Assessing the effect of a regional integrated care model over ten years using quality indicators based on claims data - the basic statistical methodology of the INTEGRAL project

• Main Authors: Dominikus Stelzer (Author), Erika Graf (Author), Ingrid Köster (Author), Peter Ihle (Author), Christian Günster (Author), Patrik Dröge (Author), Andreas Klöss (Author), Claudia Mehl (Author), Erik Farin-Glattacker (Author), Max Geraedts (Author), Ingrid Schubert (Author), Achim Siegel (Author), Werner Vach (Author)
• Format: Book
• Published: BMC, 2022-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12913-022-07573-7; 1472-6963
• Keywords: Integrated care model; Evaluation; Quality indicator; Statistical analysis; Public aspects of medicine; RA1-1270
• Source: BMC Health Services Research, Vol 22, Iss 1, Pp 1-20 (2022)

Abstract Background The regional integrated health care model "Healthy Kinzigtal" started in 2006 with the goal of optimizing health care and economic efficiency. The INTEGRAL project aimed at evaluating the effect of this model on the quality of care over the first 10 years. Methods This methodological protocol supplements the study protocol and the main publication of the project. Comparing quality indicators based on claims data between the intervention region and 13 structurally similar control regions constitutes the basic scientific approach. Methodological key issues in performing such a comparison are identified and solutions are presented. Results A key step in the analysis is the assessment of a potential trend in prevalence for a single quality indicator over time in the intervention region compared to the corresponding trends in the control regions. This step has to take into account that there may be a common - not necessarily linear - trend in the indicator over time and that trends can also appear by chance. Conceptual and statistical approaches were developed to handle this key step and to assess in addition the overall evidence for an intervention effect across all indicators. The methodology can be extended in several directions of interest. Conclusions We believe that our approach can handle the major statistical challenges: population differences are addressed by standardization; we offer transparency with respect to the derivation of the key figures; global time trends and structural changes do not invalidate the analyses; the regional variation in time trends is taken into account. Overall, the project demanded substantial efforts to ensure adequateness, validity and transparency.