EDITORIAL October 2014 Volume 89 Number 10
Multimorbidity at the Local Level: Implications and Research Directions
I
n this issue of Mayo Clinic Proceedings, Rocca et al1 report the results of a study of multimorbidity in a patient sample that represents nearly the total population of Olmsted County, Minnesota. (In this context, multimorbidity refers to the situation in which a patient receiving medical care for a sentinel condition has at least one additional chronic condition.) To our knowledge, this is the first report that uses the list of chronic conditions developed by the US Department of Health and Human Services (DHHS) to assist in systematically documenting the epidemiology and burden of chronic multimorbidity at this jurisdictional level.2 Other investigators have reported their use of the DHHS set of conditions to examine the burden of multimorbidity among nationally representative samples of persons in communities and in health care settings.3-6 In addition, the Centers for Medicare and Medicaid Services has provided statistics on the prevalence of multiple chronic conditions for Medicare beneficiaries at the state, county, and hospital referral region level.7 However, the report by Rocca et al expands this understanding substantially by taking this work directly to the local level through their examination of multimorbidity in the setting of nearly all persons in a single, highly documented county who have had encounters with the health care system. In 2010, the DHHS released its strategic framework on multiple chronic conditions.8 This framework has stimulated a growing body of research directed toward helping to improve our understanding of the complexities that multimorbidity poses for both clinical care and public health programs.3-6,9 The framework also has led directly to other related research priorities, including, for example, the
need for a research agenda addressing contextual factors (eg, health history, health system capacity, personal preferences and resources, familial and cultural norms, and expectations) influencing the care for and health of persons with multimorbidity and for data on comorbidities to conditions targeted by clinical practice guidelines.10,11 The report by Rocca et al provides epidemiological data on multimorbidity and a methodology that represent a model approach for local public health organizations and clinical systems to adapt for program planning and other purposes. Data developed through this approach can also be used to alert clinicians to the challenges that they and their patients face in managing multiple conditions, including, for example, drug-drug interactions and potential conflicts between relevant clinical practice guidelines.12 Similarly, the value of such information transcends the traditional medical officeebased clinical encounter through its potential use to facilitate clinic-community linkages13 by other health care or social service professionals who assist patients with multimorbidity in the community setting, such as pharmacists who guide patients and caregivers with disease self-management needs, including medication use. Although the Olmsted County study represents an important advance in the field, the findings also point to further research requirements to better inform clinical and public health programs that have responsibilities for individuals and populations with multiple chronic conditions. At least 4 areas of research can be expanded to assist these programs in preventing, diagnosing, and treating individual and combinations of chronic conditions and in program planning and evaluation efforts.
Mayo Clin Proc. n October 2014;89(10):1321-1323 n http://dx.doi.org/10.1016/j.mayocp.2014.08.007 www.mayoclinicproceedings.org n ª 2014 Mayo Foundation for Medical Education and Research
See also page 1336
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MAYO CLINIC PROCEEDINGS
First, this research needs to be replicated in populations in other jurisdictions. Olmsted County is one of more than 3000 counties in the United States, each with its own unique blend of sociodemographic composition, public health and health care resources, and other environmental features. Although there are many commonalities in the US health and health care landscape, there are also many important differences. For example, most counties do not have the same medical care system. Therefore, program and clinical systems design may benefit greatly from the use of local data, as well as from the types of population-level information on multimorbidity that reflect the range of models of health care delivery in the United States. Examining issues of multimorbidity in counties with common models for health service delivery will be important to inform disease prevention and management systems. Second, new methods should be developed to improve measurement of the burden of individual and multiple chronic conditions. The characterization of chronic disease burden increasingly is a complex operation that transcends the foundational action of counting conditions to now encompass a host of measurement issues, such as the sensitivity and specificity of measurement tools used to assess the severity of the condition(s) under study. For example, for persons who are within a health care delivery system, the use of International Classification of Diseases codes to identify conditions is an efficient and effective way to measure diagnosed conditions. However, many people are not receiving care within defined systems, and even among those who are, not all conditions are diagnosed with the same level of consistency and accuracy. Moreover, International Classification of Diseases codes are not the ideal means for measuring severity and burden of a given condition. Depending on the combinations of conditions, some individuals with many chronic conditions may experience fewer untoward effects on their quality of life than others who have a smaller number of conditions that are more severe and debilitating. Thus, additional work must be undertaken to develop algorithms and measurement tools that address both the burden and severity of chronic conditions. Third, research on multimorbidity at the county and other smaller population unit levels 1322
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should be used to assist in developing and delivering interventions to prevent and ameliorate these conditions individually and as multiples. Identifying and documenting the burden of multiple chronic conditions is in some respects the easy step. The harder step is to use this descriptive information to develop interventions that are appropriate, accessible, affordable, and effective in reducing the burden at the individual and population levels. These interventions may vary by condition set and population group. As documented in the reports by Rocca et al1 and others,3-6,9 there is variability in the number and types of conditions by demographic group. Describing and understanding these differences is important in the development and delivery of interventions in both the clinical and public health settings. Finally, further research should be directed toward improving the understanding of economic factors in the management of multimorbidity in counties and other locally defined areas. This issue, although outside the scope of the report by Rocca et al, has great importance. Just as the physical and mental health burdens of individual chronic conditions and multimorbidity are not equal, neither are the economic burdens. The economic aspects of multimorbidity have important implications for physicians, interventions, payment systems, and policy makers. Although in an ideal world economics would not be a paramount consideration in disease prevention and provision of care, at any level the reality of limited resources requires that economic factors be considered carefully in setting priorities. No single report can acknowledge, let alone examine in depth, all of the issues that need to be covered in fully addressing the prevention, provision of care, and policy components of multimorbidity. Rocca et al have substantially advanced the epidemiological characterization of multimorbidity by reporting on patterns in a well-defined population within a local context. This report both highlights additional research directions and serves as a model for what could be replicated in other settings to improve understanding of local variability and needs as an aid for clinicians, researchers, and programs in meeting the needs of persons with multiple chronic conditions.
October 2014;89(10):1321-1323
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http://dx.doi.org/10.1016/j.mayocp.2014.08.007 www.mayoclinicproceedings.org
EDITORIAL
Samuel F. Posner, PhD Richard A. Goodman, MD, JD, MPH Centers for Disease Control and Prevention National Center for Chronic Disease Prevention and Health Promotion Atlanta, GA Correspondence: Address to Samuel F. Posner, PhD, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 4770 Buford Hwy MS F-80, Atlanta, GA 30341 ([email protected]).
REFERENCES 1. Rocca WA, Boyd CM, Grossardt BR, et al. Prevalence of multimorbidity in a geographically defined American population: patterns by age, sex, and ethnicity. Mayo Clin Proc. 2014;89(10): 1336-1349. 2. Goodman RA, Posner SF, Huang ES, Parekh AK, Koh HK. Defining and measuring chronic conditions: imperatives for research, policy, program, and practice. Prev Chronic Dis. 2013; 10:120239. http://dx.doi.org/10.5888/pcd10.120239. 3. Ward BW, Schiller JS. Prevalence of multiple chronic conditions among US adults: estimates from the National Health Interview Survey, 2010. Prev Chronic Dis. 2013;10:120203. http://dx.doi. org/10.5888/pcd10.120203. 4. 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. http://dx.doi.org/10.5888/pcd10.120308. 5. 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. http://dx.doi. org/10.5888/pcd10.120292.
Mayo Clin Proc. n October 2014;89(10):1321-1323 www.mayoclinicproceedings.org
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6. Lochner KA, Cox CS. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. Prev Chronic Dis. 2013;10:120137. http://dx.doi.org/10.5888/ pcd10.120137. 7. Centers for Medicare & Medicaid Services. Research, Statistics, Data and Systems:Multiple Chronic Conditions Website. http:// www.cms.gov/Research-Statistics-Data-and-Systems/StatisticsTrends-and-Reports/Chronic-Conditions/MCC_Main.html. Accessed August 27, 2014. 8. US Department of Health and Human Services. Multiple Chronic ConditionsdA Strategic Framework: Optimum Health and Quality of Life for Individuals With Multiple Chronic Conditions. Washington, DC: US Dept of Health and Human Services; 2010. http://www.hhs.gov/ash/initiatives/mcc/mcc_framework. pdf. Accessed August 1, 2014. 9. Ford ES, Croft JB, Posner SF, Goodman RA, Giles WH. Cooccurrence of leading lifestyle-related chronic conditions among adults in the United States, 2002-2009. Prev Chronic Dis. 2013;10:120316. http://dx.doi.org/10.5888/pcd10.120316. 10. Bayliss EA, Bonds DE, Boyd CM, et al. Understanding the context of health for persons with multiple chronic conditions: moving from what is the matter to what matters. Ann Fam Med. 2014;12(3):260-269. 11. Goodman RA, Boyd C, Tinetti ME, Von Kohorn I, Parekh AK, McGinnis JM. IOM and DHHS meeting on making clinical practice guidelines appropriate for patients with multiple chronic conditions. Ann Fam Med. 2014;12(3):256-259. 12. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294(6):716-724. 13. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45-52.
http://dx.doi.org/10.1016/j.mayocp.2014.08.007
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Multimorbidity at the Local Level: Implications and Research Directions
I
n this issue of Mayo Clinic Proceedings, Rocca et al1 report the results of a study of multimorbidity in a patient sample that represents nearly the total population of Olmsted County, Minnesota. (In this context, multimorbidity refers to the situation in which a patient receiving medical care for a sentinel condition has at least one additional chronic condition.) To our knowledge, this is the first report that uses the list of chronic conditions developed by the US Department of Health and Human Services (DHHS) to assist in systematically documenting the epidemiology and burden of chronic multimorbidity at this jurisdictional level.2 Other investigators have reported their use of the DHHS set of conditions to examine the burden of multimorbidity among nationally representative samples of persons in communities and in health care settings.3-6 In addition, the Centers for Medicare and Medicaid Services has provided statistics on the prevalence of multiple chronic conditions for Medicare beneficiaries at the state, county, and hospital referral region level.7 However, the report by Rocca et al expands this understanding substantially by taking this work directly to the local level through their examination of multimorbidity in the setting of nearly all persons in a single, highly documented county who have had encounters with the health care system. In 2010, the DHHS released its strategic framework on multiple chronic conditions.8 This framework has stimulated a growing body of research directed toward helping to improve our understanding of the complexities that multimorbidity poses for both clinical care and public health programs.3-6,9 The framework also has led directly to other related research priorities, including, for example, the
need for a research agenda addressing contextual factors (eg, health history, health system capacity, personal preferences and resources, familial and cultural norms, and expectations) influencing the care for and health of persons with multimorbidity and for data on comorbidities to conditions targeted by clinical practice guidelines.10,11 The report by Rocca et al provides epidemiological data on multimorbidity and a methodology that represent a model approach for local public health organizations and clinical systems to adapt for program planning and other purposes. Data developed through this approach can also be used to alert clinicians to the challenges that they and their patients face in managing multiple conditions, including, for example, drug-drug interactions and potential conflicts between relevant clinical practice guidelines.12 Similarly, the value of such information transcends the traditional medical officeebased clinical encounter through its potential use to facilitate clinic-community linkages13 by other health care or social service professionals who assist patients with multimorbidity in the community setting, such as pharmacists who guide patients and caregivers with disease self-management needs, including medication use. Although the Olmsted County study represents an important advance in the field, the findings also point to further research requirements to better inform clinical and public health programs that have responsibilities for individuals and populations with multiple chronic conditions. At least 4 areas of research can be expanded to assist these programs in preventing, diagnosing, and treating individual and combinations of chronic conditions and in program planning and evaluation efforts.
Mayo Clin Proc. n October 2014;89(10):1321-1323 n http://dx.doi.org/10.1016/j.mayocp.2014.08.007 www.mayoclinicproceedings.org n ª 2014 Mayo Foundation for Medical Education and Research
See also page 1336
1321
MAYO CLINIC PROCEEDINGS
First, this research needs to be replicated in populations in other jurisdictions. Olmsted County is one of more than 3000 counties in the United States, each with its own unique blend of sociodemographic composition, public health and health care resources, and other environmental features. Although there are many commonalities in the US health and health care landscape, there are also many important differences. For example, most counties do not have the same medical care system. Therefore, program and clinical systems design may benefit greatly from the use of local data, as well as from the types of population-level information on multimorbidity that reflect the range of models of health care delivery in the United States. Examining issues of multimorbidity in counties with common models for health service delivery will be important to inform disease prevention and management systems. Second, new methods should be developed to improve measurement of the burden of individual and multiple chronic conditions. The characterization of chronic disease burden increasingly is a complex operation that transcends the foundational action of counting conditions to now encompass a host of measurement issues, such as the sensitivity and specificity of measurement tools used to assess the severity of the condition(s) under study. For example, for persons who are within a health care delivery system, the use of International Classification of Diseases codes to identify conditions is an efficient and effective way to measure diagnosed conditions. However, many people are not receiving care within defined systems, and even among those who are, not all conditions are diagnosed with the same level of consistency and accuracy. Moreover, International Classification of Diseases codes are not the ideal means for measuring severity and burden of a given condition. Depending on the combinations of conditions, some individuals with many chronic conditions may experience fewer untoward effects on their quality of life than others who have a smaller number of conditions that are more severe and debilitating. Thus, additional work must be undertaken to develop algorithms and measurement tools that address both the burden and severity of chronic conditions. Third, research on multimorbidity at the county and other smaller population unit levels 1322
Mayo Clin Proc.
n
should be used to assist in developing and delivering interventions to prevent and ameliorate these conditions individually and as multiples. Identifying and documenting the burden of multiple chronic conditions is in some respects the easy step. The harder step is to use this descriptive information to develop interventions that are appropriate, accessible, affordable, and effective in reducing the burden at the individual and population levels. These interventions may vary by condition set and population group. As documented in the reports by Rocca et al1 and others,3-6,9 there is variability in the number and types of conditions by demographic group. Describing and understanding these differences is important in the development and delivery of interventions in both the clinical and public health settings. Finally, further research should be directed toward improving the understanding of economic factors in the management of multimorbidity in counties and other locally defined areas. This issue, although outside the scope of the report by Rocca et al, has great importance. Just as the physical and mental health burdens of individual chronic conditions and multimorbidity are not equal, neither are the economic burdens. The economic aspects of multimorbidity have important implications for physicians, interventions, payment systems, and policy makers. Although in an ideal world economics would not be a paramount consideration in disease prevention and provision of care, at any level the reality of limited resources requires that economic factors be considered carefully in setting priorities. No single report can acknowledge, let alone examine in depth, all of the issues that need to be covered in fully addressing the prevention, provision of care, and policy components of multimorbidity. Rocca et al have substantially advanced the epidemiological characterization of multimorbidity by reporting on patterns in a well-defined population within a local context. This report both highlights additional research directions and serves as a model for what could be replicated in other settings to improve understanding of local variability and needs as an aid for clinicians, researchers, and programs in meeting the needs of persons with multiple chronic conditions.
October 2014;89(10):1321-1323
n
http://dx.doi.org/10.1016/j.mayocp.2014.08.007 www.mayoclinicproceedings.org
EDITORIAL
Samuel F. Posner, PhD Richard A. Goodman, MD, JD, MPH Centers for Disease Control and Prevention National Center for Chronic Disease Prevention and Health Promotion Atlanta, GA Correspondence: Address to Samuel F. Posner, PhD, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 4770 Buford Hwy MS F-80, Atlanta, GA 30341 ([email protected]).
REFERENCES 1. Rocca WA, Boyd CM, Grossardt BR, et al. Prevalence of multimorbidity in a geographically defined American population: patterns by age, sex, and ethnicity. Mayo Clin Proc. 2014;89(10): 1336-1349. 2. Goodman RA, Posner SF, Huang ES, Parekh AK, Koh HK. Defining and measuring chronic conditions: imperatives for research, policy, program, and practice. Prev Chronic Dis. 2013; 10:120239. http://dx.doi.org/10.5888/pcd10.120239. 3. Ward BW, Schiller JS. Prevalence of multiple chronic conditions among US adults: estimates from the National Health Interview Survey, 2010. Prev Chronic Dis. 2013;10:120203. http://dx.doi. org/10.5888/pcd10.120203. 4. 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. http://dx.doi.org/10.5888/pcd10.120308. 5. 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. http://dx.doi. org/10.5888/pcd10.120292.
Mayo Clin Proc. n October 2014;89(10):1321-1323 www.mayoclinicproceedings.org
n
6. Lochner KA, Cox CS. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. Prev Chronic Dis. 2013;10:120137. http://dx.doi.org/10.5888/ pcd10.120137. 7. Centers for Medicare & Medicaid Services. Research, Statistics, Data and Systems:Multiple Chronic Conditions Website. http:// www.cms.gov/Research-Statistics-Data-and-Systems/StatisticsTrends-and-Reports/Chronic-Conditions/MCC_Main.html. Accessed August 27, 2014. 8. US Department of Health and Human Services. Multiple Chronic ConditionsdA Strategic Framework: Optimum Health and Quality of Life for Individuals With Multiple Chronic Conditions. Washington, DC: US Dept of Health and Human Services; 2010. http://www.hhs.gov/ash/initiatives/mcc/mcc_framework. pdf. Accessed August 1, 2014. 9. Ford ES, Croft JB, Posner SF, Goodman RA, Giles WH. Cooccurrence of leading lifestyle-related chronic conditions among adults in the United States, 2002-2009. Prev Chronic Dis. 2013;10:120316. http://dx.doi.org/10.5888/pcd10.120316. 10. Bayliss EA, Bonds DE, Boyd CM, et al. Understanding the context of health for persons with multiple chronic conditions: moving from what is the matter to what matters. Ann Fam Med. 2014;12(3):260-269. 11. Goodman RA, Boyd C, Tinetti ME, Von Kohorn I, Parekh AK, McGinnis JM. IOM and DHHS meeting on making clinical practice guidelines appropriate for patients with multiple chronic conditions. Ann Fam Med. 2014;12(3):256-259. 12. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294(6):716-724. 13. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45-52.
http://dx.doi.org/10.1016/j.mayocp.2014.08.007
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