most expensive ORCs at the population level; each responsible for ≥$18 million annually among 100,000 individuals. HTND and osteoarthritis were much more costly among individuals with obesity than those without obesity. LIMITATIONS: Data were from a small geographic region. CONCLUSIONS: ORCs are associated with substantial economic burden, especially for those requiring continuous treatments.

The Economic Burden of Obesity by Glycemic Stage in the United States.

BACKGROUND: Electronic medical records and insurance claims data from the Geisinger Health System were examined to assess the real-world healthcare costs of being overweight or obese at different glycemic stages, including normal glycemia, pre-diabetes (PreD), and type 2 diabetes (T2D). METHODS: The medical history of the sample subjects was segmented into different glycemic stages via diagnosis codes, glycosylated hemoglobin A1c or fasting plasma glucose laboratory results, and use of antidiabetic drugs. Healthcare resource utilization captured by the claims and associated costs (in 2013 values) were examined for each glycemic stage. The association between costs and body mass index (BMI) was estimated by regressions, and adjusted for sociodemographics. We predicted the adjusted incremental annual costs associated with high BMI, relative to normal BMI (18.5-24.9 kg/m(2)). RESULTS: We identified 48,344 adults in normal glycemic stage, 3,085 in the PreD stage, and 9,526 in the T2D stage (mean age 46, 58, and 60 years, respectively; mean BMI 29, 32, and 33 kg/m(2), respectively). The adjusted incremental annual costs associated with high BMI relative to normal BMI ranged from $336 for overweight (25-29.9 kg/m(2)) to $1,850 for class III obesity (≥40 kg/m(2)) during normal glycemic stage; were only significant for class III ($2,434) during the PreD stage; and ranged from $1,139 for overweight to $4,649 for class III during the T2D stage (all p < 0.05). CONCLUSIONS: Positive associations between healthcare costs and BMI levels were observed within each glycemic stage. Management of body weight is important in reducing the overall healthcare costs, especially for subjects with PreD or T2D.

Severe hypoglycemia rates and associated costs among type 2 diabetics starting basal insulin therapy in the United States.

OBJECTIVES: To derive current real-world data on the rates and costs of severe hypoglycemia (SH) for people with type 2 diabetes mellitus (T2D) who have initiated basal insulin therapy and to examine differences in SH rates and costs stratified by history of prior SH events. METHODS: We used a nation-wide electronic health records database that included encounter and laboratory data, as well as clinical notes, to estimate the rates and costs of SH events among adults with T2D who initiated basal insulin between 2008 and 2011. Unadjusted and regression-adjusted rates and quarterly costs were calculated for all patients as well as stratified by history of a SH event before starting basal insulin and history of a SH event during the basal insulin titration period. RESULTS: We identified 7235 incident cases of basal insulin use among patients with T2D who did not use insulin during the previous 12 months. Regression-adjusted incidence and total event rates were 10.36 and 11.21 per 100 patient-years, respectively. A history of SH events during the pre-index baseline and post-index titration periods were statistically significantly associated with both the incidence and total event rates (p < 0.01). Regression-adjusted total healthcare and diabetes-related costs were statistically significantly (p < 0.01) higher in those quarters when a SH event occurred than in those quarters without any SH events ($3591 vs. $487 and $3311 vs. $406, respectively). A history of previous SH or SH events during the titration period were not statistically significantly associated with costs. CONCLUSIONS: These results suggest that the real-world burden of SH is high among people with T2D who start using basal insulin and that history of previous SH events, both before starting insulin and during the insulin titration period, influences future SH. These results can also provide insights into interventions that can prevent or delay SH. These results should, however, be interpreted in light of the key limitations of our study: not all SH events may have been captured or coded in the database, data on filled prescriptions were not available, and the post-titration follow-up period could have been divided into time units other than quarters (3 month blocks) resulting in potentially different conclusions. Further real-world studies on the frequency and costs of SH, using methods to identify as many SH events as possible, can allow healthcare providers to make more informed decisions on the risks and benefits of basal insulin therapy in T2D patients.