Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs.
ABSTRACT Persons with multiple chronic conditions are a large and growing segment of the US population. However, little is known about how chronic conditions cluster, and the ramifications of having specific combinations of chronic conditions. Clinical guidelines and disease management programs focus on single conditions, and clinical research often excludes persons with multiple chronic conditions. Understanding how conditions in combination impact the burden of disease and the costs and quality of care received is critical to improving care for the 1 in 5 Americans with multiple chronic conditions. This Medline review of publications examining somatic chronic conditions co-occurring with 1 or more additional specific chronic illness between January 2000 and March 2007 summarizes the state of our understanding of the prevalence and health challenges of multiple chronic conditions and the implications for quality, care management, and costs.
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ABSTRACT: Reducing health care costs requires the ability to identify patients most likely to incur high costs. Our objective was to evaluate the ability of the Charlson comorbidity score to predict the individuals who would incur high costs in the subsequent year and to contrast its predictive ability with other commonly used predictors. We contrasted the prior year Charlson comorbidity index, costs, Diagnostic Cost Group (DCG) and hospitalization as predictors of subsequent year costs from claims data of fund that provides comprehensive health benefits to a large union of health care workers. Total costs in the subsequent year was the principal outcome. Of the 181,764 predominantly Black and Latino beneficiaries, 70% were adults (mean age 45.7 years; 62% women). As the comorbidity index increased, total yearly costs increased significantly (P<.001). At lower comorbidity, the costs were similar across different chronic diseases. Using regression to predict total costs, top 5th and 10th percentile of costs, the comorbidity index, prior costs and DCG achieved almost identical explained variance in both adults and children. The comorbidity index predicted health costs in the subsequent year, performing as well as prior cost and DCG in identifying those in the top 5% or 10%. The comorbidity index can be used prospectively to identify patients who are likely to incur high costs. ClinicalTrials.gov NCT01761253.PLoS ONE 12/2014; 9(12):e112479. · 3.53 Impact Factor
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ABSTRACT: Background One of the most widely used self-reporting tools assessing diabetes self-management in English is the Summary of Diabetes Self-Care Activities (SDSCA) measure. To date there is no psychometric validated instrument in German to assess self-management in patients with diabetes mellitus. Therefore, this study aimed to translate the SDSCA into German and examine its psychometric properties.Methods The English version of the SDSCA was translated into German following the guidelines for cultural adaptation. The German version of the SDSCA (SDSCA-G) was administered to a random sample of 315 patients with diabetes mellitus type 2. Reliability was analyzed using Cronbach¿s alpha coefficient and item characteristics were assessed. Exploratory and confirmatory factor analysis (EFA and CFA) were carried out to explore the construct validity. A multivariable linear regression model was used to identify the influence of predictor variables on the SDSCA-G sum score.ResultsThe Cronbach¿s alpha for the SDSCA-G (all items) was ¿¿=¿0.618 and an acceptable correlation between the SDSCA-G and Self-management Diabetes Mellitus-Questionnaire (SDQ) (¿¿=¿0.664) was identified. The EFA suggested a four factor construct as did the postulated model. The CFA showed the goodness of fit of the SDSCA-G. However, item 4 was found to be problematic regarding the analysis of psychometric properties. The omission of item 4 yielded an increase in Cronbach¿s alpha (¿¿=¿0.631) and improvements of the factor structure and model fit. No statistically significant influences of predictor variables on the SDSCA-G sum score were observed.Conclusion The revised German version of the SDSCA (SDSCA-G) is a reliable and valid tool assessing self-management in adults with type 2 diabetes in Germany.Health and Quality of Life Outcomes 12/2014; 12(1):185. · 2.10 Impact Factor
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ABSTRACT: Little is known about how combinations of chronic conditions in adults affect total health care expenditures. Our objective was to estimate the annual average total expenditures and out-of-pocket spending burden among US adults by combinations of conditions. We conducted a cross-sectional study using 2009 and 2011 data from the Medical Expenditure Panel Survey. The sample consisted of 9,296 adults aged 21 years or older with at least 2 of the following 4 highly prevalent chronic conditions: arthritis, diabetes mellitus, heart disease, and hypertension. Unadjusted and adjusted regression techniques were used to examine the association between chronic condition combinations and log-transformed total expenditures. Logistic regressions were used to analyze the relationship between chronic condition combinations and high out-of-pocket spending burden. Among adults with chronic conditions, adults with all 4 conditions had the highest average total expenditures ($20,016), whereas adults with diabetes/hypertension had the lowest annual total expenditures ($7,116). In adjusted models, adults with diabetes/hypertension and hypertension/arthritis had lower health care expenditures than adults with diabetes/heart disease (P < .001). In adjusted models, adults with all 4 conditions had higher expenditures compared with those with diabetes and heart disease. However, the difference was only marginally significant (P = .04). Among adults with arthritis, diabetes, heart disease, and hypertension, total health care expenditures differed by type of chronic condition combinations. For individuals with multiple chronic conditions, such as heart disease and diabetes, new models of care management are needed to reduce the cost burden on the payers.Preventing chronic disease 01/2015; 12:E12. · 1.96 Impact Factor
Multiple Chronic Conditions: Prevalence, Health Consequences,
and Implications for Quality, Care Management, and Costs
Christine Vogeli, PhD1,2, Alexandra E. Shields, PhD1,2, Todd A. Lee, PharmD PhD4,5,
Teresa B. Gibson, PhD3, William D. Marder, PhD3, Kevin B. Weiss, MD MPH4,5,
and David Blumenthal, MD MPP1,2
1Institute for Health Policy in the Department of Medicine, Massachusetts General Hospital, Boston, MA, USA;2Harvard Medical School, Boston,
MA, USA;3Thomson Healthcare, Ann Arbor, MI, USA;4Institute for Healthcare Studies, Feinberg School of Medicine, Northwestern University,
Chicago, IL, USA;5Center for Complex Chronic Care, Hines VA Hospital, Hines, IL, USA.
Persons with multiple chronic conditions are a large
and growing segment of the US population. However,
little is known about how chronic conditions cluster,
and the ramifications of having specific combinations of
chronic conditions. Clinical guidelines and disease
management programs focus on single conditions, and
clinical research often excludes persons with multiple
chronic conditions. Understanding how conditions in
combination impact the burden of disease and the costs
and quality of care received is critical to improving care
for the 1 in 5 Americans with multiple chronic condi-
tions. This Medline review of publications examining
somatic chronic conditions co-occurring with 1 or more
additional specific chronic illness between January
2000 and March 2007 summarizes the state of our
understanding of the prevalence and health challenges
of multiple chronic conditions and the implications for
quality, care management, and costs.
KEY WORDS: chronic disease; comorbidity; prevalence; quality of health
J Gen Intern Med 22(Suppl 3):391–5
© Society of General Internal Medicine 2007
Typically, we study experience with health care conditions,
including health care costs and quality, as if these conditions
occur in isolation, one at a time. The vast majority of extant
clinical guidelines and disease management programs focus
on a single condition, although the experience of multiple
chronic illnesses is the reality for many patients—particularly
among the elderly and near elderly. The Institute of Medicine’s
report “Crossing the Quality Chasm” highlights the problem
with health system fragmentation and stresses the need for
health care systems that promote continuity of care and
integration of services.1Realigning the focus of health services
research to be more in line with the complex experience of
patients is central to developing solutions that work. This
paper provides information on what we know about multiple
chronic conditions, specifically the prevalence and health
challenges of multiple chronic conditions, and the ramifica-
tions of specific combinations of chronic conditions on quality,
patient management, and costs.
We performed a semistructured literature review to identify
relevant articles. Specifically, we queried MEDLINE for peer-
reviewed publications that examined the prevalence, out-
comes, costs, and patient management challenges associated
with multiple chronic conditions.
We first selected all articles the Mesh terms ‘chronic disease’
and ‘comorbidity,’ and limited our search to articles on adults
published in English between January 2000 and March 2007
(n=643). This set was further paired down using 2 different
strategies. The first strategy used a set of specific Mesh terms
related to prevalence, quality, access, delivery of care, patterns
of care, morbidity, mortality, and expenditures. To ensure that
we did not overlook any important articles in the original set,
we also limited the original set to articles published in core
journals. The final set of 123 articles was the union of
abstracts gained from these 2 approaches. Articles without
abstracts or whose author was anonymous were not reviewed.
The remaining abstracts were reviewed by the first author and
abstracts that did not mention at least 1 specific somatic
chronic illness, abstracts that did not examine specific comor-
bidities, and articles that focused on an acute illness or
procedure were removed. Information summarized in this
review stem from the remaining articles and prior publications
cited by these articles.
PREVALENCE OF MULTIPLE CHRONIC CONDITIONS
The number of persons in the United States who have not just
a single chronic condition, but multiple co-occurring chronic
conditions is large and growing. In 2005, 21% or roughly
63 million Americans had more than 1 chronic condition, or
multiple illnesses or impairments expected to last a year or
The authors are members of the Consortium on Complex Chronic
Illness, Quality, and Equity. The Consortium, directed by Dr. A. Shields, is
a collaboration of investigators from Harvard, MGH, Northwestern, the
VA, Thomson Healthcare, and Ingenix committed to accelerating research
on complex chronic illness and its implications for quality and the health
of vulnerable populations.
longer. A persons’ risk of having more than 1 chronic
condition, henceforth referred to as multiple chronic condi-
tions or MCC, increases with age: 62% of Americans over 65
have MCC. With the aging of the US population, the number of
Americans with MCC is projected to be 81 million by 2020.2
The Institute of Medicine’s seminal report “Crossing the
Quality Chasm” noted that 23% of Medicare beneficiaries have
5 or more chronic conditions.1
Prior research has documented the prevalence of individual
conditions in the U.S. population generally and among the
elderly and near elderly in particular. For example, based on
data from the Medicare Current Beneficiary Survey (MCBS),
the most prevalent individual conditions among the over-65
population include: arthritis (57%), hypertension (55%), pul-
monary disease (38%), diabetes (17%), cancer (17%) and
osteoporosis (16%).2However, there has been very little
research to date exploring the prevalence of particular combi-
nations or clusters of chronic conditions, and almost all
studies examining specific comorbidities do so from the
perspective of a specific index disease rather than examining
all co-occurring chronic conditions.3
Only a fragmentary portrait of the prevalence of MCC
emerges from studies examining comorbidities among patients
with specific index conditions. A case-control study of asth-
matics found that diabetes was more common in concert with
asthma, but obesity was more common in patients without
asthma4Of patients with Alzheimer’s disease, 28% also have
congestive heart failure, 27% chronic obstructive pulmonary
disease, 22% diabetes mellitus, and 20% cancer.2In comparison
to the general population, persons receiving care for schizophre-
nia or affective disorders in community-based treatment centers
were more likely to suffer from asthma, chronic bronchitis,
diabetes, and liver problems.5Finally, persons suffering from
epilepsy have higher rates of a host of chronic conditions,
including bowel disorders, bronchitis/emphysema, heart dis-
ease, and stroke in comparison to the general population.6
Fewer studies have explored the natural clustering of
chronic conditions. Using cross-sectional Medicare claims
data, Wolff and colleagues grouped a national sample of
Medicare patients into Major Diagnostic Categories (MDC)
based on a well-validated grouping algorithm. They found that
the tendency of patients to have comorbid conditions varied
from 80% among individuals in the MDC “myeloproliferative
disorders” to 32% in the “circulatory disorders” MDC. These
investigators found that specific combinations of chronic
conditions occurred more frequently than expected, and
proposed that perhaps underlying biological vulnerabilities
may help explain the clustering of diseases within individuals.7
A subsequent more limited study used cluster analysis to
identify conditions that tend to co-occur among elderly
American Indians. The specific conditions in this group
include heart disease, stroke, hypertension, diabetes, urinary
or bladder conditions, and tuberculosis. Interestingly, al-
though arthritis is 1 of the 2 most common chronic conditions
in this population, it does not commonly occur in concert with
Perhaps the most widely known example of the clustering of
chronic conditions in a biologically and clinically meaningful
way is the so-called metabolic syndrome. Found in 24% of the
U.S. population,9the metabolic syndrome is present when
patients have at least 3 of 5 chronic conditions: obesity,
hypertriglyceridemia, low-serum high-density lipoprotein
(HDL), hypertension, and glucose intolerance.10,11The meta-
bolic syndrome is associated with increased risk of cardiovas-
cular disease and all cause mortality12,13and may reflect
underlying genetic predispositions to this combination of
The health consequences of multiple chronic conditions are
poorly understood. Overall, specific chronic conditions have a
stronger relationship with functional impairment than others,
and persons with more chronic conditions become more
functionally impaired sooner than persons with fewer chronic
conditions.16The picture, however, appears to be more
complex. One of the most revealing studies to date found that
after controlling for the presence of individual conditions,
specific MCC were associated with disability far greater than
expected based on the disability observed for each disease in
isolation. The authors suggested that some diseases may be
associated with disability only in the presence of other specific
diseases, and that “a new, potentially effective strategy for
prevention or amelioration of disability would be to decrease
targeted disease-disease interactions”.17Fultz et al.18also
found synergistic interactions between some pairings of men-
tal and physical conditions, but not others. For example,
persons with stroke and cognitive impairment had a higher
level of impairment in activities of daily living than predicted by
the presence of stroke and cognitive impairment alone. Other
combinations, such as stroke and depression, did not have the
same synergistic effect on activities of daily living impair-
ments.18A recent analysis of near-elderly veterans found that
in general the risk of 5-year mortality increased with the
number of co-occurring chronic conditions; however, osteoar-
thritis in combination with any other chronic condition
actually lowered the risk of 5-year mortality.19
QUALITY AND CARE MANAGEMENT CHALLENGES
Persons with multiple chronic conditions are particularly
vulnerable to suboptimal quality care.2They tend to use
services more frequently and to use a greater array of services
than other consumers of care. This makes coordination of care
more difficult for individuals with multiple chronic conditions.
The number of different physicians seen annually by the
average Medicare patient with a chronic condition ranges from
4 with 1 condition to 14 with 5 or more. As the number of
providers involved in patients’ care increases, patients are
likely to find it increasingly challenging to understand, re-
member, and reconcile the instructions of those providers.20
Because patients with more than 1 chronic condition take on
average more medications, they are more likely to suffer
adverse drug events (ADEs), including ADEs that result from
drug-drug interactions,21–24or in the specific case of heart
failure coupled with chronic obstructive pulmonary disease,
present challenges to appropriate pharmacological manage-
ment.25Having multiple chronic conditions also makes it more
challenging for patients to participate effectively in their own
care.26Surveys of physicians confirm that they believe quality
problems are increased among their patients with multiple
Vogeli et al.: Multiple Chronic Conditions: Prevalence and Cost
However, the link between co-occurring chronic conditions
and poor quality is far from clear. An assessment of quality
among Canadians over 65 with specific combinations of
chronic conditions found deficiencies in care associated with
some combinations of conditions, but not all. For example,
patients with hyperlipidemia and chronic obstructive pulmo-
nary disease were less likely than patients with hyperlipidemia
alone to receive lipid-lowering medications. Individuals with
psychoses and arthritis were less likely to receive arthritis
medications than individuals with arthritis alone. However,
glaucoma patients with breast cancer are no less likely to
receive glaucoma medications than those without breast
cancer.28A well-known study of the predictors of initiating
psychiatric treatment found that “competing demands” from
physical problems hindered the initiation of psychiatric care.29
However, other studies have found that co-occurring chronic
conditions are actually associated with more appropriate care.
Contrary to prior expectations, researchers examining a cohort
of diabetic patients enrolled in a heart failure disease manage-
ment program found that despite their targeted heart failure
care, these patients also received comprehensive diabetes
care.30Patients with somatic chronic conditions may actually
receive more appropriate care for depression or other psychi-
atric disorders. Among elderly persons, depression care was
more likely to be adequate among elderly persons with co-
occurring diabetes than without,31and neither the number nor
specific comorbid conditions were found to impact the effective-
ness of interventions aimed at improving depression care.32
More general measures of quality also yielded mixed results.
Braunstein et al.33found that the occurrence of hospitaliza-
tions for ambulatory care sensitive conditions increased
among elderly heart failure patients when they suffered from
other comorbidities. Hospitalizations for ambulatory care
sensitive conditions are considered preventable by good pri-
mary care.34The odds of experiencing these so-called “pre-
ventable” hospitalizations were largest when heart failure
occurred in combination with hypertension or chronic renal
insufficiency. However, for unknown reasons, certain comor-
bidities, such as hypercholesterolemia or dementia, seemed to
protect heart failure patients against hospitalization for am-
bulatory care sensitive conditions.33Among vulnerable per-
sons age 65 and over, Min et al.35found that overall, persons
with more chronic conditions had higher (better) risk-adjusted
quality scores. However, specific combinations such as diabe-
tes and cardiovascular disease were associated with worse
quality of care as measured by a composite of up to 207 quality
It is reasonable to hypothesize that clinicians systematically
vary in the provision of indicated services when caring for
patients with particular combinations of conditions, just as
they systematically overlook certain issues in caring for single
illnesses.36For example, systematic differences in colon cancer
screening rates among elderly persons with chronic conditions
may reflect conscious decisions to concentrate screening on
patients whose life expectancy can be improved through
Several observers have argued that current strategies
including disease-specific health guidelines may not be suit-
able in many cases to optimizing care of individuals with
MCC.21,24Instead, it is argued, guidelines need to be tailored
to clusters of illnesses in ways that acknowledge not only the
biology of those clusters, but also the special challenges and
threats to quality of care associated with MCC in general and
specific clusters in particular. Moreover, single-disease-oriented
disease management programs, which frequently offer services
provided outside traditional health care facilities (call centers
care. Even Wagner’s Chronic Care Model, which emphasizes
coordination ofcare around chronic illness,focuses primarily on
single illnesses, not multiple chronic conditions,38so that its
relevance and effects on multiple chronic conditions remain to
The intrinsic challenges to optimizing quality and value of
care among individuals with multiple chronic conditions, the
evidence that quality may be suboptimal for some indivi-
duals with multiple chronic conditions, and indications that
quality may vary with the specific clusters of chronic
conditions, all suggest the need to explore more systemati-
cally the relationship between quality of care and clusters of
COSTS OF CARE
In a country with health care expenditures exceeding
$1.7 trillion and 15% of gross domestic product,39controlling
costs of care has become an overwhelming concern among pub-
lic and private policy makers and managers. From this perspec-
tive, individuals with multiple chronic conditions pose special
challenges and opportunities. The care of individuals with
chronic conditions is estimated to account for 78% of health
expenditures in the United States. Patients with more than 1
chronic condition are estimated to account for 95% of all
Medicare spending; those with more than 5 account for two
thirds.2The Congressional Budget Office reports that among
high-cost Medicare beneficiaries (e.g., the 25% of beneficiaries
accounting for 85% of programmatic costs), about 30% had 4
co-occurring chronic illnesses: coronary artery disease, diabe-
tes, congestive heart failure, and chronic obstructive pulmo-
nary disease.40The likelihood that patients with a particular
condition such as heart failure or diabetes will use expensive
health care resources such as hospital care increases sub-
stantially with the presence of other comorbidities.33,41For
example, the likelihood that a Medicare patient with a chronic
medical condition will use emergency department services
doubles when depression is present as a comorbidity.42
Medicare beneficiaries with heart failure who have comorbid-
ities are more likely to be readmitted for heart failure than
patients without comorbidities.43
The concentration of health care expenditures in subpopu-
lations with chronic conditions has led to the widespread
proliferation of disease management programs. In 2004, 97%
of private health plans had disease management programs for
diabetes, 86% for asthma, 83% for heart failure, and 70% for
ischemic heart disease.44State Medicaid programs have also
begun implementing similar disease management pro-
grams.45,46Under provisions of the Medicare Modernization
Act, Congress instructed the Centers for Medicare and Medic-
aid Services to undertake a variety of initiatives to improve care
for high-cost, chronically ill patients, including the Chronic
Care Improvement Program (CCIP), a large national experi-
ment with applying disease management programs to patients
in the traditional Medicare program.47,48The CCIP will target
more than 30,000 beneficiaries with 3 conditions (diabetes,
Vogeli et al.: Multiple Chronic Conditions: Prevalence and Cost
heart failure, and chronic obstructive pulmonary disease) in
10 regions of the country.
Despite the conceptual attractiveness of the disease man-
agement approach, evidence of clinical and cost-effectiveness
remain limited.49,50Recent analyses have found that cost
savings and return on investments varied by diagnosis.51,52
Most disease management programs focus on management of a
single chronic condition. This raises concerns about whether
they may undermine coordination of care for patients with
MCC,53thereby introducing new inefficiencies and potential
threats to quality of care.21,24,54Furthermore, by focusing on a
single illness, programs fail to account for the synergistic
impact of chronic conditions occurring in combination. The
Medicare program has begun experimenting with improving
management of patients with MCC under other demonstrations
including its Medicare Coordinated Care Demonstration and its
Care Management for High-Cost Beneficiaries Demonstration.
More recently, some private health plans have also shifted
toward intensive case management programs aimed at high-
risk patients with multiple complex conditions,50often using
predictive modeling applications to identify members whose
past utilization suggests they are likely to generate high health
care costs in the future.55However, even these initiatives may
suffer from the fact that they lack information necessary to take
into account the potential variation in costs and quality
associated with particular MCC and information on the most
efficacious treatments for specific disease combinations.
Understanding how to care effectively for persons with multiple
chronic conditions is among the most important challenges
our health care system faces. Despite the depth of research
into specific chronic conditions, there is little information
about the prevalence of MCC, and the health and cost impacts
of specific combinations of chronic conditions. The small
amount we do know suggests that specific chronic conditions
combine and impact health and costs in unpredictable ways,
and that specific combinations have particularly large impacts
of health or costs of care.
Currently, there are a number of methodological challenges
to research on MCC, including, fundamentally, the need for
large and preferably longitudinal, clinically meaningful data
that can be used to identify the natural history of disease, and
control for the severity of individual conditions when assessing
outcomes for MCC. The increasing adoption of health infor-
mation technology has the potential to greatly improve the
level of clinical detail of widely available data,56and may help
accelerate clinically meaningful research.
Such research should help illuminate why certain clusters
of comorbid illness may be more prone to quality lapses or be
associated with significant but unexpected clinical outcomes,
and lead to the development of targeted strategies, including
tailored MCC-specific clinical guidelines, to improve the man-
agement of patients with key MCC. Similarly, research on more
clinically detailed data may be used to develop computerized
decision support that incorporates new knowledge regarding
within a patient and anticipate the tendency of clinicians to
overlook or overprescribe certain elements in the process of
care. Although payers have begun to target high cost combina-
tions, far more research is needed to understand the clinical
impact of the clustering of chronic illness and to incorporate
this more refined understanding into targeted quality improve-
ment and clinical management strategies. With the aging of
population, these needs are ever more pressing.
Acknowledgments: No authors received funding, either internal or
external, to support this work.
Conflicts of interest: None disclosed.
Corresponding Author: Christine Vogeli, PhD; Institute for Health
Policy in the Department of Medicine, Massachusetts General
Hospital, 50 Staniford Street, 9th Floor, Boston, MA 02114, USA
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