Understanding Disparities in Persons with Multiple Chronic Conditions: Research Approaches and Datasets. 1. Executive Summary


Understanding how to better care for individuals with multiple chronic conditions (MCC) is a priority for the Department of Health and Human Services. Persons with MCC represent almost one-third of the U.S. population and account for two-thirds of health care spending, yet most research on chronic conditions focuses on single diseases. In response to this growing challenge, the Department of Health and Human Services (HHS) led the development of the Strategic Framework on Multiple Chronic Conditions(HHS 2010).

This white paper contributes to meeting the goals outlined by the HHS strategic framework by examining promising data, methods, and topics for future disparities research within the MCC population. It builds on a previous white paper titled “Understanding the High Prevalence of Low-Prevalence Chronic Disease Combinations: Databases and Methods for Research”, which describes the “long tail” of the MCC distribution: approximately one-third of all Medicare patients have one of the most common combinations of MCC, but another third of all patients have one of two million unique combinations of MCC and account for 79% of health care costs. This poses a unique challenge for research because of the small number of persons within each unique combination of MCC in the “long tail” of the distribution (Exhibit 1). For disparities research, the challenge is even greater as stratification by race, ethnicity and sociodemographic variables further reduces sample size.

The present paper summarizes the current literature on MCC disparities, describes how the methodological challenges of disparities research are further manifested in MCC research, reviews promising methods, and assesses the usability of various data systems and datasets for MCC disparities research.

Study methods for this paper included a literature review (Appendix B), interviews with nine key informants (Appendix C) who were identified by ASPE project officers and our Technical Advisory Group (Appendix D), a review of datasets and data systems identified in the first White Paper (Appendix E), to assess their potential for MCC disparities research, and integration of input and feedback from key informants and the Technical Advisory Group.

Study results showed that most of the existing disparities research to date has focused on individual chronic conditions. There has been little research on the extent, causes, and strategies for reducing disparities within the MCC population. Further research is needed to test and replicate findings from recent studies before patterns can be confirmed. Results from our literature review suggest that:

  •  Women are more likely than men to be classified as having MCC (Ashman et al., 2013; CMS, 2012; Ward et al., 2012; Machlin et al., 2013).
  •  The number of chronic conditions rises with age (Freid et al., 2012).
  •  Hispanic patients have the lowest MCC prevalence (Ward et al., 2013; Steiner et al., 2013). Mexican-Americans have lower initial levels of MCC and slower accumulation of comorbidity compared to non-Hispanic White and non-Hispanic Black patients (Quinones et al., 2011).
  • MCC prevalence among Asian Americans is lower compared to white or black MCC patients (Machlin et al. 2013), though Asians/Pacific Islanders had the highest mortality and cost per case compared to all other groups (Steiner et al., 2013).

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

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

Note on the Exhibit: The exhibit displays the first 250 Disease 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%.

Future research on disparities in the MCC population would be facilitated by the development of a conceptual model of MCC disparities that incorporates the roles of biological, behavioral, health care, socio-economic, community and environmental factors; and by further development of the research infrastructure, for example through continued efforts to improve the reporting of patient race, ethnicity, language, and other sociodemographic variables.

Several immediate MCC disparities research opportunities are identified in this paper, including secondary data analyses, intervention research, and  research using complementary methods such as qualitative methods, positive deviance research, metasynthesis and rare disease surveillance.

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