The American Journal of Managed Care January 2010
Is There a Survival Benefit Within a German Primary Care-Based Disease Management Program?
Patients with type 2 diabetes in a German disease management program had a lower mortality rate after 3 years than those not in the program.
Objective: To compare the mortality rate of patients with type 2 diabetes who were enrolled in the German diabetes disease management program (DMP) with the mortality rate of those who were not enrolled.
Study Design: This observational study was part of the ELSID study (Evaluation of a Large Scale Implementation of disease management programs) in Germany.
Methods: Participants had type 2 diabetes and were either enrolled or not enrolled in the DMP. The DMP provides systems-based, multifaceted, and patient-centered interventions. To reduce imbalances between the groups, a matched sample was created using sex, age, retirement status, federal state, pharmacy-based cost groups, and diagnostic-cost groups as matching criteria. Cox proportional hazards regression model and the Kaplan-Meier method were used to assess overall mortality. The observation period was 3 years beginning on January 1, 2006.
Results: A total of 11,079 patients were included in the analysis. As of January 1, 2006, 2300 patients were enrolled in the DMP and 8779 were receiving routine care. There were 1927 matched pairs of patients in the DMP group and the non-DMP group. The overall mortality rate was 11.3% in the DMP and 14.4% in the non-DMP group (log-rank test P <.01).
Conclusions: We found an association between participation in the German diabetes DMP and reduced mortality. This reduced mortality cannot be attributed directly to the DMP. However, further research should evaluate whether a primary care–based DMP contributes to increased life expectancy in patients with diabetes.
(Am J Manag Care. 2010;16(1):49-54)
Patients with type 2 diabetes who participate in the German primary care–based diabetes disease management program (DMP) receive systems-based, multifaceted, and patientcentered interventions.
- Participation in the DMP was associated with reduced mortality after 3 years.
- Although this reduced mortality cannot be attributed directly to the DMP, extension of the approach within primary care may contribute to increased life expectancy for patients with diabetes.
Interventions to improve the health status and life expectancy of patients with diabetes should focus on the patients and their individual problems as well as on a restructuring of care. Complex interventions that go beyond the adjustment of clinical parameters are required. The patient needs to be considered holistically in light of his or her individual circumstances. According to the literature, multifaceted interventions by multiprofessional teams with additional patient education and the enhancement of the role of practice nurses have been associated with improved diabetes care as well as patient outcomes.1,4
Diabetes disease management programs (DMPs) delivered in primary care settings were introduced into Germany nationwide in 2003.5 Using compulsory requirements determined on behalf of the German Ministry of Health, sickness funds arrange contracts with primary care physicians. Participation for doctors and patients is voluntary, but participating doctors are obliged to keep within the conditions of the program. Primary care physicians and the wider practice teams, mostly in small- to medium-size practices in Germany, have a central role in performing and coordinating the provision of care to enrolled patients with diabetes.6 The German DMPs have been designed to improve the quality of care for patients with chronic diseases, reduce complications, improve patientoriented outcomes, and lower costs. Currently about 2.7 million patients with type 2 diabetes are enrolled.
The diabetes DMP includes the implementation and audit of evidence- based clinical guidelines using quality indicators and quality assurance measures, with feedback to participants on their level of performance. It also includes regular recalls for patients and shared individual goal setting by the patient and the physician, with consideration of the individual circumstances and risk profiles. This shared goal setting is based on emphasizing both coordination and continuity of care and the physician’s knowledge of each patient.7 Patients are offered lifestyle advice with the aim of enabling them to achieve behavioral changes in diet and physical activity in support of their own self-management. The doctor-patient relationship is strengthened within the program, and continuity of care is guaranteed as patients are obliged to visit their physician regularly, either quarterly or semiannually. There also is an enhanced role for doctors’ assistants within the DMP. Patients enrolled in the program are more likely to receive diabetes care according to the chronic care model6 than patients who are not enrolled.
Statutory nationwide evaluation of all DMPs is mandatory for all sickness funds. However, patients enrolled in a DMP are not compared with patients who are not. Although reducing mortality is a patient-relevant outcome of interventions and new models in diabetes care, it often is not measured in trials.8 The reason may be that it is difficult and expensive to design studies in this area to evaluate the impact of specific interventions on mortality. The aim of this observational study was to report the mortality rates of patients with type 2 diabetes enrolled in the German diabetes DMP compared with the mortality rates of patients who are not enrolled in the program, but who receive routine care.
This analysis was carried out as part of the ELSID study (Evaluation of a Large Scale Implementation of disease management programs), which is a 2-armed controlled trial to evaluate the effectiveness of the German diabetes DMP. The first arm is described elsewhere.9 The second arm, on which the present analysis was based, was observational. The study team made no additional intervention and did not influence participation in the DMP. The ELSID study was conducted in 2 federal states of Germany (Rheinland-Pfalz and Sachsen- Anhalt) and was fully approved by the ethics committee of the Medical Faculty of the University of Heidelberg.
All of the participants in this study were insured by 1 large statutory regional healthcare fund called the Allgemeine Ortskrankenkasse (AOK), which covers about 40% of the German population. The prevalence data were provided by the AOK. For Sachsen-Anhalt, data also were provided by the regional Association of Statutory Health Insurance Physicians. Patients were identified from routine claims data from the AOK. To be included in the study, patients had to be older than age 50 years and be receiving a prescription for antidiabetic medication (oral antidiabetic drugs or insulin) in the first half-year of 2005. Patients who were managing their diabetes by diet alone were excluded from analysis. Patients in the DMP group had to be enrolled in the program by December 31, 2005, regardless of how long they had participated in the program prior to that date. Patients in the non-DMP group were not enrolled in the DMP before this appointed date. In the non-DMP group, all of the patients who joined the DMP during the observational time were excluded from analysis. Therefore, the number of patients in the non-DMP group was deliberately larger from the beginning, because we did not know how many patients would subscribe to the DMP during the study.
Participation in the DMP is voluntary for both patients and primary care physicians; physicians get financial remuneration for participating patients. Patients are informed of the existence of these programs by their primary care physician and their sickness fund. Patients could subscribe only to the program attended by their primary care physician. Using the claims data, all of the patients were assigned to their primary care physician as a cluster.
In order to reduce imbalances between DMP and non-DMP patients at baseline in terms of sex, age, and other variables, we matched the sample according to the study protocol.9 To create matched pairs with regard to the illness burden, we used a matching method based on pharmacy-based cost groups (PCGs) as an outpatient morbidity measure and marker for chronic conditions based on the prior prescription of medication10 and diagnostic cost groups (DCGs) with inpatient diagnostic information from prior hospitalizations.11,12 This method was developed in the Netherlands for application within the national risk structure compensation scheme.
The matching criteria in the ELSID study were age group (from age 50 years in consecutive steps of 5 years to >90 years), sex, retirement status (yes or no), federal state (Sachsen- Anhalt, Rheinland-Pfalz), PCG, and DCG. For each patient in the DMP group, an appropriate matching partner from the non-DMP group was identified if possible. It was necessary for the age group, sex, retirement status, and federal state, as well as the most expensive PCG and DCG and the total number of PCGs and DCGs, to be consistent between the matching partners.
We used the MatchBalance() function of R’s package “Matching” to test whether matching was balanced on baseline covariates.13
The primary outcome of this analysis was the overall mortality (death from all causes) for matched pairs of DMP and non-DMP patients. The observation period was 3 years, beginning on January 1, 2006. Survival times were censured for patients who were still alive on December 31, 2008. As a secondary outcome measure, the unadjusted overall mortality of the unmatched sample also was analyzed.
The data were analyzed using SPSS version 15.0 (SPSS Inc, Chicago, IL). Baseline characteristics were analyzed using the Student t test or the Mann-Whitney test for continuous variables and the X2 test for categorical variables as appropriate (after testing for normal distribution with the Kolmogorov-Smirnov test). Survival curves were constructed using the Kaplan-Meier method; the group differences between DMP and non-DMP patients were analyzed by the log-rank test. Hazard ratios and 95% confidence intervals (CIs) were calculated with the Cox proportional hazards regression model.
Within the primary analysis of the matched pairs, we only included group membership (DMP vs non-DMP) in the univariate model, as the other variables had been used previously as matching criteria. Within the secondary analysis of the whole sample in the final multivariate regression model, we included age, sex, federal state, and group membership (DMP vs non-DMP). A potential cluster effect also was considered by including binary variables for the clusters (ie, primary care physicians) in the model. The level of significance was P <.05.
A total of 11,079 patients were included in our analyses. Of these, 2300 were enrolled in a DMP and 8779 received routine care. Table 1 shows the sociodemographic characteristics of these patients. Compared with patients receiving routine care, patients in the DMP were younger, with a mean ± SD age of 70.47 ± 8.88 years versus 72.80 ± 9.63 years for non-DMP patients (P <.001). The patients within the DMP group had a higher total number of PCGs (P <.001) and a lower number of DCGs (P <.001), as can be seen in Table 1.
Matched pairs of patients (n = 1927 pairs) were identified and analyzed. The sociodemographic characteristics of these subgroups are shown in Table 1. We were able to confirm that matching was balanced on all baseline covariates with the MatchBalance() function of R’s package Matching.
The overall mortality (death from all causes) within the 2 groups of matched pairs was 11.3% within the DMP group and 14.4% within the non-DMP group (log-rank test P <.01). Mortality, therefore, was significantly lower in the DMP group. Panel A of the Figure shows the patient survival data for the matched pairs (Kaplan-Meier curve). In the univariate analysis of the matched sample, nonparticipation was associated with an increased hazard ratio of 1.3 (95% CI = 1.09, 1.55; P <.01).
The overall mortality in the total sample was 12.8% for the group of patients enrolled in the DMP and 21.7% for the patients receiving routine care (P <.001). Panel B of the Figure shows the unadjusted patient survival rate (Kaplan-Meier curve) for these 2 groups. Considering all of the variables within the final Cox regression model, nonparticipation in the DMP, higher age, and federal state were significant predictors of mortality. The adjusted hazard ratio for nonparticipation was 1.46 (95% CI = 1.28, 1.65; P <.001).
These data show that the mortality risk was significantly lower for patients participating in the DMP. Table 2 shows the univariate and multivariate hazard ratios of the Cox proportional regression model.