Article

Defining Multimorbidity in Older Patients Hospitalized with Medical Conditions

Authors:
  • Children’s hospital of Philadelphia
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Abstract

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.

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... However, they are in line with numbers being reported for participants taking part in a study using the ClinSearch Acceptability Score Test ® conducted by Vallet et al. [39]. The rather vulnerable profile of included old participants might be due to the recruitment site being a hospital, thereby including sicker people compared to the general population [40]. As older adults are commonly dependent on the intake of medicines, these rather vulnerable participants are a good point of reference. ...
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Multimorbidity is common among the heterogeneous primary care population, but little data exist on its association with health care utilization or cost. The aim of this observational study was to examine the prevalence and associated health care utilization and cost of patients with multimorbidity. All patients >50 years of age were eligible for the study which took place in three primary care practices in the West of Ireland. Chronic medical conditions and associated health care utilization in primary and secondary care were identified through patient record review. In a sample of 3309 patients in the community, the prevalence of multimorbidity was 66.2% (95% CI: 64.5-67.8) in those >50 years of age. Health care utilization and cost was significantly increased among patients with multimorbidity (P < 0.001). After multivariate adjustment for age, gender and free medical care eligibility, the addition of each chronic condition led to an associated increase in primary care consultations (P = 0.001) (11.9 versus 3.7 for >4 conditions versus 0 conditions); hospital out-patient visits (P = 0.001) (3.6 versus 0.6 for >4 conditions versus 0 conditions); hospital admissions (P = 0.01) [adjusted odds ratio (OR) of 4.51 for >4 conditions versus 0 conditions] and total health care costs (P < 0.001) (€4,096.86 versus €760.20 for >4 conditions versus 0 conditions) over the previous 12 months. Multimorbidity is very common in primary care and in a system with strong gatekeeping is associated with high health care utilization and cost across the health care system. Interventions to address quality and cost associated with multimorbidity must focus on primary as well as secondary care.
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In 2006, the Centers for Medicare & Medicaid Services, which administers the Medicare program in the United States, launched the Chronic Condition Data Warehouse (CCW). The CCW contains all Medicare fee-for-service (FFS) institutional and non-institutional claims, nursing home and home health assessment data, and enrollment/eligibility information from January 1, 1999 forward for a random 5% sample of Medicare beneficiaries (and 100% of the Medicare population from 2000 forward). Twenty-one predefined chronic condition indicator variables are coded within the CCW, to facilitate research on chronic conditions. The current article describes this new data source, and the authors demonstrate the utility of the CCW in describing the extent of chronic disease among Medicare beneficiaries. Medicare claims were analyzed to determine the prevalence, utilization, and Medicare program costs for some common and high cost chronic conditions in the Medicare FFS population in 2005. Chronic conditions explored include diabetes, chronic obstructive pulmonary disease (COPD), heart failure, cancer, chronic kidney disease (CKD), and depression. Fifty percent of Medicare FFS beneficiaries were receiving care for one or more of these chronic conditions. The highest prevalence is observed for diabetes, with nearly one-fourth of the Medicare FFS study cohort receiving treatment for this condition (24.3 percent). The annual number of inpatient days during 2005 is highest for CKD (9.51 days) and COPD (8.18 days). As the number of chronic conditions increases, the average per beneficiary Medicare payment amount increases dramatically. The annual Medicare payment amounts for a beneficiary with only one of the chronic conditions is 7,172.Forthosewithtwoconditions,paymentjumpsto7,172. For those with two conditions, payment jumps to 14,931, and for those with three or more conditions, the annual Medicare payments per beneficiary is $32,498. The CCW data files have tremendous value for health services research. The longitudinal data and beneficiary linkage within the CCW are features of this data source which make it ideal for further studies regarding disease prevalence and progression over time. As additional years of administrative data are accumulated in the CCW, the expanded history of beneficiary services increases the value of this already rich data source.
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To determine the prevalence of multimorbidity in primary care, by age, sex, and socio-economic class, and to analyse the trend in multimorbidity over the last 20 years. We performed an observational study using data from the Continuous Morbidity Registration (CMR) Nijmegen. This registration includes approximately 13 500 enlisted patients. To study the distribution of multimorbidity by age, sex, and socio-economic class, we analysed all patients enlisted in the CMR in 2005. To analyse the trend of multimorbidity over time, we studied the prevalence of multimorbidity from 1985 to 2005. We found that increasing age, female sex, and low socio-economic class are associated with an increasing number of patients with multimorbidity. The prevalence of chronic diseases doubled between 1985 and 2005. The proportion of patients with four or more chronic diseases increased in this period by approximately 300%. The increasing amount of multimorbidity in primary care as well as the increasing number of chronic diseases per patient leads to more complex medical care. The general practitioner needs guidelines focusing on multimorbidity to support this care. The registration of chronic diseases by the general practitioner will become more complex and time-consuming.
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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.
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Background: Adults have a higher prevalence of multimorbidity-or having multiple chronic health conditions-than having a single condition in isolation. Researchers, health care providers, and health policymakers find it challenging to decide upon the most appropriate assessment tool from the many available multimorbidity measures. Objective: The objective of this study was to describe a broad range of instruments and data sources available to assess multimorbidity and offer guidance about selecting appropriate measures. Design: Instruments were reviewed and guidance developed during a special expert workshop sponsored by the National Institutes of Health on September 25-26, 2018. Results: Workshop participants identified 4 common purposes for multimorbidity measurement as well as the advantages and disadvantages of 5 major data sources: medical records/clinical assessments, administrative claims, public health surveys, patient reports, and electronic health records. Participants surveyed 15 instruments and 2 public health data systems and described characteristics of the measures, validity, and other features that inform tool selection. Guidance on instrument selection includes recommendations to match the purpose of multimorbidity measurement to the measurement approach and instrument, review available data sources, and consider contextual and other related constructs to enhance the overall measurement of multimorbidity. Conclusions: The accuracy of multimorbidity measurement can be enhanced with appropriate measurement selection, combining data sources and special considerations for fully capturing multimorbidity burden in underrepresented racial/ethnic populations, children, individuals with multiple Adverse Childhood Events and older adults experiencing functional limitations, and other geriatric syndromes. The increased availability of comprehensive electronic health record systems offers new opportunities not available through other data sources.
Article
Background Nursing resources, such as staffing ratios and skill mix, vary across hospitals. Better nursing resources have been linked to better patient outcomes but are assumed to increase costs. The value of investments in nursing resources, in terms of clinical benefits relative to costs, is unclear.Objective To determine whether there are differential clinical outcomes, costs, and value among medical patients at hospitals characterized by better or worse nursing resources.DesignMatched cohort study of patients in 306 acute care hospitals.PatientsA total of 74,045 matched pairs of fee-for-service Medicare beneficiaries admitted for common medical conditions (25,446 sepsis pairs; 16,332 congestive heart failure pairs; 12,811 pneumonia pairs; 10,598 stroke pairs; 8858 acute myocardial infarction pairs). Patients were also matched on hospital size, technology, and teaching status.Main MeasuresBetter (n = 76) and worse (n = 230) nursing resourced hospitals were defined by patient-to-nurse ratios, skill mix, proportions of bachelors-degree nurses, and nurse work environments. Outcomes included 30-day mortality, readmission, and resource utilization-based costs.Key ResultsPatients in hospitals with better nursing resources had significantly lower 30-day mortality (16.1% vs 17.1%, p < 0.0001) and fewer readmissions (32.3% vs 33.6%, p < 0.0001) yet costs were not significantly different ($18,848 vs 18,671, p = 0.133). The greatest outcomes and cost advantage of better nursing resourced hospitals were in patients with sepsis who had lower mortality (25.3% vs 27.6%, p < 0.0001). Overall, patients with the highest risk of mortality on admission experienced the greatest reductions in mortality and readmission from better nursing at no difference in cost.Conclusions Medicare beneficiaries with common medical conditions admitted to hospitals with better nursing resources experienced more favorable outcomes at almost no difference in cost.
Chapter
As a prelude to several chapters describing the construction of a matched control group, the current chapter presents an example of a matched observational study as it might (and did) appear in a scientific journal. When reporting a matched observational study, the matching methods are described very briefly in the Methods section. In more detail, the Results section presents tables or figures showing that the matching has been effective in balancing certain observed covariates, so that treated and control groups are comparable with respect to these specific variables. The Results section then compares outcomes in treated and control groups. Because matching has arranged matters to compare ostensibly comparable groups, the comparison of outcomes is often both simpler in form and more detailed in content than it might be if separate adjustments were required for each aspect of each outcome. Treated and control groups that appear comparable in terms of a specific list of measured covariates—groups that are ostensibly comparable—may nonetheless differ in terms of covariates that were not measured. Though not discussed in the current chapter, the important issue of unmeasured covariates in this example is discussed in Part III.
Article
Objective: Estimate (1) prevalence of major depressive disorder (MDD) diagnosis; (2) risk factors associated with MDD diagnosis; (3) time at which MDD is diagnosed post-spinal cord injury (SCI) and interaction of inferred mobility status (IMS) in a commercially insured population over three years. Design: Retrospective longitudinal cohort design. Setting: A commercial insurance claims database from January 1, 2010 - December 31, 2013. Participants: Individuals with an index cervical or thoracic SCI in 2011 or 2012, without history of MDD ≤30 days pre-SCI (n=1,409). Intervention: Not applicable. Main outcomes: Prevalence of, risk factors associated with, and time to MDD diagnosis post-SCI. A stratified survival analysis using IMS, based upon durable medical equipment (DME) claims, was also completed. Results: Post-SCI, 20.87% of the sample was diagnosed with new-onset MDD. Significant (p<0.05) risk factors included: employed, length of index hospitalization, discharged from index hospitalization with healthcare services, rehabilitation services post-SCI, and two of five IMS comparisons. Median time to MDD was 86 days. Survival analysis demonstrated a significant difference between six of ten IMS comparisons. Regarding new-onset or recurring MDD, 30.66% of the sample was diagnosed post-SCI. Significant risk factors included: female, employed, length of index hospitalization, discharge from index hospitalization with healthcare services, rehabilitation services post-SCI, MDD >30 days pre-SCI, catheter claims, and two of five IMS comparisons. Median time to MDD was 74 days. Survival analysis demonstrated a significant difference between four of ten IMS comparisons. Conclusions: Prevalence of MDD post-SCI is greater than the general population. Stratification by IMS illustrated that individuals with greater inferred reliance on DME are at a greater risk for MDD and have shorter time to MDD diagnosis post-SCI.
Article
Background: Teaching hospitals typically pioneer investment in new technology and cultivate workforce characteristics generally associated with better quality, but the value of this extra investment is unclear. Objective: Compare outcomes and costs between major teaching and non-teaching hospitals by closely matching on patient characteristics. Design: Medicare patients at 339 major teaching hospitals (resident-to-bed (RTB) ratios ≥ 0.25); matched patient controls from 2439 non-teaching hospitals (RTB ratios < 0.05). Participants: Forty-three thousand nine hundred ninety pairs of patients (one from a major teaching hospital and one from a non-teaching hospital) admitted for acute myocardial infarction (AMI), 84,985 pairs admitted for heart failure (HF), and 74,947 pairs admitted for pneumonia (PNA). Exposure: Treatment at major teaching hospitals versus non-teaching hospitals. Main measures: Thirty-day all-cause mortality, readmissions, ICU utilization, costs, payments, and value expressed as extra cost for a 1% improvement in survival. Key results: Thirty-day mortality was lower in teaching than non-teaching hospitals (10.7% versus 12.0%, difference = - 1.3%, P < 0.0001). The paired cost difference (teaching - non-teaching) was 273(P<0.0001),yielding273 (P < 0.0001), yielding 211 per 1% mortality improvement. For the quintile of pairs with highest risk on admission, mortality differences were larger (24.6% versus 27.6%, difference = - 3.0%, P < 0.0001), and paired cost difference = 1289(P<0.0001),yielding1289 (P < 0.0001), yielding 427 per 1% mortality improvement at 30 days. Readmissions and ICU utilization were lower in teaching hospitals (both P < 0.0001), but length of stay was longer (5.5 versus 5.1 days, P < 0.0001). Finally, individual results for AMI, HF, and PNA showed similar findings as in the combined results. Conclusions and relevance: Among Medicare patients admitted for common medical conditions, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used.
Article
Objective: To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics. Background: Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear. Methods: A study of Medicare patients at 340 teaching hospitals (resident-to-bed ratios ≥ 0.25) and matched patient controls from 2444 nonteaching hospitals (resident-to-bed ratios < 0.05).We studied 86,751 pairs admitted for general surgery (GS), 214,302 pairs of patients admitted for orthopedic surgery, and 52,025 pairs of patients admitted for vascular surgery. Results: In GS, mortality was 4.62% in teaching hospitals versus 5.57%, (a difference of -0.95%, <0.0001), and overall paired cost difference = 915(P<0.0001).FortheGSquintileofpairswithhighestriskonadmission,mortalitydifferenceswerelarger(15.94915 (P < 0.0001). For the GS quintile of pairs with highest risk on admission, mortality differences were larger (15.94% versus 18.18%, difference = -2.24%, P < 0.0001), and paired cost difference = 3773 (P < 0.0001), yielding $1682 per 1% mortality improvement at 30 days. Patterns for vascular surgery outcomes resembled general surgery; however, orthopedics outcomes did not show significant differences in mortality across teaching and nonteaching environments, though costs were higher at teaching hospitals. Conclusions: Among Medicare patients, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used in general surgery, and to some extent vascular surgery, but this was not apparent in orthopedic surgery.
Article
Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.
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.
Article
Background: Little is known about differences in the clinical course between patients receiving maintenance dialysis who do and do not withdraw from dialysis therapy. Study design: Case-control analysis. Setting & participants: US patients with Medicare coverage who received maintenance hemodialysis for 1 year or longer in 2008 through 2011. Predictors: Comorbid conditions, hospitalizations, skilled nursing facility stays, and a morbidity score based on durable medical equipment claims. Outcome: Withdrawal from dialysis therapy. Measurements: Rates of medical events, hospitalizations, skilled nursing facility stays, and a morbidity score. Results: The analysis included 18,367 (7.7%) patients who withdrew and 220,443 (92.3%) who did not. Patients who withdrew were older (mean age, 75.3±11.5 [SD] vs 66.2±14.1 years) and more likely to be women and of white race, and had higher comorbid condition burdens. The odds of withdrawal among women were 7% (95% CI, 4%-11%) higher than among men. Compared to age 65 to 74 years, age 85 years or older was associated with higher adjusted odds of withdrawal (adjusted OR, 1.61; 95% CI, 1.54-1.68), and age 18 to 44 years with lower adjusted odds (adjusted OR, 0.36; 95% CI, 0.32-0.40). Blacks, Asians, and Hispanics were less likely to withdraw than whites (adjusted ORs of 0.36 [95% CI, 0.35-0.38], 0.47 [95% CI, 0.42-0.53], and 0.46 [95% CI, 0.44-0.49], respectively). A higher durable medical equipment claims-based morbidity score was associated with withdrawal, even after adjustment for traditional comorbid conditions and hospitalization; compared to a score of 0 (lowest presumed morbidity), adjusted ORs of withdrawal were 3.48 (95% CI, 3.29-3.67) for a score of 3 to 4 and 12.10 (95% CI, 11.37-12.87) for a score ≥7. Rates of medical events and institutionalization tended to increase in the months preceding withdrawal, as did morbidity score. Limitations: Results may not be generalizable beyond US Medicare patients; people who withdrew less than 1 year after dialysis therapy initiation were not studied. Conclusions: Women, older patients, and those of white race were more likely to withdraw from dialysis therapy. The period before withdrawal was characterized by higher rates of medical events and higher levels of morbidity.
Article
Background Multimorbidity is an important health outcome but is difficult to quantify. We recently developed a multimorbidity-weighted index (MWI) and herein assess its performance in an independent nationally-representative cohort. Methods Health and Retirement Study (HRS) participants completed an interview on physician-diagnosed chronic conditions and physical functioning. We determined the relationship of chronic conditions on physical functioning and validated these weights with the original, independently-derived MWI. We then determined the association between MWI with physical functioning, grip strength, gait speed, basic and instrumental activities of daily living (ADL/IADL) limitations, and the modified Telephone Interview for Cognitive Status (TICS-m) in adjusted models. Results Among 20,509 adults, associations between chronic conditions and physical functioning varied several-fold. MWI values based on weightings in the HRS and original cohorts correlated strongly (Pearson’s r=0.92) and had high classification agreement (Kappa statistic=0.80, p<0.0001). Participants in the highest vs. lowest MWI quartiles had weaker grip strength (-2.91 kg, 95%CI: -3.51, -2.30), slower gait speed (-0.29 m/s, 95%CI: -0.35, -0.23), more ADL (0.79, 95%CI: 0.71, 0.87) and IADL (0.49, 95%CI: 0.44, 0.55) limitations, and lower TICS-m (-0.59, 95%CI: -0.77, -0.41) (all P<0.001). We observed monotonic graded relationships for all outcomes with increasing MWI quartiles. Conclusion A multimorbidity index weighted to physical functioning performed nearly identically in a nationally-representative cohort as it did in its development cohorts, confirming broad generalizability. MWI was strongly associated with subjective and objective physical and cognitive performance. Thus, MWI serves as a valid patient-centered measure of multimorbidity, an important construct in research and clinical practice.
Article
Purpose The incidence of treatment-related toxicity for adjuvant chemotherapy in breast cancer is well documented in clinical trials. However, the effect of chemotherapy on functional outcomes in older patients is less well known. We identified a cohort of older women diagnosed with early stage breast cancer to examine the association between exposure to chemotherapy and a claims-based measure of function-related adverse events (FAE). Methods Using the Surveillance, Epidemiology, and End Results (SEER)-Medicare dataset, we identified women aged ≥66 diagnosed with stage I or II breast cancer from 2004 to 2011. FAE were defined as one or more claims suggestive of functional impairment within 24 months following chemotherapy including claims for durable medical equipment and skilled care. Women who did not receive chemotherapy were weighted to reflect the covariate distribution of chemotherapy recipients using propensity score weighting for age, stage, baseline healthcare utilization, and comorbidities. Results The cohort included 44,626 patients, 6892 (15%) received chemotherapy. 19% of the population experienced ≥1 FAE. After propensity weighting, chemotherapy was associated with a small increased risk of FAEs (HR 1.12, 95% confidence interval: 1.04, 1.20). Results were similar in patients 75 years and older versus younger patients. In the chemotherapy group, the highest risk of FAE occurred in the first 3 months, but persisted through follow-up. Conclusions Exposure to chemotherapy was associated with a small increased risk of FAE which did not vary by age. These data can be used to inform treatment decision making for older patients with breast cancer who are eligible for adjuvant chemotherapy.
Article
Importance Few studies have analyzed contemporary data on outcomes at US teaching hospitals vs nonteaching hospitals. Objective To examine risk-adjusted outcomes for patients admitted to teaching vs nonteaching hospitals across a broad range of medical and surgical conditions. Design, Setting, and Participants Use of national Medicare data to compare mortality rates in US teaching and nonteaching hospitals for all hospitalizations and for common medical and surgical conditions among Medicare beneficiaries 65 years and older. Exposures Hospital teaching status: major teaching hospitals (members of the Council of Teaching Hospitals), minor teaching hospitals (other hospitals with medical school affiliation), and nonteaching hospitals (remaining hospitals). Main Outcomes and Measures Primary outcome was 30-day mortality rate for all hospitalizations and for 15 common medical and 6 surgical conditions. Secondary outcomes included 30-day mortality stratified by hospital size and 7-day mortality and 90-day mortality for all hospitalizations as well as for individual medical and surgical conditions. Results The sample consisted of 21 451 824 total hospitalizations at 4483 hospitals, of which 250 (5.6%) were major teaching, 894 (19.9%) were minor teaching, and 3339 (74.3%) were nonteaching hospitals. Unadjusted 30-day mortality was 8.1% at major teaching hospitals, 9.2% at minor teaching hospitals, and 9.6% at nonteaching hospitals, with a 1.5% (95% CI, 1.3%-1.7%; P < .001) mortality difference between major teaching hospitals and nonteaching hospitals After adjusting for patient and hospital characteristics, the same pattern persisted (8.3% mortality at major teaching vs 9.2% at minor teaching and 9.5% at nonteaching), but the difference in mortality between major and nonteaching hospitals was smaller (1.2% [95% CI, 1.0%-1.4%]; P < .001). After stratifying by hospital size, 187 large (≥400 beds) major teaching hospitals had lower adjusted overall 30-day mortality relative to 76 large nonteaching hospitals (8.1% vs 9.4%; 1.2% difference [95% CI, 0.9%-1.5%]; P < .001). This same pattern of lower overall 30-day mortality at teaching hospitals was observed for medium-sized (100-399 beds) hospitals (8.6% vs 9.3% and 9.4%; 0.8% difference between 61 major and 1207 nonteaching hospitals [95% CI, 0.4%-1.3%]; P = .003). Among small (≤99 beds) hospitals, 187 minor teaching hospitals had lower overall 30-day mortality relative to 2056 nonteaching hospitals (9.5% vs 9.9%; 0.4% difference [95% CI, 0.1%-0.7%]; P = .01). Conclusions and Relevance Among hospitalizations for US Medicare beneficiaries, major teaching hospital status was associated with lower mortality rates for common conditions compared with nonteaching hospitals. Further study is needed to understand the reasons for these differences.
Article
The terms multimorbidity and frailty are increasingly used in the medical literature to measure the risk profile of an older individual in order to support clinical decisions and design ad hoc interventions. The construct of multimorbidity was initially developed and used in nongeriatric settings. It generates a monodimensional nosological risk profile, grounding its roots in the somewhat inadequate framework of disease. On the other hand, frailty is a geriatric concept that implies a more exhaustive and comprehensive assessment of the individual and his/her environment, facilitating the implementation of multidimensional and tailored interventions. This article aims to promote among geriatricians the use of terms that may better enhance their background and provide more value to their unrivaled expertise in caring for biologically aged persons.
Article
We discuss observational studies that test many causal hypotheses, either hypotheses about many outcomes or many treatments. To be credible an observational study that tests many causal hypotheses must demonstrate that its conclusions are neither artifacts of multiple testing nor of small biases from nonrandom treatment assignment. In a sense that needs to be defined carefully, hidden within a sensitivity analysis for nonrandom assignment is an enormous correction for multiple testing: in the absence of bias, it is extremely improbable that multiple testing alone would create an association insensitive to moderate biases. We propose a new strategy called "cross-screening", different from but motivated by recent work of Bogomolov and Heller on replicability. Cross-screening splits the data in half at random, uses the first half to plan a study carried out on the second half, then uses the second half to plan a study carried out on the first half, and reports the more favorable conclusions of the two studies correcting using the Bonferroni inequality for having done two studies. If the two studies happen to concur, then they achieve Bogomolov-Heller replicability; however, importantly, replicability is not required for strong control of the family-wise error rate, and either study alone suffices for firm conclusions. In randomized studies with a few hypotheses, cross-split screening is not an attractive method when compared with conventional methods of multiplicity control, but it can become attractive when hundreds or thousands of hypotheses are subjected to sensitivity analyses in an observational study. We illustrate the technique by comparing 46 biomarkers in individuals who consume large quantities of fish versus little or no fish.
Article
Importance The literature suggests that hospitals with better nursing work environments provide better quality of care. Less is known about value (cost vs quality).Objectives To test whether hospitals with better nursing work environments displayed better value than those with worse nursing environments and to determine patient risk groups associated with the greatest value.Design, Setting, and Participants A retrospective matched-cohort design, comparing the outcomes and cost of patients at focal hospitals recognized nationally as having good nurse working environments and nurse-to-bed ratios of 1 or greater with patients at control group hospitals without such recognition and with nurse-to-bed ratios less than 1. This study included 25 752 elderly Medicare general surgery patients treated at focal hospitals and 62 882 patients treated at control hospitals during 2004-2006 in Illinois, New York, and Texas. The study was conducted between January 1, 2004, and November 30, 2006; this analysis was conducted from April to August 2015.Exposures Focal vs control hospitals (better vs worse nursing environment).Main Outcomes and Measures Thirty-day mortality and costs reflecting resource utilization.Results This study was conducted at 35 focal hospitals (mean nurse-to-bed ratio, 1.51) and 293 control hospitals (mean nurse-to-bed ratio, 0.69). Focal hospitals were larger and more teaching and technology intensive than control hospitals. Thirty-day mortality in focal hospitals was 4.8% vs 5.8% in control hospitals (P < .001), while the cost per patient was similar: the focal-control was −163(95163 (95% CI = −542 to 215;P=.40),suggestingbettervalueinthefocalgroup.Forthefocalvscontrolhospitals,thegreatestmortalitybenefit(17.3215; P = .40), suggesting better value in the focal group. For the focal vs control hospitals, the greatest mortality benefit (17.3% vs 19.9%; P < .001) occurred in patients in the highest risk quintile, with a nonsignificant cost difference of 941 per patient (53701vs53 701 vs 52 760; P = .25). The greatest difference in value between focal and control hospitals appeared in patients in the second-highest risk quintile, with mortality of 4.2% vs 5.8% (P < .001), with a nonsignificant cost difference of −862(862 (33 513 vs $34 375; P = .12).Conclusions and Relevance Hospitals with better nursing environments and above-average staffing levels were associated with better value (lower mortality with similar costs) compared with hospitals without nursing environment recognition and with below-average staffing, especially for higher-risk patients. These results do not suggest that improving any specific hospital’s nursing environment will necessarily improve its value, but they do show that patients undergoing general surgery at hospitals with better nursing environments generally receive care of higher value.
Article
The concept of multimorbidity is still poorly understood and not well integrated into medical care and research. For clinicians involved in rheumatology care for an ageing patient population who have multiple diseases, multimorbidity is the rule not the exception. The interaction of different diseases and the impact they have on important clinical outcomes, such as physical function, quality of life and mortality, should all be considered by the rheumatologist. Treatment decisions must be adapted for the patient with multimorbidity to best serve the individual and society. This Perspectives article describes the concept of multimorbidity, how it differs from comorbidity, and outlines why an increased understanding of multimorbiditiy will enhance our overall clinical practice and research focus.
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.
Article
Multimorbidity is increasing in frequency. It can be quantitatively measured and is a major correlate of high use of health services resources of all types, especially over time. The ACG System for characterizing multimorbidity is the only widely used method that is based on combinations of different TYPES of diagnoses over time, rather than the presence or absence of particular conditions or numbers of conditions. It incorporates administrative data (as from claims forms or medical records) on all types of encounters and is not limited to diagnoses captured during hospitalizations or other places of encounter. It can be employed in any one or combination of analytic models, and can incorporate medication use if desired. It is being used in clinical care, management of health services resources, in health services research to control for degree of morbidity, and in understanding morbidity patterns over time. In addition to its research uses, it is being employed in many countries in various applications as a policy to better understand health needs of populations and tailor health services resources to health needs.
Article
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.
Article
Multimorbidity, defined as the coexistence of 2 or more chronic diseases, is a common phenomenon especially in older people. Numerous efforts to establish a standardized instrument to assess the level of multimorbidity have failed until now, and indices are primarily characterized by their high heterogeneity. Thus, the objective is to provide a comprehensive overview on existing instruments on the basis of a systematic literature review. The review was performed in MedLine. All articles published between January 1, 1960 and August 31, 2009 in German or English language, with the primary focus either on the development of a weighted index or on the effect of multimorbidity on different outcomes, were identified. A total of 39 articles met the inclusion criteria. In the majority of studies (59.0%), the list of included diseases was presented without any selection criteria. Only the high prevalence of diseases (17.9%), their impact on mortality, function, and health status served as a point of reference. Information on the prevalence of chronic conditions mostly rely on self-reports. On average, the 39 indices included 18.5 diseases, ranging between 4 and 102 different conditions. Most frequently mentioned diseases were diabetes mellitus (in 97.5% of indices), followed by stroke (89.7%), hypertension, and cancer (each 84.6%). Overall, three different weighting methods could be distinguished. The systematic literature further emphasis the heterogeneity of existing multimorbidity indices. However, one important similarity is that the focus is on diseases with a high prevalence and a severe impact on affected individuals.
Article
In this study, we evaluate the impact of disability and multimorbidity on the risk of all-cause death in a population of frail older persons living in community. We analyzed data from the Aging and Longevity Study in the Sirente geographic area, a prospective cohort study that collected data on all subjects aged 80 years and older (n=364). The main outcome measure was all-cause mortality over 4-year follow-up. A total of 150 deaths occurred. Sixty-seven subjects (44.6%) died in the nondisabled group compared with 83 subjects (55.3%) in the disabled group (P<0.01). Thirty-nine subjects (31.7%) died among subjects without multimorbidity compared with 111 subjects (46.0%) with two or more diseases (P<0.01). When examining the combined effect of multimorbidity and disability, the effect of disability on the risk of death was higher than that of multimorbidity. After adjusting for potential confounders, relative to those without disability and multimorbidity, disabled subjects showed an increased risk of death when multimorbidity was associated (hazard ratio [HR]=3.91; 95% confidence interval [CI]=1.53-10.00) and in absence of multimorbidity (HR=2.36; 95% CI=0.63-8.83). Our results show that disability exerts an important influence on mortality, independently of age and other clinical and functional variables.
Article
To describe patterns of comorbidity and multimorbidity in elderly people. A community-based survey. Data were gathered from the Kungsholmen Project, a urban, community-based prospective cohort in Sweden. Adults aged 77 and older living in the community and in institutions of the geographically defined Kungsholmen area of Stockholm (N=1,099). Diagnoses based on physicians' examinations and supported by hospital records, drug use, and blood samples. Patterns of comorbidity and multimorbidity were evaluated using four analytical approaches: prevalence figures, conditional count, logistic regression models, and cluster analysis. Visual impairments and heart failure were the diseases with the highest comorbidity (mean 2.9 and 2.6 co-occurring conditions, respectively), whereas dementia had the lowest (mean 1.4 comorbidities). Heart failure occurred rarely without any comorbidity (0.4%). The observed prevalence of comorbid pairs of conditions exceeded the expected prevalence for several circulatory diseases and for dementia and depression. Logistic regression analyses detected similar comorbid pairs. The cluster analysis revealed five clusters. Two clusters included vascular conditions (circulatory and cardiopulmonary clusters), and another included mental diseases along with musculoskeletal disorders. The last two clusters included only one major disease each (diabetes mellitus and malignancy) together with their most common consequences (visual impairment and anemia, respectively). In persons with multimorbidity, there exists co-occurrence of diseases beyond chance, which clinicians need to take into account in their daily practice. Some pathological mechanisms behind the identified clusters are well known; others need further clarification to identify possible preventative strategies.
Article
To examine financial implications of the Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC) risk-adjustment model on Medicare payments for individuals with comorbid chronic conditions. The study used 1992-2000 data from the Medicare Current Beneficiary Survey and corresponding Medicare claims. Pairs of comorbidities were formed based on prior evidence about possible synergy between these conditions and activities of daily living (ADLs) deficiencies, and included heart disease and cancer, lung disease and cancer, stroke and hypertension, stroke and arthritis, congestive heart failure (CHF) and osteoporosis, diabetes and coronary artery disease, and CHF and dementia. For each beneficiary, we calculated the actual Medicare cost ratio as the ratio of the individual's annualized costs to the mean annual Medicare cost for all people in the study. The actual Medicare cost ratios, by ADLs, were compared with HCC ratios under the CMS-HCC payment model. Using multivariate regression models, we tested whether having the identified pairs of comorbidities affected the accuracy of CMS-HCC model predictions. The CMS-HCC model underpredicted Medicare capitation payments for patients with hypertension, lung disease, CHF, and dementia. The difference between the actual costs and predicted payments was partially explained by beneficiary functional status and less-than-optimal adjustment for these chronic conditions. Information about beneficiary functional status should be incorporated in reimbursement models. Underpaying providers who care for populations with multiple comorbidities may provide severe disincentives for managed care plans to enroll such individuals and to appropriately manage their complex and costly conditions.
Article
This study uses national Medicare data as well as data that were abstracted to calibrate the Medicare Mortality Predictor System to assess the usefulness of a risk adjustment system in interpreting hospital mortality rates. The majority of variation in annual hospital death rates for the four conditions studied (stroke, pneumonia, myocardial infarction, and congestive heart failure) is chance variability that results from the relatively small numbers of patients treated in most hospitals in a year. For hospitals in the highest and lowest quartiles of observed death rates, the difference between observed rates and those predicted by the Medicare Mortality Predictor System is not quite on third smaller than the difference between observed rates and unadjusted national rates. Risk adjustment methods do not show whether the unexplained difference in mortality rates results from differences in effectiveness of care or unmeasured differences in patient risk at the time of admission. Risk-adjusted mortality rates, therefore, should be supplemented by review of the actual care rendered before conclusions are drawn regarding effectiveness of care.
Article
This article evaluates the behavior of an adaptation of the Charlson Index (CHI) applied to administrative databases to measure the relationship between chronic comorbidity and the hospital care outcomes of length of stay (LOS), in-hospital mortality, and emergency readmissions at 30 and 365 days. These outcomes were analyzed in 106,673 hospitalization episodes whose records are registered in a minimum basic data set maintained by the public health authorities of the community of Valencia, Spain. The highest comorbidity measured by the CHI was associated with greater LOS and in-hospital mortality and increased readmission at 30 and 365 days. The rate of readmissions at 1 year dropped, however, in the group with the greatest comorbidity, probably owing to an increase in mortality after hospitalization. While comorbidity does appear to increase the risk of adverse outcomes in general and mortality and readmission specifically, the second outcome is only possible if the first has not occurred. For this reason, information and selection biases derived from administrative databases, or from the CHI itself, should be taken into account when using and interpreting the index.
Article
Teaching hospitals are widely reputed to provide high-quality care, eliciting very positive public opinions in surveys across the United States (Boscarino 1992). The U.S. News and World Report's listing of “America's Best Hospitals” (2000), based in part on the opinions of academic and community physicians, highly ranks many major teaching hospitals. These public and professional views may reflect features of teaching hospitals that are perceived to foster a higher quality of care, including the treatment of rare diseases and complex patients, the provision of specialized services and advanced technology, and the conduct of biomedical research (Neely and McInturff 1998). Some services, such as specialized surgery and bone marrow transplants, are provided predominantly at teaching hospitals (Levin, Moy, and Griner 2000). Other distinctive missions of teaching hospitals include medical education and training, innovations in clinical care, and treatment of indigent patients, particularly at public teaching hospitals (Blumenthal, Weissman, and Campbell 1997). Because teaching hospitals face increasing pressure to justify their higher charges for clinical care, the quality of care in teaching and nonteaching hospitals is an important policy question. The most rigorous peer-reviewed studies published between 1985 and 2001 that assessed quality of care by hospital-teaching status in the United States provide moderately strong evidence of better quality and lower risk-adjusted mortality in major teaching hospitals for elderly patients with common conditions such as acute myocardial infarction, congestive heart failure, and pneumonia. A few studies, however, found nursing care, pediatric intensive care, and some surgical outcomes to be better in nonteaching hospitals. Some factors related to teaching status, such as organizational culture, staffing, technology, and volume, may lead to higher-quality care.