Advancing current approaches to disease management evaluation

Capitalizing on heterogeneity to understand what works and for whom

A.M.J. Elissen, J.L. Adams, M.D. Spreeuwenberg, I.G.P. Duimel-Peeters, C. Spreeuwenberg, A. Linden, H.J.M. Vrijhoef

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Abstract

Background
Evaluating large-scale disease management interventions implemented in actual health care settings is a complex undertaking for which universally accepted methods do not exist. Fundamental issues, such as a lack of control patients and limited generalizability, hamper the use of the ‘gold-standard’ randomized controlled trial, while methodological shortcomings restrict the value of observational designs. Advancing methods for disease management evaluation in practice is pivotal to learn more about the impact of population-wide approaches. Methods must account for the presence of heterogeneity in effects, which necessitates a more granular assessment of outcomes.
Methods
This paper introduces multilevel regression methods as valuable techniques to evaluate ‘real-world’ disease management approaches in a manner that produces meaningful findings for everyday practice. In a worked example, these methods are applied to retrospectively gathered routine health care data covering a cohort of 105,056 diabetes patients who receive disease management for type 2 diabetes mellitus in the Netherlands. Multivariable, multilevel regression models are fitted to identify trends in clinical outcomes and correct for differences in characteristics of patients (age, disease duration, health status, diabetes complications, smoking status) and the intervention (measurement frequency and range, length of follow-up).
Results
After a median one year follow-up, the Dutch disease management approach was associated with small average improvements in systolic blood pressure and low-density lipoprotein, while a slight deterioration occurred in glycated hemoglobin. Differential findings suggest that patients with poorly controlled diabetes tend to benefit most from disease management in terms of improved clinical measures. Additionally, a greater measurement frequency was associated with better outcomes, while longer length of follow-up was accompanied by less positive results.
Conclusions
Despite concerted efforts to adjust for potential sources of confounding and bias, there ultimately are limits to the validity and reliability of findings from uncontrolled research based on routine intervention data. While our findings are supported by previous randomized research in other settings, the trends in outcome measures presented here may have alternative explanations. Further practice-based research, perhaps using historical data to retrospectively construct a control group, is necessary to confirm results and learn more about the impact of population-wide disease management.
Keywords: Chronic disease management, Quality measurement, Evaluation methodology, Multilevel regression methods, Statistical heterogeneity
Original languageEnglish
Article number40
JournalBMC Medical Research Methodology
Volume13
DOIs
Publication statusPublished - 2013

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Disease Management
Delivery of Health Care
Glycosylated Hemoglobin A
Gold
Netherlands
Type 2 Diabetes Mellitus

Cite this

Elissen, A. M. J., Adams, J. L., Spreeuwenberg, M. D., Duimel-Peeters, I. G. P., Spreeuwenberg, C., Linden, A., & Vrijhoef, H. J. M. (2013). Advancing current approaches to disease management evaluation: Capitalizing on heterogeneity to understand what works and for whom. BMC Medical Research Methodology, 13, [40]. https://doi.org/10.1186/1471-2288-13-40
Elissen, A.M.J. ; Adams, J.L. ; Spreeuwenberg, M.D. ; Duimel-Peeters, I.G.P. ; Spreeuwenberg, C. ; Linden, A. ; Vrijhoef, H.J.M. / Advancing current approaches to disease management evaluation : Capitalizing on heterogeneity to understand what works and for whom. In: BMC Medical Research Methodology. 2013 ; Vol. 13.
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Elissen, AMJ, Adams, JL, Spreeuwenberg, MD, Duimel-Peeters, IGP, Spreeuwenberg, C, Linden, A & Vrijhoef, HJM 2013, 'Advancing current approaches to disease management evaluation: Capitalizing on heterogeneity to understand what works and for whom', BMC Medical Research Methodology, vol. 13, 40. https://doi.org/10.1186/1471-2288-13-40

Advancing current approaches to disease management evaluation : Capitalizing on heterogeneity to understand what works and for whom. / Elissen, A.M.J.; Adams, J.L.; Spreeuwenberg, M.D.; Duimel-Peeters, I.G.P.; Spreeuwenberg, C.; Linden, A.; Vrijhoef, H.J.M.

In: BMC Medical Research Methodology, Vol. 13, 40, 2013.

Research output: Contribution to journalArticleScientificpeer-review

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T1 - Advancing current approaches to disease management evaluation

T2 - Capitalizing on heterogeneity to understand what works and for whom

AU - Elissen, A.M.J.

AU - Adams, J.L.

AU - Spreeuwenberg, M.D.

AU - Duimel-Peeters, I.G.P.

AU - Spreeuwenberg, C.

AU - Linden, A.

AU - Vrijhoef, H.J.M.

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N2 - BackgroundEvaluating large-scale disease management interventions implemented in actual health care settings is a complex undertaking for which universally accepted methods do not exist. Fundamental issues, such as a lack of control patients and limited generalizability, hamper the use of the ‘gold-standard’ randomized controlled trial, while methodological shortcomings restrict the value of observational designs. Advancing methods for disease management evaluation in practice is pivotal to learn more about the impact of population-wide approaches. Methods must account for the presence of heterogeneity in effects, which necessitates a more granular assessment of outcomes.MethodsThis paper introduces multilevel regression methods as valuable techniques to evaluate ‘real-world’ disease management approaches in a manner that produces meaningful findings for everyday practice. In a worked example, these methods are applied to retrospectively gathered routine health care data covering a cohort of 105,056 diabetes patients who receive disease management for type 2 diabetes mellitus in the Netherlands. Multivariable, multilevel regression models are fitted to identify trends in clinical outcomes and correct for differences in characteristics of patients (age, disease duration, health status, diabetes complications, smoking status) and the intervention (measurement frequency and range, length of follow-up).ResultsAfter a median one year follow-up, the Dutch disease management approach was associated with small average improvements in systolic blood pressure and low-density lipoprotein, while a slight deterioration occurred in glycated hemoglobin. Differential findings suggest that patients with poorly controlled diabetes tend to benefit most from disease management in terms of improved clinical measures. Additionally, a greater measurement frequency was associated with better outcomes, while longer length of follow-up was accompanied by less positive results.ConclusionsDespite concerted efforts to adjust for potential sources of confounding and bias, there ultimately are limits to the validity and reliability of findings from uncontrolled research based on routine intervention data. While our findings are supported by previous randomized research in other settings, the trends in outcome measures presented here may have alternative explanations. Further practice-based research, perhaps using historical data to retrospectively construct a control group, is necessary to confirm results and learn more about the impact of population-wide disease management.Keywords: Chronic disease management, Quality measurement, Evaluation methodology, Multilevel regression methods, Statistical heterogeneity

AB - BackgroundEvaluating large-scale disease management interventions implemented in actual health care settings is a complex undertaking for which universally accepted methods do not exist. Fundamental issues, such as a lack of control patients and limited generalizability, hamper the use of the ‘gold-standard’ randomized controlled trial, while methodological shortcomings restrict the value of observational designs. Advancing methods for disease management evaluation in practice is pivotal to learn more about the impact of population-wide approaches. Methods must account for the presence of heterogeneity in effects, which necessitates a more granular assessment of outcomes.MethodsThis paper introduces multilevel regression methods as valuable techniques to evaluate ‘real-world’ disease management approaches in a manner that produces meaningful findings for everyday practice. In a worked example, these methods are applied to retrospectively gathered routine health care data covering a cohort of 105,056 diabetes patients who receive disease management for type 2 diabetes mellitus in the Netherlands. Multivariable, multilevel regression models are fitted to identify trends in clinical outcomes and correct for differences in characteristics of patients (age, disease duration, health status, diabetes complications, smoking status) and the intervention (measurement frequency and range, length of follow-up).ResultsAfter a median one year follow-up, the Dutch disease management approach was associated with small average improvements in systolic blood pressure and low-density lipoprotein, while a slight deterioration occurred in glycated hemoglobin. Differential findings suggest that patients with poorly controlled diabetes tend to benefit most from disease management in terms of improved clinical measures. Additionally, a greater measurement frequency was associated with better outcomes, while longer length of follow-up was accompanied by less positive results.ConclusionsDespite concerted efforts to adjust for potential sources of confounding and bias, there ultimately are limits to the validity and reliability of findings from uncontrolled research based on routine intervention data. While our findings are supported by previous randomized research in other settings, the trends in outcome measures presented here may have alternative explanations. Further practice-based research, perhaps using historical data to retrospectively construct a control group, is necessary to confirm results and learn more about the impact of population-wide disease management.Keywords: Chronic disease management, Quality measurement, Evaluation methodology, Multilevel regression methods, Statistical heterogeneity

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DO - 10.1186/1471-2288-13-40

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