Toward Tailored Disease Management for Type 2 Diabetes | Page 2

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: http://www.diabetesfederatie.nl/zorg/zorgstandaard.html.

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

We used a 2-step approach to population-based, multilevel meta-analysis, in which we clustered the individual patient data by care group.^26,27 During the first step, we conducted paired-sampled t tests (2 sided; α = .05) using SPSS version18 (IBM Corporation, Armonk, New York) to calculate the group-specific mean differences in clinical values and associated standard deviations. In the second step, these were synthesized with Review Manager (RevMan version 5.1.1; The Cochrane Collaboration) into pooled mean differences and 95% confidence intervals, for which—owing to significant heterogeneity in results—we used the random-effects metaanalysis model of DerSimonian and Laird.²⁸ Using this model, we weighted the aggregate effects by the inverse of their variances while assuming random treatment effects across care groups.²⁹ To quantify heterogeneity, we calculated the I2 statistic—which can range from 0% to 100%—on the basis of the χ2 test.³⁰ For outcomes showing moderate (I² >50%) to\high (I² >75%) heterogeneity, subgroup analyses were conducted to examine the consistency of results across diabetes care processes and patients.²⁶ For this purpose, most continuous variables were categorized into 2 or 3 groups on the basis of either scientific literature (age^31,32 and disease duration³³) or median values (measurement frequency and length of followup). We dichotomized measurement range as 8 outcomes (the maximum number of clinical indicators that could be registered) versus fewer than 8 outcomes; first-year health status was categorized according to the target range values for clinical outcomes included in the diabetes care standard.²⁵

We used multilevel meta-regression analysis of individual patient data, stratified by care group, to further assess the influence of potential effect modifiers as well as to investigate potential 3-way interactions between care processes, patient characteristics, and clinical outcomes.^34,35 To conduct the meta-regressions, we used the PROC MIXED command in the SAS 9.2 software (SAS Institute Inc, Cary, North Carolina), which uses a random-effects iterative method to provide a maximum likelihood estimate of the regression parameters. The meta-regression models were multivariable, though process and patient characteristics were included separately; continuous covariates were included as such. For each clinical outcome, we calculated the intraclass correlation coefficient to assess the percentage of total heterogeneity in effects occurring between care groups. The larger the intraclass correlation coefficient, the greater the proportion of variance that can be attributed to differences between rather than within groups.³⁶ We examined collinearity with the variance inflation factor: a variance inflation factor value of greater than 10 is generally taken as an indication of serious multi-collinearity.³⁷ Explained heterogeneity was expressed as the percent change in between-group variance (τ²) and within-group variance (σ²).

RESULTS

Diabetes Care Processes and Patients