Comorbidity: From Bedside to BenchSummary of the NIA/AGS R13 ConferenceASG Annual Meetings, May 13, 2005, Orlando

Comorbidity, Multi-Morbidityany distinct clinical entity that has existed or may occur during the clinical course of a patient who has an index disease [or condition] under study. (Feinstein, 1970)distinct clinical entities coexisting or likely to co-occur during a patients clinical courseASG Annual Meetings, May 13, 2005, Orlando

Symposium PresentersRebecca Silliman, MD: The NIA Comorbidity TaskforceAlison Moore, MD: Comorbidity in Relation to the Study and Treatment of Index ConditionsChristine Ritchie, MD: The Health and Social Burden of Multiple MorbidityStephanie Studenski, MD: The Research AgendaASG Annual Meetings, May 13, 2005, Orlando

Multimorbidity: Concepts and Research RecommendationsThanks to Linda Fried for the use of some of her presentation and to the members of the preclinical break out session

Stephanie Studenski MD MPHProfessor, Department of Medicine (geriatrics) Staff Physician, VA Pittsburgh GRECC3471 Fifth Avenue Suite 500Pittsburgh Pa 15213office 412 692 2360fax 412 692 2370email studenskis@msx.dept-med.pitt.edu ASG Annual Meetings, May 13, 2005, Orlando

OutlineMultimorbidity: the burden of illnessClusters of diseases and conditions: causes and consequences ASG Annual Meetings, May 13, 2005, Orlando DefinitionsComorbidity: additional diseases beyond the index diseaseMultimorbidity: co-occurrence of diseases

MultimorbidityOften no index condition.Systems serve as reserve capacity for each others losses.Multimorbidity reflects total burden of illness and has implications for reserve and tolerance to stress. ASG Annual Meetings, May 13, 2005, Orlando

Measuring the Burden of Illness: ChallengesWhen burden is assessed by diagnoses, factors that influence the process of clinical diagnosis affect reports.

Eg: Clinical thresholds for the diagnosis of disease vary by provider recognition, shifts over time in definitions eg DM, hyperlipidemia, HBP

Eg: Severity measures may be affected by coexisting conditions eg treadmill testing and CAD. Subspecialists may ignore the effect of coexisting conditions. ASG Annual Meetings, May 13, 2005, Orlando

Physiological system indicators may eliminate variability due to clinical thresholdsWhen burden is assessed by diagnoses, factors that influence the process of clinical diagnosis affect reports.

Eg: Clinical thresholds for the diagnosis of disease vary by provider recognition, shifts over time in definitions eg DM, hyperlipidemia, HBP

Eg: Severity measures may be affected by coexisting conditions eg treadmill testing and CAD. Subspecialists may ignore the effect of coexisting conditions. ASG Annual Meetings, May 13, 2005, Orlando

Physiological system indicators may eliminate variability due to clinical thresholdsSeverityPhysiologicalSystemDx

Physiological system indicators may eliminate variability due to clinical thresholdsSeverityPhysiologicalSystemDx

OpportunitiesCreate a system of basic markers of physiological functions across key systems (a battery like APACHE)Basic indicators by system (e.g., Hb, Creat).Develop and evaluate using existing data.

ApplicationsCompare to measures based on diagnoses. Might help ease barriers to including research on elders with comorbidity.Use battery to examine interactions and demands between physiological systems.Trials could look at subclinical adverse effects across subgroups

The Physiologic Battery as an indicator of burden of illness/multimorbidity

Expand battery to include axes within physiological systems/diseaseDuration and pattern over timeTreatment effectsAdds detail but increases complexity and demand of measure

Next StepsModeling that accounts for time and patterns: the NIA longitudinal analysis RFANovel analytic methodsTraining (K awards), methodological publicationsData sets (core data) with physiological indicatorsHealth ABC, InChianti, BLSA

Other Measures of Burden of Illness/Multimorbidity Physical Performance measures can be thought of as summary measures of preclinical & clinical conditions; they are composites and are non-specific.One kind of indicator; an integrative summary of multiple morbidityDo not attribute symptoms and function only to index condition

Clusters: what can they tell us?Cluster: system abnormalities that co-occur at a rate that is higher than expected by chance alone.Types of clusters single underlying common cause secondary consequences of index condition Clusters can provide insights into common causes and into combined effects on consequences like disability.

Clusters in late life: implications for causation24 year old woman with rash, arthritis and kidney disease84 year old woman with rash, arthritis and kidney disease

Since conditions are more rare in younger adult, a cluster is unlikely to be due to chance, and is likely to have a common cause. Conversely, since conditions are more common in older adults, clusters are more likely to be due to chance and may not have a common cause.

Late Life ClustersUnrecognized underlying process or condition precipitates multiple abnormalities; inflammation as cause of atherosclerosis, malnutrition, frailty, neurodegeneration: creates new target for intervention. (Ferrucci L et al A flame burning within. Aging Clin Exp Res. 2004)

A known condition precipitates others eg diabetes, atherosclerosis, renal failure: target precipitating condition for intervention (Volpato et al Diabetes Care 2002)

Primary clustersPotential underlying causeDisease BDisease CDisease DClusters with no recognized underlying common cause are an opportunity for research into prevention and treatment of late life multimorbidity

Secondary Clusters:Diabetes and complicationsDuration of diabetes associated with presence of: CHD, CHF, PAD, HTN, DepressionDiabetes associated with:Peripheral neuropathy, CVD, visual impairment, obesityDisability: mobility, ADL, IADLVolpato,Diabetes Care 2002

Consequences: combinations of diseases synergistically associated with disability

Two Diseases Present Concurrently have Joint EffectsRisk of Mobility DisabilityHeart Disease Only: OR = 2.3Arthritis Only: OR = 4.3Both Heart Disease and Arthritis: OR = 13.6

NHANES IIIEttinger et al;

Clusters and ConsequencesMuch of the action is in the interactionThe interactions between diseases contribute to disability, over and above the independent contribution of each disease.Research questions: Interactions between specific disease pairs might have effects specific to different types of function.Clinical implications: target preservation of specific functions by minimizing specific interactions?

Comorbidity in relation to study and treatment of index disease Alison A. Moore, MD, MPH David Geffen School of Medicine at UCLADivision of Geriatric Medicine

HIV/AIDS as a Chronic Disease: the Veterans Aging Cohort Study

Amy C. Justice, MD, PhDPI, Veterans Aging Cohort StudyGIM Section Chief, West Haven VAMC Yale University

Why Study HIV and Comorbidity?Clinical Reasons:Prevalence: People with HIV are living long enough to age Incidence: As more people with HIV are aging, more older individuals will contract HIVToxicity: Difficult to determine what is due to treatment if we dont understand underlying risk of comorbid disease

Research Reasons:Bench: effect modification may lead to pathophysiologic insightsOutcomes: due to implications for survival: optimal management of HIV may differ by age; optimal management of comorbidity may differ by HIV status

Life Expectancy after HIV diagnosis with and without HAART CD4 = 750CD4 = 500 CD4 = 200with withoutwithwithoutwith withoutAge 40Age 50Age 30Years

Chart4

24.808333333322.191666666719.55

11.266666666710.19166666679.9833333333

23.220.991666666718.675

9.09166666677.76666666677.7166666667

20.091666666717.816666666716.2583333333

6.90833333335.50833333335.4416666667

Sheet1

noHAARTHAARTnoHAARTnonHIVHIV

Age 30

no HIV467.6467.60

CD4 750135.2297.7135.224.8083333333

CD4 500109.1278.4109.123.2

CD4 20082.9241.182.920.0916666667

7506.135.1

5004.830.8

2003.425.1

CD4 750135.2162.511.266666666713.5416666667

6.1296.129

CD500109.1169.39.091666666714.1083333333

4.8264.826

CD4 20082.9158.26.908333333313.1833333333

3.421.73.421.7

304050

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30.846.963.8

5004.89.115.4

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10 5

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304050

297.7266.3234.624.808333333322.191666666719.55

750135.2122.3119.811.266666666710.19166666679.9833333333

278.4251.9224.123.220.991666666718.675

500109.193.292.69.09166666677.76666666677.7166666667

241.1213.8195.120.091666666717.816666666716.2583333333

20082.966.165.36.90833333335.50833333335.4416666667

Sheet1

000

000

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000

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Non-AIDS Deaths with and without HAART (Virtual Cohort) CD4 = 750 CD4 = 500 CD4 = 200with withoutwithwithoutwith withoutAge 40Age 50Age 30%

Chart1

35.151.170.1

6.113.420.7

30.846.963.8

4.89.115.4

25.137.452.7

3.46.311

Sheet1

noHAARTHAARTnoHAARTnonHIVHIV

Age 30

no HIV467.6467.60

CD4 750135.2297.7135.224.8083333333

CD4 500109.1278.4109.123.2

CD4 20082.9241.182.920.0916666667

7506.135.1

5004.830.8

2003.425.1

CD4 750135.2162.511.266666666713.5416666667

6.1296.129

CD500109.1169.39.091666666714.1083333333

4.8264.826

CD4 20082.9158.26.908333333313.1833333333

3.421.73.421.7

304050

35.151.170.1

7506.113.420.7

30.846.963.8

5004.89.115.4

25.137.452.7

2003.46.311

Sheet1

467.60

135.224.8083333333

109.123.2

82.920.0916666667

Sheet2

00

00

00

Sheet3

000

000

000

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000

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ConclusionsMore HIV pts will die from non-HIV causesNearly half of patients with age>40 years

If mean age at HIV diagnosis remains 38,Mean survival will approach 19.6 yearsMean age at death will approach 58 years

Guidelines for management of diseases occurring with complex chronic disease must account forShortened life expectancyIncreased risk due to primary disease and its treatment

Late Life Depression and Medical ComorbidityIra R. Katz, MD, PhDProfessor of PsychiatryUniversity of PennsylvaniaDirector, MIRECCPhiladelphia VA Medical Center

Depression amplifies morbidityDisabilityCognitive impairmentPain (and other symptoms)SubnutritionDecreased treatment adherenceIncreased use of health servicesIncreased mortalitySuicide and non-Suicide

Associations between Depression and Frailty

Proportion with CES-D > 10 by Frailty Status From Fried et al, J Gerontol Med Sci 56A: M146-M156, 2001 CHS data

Depressive Symptoms Confer VulnerabilityGlaser et al, Arch Gen Psych 60: 1009-1014, 2003Changes in IL-6 after influenza vaccination in normal older individuals

ConclusionsDepression is a manifestation of morbidity and a source of vulnerability that arises from multiple comorbidities and paths and leads to multiple adverse health effectsTherefore, it can be considered a frailty

Cardiovascular DiseaseThe # 1 Comorbidity in Aging PatientsAnne B. Newman, MD, MPHProfessor of Epidemiology and MedicineUniversity of Pittsburgh

Comorbidity - CVD and other diseasesCVD and OsteoarthritisMost common combination CVD and DepressionNumerous studies show depression increases risk of CVD Also possible that there is a vascular depressionCVD and DementiaVascular dementia vs. AD with vascular disease? CVD and CancerCVD and Chronic Lung Disease

Multivariate Analysis of Subclinical Cardiovascular Disease for 1st MI CHS; n=4,946; follow-up: 4.8 yrs.* Adjusted for age, race, gender, SBP, glucose, AAI, ICA-IMT, and EF. Variables that did not make into final model: LV mass by ECG, FVC, HDL-C, smoking, and fibrinogen.Psaty BM, Furberg CD, Kuller LH, Bild DE, Rautaharju PM, Polak JF, Bovill E Gottniener JS. Traditional risk factors and subclinical disease measures as predictors of first myocardial infarction in older adults: The Cardiovascular Health Study. Arch Intern Med. 1999; 159:1339-1347.

Probability of Successful Aging by Age, Gender, and Subclinical Cardiovascular Disease65-6970-7475-7980+Women 65-6970-7475-7980+Men Newman AB, Arnold AM, Naydeck BL, Fried LP, Burke GL, Enright P, Gottdiener J, Hirsch C, OLeary D, Tracy R. Successful Aging: Effects of Subclinical Cardiovascular Disease. Arch Intern Med. 2003;163:2315-2322.

SummaryCVD is so common that it will - more often than not - be comorbid with something elseClinically diagnosed CVD represents less than half of the total burden of CVDAn equal proportion have subclinical CVDSubclinical CVD is related to Physical performanceFrailtyCognitive declineDementia

Diabetes and Comorbidity in Older AdultsCaroline S. BlaumUniversity of MichiganAnn Arbor VA Medical Center

March, 2005

Research questions and hypothesesIn type 2 diabetes, do frailty and disability result from accumulating comorbidities or is it the underlying pathophysiological disruption that causes comorbidity accumulation, frailty and disability development?Is there a stepwise relationship between the MS, Diabetes, and Diabetes+comorbidities, and frailty and disability?

Percent change in mobility score associated with metabolic syndrome group

Model 1

Model 2

Model 3

MS-DM-

ref

ref

ref

MS+DM-

19.2% ***

14.5% ***

13.5% ***

MS-DM+

45.2% ***

54.8% ***

41.9% ***

MS+DM+

56.4% ***

58.9% ***

44.9% ***

Gender (Male=ref)

29.2% ***

14.5% ***

Education (yrs)

-2.4% ***

-1.4% ***

MI

15.0%

CHF

32.0% **

Intermittent claudication

26.7% ***

Stroke

43.8% ***

CES-D

2.4% ***

Arthritis in knee or hip

38.0% ***

From regression models using GEE after multiple imputation procedure for missing values

Models 2 and 3 include a time x age interaction term

Time-varying covariates * p < .05, ** p < .01, *** p< .001

Summary Comorbidity prevalent in older adults with diabetesIncreases with ageStepwise progression from MS to new diabetes to longstanding diabetesMS related to worsening in mobility disability but diabetes has much stronger associationObesity and diabetes are independently related to prevalent frailty.MS is related to incident frailty and may maintain association in the presence of incident diabetesDiabetes and many comorbidities are related to incident frailty

Clinical Epidemiology of Comorbidity in Aging Patients: Findings and Insights from Geriatric OncologyWilliam A. Satariano, Ph.D, MPHSchool of Public HealthUniversity of California, Berkeley

Reasons for Research on Comorbidity and CancerThere are age-associated patterns of cancer incidence, stage, treatment, and survival (both duration and quality of life).It is hypothesized that age-associated patterns of comorbidity may help to account for those age-associated differences in cancer outcomes.

Reasons for Research on Comorbidity and CancerThere is an extensive network of hospital-based and, more important, population-based cancer registries and surveillance systems.Assessment of large number of cancer cases by cancer site, stage, histology, first-course of treatment.System of linkage with other sources of health data that include records of diagnosis and treatment for other health conditions.Affords opportunity to conduct detail analysis of cancer outcomes.

Reasons for Research on Comorbidity and CancerThere is a significant area of clinical and epidemiological research on multiple primary cancers, a history of two or more primary cancers dx in a single individual.

Benefits and Risks of Alcohol Use among Older Persons Alison A. Moore, MD, MPH Division of Geriatric Medicine

Conditions which may be prevented by light to moderate alcohol useAll-cause mortalityCoronary heart diseaseCongestive heart failureCerebrovascular diseaseIschemic strokeDiabetesCholelithiasisDementia?Falls

Conditions that may be caused or worsened by alcohol useLip and oropharyngeal cancerEsophageal varices and cancerLaryngeal cancerLiver cirrhosis and cancerGastro-esophageal hemorrhageAcute and chronic pancreatitisFemale breast cancerEpilepsyHypertensionCardiac arrhythmiasHemorrhagic strokePsoriasisDepressionGoutAlcohol use disorders

What is the effect of moderate drinking if you have comorbidities for which alcohol is beneficial? Evidence that moderate alcohol use is beneficial among those persons having:CHDStrokeDiabetes

What about the effects of drinking and multiple comorbidity? No data!Studies have included comorbidity as covariates rather than considering the combination of alcohol use and selected comorbidity on outcomes

Drinking Patterns in Older Persons

Mortality risks among at-risk drinkers and abstainers as compared to not at-risk drinkersN=3726 persons aged 60+ participating in NHANES I (1971-75) and NHEFS 1992

Chart1

1.121.080.150.120.110.1

1.21.060.190.210.140.12

0.871.090.260.370.170.15

*

AT RISK

ABSTAINER

HAZARD RATIO

Sheet1

AT RISKABSTAINER

ALL1.120.150.121.080.10.11

MEN1.20.190.211.060.120.14

WOMEN0.870.260.371.090.150.17

Sheet1

0.150.120.110.1

0.190.210.140.12

0.260.370.170.15

*

AT RISK

ABSTAINER

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Sheet2

Sheet3

Conclusions40-60% of older persons drink alcohol and many have comorbiditiesAlcohol has benefits or risks in regard to CHD and CHD-related outcomes depending on amount of alcohol useAlcohol is a risk for many other adverse outcomes It is unknown whether the CHD-related benefits of light to moderate alcohol use persist in the face of multiple comorbidity

The Health and Societal Burden of Multiple Morbidity Christine S. Ritchie, MD, MSPHAssociate Professor of MedicineUniversity of Alabama at Birmingham

Multimorbidity (Comorbidity)The co-occurrence of multiple diseases in an individual person

The total burden of all concurrently occurring biological processes (clinical and sub-clinical ) that are intrinsic to the individual

Explicitly excludes socioeconomic factors, lifestyle factors, and access to health care

In the Nagi pathway terminology, impairment is included, while disability is excluded, since disability is environment-dependent.

Adapted from Karlamangla A, NIA Comorbidity Conference, 2005

Prevalence of MultimorbidityUsing 24 major diagnostic categories82 percent of people 65 and older had one or more chronic conditions65 percent two or more43 percent two or more24 percent four or more.

On average there are 2.3 chronic conditions reported by people 65 and older Wolff JL, Starfield B, Anderson G. Arch Intern Med.2002;162:2269-2276

Prevalence of MultimorbidityWolff JL, Starfield B, Anderson G. Arch Intern Med.2002;162:2269-2276

Multimorbidity identifies those at risk for more diseasesPeople with multimorbidity at higher risk of getting 2 or more new diseases than those with no disease, people >18 years

(Netherlands; van den Akker 1998)From Fried L, NIA Comorbidity Conference 2005

Multimorbidity: Joint Effects of Two DiseasesRisk of Mobility DisabilityHeart Disease Only: OR = 2.3Arthritis Only: OR = 4.3Both Heart Disease and Arthritis: OR = 13.6

NHANES IIIEttinger et al; From Fried L, NIA Comorbidity Conference 2005

Impact of multi-morbidity on physical limitationsKriegsman et al. Disability Rehabilitation 1997;19:71-83

Impact of multimorbidity on 3-year decline in physical functioningKriegsman et al. J Clin Epidemiol 2004;57:55-65

Impact of Multimorbidity on Quality of LifeIn a systematic reviewInverse relationship between the number of medical conditions and QOL related to physical domains. For social and psychological dimensions of QOL, studies reveal a similar inverse relationship in patients with 4 or more diagnoses Fortin et al. Health Qual Life Outcomes. 2004; 2: 51

Multimorbidity and depressive symptomsPenninx et al. J Psychosom Res 1996;40:521-534;Adapted from Kriegsman D, NIA Comorbidity Conference, 2005

Impact of multimorbidity on coping resourcesNegatively influences coping resources:Self-esteemMasterySelf-efficacyKriegsman D, NIA Comorbidity Conference, 2005

Impact of Multimorbidity on Hospitalization: Hospitalization for Ambulatory Care Sensitive Condition by Number of Chronic ConditionsWolff, J. NIA Comorbidity Conference, 2005

Chart1

0.9922177209

7.7093503746

19.5118458048

40.1855458066

74.4534640461

118.6505394886

168.5654439563

216.1624683621

266.6879082364

296.3140258497

362.4678663239

ACSCs

Number of Chronic Conditions

Potentially Preventable Hospitalizations Per 1000 Beneficiaries

Table1

Table 1

Demographic Characteristics of Study Sample, 1997

Study SampleEnrollment File

Mean Age (Years)7575

FrequencyPercentFrequencyPercent

Gender

Male511,61539.8%626,26040.3%

Female773,02960.2%928,88059.7%

Race

White1,137,70788.6%1,368,70188.0%

Black95,7487.5%117,3907.5%

Hispanic22,7621.8%30,1731.9%

Other28,4272.2%38,8762.5%

Other13,3991.0%3,5480%

Asian10,4980.8%18,7561%

Unknown3,0510.2%14,9441%

North American Native1,4790.1%1,6280%

Total1,284,6441,555,140

Table 1

Demographic Characteristics of Study Sample

Study SampleEnrollment File

Mean Age (Years)75.475.2

FrequencyPercentFrequencyPercent

Gender

Male484,84239.8%621,79440.3%

Female732,26160.2%920,00959.7%

Race

White1,079,58088.7%1,356,19588.0%

Black90,0397.4%116,9587.6%

Hispanic16,3111.3%24,2561.6%

Other31,1732.6%44,3942.9%

Other17,0251.4%24,8921.6%

Asian9,9600.8%14,5470.9%

Unknown2,8220.2%3,4230.2%

North American Native1,3660.1%1,5320.1%

Total1,217,1031,541,803

&L&D&RExhibits

Table2

Table 2

Summary of Chronic Disease Prevalence and Costs by Age, 1997

Age Group

65-6970-7475-8080-8585+Total

PercentMeanPercentMeanPercentMeanPercentMeanPercentMeanPercentMean

Age GroupPaidAge GroupPaidAge GroupPaidAge GroupPaidAge GroupPaidBeneficiariesPaid

90,51063,56340,37522,61517,837234,900

No Chronic Conditions25.9%$17119.25%$17315.18%$19312.35%$22411.44%$31118.29%$191

74,92263,40546,00629,02224,478237,833

One Type of Chronic Condition21.4%$87919.20%$94017.30%$1,06315.85%$1,20115.70%$1,64718.51%$1,049

74,57271,95856,27938,13332,874273,816

Two Types of Chronic Conditions21.3%$1,96021.79%$2,08521.16%$2,36220.82%$2,79221.09%$3,48621.31%$2,374

53,34358,74350,58136,45031,602230,719

Three Types of Chronic Conditions15.3%$4,08517.79%$4,08919.02%$4,48519.90%$5,15920.27%$6,16817.96%$4,629

56,12272,53872,69356,93249,091307,376

Four + Types of Chronic Conditions16.1%$12,65021.97%$12,43927.33%$12,82431.08%$13,41531.49%$14,15423.93%$13,023

Overall Age Group100.0%$3,306100.0%$4,128100.0%$5,072100.0%$5,996100.0%$6,737100.0%$4,683

Mean # Chronic Conditions1.872.232.512.702.732.32

Total - All Study Participants349,469330,207265,934183,152155,8821,284,644

Table 2

Summary of Chronic Disease Prevalence and Costs by Age

Age Group

65-6970-7475-8080-8585+Total

PercentMeanPercentMeanPercentMeanPercentMeanPercentMeanPercentMean

Age GroupPaidAge GroupPaidAge GroupPaidAge GroupPaidAge GroupPaidBeneficiariesPaid

78,04358,96639,75622,65219,285218,702

No Chronic Conditions25.7%$19518.86%$20315.19%$20512.56%$22212.20%$30317.97%$211

62,01956,31641,84826,85523,745210,783

One Type of Chronic Condition20.4%$99918.02%$1,07315.99%$1,17514.88%$1,27115.02%$1,57917.32%$1,154

67,57870,33256,52537,69933,192265,326

Two Types of Chronic Conditions22.2%$2,05522.50%$2,18621.59%$2,34820.89%$2,67720.99%$3,28421.80%$2,394

48,68058,54952,20936,90332,174228,515

Three Types of Chronic Conditions16.0%$4,22718.73%$4,32819.94%$4,59720.45%$4,99720.35%$5,92918.78%$4,701

47,90968,40671,44156,31349,708293,777

Four + Types of Chronic Conditions15.7%$14,10921.89%$13,77427.29%$13,85731.21%$13,97531.44%$14,28224.14%$13,973

Overall Age Group100.0%$3,609100.0%$4,548100.0%$5,424100.0%$6,160100.0%$6,660100.0%$5,015

Mean # Chronic Conditions1.882.252.522.712.712.34

Total - All Study Participants304,229312,569261,779180,422158,1041,217,103

&L&D&RExhibits

Table3

Table 3

Prevalence, Costs, and Comorbidity by MDC, 1997

TotalTotal HospitalizationsPercent withMeanAdmits/1,000 Beneficiaries for:PrevalenceNumber ofPrevalence of

Major Diagnostic CategoryPaidASCSsComplications4+ ConditionsAmt PaidASCSs (1)Complications (2)RankBeneficiariesType of Condition

Circulatory$5,252,442,66553,8038,82738%$7,24074.1612.171725,46756%

Endocrine$3,299,777,50433,9885,07443%$6,89270.9910.602478,79537%

Eye$1,793,519,00715,7532,30646%$5,48048.137.053327,27525%

Musculoskeletal$2,465,104,26020,8413,37851%$7,98767.5210.944308,65724%

Respiratory$2,580,594,89727,2815,35760%$13,252140.0927.515194,73315%

Mental$1,965,148,69619,1684,62962%$12,381120.7629.166158,72312%

Nervous$2,034,837,31918,8534,73766%$13,357123.7531.097152,34212%

Male Reproductive$829,710,7597,2171,46746%$6,16153.5910.898134,66310%

Skin/Subcutaneous Tissue$878,489,6785,8231,52755%$7,83451.9313.629112,1359%

Kidney$1,176,000,87212,0092,14774%$17,345177.1331.671067,7995%

Digestive$662,383,6644,9381,25564%$12,22991.1723.171154,1654%

Blood and Immunological$571,352,8644,83884866%$12,604106.7218.711245,3324%

ENT$289,154,8592,37257463%$8,72771.5917.321333,1343%

Myleproferative/Neoplasms$452,539,9762,94063779%$17,038110.6923.981426,5602%

Hepatobiliary$251,767,9501,77437873%$16,238114.4124.381515,5051%

Female Reproductive$149,233,43987622360%$10,12259.4115.121614,7441%

Drug/Alcohol$129,536,2421,02820374%$18,094143.6028.36177,1591%

$24,781,594,650233,50243,567

Total Patients1,284,644

Table 3

Prevalence, Costs, and Comorbidity by MDC

Percent withMeanPrevalence ofTotalTotal HospitalizationsAdmits/1,000 Beneficiaries for:PrevalenceNumber of

Major Diagnostic Category4+ ConditionsAmount PaidType of ConditionPaidASCSsComplicationsASCSs (1)Complications (2)RankBeneficiaries

Injury86%$38,2010%$39,614,684143318137.90306.65201,037mdc21

Myeloproliferative80%$19,8392%$476,493,1012,8752,248119.7093.601524,018mdc17

Kidney74%$18,8966%$1,383,651,24811,3725,203155.3171.061173,223mdc11

Alcohol/Drug74%$19,1670%$114,525,224924435154.6472.80185,975mdc20

Other Factors74%$17,3739%$1,931,362,81212,3169,456110.7885.069111,173mdc23

Pregnancy73%$14,3620%$1,364,3559894.7484.212395mdc14

Hepatobiliary72%$17,1231%$268,743,9121,5531,13998.9572.571615,695mdc07

Newborn71%$15,0030%$1,935,41451138.7685.2722129mdc15

HIV69%$17,4040%$4,751,1743111113.5540.2921273mdc25

Infectious & Parasitic67%$11,2260%$14,986,571926068.9144.94191,335mdc18

Blood & Immunological67%$13,3664%$587,882,4324,4181,922100.4543.701343,983mdc16

Nervous System66%$13,51612%$1,929,797,04417,1986,563120.4545.977142,781mdc01

Digestive63%$13,0934%$667,730,6974,4293,84886.8475.451250,999mdc06

Mental62%$12,53713%$1,996,168,05819,6306,109123.2938.376159,217mdc19

ENT62%$9,6862%$253,465,8211,84596070.5136.691426,168mdc03

Respiratory60%$14,30315%$2,567,866,12725,58210,642142.5059.285179,528mdc04

Female Reproductive59%$10,3641%$137,727,69066595650.0471.941713,289mdc13

Skin, Subcutaneous Tissue & Breast54%$8,9788%$883,610,0875,1003,28651.8233.391098,423mdc09

Eye50%$6,29620%$1,513,326,07311,0365,07145.9121.103240,360mdc08

Musculoskeletal48%$8,23025%$2,505,265,14118,7469,48961.5831.174304,408mdc02

Male Reproductive46%$6,86811%$897,135,6346,2424,11347.7831.498130,631mdc12

Endocrine41%$6,94143%$3,633,133,85628,68414,25954.8027.242523,437mdc10

Circulatory38%$7,52158%$5,307,212,83145,35721,20764.2830.051705,608mdc05

Any MDC29%$6,06782%$6,057,765,85349,37623,41749.4623.45998,401mdc00

Total Patients1,217,103

&L&D&RExhibits

Table4

Table 4

Multiple Logistic Regression Results for Incurring an Inpatient Admission

for an Adverse Event, 1997

ACSCsPreventable Complications

Odds95% CIOdds95% CI

RatioLowerUpperRatioLowerUpper

# Chronic Conditions

One6.325.517.255.540.476.51

Two16.4914.4618.8013.8711.9116.16

Three30.8227.0635.1027.9424.0332.49

Four +76.7067.4487.2481.1469.9294.16

Age

70-741.081.051.120.960.930.99

75-791.271.221.310.940.910.98

80-841.661.601.720.850.810.88

85+2.402.322.480.680.650.71

Gender0.900.880.920.760.750.78

Table 5

Multiple Logistic Regression Results for Incurring an Inpatient Admission

for an Adverse Event

ACSCsPreventable Complications

Odds95% CIOdds95% CI

RatioLowerUpperRatioLowerUpper

# Chronic Conditions

One7.496.508.656.024.997.25

Two18.1015.7920.7613.6011.3916.24

Three36.4331.8141.7329.1724.4934.75

Four +98.5286.11112.7291.3576.85108.59

Age

70-740.980.951.021.000.961.04

75-791.181.141.210.970.931.01

80-841.581.531.630.900.860.94

85+2.492.412.570.680.640.72

Gender0.890.870.900.770.750.79

Notes: ACSCs = Hospitalizations for ambulatory care sensitive conditions (ACSCs).

(excludes overlapping diagnoses with chronic conditions)

Preventable Complications = Hospitalizations for preventable complications.

(Elixhauser list of complications using any diagnosis position)

Reference groups: chronic conditions = none; age = 65-69 years; gender = male

&L&D&RExhibits

Figure1

1999

Inpatient Admits Per 1,000 BeneficiariesAverageTotal

# MDCsFor ASCSs (1)With Complications (2)Amount PaidTotal ASCSComplicationsTotal Cases

ACSCsComplications

011$211217134218702

184$1,1541625754210783

2208$2,39451772138265326

34017$4,70191833983228515

47434$8,587109975028147703

511957$14,1689633465281188

616986$21,1036599338039148

7216119$28,9893587196716594

8267152$37,81716749536277

9296182$46,8276193812089

10+362233$56,588282181778

Overall4119$5,01549593235511217103

Notes: (1) Hospitalizations for ambulatory care sensitive conditions (ACSCs).

(2) Hospitalizations with preventible complications.

1997

Inpatient Admits Per 1,000 BeneficiariesAverage

# MDCsFor ASCSs (1)With Complications (2)Amount Paid

ACSCsComplications

01.010.76$191

16.794.22$1,049

218.4210.27$2,374

335.7520.69$4,629

462.3137.62$8,204

594.8862.03$13,063

6130.7593.63$19,293

7169.39127.49$25,817

8218.70166.60$33,155

9235.54197.93$40,131

10+260.92270.74$50,381

Total33.8921.68$4,683

Notes: (1) Hospitalizations for ambulatory care sensitive conditions (ACSCs).

(2) Hospitalizations with preventible complications.

Figure1

00

00

00

00

00

00

00

00

00

00

00

ACSCs

Complications

Number of Types of Chronic Conditions (by MDC)

Rate Per 1000 Beneficiaries

Figure 1.Inpatient Hospitalizations Associated With Avoidable Events

NA1

Table 5

Summary of Avoidable Inpatient Admissions, 1997

A. Number ofB. InpatientC. PercentNumber of# of PreventablePer 100 Inpt AdmitsPer 1000 Study Participants

Age GroupBeneficiariesAdmitsof Total (%)ACSCsComplicationsACSCsPreventable ComplicationsACSCsPreventable Complications

65-69349,46990,90019.19%6,7746,5237.57.219.418.7

70-74330,207105,43322.25%8,5177,2618.16.925.822.0

75-80265,934104,65922.09%9,0586,6328.76.334.124.9

80-85183,15287,67118.50%8,7634,43810.05.147.824.2

85+155,88285,13717.97%10,4213,00012.23.566.919.2

Gender

Male511,615196,07641.38%17,77813,1149.16.734.725.6

Female773,029277,72458.62%25,75514,7409.35.333.319.1

Total1,284,644473,800100.00%43,53327,85412.25.933.921.7

Notes: ACSCs = Hospitalizations for ambulatory care sensitive conditions (ACSCs). Excludes overlapping dx with chronic illness

Preventable Complications = Hospitalizations for preventable complications. (Elixhauser list and any dx position).

Table 5

Summary of Avoidable Inpatient Admissions, 1999

A. Number ofB. InpatientC. PercentNumber of# of PreventablePer 100 Inpt AdmitsPer 1000 Study Participants

Age GroupBeneficiariesAdmitsof Total (%)ACSCsComplicationsACSCsPreventable ComplicationsACSCsPreventable Complications

65-69304,22977,37716.83%6,4894,9228.46.421.316.2

70-74312,569100,32421.83%9,2066,2159.26.229.519.9

75-80261,779104,02422.63%10,5475,73510.15.540.321.9

80-85180,42287,82119.11%10,3744,02611.84.657.522.3

85+158,10490,11219.60%12,9772,65314.42.982.116.8

Gender

Male484,842187,80140.86%20,61011,13911.05.942.523.0

Female732,261271,85759.14%28,98312,41210.74.639.617.0

Total1,217,103459,658100.00%49,59323,55112.25.140.719.4

Notes: ACSCs = Hospitalizations for ambulatory care sensitive conditions (ACSCs). Excludes overlapping dx with chronic illness

Preventable Complications = Hospitalizations for preventable complications. (Elixhauser list and any dx position).

&L&D&RExhibits

NA3

Table 2

Summary of Chronic Disease Prevalence and Costs by Age and Gender, 1997

TotalMean #No Chronic ConditionsOne Chronic ConditionTwo Chronic Conditions3+ Chronic Conditions

AgeNumber ofof ChronicNumberPercentMeanNumberPercentMeanNumberPercentMeanNumberPercentMean

GroupBeneficiariesConditionsBeneficiariesBeneficiariesPaidBeneficiariesBeneficiariesPaidBeneficiariesBeneficiariesPaidBeneficiariesBeneficiariesPaid

65-69349,4691.8790,51025.90%$17174,92221.44%$87974,57221.34%$1,960109,46531.32%$8,476

70-74330,2072.2363,56319.25%$17363,40519.20%$94071,95821.79%$2,085131,28139.76%$8,703

75-80265,9342.5140,37515.18%$19346,00617.30%$1,06356,27921.16%$2,362123,27446.36%$9,402

80-85183,1522.7022,61512.35%$22429,02215.85%$1,20138,13320.82%$2,79293,38250.99%$10,192

85+155,8822.7317,83711.44%$31124,47815.70%$1,64732,87421.09%$3,48680,69351.77%$11,027

Total1,284,6442.83234,90018.29%$191237,83318.51%$1,049273,81621.31%$2,374538,09541.89%$9,424

Table 2

Summary of Chronic Disease Prevalence and Costs by Age and Gender, 1999

TotalMean #No Chronic ConditionsOne Chronic ConditionTwo Chronic Conditions3+ Chronic Conditions

AgeNumber ofof ChronicNumberPercentMeanNumberPercentMeanNumberPercentMeanNumberPercentMean

GroupBeneficiariesConditionsBeneficiariesBeneficiariesPaidBeneficiariesBeneficiariesPaidBeneficiariesBeneficiariesPaidBeneficiariesBeneficiariesPaid

65-69304,2291.8878,04325.65%$19562,01920.39%$99967,57822.21%$2,05596,58931.75%$9,128

70-74312,5692.2558,96618.86%$20356,31618.02%$1,07370,33222.50%$2,186126,95540.62%$9,417

75-80261,7792.5239,75615.19%$20541,84815.99%$1,17556,52521.59%$2,348123,65047.23%$9,947

80-85180,4222.7122,65212.56%$22226,85514.88%$1,27137,69920.89%$2,67793,21651.67%$10,421

85+158,1042.7119,28512.20%$30323,74515.02%$1,57933,19220.99%$3,28481,88251.79%$11,000

Total1,217,1032.34218,70217.97%$211210,78317.32%$1,154265,32621.80%$2,394522,29242.91%$9,917

&L&D&RExhibits

NA4

Table 2

Summary of Chronic Disease Prevalence and Medical Costs by Number of Comorbidities, 1997

(Categorized by MDC)

TotalPercent ofCumulativeAveragePercent ofCumulative

# MDCsBeneficiariesBeneficiariesPercent (%)Amount PaidAmount PaidPercent (%)

0234,90018.29%18.29%$1910.75%0.75%

1237,83318.51%36.80%$1,0494.15%4.89%

2273,81621.31%58.11%$2,37410.81%15.70%

3230,71917.96%76.07%$4,62917.75%33.45%

4152,95611.91%87.98%$8,20420.86%54.32%

584,6236.59%94.57%$13,06318.38%72.69%

641,2703.21%97.78%$19,29313.24%85.93%

7+28,5272.22%100.00%$29,67114.07%100.00%

717,9941.40%99.18%$25,8177.72%93.65%

87,1970.56%99.74%$33,1553.97%97.62%

92,4200.19%99.93%$40,1311.61%99.23%

10+9160.07%100.00%$50,3810.77%100.00%

Total1,284,644100.00%$4,683100.00%

Table 2

Summary of Chronic Disease Prevalence and Medical Costs by Number of Comorbidities, 1999

(Categorized by MDC)

0218,70217.97%17.97%$2110.76%0.76%

1210,78317.32%35.29%$1,1543.98%4.74%

2265,32621.80%57.09%$2,39410.41%15.15%

3228,51518.78%75.86%$4,70117.60%32.75%

4147,70312.14%88.00%$8,58720.78%53.53%

581,1886.67%94.67%$14,16818.84%72.37%

639,1483.22%97.89%$21,10313.54%85.91%

7+25,7382.12%100.00%$33,42414.09%100.00%

716,5941.36%99.25%$28,9897.88%93.79%

86,2770.52%99.76%$37,8173.89%97.68%

92,0890.17%99.94%$46,8271.60%99.28%

10+7780.06%100.00%$56,5880.72%100.00%

Total1,217,103$5,015

1999

Data for Chart

0$21117.97%

1$1,15417.32%

2$2,39421.80%

3$4,70118.78%

4$8,58712.14%

5$14,1686.67%

6$21,1033.22%

7$28,9891.36%

8$37,8170.52%

9$46,8270.17%

10+$56,5880.06%

Total100.00%

&L&D&RExhibits

NA4

00

00

00

00

00

00

00

00

00

00

00

&A

Page &P

Number of Conditions (by MDC)

Average Annual Medical Expenditures

Percent of Beneficiaries

Total Annual Medical Costs by Number of Conditions, 1999

NA5

Table 4

15 Most Prevalent Chronic Condition Combinations, 1997

Actual Versus Expected Probability of Co-Occurrence

ActualTotalTotalTotal HospitalizationsBeneficiariesExpectedActual to

Major Diagnostic CategoryProbabilityChargesPaidASCSsComplicationsWithout ComorbidityProbabilityExpected

Circulatory X Endocrine28%$11,119,130,149$5,252,442,66559,2568,82784,08521%1.34

Circulatory X Musculoskeletal17%$7,038,691,633$3,299,777,50437,7775,07437,65613%1.26

Circulatory X Eye17%14%1.20

Endocrine X Eye12%9%1.26

Circulatory X Respiratory12%8%1.38

Endocrine X Musculoskeletal11%9%1.26

Circulatory X Nervous9%7%1.41

Circulatory X Mental9%7%1.41

Eye X Musculoskeletal8%6%1.33

Circulatory X Male Reproductive7%6%1.23

Endocrine X Respiratory7%6%1.20

Circulatory X Skin, Subcutaneous Tissue, Breast6%5%1.16

Endocrine X Nervous6%4%1.31

Endocrine X Mental6%4%1.24

Musculoskeletal X Respiratory5%4%1.37

Table 4

15 Most Prevalent Chronic Condition Combinations, 1999

ActualTotalTotalTotal HospitalizationsBeneficiariesExpectedActual to

Major Diagnostic CategoryProbabilityChargesPaidASCSsComplicationsWithout ComorbidityProbabilityExpected

Circulatory X Endocrine40%$11,119,130,149$5,252,442,66559,2568,82784,08521%1.34

Circulatory X Musculoskeletal22%$7,038,691,633$3,299,777,50437,7775,07437,65613%1.26

Circulatory X Eye16%14%1.20

Endocrine X Musculoskeletal16%9%1.26

Circulatory X Respiratory14%8%1.38

Endocrine X Eye13%9%1.26

Circulatory X Mental12%7%1.41

Circulatory X Nervous System11%7%1.41

Circulatory X Other Factors9%6%1.33

Endocrine X Respiratory9%6%1.23

Circulatory X Male Reproductive9%6%1.20

Endocrine X Mental8%5%1.16

Endocrine X Nervous System8%4%1.31

Musculoskeletal X Eye8%4%1.24

Circulatory X Skin, SubQTissue/Breast7%4%1.37

&L&D&RExhibits

NA6

Table 4

Most Prevalent Types of Co-Occurring Chronic Conditions

Individuals with Diseases and Disorders of the Circulatory System (MDC5), 1997

Number ofPercent ofPercent of AllMean

ParticipantsMDC SubtotalBeneficiariesPaidPercent (%)

Circulatory and

Endocrine356,30849%28%$8,531118%

Musculoskeletal217,00930%17%$10,092139%

Eye216,05630%17%$7,324101%

Respiratory148,73121%12%$15,812218%

Nervous121,67517%9%$15,381212%

Mental121,49117%9%$14,749204%

Male Reproductive88,74512%7%$8,135112%

Skin, Subcutaneous Tissue, & Breast75,08010%6%$10,430144%

Kidney & Urinary Tract57,4618%4%$19,286266%

Digestive System37,6485%3%$15,204210%

Blood and Immunological32,9975%3%$15,239210%

ENT22,8303%2%$11,173154%

Myeloproferative Disease & Poorly Differentiated Neoplasm18,6763%1%$19,735273%

Hepatobiliary System & Pancreas11,6462%1%$19,016263%

Female Reproductive System9,8071%1%$12,513173%

Circulatory Alone84,08512%7%$1,24117%

Total Circulatory725,467100%56%$7,240

Table 4

Most Prevalent Types of Co-Occurring Chronic Conditions

Individuals with Diseases and Disorders of the Circulatory System (MDC5), 1999

Number ofPercent ofPercent of AllMean

ParticipantsMDC SubtotalBeneficiariesPaidPercent (%)

Circulatory and

Endocrine394,40956%40%$8,449112%

Musculoskeletal216,54231%22%$10,238136%

Eye164,45423%16%$8,078107%

Respiratory138,91620%14%$16,837224%

Mental123,23617%12%$14,784197%

Nervous System114,28616%11%$15,578207%

Other Factors94,18713%9%$18,989252%

Male Reproductive87,91412%9%$8,815117%

Skin, SubQTissue/Breast67,35010%7%$11,566154%

Kidney62,7939%6%$20,669275%

Digestive36,1465%4%$15,928212%

Blood & Immunological32,2615%3%$15,850211%

ENT18,4153%2%$12,108161%

Myeloproliferative17,1132%2%$22,429298%

Hepatobiliary11,9042%1%$19,831264%

Female Reproductive8,9471%1%$12,311164%

Alcohol/Drug4,6561%0%$21,779290%

Infectious & Parasitic9280%0%$14,253190%

Injury9100%0%$41,755555%

HIV1790%0%$22,481299%

Newborn1030%0%$17,443232%

Pregnancy780%0%$17,077227%

Circulatory Alone73,42010%7%$1,25317%

.71%.

&L&D&RExhibits

NA7

Table 4

Hospitalizations with Preventable Complications

by Number of Types of Chronic Conditions, 1997

Number ofB. People WhoTotalC. People WhoTotalPercent of BeneficiariesNumber of PreventableACSCs as

Types ofA. People WhoTotalIncurred 1 or MoreNumber ofIncurred 1 orNumber ofHospitalized Who IncurredComplications as aPercent of

ChronicIncurred 1 or MoreNumber ofPreventablePreventableMore InpatientInpatientOne or More PreventablePercentage of InpatientInpatient

ConditionsBeneficiariesACSCsASCSsComplications*ComplicationsAdmissionsAdmissionsComplications (A/C)AdmissionsAdmits

0234,9002381761792,4232,5887.26%6.92%9.2%

1237,8331,6159531,00314,31216,6616.66%6.02%9.7%

2273,8165,0452,6972,81138,20248,5317.06%5.79%10.4%

3230,7198,2484,4974,77459,24183,6557.59%5.71%9.9%

4152,9569,5315,2635,75462,541100,5388.42%5.72%9.5%

584,6238,0294,6775,24948,14490,0879.71%5.83%8.9%

641,2705,3963,3343,86429,36964,44911.35%6.00%8.4%

717,9943,0481,8962,29414,52736,99213.05%6.20%8.2%

87,1971,5749661,1996,38819,12915.12%6.27%8.2%

92,4205703874792,2397,63017.28%6.28%7.5%

10+0.00.01752488783,54019.93%7.01%0.0%

Total1,283,72843,29425,02127,854278,264473,80010.01%5.88%

Table 4

Hospitalizations with Preventable Complications

by Number of Types of Chronic Conditions

Number ofTotalTotalNumber of PreventableB. People WhoC. People WhoPercent of BeneficiariesACSCs as

Types ofNumber ofNumber ofComplications as aA. People WhoTotalIncurred 1 or MoreIncurred 1 orHospitalized Who IncurredPercent of

ChronicPreventableInpatientPercentage of InpatientIncurred 1 or MoreNumber ofPreventableMore InpatientOne or More PreventableInpatient

ConditionsComplicationsAdmissionsAdmissionsBeneficiariesACSCsASCSsComplications*AdmissionsComplications (A/C)Admits

01342,2156.05%218,7022142171302,0656.30%9.8%

175414,1625.32%210,7831,5821,62573012,4005.89%11.5%

22,13843,1214.96%265,3264,8455,1772,05234,4555.96%12.0%

33,98379,3865.02%228,5158,4419,1833,74357,0736.56%11.6%

45,02899,0875.07%147,7039,85810,9974,59561,8507.43%11.1%

54,65290,8025.12%81,1888,3179,6334,13548,3488.55%10.6%

63,38065,0195.20%39,1485,4846,5992,90329,1389.96%10.1%

71,96737,5025.25%16,5942,9153,5871,64914,09111.70%9.6%

895317,9305.32%6,2771,3131,6747865,68913.82%9.3%

93817,2775.24%2,0894746193081,96315.69%8.5%

10+1813,1577.01%77820128214974619.93%8.9%

Total23,551459,6585.12%1,217,10343,64449,59321,180267,8188.79%

&L&D&RExhibits

Multimorbidity and Clinical Outcomes in the VAPetersen et al. Med Care 2005;43:61; from Berlowitz D, NIA Comorbidity Conference 2005*Adjusted Clinical Groups (ACGs)**Diagnostic Cost Groups (DCGs)

Impact of Multimorbidity on Medicare ExpendituresWolff JL, Starfield B, Anderson G. Arch Intern Med.2002;162:2269-2276 63%95%

Impact of Multimorbidity on Medicare ExpendituresWolff JL, Starfield B, Anderson G. Arch Intern Med.2002;162:2269-2276

Impact of multimorbidity on 3-year mortalityKriegsman & Deeg. In: Autonomy and well-being in the aging population 2 (1997)

The Problem of Single Disease Focus vs Multimorbidity FocusEvaluation of severity of disease and impact on function

Evaluation of patient experiences and preferences

Disease management and health system practice

Index Diseases vs Multimorbidity: Evaluation of severity of disease and impact on functionAggregate effects of Multimorbidity on functionNot known: dose response effects of disease prevention (decreasing by 1, 2, 3)Not known: Whether additive and synergistic have joint mechanisms to be targeted

Index Diseases vs Multimorbidity: Evaluation of severity of disease and impact on functionNeed to develop systems to measure severity of individual diseases, designed to be used both within and across diseases, in patients with multimorbidity

categorization of severity perhaps based on similar domains across diseases

From Boyd C, NIA Comorbidity Conference 2005

Impact of Multimorbidity on Patient ExperiencesPoor functioning Negative psychological reactions Negative effects on relationships and interference with work or leisure Concerns about polypharmacyProblematic interactions with providers and the health care system including incidents in which providers had ignored concerns or provided conflicting advice Knowledge and skills deficits interfered with self-management Noel et al. Health Expectations. 2005;8; 54-63.

Multimorbidity and Problems with Quality Chronic Care in the Medicare ProgramOrientation toward acute care, including coverage criteria Exclusion of catastrophic coverageLack of incentives to provide state-of-the art chronic care. In particular:Reliance on physician orders Reimbursement for visits of short duration No impetus to coordinate careAbsence of information technology infrastructureInadequate training of health professionalsFrom Wolff J Comorbidity Conference 2005

Multimorbidity and Chronic Disease ManagementApplicability of evidence-based guidelines:Focus has been on single disease despite high prevalence of multi-morbidityPatient preferences/sx often not included in outcomesTranslation of education & self-management techniques:Often does not account for polypharmacy accompanying multimorbidityDoes not account for fragmented, single disease focused careAbility to engage physiciansPhysicians predominantly fee-for-service with time constraintsLarge numbers of physicians, not a restricted network

Multimorbidity and Questions that RemainWhat are realistic assessments/interventions?Taxonomy of goalsWhat is the effectiveness of following disease-specific guidelines in multi-morbidity?What are the tensions between prevention, treatment, palliation?How do we change provider behavior?How can current care be better integrated/coordinated?Are specialists really better than generalists for outcomes that matter in the multi-morbidity patient population?

Multimorbidity and Untapped Health and Societal OutcomesPts goals of care/Shared decision making tools/Goal attainment scalingPain/Symptom burdenTrajectories of decline that incorporate multiple outcomes (transition probability)Self management/caregiver managementAdvance care planningMedication reviewPatient experience (AHRQ survey currently being investigated by Medicare and mentioned in the MedPAC report)

Multimorbidity and Health and Societal OutcomesX axis: Quantitative measures/Qualitative measures/Safety/Medical errors

Y axis: Process/Outcomes

Z axis: Time (short vs long term)

Report from the NIA Task Force on ComorbidityRebecca A. Silliman, MD, PhD

NCI Cancer in the Elderly InitiativeOutgrowth of an NCI/NIA working conference in 1981

1983 RFA: Patterns of Care for Elderly Cancer Patients: Implications for Cancer Control

Rosemary Yancik, PhD, Project Officer

Subsequent NCI Aging Initiatives1991 NCI RFA The goal of this project is to decrease morbidity and enhance survival from breast cancer in women 65 years of age and older.Program Announcements Breast and Prostate - 1996P20 RFA - 2003

Geriatric OncologyAGS Geriatric Oncology Interest Group - 1991John A. Hartford Foundation Geriatric Education Retreat (Oncology) - 1997International Society of Geriatric Oncology (SIOG) - 2000 ASCO/Hartford Geriatric Oncology Fellowship Programs - 2002

Comorbidity and Cancer in Older Adults

Workshop - Comorbidity Assessment of Older Cancer Patients, July 29-30, 1999, National Institute on Aging and National Cancer Institute Workshop

Convened by Rosemary Yancik, PhD

Parallel DevelopmentsCase-mix adjustment in health services research

Understanding the relationships among aging, comorbidity, functional status, and frailty

Improving chronic illness care

NIA Comorbidity Task ForceGeriatric Oncology: Harvey Cohen, William Ershler, Martine Extermann, Carrie Klabunde, Jeanne Mandelblatt, Vincent Mor, William Satariano, Rebecca Silliman

Geriatrics/Gerontology: Luigi Ferrucci, Linda Fried, Jack Guralnik, Jerry Gurwitz, Jeffrey Halter, William Hazzard, Marco Pahor, Stephanie Studenski, Mary Tinetti, Terrie Wetle, Darryl Wieland

Convened by Rosemary Yancik with participation from key NIA staff

Task Force ObjectivesIdentify research opportunities:

interactive health issues affecting older adults

impacts of comorbidity on treatment efficacy and tolerance

diagnostic, prognostic, treatment, and prevention strategies in the presence of comorbidity

Thinking about ComorbidityWhat is comorbidity?The extent to which comorbidity affects treatment for an index conditionThe extent to which the management of an index condition affects ongoing treatment of pre-existing or concurrent comorbidityThe interaction of specific conditionsOverall comorbidity burden

Thinking about ComorbidityComplicating factors:Severity of diseasesContributions of treatment as well as diseaseFunctional status as comorbidity versus an outcomeInfluence of behavioral/lifestyle issues

Commissioned PapersThe Nosology of Impairments, Diseases, and ConditionsSeverity of Disease Classification Systems: The Continuum of Conditions, Impairments and Diseases Methodology, Design, and Analytic Techniques Data Sources Relevant to Comorbidity and Aging Research

I. Nosology of Impairments, Diseases, and ConditionsOrganizing Principles:Classified by organ/physiologic/psychological systemsDecrements in health start before onset of symptomsAccommodates both positive (protective) and negative (deleterious) changesAvoids arbitrary diagnostic thresholds

Includes Thirteen Systemsmental respiratorysensory digestive voice/speech metaboliccardiovascular endocrinehematologic immunologicneuromuscular genitourinaryskin

Domains within Individual Systems: StreamsWithin each system, there will be one or more domains, one for each physiological/ psychological/functioning measure

Each domain will be conceptualized as a stream, from protective to sub-clinical to overt disease Examples: Glycosylated hemoglobin Blood pressure

Using the Nosology Comorbidity indices can be created by: assigning monotonically increasing points within each stream; combining streams using weights;creating interactions between streams Interactions between comorbidity and lifestyle/social factors may be important

II. Severity of Disease Classification SystemsConceptual Framework

Severity approaches have been developed for different purposes:ScreeningPrognosisDefining impact of disease on well-beingMaking treatment decisionsDetermining if treatment alters severity Answering specific research questions

Pathology

PhysiologySymptoms

ImpairmentsExercise Tolerance

Physical PerformanceDisease ProcessExperientialPhysical Function

Quality of Life

Treatment Required for ControlPoints on Causal Pathway for Disease Severity Assessment: Conceptual FrameworkOutcomesIncreasing Etiologic Specificity Other/Secondary DiseasesIncreasing Relevance to Older Patients

II. Severity of Disease Classification SystemsA three-stage system:Goal of measurement: prognosis, treatmentDomain classification: symptoms, treatments required to control symptoms, function, quality of lifeSources of data: patient-report, laboratory tests, functional tests, health care utilization

CHF: Goals of Classification Systems

CHF: Domains for Classification

CHF: Sources of Information

DiscussionThe single disease focus of severity classification systems has led to a chaotic array of systems not readily amenable to use in the study of the import of disease severity.

III. Methodology, Design, and Analytic TechniquesData Sources:

Inverse relationship between dataset size and data quality and quantityMissingess varies as a function of data sourceCost, privacy, feasibility of collection

III. Methodology, Design, and Analytic TechniquesMeasurement issues:Lack of equivalency in relation to outcome: e.g., metastatic cancer and diabetes Lack of independence: e.g., hypertension and diastolic dysfunctionDouble counting: e.g., when organ dysfunction/disease is a severity indicator

III. Methodology, Design, and Analytic TechniquesAnalytic Techniques:

Sensitivity analysis: estimates uncertainty due to non-random error

Multiple informants: uses all available measures simultaneously

Data Sources Relevant to Comorbidity and Aging ResearchReview of:Large comorbidity databases (5000+ patients) Studies (>500 patients) containing functional status information with either comorbidity information, or a potential to retrieve it

Epidemiological StudiesAging cohorts: EPESE, WHAS, LSOAInsurance: Medicare Oncologic: SEER, NCCNAdult cohorts: NHS & PHS, WHI

ObservationsStudies that have functional information frequently do not have detailed comorbidity information and vice versa

Retrospective retrieval of either is limited and/or not feasible.

Cooperative Clinical Trials in OncologySelection factors (exclusions; volunteers) are challengingMost have reliable and systematic functional informationComorbidity data usually are poorExcellent opportunity for retrospective retrieval of comorbidity information

Why Should We Care about Comorbidity? EtiologyPreventionTreatment Decisions and Benefits/RisksPrognosis

In Short: All That We Do

Now that Ive shown you how the model has been calibrated and validated, I would like to show you some of its predictions over longer time intervals. This slide shows the life expectancy after HIV diagnosis with and without HAART for patients of different CD4 counts and ages. Life expectancy is shown on the vertical axis. On the axis going from left to right, patients are grouped by CD4 count. ON the axis going from front to back, patients are grouped by age. Additionally, each age and CD4 grouping is further stratified depending upon whether treatment is WITH or WITHOUT HAART. Starting at the front left, those patients with CD4 counts of 750 and treatment WITH HAART have life expectancies approaching 25 years. Without HAART, life expectancy decreases sharply to approximately 10 years. Heading towards the back, life expectancies diminish gradually as age at diagnosis increases towards 50. However, this decrease is far smaller than the decrement in life expectancy without HAART. Moving towards the right to middle grouping of patients, who have CD4 counts of 500, life expectancies are similar but approximately 1 to 2 years less than for patients with CD4 counts of 750. HAART continues to have a large and positive impact on life expectancy, and age at diagnosis continues to have a more modest and negative impact. Continuing to the right, patients with CD4 counts of 200 have life expectancies approximately 3 years less than those with CD4 counts of 500, and the effects HAART and increasing age persist. The format of this slide is similar to the previous slide, except that the vertical axis now shows the percentage of deaths from non-AIDS causes with and without HAART. Starting at the front left, patients with CD4 counts of 750 and who are treated with HAART have just over a 30% chance of dying from a cause unrelated to AIDS. Without HAART, this probability decreases sharply to less than 10%. Moving towards the back, later age at HIV diagnosis increases this probability sharply, with 50 year-olds having a 70% of dying of causes unrelated to AIDS. Moving towards the right, patients with CD4 counts of 500 have decreased probabilities of dying of causes unrelated to AIDS, but the impact of decreasing CD4 count is much less than the impact of HAART or of later age at diagnosis. Continuing to the right, these relationships also persist for patients with CD4 counts of 200. Note that regardless of CD4 count, most patients diagnosed with HIV at age 40 or greater and who are on HAART have more than a 50% chance of dying with a non AIDS related illness.

In conclusion, more HIV patients will die of non-HIV related causes in the future, including nearly half of all patients with age at diagnosis greater than 40. If the mean age at HIV diagnosis remains 38, the mean survival of HIV patients will approach 19.6 years, and the mean age at death will approach 58. It will become increasingly important for HIV+ care to involve practitioners who prevent and treat a broad spectrum of disorders. First I would like to describe the theoretical framework we developed to characterize the current state of the field in assessing disease severity.

After review of the literature and much discussion, we developed conceptual framework.Drawing on Iom pathway disease disability, we operationalized a classification system

Orient to the figure then distill the points from the figure In overview fashion

Sentinel pointsa- presence of other diseases (or even sociodemographic factors) as part of the classification system could lead to overadjustment b-e.g. tumor burden or evidence of end-organ damage (diabetic retinopathy, Glomerular Filtration Rate)c-e.g. inflammatory markers, blood glucose, blood pressured-e.g. cough, sputum production, shortness of breathe-e.g. number of blood pressure or glucose control medsf-e.g. dyspnea with a certain task, exercise toleranceg-e.g. difficulty or dependency with a task or observed performanceh- components of quality of life that do not fit neatly into one of these other categories a-(e.g. symptoms, QOL, disability, behaviors, other medical history)b-( e.g. blood or body fluid assay)c-(e.g. auscultation for rales, inspection for ulcer, get up and go)d-(e.g. histological specimen, echocardiogram, PFTs, slit lamp exam, MRI)e-(e.g. number of medicines, number of ER visits or hospitalizations) Or in the evaluation of severit of multiple diseases at conceptually parallel levels

Use chd* OA

Otherwise, the questions about severity and comorbidity will arise after a study is underway or complete, which limits the ability to appropriately weigh the pros and cons and decide how severity would best be ascertained.