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The American Journal of Managed Care October 2012
Change to FIT Increased CRC Screening Rates: Evaluation of a US Screening Outreach Program
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Toward Tailored Disease Management for Type 2 Diabetes
Arianne M. J. Elissen, MSc; Inge G. P. Duimel-Peeters, PhD; Cor Spreeuwenberg, PhD; Marieke Spreeuwenberg, PhD; and Hubertus J. M. Vrijhoef, PhD
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Toward Tailored Disease Management for Type 2 Diabetes

Arianne M. J. Elissen, MSc; Inge G. P. Duimel-Peeters, PhD; Cor Spreeuwenberg, PhD; Marieke Spreeuwenberg, PhD; and Hubertus J. M. Vrijhoef, PhD
The heterogeneous nature of care processes and patients should be taken into account in both the design and evaluation of disease management programs for diabetes.
Objectives: To assess the differentiated effects of population-based disease management programs (DMPs) for type 2 diabetes on intermediary clinical outcomes in The Netherlands.

Methods: Data covering a period from 20 to 24 months between January 2008 and December 2010 were collected from 18 Dutch care groups (primary care provider networks that have bundled payment contracts for delivery of diabetes DMPs). Meta-analysis and meta-regression methods were used to conduct differentiated analyses of these programs’ effects over time on 4 clinical indicators: glycated hemoglobin, lowdensity lipoprotein, systolic blood pressure, and body mass index. Heterogeneous average results were stratified according to various patient and process characteristics to investigate whether differences in these features could explain variation in outcomes.

Results: Between 56% and 71% of patients (N = 105,056) had valid first- and second-year measurements of the study outcomes. Although average changes in these measures over time were small, stratified analyses demonstrated that clinically relevant improvements were achieved in patients with poor first-year health values. Interactions with age, disease duration, comorbidity, and smoking status were not consistent across outcomes; nonetheless, heterogeneity in results decreased considerably when simultaneously correcting for known patient characteristics. Positive effects tended to diminish with longer length of follow-up, while greater measurement frequency was associated with improved results, especially in patients with poor health.

Conclusions: Our data suggest that tailored disease management, in which not only evidencebased guidelines but also patient characteristics directly determine care processes, including self-management support, has great potential to improve the cost-effectiveness of current chronic care delivery.

(Am J Manag Care. 2012;18(10):619-630)
Correcting for heterogeneous care processes and patients resulted in differentiated insight into the effects of population-based disease management on intermediary outcomes of diabetes care.

  • Patients with poorly controlled diabetes benefit more from standardized disease management than patients with better health, for whom self-management support might be equally effective.

  • Positive effects of disease management diminish with longer length of follow-up; greater measurement frequency improves results, especially in patients with poor health.

  • Tailored disease management, in which care content and dose are determined by patients’ health, demographics, and social characteristics, has great potential for improving cost-effectiveness in chronic care.
Rising costs, an aging population, and the recognition of severe deficiencies in traditional care have motivated the development of innovative models for chronic illness management. Examples include shared care, case management, and stepped care, but perhaps most well known internationally are disease management and the Chronic Care Model.1-3 Differences aside, these concepts have in common their basic assumption that better treatment today will result in better health and less expensive care in the future. Specific quality improvement efforts tend to focus on (1) reducing fragmentation between providers and settings, (2) stimulating evidence-based practice, (3) promoting active and planned follow-up, and (4) supporting patients’ self-management.4-7

Developed in the United States, disease management programs (DMPs) have quickly spread to other countries such as Canada, the United Kingdom, and Germany, which now has some of the largest DMPs in the world.8,9 In the primary care–oriented Dutch health system, chronic disease management initiatives developed from the 1990s onward. Initially, uptake of these initiatives remained limited, mainly due to lack of a structured framework and fragmentary funding.10,11 The 2006 health insurance reform granted health insurers extended power to negotiate with care providers and, in so doing, facilitated the development of a more integrated funding method, the so-called “bundled payment system.”12-14 Under this system, insurers pay care groups, which are provider networks based in primary care, a single fee for the full range of outpatient services for a specific chronic condition.15 Bundled payments are seen as a way to stimulate primary care providers, predominantly general practitioners (GPs), to engage in multidisciplinary cooperation and deliver integrated,  evidence-based disease management in an ambulatory setting, therefore limiting the need for specialist care.16,17

Although the evidence base for their impact on the quality and outcomes of care is limited, integrally financed DMPs for type 2 diabetes quickly achieved national coverage in The Netherlands. In March 2010, more than 100 groups representing approximately 80% of GPs had a bundled payment contract for diabetes care.17,18 In the same year, the payment system was expanded to cover the management of chronic obstructive pulmonary disease and vascular risks. Hence, the support for disease management in The Netherlands appears to be a matter more of faith than fact, as is the case in other countries as well. International studies and reviews of DMPs demonstrate highly heterogeneous results and so far have failed to answer the basic question of what works best for whom, not least because of variation in both methodology and nomenclature.3,19-22 It is, however, especially the multicomponent and populationbased nature of disease management that makes it difficult to draw unambiguous conclusions concerning effectiveness. Analyzing complex DMPs necessitates a clear framework that links expected outcomes to the characteristics of both the program and its target population, and measures effects over an adequate period of time.23

As part of the DISMEVAL (Developing and Validating Disease Management Evaluation Methods for European Healthcare Systems) project, this study’s objective was to assess the effects of the Dutch diabetes DMPs on a range of intermediary clinical outcomes. To investigate heterogeneity in results across different care processes and patients, we designed the study as a population-based, multilevel meta-analysis and meta-regression. Given that experimental comparisons were not possible because of the nationwide rollout of the DMPs and the unsuitability of using historical controls,9 these methods allow for the most in-depth assessment of effectiveness. Such differentiated insight, which goes beyond the “grand means” that currently inform many health system redesigns, can support professionals and policy makers in their efforts to better meet the complex care needs of the growing and inherently diverse population of chronically ill patients.


Study Design and Participants

The bundled payment system for generic diabetes disease management in The Netherlands obliges care groups to provide insurers with a specific number of performance indicators for both processes and outcomes on an annual basis.24 We retrospectively gathered individual patient data on these indicators from a convenience sample of 18 groups, which were set up between 2006 and 2009 and represent nearly all regions of The Netherlands. Nine groups were part of an experimental pilot concerning bundled payments evaluated by the National Institute for Public Health and the Environment15; to include 9 other, nonexperimental groups, 14 care groups were approached (response rate 64.3%).

Across the included groups, 106,623 patients had at least 1 registered visit during the research period, which—depending on the availability of data—was either 20 or 24 months between January 2008 and December 2010. We excluded type 1 diabetes patients (n = 1567) because they are treated primarily by specialists. Since patient data were drawn from groups’ clinical information systems, plausibility was verified through range checks. Outliers were removed based on cutoff points determined by Dutch diabetes experts (see eAppendix, available at Missing values were not imputed.

Because patient data were not available for the period before introduction of the bundled payment system, we assessed the effects of the diabetes DMPs by comparing the last measurement of each clinical outcome during the first year of the research period (or the first 8 months for the 2 groups with a 20-month research period) with the last measurement of that outcome in the second year. Per outcome-specific analysis, we excluded patients who (1) lacked registrations of the first- or second-year measurements, or both; (2) missed registrations of 1 or more of the patient and/or process characteristics used for stratification; and/or (3) had an observation period between first- and second-year outcome measurement of fewer than 3 months. The maximum length of follow-up per patient was 23 months. The study flow chart is shown in Figure 1.

Diabetes Disease Management Programs in The Netherlands

In 2007, 10 Dutch care groups started experimenting with a bundled payment system that allows the different components of care for generic type 2 diabetes to be purchased, delivered, and billed as a single product or service. Care groups are legal entities in primary care, which consist of multiple care providers, are most commonly owned by GPs, and form the principal contracting partners for health insurers involved in bundled payment contracts.17 Groups either deliver the various components of a diabetes DMP themselves or subcontract other providers, including GPs, physical therapists, dieticians, laboratories, and/or specialists, to do so. The price of a specific program (ie, care bundle) as well as the fees for individual subcontractors are freely negotiable between care groups on the one hand and health insurers or care professionals on the other. Patients are automatically enrolled by their GP.15,16

The services to be covered in the diabetes DMPs are codified by a national care standard for generic diabetes care.25 This standard is based on existing evidence-based guidelines for GPs and includes general modules (eg, information, education and self-management support, smoking cessation, physical activity, nutrition and diet) as well as disease-specific modules.24 The latter comprise a defined frequency of GP visits, regular foot and eye examinations, and laboratory testing. To stimulate task redistribution from GPs to practice nurses, who traditionally play an important role in Dutch chronic care, the standard describes care in terms of functions rather than providers, defining what services must be delivered rather than by whom or where.16,25 For more information on the contents of the Dutch diabetes DMPs, the diabetes care standard can be accessed online:

Definition of Variables and Data Validation

We defined intermediary clinical outcomes as mean changes in glycated hemoglobin (A1C), low-density lipoprotein (LDL), systolic blood pressure (SBP), and body mass index (BMI) between the first- and second-year measurements. In addition, we assessed and compared the proportions of patients within 3 target range groups for glycemic control (A1C <53 mmol/mol; A1C = 54-74 mmol/mol; A1C >75 mmol/mol) at these 2 measurement points.25

Care processes were described in terms of measurement frequency and range, and duration of care. We codified measurement frequency as the number of registrations of each clinical outcome during follow-up; to describe measurement range, we assessed the number of different outcomes registered, which across care groups could be a maximum of 8 (ie, A1C, total cholesterol, LDL and high-density lipoprotein, SBP and diastolic blood pressure, BMI, and triglycerides). Duration of care was defined as the number of months between the first- and second-year measurements of each clinical outcome.

To describe patients, we used age in years, disease duration in years, health status, comorbidity, and smoking status. Health status was determined by the first-year values of each clinical outcome. Comorbidity was defined as the presence of 1 or more of the 4 most frequently registered co-occurring conditions across the included care groups (ie, angina pectoris, myocardial infarction, stroke, transient ischemic attack). We dichotomized smoking status as previous smoker or nonsmoker versus current smoker.

Data Analysis

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