synergizing queri research and operations analyses in monitoring and improving the population health...

Synergizing QUERI Research and Operations Analyses in Monitoring

and Improving the Population Health of Veterans with Diabetes

D. Aron, L. Pogach, E. Kerr and D. MillerWORKSHOP OBJECTIVES

• (1) address issues surrounding current diabetes quality measures and discuss alternatives and how they can be used in both research and clinical practice; and

• (2) describe two data sources: the Patient Care Services (PCS)-VSSC Diabetes Cube and Diabetes Epidemiological Cohort (DEPIC) and their potential use in operations and research.

Introduction and Context for Diabetes Quality Measurement

David C. Aron, MD, MS

Co-Clinical Coordinator, DM QUERIACOS/Education, Louis Stokes

Cleveland DVAMCProfessor of Medicine

Case Western Reserve University School of Medicine

QUERI WORKSHOP Agenda1. Context for performance

measurement2. Limitations of measurement3. Alternative means for cross

sectional measurement4. Cross sectional measures vs

Longitudinal Measures5. Individuals versus populations6. Sources of Data

How Do We Use Performance Measurement

Public Accountability (External)• External transparency• To compare healthcare plans or physicians

based upon a metric• To financially reward plans and physicians

Quality Improvement (Internal)• Internal to plan or practice• To guide population based improvement• Can be used for internal reimbursement

LowerMedical

loss ratio

Proftability

Offer better

products

↑MarketShare

PayersPayersSeekingSeekingValueValue

QI

↑SustainabilityQuality

Improved practice

Re-Assess

IdentifyGaps

Provider inProvider inbroadbroadsense-sense-

PDCA/QIPDCA/QI

GetInformation

MakeHealthcare

choices

Assess

Chooseamongplayers

InformedInformedConsumerConsumer

ChoiceChoice

MarketingMarketing

UtilizationUtilization(Macro-(Macro-Choice)Choice)

UtilizationUtilization(Micro-(Micro-Choice)Choice)

MarketingMarketing

Encourage Disease ManagementEncourage Disease Management

Inform product designInform product design

Research

Evidence

Reports

Measuresand

ReportsMeasures

NON-TRANSPARENT INFLUENCES ON MEASURES AND PUBLIC REPORTING OF

QUALITY DATA

Specialty Societies, Big Pharma, Disease Group Advocacy

Direct toConsumer

Advertising

Drug Detailing

Formulary Management

WHO RAN THE CAMPAIGN?

Who are the patient?Age Distribution of VA

Patients - 2005

0

0.5

1

1.5

2

2.5

3

3.5

4

20 30 40 50 60 70 80 90 100 AGE

%

14%14%

24%24%

40%40%

23%23%

WWIIKorea WarVietnam WarPost-Vietnam

Dramatic increase in proportion of younger patient recent years.

Pogach et al. AJMC March, 2007

5-year Mortality Rates in Veterans with Diabetes <65 Years with Co-morbidities

Evolving Evidence Base:Studies of Glycemic Control and Its

Relationship to CVD Outcomes

Study Acronym

Number of Participants

Follow-up

(years)

Intensive A1c Target

Standard A1c Target

Results

ACCORD 10,251 4 <6.0%

Achieved 6.4

7.0%-7.9% Achieved

7.5

higher mortality

in intensive

group

ADVANCE 11,140 ~4.5 ≤6.5% Achieved

6.4

Usual care

Achieved 7.0

No difference

VADT ~1700 5-7 ≤6.0% 8%-9% Difference not

significant

What populations do worse on glycemic control ?

• Longer duration of diabetes African Americans and White Hispanic

• <45 yrs worse than 45-54 than 55-64• Mental Health Conditions: Psychoses,

Substance Abuse and/or anxiety/PTSD disorders

• Lower socioeconomic-educational status (buying healthy food, diabetes numeracy?)

• Less social support

QUERI WORKSHOP 12-091. Context for performance




Technical Elements of Performance Measurement

• Measurement uncertainty

• Population at risk

• Bias (differences in population)

• Effectiveness in practice

• Feasibility and cost of data collection

• Baseline status, patient safety, patient preferences

Facility Variation in Factors Impacting Glycemic Control (1999-2000) Maney et al, Diabetes Care, 2007

Factor Mean P10 P90

<55 yrs 19.8 14.2 25.6

11+yr duration 35.9 31.3 41.8

Not high school grad 30.7 18.9 44.2

Food insufficiency 13.9 9.3 19.5

MHS (SF36V) 43.8 41.2 46.2

BMI =>35 15.7 11.2 19.9

No exercise in past month 43.2 36.1 49.1

Risk adjustment 30% of best/worse change >2 decile ranks

Dominant conditions/Decreased life expectancy <65 years

Range 20.9-52.6; 20% of facilities change =>2 deciles

FY08 Performance Measures A1c

• A1c FACILITY VARIATION RANGE (730-2270, mean 1560 EPRP charts)– <7=46% [range 42-49 VISNS]– <8=72% [range 66-72 VISNS]– >9=16% [range 15-20 VISNS]

• LDL-C– <120 mg/dl=81%– <100 mg/dl=68%

– Based upon ~32K charts

Vijan, S. et. al. Ann Intern Med 1997;127:788-795

Age is Important:Lifetime Risk for Blindness Due to Diabetic Retinopathy*

Vijan, S. et. al. Ann Intern Med 1997;127:788-795

Lifetime Risks for End-Stage Renal Disease*

Benefits of Glycemic Risk Reduction (7.9% to 7.0%) over 10 yrs from the UKPDS

(Budenholzer et al, BMJ 1245, 2001)

Outcome OR

(95%CI)

P NNT Per PY

ARR/ 1000PY

Rate per 1000 PY - Intensive Control

Any DM Endpoint

0.88

(.79-.99)

0.02 196

(153-272)

5.1 40.9 46

MI 0.84

(.71-1.00)

0.052 370

(279-551)

2.7 14.7 17.4

Stroke ----- ---- --- ---- 5.6 5.0

Microvasc 0.75

(.6-.93)

0.01 357

(285-478)

2.8 8.6 11.4

Laser Treatment

0.71

(.58-.98)

0.003 323 3.1 7.9 11.0

Diabetes Mortality

0.90 NS ----- -- 10.4 11.5

Outcome NNT x 10 years

ARR per 1000 person years

Rate per 1000 person years

Intensive Control

Any DM Endpoint

6.1 16.5 50.9 67.4

MI -- ---- 18.6 23.5

Stroke 19.6 (15.9-25.7)

5.1 6.5 11.6

Microvascular 13.8 (11.4-17.8)

7.2 12.0 19.2

Diabetes Mortality

15.2 (12.2-20.1)

6.6 13.7 20.3

Benefits of Blood Pressure Reduction 154/87 to Benefits of Blood Pressure Reduction 154/87 to 144/82 over 8.5 yrs from the UKPDS144/82 over 8.5 yrs from the UKPDS

Cross Sectional “Good” Measure Options

• Case mix adjustments: What is not under plan control? – Socio-positioning– Age– Duration, Type 1 or 2, others

• Exclusions: – What to exclude? Life expectancy, health

risk, side-effects• How To Score

– Pass/Fail? “Partial” credit:– How to weight?

Curvilinear relationship between A1c and Microvascular Disease: Risk of retinopathy and by A1C level

DCCT Research Group NEJM 1993DCCT Research Group NEJM 1993

Stringent Dichotomous Outcome Measures

• Don’t target patients most likely to benefit– Ignore the heterogeneity of patient risk factors

• Don’t help providers do the “right” thing– Do not give “partial credit” for actions or

improvements that may yield considerable benefits • Don’t take into account patient preferences

– Could mandate care that is contrary to the wishes of a reasonable, well informed patient

• Could result in unintended consequences– Polypharmacy, hypoglycemia, worse outcomes

Aron & Pogach JCJQS 2007;33:636-643

Implementing Linked Clinical Action Measures for Assessment and

Improvement

Eve A. Kerr, MD, MPH

PI, Ann Arbor VA Center

for Clinical Management Research

Research Coordinator, DM QUERI

Associate Professor of Internal Medicine

University of Michigan Health System

The Paradox of Performance Measurement

"Not everything that can be counted counts, and not everything that counts can be counted."

- Albert Einstein (1879-1955) From a sign hanging in Albert Einstein's office at Princeton.

How can we measure what counts?

What Makes a Good Quality Measure?AHRQ/NIDDK/VA Scientific Conference on Diabetes Quality Assessment

• Target patients most likely to benefit

• Help providers do the “right” thing

• Incorporate (or at least don’t ignore) patient preferences

• Avoid unintended consequences

• Acknowledge limitations of current data sources and resulting measures (and motivate collection and use of clinically detailed data)

http://www.ahrq.gov/QUAL/diabetescare/

Measuring Quality in Diabetes

Patients with diabetes who are 75 years or younger should have

A1c < 7%

BP < 130/80

Do stringent dichotomous outcomes measure what counts?

Do stringent dichotomous outcomes target patients most likely to benefit?

45 55 65 757 0.3% 0.1% 0.0% 0.0%

Hgb 9 2.6% 1.2% 0.5% 0.1%A1c 11 7.9% 4.4% 1.9% 0.5%

13 12.5% 7.9% 4.2% 1.5%

Lifetime Risk of Blindness due to Retinopathy

Vijan et al. Ann Int Med, 1997

Stringent Dichotomous Outcome Measures

• Don’t target patients most likely to benefit– Ignore the heterogeneity of patient risk factors

• Don’t help providers do the “right” thing– Do not give “partial credit” for actions or improvements that

may yield considerable benefits

• Don’t take into account patient preferences– Could mandate care that is contrary to the wishes of a

reasonable, well informed patient

• Could result in unintended consequences– Polypharmacy, hypoglycemia, worse outcomes

Tightly Linked Clinical Action Measures

• Identify high risk populations by diagnosis or by a poor intermediate outcome or other assessment of high risk

• Evaluate processes of care that are strongly associated with important outcomes for that population

• Intrinsically identify appropriate quality improvement responses within the measure that are under a health system’s control

- Kerr et al. Am J Manag Care 2001

Linked Clinical Action Measures:Adequate Quality for Hyperlipidemia Treatment

• Tightly Linked Clinical Action Measure– LDL <130 mg/dl; or

– LDL>= 130 mg/dl with appropriate clinical action:

1) were on a high dose statin; or 2) had statin started or dose increase within 6

months; or 3) repeat LDL <130 mg/dl within 6 months; or 4) had contraindications noted to statin

treatment

Percentage with Adequate Quality

Type of Measure Adequate Quality N (%)

Dichotomous Intermediate Outcome (LDL<130)

847/1154 (73%)

Tightly Linked Measure

(LDL<130 OR appropriate clinical action OR contraindication)

1006/1154 (87%)

Kerr et al. Medical Care 2003

Linked Clinical Action Measures

• Target patients most likely to benefit by :– accounting for patients’ risk factors and benefits of

intervention– Incorporating exceptions

• Help providers do the “right” thing– Intrinsically incorporate quality improvement response

• Can take into account patient preferences– Incorporate refusals or patient priorities

• Diminish but don’t eliminate potential for unintended consequences

Kerr et al. Am J Managed Care 2001

Measuring What Counts

• Focus on high risk populations and high benefit interventions

• Consider the costs, burden and safety of the treatments needed to achieve the goals

• Give at least partial credit for processes under providers’ control

• Insist on improvements in availability of clinically meaningful data

• Guard against unintended consequences

“Everything should be made as simple as possible, but not one bit simpler.”

– Albert Einstein (1879 - 1955)

ACCORD: More deaths in intensive vs standard glycemic control groups

National Heart, Lung, and Blood Institute. ACCORD telebriefing prepared remarks. February 6, 2008.

Standard glycemic control

Intensive glycemic control

Deaths, n 203 (11/1000/y) 257 (14/1000/y)

Do stringent dichotomous outcomes target patients most likely to benefit?

The Universe of Performance Measures

Sampled performancemeasures = SUBSET OFQUALITY(+ MORE NOISE)

Universeof possible performance measures= OVERALL QUALITY (+ SOME NOISE)

VA Compared to Community

0%

20%

40%

60%

80%

100%

Acute Care Chronic Care Preventive Care

VA Community

Asch et al. Annals of Internal Medicine, 2004

*

*

*P<0.01

Designing Clinically Meaningful Measures

• Tightly-Linked Clinical Action Measures

• Weighted or QALY-adjusted Measures

Weighted MeasuresDavid Aron and Len Pogach

• Differentially weighting measures in a composite can reflect the relative contributions of each measure to outcomes of interest

• Outcomes of interest may be defined specifically: e.g., cardiovascular events, mortality, or QALYs

• Certain measures can be weighted (or stratified) to reflect the importance of achieving the measure to different populations

kerr

ADA-NCQA Diabetes Physician Recognition “Weights and Measures”

Scored Measures Threshold Weight

(% of patients in sample) HbA1c Control <7.0% 40%

10.0 HbA1c Poor Control >9.0 % 15%

15.0 Blood Pressure Control >140/90 mm Hg 35% 15.0 Blood Pressure Control <130/80 mm Hg 25%

10.0 LDL Control >130 mg/dl 37% 10.0 LDL Control <100 mg/dl 36% 10.0 Eye Examination 60%

10.0 Foot Examination 80% 5.0 Nephropathy Assessment 80%

5.0 Smoking Status and Cessation Advice or Rx 80%

10.0

Total Points = 100.0 Points to Achieve Recognition = 75.0

What Are Quality Adjusted Life Years?

• Trade off between quality and length of life• QALY for a given intervention is the

average number of years of life gained by the intervention, multiplied by a judgment of quality of life in those years, summed over a lifetime

• Can address summary benefits and harms• Can address issues of life expectancy at

time of intervention

QALY Model

• Trade off between quality and length of life• QALYs are the most important and broadly

used method for evaluating health quality.

• Panel on Cost Effectiveness in Health and Medicine (Gold et al. 1996): Medical CE studies should incorporate morbidity and mortality consequences into a single measure using QALYs.

s

Time in state s Quality of life in sQALYs

The Case for QALYs to Assess Quality

• A1c reduction improves QOL by reducing complications, which differ in their impact upon QALYs

• Prioritization of public health measures requires an assessment of the impact of an intervention (ARR)

• The relationship of A1c to absolute reduction of complications is log-linear over a wide A1c range and is a function of life expectancy

Problems with QALYs

• Numerous studies have demonstrated that the correlation between one’s current health and the time-tradeoff or standard gamble utility for that health state is at best modest. (Tsevat 2000)

• Maximum endurable time: Subjects can tolerate no more than a particular time in an undesirable health state, beyond which each additional increment of time decreases overall utility. Miyamoto et al (1998)

• Such behavior cannot be accommodated within the QALY model.

QALY model and Extrinsic Goals

• In the QALY model, quality of health is given weight proportional to health duration; It follows that the QALY model cannot directly account for extrinsic goals, whose importance is by definition independent of duration– an author might want to complete a book;– many individuals seek to have children and

raise families.

Additional Considerations (QALYs)

• Equity considerations– interventions for young preferred to interventions for

old• Young have more life years remaining

– life extensions for healthy preferred to life extensions to less-healthy

• Healthy have a higher quality than chronically ill

CE Ratios (Cost/QALY)

Age Intensive GlycCntr

Intensive BloodPr Cntr

CholesterolReduction

35-44 $18,309 -$5,407 $79,473

45-54 $37,086 -$2,534 $52,544

55-64 $71,816 -$949 $43,331

65-74 $154,376 -$468 $40,471

75-84 $401,883 -$188 $51,459

All $41,384 -$1,959 $51,889

CDC, JAMA 2002

• Compares loss in QALY with expected QALY– The higher the proportion– The higher the need for equity compensation

Proportional short fall

Prop. Short Fall = 25% Prop. Short Fall = 50% Prop. Short Fall = 60%

QALY lostQALY gain

t

QoL

Prop. Short Fall = 50%

Now

Comparison of Weighted Performance Measurement and Dichotomous Thresholds for Glycemic Control (Pogach, Rajan, Aron, Diabetes Care, February 2006):

• 141 VA Centers• Thresholds <7%, <8%, or QALYsS• Incremental lifetime QALYs gained are

based on assumptions:– Linear relationship between QALY and

A1C between 7.0 and 7.9%– Lifetime QALYs range by age from 0 to

0.648– Maintaining A1c over lifetime– No adjustment for comorbid conditions

Weighted Measures• Gives “partial credit” to

achieved A1c levels ranging from 7.0 – 7.9%

• Differential credit based on potential for Quality Life Years Saved (QALYS) in different age groups of moving from 7.9 - 7.0%

• A1c values ≥7.9% (or not performed) received zero credit; A1c values <7% received full credit

*Pogach, Rajan, Aron. Diabetes Care, 2006.

Top and bottom decile ranking using <7% or QALY weighted

measure

http://care.diabetesjournals.org/content/vol29/issue2/images/large/zdc0020652010002.jpeg

Advantages of Weighted A1c Measure

• Assesses progress toward achieving thresholds, rather than whether the targets were completely met

• Motivates movement toward target even if target cannot be fully achieved

• Takes differential benefit of decreasing A1c by age into account

• Can be easily calculated using A1c data currently collected

kerr

Disadvantages of Weighted Measures

• Need underlying QALY information

• “Credit” given for a narrow range of A1c levels and not for intensity or modification of treatments

• Maximal QALYs calculations are based only on age strata

kerr

Use of Continuous Weighted Measures and Exclusion Criteria Can Address Possible

Unintended Consequences of <7% Measure for Public Reporting

• Avoids selection of marginal A1cs closest to 7% which may decrease

• Adverse Selection biases• Incorporation of Patient Preferences into

target setting • Use of additional medications without

consideration of actual benefit • Adverse events from additional

medications

Diabetes Epidemiology Cohorts:A Resource for Quality Measures

Donald R. Miller

Monitoring and Improving the Population Health of Veterans with Diabetes:

Cross-sectional vs. Longitudinal Measures

DEpiC and affiliated projects are maintained by over 30 people across U.S.

A National Registry of Diabetes since 1998A National Registry of Diabetes since 1998

A Resource for Studying Diabetes in the VAA Resource for Studying Diabetes in the VA

DEpiC - DEpiC - Diabetes Epidemiology Cohorts,Diabetes Epidemiology Cohorts,VA EpidemiologyVA Epidemiology

Donald R. Miller – Epidemiologist - Bedford VA, Boston UniversityLeonard Pogach – Endocrinologist - East Orange VA, UMDNJB. Graeme Ficnke – Internal Medicine - Bedford VA, Boston UniversityMonika Safford – Internal Medicine – Birmingham VA, U. Alabama Susan Frayne – Internal Medicine – Palo Alto VA, Stanford Univ. Cindy Christianson - Statistician - Bedford VA, Boston UniversityChin-lin Tseng - Statistician - East Orange VA, UMDNJAnn Hendricks – Health Economist - Boston VA, Boston UniversityYujing Shen – Health Economist - East Orange VA, Rutgers UniversityMangala Rajan – Program Analyst - East Orange VA, UMDNJQing Shao – Program Analyst - Bedford VAXi Chen - Program Analyst – Boston University

Applications for DEpiC

Distribution of people and diseases Epidemiology

Monitoring processes of care Quality assessment

Measuring outcomes of care Quality improvement

Risk adjustment Costs of treatment

Medication safety & effectiveness Evidence basedBroad collaboration; over 20 funded

projects

DEpiCDiabetes Epidemiology Cohorts

Other data links: - Vital signs - Health care costs - Disease Registries

VA National Patient Care

Databases

VA PharmacyPrescriptions

Laboratory Test Results

VA National Health Surveys

Medicare ClaimsCMS

Mortality Records

• Define explicitly, evaluate rigorously.

• Working definition of 2 or more diabetes diagnostic codes over a 24 month period or prescribed diabetes medication in the year + other restrictions and conditions.– Specificity of 98.3%– Positive predictive value of 93.4%

• Fixed cohorts + linked longitudinal data; or dynamic cohort analysis

Who Has Diabetes?

Diabetes Prevalence and Incidence in VA FY98-FY05

0%

5%

10%

15%

20%

25%

FY98 FY99 FY00 FY01 FY02 FY03 FY04 FY05

Incident New to VA Prevalent

2.8% 1.9% 1.7% 2.0% 2.2% 2.0% 2.6%

2.9% 3.1% 2.6% 2.0% 1.0% 1.1%

16.6% 18.1%19.3%

20.9%22.0% 22.9% 23.1%

23.7%*499,243 → → → → 117% increase → → → → 1,082,678

* projected

0

2000

4000

6000

8000

10000

12000

14000

16000

180002

.3

3.4

4.1

4.8

5.5

6.2

6.9

7.6

8.3 9

9.7

10

.4

11

.1

11

.8

12

.5

13

.2

13

.9

14

.6

15

.3 16

16

.7

17

.4

>9.5 - 81,386 (14.0%)

>8.0 - 117,401 (30.6%)

Hemoglobin A1c in VA 2000 - Maximum ValuesTotal number of diabetic patients: 654,677 Total # of tests: 1,061,350Total # of patients with tests: 579,891

>7.0 – 54%

Intermediate Outcome of Diabetes Care

Hemoglobin A1c Trends in VA

What are the trends and what do they mean?

Is better treatment progressively improving glucose control?

Are there differences by race, comorbidities, or other population groups?

Can these be used in quality monitoring to improve care?

Questions

All

Mean <7% <8%

2000 7.7 54% 76%

2001 7.5 59% 80%

2002 7.4 59% 81%

2003 7.3 60% 83%

Cross Sectional Trends in A1c Measures

All Prevalent, since 98

Mean <7% <8% Mean <7% <8%

2000 7.7 54% 76% 7.9 47% 72%

2001 7.5 59% 80% 7.8 50% 75%

2002 7.4 59% 81% 7.8 49% 75%

2003 7.3 60% 83% 7.7 50% 76%


All Prevalent, since 98 Recent-onset

Mean <7% <8% Mean <7% <8% Mean <7% <8%

2000 7.7 54% 76% 7.9 47% 72% 7.0 76% 89%

2001 7.5 59% 80% 7.8 50% 75% 7.0 78% 91%

2002 7.4 59% 81% 7.8 49% 75% 7.0 75% 90%

2003 7.3 60% 83% 7.7 50% 76% 6.9 75% 90%


Adherence in meeting threshold measures in overall diabetes population is dependent upon diabetes “duration”

27%

32%

36%

39%

Longitudinal HbA1c Trends in VA FY99-05

Methods

• 13 million measures from 1.2 million patients

• Growth curve model: longitudinal linear regression with random effects (slopes and intercepts) for individuals nested within facility and year

• Adjustment for age, sex, race, facility, seasonality

Trends in HbA1c in VA FY2000-2004

HbA1c declines steadily by nearly 0.5% over 5 years

FY2000 2001 2002 2003 2004

N= 1,351,551 patients with 9,400,875 A1c values

6.6

6.8

7.0

7.2

7.4

7.6

7.8

8.0

8.2

Mean annual decline of 0.09% per year

Mean HbA1c by month

7

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

7.9

O N D J F MA M J J A S O N D J F MA M J J A S O N D J F MA M J J A S O N D J F MA M J J A S

Average A1c is highest in late winterAverage A1c is highest in late winter

Average A1c is lowest in late summerAverage A1c is lowest in late summer

Seasonality of HbA1c in VA - FY1999-2003

Monthly mean HbA1c

6.8

7

7.2

7.4

7.6

7.8

8

1999 2000 2001 2002 2003

Average HbA1c by Year FY1999-2003

ALL diabetes patients -0.09% per year

HighestHighest

LowestLowest

Substantial within year variation.Industry measures last a1c in year but

clinicians treat last value.

6.6

6.8

7.0

7.2

7.4

7.6

7.8

8.0

8.2O

ct

De

c

Fe

b

Ap

r

Jun

Au

g

Oct

De

c

Fe

b

Ap

r

Jun

Au

g

Oct

De

c

Fe

b

Ap

r

Jun

Au

g

Oct

De

c

Fe

b

Ap

r

Jun

Au

g

Oct

De

c

Fe

b

Ap

r

Jun

Au

g

Trends in HbA1c by Subgroup in VA FY2000-2004

Prevalent panel surviving:Prevalent panel surviving: -0.06% per year -0.06% per year

IncidentIncident:: -0.01% per year -0.01% per year

Prevalent panel diedPrevalent panel died:: -0.14% per year -0.14% per year

Prevalent new to VAPrevalent new to VA:: -0.12% per year -0.12% per year

Lower HbA1c level and little trend with incident diabetes.

Steepest trend if prevalent and near death or new to VA

FY2000 2001 2002 2003 2004

N= 1,351,551 patients with 9,400,875 A1c values

6.6

6.8

7.0

7.2

7.4

7.6

7.8

8.0

8.2

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Trends in HbA1c in VA FY2000-2004in National Panel of 248,768 patients

FY2000 2001 2002 2003 2004

Prevalent panel surviving:Prevalent panel surviving: -0.06% per year -0.06% per year

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

8.1

FY2000 2001 2002 2003 2004

Sicker patients have lower HbA1c and flatter trends

Without co-morbidities

With stroke, CVD, recent cancer, liver failure, COPD

Trends in HbA1c by Month by Co-morbidity inNational Panel of VA Diabetes Patients FY2000-2004

-0.06%

-0.03%

A1c %

Mean Monthly A1C for Veteran Clinic Users By Age Category

7

7.2

7.4

7.6

7.8

8

8.2

8.4

8.6

8.8

Oct-98

Nov-98

Dec-98

Jan-99

Feb-99

Mar-99

Apr-99

May-99

Jun-99

Jul-99

Aug-99

Sep-99

Oct-99

Nov-99

Dec-99

Jan-00

Feb-00

Mar-00

Apr-00

May-00

Jun-00

Jul-00

Aug-00

Sep-00

Month Year

Mea

n A

1C %

Age <55 Age 55 - 65 Age 65 - 75 Age 75 + Total

Additionally, a regression model that adjusts for clustering (patient and facility) and seasonal effects was used to confirm thedownward linear trend in monthly A1c values overall (-0.013, p=<.0001) and minimal differences in this trend by each age category (p=.492)HbA1c declines steadily with age

Chart Title

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

8.1

White, non-Hispanic

FY2000 2001 2002 2003 2004

A1c %

Trends in HbA1c by Month by Race/Ethnicity inNational Panel of VA Diabetes Patients FY2000-2004

Standardized to combined age & sex distribution

FY2000 yearly Δ FY2004

White, non-Hispanic 7.60 -0.06 7.32

70.2% 152,352

Whites similar to overall

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

8.1

Oct J an

Apr J ul

Oct J an

Apr J ul

Oct J an

Apr J ul

Oct J an

Apr J ul

Oct J an

Apr J ul

White, non-Hispanic

African American

FY2000 2001 2002 2003 2004

A1c %


FY2000 yearly Δ FY2004African American 7.89 -0.08 7.48

White, non-Hispanic 7.60 -0.06 7.32

20.1% 43,641

70.2% 152,352


AA have higher A1c but steeper decline & less difference in 2004

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

8.1

Oct J an

Apr J ul

Oct J an

Apr J ul

Oct J an

Apr J ul

Oct J an

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White, non-Hispanic

Hispanic, non-White

FY2000 2001 2002 2003 2004

A1c %


FY2000 yearly Δ FY2004African American 7.89 -0.08 7.48

Hispanic, White 7.81 -0.03 7.68White, non-Hispanic 7.60 -0.06 7.32

8.6% 18,716

70.2% 152,352

African American20.1% 43,641


Hispanics are intermediate but less steep decline & highest in 2004

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

8.1

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FY2000 yearly Δ FY2004African American 7.89 -0.08 7.48Pacific Isl./Asian 7.82 -0.08 7.40Native American 7.82 -0.08 7.40Hispanic, White 7.81 -0.03 7.68White, nonhispanic 7.60 -0.06 7.32

White, non-Hispanic

Native American

Pacific Isl./Asian

Hispanic, non-White

African American

FY2000 2001 2002 2003 2004

A1c %


0.7% 1,426

0.5% 1,030 8.6% 18,716

20.1% 43,641

70.2% 152,352


Other racial groups just below AA but similar trends

Longitudinal HbA1c Trends in VA FY99-04

Mean HbA1c decreased (0.06% per year) and proportion below target levels increased (53.8%→60.4% -- <7%).

This may be due in part to better treatment of diabetes but there are other factors to consider, including:

• Incident versus prevalent diabetes

• Medical, psychiatric, and social conditions that contraindicate intensification of glucose control

• Seasonality and age modification

Trends varied by race, age, and comorbidity.

Can VA Longitudinal HbA1c Trends be used for Quality Monitoring?

Yes, but methodologic issues must be addressed?

Standardize data collection and data quality

Develop method for more rapid analysis – currently computationally demanding

Better understanding of external sources of variation

How to interpret and use in improving quality of care

Two options: Interventions for Everybody or Targeted

A moderate intervention for all patients with DM (1 point improvement in A1c)

Vs.

An intensive intervention for the 20% at the highest risk (2 point improvement in A1c)

# of Treatment Years Needed to Prevent 1 Yr of Blindness

(Vijan Ann Intern Med 1997)

A1c 9% 7%

Pt Age (Pt Years)

45 yrs 40

65 yrs 180

# of Treatment Years Needed to Prevent 1 Yr of Blindness (Estimates if BP controlled)

A1c 8% 7%

Pt Age (Pt Years)

45 yrs > 400

65 yrs > 6000

Possible Action Steps• Identify sub-populations of veterans not doing

well (especially <65, MHCs)• >9% applies to all; <8% to many; <7% to some• Focus on shared care (mental health-primary

care)• Insulin initiation and management teams (NPs,

Pharm Ds) with “graduation”• Use of telehealth for patients who may not

have access during the day • Identify “innovative practices” by comparing

like facilities with like facilities.

DiabetesDiabetesCare ManagementCare Management

Data MartData Mart

Diabetes ResearchDiabetes ResearchData MartData Mart

DataDataWarehouseWarehouse

Health DataHealth DataRepositoryRepository

SourceSourceSystemsSystems

AcquireAcquire Populate Populate Create Create Access Access Data Data Warehouse Warehouse Data Marts Data Marts Information Information

11 33 44

Common Query, Reporting, Common Query, Reporting, Analysis, & Data Mining ToolsAnalysis, & Data Mining Tools

22

VHA Corporate Data Warehouse ArchitectureUnder Development

Clinical Care Management Site

Research site

Updated continuously

DiabetesDiabetesCare ManagementCare Management

Data MartData Mart

Diabetes ResearchDiabetes ResearchData MartData Mart

DataDataWarehouseWarehouse

AdministrativeAdministrative(DSS, PBM, etc.)(DSS, PBM, etc.)

OtherOther(Medicare, DoD)(Medicare, DoD)

VSSC Diabetes Cube - History

• Developed in 2005-2006.

• Definitions per evidence available at that time.

• As additional data (example: Vital Signs) are sent to/stored in HDR, additional “Hierarchies” can be added.

• ProClarity software used.

--------Facilities ------

----

----

Pat

ien

ts--

----

-

--Tim

e--

A Patient atA Facility on

A Date

What’s a Cube?

Multi-Dimensional Store of Data

What can a Cube Do?

• Organizes and optimizes data

• Efficient and fast querying

• Aggregated or detailed data

• Numerical analysis

• Graphical interface

• Easy to use

Hierarchies

• Diabetes Definitions– Definite DM– Possible Unrecognized

DM– Possible Pre-Diabetes

• Admin/Demographic– Employee– Fee Patient– Location, Preferred

Location

• Admin/Demog, Contd– Age (ranges)– OEF-OIF– SC status– PCP Assigned– Priority Status– FY– Home County– (Race: not reliable)

Hierarchies

• Lab Values (ranges)– A1C– Urine Alb/Cr Ratio– Total Cholesterol– HDL Cholesterol– LDL Cholesterol– Creatinine

• Complications (Y/N)– Retinopathy– ESRD– Amputations– Foot Ulcers– IHD– Stroke

Hierarchies

• Glycemice Rx’s (Y/N)– Insulins, any– Glargine– SU’s– Biguanides– TZD’s– Exenatide– AGI’s– Metaglitinide

• CV Rx’s (Y/N)– ACEI’s– ARB’s– Statins– Thiazides

Hierarchies

• Comorbidities (codes)– PTSD– Serious Mental Illness– Substance Abuse– Tobacco Use Disorder

• Costs– Overall Pharmacy $– Diabetes Pharmacy $– Inpatient/Outpatient

costs per encounter, per patient, etc.

Point Prevalence of possible pre-diabetes, possible

unrecognized diabetes and definite diabetes

With ProClarity, we can sort patients by any combinations) of hierarchies.

View of A1c control in patients on insulin versus no insulin (no

evaluation oral agents)

View of A1c control on insulin versus no insulin for patients on

metformin and sulfonylurea therapy

View of A1c control on insulin versus no insulin for patients on

metformin, sulfonylurea and TZD therapy

Example: Costs

A1C’s for Hi/Lo Cost VISNs

Diabetes TZD Cost view – TZD RX cost compared to Diabetes RX cost

TZD Study effectiveness ViewA1C changes within 8 months

After first TZD dose

37% have an increase or no change In the A1C value

Example: A1C by Site

VISN Service Support Center developmental database

Diabetes Data MartLatest Results of Critical Labs

Access via VSSC Site:http://klfmenu.med.va.gov/

Summary: Where Future Research is Relevant

• Defining exclusion criteria – establishing the proper denominator

• Developing linked and weighted measures

• Defining risk adjustment

• Evaluation of outlier status

• Evaluation of unit of measurement (provider vs clinic vs facility)

• Time Frame

synergizing queri research and operations analyses in monitoring and improving the population health...

Documents

sources of data slide

internal reimbursement

diabetes care

diabetes facility variation

cross sectional measurement

limitations of measurement

synergizing queri research

clinical practice