Understanding Disparities in Persons with Multiple Chronic Conditions: Research Approaches and Datasets. 4.1 Non-Disease Specific Disparities in the MCC Population


For the purposes of this white paper, non-disease specific disparities are defined as disparities that relate to MCC in general, rather than a specific combination of chronic conditions. As discussed in depth in the first white paper (Rezaee, 2013) many MCC studies use counts of chronic conditions as a way of categorizing groups because of the complexity of parsing the myriad disease combinations that exist. Groups of consumers are categorized as having two, three, four, etc. chronic conditions but the conditions are not necessarily the same ones. One person in a group with three chronic conditions may have diabetes, hypertension and Multiple Sclerosis while another might have diabetes, COPD and arthritis. By contrast, disease specific disparities investigate differences that occur among patients with a specific combination of chronic conditions (e.g. hypertension, diabetes, and MS). Only individuals with those specific MCC combinations are considered in the research.

Non-disease specific MCC studies use counts of the number of MCC that a person has (2, 3, 4, etc.). Cases are grouped by the number of MCC although the specific conditions may differ.

Non-disease specific disparities research has examined MCC prevalence, healthcare utilization and cost, and the occurrence of common chronic disease combinations across different MCC patient groups. Exhibit 3 describes studies conducted on non-disease specific disparities to-date. Several of the papers represent a coordinated effort by the HHS Interagency Workgroup to review national datasets that could be used for MCC research, and the findings were published in the Journal of Preventing Chronic Disease The articles are available online at: http://www.cdc.gov/pcd/collections/pdf/PCD_MCC_Collection_5-17-13.pdf. The authors chose 20 chronic conditions in order to compare the ability of each dataset to address specific MCC (Goodman, 2013).

The datasets focus on adult and elderly populations (versus children) and use the number of chronic conditions a person has to create groups. The most common differences explored in MCC groups are by gender, age, and race/ethnicity differences. In the sections that follow, we discuss the findings of the research conducted to investigate disparities among people with the same number (non-disease specific) of MCC. The findings are organized by gender, age, race/ethnicity, insurance status, and education.

Exhibit 3: Summary of Non Disease Specific MCC Disparities Studies

Citation Year Sample Data Source # of CC studied by authors Disparities Investigated Disease Clusters Investigated
Note: The Office of the Assistant Secretary for Health (OASH ) developed a list of 20 CCs that they then studied across a number of datasets (Goodman,For these studies, the number of CCs from this list that authors chose to look at is represented by an asterisk, *.
Ashman JJ, Beresovsky V. Multiple chronic conditions among US adults who visited physician offices: data from the National Ambulatory Medical Care Survey, 2009. Prev Chronic Dis 2013; 10:120308. 2013 Adult Civilian Patients N=28,693 National Ambulatory Medical Care Survey 13* Gender Age Race/Ethnicity Insurance Type Yes
Centers for Medicare & Medicaid Services (CMS). Chronic Conditions among Medicare Beneficiaries, Chartbook. 2012 Edition. Baltimore, MD. 2012. 2012 Medicare Patients N=31,313,344 CMS Chronic Condition Warehouse 15 Gender Age Race/Ethnicity Dual Eligibility Status No
Ford ES, Croft JB, Posner SF, Goodman RA, Giles WH. Co-occurrence of leading lifestyle-related chronic conditions among adults in the United States, 2002-2009. Prev Chronic Dis 2013;10:120316. 2013 Adult Civilians N =196,240 National HealthInterview Survey 9* Gender Age Race/Ethnicity Education No
Freid VM, Bernstein AM, and Bush MA. Multiple chronic conditions among adults aged 45 and over: Trends over the past 10 years. NCHS data brief, no.100. Hyattsville, MD: National Center for Health Statistics. 2012. 2012 Adult Civilians N = 30,682 National HealthInterview Survey 9 Age Race/Ethnicity No
Hidalgo CA, Blumm N, Barabási A-L, Christakis NA(2009) A Dynamic Network Approach for the Study of Human Phenotypes. PLoS Comput Biol 5(4): e1000353. 2009 Medicare Patients Medicare Provider and Analysis Review File 16,459 Race & Ethnicity Yes
Lochner KA, Cox CS. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. Prev Chronic Dis 2013;10:120137. 2013 Medicare Patients N=31 million Medicare Claims 15* Gender Age Race & Ethnicity Dual Eligibility Status Yes
Machlin SR, Soni A. Health care expenditures for adults with multiple treated chronic conditions: estimates from the Medical Expenditure Panel Survey, 2009. Prev Chronic Dis 2013;10:120172. 2013 Adult Civilians N=24,870 Medical Expenditure Panel Survey 20* Gender Age Race & Ethnicity Insurance Type Utilization No
Steiner CA, Friedman B. Hospital utilization, costs, and mortality for adults with multiple chronic conditions, Nationwide Inpatient Sample, 2009. Prev Chronic Dis 2013;10;120292. 2013 Adult Inpatients N=7,810,762 Nationwide Inpatient Sample 15* Gender Age Race & Ethnicity Insurance Type Mortality Utilization & Cost Yes
Steinman, M.A., Lee, S.J., John, B.W. et al. Patterns of Multimorbidity in elderly veterans. J Am Geriatr Soc. 2012 Oct;60(10):1872-80. 2012 VA Patients N=2,002,693 VA Databases 23 Gender Yes
Ward BW, and Schiller JS. Prevalence of multiple chronic conditions among US adults: estimates from the National Health Interview Survey, 2010. Prev Chronic Dis. 2013;10:E65. 2013 Adult Civilians N=27,157 National HealthInterview Survey 10* Gender Age Race & Ethnicity Insurance Type Yes


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