A Meta-analysis of Interventions to Improve Care for Chronic Illnesses

Published Online: August 01, 2005
Alexander C. Tsai, PhD; Sally C. Morton, PhD; Carol M. Mangione, MD, MSPH; and Emmett B. Keeler, PhD

Objective: To use empirical data from previously published literature to address 2 research questions: (1) Do interventions that incorporate at least 1 element of the Chronic Care Model (CCM) result in improved outcomes for specific chronic illnesses? (2) Are any elements essential for improved outcomes?

Study Design: Meta-analysis.

Methods: Articles were identified from narrative literature reviews and quantitative meta-analyses, each of which covered multiple bibliographic databases from inception to March 2003. We supplemented this strategy by searching the MEDLINE database (1998-2003) and by consulting experts. We included randomized and nonrandomized controlled trials of interventions that contained 1 or more elements of the CCM for asthma, congestive heart failure (CHF), depression, and diabetes. We extracted data on clinical outcomes, quality of life, and processes of care. We then used random-effects modeling to compute pooled standardized effect sizes and risk ratios.

Results: Of 1345 abstracts screened, 112 studies contributed data to the meta-analysis: asthma, 27 studies; CHF, 21 studies; depression, 33 studies; and diabetes, 31 studies. Interventions with at least 1 CCM element had consistently beneficial effects on clinical outcomes and processes of care across all conditions studied. The effects on quality of life were mixed, with only the CHF and depression studies showing benefit. Publication bias was noted for the CHF studies and a subset of the asthma studies.

Conclusions: Interventions that contain at least 1 CCM element improve clinical outcomes and processes of care—and to a lesser extent, quality of life—for patients with chronic illnesses.

(Am J Manag Care. 2005;11:478-488)

Approximately 90 million persons in the United States live with 1 or more chronic illnesses.1 The management of healthcare delivery for persons with chronic illnesses has advanced substantially in recent decades, yet considerable deficiencies in the quality of chronic-illness care remain.2-6 Critics argue that systems of care designed to deal with acute episodes do not serve the needs of patients with chronic illness well.7 Chronic illnesses rank among the nation's costliest conditions8; and 5 chronic illnesses—asthma, diabetes, heart disease, hypertension, and mood disorders—account for nearly one half of US healthcare expenditures.9 Managed care organizations are increasingly becoming the main source of healthcare services for persons with chronic illnesses such as diabetes10 and may be particularly well suited to adopting reforms to optimize care for such patients.

The Chronic Care Model (CCM) is a primary care-based framework aimed at improving the care of patients with chronic illnesses.11-14 The model integrates a number of elements into a plausible package designed to foster more productive interactions between prepared, proactive teams and well-informed, motivated patients. Details on the concept, implementation, and evidence base of the CCM are available at http://www.improvingchroniccare.org.

Although individual components of the CCM have been rigorously studied, accounts of the CCM's beneficial effects as a whole on processes, outcomes, and/or costs come largely from self-reported, uncontrolled studies.11,14,15 Therefore, we sought in this meta-analysis to classify previously published studies according to the CCM component(s) implemented, in order to address 2 related research questions: (1) To what extent do interventions that incorporate 1 or more elements of the CCM result in improved outcomes of interest for specific chronic illnesses? (2) Are some elements of the CCM more effective than others? Although the CCM is intended to be generic, applicable across all types of chronic illnesses,11,16 we focused on evaluating the effects of diverse interventions on clinical outcomes, quality of life, and processes of care for 4 chronic illnesses that would be of particular interest to managed care organizations: asthma, congestive heart failure, depression, and diabetes.


Conceptual Model

The CCM identifies 6 elements deemed to be essential for providing high-quality care to patients with chronic illnesses: delivery system design, self-management support, decision support, clinical information systems, community resources, and healthcare organization.11,13,14 Because of the limited information available in published descriptions of interventions, and because the CCM elements are broadly defined, we categorized interventions according to the CCM elements they incorporated (Box).17 Any given intervention could contain more than 1 element, up to a maximum of 6.


Article Selection and Data Abstraction

One author (ACT) was responsible for all aspects of study selection and data abstraction. First, we used MEDLINE and the Cochrane Library to identify 20 recently published systematic reviews and meta-analyses of the 4 chronic illnesses of interest: asthma,18-22 congestive heart failure,23-28 non-insulin dependent diabetes mellitus,29-35 and depression.36,37 These 20 reviews, in addition to 3 other reviews that were not condition specific,38-40 formed the primary substrate for identifying studies for potential inclusion in our study sample. These previously published reviews covered multiple databases—including the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, the Dissertation Index, Embase, Healthstar, the Health Management Information Service (HELMIS), MEDLINE, PsycINFO, PsychLit, and the System for Information on Grey Literature in Europe (SIGLE)—from inception to March 2003 (depending on the date the review was published). We also performed a MEDLINE search for more recent studies (January 1998-June 2003) that may have been missed. Finally, additional studies were identified by consulting experts and searching independently maintained bibliographies (eg, the Chronic Care Bibliography41).

In identifying abstracts and articles for potential inclusion in our sample, the authors and their affiliations were not masked. We screened all identified abstracts for potentially relevant studies that assessed the effects of interventions containing 1 or more of the CCM elements. Randomized and nonrandomized controlled studies were eligible for inclusion. Uncontrolled studies were excluded, as were studies written in a language other than English. Because we sought to include only the most recent decade of published evidence in our report, we excluded studies published before 1993.

These studies used a wide range of outcome variables. We identified a clinical outcome, a quality-of-life outcome, and a key process of care for each condition (Table 1). To make the analysis more tractable and to maximize our ability to pool data, we opted to collect data on the variables that were reported by the greatest number of studies (eg, for the diabetes studies we selected glycosylated hemoglobin [HbA1c] as the clinical outcome rather than cholesterol level, and HbA1c monitoring as the key process of care rather than funduscopic testing). We also accepted a variety of similar measures for some domains. For example, we accepted any of the most commonly used measures for the clinical outcome among the depression studies, including the Hopkins Symptom Checklist-20 (9 studies), the Hamilton Depression Rating scale (6 studies), and the Center for Epidemiological Studies-Depression scale (6 studies). The most commonly used measures of disease-specific quality of life were the St. George's Respiratory Questionnaire (4 studies), the Juniper Asthma Quality of Life Questionnaire (4 studies), and the Minnesota Living with Heart Failure Questionnaire (3 studies).


Among the selected studies with data on the clinical outcomes, some reported data as count variables or continuous variables, whereas others reported data as dichotomous variables. We extracted data in both forms where possible. Too few studies on asthma and congestive heart failure reported the clinical outcome data as count variables (number of emergency department visits or readmissions during the study period), so for those 2 condition-specific analyses we used only the data that were reported as dichotomous variables (number with any emergency department visit during study period, number readmitted during study period).

Using a standardized data collection form, we abstracted data on study characteristics, including CCM elements incorporated in the interventions and effects on clinical outcomes, quality of life, and processes of care. If the results of a study were reported in multiple articles, data were abstracted from all articles and attributed to the primary citation. If a study reported comparisons at multiple follow-up times, we abstracted data on outcomes closest to 12 months of follow-up. For each study that reported data as continuous variables, the mean and standard deviation values were extracted (if available). For studies that did not report a standard deviation value at follow-up, the standard deviation was assumed to be equal to one quarter of the theoretical range for that measure. This imputation approach is based on the assumption that the underlying distributions were approximately normal, and it is a very conservative assumption in that the multiplication factor for truly normal distributions should be closer to one sixth. For the clinical outcomes, a higher score represented a worse outcome, whereas for quality of life and processes of care, a higher score represented a better outcome; some studies required recoding of quality-of-life measures for consistency. For example, if a study used the St. George's Respiratory Questionnaire as a measure of quality of life (0-100, with higher scores indicating poorer quality of life), we recoded the variables so that higher scores represented better quality of life. We excluded studies that did not provide sufficient statistics for data abstraction if our efforts to contact the study's authors for additional data were unsuccessful.

Statistical Analysis

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