Research Addressing the HHS Strategic Framework on Multiple Chronic Conditions. White paper #1: Understanding the High Prevalence of Low-Prevalence Chronic Disease Combinations: Databases and Methods for Research


The purpose of the first white paper was to explore how the “long tail” of the MCC population can be appropriately studied. As a first step, ASPE wanted to identify the existing data sources that could be used to understand the population, and to consider what steps might be taken in the future to improve the knowledgebase. ASPE’s guiding study questions were:

  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)?
  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?
  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?
  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?

The white paper identifies the challenges in studying people with MCC, weaknesses in national datasets, methodological constraints of studying many groups with unique disease combinations, and opportunities for future research.

Exhibit 1: Percent of MCC Prevalence and Cost in the Beginning of Medicare’s Long Tail

Exhibit 1: Percent of MCC Prevalence and Cost in the Beginning of Medicare’s Long Tail

Note on the Exhibit: The exhibit displays the first 250 Diseases Combinations (ranked by prevalence) from the baseline HCC analysis as calculated by Sorace and colleagues (Sorace et al. 2011). Chronic disease combination classifications (e.g. high, moderate and low) were assigned, but only represent rough approximations; specific criteria for each classification have not been defined. Note that the left Y-axis represents the proportion of the population that is included in each unique disease combination, and is adjusted for the 32% of beneficiaries and 6% of expenditures that are associated with the no-HCC population. The right Y-axis represents the cumulative percent of the total population (red format) and the total expenditure (blue format). Note that approximately 75% of expenditures are associated with the 27% of patients that are not represented by the most prevalent 250 disease combinations. As there are over 2 million disease combinations calculated by this methodology, the figure’s X-axis would need to be extended over 8,000 fold to the reader’s right before both cumulative lines reached 100%.

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"rpt_MCCResearchFinal.pdf" (pdf, 196Kb)

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