Real life treatment of diabetes mellitus type 2 patients: An analysis based on a large sample of 394,828 German patients

diabetes research and clinical practice 106 (2014) 275–285

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Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres

Real life treatment of diabetes mellitus type 2 patients: An analysis based on a large sample of 394,828 German patients Thomas Wilke a,*, Antje Groth a, Andreas Fuchs b, Lisa Seitz c, Joachim Kienho¨fer c, Rainer Lundershausen d, Ulf Maywald b a

Institut fu¨r Pharmakoo¨konomie und Arzneimittellogistik (IPAM), Hochschule Wismar, Germany AOK PLUS, Dresden, Germany c Novo Nordisk Pharma GmbH, Germany d Diabeteszentrum Erfurt/Bad Berka, Germany b

article info

abstract

Article history:

Objectives: The aim of this claims-based data analysis was to describe the care of German

Received 20 January 2014

T2DM patients and to determine which subgroups could be differentiated in terms of the

Received in revised form

achieved T2DM-related treatment results, the underlying comorbidities, and the achieved

23 May 2014

comorbidity-related treatment results.

Accepted 3 August 2014

Methods: We included all T2DM patients insured by a large sickness fund in 2010/2011. We

Available online 10 August 2014

defined 12 subgroups according to observed HbA1C, blood pressure and Charlson Comor-

Keywords:

were reported. Different treatment variables were described. T2DM-related events leading

bidity Index (CCI). For each subgroup, available sociodemographic and clinical information Type 2 diabetes mellitus

to acute hospitalisations were reported.

Real life treatment

Results: We included 394,828 T2DM patients in our analysis; for 228,703 patients’ detailed

Patient profiles

data as basis for subgroup classification were available. For 4.5% of these patients, a HbA1C

Micro- and macrovascular events

>9% was observed. 21,833 of the T2DM patients were affected by a T2DM-related event; the risk was 5.53% per patient year; 1.74% of the patients suffered from more than one event. Most frequent event types were hospitalisation with T2DM as primary diagnosis (2.39%), vascular interventions/stent implantations (1.92%), and ischaemic stroke (1.19%). There were significant differences between the observed subgroups in terms of T2DM-related event risk. Conclusion: Overall, our data indicate that the typically treated T2DM patient has a number of comorbidities and thus treatment focused solely on T2DM is neither possible nor clinically meaningful. Particularly those patients who reached HbA1C goals, but had also achieved relevant additional treatment goals reached low yearly T2DM event rates whereas subgroups failing to achieve one or several treatment goals are facing much higher event risks. # 2014 Elsevier Ireland Ltd. All rights reserved.

* Corresponding author at: IPAM, Hochschule Wismar, PF 1210, 23952 Wismar, Germany. Tel.: +49 3841 753 504; fax: +49 3841 7581 011. E-mail address: [email protected] (T. Wilke). http://dx.doi.org/10.1016/j.diabres.2014.08.002 0168-8227/# 2014 Elsevier Ireland Ltd. All rights reserved.

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1.

diabetes research and clinical practice 106 (2014) 275–285

Introduction

Amongst the most common chronic diseases, type 2 diabetes mellitus (T2DM) presents some of the greatest clinical and health-economic challenges [1]. In addition to the burdens directly associated with this disease, T2DM patients also face an increased frequency of micro- and macrovascular complications and increased mortality rates [2]. Current data show the high prevalence of T2DM in industrialised countries; based on the whole population in these regions, figures reach at least 5% and prevalence rises sharply with increasing age to at least 25% in those over 85 years-old [3–5]. In light of the numerous options available for T2DM treatment and the fact that many T2DM patients already have a multitude of comorbidities, it is obvious that there is no such thing as a ‘typical’ T2DM patient. Instead, in order to estimate the patient-specific risk for a micro-/macrovascular event and to sensibly assess the T2DM treatment and its results, patients should be divided into subgroups based on T2DM status in terms of progression of the disease, quality of T2DM care, existence of comorbidities potentially related to micro-/ macrovascular risk, and the quality of care related to these comorbidities [6]. There are only limited detailed data available on the reallife care of T2DM patients; this is also true for information regarding micro- and macrovascular event rates and/or T2DM-related treatment costs [7–12]. In some cases, such information was previously derived from cohort studies or surveys with selected centres/patients or from registers that did not include all of the important levels of care. Thus, such studies were subject to low sample sizes and to a potential, often unquantified, bias [6,9]. In addition, previous studies have analysed the care of T2DM patients almost exclusively by focusing on the development of T2DMrelated surrogate outcomes such as HbA1C and BMI [7–12]. Other clinical parameters related to additional comorbidities such as blood pressure or even the pure existence of these comorbidities rarely play a role in the literature focusing on the description of treatment of T2DM patients [7,9]. Studies based on large claims data sets do not have the drawbacks mentioned above [11,13,25,28]. Claims datasets usually contain information for large unselected patient collectives regarding all diagnosed diseases; the size of the samples also allows subgroup analysis. Nevertheless, to date, such datasets have only been used occasionally to describe the care of T2DM patients. The main reason for this is that essential clinical information is usually not available in these datasets. In our analysis, we had access to a large dataset that included this clinical information. The aim of this article, therefore, is to describe the inpatient/outpatient care of German T2DM patients and to determine which subgroups could be differentiated in terms of the achieved T2DMrelated treatment results, the underlying comorbidities, and the achieved comorbidity-related treatment results. We use data from one of the largest German sickness funds (AOK PLUS).

2.

Methods

2.1.

T2DM sample and T2DM prevalence

The basis of the analysis was a pseudonymised dataset from AOK PLUS which included all T2DM patients from 2010 and 2011 who were insured by this sickness fund for the entire study period. The dataset used available information on the sociodemographic characteristics of the patients, their treatment with prescription aids and medication, their outpatient and inpatient treatment, and T2DM-specific parameters and any treatment results for the patients who were registered with AOK PLUS in a disease management programme (DMP) for T2DM during 2011. A patient was confirmed as being T2DM prevalent if physicians documented at least two outpatient T2DM diagnoses (ICD E11.-) and/or at least one inpatient T2DM diagnosis [3–5]. The T2DM prevalence was shown based on the structure of those insured by AOK PLUS differentiated by sex in 5-year age classes for the year 2011.

2.2.

Defined T2DM subgroups

For several years, DMPs have been offered in Germany for specific chronic diseases and patients. Patients can, on a voluntary basis, be included in these programmes. As part of these DMPs, physicians document treatment-specific information which is then also accessible to sickness funds in addition to the claims data that is already available. As part of the T2DM DMP, at least once a year and at most quarterly, the HbA1C, BMI, blood pressure, and the foot and kidney status of the patient are documented by the treating physician. If there was more than one value for a DMP variable available in 2011 for a patient, the arithmetic mean derived from these values was used. The available claims and DMP data were used to describe the real life treatment of T2DM patients for 2011. As per the definition above, all T2DM patients who were insured by AOK PLUS throughout the entire year of 2011 were included in the analysis unless they died during the course of the study. The care of T2DM patients is characterised by the challenge of achieving diabetes-related treatment goals (particularly HbA1C values <6.5–7.5%) [14,15], while at the same time, also taking common comorbidities and micro-/macrovascular complications into consideration [6,16,25,27,28]. Based on the available documented data, we therefore defined T2DM patient subgroups according to three criteria:  HbA1C less than 7%, between 7% and 9%, and greater than 9%  Blood pressure (systolic) either greater than or less than 130 mmHg  Charlson Comorbidity Index (CCI) [17] either greater than or equal to/less than 6 based on documented outpatient and inpatient diagnoses in the previous year (2010). Depending on the extent and combination of the above criteria, 12 different T2DM patient subgroups were formed. In addition, all patients for whom there were no DMP data available were analysed as a 13th patient subgroup. However,

diabetes research and clinical practice 106 (2014) 275–285

their analysis only included information that could be derived from claims data and not from (clinical) DMP data. For each subgroup, available sociodemographic information as well as HbA1C, systolic/diastolic blood pressure, CCI referring to diagnoses in 2010, BMI, and kidney status were analysed. The kidney status of the patients was reported in 5 stages: I (GFR  90 mL/min); II (60 mL/min  GFR < 90 mL/ min); III (30 mL/min  GFR < 60 mL/min); IV (15 mL/ min < GFR < 30 mL/min); and V (GFR < 15 mL/min). The status was determined using the serum creatinine values documented in the DMP dataset. If there was no DMP kidney function value available, any documented outpatient/inpatient diagnosis of a stages I–V kidney function disorder was used to describe the status (ICD 10 N18.1 = stage I, N18.2 = stage II, N18.3 = stage III, N18.4 = stage IV, N18.5/Z49 (dialysis treatment) = stage V). If several diagnoses were available, the worst kidney status was used to describe the status quo. If neither kidney-relevant diagnoses nor DMP-related kidney function values were available, the kidney status was not reported. The micro-/macrovascular complication status of the patients [25] at the start of the observation year (2011) was described for each subgroup, with the following elements (based on at least one outpatient/inpatient diagnosis in 2010):

 Microvascular status – presence of  Retinopathy (ICD 10 H36.0/H28.0)  Nephropathy (ICD 10 N08.3/N18.-)  Polyneuropathy (ICD 10 G59.0/G63.2/G73.0/G99.0)  Angiopathy (ICD 10 I79.2)  Macrovascular status – presence of/history of a  Myocardial infarction (ICD 10 I21.-/I22.-/I23.-)  Coronary heart disease (ICD 10 I25.-)  Ischaemic stroke (ICD 10 I63.-/I64.-)  Atherosclerosis (ICD 10 I70.-)  PAOD (ICD 10 I73.9).

2.3.

Treatment of T2DM subgroups

For each of the 13 patient subgroups, the treatment in 2011 was analysed across three areas:

277

placed in the following categories (each referring to 2011): (a) no anti-diabetic therapy, (b) metformin monotherapy, (c) sulphonylureas monotherapy, (d) combination therapy of sulphonylureas/metformin, (e) combination therapy of oral anti-diabetics including GLP-1 receptor antagonists, (f) combination therapy of OAD/insulin, (g) insulin monotherapy, or (h) other combinations. If a different medication was administered sequentially in 2011, this was described as a combination medication. The frequency of diabetic training (based on insurer-specific billing codes) was also evaluated.

2.4.

T2DM-related events

In each of the defined T2DM subgroups, the frequency of documented T2DM-related events in 2011 was analysed. In order to reliably differentiate new events from outpatient complication diagnoses based on previous events, only acute hospitalisations in 2011 were considered in this evaluation in the context of the following diagnoses:

Hospitalisations with T2DM primary diagnosis (ICD 10 E11.-) Hospitalisations with ischaemic stroke (ICD 10 I63.-/I64.-) Hospitalisations with acute myocardial infarction (ICD 10 I21.-) Hospitalisations with amputation of the lower extremities (procedural code OPS: 5-864/5-865)  Hospitalisations with percutaneous transluminal vascular interventions and stent implantations (procedural code OPS 8-836/8-837/8-84).

   

The event frequency was reported as event-specific event probability per patient year in 2011 and, as a composite outcome, as percentage of T2DM patients who were affected by at least one of the above events in 2011.

2.5.

T2DM-related costs

T2DM-related treatment costs in 2011 were evaluated for each patient subgroup. The following items were defined as T2DMrelated resource use positions:

 outpatient treatment  inpatient treatment  pharmacotherapy.

 All outpatient doctor’s visits, including diabetes training  Hospitalisations as per the above event definition  Anti-diabetic outpatient medication costs (ATC groups A10A/A10B) including blood glucose test strips.

Other treatment areas were not considered in detail. The evaluated data were reported as annual means for 2011. In terms of the outpatient care, the frequency of GP and specialist visits that could be observed was analysed. For evaluation of the inpatient care, the all-cause hospitalisation rate was reported. The pharmacotherapy was analysed based on active substances (ATC groups) by determining the number of active substances prescribed in 2011 per patient. Only those medications for which there were at least two prescriptions available for a patient during the 12-month period were included in the analysis. In order to show T2DM-relevant medication patterns (anti-diabetic medication – ATC groups A10A and A10B), patients from the particular subgroups were

The treatment costs were shown in Euros per patient in 2011. Outpatient visit costs were calculated based on documented treatment frequency/complexity (points, multiplied by the publicly available cost per point of 0.035048 s) and additional material costs documented in the outpatient treatment database. Hospitalisation costs were calculated based on DRGs while medication costs and blood glucose test strip costs were based on list prices for the specific items. All descriptive evaluations were performed using Microsoft SQL Server 2008 and Microsoft Excel 2010. The study was carried out with a positive ethical vote from the Thuringia State Medical Association.

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diabetes research and clinical practice 106 (2014) 275–285

Table 1 – T2DM-prevalence in 2011 based on all persons insured by the AOK PLUS. Age groups

AOK PLUS-insured in 2011 Male

To below 15 years 15 to below 20 years 20 to below 25 years 25 to below 30 years 30 to below 35 years 35 to below 40 years 40 to below 45 years 45 to below 50 years 50 to below 55 years 55 to below 60 years 60 to below 65 years 65 to below 70 years 70 to below 75 years 75 to below 80 years 80 to below 85 years 85 to below 90 years 90 and older All age groups

Female

Total

144,204 42,022 77,573 79,732 67,997 59,357 81,605 100,924 101,883 99,572 82,861 67,605 95,629 72,789 48,974 22,880 8655

137,371 40,394 74,662 76,048 66,355 55,061 80,484 101,399 100,413 97,455 85,972 74,938 116,023 112,214 101,901 70,369 36,879

281,575 82,416 152,235 155,780 134,352 114,418 162,089 202,323 202,296 197,027 168,833 142,543 211,652 185,003 150,875 93,249 45,534

1,254,262

1,427,938

2,682,200

3.

Results

3.1.

T2DM sample and T2DM prevalence

T2DM-prevalent insured in 2011

A total of 449,368 patients could be characterised as T2DMprevalent for 2011 (Table 1); 1.6% of patients were included because of an inpatient T2DM diagnosis as first observed

Male 56 54 136 234 450 826 2,584 6,412 11,732 18,077 22,932 21,617 35,961 34,428 24,149 11,888 5049

Female

(0.04%) (0.13%) (0.18%) (0.29%) (0.66%) (1.39%) (3.17%) (6.35%) (11.52%) (18.15%) (27.68%) (31.98%) (37.60%) (47.30%) (49.31%) (51.96%) (58.34%)

196,585 (15.67%)

49 85 200 391 586 787 1,879 4,247 7918 12,761 18,043 19,617 38,439 47,161 45,463 34,193 20,964

Total

(0.04%) (0.21%) (0.27%) (0.51%) (0.88%) (1.43%) (2.33%) (4.19%) (7.89%) (13.09%) (20.99%) (26.18%) (33.13%) (42.03%) (44.61%) (48.59%) (56.85%)

252,783 (17.70%)

105 139 336 625 1036 1613 4,463 10,659 19,650 30,838 40,975 41,234 74,400 81,589 69,612 46,081 26,013

(0.04%) (0.17%) (0.22%) (0.40%) (0.77%) (1.41%) (2.75%) (5.27%) (9.71%) (15.65%) (24.27%) (28.93%) (35.15%) (44.10%) (46.14%) (49.42%) (57.13%)

449,368 (16.75%)

diagnosis. The prevalence of T2DM in this sickness fund increased sharply with age from 15.65% in the 55–60 age group to 57.13% in the highest age group (>90 years). T2DM prevalence was also higher in women than in men. The mean age of all T2DM-prevalent patients in the AOK PLUS sample in 2011 was 72.63 years (SD 12.20 years), of whom 56.25% were female (Table 2).

Table 2 – Sociodemographic characteristics of the T2DM-samples. Variables

T2DM-prevalent T2DM-prevalent T2DM-prevalent T2DM-prevalent in 2011 in 2010 and 2011 in 2010 and 2011 with in 2010 and 2011 w/o DMP-documentation DMP-documentation

Number 449,368 394,828 72.63 (SD 12.20) 73.05 (SD 11.80) Ø age in years (per 31.12.2011) 43.75%/56.25% 43.54%/56.46% Gender (male/female) Ø number of prescribed medication 5.58 (SD 3.67) 5.80 (SD 3.65) 6.33 (SD 3.28) 6.62 (SD 3.04) Ø CCI (base: 2010) Five most common comorbidities (in- and outpatient diagnoses 2010) 78.0% 86.5% Hypertension (ICD: I10) 43.5% 48.4% Disorders of lipoprotein metabolism (ICD: E78) 33.3% 36.7% Chronic ischaemic heart disease (ICD: I25) 34.1% Disorders of refraction and 38.3% accommodation (ICD: H52) 30.8% 34.3% Dorsalgia (ICD: M54) Ø number of visits to a GP in 2011 4.35 (SD 1.86) 4.43 (SD 1.83) Only for patients with DMP-documentation n.a. n.a. Ø HbA1C in 2011 Patients with Ø HbA1C <6.5% Patients with Ø HbA1C <7.0% Patients with Ø HbA1C <7.5% Ø BMI in 2011 Ø blood pressure in 2011 Patients with kidney complications in 2011 Patients with diabetic foot in 2011 33.33% Patients with at least one 33.69% hospitalisation in 2011 Ø number of hospital stays 1.94 1.93 9.54 (SD 8.80) 9.47 (SD 8.73) Ø length of hospital stays in days

228,703 71.85 (SD 11.08) 45.09%/54.91% 6.22 (SD 3.51) 6.37 (SD 2.85)

166,125 74.70 (SD 12.53) 41.42%/58.58% 5.23 (SD 3.77) 6.97 (SD 3.26)

88.1% 52.5%

84.4% 42.7%

36.1%

37.5%

45.3%

28.7%

36.4% 4.76 (SD 1.69)

31.4% 3.98 (SD 1.91)

6.98 (SD 1.02) 79,987 (34.9%) 135,046 (59.0%) 173,024 (75.6%) 30.17 (SD 6.38) 135.55/78.81 mmHg 73.41% 6.95% 32.34%

n.a.

1.91 9.15 (SD 8.50)

1.96 9.87 (SD 8.99)

34.69%

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diabetes research and clinical practice 106 (2014) 275–285

The sample set used for the subsequent analysis (N = 394,828), comprising all T2DM-prevalent patients in 2010 and 2011, was characterised by a high mean age (73.05 years) and a high number of comorbidities with a mean CCI of 6.62 (SD 3.04) based on diagnoses from 2010 (Table 2). The five most common comorbidities were hypertension (86.5% of the patients), lipid metabolism disorders (48.4% of the patients), chronic ischaemic heart disease (36.7% of the patients), disorders of accommodation and refraction (38.3% of the patients), and back pain (34.3% of the patients). On average, the T2DM patients in the study visited a general practitioner 4.43 times and a specialist 6.94 times in 2011; 33.33% of the patients underwent an inpatient treatment during the year with a mean duration of 9.47 days per hospitalisation.

3.2.

T2DM subgroups

There were DMP data available for 57.92% of the 394,828 T2DM patients considered; 228,703 patients who participated in the T2DM-DMP (T2DM-DMP group) were younger and made up of more male patients than the comparative T2DM-Non-DMP group. At the same time, the T2DM-DMP group had a higher

number of comorbid patients for most of the top 5 comorbidities (Table 2). The average blood pressure was 135.55/ 78.81 mmHg and the average BMI 30.17 in that patient group (Table 2). Based on the 228,703 T2DM-DMP patients for whom detailed T2DM-related data were available, we defined the 12 predefined subgroups (Fig. 1). According to the first classification criterion of mean HbA1C measured in 2011, 59.0% of the patients realised an HbA1C mean annual value of less than 7% (subgroup A), 36.5% had a value between 7% and 9% (subgroup B), and 4.5% of the T2DM-DMP patients had a HbA1C value greater than 9% (subgroup C); on average, 3.19 HbA1C values could be evaluated per patient in the total sample. Within the T2DM-DMP patient group A (HbA1C <7%), 39.8% of the patients had a systolic blood pressure of 130 mmHg (subgroup AA); whereas 60.2% of the patients had a systolic blood pressure of >130 mmHg (subgroup AB). The respective numbers for subgroups B and C can be derived from Fig. 1. Table 3 describes the sociodemographic and morbidity-related characteristics of the different T2DM subgroups. Overall, 14.35–65.54% of all T2DM patients already had microvascular subgroup

All prevalent T2DMpatients in 2011 HbA1C <7%

449,368 pat.

135,046 pat. (59.0%)

All prevalent T2DM- patients (2010+2011) 394,828 pat.

With DMPdocumentation 228,703 pat.

HbA1C 7-9% 83,407 pat. (36.5%)

HbA1C >9% 10,250 pat. (4.5%) All prevalent T2DMpatients in 2010 405,215 pat.

syst. BP ≤ 130 mmHg

CCI ≤ 6 29,235 pat. (12.8%)

AAA

53,806 pat. (23.5%)

CCI > 6 24,571 pat. (10.7%)

AAB

syst. BP> 130 mmHg

CCI ≤ 6 49,345 pat. (21.6%)

ABA

81,240 pat. (35.5%)

CCI > 6 31,895 pat. (13.9%)

ABB

syst. BP ≤ 130 mmHg

CCI ≤ 6 13,851 pat. (6.1%)

BAA

27,896 pat. (12.2%)

CCI > 6 14,045 pat. (6.1%)

BAB

syst. BP> 130 mmHg

CCI ≤ 6 31,947 pat. (14.0%)

BBA

55,511 pat. (24.3%)

CCI > 6 23,564 pat. (10.3%)

BBB

syst. BP ≤ 130 mmHg

CCI ≤ 6 1,647 pat. (0.7%)

CAA

3,162 pat. (1.4%)

CCI > 6 1,515 pat. (0.7%)

CAB

syst. BP> 130 mmHg

CCI ≤ 6 4,238 pat. (1.9%)

CBA

7,088 pat. (3.1%)

CCI > 6 2,850 pat. (1.2%)

CBB

Without DMP-documentation 166,125 pat.

Fig. 1 – Prevalent T2DM patients and patient subgroups as defined by the mean HbA1C, systolic blood pressure (syst. BP), and Charlson Comorbidity Index (CCI). Percentage of patients in each group based on mean values for the variables as defined above are shown

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Table 3 – Sociodemographic and morbidity-related characteristics of the T2DM-DMP subgroups. Subgroup (N)

AAA (29,235) AAB (24.571) ABA (49,345) ABB (31,895) BAA (13,851) BAB (14,045) BBA (31,947) BBB (23,564) CAA (1647) CAB (1515) CBA (4238) CBB (2850) Without DMPdocumentation (166,125)

Definition

HbA1c <7%; sBP 130 mmHg; CCI HbA1c <7%; sBP 130 mmHg; CCI HbA1c <7%; sBP >130 mmHg; CCI HbA1c <7%; sBP >130 mmHg; CCI HbA1c 7–9%; sBP 130 mmHg; CCI HbA1c 7–9%; sBP 130 mmHg; CCI HbA1c 7–9%; sBP >130 mmHg; CCI HbA1c 7–9%; sBP >130 mmHg; CCI HbA1c >9%; sBP 130 mmHg; CCI HbA1c >9%; sBP 130 mmHg; CCI HbA1c >9%; sBP >130 mmHg; CCI HbA1c >9%; sBP >130 mmHg; CCI –

Ø Age

Female

Ø HbA1C

Ø Blood pressure

Ø CCI

Ø BMI

Patients with microvascular complications

Patients with macrovascular complications

66.41

54.61%

6.30

124.7/76.7

4.32

29.49

14.35%

24.16%

79.09

54.99%

6.31

123.6/74.8

9.14

28.28

45.98%

72.32%

68.15

53.49%

6.35

141.7/81.2

4.41

30.60

15.04%

22.81%

78.69

57.44%

6.36

141.5/79.2

8.80

29.22

45.23%

69.61%

65.41

52.93%

7.62

125.1/76.9

4.37

30.62

23.54%

26.58%

78.16

55.22%

7.66

123.9/74.7

9.25

29.58

57.67%

75.92%

67.04

52.95%

7.66

142.9/81.6

4.45

31.78

25.01%

23.31%

77.74

58.78%

7.68

142.3/79.1

8.90

30.36

57.72%

72.28%

60.52

47.78%

10.03

124.7/77.4

4.11

31.77

28.29%

26.98%

76.63

54.65%

9.93

123.9/74.7

9.48

30.39

65.54%

75.64%

62.78

51.69%

10.00

144.5/83.0

4.15

33.29

29.09%

23.92%

75.74

60.42%

9.90

143.9/80.2

8.96

31.77

63.50%

73.75%

74.70

58.58%

–

–

6.92

25.68%

46.47%

6 >6 6 >6 6 >6 6 >6 6 >6 6 >6

complications, while 22.81–75.92% of the patients had macrovascular complications.

3.3.

Treatment of T2DM subgroups

Figs. 2 and 3 show the treatment parameters evaluated in the 13 subgroups considered. Patients not included in the DMP programme had the lowest frequency of outpatient physician visits. Within the other subgroups, subgroup CBA (HbA1C >9%; BP >130 mmHg; CCI 6), with a mean of 5.09 GP- and 6.09 specialist visits, had the lowest outpatient treatment intensity, while subgroup CAB (HbA1C >9%; BP 130 mmHg; CCI >6), with a mean of 5.46 GP-, and subgroup AAB, with 9.27 specialist visits, had the highest treatment intensities. In terms of the all-cause acute inpatient hospitalisations, subgroup ABA (HbA1C <7%; BP >130 mmHg; CCI 6) had the lowest percentage of patients affected by inpatient hospitalisation (21.76% of patients). In subgroup CAB (HbA1C >9%; BP 130 mmHg; CCI >6), 52.94% of the patients experienced an inpatient hospitalisation, which was the highest percentage recorded in the different subgroups. In terms of the anti-diabetic medication therapy, the results can be summarised as follows:  In the subgroups with a HbA1C value between 7% and 9% (B), the proportion of patients without any diabetic therapy was about 8%; in subgroup C, that percentage was about 4%.  Metformin was the dominant oral anti-diabetic therapy in the subgroups A and B (HbA1C <7% or 7–9%, respectively).  In the subgroups B and C (HbA1C 7–9% or >9%, respectively), insulin/OAD combination therapy played an important role.

–

However, OAD/GLP-1 receptor agonist combinations were also important for anti-diabetic therapy.  The insulin ratio in all subgroups (A–C) (medication clusters f and g in Fig. 3) depended not only on the level of the HbA1C but also appeared to correlate considerably with the comorbidities. Patients with a CCI >6 had an insulin proportion (combination therapy and monotherapy) average of 39.64% compared to a proportion of 23.81% for those with a CCI 6.

3.4.

T2DM-related events

Fig. 4 shows the frequency of the defined T2DM-related hospitalisations in terms of the five defined event groups (hospitalisations with T2DM as primary diagnosis, ischaemic stroke, acute myocardial infarction, amputation of the lower extremities or percutaneous transluminal vascular intervention, and stent implantation) for the 13 different subgroups. Across all patient subgroups, a total of 21,833 T2DM patients involved were affected by an event; the risk was 5.53% per patient year. A total of 1.74% of the patients considered suffered from more than one event in 2011. The quantitatively most frequent event type was hospitalisation with T2DM as the primary diagnosis (2.39% of patients affected), followed by vascular interventions and stent implantations (1.92% of patients affected), and ischaemic stroke (1.19% of patients affected). Subgroup CAB (HbA1C >9%; BP 130 mmHg; CCI >6) was affected by the highest T2DM-related event risk; 18.35% of these patients suffered from at least one event (28.25% per

diabetes research and clinical practice 106 (2014) 275–285

281

Fig. 2 – Real-life treatment of T2DM patients in 2011. Percentage of patients in each subgroup who experienced an all-cause hospitalisation in 2011. Furthermore, frequency of visits to GPs and frequency of visits to diabetes specialists are shown for the year 2011

Fig. 3 – Real-life antidiabetic medication therapy of T2DM patients in 2011. Percentage of patients having received the predefined types of antidiabetic medication therapy in 2011 are shown. Please note that therapies that accounted for more than 5% are reported separately; all others were included in group (h).

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diabetes research and clinical practice 106 (2014) 275–285

Fig. 4 – Real-life frequency of T2DM-related hospitalisations. Percentage of patients having experienced the pre-defined events in 2011 are shown

patient year). The subgroups in second and third positions were CBB (HbA1C >9%; BP >130 mmHg; CCI >6), with 16.35% of patients affected, and BAB (HbA1C 7–9%; BP 130 mmHg; CCI >6), with 11.41% of patients affected. The subgroups with the lowest event frequencies were ABA (HbA1C <7%; BP >130 mmHg; CCI 6), with 2.36% of patients affected, AAA (HbA1C <7%; BP 130 mmHg; CCI 6), with 2.40% affected patients, and BBA (HbA1C 7–9%; BP >130 mmHg; CCI 6), with 4.22% of patients affected.

3.5.

AAA (HbA1C <7%; BP >130 mmHg; CCI 6), with costs of s661.09 per patient, ABA (HbA1C <7%; BP 130 mmHg; CCI 6), with an average cost of s671.50 per patient, and the T2DM patient group without DMP documentation with a cost of s1028.98 per patient.

4.

Discussion

4.1.

Objectives and main results

T2DM-related costs

Fig. 5 shows the T2DM-related treatment costs in 2011 (outpatient, inpatient and medication costs). Overall, the mean T2DM-related costs for the total sample were s1178.10 per patient. The most cost-intensive subgroup, CAB (HbA1C >9%; BP 130 mmHg; CCI >6), had 148.0% higher treatment costs than the mean of all observed patients, with costs of s2921.63 per patient; these were influenced in particular by above average medication and inpatient costs. The second and third most expensive subgroups in this regard were subgroups CBB (HbA1C >9%; BP >130 mmHg; CCI >6), with a cost of s2734.63 per patient and BAB (HbA1C 7–9%; BP 130 mmHg; CCI >6), with a cost of s2313.87 per patient. The subgroups with the lowest treatment costs were subgroups

The aim of our study was to describe the real-life treatment of T2DM patients in Germany. Specific features of this study are the use of a very large and, in terms of the variety of data, very comprehensive sample and the explicit differentiation of treatment-relevant T2DM subgroups. Overall, our data indicate that the typically treated T2DM patient has a number of comorbidities and thus treatment focused solely on T2DM is neither possible nor clinically meaningful. For example, it is worth noting that almost threequarters of the total T2DM patients (73.4%) had kidney complications, which may have considerable consequences on the choice and dosage of medication in a number of cases. On the other hand, it should be noted that our T2DM sample had a high mean age, which can generally be ascribed to the

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Fig. 5 – T2DM-related treatment costs in 2011 (Ø E1178.10). T2DM-related treatment costs (mean) as defined in the methodology part of the paper are shown for each subgroup.

high mean age of T2DM patients in industrialised countries, but which also represents the specifics of our database (patients insured with the specific sickness fund AOK PLUS). Our data show that about 60% of the T2DM-DMP patients had good glycaemic control; the mean observed HbA1C of 6.98% confirms that observation. In particular, in those patients who reached HbA1C goals but had also achieved relevant treatment goals in terms of blood pressure and who developed few comorbidities (subgroup AAA: HbA1C <9%; BP 130 mmHg; CCI 6), the T2DM-related hospitalisation rate was relatively low at 2.4% per patient year. If, on the other hand, HbA1C values fell far short of the goals (subgroups C), the hospitalisation rate was at least 9.27% (independent of blood pressure and comorbidities); if there were many comorbidities (CCI >6) and if blood pressure goals were not achieved, the T2DM hospitalisation rate per patient year increased to 16.35% (subgroup CBB), which is nearly 7 times the rate reported for subgroup AAA. Our data correspond with those from available treatment research studies [16]. We thus confirm the finding that the management of T2DM patients is challenged by the fact that a number of comorbidities must be treated [6,24,25,26,28]. In comparison to other studies, however, our data reveal a

comparably higher proportion of patients who achieve the goal of an HbA1C value <7%. Other studies have reported considerably lower patient numbers in this regard [8,18–20,27]. Nevertheless, our data also reveal that a large proportion of the T2DM patients do not achieve combined treatment goals: an HbA1C value <7% with a blood pressure <130 mmHg (subgroups AAA and AAB) was only achieved by 23.5% of the patients considered. At the same time, our study results reveal a high rate of complications for treated T2DM patients. With a T2DM-related mean hospitalisation risk of 5.53% for the defined T2DMrelated events, the event rate must be considered high. It is, however, comparable with event rates in other studies for a T2DM population with the same mean age [21,22]. We report average treatment costs of T2DM patients amounting to approximately s1178. Compared to previous studies focused in Germany, these costs are low; another study reported average treatment costs of diabetes type 1/2 patients of s5958, including so-called diabetes-related excess costs of s2608 [29]. However, in this previous study, the definition of treatment costs was less diabetes-specific than in our study. Diabetes-related excess costs in this study were derived from a matched-pairs cost comparison with

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non-diabetic patients in the same study. Instead, we used a bottom-up approach by selecting specific items only to be T2DM-related resource use. The mean final all-cause treatment costs of our T2DM patients amounted to about s4790; therefore, we assumed that only 24.5% of all costs were diabetes-related.

4.2.

Limitations

We acknowledge some limitations of this study. Firstly, not all of our data were gathered in light of the study aims but rather as part of the billing of health services (claims data) or documentation as part of a DMP. For example, in the T2DM DMP, cholesterol values are not documented, which would be an additional important treatment parameter. Furthermore, we did not have any opportunity to check the correctness and plausibility of each diagnosis, laboratory parameter, or other analysed variables. To address this reasonably, we carried out a plausibility test of all variables based on medical databases. Nevertheless, there remains some uncertainty regarding the validity of the data. This limitation, however, applies to most retrospective studies and certainly to data analysis of every claim. There were also gaps in the data in the DMP dataset. For 42% of the T2DM patients, there were no DMP records available; these patients differed from the T2DM DMP samples in terms of age, gender, and number of comorbidities. Secondly, although the criteria defined by us for delimiting the subgroups were derived from the available sources and guidelines as ‘clinical common sense’, they are nevertheless arbitrary to a certain degree. It is possible that other limits would enable the allocation of patients to subgroups that allow the better prediction of event rates. To be able to define clear limits, we also used systolic blood pressure alone as a relevant criterion. Thirdly, our definition of T2DM-related events is also arbitrary to a certain degree. On the one hand, we restricted ourselves to events that led to acute hospitalisations. We chose this method to avoid falsely interpreting possibly incorrect outpatient diagnoses as events. Moreover, we only defined selected micro- and macrovascular complications as well as T2DM-related hospitalisations as events; this was done on the basis of the most frequently used outcomes in T2DM studies but it is a matter of debate whether other event groups should have also been included.

5.

Conclusions

The present study is characterised by an above average detailed description of T2DM-relevant subgroups and their treatment on the basis of a very large sample of T2DM patients. As a descriptive study, it may form the basis of future population-related T2DM treatment models and health economics models. Moreover, it very clearly reveals different medication patterns in different T2DM patient subgroups, which, in turn, have a critical effect on treatment costs, like the T2DM-related events themselves. The overall aim of the treatment of T2DM patients is to prevent the occurrence of microvascular/macrovascular complications or to reduce their severity; achieving surrogate goals

such as HbA1C, BMI or blood pressure is not a final treatment goal but is rather a means to an end [15,23]. To enable the necessary considerations to be taken when treating supposedly less critical diseases/complications, a valid risk assessment in terms of the microvascular/macrovascular event risk is therefore of key importance. Our subgroup analysis shows that a combined risk assessment, which goes well beyond measuring HbA1C levels alone but incorporates these values prominently, allows a considerably higher predictive quality than considering one treatment goal alone. This is, however, only a first indication of a possible cause/effect mechanism that has not been addressed in multivariate regression models so far. The task of future research is to develop empirically validated predictor models from these findings which can then also be used for complication risk screening of T2DM patients.

Conflicts of interest Thomas Wilke has received honoraria from several pharmaceutical companies (Novo Nordisk, GSK, BMS, Astra Zeneca, Bayer, Boehringer Ingelheim). Antje Groth participated as staff member in the study described in this paper which was sponsored by Novo Nordisk. Ulf Maywald, Andreas Fuchs, Lisa Seitz, and Joachim Kienho¨fer do not have any conflicts of interest except those potentially related to their employer. Rainer Lundershausen received honoraria for lectures from Novo Nordisk, Lilly, MSD, BMS, Boehringer Ingelheim and Bayer. Further he is member of scientific boards of Novo Nordisk, Boehringer Ingelheim, BMS and MSD.

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