Understanding the High Prevalence of Low-Prevalence Chronic Disease Combinations: Databases and Methods for Research. Purpose of the Paper

09/20/2013

The Assistant Secretary for Planning and Evaluation (APSE) Office of Science and Data Policy contracted Abt Associates to explore how the “long tail” of the MCC population can be appropriately studied. As a first step, ASPE wanted to identify and review the existing data sources that can be used to understand the population, and to describe relevant methodological research issues. The paper is intended to serve as a resource for investigators working on MCC by describing the strengths and limitations of currently available databases and methods. The information can help both researchers and stakeholders better understand and interpret research results, as well as consider what steps might be taken in the future to improve the knowledgebase on health care for MCC. Specifically, ASPE’s guiding study questions were as follows:

Study question #1 – What are the findings from MCC research related to prevalence and patterns of chronic disease combinations, health care utilization and cost, with particular attention to addressing less prevalent combinations of chronic conditions (i.e., the long tail)?

Study question #2 – What methodologies and analytic techniques have been used to study MCC? What are the potential limitations of these approaches in considering less prevalent combinations of MCC?

Study question #3 – What data systems and data sets exist that can be analyzed to better improve HHS’s understanding of and approaches to addressing numerous less prevalent combinations of chronic conditions?

Study question #4 – What combinations of less prevalent combinations of chronic comorbidities are most critical to address in terms of care utilization and cost? What are the future research considerations for MCC research?

In the Background section of the paper (Section 4) we describe why less prevalent MCC are an important area of study, as well as address the definitional problems and the interests of various stakeholders in MCC research. We describe the data collection and analysis methods we have used in Section 5: (1) literature review, (2) Technical Advisory Group, (3) key informant interviews, and (4) datasets and grouping systems. In Section 6, we characterize the literature on prevalence and patterns of MCC that has been conducted to-date. Methodological and analytic considerations of MCC research, such as grouping systems and study designs, are discussed in Section 7. Section 8 contains a review of potential datasets for MCC research. Section 9 discusses consideration for future areas of inquiry.

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