Distribution and drivers of costs in type 2 diabetes mellitus treated with oral hypoglycemic agents: a retrospective claims data analysis

Identification of the risk factors of type 2 diabetes and its prediction using machine learning techniques

This study identified the risk factors for type 2 diabetes (T2D) and proposed a machine learning (ML) technique for predicting T2D. The risk factors for T2D were identified by multiple logistic regression (MLR) using p-value (p<0.05). Then, five ML-based techniques, including logistic regression, naïve Bayes, J48, multilayer perceptron, and random forest (RF) were employed to predict T2D. This study utilized two publicly available datasets, derived from the National Health and Nutrition Examination Survey, 2009-2010 and 2011-2012. About 4922 respondents with 387 T2D patients were included in 2009-2010 dataset, whereas 4936 respondents with 373 T2D patients were included in 2011-2012. This study identified six risk factors (age, education, marital status, SBP, smoking, and BMI) for 2009-2010 and nine risk factors (age, race, marital status, SBP, DBP, direct cholesterol, physical activity, smoking, and BMI) for 2011-2012. RF-based classifier obtained 95.9% accuracy, 95.7% sensitivity, 95.3% F-measure, and 0.946 area under the curve.

Delineating the Type 2 Diabetes Population in the Netherlands Using an All-Payer Claims Database: Specialist Care, Medication Utilization and Expenditures 2016-2018

OBJECTIVES

The aim of this study was to describe the healthcare utilization and expenditures related to medical specialist care and medication of the entire type 2 diabetes population in the Netherlands in detail.

METHODS

For this retrospective, observational study, we used an all-payer claims database. Comprehensive data on specialist care and medication utilization and expenditures of the type 2 diabetes population (n = 900,522 in 2018) were obtained and analyzed descriptively. Data were analyzed across medical specialties and for various types of diabetes medication (or glucose-lowering drugs [GLDs]) and other medication.

RESULTS

Specialist care utilization was diverse: different medical specialties were visited by a considerable fraction of the type 2 diabetes population. Total expenditures on specialist care were €2498 million in 2018 (i.e., 10.6% of the national specialist care expenditures). In total, 97.8% of patients used other medication (not GLDs) and 81.8% used GLDs; 25.6% of medication expenditures were for GLDs. For both specialist care and medication, mean expenditures per treated patient were higher than median expenditures, indicating a skewed distribution of spending.

CONCLUSION

Use of and expenditures on specialist care and medication of the type 2 diabetes population is diverse. These heterogeneous healthcare use patterns are likely caused by the presence of comorbidities. Additionally, we found that a small fraction of the population is responsible for a large share of the expenditures. A shift towards more patient-centered care could lead to health improvements and a reduction in overall costs, subsequently promoting the sustainability of healthcare systems.

Real-world evidence: the devil is in the detail

Much has been written about real-world evidence (RWE), a concept that offers an understanding of the effects of healthcare interventions using routine clinical data. The reflection of diverse real-world practices is a double-edged sword that makes RWE attractive but also opens doors to several biases that need to be minimised both in the design and analytical phases of non-experimental studies. Additionally, it is critical to ensure that researchers who conduct these studies possess adequate methodological expertise and ability to accurately implement these methods. Critical design elements to be considered should include a clearly defined research question using a causal inference framework, choice of a fit-for-purpose data source, inclusion of new users of a treatment with comparators that are as similar as possible to that group, accurately classifying person-time and deciding censoring approaches. Having taken measures to minimise bias 'by design', the next step is to implement appropriate analytical techniques (for example propensity scores) to minimise the remnant potential biases. A clear protocol should be provided at the beginning of the study and a report of the results after, including caveats to consider. We also point the readers to readings on some novel analytical methods as well as newer areas of application of RWE. While there is no one-size-fits-all solution to evaluating RWE studies, we have focused our discussion on key methods and issues commonly encountered in comparative observational cohort studies with the hope that readers are better equipped to evaluate non-experimental studies that they encounter in the future. Graphical abstract.

Medical Costs of Patients with Type 2 Diabetes in a Single Payer System: A Classification and Regression Tree Analysis

BACKGROUND AND OBJECTIVES

Many studies and systematic reviews have estimated the healthcare costs of diabetes using a cost-of-illness approach. However, in the studies based on this approach patients' heterogeneity is rarely taken into account. The aim of this study was to stratify patients with type 2 diabetes into homogeneous cost groups based on demographic and clinical characteristics.

METHODS

We conducted a retrospective cost-of-illness study by linking individual data on health services utilization retrieved from the administrative databases of Emilia-Romagna Region (Italy). Direct medical costs (either all-cause or diabetes-related) were calculated from the perspective of the regional health service, using tariffs for hospitalizations and outpatient services and the unit costs of prescriptions for drugs. The determinants of costs identified in a generalized linear regression model were used to characterize subgroups of patients with homogeneous costs in a classification and regression tree analysis.

RESULTS

The study population consisted of a cohort of 101,334 patients with type 2 diabetes, followed up for 1 year, with a mean age of 70.9 years. Age, gender, complications, comorbidities and living area accounted significantly for cost variability. The classification tree identified ten patient subgroups with different costs, ranging from a median of €483 to €39,578. The two subgroups with highest costs comprised dialysis patients, and the largest subgroup (57.9%) comprised patients aged ≥ 65 years without renal, cardiovascular and cerebrovascular complications.

CONCLUSIONS

Classification of patients into homogeneous cost subgroups can be used to improve the management of, and budget allocation for, patients with type 2 diabetes.

Dipeptidyl Peptidase-4 Inhibitor Development and Post-authorisation Programme for Vildagliptin - Clinical Evidence for Optimised Management of Chronic Diseases Beyond Type 2 Diabetes

The last decade has witnessed the role of dipeptidyl peptidase-4 (DPP-4) inhibitors in producing a conceptual change in early management of type 2 diabetes mellitus (T2DM) by shifting emphasis from a gluco-centric approach to holistically treating underlying pathophysiological processes. DPP-4 inhibitors highlighted the importance of acknowledging hypoglycaemia and weight gain as barriers to optimised care in T2DM. These complications were an integral part of diabetes management before the introduction of DPP-4 inhibitors. During the development of DPP-4 inhibitors, regulatory requirements for introducing new agents underwent substantial changes, with increased emphasis on safety. This led to the systematic collection of adjudicated cardiovascular (CV) safety data, and, where 95% confidence of a lack of harm could not be demonstrated, the standardised CV safety studies. Furthermore, the growing awareness of the worldwide extent of T2DM demanded a more diverse approach to recruitment and participation in clinical trials. Finally, the global financial crisis placed a new awareness on the health economics of diabetes, which rapidly became the most expensive disease in the world. This review encompasses unique developments in the global landscape, and the role DPP-4 inhibitors, specifically vildagliptin, have played in research advancement and optimisation of diabetes care in a diverse population with T2DM worldwide.

Costs of Managing Patients with Diabetes in a Large Health Maintenance Organization in Israel: A Retrospective Cohort Study

INTRODUCTION

The aim of this study was to evaluate the direct costs of patients with diabetes ensured in a large health maintenance organization, Maccabi Health Services (MHS), in order to compare the medical costs of these patients to the medical costs of other patients insured by MHS and to assess the impact of poorly controlled diabetes on medical costs.

METHODS

A retrospective analysis of patients insured in MHS during 2012 was performed. Data were extracted automatically from the electronic database. A glycated hemoglobin (HbA1c) level of >9% (75 mmol/mol) was considered to define poorly controlled diabetes, and that of <7% (53 mmol/mol) and <8% (64 mmol/mol) to define controlled diabetes for patients aged <75 and ≥75 years, respectively. Multivariate analysis analyses were done to assess factors affecting cost.

RESULTS

Data on a total of 99,017 patients with diabetes were obtained from the MHS database for 2012. Of these, 54% were male and 72% were aged 45-75 years. The median annual cost of treating diabetes was 4420 cost units (CU), with hospitalization accounting for 56% of the total costs. The median annual cost per patient in the age groups 35-44 and 75-84 years was 2836 CU and 7033 CU, respectively. Differences between costs for patients with diabetes and those for patients without diabetes was 85% for the age group 45-54 years but only 24% for the age group 75-84 years. Medical costs increased similarly with age for patients with controlled diabetes and those with poorly controlled diabetes costs, as did additional co-morbidities. Costs were significantly impacted by kidney disease. The costs for patients with an HbA1c level of 8.0-8.99% (64-74 mmol/mol) and 9.0-9.99% (75-85 mmol/mol) were 5722 and 5700 CU, respectively. In a multivariate analysis the factors affecting all patients' costs were HbA1C level, male gender, chronic diseases, complications of diabetes, disease duration, and stage of kidney function.

CONCLUSIONS

The direct medical costs of patients with diabetes were significantly higher than those of patients without diabetes. The main drivers of these higher costs were hospitalizations and renal function. In poorly controlled patients the effect of HbA1c on costs was limited. These findings suggest that it is cost effective to identify patients with diabetes early in the course of the disease.

FUNDING

The work was sponsored by internal funds of the authors. Article processing charges for this study was funded by Novo Nordisk.

Medical Care Costs Associated With Long-term Weight Maintenance Versus Weight Gain Among Patients With Type 2 Diabetes

OBJECTIVE

Weight loss is recommended for overweight patients with diabetes but avoidance of weight gain may be a more realistic goal. We calculated the 4-year economic impact of maintaining body weight versus gaining weight.

RESEARCH DESIGN AND METHODS

Among 8,154 patients with type 2 diabetes, we calculated weight change as the difference between the first body weight measure in 2010 and the last measure in 2013 and calculated mean glycated hemoglobin (A1C) from all measurements from 2010 to 2013. We created four analysis groups: weight change <5% and A1C <7%; weight gain ≥5% and A1C <7%; weight change <5% and A1C ≥7%; and weight gain ≥5% and A1C ≥7%. We compared change in medical costs between 2010 and 2013, adjusted for demographic and clinical characteristics.

RESULTS

Patients who maintained weight within 5% of baseline experienced a reduction in costs of about $400 regardless of A1C. In contrast, patients who gained ≥5% of baseline weight and had mean A1C ≥7% had an increase in costs of $1,473 (P < 0.001). Those who gained >5% of their baseline weight with mean A1C <7% had a modest increase in costs ($387, NS).

CONCLUSIONS

Patients who gained at least 5% of their baseline body weight and did not maintain A1C <7% over 4 years experienced a 14% increase in medical costs, whereas those who maintained good glycemic control had a mean cost increase of 3.3%. However, patients who maintained weight within 5% of baseline had costs that were ∼5% lower than baseline. Avoidance of weight gain may reduce costs in the long-term.

Drug usage patterns and treatment costs in newly-diagnosed type 2 diabetes mellitus cases, 2007 vs 2012: findings from a large US healthcare claims database analysis

Objective To explore trends in demographics, comorbidities, anti-diabetic drug usage, and healthcare utilization costs in patients with newly-diagnosed type 2 diabetes mellitus (T2DM) using a large US claims database. Methods For the years 2007 and 2012, Truven Health Marketscan Research Databases were used to identify adults with newly-diagnosed T2DM and continuous 12-month enrollment with prescription benefits. Variables examined included patient demographics, comorbidities, inpatient utilization patterns, healthcare costs (inpatient and outpatient), drug costs, and diabetes drug claim patterns. Results Despite an increase in the overall database population between 2007-2012, the incidence of newly-diagnosed T2DM decreased from 1.1% (2007) to 0.65% (2012). Hyperlipidemia and hypertension were the most common comorbidities and increased in prevalence from 2007 to 2012. In 2007, 48.3% of newly-diagnosed T2DM patients had no claims for diabetes medications, compared with 36.2% of patients in 2012. The use of a single oral anti-diabetic drug (OAD) was the most common diabetes medication-related claim (46.2% of patients in 2007; 56.7% of patients in 2012). Among OAD monotherapy users, metformin was the most commonly used and increased from 2007 (74.7% of OAD monotherapy users) to 2012 (90.8%). Decreases were observed for sulfonylureas (14.1% to 6.2%) and thiazolidinediones (7.3% to 0.6%). Insulin, predominantly basal insulin, was used by 3.9% of patients in 2007 and 5.3% of patients in 2012. Mean total annual healthcare costs increased from $13,744 in 2007 to $15,175 in 2012, driven largely by outpatient services, although costs in all individual categories of healthcare services (inpatient and outpatient) increased. Conversely, total drug costs per patient were lower in 2012 compared with 2007. Conclusions Despite a drop in the rate of newly-diagnosed T2DM from 2007 to 2012 in the US, increased total medical costs and comorbidities per individual patient suggest that the clinical and economic trends for T2DM are not declining.

Characterizing downstream healthcare resource utilization and costs based on prior utilization patterns of immediate-release hydrocodone

OBJECTIVE

To assess downstream healthcare resource utilization (HRU) and costs among immediate release (IR) hydrocodone patients by days' supply and average doses/month in the prior 6 months.

METHODS

Retrospective analysis using healthcare claims from Truven MarketScan commercial, Medicare supplemental, and Medicaid multistate databases was performed. Patients prescribed IR hydrocodone during the 6-month baseline (July-December 2011), and with continuous enrollment during baseline and the 12-month follow-up (2012) were selected. HRU and per-patient-per-month (PPPM) costs (2014 US dollars) were assessed at follow-up. Descriptive analyses and multivariate regressions were conducted to compare HRU and costs at follow-up by days' supply (<60 vs ≥60 days) and average doses per month (≤60 vs >60 doses/month) of IR hydrocodone at baseline.

RESULTS

In total, 1,698,845 commercial, 264,038 Medicare, and 151,063 Medicaid IR hydrocodone patients were identified. During follow-up, commercial patients with prior ≥60 days' supply were more likely to have an inpatient admission (13.2% vs 7.5%), outpatient hospital visit (69.1% vs 57.0%), office visit (97.6% vs 91.0%), emergency room (ER) visit (28.1% vs 21.4%), and had higher PPPM total costs ($1494 vs $842) than the <60 days' supply sub-group (all p < 0.05). Among commercial patients the adjusted odds ratio for prior ≥60 days' supply of IR hydrocodone vs prior <60 days' supply was 1.62 (inpatient), 1.33 (outpatient), 2.58 (office visit) and 1.48 (ER) (all p-values <0.05). Adjusted all-cause total costs were higher ($1245 vs $851, p <0.05) among commercial patients with longer days' supply than those with shorter days' supply. Trends were similar with ≤60 vs >60 doses per month sub-groups and across all plan types.

CONCLUSION

Increased days' supply and higher doses/month of IR hydrocodone in the prior 6 months may help to predict levels of HRU and costs in the following year, providing an opportunity to identify patients in order to implement interventions to improve their quality of care.

Flare frequency, healthcare resource utilisation and costs among patients with gout in a managed care setting: a retrospective medical claims-based analysis

OBJECTIVES

For most gout patients, excruciatingly painful gout attacks are the major clinical burden of the disease. The goal of this study was to assess the association of frequent gout flares with healthcare burden, and to quantify how much lower gout-related costs and resource use are for those with infrequent flares compared to frequent gout flares.

DESIGN

Retrospective cohort study.

SETTING

Administrative claims data from a large US health plan.

PARTICIPANTS

Patients aged 18 years or above, and with evidence of gout based on medical and pharmacy claims between January 2009 and April 2012 were eligible for inclusion. Patient characteristics were assessed during a 12-month baseline period.

OUTCOME MEASURES

Frequency of gout flares, healthcare costs and resource utilisation were assessed in the 12 months following the first qualifying gout claim. Generalised linear models were employed to assess the impact of flare frequency on cost outcomes after adjusting for covariates.

RESULTS

102,703 patients with gout met study inclusion criteria; 89,201 had 0-1 gout flares, 9714 had 2 flares, and 3788 had 3+ flares. Average counts of gout-related inpatient stays, emergency room visits and ambulatory visits were higher among patients with 2 or 3+ flares, compared to those with 0-1 flares (all p<0.001). Adjusted annual gout-related costs were $1804, $3014 and $4363 in those with 0-1, 2 and 3+ gout flares, respectively (p<0.001 comparing 0-1 flares to 2 or 3+ flares).

CONCLUSIONS

Gout-related costs and resource use were lower for those with infrequent flares, suggesting significant cost benefit to a gout management plan that has a goal of reducing flare frequency.