Article

The Complexity of Disease Combinations in the Medicare Population

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Abstract

Developing systems of care that address the mortality, morbidity, and expenditures associated with Medicare beneficiaries with multiple diseases would benefit from a greater understanding of the complexity of disease combinations (DCs) found in the Medicare population. To develop estimates of the number of DCs, we performed an observational analysis on 32,220,634 beneficiaries in the Medicare Fee-for-Service claims database based on a set of records containing each beneficiary's Part A and B International Classification of Diseases, 9(th) Revision, Clinical Modification (ICD-9-CM) claims data for the year of 2008. We made 2 simplifying adjustments. First, we mapped the individual ICD-9-CM codes to the Centers for Medicare and Medicaid Services-Hierarchical Conditions Categories (HCC) model that was developed in 2004 to risk adjust capitation payments to private health care plans based on the health expenditure risk of their enrollees. Second, we aggregated beneficiaries with identical HCCs regardless of the temporal order of these findings within the 2008 claims year; thus the DC to which they are assigned represents the summation of their 2008 claims data. We defined 3 distinct populations at the HCC level. The first consisted of 35% of the beneficiaries who did not fall into any HCC category and accounted for 6% of expenditures. The second was represented by the 100 next most prevalent DCs that accounted for 33% of the beneficiaries and 15% of expenditures. The final population, accounting for 32% of the beneficiaries and 79% of expenses, was complex and consisted of over 2 million DCs. Our results indicate that the majority of expenditures are associated with a complex set of beneficiaries.

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... One example is a previously published study of the distribution of multiple comorbidities in the Medicare population (32 million people) using Medicare claims data from 2008. [14] This study calculated the number of disease combinations (DCs) in terms of CMS's hierarchical condition categories (HCCs). The HCC system used in this study grouped approximately 3000 ICD-9-CM codes (selected based on increase 12-month prospective expense) into 70 HCC categories. ...
... See Table 2 for examples of specific DCs. [14] Finally, a follow-up study demonstrated that the national distribution of DCs changes significantly over time with new combinations constantly emerging. [15] Our current approach to this level of complexity is to ignore it and rely on measurement and quality improvement approaches [14] Twin studies offer another way of exploring individual disease variation. ...
... [14] Finally, a follow-up study demonstrated that the national distribution of DCs changes significantly over time with new combinations constantly emerging. [15] Our current approach to this level of complexity is to ignore it and rely on measurement and quality improvement approaches [14] Twin studies offer another way of exploring individual disease variation. Specifically, monozygotic (MZ) twins are expected to have reduced variation, or increased correlation, in disease states due to their near genetic identity and shared family environment. ...
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Health care is undergoing a profound transformation driven by an increase in new types of diagnostic data, increased data sharing enabled by interoperability, and improvements in our ability to interpret data through the application of artificial intelligence and machine learning. Paradoxically, we are also discovering that our current paradigms for implementing electronic health-care records and our ability to create new models for reforming the health-care system have fallen short of expectations. This article traces these shortcomings to two basic issues. The first is a reliance on highly centralized quality improvement and measurement strategies that fail to account for the high level of variation and complexity found in human disease. The second is a reliance on legacy payment systems that fail to reward the sharing of data and knowledge across the health-care system. To address these issues, and to better harness the advances in health care noted above, the health-care system must undertake a phased set of reforms. First, efforts must focus on improving both the diagnostic process and data sharing at the local level. These efforts should include the formation of diagnostic management teams and increased collaboration between pathologists and radiologists. Next, building off current efforts to develop national federated research databases, providers must be able to query national databases when information is needed to inform the care of a specific complex patient. In addition, providers, when treating a specific complex patient, should be enabled to consult nationally with other providers who have experience with similar patient issues. The goal of these efforts is to build a health-care system that is funded in part by a novel fee-for-knowledge-sharing paradigm that fosters a collaborative decentralized approach to patient care and financially incentivizes large-scale data and knowledge sharing.
... To risk adjust payments, the Centers for Medicare and Medicaid Services (CMS) uses hierarchical condition categories (HCCs), which are mainly CCs. In 2008, 32% of Medicare beneficiaries had two or more HCCs, including >2,000,000 unique combinations, and accounted for 79% of costs [11]. Developing integrated guidelines for even a small percentage of >2,000,000 unique HCC groups is impractical. ...
... In this patient cohort, the number of CCs accounted for 36% of the variance (R 2 = 0.36) in Medicare paid claims, which is consistent with previous reports [1,2,11,30]. Moreover, the linear relationship between the mean number of CCs and mean costs per beneficiary in each cluster is evident (Fig 2). ...
... The integrated guideline could also include guidance on the resources and expertise as well as the patient education and support resources required for integrated, comprehensive management of each cluster. Our analysis addresses one of several barriers to integrated guidelines, i.e., the number of unique combinations of CCs [11]. Despite limitations, application of the conceptual approach outlined in this report could potentially provide an important starting point for a more integrated approach to improving care quality and outcomes at lower cost. ...
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Background Approximately 28% of adults have ≥3 chronic conditions (CCs), accounting for two-thirds of U.S. healthcare costs, and often having suboptimal outcomes. Despite Institute of Medicine recommendations in 2001 to integrate guidelines for multiple CCs, progress is minimal. The vast number of unique combinations of CCs may limit progress. Methods and findings To determine whether major CCs segregate differentially in limited groups, electronic health record and Medicare paid claims data were examined in one accountable care organization with 44,645 Medicare beneficiaries continuously enrolled throughout 2015. CCs predicting clinical outcomes were obtained from diagnostic codes. Agglomerative hierarchical clustering defined 13 groups having similar within group patterns of CCs and named for the most common CC. Two groups, congestive heart failure (CHF) and kidney disease (CKD), included 23% of beneficiaries with a very high CC burden (10.5 and 8.1 CCs/beneficiary, respectively). Five groups with 54% of beneficiaries had a high CC burden ranging from 7.1 to 5.9 (descending order: neurological, diabetes, cancer, cardiovascular, chronic pulmonary). Six groups with 23% of beneficiaries had an intermediate-low CC burden ranging from 4.7 to 0.4 (behavioral health, obesity, osteoarthritis, hypertension, hyperlipidemia, ‘other’). Hypertension and hyperlipidemia were common across groups, whereas 80% of CHF segregated to the CHF group, 85% of CKD to CKD and CHF groups, 82% of cancer to Cancer, CHF, and CKD groups, and 85% of neurological disorders to Neuro, CHF, and CKD groups. Behavioral health diagnoses were common only in groups with a high CC burden. The number of CCs/beneficiary explained 36% of the variance (R² = 0.36) in claims paid/beneficiary. Conclusions Identifying a limited number of groups with high burdens of CCs that disproportionately drive costs may help inform a practical number of integrated guidelines and resources required for comprehensive management. Cluster informed guideline integration may improve care quality and outcomes, while reducing costs.
... 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. ...
... How this group may change over time as individuals acquire new chronic conditions, or certain conditions change in intensity, has only recently been examined. Overall, there are many unique constellations of MCC; for example, a recent study of approximately 32 million Medicare beneficiaries found over 2,000,000 unique disease combinations (Sorace et al. 2011). The distribution of constellations of diseases results in a curve with a very " long tail " of complex patients. ...
... Constellations categorized as " rare " can result from combinations of common chronic conditions and/or less common or rare diseases. In other words, there are multiple pathways to becoming less prevalent (See Exhibit 2), and combining less prevalent combinations may account for as much as 79% of Medicare expenditures and 32% of beneficiaries (Sorace et al. 2011). Unique constellations are especially complex when multiple organ systems are involved and the combination of diseases, or treatments interact with one another. ...
... To date, research on the extent of medical conditions and disease burden in individuals with alcohol, cannabis, and opioid use disorders has largely focused on single SUDs (Schukit, 2009;Hall and Degenhardt, 2009;Whiteford et al., 2013;Rehm et al., 2009), narrowly defined patient samples (e.g., age/gender distinguished; psychiatric patients) (Kronik et al., 2009;Clark et al., 2009;), or has used data aggregated at the population-level (Rehm et al., 2009;Degenhardt and Hall, 2012;Degenhardt et al., 2013) or selected from publicly insured patient samples (e.g., Medicaid or Medicare) (Boyd et al., 2010;Sorace et al., 2011). There is little individual-level data on the extent of medical conditions and disease burden for those with alcohol, cannabis, and opioid use disorders, who have private insurance and access to integrated health services. ...
... We also found higher disease burden estimates for SUD patients compared to patients without SUDs; those with opioid use disorders had particularly high disease burden. Thus, similar to investigations conducted with other populations and in other types of healthcare systems (Kronik et al., 2009;Clark et al., 2009;Boyd et al., 2010;Sorace et al., 2011), our results show that medical comorbidities are not only common among patients with prevalent SUDs, but are associated with substantial disease burden even for those with access to private insurance and integrated medical and SUD treatment services. Given that integrated healthcare systems are becoming increasingly more common in the post-Affordable Care Act environment (Barry and Huskamp, 2011), our results highlight the important role of such health systems in developing strategies to improve health outcomes for patients with SUDs. ...
Article
Objectives: We examined prevalence of major medical conditions and extent of disease burden among patients with and without substance use disorders (SUDs) in an integrated health care system serving 3.8 million members. Methods: Medical conditions and SUDs were extracted from electronic health records in 2010. Patients with SUDs (n = 45,461; alcohol, amphetamine, barbiturate, cocaine, hallucinogen, and opioid) and demographically matched patients without SUDs (n = 45,461) were compared on the prevalence of 19 major medical conditions. Disease burden was measured as a function of 10-year mortality risk using the Charlson Comorbidity Index. P-values were adjusted using Hochberg's correction for multiple-inference testing within each medical condition category. Results: The most frequently diagnosed SUDs in 2010 were alcohol (57.6%), cannabis (14.9%), and opioid (12.9%). Patients with these SUDs had higher prevalence of major medical conditions than non-SUD patients (alcohol use disorders, 85.3% vs 55.3%; cannabis use disorders, 41.9% vs 23.0%; and opioid use disorders, 44.9% vs 26.1%; all P < 0.001). Patients with these SUDs also had higher disease burden than non-SUD patients; patients with opioid use disorders (M = 0.48; SE = 1.46) had particularly high disease burden (M = 0.23; SE = 0.09; P < 0.001). Conclusions: Common SUDs, particularly opioid use disorders, are associated with substantial disease burden for privately insured individuals without significant impediments to care. This signals the need to explore the full impact SUDs have on the course and outcome of prevalent conditions and initiate enhanced service engagement strategies to improve disease burden.
... As described by Sorace and colleagues, the distribution of constellations of MCCs results in a curve with a very ''long tail'' of complex patients that changes nationally over time. 44,45 More than 79% of Medicare expenditures can be associated with patients with unique, low-prevalence MCCs belonging to this ''long tail.'' 44 The study team conducted a similar analysis looking at all the different permutations of chronic conditions in the employer-based health plan population. ...
... 44,45 More than 79% of Medicare expenditures can be associated with patients with unique, low-prevalence MCCs belonging to this ''long tail.'' 44 The study team conducted a similar analysis looking at all the different permutations of chronic conditions in the employer-based health plan population. The team found 1560 unique disease constellations. ...
Article
Patients with multiple chronic conditions (MCCs) are a significant concern for the US health care system. MCC patients represent an increasing proportion of the US population and are associated with increased health care cost and utilization, and poor quality of care. Research that has been conducted on MCC patients to date has been at the national level using large data sets, such as Medicare and Medicaid claims and the National Inpatient Sample. These studies have produced research evidence that may be of little utility to individual employer-based health plans given the inherent differences in the patient populations they serve. This study analyzed evaluation and management claims for patients ages 18 years and older (n=632,477) from the Beaumont Employee Health Plan (BEHP), a regional health insurance provider serving Beaumont Health System employees and their families across Southeastern Michigan. The study found that individuals with MCCs are associated with increased cost and visits, and decreased time between appointments in the outpatient setting. Despite decreasing prevalence of MCCs over the study period, substantial increases in cost and visits, and a decrease in time between appointments was observed for MCC patients. Asthma and chronic back pain were uniquely identified as additional primary targets for disease management programs for employer-based health plans. These findings speak to the value of studying MCCs at the employer-based health plan level, where population-specific MCCs can be identified for meaningful intervention and management. Significant opportunity exists for employer-based health plans to study, prevent, and manage MCCs among adult patients. (Population Health Management 2015;xx:xxx-xxx).
... [1][2][3][4][5] Identifying the most common disease clusters in older adults may help the next generation of clinical practice guidelines explicitly account for common co-occurring conditions so that they can help clinicians avoid drug-drug and drug-disease interactions. Previous work has shed important light on this question by quantifying the number of co-occurring diseases within populations, examining linkages between common conditions, and determining the healthcare outcomes and costs associated with different patterns of multimorbidity, [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] but the existing literature has certain limitations. Most of studies in this area have evaluated a limited number of conditions or limited populations, ranked conditions based on costs and outcomes rather than prevalence, focused on two-way disease combinations (and not more-complex patterns of disease co-occurrence), and provide little information on how patterns of multimorbidity may vary between the young-old (e.g., 65-74) and the "old-old" (e.g., 85). ...
... Most of studies in this area have evaluated a limited number of conditions or limited populations, ranked conditions based on costs and outcomes rather than prevalence, focused on two-way disease combinations (and not more-complex patterns of disease co-occurrence), and provide little information on how patterns of multimorbidity may vary between the young-old (e.g., 65-74) and the "old-old" (e.g., 85). 8,[10][11][12][16][17][18][19][20][21] Thus, important knowledge gaps remain, particularly in identifying the specific patterns of multimorbidity that are most common in older adults and how these patterns differ between young-old and old-old people. ...
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To determine patterns of co-occurring diseases in older adults and the extent to which these patterns vary between the young-old and the old-old. Observational study. Department of Veterans Affairs. Veterans aged 65 years and older (1.9 million male, mean age 76 ± 7; 39,000 female, mean age 77 ± 8) with two or more visits to Department of Veterans Affairs (VA) or Medicare settings in 2007 and 2008. The presence of 23 common conditions was assessed using hospital discharge diagnoses and outpatient encounter diagnoses from the VA and Medicare. The mean number of chronic conditions (out of 23 possible) was 5.5 ± 2.6 for men and 5.1 ± 2.6 for women. The prevalence of most conditions increased with advancing age, although diabetes mellitus and hyperlipidemia were 11% to 13% less prevalent in men and women aged 85 and older than in those aged 65 to 74 (P < .001 for each). In men, the most common three-way combination of conditions was hypertension, hyperlipidemia, and coronary heart disease, which together were present in 37% of men. For women, the most common combination was hypertension, hyperlipidemia, and arthritis, which co-occurred in 25% of women. Reflecting their high population prevalence, hypertension and hyperlipidemia were both present in 9 of the 15 most common three-way disease combinations in men and in 11 of the 15 most common combinations in women. The prevalence of many disease combinations varied substantially between young-old and old-old adults. Specific combinations of diseases are highly prevalent in older adults and inform the development of guidelines that account for the simultaneous presence of multiple chronic conditions.
... As physicians on the front lines of the readmitted patient, hospitalists are uniquely situated to see the challenges of populations with increasing disease complexity and disease combinations. 7 The HRRP policy remains controversial. This is due in large part to recent work suggesting that while the HRRP may have helped reduce readmissions, its implementation may have driven the unintended consequence of increased mortality. ...
... It has been reported that quality of life and its relationship with basic and instrumental activities of daily living (ADL) decline with the occurrence of medical illnesses in elderly populations 4,5 . In addition, considerable research has indicated a link of comorbidities with prognostics, quality of life, and health system utilization, such as hospitalization, and a consequent increase in the total costs of health care [6][7][8][9][10][11] . ...
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The detailed comorbidity status of hospitalized elderly patients throughout Japan has remained largely unknown; therefore, our goal was to rigorously explore this situation and its implications as of the 2015 fiscal year (from April 2015 to March 2016). This study was based on a health insurance claims database, covering all insured policy holders in Japan aged ≥60 years (male: n = 2,135,049, female: 1,969,019) as of the 2015 fiscal year. Comorbidity status was identified by applying principal factor analysis to the database. The factors identified in male patients were [1] myocardial infarction, hypertension, dyslipidemia, and diabetes mellitus; [2] congestive heart failure (CHF), cardiac arrhythmia, and renal failure; [3] Parkinson’s disease, dementia, cerebrovascular disease, and pneumonia; [4] cancer and digestive disorders; and [5] rheumatoid arthritis and hip fracture. However, in female patients, the results obtained for the quaternary and quinary factors were the opposite of those obtained in male patients. In superelderly patients, dementia, cerebrovascular disease, and pneumonia appeared as the tertiary factor, and hip fracture and osteoporosis appeared as the quaternary factor. The comorbidities in the elderly patients suggest the importance of coronary heart disease and its related metabolic disorders; in superelderly patients, fracture and osteoporosis appeared as factors, in addition to dementia and pneumonia.
... First, it will be challenging to define complex patients in a way that enables the design of specific interventions: A recent study of chronic conditions among approximately 32 million Medicare beneficiaries found more than 2 million unique combinations of conditions, reflecting a very "long tail" that changes nationally over time. [7][8][9] This distribution suggests that developing interventions tailored to each combination of disease is infeasible. ...
... Caring for older adults with multiple chronic conditions (MCC) is challenging. [1][2][3] In 2010, the American Geriatrics Society (AGS) convened an expert panel to address how to provide patient-centered care for this growing population. The AGS Guiding ...
Article
Caring for older adults with multiple chronic conditions (MCCs) is challenging. The American Geriatrics Society (AGS) previously developed The AGS Guiding Principles for the Care of Older Adults with Multimorbidity using a systematic review of the literature and consensus (Table 1). The objective of the current work was to translate these principles into a framework of Actions and accompanying Action Steps for decision‐making for clinicians who provide both primary and specialty care to older people with MCCs. A workgroup of geriatricians, cardiologists, and generalists: 1) articulated the core MCC Actions and the Action Steps needed to carry out the Actions; 2) provided decisional tips and communication scripts for implementing the Actions and Action Steps, using commonly encountered situations: 3) performed a scoping review to identify evidence‐based, validated tools for carrying out the MCC Actions and Action Steps; and 4) identified potential barriers to, and mitigating factors for, implementing the MCC Actions. The recommended MCC Actions include: 1) Identify and communicate patients' health priorities and health trajectory; 2) Stop, start, or continue care based on health priorities, potential benefit versus harm and burden, and health trajectory; and 3) align decisions and care among patients, caregivers, and other clinicians with patients' health priorities and health trajectory. The tips and scripts for carrying out these Actions are included in the full MCC Action Framework available in the supplement (www.GeriatricsCareOnline.org). This article is protected by copyright. All rights reserved.
... The number of those individuals and their weighted percentage were calculated. Rao-Scott chi-square (a design-adjusted Pearson chi-square test) [32] analyses were performed to examine significant subgroup differences across strata for the two groups (having PCMH and having no PCMH). Adjusted multiple logistic regression analyses were then conducted to assess predictors associated with having a PCMH. ...
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Background The Patient-Centered Medical Home (PCMH) model is a coordinated-care model that has served as a means to improve several chronic disease outcomes and reduce management costs. However, access to PCMH has not been explored among adults suffering from chronic conditions in the United States. Therefore, the aim of this study was to describe the changes in receiving PCMH among adults suffering from chronic conditions that occurred from 2010 through 2015 and to identify predisposing, enabling, and need factors associated with receiving a PCMH. Methods A cross-sectional analysis was conducted for adults with chronic conditions, using data from the 2010–2015 Medical Expenditure Panel Surveys (MEPS). Most common chronic conditions in the United States were identified by using the most recent data published by the Agency for Healthcare Research and Quality (AHRQ). The definition established by the AHRQ was used as the basis to determine whether respondents had access to PCMH. Multivariate logistic regression analyses were conducted to detect the association between the different variables and access to PCMH care. Results A total of 20,403 patients with chronic conditions were identified, representing 213.7 million U.S. lives. Approximately 19.7% of the patients were categorized as the PCMH group at baseline who met all the PCMH criteria defined in this paper. Overall, the percentage of adults with chronic conditions who received a PCMH decreased from 22.3% in 2010 to 17.8% in 2015. The multivariate analyses revealed that several subgroups, including individuals aged 66 and older, separated, insured by public insurance or uninsured, from low-income families, residing in the South or the West, and with poor health, were less likely to have access to PCMH. Conclusion Our findings showed strong insufficiencies in access to a PCMH between 2010 and 2015, potentially driven by many factors. Thus, more resources and efforts need to be devoted to reducing the barriers to PCMH care which may improve the overall health of Americans with chronic conditions.
... Much heterogeneity exists among people with CI in terms of severity (Mungas et al., 2010), and the cause-specific mortality likely differs for people with different combinations of multimorbidity. However, because of the many different possible combinations of morbidity Sorace et al., 2011), examining them all is impractical with traditional methods. However, data mining approaches can empirically identify relevant combinations that influence an outcome (Koroukian, Schiltz, Warner, Sun, Bakaki, et al., 2016;, in this case, cause-specific mortality. ...
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Objective: The aim of this study is to evaluate the relationship of leading causes of death with gradients of cognitive impairment and multimorbidity. Method: This is a population-based study using data from the linked 1992-2010 Health and Retirement Study and National Death Index ( n = 9,691). Multimorbidity is defined as a combination of chronic conditions, functional limitations, and geriatric syndromes. Regression trees and Random Forest identified which combinations of multimorbidity associated with causes of death. Results: Multimorbidity is common in the study population. Heart disease is the leading cause in all groups, but with a larger percentage of deaths in the mild and moderate/severe cognitively impaired groups than among the noncognitively impaired. The different "paths" down the regression trees show that the distribution of causes of death changes with different combinations of multimorbidity. Discussion: Understanding the considerable heterogeneity in chronic conditions, functional limitations, geriatric syndromes, and causes of death among people with cognitive impairment can target care management and resource allocation.
... In addition, the increase in the number of associated comorbidities causes a multiplier effect rather than additive effects in the structural costs. These facts suggest that the management and coordination of healthcare programmes will face the added challenge of heterogeneity in the health status of this population group [20]. ...
... First, it will be challenging to define complex patients in a way that enables the design of specific interventions: A recent study of chronic conditions among approximately 32 million Medicare beneficiaries found more than 2 million unique combinations of conditions, reflecting a very "long tail" that changes nationally over time. [7][8][9] This distribution suggests that developing interventions tailored to each combination of disease is infeasible. ...
Article
In the United States, a relatively small proportion of complex patients---defined as having multiple comorbidities, high risk for poor outcomes, and high cost---incur most of the nation's health care costs. Improved care coordination and management of complex patients could reduce costs while increasing quality of care. However, care coordination efforts face multiple challenges, such as segmenting populations of complex patients to better match their needs with the design of specific interventions, understanding how to reduce spending, and integrating care coordination programs into providers' care delivery processes. Innovative uses of analytics and health information technology (HIT) may address these challenges. Rudin and colleagues at RAND completed a literature review and held discussions with subject matter experts, reaching the conclusion that analytics and HIT are being used in innovative ways to coordinate care for complex patients but that the capabilities are limited, evidence of their effectiveness is lacking, and challenges are substantial, and important foundational work is still needed.
... It is more challenging to address the health care needs of patients with chronic conditions who typically require comprehensive and continuous care by both primary care providers and specialists. Moreover, the complexity of the needs for health care increases with the number of chronic conditions a patient has (Sorace et al., 2011). According to the 2009 Medical Expenditure Panel Survey, the average numbers of ambulatory visits, emergency department visits, inpatient stays, and prescribed medicine purchases were much higher among patients with 2 or more chronic conditions than those with no chronic condition (Machlin & Soni, 2013). ...
Article
This study examined access to care and satisfaction among health center patients with chronic conditions. Data for this study were obtained from the 2009 Health Center Patient Survey. Dependent variables of interest included 5 measures of access to and satisfaction with care, whereas the main independent variable was number of chronic conditions. Results of bivariate analysis and multiple logistic regressions showed that patients with chronic conditions had significantly higher odds of reporting access barriers than those without chronic conditions. Our results suggested that additional efforts and resources are necessary to address the needs of health center patients with chronic conditions.
... These patients are among the highestcost users of health organization and they constitute an important source of growth in expenditures and increasing the number of comorbidities induces a multiplicative rather than an additive cost structure. This suggests that disease management and care coordination programs will face a difficult challenge in coping with the heterogeneity of patient health conditions [20]. ...
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Introduction: In the developed countries, around 3-4% of the people could be identified as chronic complex patient and they are increasingly at risk of atrial fibrillation and cognitive impairment. The main objective of this study was to evaluate association of
... The U.S. Department of Health and Human Services has recommended identification of similar subgroups among the population of patients with multiple chronic conditions as a critical step to improve the health status of the total population (Kronick et al. 2007;Parekh et al. 2011). Past work shows that it is not practical to consider every possible combination of conditions separately as there are over 2 million combinations in the Medicare population (Sorace et al. 2011). Therefore, it is necessary to find a manageable number of combinations of comorbidities in diabetes to measure the relationship between multiple chronic conditions and diabetes care outcomes in a clinically meaningful way. ...
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Objective: To find clinically relevant combinations of chronic conditions among patients with diabetes and to examine their relationships with six diabetes quality metrics. Data sources/study setting: Twenty-nine thousand five hundred and sixty-two adult patients with diabetes seen at eight Midwestern U.S. health systems during 2010-2011. Study design: We retrospectively evaluated the relationship between six diabetes quality metrics and patients' combinations of chronic conditions. We analyzed 12 conditions that were concordant with diabetes care to define five mutually exclusive combinations of conditions ("classes") based on condition co-occurrence. We used logistic regression to quantify the relationship between condition classes and quality metrics, adjusted for patient demographics and utilization. Data collection: We extracted electronic health record data using a standardized algorithm. Principal findings: We found the following condition classes: severe cardiac, cardiac, noncardiac vascular, risk factors, and no concordant comorbidities. Adjusted odds ratios and 95 percent confidence intervals for glycemic control were, respectively, 1.95 (1.7-2.2), 1.6 (1.4-1.9), 1.3 (1.2-1.5), and 1.3 (1.2-1.4) compared to the class with no comorbidities. Results showed similar patterns for other metrics. Conclusions: Patients had distinct quality metric achievement by condition class, and those in less severe classes were less likely to achieve diabetes metrics.
... A latent class is defined as a subpopulation of individuals; in this case, the subpopulation was defined by co-existing conditions within an individual with or without a causal link (van den Akker, Buntinx, Metsemakers, Roos, & Knottnerus, 1998;Yancik et al., 2007). As the number of potential combinations of MCCs based on disease codes is huge (Sorace et al., 2011), the chronic conditions selected for the LCA were based on the Charlson Comorbidity Index (CCI), an accepted comorbidity risk score designed to predict mortality that has also been used as a general summary measure of the overall burden of comorbidities (Austin, Wong, Uzzo, Beck, & Egleston, 2015;. These chronic condition categories were: AIDS/HIV; cerebrovascular disease; congestive heart failure; chronic pulmonary disease; dementia; diabetes without chronic complications; diabetes with chronic complications; hemiplegia or paraplegia; mild, moderate, or severe liver disease; any malignancy, including leukemia and lymphoma, metastasis, or solid tumor; myocardial infarction; peripheral vascular disease; peptic ulcer disease; rheumatologic disease; and renal diseases. ...
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Background: Among older adults receiving long-term services and supports (LTSS), debilitating hospitalizations is a pervasive clinical and research problem. Multiple chronic conditions (MCCs) are prevalent in LTSS recipients. However, the combination of MCCs and diseases associated with hospitalizations of LTSS recipients is unclear. Objective: The purpose of this analysis was to determine the association between classes of MCCs in newly enrolled LTSS recipients and the number of hospitalizations over a 1-year period following enrollment. Methods: This report is based on secondary analysis of extant data from a longitudinal cohort study of 470 new recipients of LTSS, 60 years and older, receiving services in assisted living facilities, nursing homes, or through home- and community-based services. Using baseline chronic conditions reported in medical records, latent class analysis was used to identify classes of MCCs and posterior probabilities of membership in each class. Poisson regressions were used to estimate the relative ratio between posterior probabilities of class membership and number of hospitalizations during the 3-month period prior to the start of LTSS (baseline) and then every 3 months forward through 12 months. Results: Three latent MCC-based classes named Cardiopulmonary, Cerebrovascular/Paralysis, and All Other Conditions were identified. The Cardiopulmonary class was associated with elevated numbers of hospitalizations compared to the All Other Conditions class (relative ratio [RR] = 1.88, 95% CI [1.33, 2.65], p < .001). Conclusion: Older LTSS recipients with a combination of MCCs that includes cardiopulmonary conditions have increased risk for hospitalization.
... Research has established that health plan members associated with the highest costs in a health plan tend to be very young [9,19] or very old [26,27]; have multiple health risks [8]; are functionally limited [26,28]; have complex [25,29] or multiple conditions [24]; experience multiple readmissions [8,9] are associated with unexpected treatment complications [8]; or have a terminal illness [7]. Studies have also shown that inpatient charges associated with high-cost members are positively correlated with hospital size, and that these charges tend to be higher in academic medical centers than in general hospitals unaffiliated with a university [9]. ...
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This research was undertaken to develop an operational definition of high health care costs, identify potential threshold values based upon the underlying probability distribution, and explore the clinical foundationsof high-cost health care cases through an application of probability theory. Historically, high-cost thresholds were set on the basis of specific stop-loss insurance policies that estimated the probability of loss. These estimates can be made with more precision when clinical conditions are taken into account. By scaffolding clinical conditions to the probability of loss, a health plan can estimate the types of patients that could benefit from enhanced case management. This study used a retrospective observational study of a health plan to estimate the probability distribution of health care claims, determine outlier values and tie health care costs to hierarchical condition categories. This transformation makes it possible to identify high-cost cases before they reach those thresholds.
... It is estimated that there are about 2 million unique disease combinations among approximately 32 million Medicare beneficiaries. 17 Most of the disease constellations have low prevalence, and a challenge for practitioners is to provide the best treatment for those "rare" patients, a subset of whom will appear in any specific health plan or clinical practice. Table 2 summarizes AHRQ MCC RN studies focused on low-prevalence MCC. ...
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Background: By 2030, 171 million Americans are expected to have more than one chronic condition. The cohort of individuals with multiple chronic conditions (MCC) is growing and two thirds of healthcare costs for the US population are currently spent on the 20% of people who have MCC. Objectives: Recognizing the need for increased investment in MCC programs and research, Health and Human Services (HHS) developed the HHS Strategic Framework on MCC. The Agency for Healthcare Research and Quality (AHRQ) contributed to the goals of the framework by funding the MCC Research Network, comprising 45 diverse grants and representing one of the largest federal investment in MCC studies to date. Results: The initial body of research emerging from the AHRQ MCC Research Network included: comanagement of commonly co-occurring conditions (including by caregivers); care for patients with low-prevalence combinations of MCC; the effect of MCC patients on provider performance metrics; guidelines for preventive services; medication management in individuals with MCC; as well as MCC-specific methodological and analytical techniques. Conclusions: The authors describe a subset of research contributions made in each topic area and make 3 recommendations for future MCC research: (1) include person-centered and person-driven measures and outcomes, (2) consider the person in the context of their relationships and community, and (3) include mental healthcare as an essential part of overall healthcare.
... More than three-fourths of Americans aged 65 and older have two or more chronic conditions. 1 The intensity and complexity of treating persons with MCCs accounts for a large proportion of healthcare costs, accounting for more than 80% of Medicare expenditures. 2 Chronic disease treatments are developed and tested for their effect on disease-specific outcomes, frequently in populations with a single disease or a few comorbidities. Individuals with MCCs typically receive multiple interventions, each of which may affect other coexisting conditions (positively or negatively) and potentially interact with other interventions. ...
Article
Older adults with multiple chronic conditions (MCCs) require considerable health services and complex care. Because the persistence and progression of diseases and courses of treatments affect health status in multiple dimensions, well-validated universal outcome measures across diseases are needed for research, clinical care, and administrative purposes. An expert panel meeting held by the National Institute on Aging in September 2011 recommends that older persons with MCCs complete a brief initial composite measure that includes general health; pain; fatigue; and physical health, mental health, and social role function, along with gait speed measurement. Suitable composite measures include the Medical Outcomes Study 8 (SF-8) and 36 (SF-36) -item Short-Form Survey and the Patient Reported Outcomes Measurement Information System 29-item Health Profile. Based on responses to items in the initial measure, short follow-on measures should be selectively targeted to symptom burden, depression, anxiety, and daily activities. Persons unable to walk a short distance to assess gait speed should be assessed using a physical function scale. Remaining gaps to be considered for measure development include disease burden, cognitive function, and caregiver burden. Routine outcome assessment of individuals with MCCs could facilitate system-based care improvement and clinical effectiveness research. J Am Geriatr Soc 60: 2333-2341, 2012.
... Second, research should focus not only on comorbidity in reference to index conditions, or issues associated with particular combinations of conditions, but also on an understanding of the generic, underlying commonalities of MCCs in light of the enormous heterogeneity of the population with MCCs (see [9,10]). Examples include developing and evaluating pragmatic approaches to shared decision-making for treatment and care plans, regardless of what the specifi c conditions may be, or evaluating and refi ning electronic health records in order to enhance patient-centered care for people with MCCs [11]. ...
Article
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“Multiple Chronic Conditions: A Strategic Framework” is a seminal report and the heart of a US strategic initiative, released by the U.S. Department of Health and Human Services (HHS) in December 2010, to focus the attention and resources of the US government on the research, practice, and policy implications of multiple chronic conditions (MCCs) [1]. The specific purpose of the report is “to catalyze change within the context of how chronic illnesses are addressed in the United States – from an approach focused on individual chronic diseases to one that uses a multiple chronic condition approach” [1]. The report observes that this process represents “a culture change, or paradigm shift, and the subsequent implementation of these strategies that will provide a foundation for realizing the vision of optimal health and quality of life for individuals with multiple chronic conditions” [1].
... [15] A limit on the number of well-researched, clear-cut medical decisions may explain the difficulty of rule-based utilization management programs like the Centers for Medicare and Medicaid Services' Clinical Quality Measures, or HEDIS to expand past a few hundred rules, even as the lower estimate on the number of medical scenarios exceeds this number by multiple orders of magnitude. [16] Rules may become more numerous and complex, but the rules-based approach shares many of the same limitations as another long-standing issue in the evaluation of medical decisions, the determination of pretest probability (PTP). [17][18][19][20][21][22] Both attempt to probe the black-box that is the patient-physician relationship, whose nuances may not easily translate to structured fields in a database. ...
Article
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Laboratory utilization management describes a process designed to increase healthcare value by altering requests for laboratory services. A typical approach to monitor and prioritize interventions involves audits of laboratory orders against specific criteria, defined as rule-based laboratory utilization management. This approach has inherent limitations. First, rules are inflexible. They adapt poorly to the ambiguity of medical decision-making. Second, rules judge the context of a decision instead of the patient outcome allowing an order to simultaneously save a life and break a rule. Third, rules can threaten physician autonomy when used in a performance evaluation. We developed an alternative to rule-based laboratory utilization. The core idea comes from a formula used in epidemiology to estimate disease prevalence. The equation relates four terms: the prevalence of disease, the proportion of positive tests, test sensitivity and test specificity. When applied to a laboratory utilization audit, the formula estimates the prevalence of disease (pretest probability [PTP]) in the patients tested. The comparison of PTPs among different providers, provider groups, or patient cohorts produces an objective evaluation of laboratory requests. We demonstrate the model in a review of tests for enterovirus (EV) meningitis. The model identified subpopulations within the cohort with a low prevalence of disease. These low prevalence groups shared demographic and seasonal factors known to protect against EV meningitis. This suggests too many orders occurred from patients at low risk for EV. We introduce a new method for laboratory utilization management programs to audit laboratory services.
... dressed by comprehensive, continuous, and coordinated primary care. [25][26][27] The persistent racial/ethnic disparities in access to primary care demonstrated in this study are expected to lead to unmet health needs, poor access to care, and low quality of care among minority groups. 19,28 -30 In addition, people who were unsatisfied with their USC or failed to get continuous primary care from their USC are more likely to have nonurgent emergency department visits and hospitalizations. ...
Article
Objective: The purpose of this study was to examine racial and socioeconomic disparities in access to primary care among people with chronic conditions. Data for this study were taken from the household component of the 2010 Medical Expenditure Panel Survey. The analysis primarily focused on adults ≥18 years old. Logistic regressions were conducted among people with chronic conditions to compare primary care attributes between each minority group and their non-Hispanic white counterparts and between individuals with high, above average, or below average socioeconomic status and their low socioeconomic status counterparts, controlling for other individual factors. Racial disparities were found in having usual source of care (USC), USC provider type, and USC location. However, no disparities were found in ease of contacting or getting to USC as well as the services received. Furthermore, very limited socioeconomic disparities were found after controlling for other individual characteristics, in particular race and insurance status. More efforts need to be devoted to racial/ethnic minorities with chronic conditions to improve their access to continuous and high-quality primary care.
... It is estimated that there are about 2 million unique disease combinations among approximately 32 million Medicare beneficiaries. 17 Most of the disease constellations have low prevalence, and a challenge for practitioners is to provide the best treatment for those "rare" patients, a subset of whom will appear in any specific health plan or clinical practice. Table 2 summarizes AHRQ MCC RN studies focused on low-prevalence MCC. ...
Article
Background: By 2030, 171 million Americans are expected to have more than one chronic condition. The cohort of individuals with multiple chronic conditions (MCC) is growing and two thirds of healthcare costs for the US population are currently spent on the 20% of people who have MCC. Objectives: Recognizing the need for increased investment in MCC programs and research, Health and Human Services (HHS) developed the HHS Strategic Framework on MCC. The Agency for Healthcare Research and Quality (AHRQ) contributed to the goals of the framework by funding the MCC Research Network, comprising 45 diverse grants and representing one of the largest federal investment in MCC studies to date. Results: The initial body of research emerging from the AHRQ MCC Research Network included: co-management of commonly co-occurring conditions (including by caregivers); care for patients with low-prevalence combinations of MCC; the effect of MCC patients on provider performance metrics; guidelines for preventive services; medication management in individuals with MCC; as well as MCC-specific methodological and analytical techniques. Conclusions: The authors describe a subset of research contributions made in each topic area and make 3 recommendations for future MCC research: (1) include person-centered and person-driven measures and outcomes, (2) consider the person in the context of their relationships and community, and (3) include mental healthcare as an essential part of overall healthcare.
... The prospect may seem daunting, as the myriad combinations of MCC will magnify the complexity of guidelines. To wit, Sorace et al 23 showed that a cohort of over 32 million Medicare beneficiaries exhibited >2 million disease combinations. A focus on patient-centered outcomes, facilitated by the judicious use of electronic health records and decision aids to facilitate shared decision-making, may help manage the increased complexity that is likely to result from evidence-based treatment guidelines for patients with MCC. ...
... The question arises: How useful is multimorbidity as a concept for future studies? Considerable research has indicated a link between multimorbidity and health-service utilization, including hospitalization and total costs of health care (11,25). Although rooted in the medical model of disease, multimorbidity, as examined in this review, omits the geriatric syndromes, which are important clinical entities that are chronic and prevalent and impact the health of the older population (26). ...
Article
Multimorbidity, the coexistence of 2 or more chronic conditions, has become prevalent among older adults as mortality rates have declined and the population has aged. We examined population-based administrative claims data indicating specific health service delivery to nearly 31 million Medicare fee-for-service beneficiaries for 15 prevalent chronic conditions. A total of 67% had multimorbidity, which increased with age, from 50% for persons under age 65 years to 62% for those aged 65-74 years and 81.5% for those aged ≥85 years. A systematic review identified 16 other prevalence studies conducted in community samples that included older adults, with median prevalence of 63% and a mode of 67%. Prevalence differences between studies are probably due to methodological biases; no studies were comparable. Key methodological issues arise from elements of the case definition, including type and number of chronic conditions included, ascertainment methods, and source population. Standardized methods for measuring multimorbidity are needed to enable public health surveillance and prevention. Multimorbidity is associated with elevated risk of death, disability, poor functional status, poor quality of life, and adverse drug events. Additional research is needed to develop an understanding of causal pathways and to further develop and test potential clinical and population interventions targeting multimorbidity.
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Background: Patients with both diabetes and hypertension could face more health risks than those with either condition alone, and less attention has been paid to their management outcomes, so this study may be the first to specifically address this problem. We aimed to examine the management outcomes of blood pressure (BP) in hypertensive patients with/without diabetes and fasting plasma glucose (FPG) in diabetic patients with/without hypertension. Methods: Follow-up data were obtained from the National Basic Public Health Service Project in Sanming (2017-2021). A total of 25,795 adults with hypertension only, 4111 adults with diabetes only, and 5729 comorbid adults (namely, hypertensive patients with diabetes) were included. Generalized estimating equations were applied. Results: Systolic blood pressure (SBP) and diastolic blood pressure (DBP) in patients with hypertension only both dropped significantly (Coef. = -0.00088, P < 0.001; Coef. = -0.00081, P < 0.001). DBP in comorbid patients decreased considerably (Coef. = -0.00033, P < 0.001). Pulse pressure in comorbid patients grew rapidly (Coef. = 0.00044, P < 0.001). BP control rate in patients with hypertension only increased significantly (OR = 1.00039, P < 0.001). FPG control rates in diabetic patients with/without hypertension grew markedly (OR = 1.00013, P < 0.001; OR = 1.00020, P < 0.001). Comorbid patients had lower baseline SBP and DBP but higher latest SBP than patients with hypertension only (Coef. = -1.18872, P < 0.001; Coef. = -1.16049, P < 0.001; Coef. = 1.0634, P < 0.001). Comorbid patients had lower baseline BP and FPG control rates than those with either condition alone, and differences were greater at the latest follow-up (OR = 0.28086, P < 0.001; OR = 0.91012, P = 0.049; OR = 0.04020, P < 0.001; OR = 0.69465, P < 0.001). Conclusion: BP and FPG management outcomes have achieved progress. Comorbid patients have poorer performance than patients with either disease alone in BP levels especially the SBP level and control rates of SBP, DBP, and FPG. Future studies should be conducted using national data and include more confounding factors.
Article
Background The term “multimorbidity” identifies high-risk, complex patients and is conventionally defined as ≥2 comorbidities. However, this labels almost all older patients as multimorbid, making this definition less useful for physicians, hospitals, and policymakers.Objective Develop new medical condition-specific multimorbidity definitions for patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia patients. We developed three medical condition-specific multimorbidity definitions as the presence of single, double, or triple combinations of comorbidities — called Qualifying Comorbidity Sets (QCSs) — associated with at least doubling the risk of 30-day mortality for AMI and pneumonia, or one-and-a-half times for HF patients, compared to typical patients with these conditions.DesignCohort-based matching studyParticipantsOne hundred percent Medicare Fee-for-Service beneficiaries with inpatient admissions between 2016 and 2019 for AMI, HF, and pneumonia.Main MeasuresThirty-day all-location mortalityKey ResultsWe defined multimorbidity as the presence of ≥1 QCS. The new definitions labeled fewer patients as multimorbid with a much higher risk of death compared to the conventional definition (≥2 comorbidities). The proportions of patients labeled as multimorbid using the new definition versus the conventional definition were: for AMI 47% versus 87% (p value<0.0001), HF 53% versus 98% (p value<0.0001), and pneumonia 57% versus 91% (p value<0.0001). Thirty-day mortality was higher among patients with ≥1 QCS compared to ≥2 comorbidities: for AMI 15.0% versus 9.5% (p<0.0001), HF 9.9% versus 7.0% (p <0.0001), and pneumonia 18.4% versus 13.2% (p <0.0001).Conclusion The presence of ≥2 comorbidities identified almost all patients as multimorbid. In contrast, our new QCS-based definitions selected more specific combinations of comorbidities associated with substantial excess risk in older patients admitted for AMI, HF, and pneumonia. Thus, our new definitions offer a better approach to identifying multimorbid patients, allowing physicians, hospitals, and policymakers to more effectively use such information to consider focused interventions for these vulnerable patients.
Article
Background Food insecurity has been identified as an important social determinant of health and is associated with many health issues prevalent in Medicaid members. Despite this, little research has been done around food insecurity within Medicaid populations. Objective Our objective was to estimate the prevalence of household food insecurity and identify factors associated with experiencing food insecurity in Iowa’s Medicaid expansion population. Design We conducted a cross-sectional telephone survey between March and May of 2019. Participants Our sample was drawn from Medicaid members enrolled in Iowa’s expansion program at least 14 months, stratified by Federal Poverty Level (FPL) category. Members who did not have valid contact information were excluded. We selected one individual per household to reduce the interrelatedness of responses. We sampled 6,000 individuals and had 1,349 respondents in the analytic sample. Main outcome measure Our main outcome was whether a respondent’s household experienced food insecurity in the previous year, using the Hunger Vital Sign screening tool. Statistical analyses performed We weighted responses to account for the sampling design and differential nonresponse between strata. We estimated the prevalence of food insecurity and used logistic regression to model food insecurity as a function of demographic (age, FPL category, gender, employment, education, race, rurality, and Supplemental Nutrition Assistance Program [SNAP] participation) and health-related (self-rated health, self-rated oral health, health literacy) factors. Results The estimated prevalence of experiencing food insecurity was 51.3%. Race, gender, education, employment, health literacy, and self-rated health were all significantly associated with food insecurity. Conclusions Our findings show that food insecurity is prevalent in Iowa’s Medicaid expansion population. Food insecurity should be more widely measured as a critical social determinant of health in Medicaid populations. Policymakers and clinicians should consider interventions that connect households experiencing food insecurity to food resources (eg, produce prescriptions and food pantry referrals) and policies that increase food access. Abbreviations Iowa Wellness Plan (IWP); Federal Poverty Level (FPL); Healthy Behavior Program (HBP); Supplemental Nutrition Assistance Program (SNAP)
Article
Background: There are numerous definitions of multimorbidity (MM). None systematically examines specific comorbidity combinations accounting for multiple testing when exploring large datasets. Objectives: Develop and validate a list of all single, double, and triple comorbidity combinations, with each individual qualifying comorbidity set (QCS) more than doubling the odds of mortality versus its reference population. Patients with at least 1 QCS were defined as having MM. Research design: Cohort-based study with a matching validation study. Subjects: All fee-for-service Medicare patients between age 65 and 85 without dementia or metastatic solid tumors undergoing general surgery in 2009-2010, and an additional 2011-2013 dataset. Measures: 30-day all-location mortality. Results: There were 576 QCSs (2 singles, 63 doubles, and 511 triples), each set more than doubling the odds of dying. In 2011, 36% of eligible patients had MM. As a group, multimorbid patients (mortality rate=7.0%) had a mortality Mantel-Haenszel odds ratio=1.90 (1.77-2.04) versus a reference that included both multimorbid and nonmultimorbid patients (mortality rate=3.3%), and Mantel-Haenszel odds ratio=3.72 (3.51-3.94) versus only nonmultimorbid patients (mortality rate=1.6%). When matching 3151 pairs of multimorbid patients from low-volume hospitals to similar patients in high-volume hospitals, the mortality rates were 6.7% versus 5.2%, respectively (P=0.006). Conclusions: A list of QCSs identified a third of older patients undergoing general surgery that had greatly elevated mortality. These sets can be used to identify vulnerable patients and the specific combinations of comorbidities that make them susceptible to poor outcomes.
Chapter
The focus on value and accountable care opens unprecedented opportunities to create effective care systems for older and/or high-need, high-cost patients. The journey to accountable care is far from linear, however. Multimorbidity and the social complexity of these patients resist easy off-the-shelf interventions, particularly given the entrenched fragmentation across primary and specialty care, post-acute and long-term care, palliative care, and community-based services. Each delivery system must find its own way across a landscape strewn with false starts, failures, and avoidable suffering. This chapter will review a broad array of innovations with particular attention to program development and adaptation, as well as the levers of organizational change. Readmissions are one such lever. Readmission reviews provide line of sight into the interstitial spaces of our health delivery systems. Readmission reduction entails addressing multiple services across multiple sites of care, e.g., medication management, advance care planning, and palliative care. Similarly, new partnerships in post-acute care and community care promise to reduce high-cost facility utilization. Predictive analytics tools help match high-touch resources to patients with remediable needs. Your ability to develop and sustain geriatric programs will depend upon your ability to obtain and present credible data on clinical and financial performance. Challenges remain, but the focus on value has begun to align the transactional logic of operating margins and the mission-driven logic of healing relationships. We should take advantage of this overlap wherever we can.
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The impact of dynapenia on the complexity of care for residents of long-term care facilities (LTCF) remains unclear. The present study evaluated associations between dynapenia, care problems and care complexity in 504 residents of Veterans Care Homes (VCHs) in Taiwan. Subjects with dynapenia, defined as low muscle strength (handgrip strength <26 kg), were older adults with lower body mass index (BMI), slow gait speed, and higher numbers of Resident Assessment Protocol (RAP) triggers. After adjusting for age, education, BMI, and Charlson’s comorbidity index (CCI), only age, education, BMI and gait speed were independently associated with higher numbers of RAP triggers, but not dynapenia or handgrip strength (kg). Dividing subjects into groups based on quartiles of gait speed, those with gait speed ≤0.803 m/s were significantly associated with higher complexity of care needs (defined as ≥4 RAP triggers) compared to the reference group (gait speed >1 m/s). Significantly slow gait speed was associated with RAP triggers, including cognitive loss, poor communication ability, rehabilitation needs, urinary incontinence, depressed mood, falls, pressure ulcers, and use of psychotropic drugs. In conclusion, slow gait speed rather than dynapenia is a simple indicator for higher complexity of care needs of older male LTCF residents.
Article
Purpose: Patients who attend cardiac rehabilitation programs have a high prevalence of multiple chronic conditions (MCCs). The extent to which different constellations of MCC influence lifestyle exercise in the year after completion of an outpatient phase 2 cardiac rehabilitation program (CRP) is unknown. Our objective was to examine the effects of MCC on lifestyle exercise in the year after completion of a CRP. Methods: The effects of different constellations of comorbidities on objectively measured lifestyle exercise were examined using data from a randomized controlled trial testing lifestyle behavior change interventions in patients with cardiac events (n = 379) who completed a phase 2 CRP. Adjusting for important covariates, the relationships between the primary outcome, exercise amount, and the presence of common chronic conditions (hypertension, obesity, diabetes, and arthritis) were studied using robust linear mixed-effects models. Results: Diabetes, hypertension, obesity, and their dyads, triads, and quads have a negative impact on amount of exercise. For example, the cooccurrences of obesity and hypertension reduced lifestyle exercise by 2.83 hours per month (95% CI, 1.33-4.33) after adjustment for the effects of covariates. The presence of obesity was a major factor in the comorbid constellations affecting lifestyle exercise. Conclusions: The presence of obesity and other chronic conditions negatively impacts lifestyle exercise in the year after a CRP. The magnitude of the effect depends on the comorbidities. Different constellations of comorbid conditions can be used to identify those persons at greatest risk for not exercising after cardiac rehabilitation.
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The objective is to modify the longitudinal extension of the average attributable fraction (LE-AAF) for recurrent outcomes with time-varying exposures and control for covariates. We included Medicare Current Beneficiary Survey participants with two or more chronic conditions enrolled from 2005 to 2009 with follow-up through 2011. Nine time-varying medications indicated for nine time-varying common chronic conditions and 14 of 18 forward-selected participant characteristics were used as control variables in the generalized estimating equations step of the LE-AAF to estimate associations with the recurrent universal health outcome self-rated health (SRH). Modifications of the LE-AAF were made to accommodate these indicated medication-condition interactions and covariates. Variability was empirically estimated by bias-corrected and accelerated bootstrapping. In the adjusted LE-AAF, thiazide, warfarin, and clopidogrel had significant contributions of 1.2%, 0.4%, 0.2%, respectively, to low (poor or fair) SRH; whereas there were no significant contributions of the other medications to SRH. Hyperlipidemia significantly contributed 4.6% to high SRH. All the other conditions except atrial fibrillation contributed significantly to low SRH. Our modifications to the LE-AAF method apply to a recurrent binary outcome with time-varying factors accounting for covariates. Copyright © 2015 Elsevier Inc. All rights reserved.
Article
To quantify heredity's effects on the burden of illness in the Medicare population, this study linked information between participants in a research twin registry to a comprehensive set of Medicare claims. To calculate disease categories, the authors used the Centers for Medicare & Medicaid Services Hierarchical Conditions Categories (HCC) model that was developed to risk adjust Medicare's capitation payments to private health care plans based on the health expenditure risk of their enrollees. Using the Medicare database, 2 sets of unrelated but demographically matched control pairs (MCPs) were generated, one specific for the monozygotic twin population and the second specific for the dizygotic twin population. The concordance and correlation rates of the 70 HCC categories for the 2 twin populations, in comparison to their corresponding MCP, was then calculated using Medicare claims data from 1991 through 2011. When indicated, HCCs for which there was a statistically significant difference between the twin and corresponding MCP control group were analyzed by calculating concordance and correlation rates of the International Classification of Diseases, Ninth Revision codes that compose the HCC. Findings reveal that monozygotic twins share 6.5% more HCC disease categories than their MCP while dizygotic twins share 3.8% more HCC disease categories than their MCP. Atrial fibrillation is a highly heritable disease category, a finding consistent with prior literature describing the heritability of the cardiac arrhythmias. These findings are consistent with qualitative assessments of heredity's role found in previous models of population health, and provide both novel methods and quantitative evidence to support future model development. (Population Health Management 2015;xx:xxx-xxx).
Article
Abstract It is widely accepted that Medicare beneficiaries with multiple comorbidities (ie, patients with combinations of more than 1 disease) account for a disproportionate amount of mortality and expenditures. The authors previously studied this phenomenon by analyzing Medicare claims data from 2008 to determine the pattern of disease combinations (DCs) for 32,220,634 beneficiaries. Their findings indicated that 22% of these individuals mapped to a long-tailed distribution of approximately 1 million DCs. The presence of so many DCs, each populated by a small number of individuals, raises the possibility that the DC distribution varies over time. Measuring this variability is important because it indicates the rate at which the health care system must adapt to the needs of new patients. This article analyzes Medicare claims data for 3 consecutive calendar years, using 2 algorithms based on the Centers for Medicare & Medicaid Services (CMS)-Hierarchical Conditions Categories (HCC) claims model. These algorithms make different assumptions regarding the degree to which the CMS-HCC model could be disaggregated into its underlying International Classification of Diseases, Ninth Revision, Clinical Modification codes. The authors find that, although a large number of beneficiaries belong to a set of DCs that are nationally stable across the 3 study years, the number of DCs in this set is large (in the range of several hundred thousand). Furthermore, the small number of beneficiaries associated with the larger number of variable DCs (ie, DCs that were not constantly populated in all 3 study years) represents a disproportionally high level of expenditures and death. (Population Health Management 2013;16:XX-XX).
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Sufficient numbers of patients are necessary to generate statistically reliable measurements of physicians' quality and cost performance. To determine whether primary care physicians in the same physician practice collectively see enough Medicare patients annually to detect meaningful differences between practices in ambulatory quality and cost measures. Primary care physicians in the United States were linked to their physician practices using the Healthcare Organization Services database maintained by IMS Health. Patients who visited primary care physicians in the 2005 Medicare Part B 20% sample were used to estimate Medicare caseloads per practice. Caseloads necessary to detect 10% relative differences in costs and quality were calculated using national mean ambulatory Medicare spending, rates of mammography for women 66 to 69 years, and hemoglobin A(1c) testing for 66- to 75-year-olds with diabetes, preventable hospitalization rate, and 30-day readmission rate after discharge for congestive heart failure (CHF). Percentage of primary care physician practices with a sufficient number of eligible patients to detect a 10% relative difference in each performance measure. Primary care physician practices had annual median caseloads of 260 Medicare patients (interquartile range [IQR], 135-500), 25 women eligible for mammography (IQR, 10-50), 30 patients with diabetes eligible for hemoglobin A(1c) testing (IQR, 15-55), and 0 patients hospitalized for CHF. For ambulatory costs, mammography rate, and hemoglobin A(1c) testing rate, the percentage of primary care physician practices with sufficient caseloads to detect 10% relative differences in performance ranged from less than 10% of practices with fewer than 11 primary care physicians to 100% of practices with more than 50 primary care physicians. None of the primary care physician practices had sufficient caseloads to detect 10% relative differences in preventable hospitalization or 30-day readmission after discharge for CHF. Relatively few primary care physician practices are large enough to reliably measure 10% relative differences in common measures of quality and cost performance among fee-for-service Medicare patients.
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This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees. We explain the model's principles, elements, organization, calibration, and performance. Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed.
Article
In Medicaid, the elderly and adults with disabilities make up only 25 percent of beneficiaries, but account for the majority of program spending. Within this population, fewer than 5% of beneficiaries account for more than 50% of overall Medicaid costs. By better understanding the specific health conditions of these beneficia-ries, states can make more informed decisions about how to best manage care, thereby improving health outcomes, increasing quality of life, and controlling program costs. This third edition of the Faces of Medicaid was commissioned by the Center for Health Care Strategies (CHCS) to further refine what is known about Medicaid beneficiaries with multiple chronic conditions, particularly those with serious mental illness. It builds on an earlier Faces of Medicaid II analysis published in 2007 that presented a groundbreaking examination of the prevalence and patterns of chronic conditions within Medicaid populations. This new analysis adds two data elements to the original study. First, 12 months of pharmacy data were added to determine the number of individuals with comorbidities who might not be identified solely via claims-based diagnostic codes, but who could be identified through pharmacy utilization. In addition, five-years of diagnostic data were analyzed to identify how the portrait of Medicaid beneficiaries with multiple comorbidities would be enhanced by examining a longer period of time. A more complete portrait of Medicaid beneficiaries emerges through this new analysis. In sum, it revealed that Medicaid beneficiaries with disabilities frequently have multiple chronic conditions and particularly high rates of psychiatric illness and cardiovascular disease. The analysis also reinforced that spending for beneficiaries with disabili-ties is skewed disproportionately to those with high levels of comorbidity — 45% of those with three or more chronic conditions account for 75% of costs (see next page for detailed findings). These enhanced insights can help states and health plans better prioritize high-opportunity groups of beneficiaries and design programs that integrate physical, behavioral, and social supports to more effectively meet beneficiaries' needs.
Article
This chartbook includes more than 25 graphics and tables developed by Mathematica to analyze the characteristics and service use of Medicaid beneficiaries with special health care needs, using State Medicaid Research Files data for four states. Offers recommendations for designing managed care approaches to meet the needs of people with chronic illnesses and disabilities and outlines the clinical and fiscal policy implications for states and health plans enrolling these populations in managed care. To order, contact the Center for Health Care Strategies at 609-279-0700.
Article
The aging of the US population, combined with improvements in modern medicine, has created a new challenge: approximately 75 million people in the United States have multiple (2 or more) concurrent chronic conditions, defined as “conditions that last a year or more and require ongoing medical attention and/or limit activities of daily living.”1,2 Is the 21st-century US health care system prepared to deal with the consequences of successfully treating patients who have conditions, often multiple, that they would not have survived in the early 20th century? Current indications suggest that it is not.
Article
This article has no abstract; the first 100 words appear below. When the Medicare program became operational in 1966, its primary orientation was the treatment of acute, episodic illness.¹,² The design of the program's benefits, coverage policies, payments to providers, and criteria for determining medical necessity were all oriented toward the treatment of acute diseases. Medicare retained this orientation for the next 40 years in spite of the growing number of Americans with chronic conditions.³,⁴ The Medicare Prescription Drug Improvement and Modernization Act of 2003 was an important first step in the reorientation of the Medicare program toward the care of patients with chronic disorders. Additional changes, however, will . . . Supported by the Robert Wood Johnson Foundation. Source Information From the Center for Hospital Finance and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore.
Article
In response to the substantial and increasing healthcare requirements of older adults with multiple chronic conditions and acknowledgment of major gaps in knowledge and funding, two expert meetings were convened to identify a research agenda addressing the needs of this population. Research priorities are to develop and evaluate more-effective models of health care, develop and evaluate management practices and organizational structures that lead to improved long-term care, develop and implement relevant and effective preventive health strategies, determine the most effective interventions in patients who have concurrent cognitive or emotional impairments, and determine how interventions during and after hospitalization affect the outcomes of hospitalized patients.
ICD-10-CM 2010 Summary of Revisions Available at: http:/ /www .cms.gov
  • Centers Medicare
  • Medicaid Services
Centers for Medicare and Medicaid Services. ICD-10-CM 2010 Summary of Revisions. Available at: http:/ /www.cms.gov/ICD10/Downloads/1_2010_Whats_New.pdf. Accessed July 14, 2010.
The faces of Medicaid II: Recognizing the care needs of people with multiple chronic conditions
  • Bella M Kronick Rg
  • Gilmer Tp
  • Somers
  • Sa
Kronick RG, Bella M, Gilmer TP, and Somers SA. The faces of Medicaid II: Recognizing the care needs of people with multiple chronic conditions. Available at: http:/ /www.chcs.org/publications3960/pub-lications_show.htm?doc_id¼540806. Accessed July 14, 2010.