Understanding the High Prevalence of Low-Prevalence Chronic Disease Combinations: Databases and Methods for Research. 1. Executive Summary

09/20/2013

Understanding how to better care for individuals with multiple chronic conditions (MCC) is a priority for the Department of Health and Human Services due to the growing cohort of people with MCC and the associated health care cost and quality of care implications. In recent decades health services research has focused on one disease at a time, or on highly prevalent co-occurring conditions, leaving a large gap in our knowledge about how to optimally treat individuals who have more than one chronic illness. Individuals living with MCC know firsthand the difficulty of navigating the health care system, the lack of coordination between different health care clinicians, the impact of illness on daily living, the toll on family and friends, and the impediments to maximizing quality of life, among other difficulties. For the numerous patients with rare combinations of multiple chronic conditions, the difficulties are exacerbated by having few peers to talk with and learn from, and few clinicians who are familiar with best treatment and options for their particular combination of conditions.

Current MCC research has focused primarily on studying the impact of high-prevalence diseases (i.e. hypertension, hyperlipidemia, diabetes, arthritis, etc.) in terms of patient outcomes, care utilization and cost. However, an understudied group comprises patients with less prevalent combinations of MCC. How the group may change over time as individuals acquire new chronic conditions, or certain conditions change in intensity, has not been well examined. There are many unique constellations of MCC; for example, a recent study of approximately 32 million Medicare beneficiaries found over 2,000,000 unique combinations of MCC (Sorace et al. 2011). The distribution of constellations of MCC results in a curve with a very “long tail” of complex patients that changes nationally over time. Sources and methods for studying the long tail and recommendations for future research on less prevalent MCC are the primary focus of our paper.

Our methods included a review of the peer-reviewed and grey literature, facilitating discussions of a Technical Advisory Group, and interviews with key informants. Most of the published studies examined a small number of high prevalence conditions ( e.g., hypertension, hyperlipidemia, ischemic heart disease, diabetes, arthritis) and almost none focused on low prevalence MCC. Claims data and large surveys are most appropriate for exploring rare combinations because of the small cell size for any one unique combination of conditions but are limited by code misspecification, upcoding to maximize reimbursement and poor demographic and socioeconomic variables in the case of claims, and recall bias and insufficient diagnostic detail in the case of surveys, as well as other limitations. The sheer volume of data needed to study the long tail distribution necessitates using a diagnostic grouping system. Of the 14 grouping systems we reviewed, the number of diagnostic groups ranged from 25 to 272 with 1080 subgroups. The number of diagnoses that are included determines the number of groups that can be studied. Grouping classifications are not well documented or explained by the researchers who utilize them. We found other methodological and analytical issues that complicate our ability to study MCC in general, and the long tail in particular. The paper serves as a resource for researchers interested in building the knowledgebase on MCC.

There is much to be learned about individuals who have less prevalent combinations of MCC and therefore many opportunities for future research, both substantive and methodological. We need to understand who comprises the long tail (including when looking at data other than Medicare claims), and better understand their demographic characteristics, cost patterns, and clusters of biologically related and unrelated conditions. Comparisons with similar populations in other countries will help shed light on treatment options. Self-management techniques and disease management for MCC combinations are critical to achieving improved quality of life, but we know little about those interventions in the low prevalence MCC population. Research methods need to be adapted and documented to help build the knowledge base about persons with MCC and lead to more valid, reliable findings. We need quality measures that take multiple illnesses into account, and much better research on the service utilization patterns in order to accurately attribute and address costs.

Finally, there is much to be learned from individuals who have less prevalent conditions of MCC: how they prioritize and manage their own illnesses, what outcomes are most important to them, where they obtain information, and how their conditions relate to one another. In the paper which follows we identify gaps in the current knowledge base, methodological constraints with existing analytical tools, and opportunities for future research to improve the care and lives of a growing, disadvantaged population.

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