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A Meta-Analysis of Interventions to Improve Chronic Illness Care

A Meta-Analysis of Interventions to Improve Chronic Illness Care. Alexander Tsai 1 ( act2@case.edu ), S.C. Morton 2 , C.M. Mangione 3 , E.B. Keeler 2. AcademyHealth Annual Research Meeting, June 7, 2004. 1 Case School of Medicine; 2 RAND Health; 3 David Geffen School of Medicine at UCLA.

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A Meta-Analysis of Interventions to Improve Chronic Illness Care

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  1. A Meta-Analysis of Interventions to Improve Chronic Illness Care Alexander Tsai1 (act2@case.edu), S.C. Morton2, C.M. Mangione3, E.B. Keeler2 AcademyHealth Annual Research Meeting, June 7, 2004 1 Case School of Medicine; 2 RAND Health; 3 David Geffen School of Medicine at UCLA

  2. The Chronic Care Model

  3. Objective • Lack of controlled studies of the CCM • But there have been controlled studies of interventions that incorporate one or more CCM elements • Using meta analysis, we sought to: • Determine the extent to which CCM-style interventions improve chronic illness care • Determine whether any specific CCM elements were essential to improved outcomes

  4. Table 1. Outcomes of Interest

  5. Data Sources • Bibliographies of 23 recently published systematic reviews and meta-analyses: asthma (5), CHF (6), diabetes (7), depression (2), general chronic care (2), information systems (1) • MEDLINE 1998-2003 • Chronic Care Bibliography

  6. Inclusion/Exclusion Criteria • Inclusion criteria • 1993-2003 • Asthma, CHF, depression, diabetes • Controlled (randomized or non-randomized) • Outcomes of Interest • Exclusion criteria • Not written in English • Non-adult patient population • Insufficient statistics

  7. Data Abstraction • Data obtained from all relevant associated articles and attributed to the primary citation • Only 12-month follow-up data recorded if multiple follow-up times assessed • If missing data, SD conservatively assumed to be 1/4 of the theoretical range for that measure

  8. Statistical Analysis • Comparisons at follow-up • Pooled analysis by condition • Hedges’ g (continuous), risk ratio (binary) • Relative effectiveness of CCM elements • Random-effects meta-regression model • Funnel plots to detect publication bias • Cochran’s Q to assess heterogeneity • Sensitivity analysis for Jadad score ≥3

  9. Table 2. Summary Statistics (N=112)

  10. Table 3. By Condition * P<0.05

  11. Table 4. By CCM Element * P<0.05

  12. Conclusions • Interventions that contained one or more CCM elements improved clinical outcomes and processes of care for four chronic illnesses • Effect on quality of life was mixed • The specific CCM elements most responsible for the beneficial effects could not be determined

  13. Limitations • Testing the CCM vs. testing CCM elements • Unable to assess intensity of implementation • Unexplained heterogeneity in aggregating across conditions and types of interventions • Conclusions limited to selected outcomes and selected conditions

  14. For additional information: http://www.rand.org/health/ICICE E-mail: act2@case.edu

  15. Fig 1. Clinical Outcomes (Continuous) Depression Diabetes Q=230, df=51, P<0.001 Pooled Effect Size = -0.23 (-0.31, -0.15) favoring intervention

  16. Fig 2. Clinical Outcomes (Binary) Asthma CHF Depression Diabetes Q=135, df=45, P<0.001 Pooled RR = 0.84 (0.78, 0.90) favoring intervention

  17. Fig 3. Quality of Life Asthma CHF Depression Diabetes Q=93, df=23, P<0.001 Pooled Effect Size = 0.11 (0.02, 0.21) favoring intervention

  18. Fig 4. Processes of Care Asthma CHF Depression Diabetes Q=312, df=31, P<0.001 Pooled RR = 1.19 (1.10, 1.28) favoring intervention

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