Health care resource utilization and costs among patients with spasticity or cervical dystonia.

BACKGROUND: Spasticity and cervical dystonia (CD) are movement disorders with considerable direct and indirect health care cost implications. Although several studies have discussed their clinical impact, few have calculated the economic burden of these disorders. OBJECTIVE: To assess the all-cause health care resource utilization (HCRU) and costs in adults and children with spasticity or CD. METHODS: This retrospective, observational cohort-based study was conducted using administrative insurance claims from the IQVIA PharMetrics Plus database from October 1, 2015, to December 31, 2019. Patients were selected based on International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes for first evidence of spasticity (associated with a spasticity etiology) or CD (index date) during the selection window, from April 1, 2016, through December 31, 2018. Cases were stratified into 3 mutually exclusive cohorts: adult patients with spasticity, pediatric patients with spasticity, and patients with CD; those with spasticity who had a history of stroke or cerebral palsy were also evaluated in subcohorts. Patients without evidence of spasticity or CD during the study period were identified as a matched comparator group and were randomly assigned an index date. Patients with spasticity were matched 1:1 to the comparator group based on age, sex, index year, and payer type using descriptive analyses. RESULTS: 215,739 adult patients with spasticity, 29,644 pediatric patients with spasticity, and 9,035 adult patients with CD were identified after matching. Adult patients with spasticity and CD had mean (SD) ages of 48.4 (15.6) years and 48.0 (13.1) years, respectively. Stroke was identified in 31.9% (n = 68,928) of adult patients with spasticity, and cerebral palsy was identified in 11.3% (n = 3,364) of pediatric patients with spasticity. Adult and pediatric patients with spasticity and patients with CD had significantly higher HCRU (including mean number of outpatient, emergency department, and inpatient visits and proportions of patients with prescription fills) and higher mean total health care costs per patient (adult patients with spasticity $29,912 vs $7,464; pediatric patients with spasticity $16,089 vs $2,963; and patients with CD $20,168 vs $7,141) than matched comparators (all P<0.0001). CONCLUSIONS: The management of patients with spasticity or CD results in considerably higher health care expenses. Within managed health care systems, more effective management of spasticity and CD in adult and pediatric patients represents a significant opportunity for cost savings.

Botulinum Toxin Utilization, Treatment Patterns, and Healthcare Costs Among Patients with Spasticity or Cervical Dystonia in the US.

INTRODUCTION: Spasticity and cervical dystonia (CD) are movement disorders with considerable direct and indirect healthcare cost implications. Although several studies have discussed their clinical impact, few have calculated the economic burden of these disorders. This study aimed to understand treatment/injection patterns of botulinum toxins type A (BoNT-As) and the characteristics, healthcare resource utilization (HCRU), and costs among patients with spasticity or CD. METHODS: Retrospective analyses were conducted using administrative healthcare claims from the IQVIA PharMetrics® Plus database, from October 1, 2015 to December 31, 2019. Eligible patients were selected based on Healthcare Common Procedure Coding System codes for BoNT-A (index date) and ICD-10 diagnosis codes for spasticity or CD with 6 months of continuous enrollment pre-index and 12 months post-index. Patients were stratified into adult spasticity, pediatric spasticity, and CD cohorts, and were evaluated for injection patterns, HCRU, and costs in the post-index period. RESULTS: Overall, 2452 adults with spasticity, 1364 pediatric patients with spasticity, and 1529 adults with CD were included. Total mean all-cause healthcare costs were US$42,562 (adult spasticity), $54,167 (pediatric spasticity), and $25,318 (CD). Differences were observed in the cost of BoNT-A injection visits between toxins, with abobotulinumtoxinA (aboBoNT-A) having the lowest injection cost across all indications. CONCLUSIONS: AboBoNT-A had the lowest injection visit costs across indications. These results are suggestive of real-world resource utilization patterns and costs, and, while helpful in informing insurers' BoNT-A management strategies, further research into cost differences is warranted.

Spasticity is an abnormal, involuntary muscle tightness due to extended muscle contraction. This resistance in movement can be caused by stroke, multiple sclerosis, or traumatic injuries to the brain or spinal cord. Cervical dystonia is a form of sustained involuntary muscle contractions that result in abnormal or repetitive muscle movements in the neck and upper shoulders. Spasticity and cervical dystonia are both associated with significant decrease in quality of life and work productivity as well as significant economic burden. It is therefore important to understand how disease management impacts these patients. Many studies have shown that botulinum toxins type A (BoNT-As) are safe and effective in reducing muscle tightness and improving normal range of motion. This study was conducted to better understand BoNT-A injection patterns, use of healthcare services, and the resulting costs in patients with spasticity or cervical dystonia.

Performance evaluation of the mouse version of the LabPET II PET scanner.

The LabPET II is a new positron emission tomography technology platform designed to achieve submillimetric spatial resolution imaging using fully pixelated avalanche photodiodes-based detectors and highly integrated parallel front-end processing electronics. The detector was designed as a generic building block to develop devices for preclinical imaging of small to mid-sized animals and for clinical imaging of the human brain. The aim of this work is to assess the physical characteristics and imaging performance of the mouse version of LabPET II scanner following the NEMA NU4-2008 standard and using high resolution phantoms and in vivo imaging applications. A reconstructed spatial resolution of 0.78 mm (0.5 µ l) is measured close to the center of the radial field of view. With an energy window of 350 650 keV, the system absolute sensitivity is 1.2% and its maximum noise equivalent count rate reaches 61.1 kcps at 117 MBq. Submillimetric spatial resolution is achieved in a hot spot phantom and tiny bone structures were resolved with unprecedented contrast in the mouse. These results provide convincing evidence of the capabilities of the LabPET II technology for biomolecular imaging in preclinical research.

Healthcare Costs of Diabetes and Microvascular and Macrovascular Disease in Individuals with Incident Type 2 Diabetes Mellitus: A Ten-Year Longitudinal Study.

OBJECTIVE: The objective of this study was to estimate the incremental long-term costs associated with T2DM attributable to vascular diseases. RESEARCH DESIGN AND METHODS: This retrospective cohort study identified newly diagnosed (incident) T2DM patients in 2007 (baseline to 01/01/2006) using the HealthCore Integrated Research Database, a repository of nationally representative claims data. Incident T2DM patients were 1:1 exact matched on age, gender and other factors of interest to non-DM patients, and followed until the earlier of 8 follow-up years or death. Patients with documented vascular disease diagnosis were identified during the study period. All-cause and T2DM/vascular disease-related annual healthcare costs were examined for each follow-up year. RESULTS: The study included 13,883 individuals with T2DM and matched non-DM controls. Among individuals with T2DM, 11,792 (85%) had vascular disease versus 9251 (66.6%) non-T2DM between 01/01/2006 and 12/31/2015. Among T2DM patients, mean all-cause annual costs were greater than in non-T2DM patients ($13,806 vs $7,243, baseline, $21,745 vs $8,524, post-index year 1, $12,756-$14,793 vs $8,349-$9,940 years 2-8, p< 0.001), respectively. A similar trend was observed for T2DM/vascular disease-related costs (p< 0. 001). T2DM/vascular disease-related costs were largest during post-index year 1, accounting for the majority of all-cause cost difference between T2DM patients and matched non-DM controls. Incident T2DM individuals without vascular disease at any time had significantly lower costs compared to non-DM controls (p< 0. 001) between years 2-8 of follow-up. CONCLUSION: Vascular disease increased the cost burden for individuals with T2DM. The cost impact of diabetes and vascular disease was highest in the year after diagnosis, and persisted for at least seven additional years, while the cost of T2DM patients without vascular disease trended lower than for matched non-DM patients. These data highlight potential costs that could be offset by earlier and more effective detection and management of T2DM aimed at reducing vascular disease burden.

MRI-compatibility study of a PET-insert based on a low-profile detection front-end with submillimeter spatial resolution.

PURPOSE: The LabPET II detection module is a potential candidate to create an magnetic resonance imaging (MRI) compatible positron emission tomography (PET)-insert with submillimeter spatial resolution for small animal applications. However, the feasibility of such an insert is hampered by the large radial size of the LabPET II front-end electronics and by components containing ferromagnetic materials. In this paper, a new low-profile front-end design based on the LabPET II architecture, called "low-profile detection module," is investigated. MATERIALS AND METHODS: The performance of the low-profile detection module in the presence of MRI-like RF signals and gradient coil pulses was independently examined. The baseline of the analog signal, its RMS noise level, and the energy resolution, determined by a dual time-over-threshold (dTOT) method for each pixel of the new low-profile detection module, was measured in the presence of RF signals at different frequencies equivalent to the Larmor frequency of 3, 7, and 9.4 T MRI. The same parameters were investigated in the presence of a gradient coil switching at frequencies from 10 to 100 kHz. The performance of the low-profile detection module inside a 7 T MRI and its effects on an MR image have also been studied using gradient echo sequences. The same measurements were repeated for the shielded low-profile detection module, inside and outside the MRI. RESULTS: Our results show that pulses in both the kilohertz and megahertz ranges cause up to 50% increase in the noise level of the baseline (DC analog signal at the output of the shaper filter) and up to 17% degradation in TOT energy resolution. By inserting a conducting composite layer as shielding around the low-profile detection module, these degrading effects were avoided. The performance measurement of the low-profile PET detection module inside a 7 T small animal MRI scanner confirmed that the shielded low-profile detection module behavior was similar inside and outside the MRI bore. In addition, gradient echo images of a water-filled phantom without and with the shielded and unshielded low-profile detection modules were acquired. The results demonstrated no evidence of artifacts in the MR image, either due to eddy currents or ferromagnetic materials with the shielded modules. CONCLUSION: A low-profile detection module based on the LabPET II technology was shown to be a viable candidate as a PET-insert for simultaneous PET/MRI applications considering its thin radial size and its EMI immunity due to placing it between two electronic boards. In comparison to the standard LabPET II detection module, it provides better performance in the presence of electromagnetic interferences, but a shielding layer is still required. When properly shielded, the proposed low-profile detection module can be operated inside an MRI without degrading the PET count rate or the MRI performance.

Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes.

INTRODUCTION: To identify predictors of hypoglycemia and five other clinical and economic outcomes among treated patients with type 2 diabetes (T2D) using machine learning and structured data from a large, geographically diverse administrative claims database. METHODS: A retrospective cohort study design was applied to Optum Clinformatics claims data indexed on first antidiabetic prescription date. A hypothesis-free, Bayesian machine learning analytics platform (GNS Healthcare REFS™: Reverse Engineering and Forward Simulation) was used to build ensembles of generalized linear models to predict six outcomes defined in patients' 1-year post-index claims history, including hypoglycemia, antidiabetic class persistence, glycated hemoglobin (HbA1c) target attainment, HbA1c change, T2D-related inpatient admissions, and T2D-related medical costs. A unified set of 388 variables defined in patients' 1-year pre-index claims history constituted the set of predictors for all REFS models. RESULTS: The derivation cohort comprised 453,487 patients with a T2D diagnosis between 2014 and 2017. Patients with comorbid conditions had the highest risk of hypoglycemia, including those with prior hypoglycemia (odds ratio [OR] = 25.61) and anemia (OR = 1.29). Other identified risk factors included insulin (OR = 2.84) and sulfonylurea use (OR = 1.80). Biguanide use (OR = 0.75), high blood glucose (> 125 mg/dL vs. < 100 mg/dL, OR = 0.47; 100-125 mg/dL vs. < 100 mg/dL, OR = 0.53), and missing blood glucose test (OR = 0.40) were associated with reduced risk of hypoglycemia. Area under the curve (AUC) of the hypoglycemia model in held-out testing data was 0.77. Patients in the top 15% of predicted hypoglycemia risk constituted 50% of observed hypoglycemic events, 26% of T2D-related inpatient admissions, and 24% of all T2D-related medical costs. CONCLUSIONS: Machine learning models built within high-dimensional, real-world data can predict patients at risk of clinical outcomes with a high degree of accuracy, while uncovering important factors associated with outcomes that can guide clinical practice. Targeted interventions towards these patients may help reduce hypoglycemia risk and thereby favorably impact associated economic outcomes relevant to key stakeholders.

Performance investigation of LabPET II detector technology in an MRI-like environment.

The EMI-compatibility of the LabPET II detection module (DM) to develop a high-resolution simultaneous PET/MRI system is investigated. The experimental set-up evaluates the performance of two LabPET II DMs in close proximity to RF coils excited at three different frequencies mimicking the electromagnetic environments of 3 T, 7 T, and 9.4 T MRI scanners. A gradient coil, with switching frequency from 10 kHz to 100 kHz, also surrounds one of the DMs to investigate the effects of the gradient field on the individual detector performance, such as the baseline of the DC-voltage and noise level along with both the energy and coincidence time resolutions. Measurements demonstrate a position shift of the energy photopeaks (⩽9%) and a slight deterioration of the energy and coincidence time resolutions in the presence of electromagnetic interferences from the gradient and RF coils. The electromagnetic interferences cause an average degradation of up to ~50% of the energy resolution (in time-over-threshold spectra) and up to 18% of the timing resolution. Based on these results, a modified version of the DM, including a composite shielding as well as an improved heat pipe-based cooling mechanism, capable of stabilizing the temperature of the DM at ~40 °C, is proposed and investigated. This shielded version shows no evidence of performance degradation inside an MRI-like environment. The experimental results demonstrate that a properly shielded version of the LabPET II DM is a viable candidate for an MR-compatible PET scanner.

Incidence and Prevalence of Microvascular and Macrovascular Diseases and All-cause Mortality in Type 2 Diabetes Mellitus: A 10-year Study in a US Commercially Insured and Medicare Advantage Population.

PURPOSE: The relationship between type 2 diabetes mellitus (T2DM) and increased microvascular and macrovascular disease and mortality is well established; however, data for the broad US T2DM population, especially by age, are limited. To help address this issue, we conducted a cohort study in a large national US commercially insured/Medicare Advantage population that incorporated a broad range of different age groups, including a large subset of younger individuals, during a 10-year study period. METHODS: This longitudinal study combined health plan claims and mortality data to identify incident T2DM patients and 1:1 directly matched non-DM controls. T2DM individuals (n = 13,883) were identified by a medical claim with a T2DM diagnosis or T2DM medication pharmacy claim in 2007; non-DM controls had no DM medical or pharmacy claims over the entire study period (January 1, 2006 to December 31, 2015). The outcomes assessed were incidence, prevalence, time to vascular disease and all-cause mortality, as well as age-stratified incidence and mortality based on Centers of Disease Control and Prevention-defined age strata. FINDINGS: Individuals with T2DM developed vascular disease at twice the rate as non-DM controls, 197 versus 98 per 1000 person-years, respectively. Vascular disease (composite) rates increased by age in T2DM/non-DM groups, 107.1/28.2 (18-44 years), 166.3/70.3 (45-64 years), and 391.0/199.7 (≥65 years) per 1000 person-years. The largest rate ratio was observed in younger individuals. All-cause mortality over follow-up was higher in T2DM individuals (27.5%) than in non-DM controls (19.6%). The largest increases in vascular disease prevalence and mortality among T2DM individuals were observed in the first year of follow-up. IMPLICATIONS: T2DM has a substantial effect on microvascular and macrovascular disease and all-cause mortality rates in all age groups. These outcomes appear to occur early after T2DM diagnosis, and have more pronounced, nearly fourfold, relative impact on younger individuals with T2DM compared to matched non-DM controls.

Studying the effects of metallic components of PET-insert on PET and MRI performance due to gradient switching.

Inserting positron emission tomography (PET) detection modules inside an MRI bore imposes extra challenges owing to the behavior of metallic materials in a strong magnetic field. The metallic parts even when placed outside an MRI field of view may not only disturb MRI performance, but could also increase temperature and vibrations, leading to premature failure of PET electronics. To investigate the compatibility of detection modules inside 3 T, 7 T and 9.4 T MRI bore, a theoretical study of the metal induced artifacts originating from component materials of electronic circuit is presented. The LabPET II detection module and a modified version of it in which the connector was replaced by ball grid array (BGA) were studied. In addition, the effect of eddy current and the associated heat loss on the PET detection module have been examined using COMSOL Multiphysics® simulations for 10 kHz and 100 kHz gradient switching. Results show that displacement artifacts resulting from the presence of small amounts of ferromagnetic metal and the heating effects of metal due to gradient switching can be compensated by using the slightly modified LabPET II detection module. Thus, the LabPET II system would be MR-compatible with some minor adjustments to operate effectively inside an MRI bore without interfering with its performance.

Estimating the Real-World Cost of Diabetes Mellitus in the United States During an 8-Year Period Using 2 Cost Methodologies.

BACKGROUND: Diabetes is associated with substantial clinical and economic burdens on patients and on the US healthcare system. Treatment options for patients with type 1 or type 2 diabetes have increased significantly, from only 3 drug classes in 1995 to more than 12 distinct classes today. Although several of the newer treatments are reported to have improved efficacy and safety profiles, they are often substantially more costly than older medications. Consequently, as drug options increase, the cost of diabetes management continues to grow. OBJECTIVES: To estimate the annual real-world costs of type 1 and 2 diabetes, as well as diabetes prevalence, treatment patterns, care quality, and resource utilization during 8 years. METHODS: In this cross-sectional study, we examined 8 annual cohorts of patients with type 1 or type 2 diabetes, on a biennial basis, using claims data from the HealthCore Integrated Research Database between 2006 and 2014. Patients were matched with controls by age, sex, residency, and health plan type. We assessed the prevalence of diabetes, treatment patterns, care quality measures, and all-cause and diabetes-related healthcare costs using 2 methods. Method 1 calculated the annual costs as the difference in all-cause costs between patients with diabetes and matched controls. Method 2 calculated the costs for healthcare encounters based on specific codes for a diabetes diagnosis or for antidiabetes medications. RESULTS: Between 346,486 and 410,234 patients with type 2 diabetes and between 21,176 and 26,228 patients with type 1 diabetes were included in each study year cohort. Between 2007 and 2014, the prevalence of type 2 diabetes increased from 4.9% to 6.3%. The costs associated with using Method 1 were almost double the cost estimates in Method 2 during most of the study period. For patients with type 1 diabetes, the associated costs were twice greater with Method 1 than with Method 2. Projections to the entire US population in 2014 indicated a total of 19.3 million individuals with diabetes and associated direct costs of $314.8 billion that year. CONCLUSION: Cost estimates can guide the prioritization of healthcare expenditures. The results of this study showed that costs attributable to diabetes differed by approximately 2-fold, depending on the estimation method. The management of the escalating expenses for diabetes management in the United States requires judicious selection of the methods for estimating costs.

Understanding Prediabetes in a Medicare Advantage Population Using Data Adaptive Techniques.

The objective was to identify individuals with undiagnosed prediabetes from administrative data using adaptive techniques. The data source was a national Medicare Advantage Prescription Drug (MAPD) plan administrative data set. A retrospective, cross-sectional study developed and evaluated data adaptive logistic regression, decision tree, neural network, and ensemble predictive models for metabolic syndrome and prediabetes using 3 mutually exclusive cohorts (N = 279,903). The misclassification rate (MCR), average squared error (ASE), c-statistics, sensitivity (SN), and false positive (FP) rates were compared to select the final predictive models. MAPD individuals with continuous enrollment from 2013 to 2014 were included. Metabolic syndrome and prediabetes were defined using clinical guidelines, diagnosis, and laboratory data. A total of 512 variables identified through subject matter expertise in addition to utilizing all data available were evaluated for the modeling. The ensemble model demonstrated better discrimination (c-statistics, MCR, and ASE of 0.83, 0.24, and 0.16, respectively), high SN, and low FP rate in predicting metabolic syndrome than the individual data adaptive modeling techniques. Logistic regression demonstrated better discrimination (c-statistics, MCR, and ASE of 0.67, 0.13, and 0.11 respectively), high SN, and low FP rate in predicting prediabetes than the other adaptive modeling techniques or ensemble methods. The scored data predicted prediabetes in 44% of the MAPD population, which is comparable to 2005-2006 National Health and Nutrition Examination Survey prediabetes rates of 41%. The logistic regression model demonstrated good performance in predicting undiagnosed prediabetes in MAPD individuals.

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.

Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system.

OBJECTIVE: To determine the prevalence of obesity and its related comorbidities among patients being actively managed at a US academic medical centre, and to examine the frequency of a formal diagnosis of obesity, via International Classification of Diseases, Ninth Revision (ICD-9) documentation among patients with body mass index (BMI) ≥30 kg/m2. DESIGN: The electronic health record system at Cleveland Clinic was used to create a cross-sectional summary of actively managed patients meeting minimum primary care physician visit frequency requirements. Eligible patients were stratified by BMI categories, based on most recent weight and median of all recorded heights obtained on or before the index date of 1July 2015. Relationships between patient characteristics and BMI categories were tested. SETTING: A large US integrated health system. RESULTS: A total of 324 199 active patients with a recorded BMI were identified. There were 121 287 (37.4%) patients found to be overweight (BMI ≥25 and <29.9), 75 199 (23.2%) had BMI 30-34.9, 34 152 (10.5%) had BMI 35-39.9 and 25 137 (7.8%) had BMI ≥40. There was a higher prevalence of type 2 diabetes, pre-diabetes, hypertension and cardiovascular disease (P value<0.0001) within higher BMI compared with lower BMI categories. In patients with a BMI >30 (n=134 488), only 48% (64 056) had documentation of an obesity ICD-9 code. In those patients with a BMI >40, only 75% had an obesity ICD-9 code. CONCLUSIONS: This cross-sectional summary from a large US integrated health system found that three out of every four patients had overweight or obesity based on BMI. Patients within higher BMI categories had a higher prevalence of comorbidities. Less than half of patients who were identified as having obesity according to BMI received a formal diagnosis via ICD-9 documentation. The disease of obesity is very prevalent yet underdiagnosed in our clinics. The under diagnosing of obesity may serve as an important barrier to treatment initiation.

Treatment patterns and clinical characteristics of patients with type 2 diabetes mellitus according to body mass index: findings from an electronic medical records database.

OBJECTIVE: This study evaluated relationships between glycaemic control, body mass index (BMI), comorbidities and pharmacological treatment in patients with type 2 diabetes mellitus (T2D). RESEARCH DESIGN AND METHODS: This was a retrospective, cross-sectional analysis of Quintiles electronic medical records research data (study period 1 October 2013-30 September 2014). Eligibility included age ≥18 years, T2D diagnosis, and at least one available BMI measurement. RESULTS: The study included 626 386 patients (mean age, 63.8 year; 51.3% female; 78.5% white; 62.6%, BMI ≥30 kg/m2). A1c data were available for 414 266 patients. The proportion of patients with good glycaemic control (A1c ≤6.5) decreased as BMI category increased, ranging from 40.1% of patients with BMI <30% to 30.1% of patients with BMI ≥40. The proportions of patients with poor glycaemic control (A1c >8% and A1c ≥9%) increased with increasing BMI category. Oral antidiabetic drugs (OAD) were the most frequently used (54.4% of patients with A1c values). Among patients using insulin-based therapy, 50% had an A1c ≥8% and 29% had an A1c ≥9% regardless of concomitant OAD or glucagon-like peptide 1 receptor agonist use. Among patients using three or more OADs, 34.3% and 16.1% had A1c values ≥8% and ≥9%, respectively. There was no common trend observed for changes in the proportion of patients with T2D-related comorbidities according to BMI category. The most notable trend was a 7.6% net increase in the percentage of patients with hypertension from BMI <30 to BMI ≥40. CONCLUSIONS: This large dataset provides evidence that roughly one out of four patients with T2D is not well controlled, and the prevalence of poor glycaemic control increases as BMI increases.

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.

Predictors of all-cause 30 day readmission among Medicare patients with type 2 diabetes.

OBJECTIVE: Readmission is costly among patients with type 2 diabetes (T2DM) in Medicare Advantage Prescription Drug Plans; identifying high-risk patients is necessary for targeting reduction programs. The objective of this study was to develop a claims-based algorithm to predict all-cause 30 day readmission among patients with T2DM. METHODS: This study used administrative data from 1 January 2012 through 31 January 2014. The cohort included hospitalized T2DM patients, aged 18-90 with ≥12 months' continuous enrollment before an unplanned hospital admission and ≥1 month of enrollment post-discharge, excluding patients in long-term care >30 days pre-index. Multivariate logistic regression predicted the likelihood of readmission following hospitalization in 2013. The analytic file was randomly split into training and test datasets to build and validate the model. Candidate variables included physician and patient demographics, baseline clinical conditions, and healthcare utilization metrics. Clinical conditions were classified using the Healthcare Cost and Utilization Project clinical classification system for ICD-9-CM. RESULTS: Of 63,237 individuals, 17.1% experienced a readmission. Of nearly 200 candidate variables, 14 were predictors of readmission, including total cumulative number of days for inpatient stays and the number of emergency department visits in the baseline period. Male gender, older age, and certain comorbidities were associated with higher likelihood of readmission. The final model demonstrated good discriminant ability (c-statistic = 0.82). CONCLUSIONS: This study provided evidence that certain patient characteristics and healthcare utilization are predictive of readmission. An algorithm with good discriminant ability was developed which could be used to target readmission reduction programs. Physician gender, specialty, and ownership status did not appear to influence the likelihood of readmission.

A Historical Retrospective Analysis of the Impact of Diabetes Quality Measure Attainment on Outcomes in Medicare Advantage Members.

The objective of this study was to assess achievement of 4 diabetes mellitus (DM)-related quality measures (QMs) and examine the relationship between QM attainment, concurrent health care costs, and DM complications over 1 year by conducting a retrospective analysis of claims data for Medicare Advantage Prescription Drug plan members with DM. Claims and member-level quality data were used to assess QM achievement, concurrent health care costs, and presence of new or worsening DM complications during the QM year. Multivariable regression models were used to examine the relationship between QM achievement and outcome measures controlling for potentially confounding baseline characteristics. QM attainment rates ranged from 54.2% for DM Treatment measure to 83.4% for Cholesterol Screening measure. Odds of new or worsening complications were greater for members who did not meet the Blood Sugar Controlled performance goal (odds ratio [OR]: 1.12, P < 0.001), DM Treatment goal (OR: 1.40, P < 0.001), or Cholesterol Screening goal (OR: 1.32, P < 0.001). Failure to attain the DM Medication Adherence goal was associated with lower odds of new or worsening complications (OR: 0.94, P < 0.001). In the regression models, all-cause health care costs were greater for members who achieved the Blood Sugar Controlled quality goal (P < 0.001), but lower for members who attained DM Treatment (P < 0.001) and low-density lipoprotein Cholesterol Screening goals (P < 0.001). There was no statistically significant relationship between attaining the DM Medication Adherence measure and all-cause costs. Achievement rates for individual QMs varied across the study population and relationships between QM attainment, health care costs, and DM complications during the QM measurement year were mixed.

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.

Changes in Characteristics and Treatment Patterns of Patients with Newly Diagnosed Type 2 Diabetes in a Large United States Integrated Health System between 2008 and 2013.

To assess changes in the clinical characteristics and treatment patterns of patients with newly diagnosed type 2 diabetes (T2D), the electronic health record system at Cleveland Clinic was used to create cross-sectional summaries of all patients with new-onset T2D in 2008 and 2013. Differences between the 2008 and 2013 data sets were assessed after adjusting for age, gender, race, and income. Approximately one-third of patients with newly diagnosed T2D in 2008 and 2013 had an A1C ≥8%, suggesting the continued presence of a delayed recognition of the disease. Patients with newly diagnosed T2D in 2008 were older than those in 2013. Hypertension, cardiovascular disease, and neuropathy were highly prevalent among patients diagnosed with T2D. The prevalence of neuropathy, cerebrovascular disease, and peripheral vascular disease increased from 2008 to 2013. Metformin was the most commonly prescribed antidiabetic medication. Sulfonylurea usage remained unchanged, while use of thiazolidinediones decreased considerably.