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New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status: Diabetic HbA1c Predictive Model Brenton B. Fargnoli Blue Cross & Blue Shield of Rhode Island

New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

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New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:. Diabetic HbA1c Predictive Model Brenton B. Fargnoli Blue Cross & Blue Shield of Rhode Island. Outline. Background Predictive Rules Validity Applications. Background. The Diabetic Epidemic. - PowerPoint PPT Presentation

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Page 1: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

New Approaches Focusing on Dynamic Variables Related to Changes in

Member’s Health Status:

Diabetic HbA1c Predictive Model

Brenton B. Fargnoli

Blue Cross & Blue Shield of Rhode Island

Page 2: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

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Outline

• Background

• Predictive Rules

• Validity

• Applications

Page 3: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Background

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Page 4: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

The Diabetic Epidemic

• Prevalent– 23.6 million people (7.8% of population)

• Expensive– Medical Expenditures: $116 Billion

National Diabetes Statistics, 2007

American Diabetes Association, 2007

• National Diabetes Statistics, 2007

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Page 5: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Lab Data Gap

Clinical and Economic Effectiveness:• HbA1c<7%: (6, 4.5)• HbA1c>9%: (6, 4.5)• Annual HbA1c Screening: (1,1)

• Thus, it is the lab values, not the presence of screenings which are significant.

de Brantes et al., Am J Managed Care, 2008

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Page 6: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Variables Associated with HbA1c Level

Association• Age• Drug Adherence• Drug Therapy • Co-Morbidities• Physician Visits• Ethnicity

Shectman et al., Diabetes Care, 2002

No Association• Gender• Income• A1c screenings

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Predictive Rules

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Page 8: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

HbA1c’s Continuous Risk Gradient

• 1% HbA1c Reduction Associated with Decreases:– 43% Amputations– 36% Nephropathy, Neuropathy, Retinopathy– 30% Depression– 24% ESRD– 14.5% Cataracts– 14% MI– 12.5% Stroke

IMPACT Product

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Page 9: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Applied HbA1c-Comorbidity RelationshipRetinopathy Example:

A1C %: 9.4 8.4 7.4 6.4 5.4

Retinopathy Prevalence: 0.5566 0.3563 0.228 0.1459 0.0934

(1-Prevalence) 0.4433 0.6438 0.772 0.8541 0.9066

P (0 Co-Morbidities) 0.1151 0.2892 0.4236 0.5307 0.6123

P(Only Retinopathy) 0.1446 0.1601 0.1251 0.0907 0.0631

P(Ret&Neur Only) 0.0601 0.0371 0.0175 0.0077 0.0033

P(Ret + 1) 0.1844 0.1435 0.0823 0.0465 0.0264

P(R, Neur, Dep Only) 0.0057 0.0027 0.0009 0.0004 0.0002

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Performed for 156 combinations of 9 Co-Morbidities

Page 10: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Predicted A1c from # of Co-Morbidities

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9.4 8.4 7.4 6.4 5.4 Predicted A1c

P(0) 0.1152 0.2894 0.4236 0.5307 0.61228 6.7732

P(1 Only) 0.2915 0.4195 0.4038 0.3630 0.31888 7.4010

P(2 Only) 0.2544 0.2270 0.1460 0.0943 0.06284 8.0573

P(3 Only) 0.2934 0.2530 0.1659 0.0872 0.04873 8.1723

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Polynomial Extrapolation

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Page 12: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Drug Intensity-Disease Intensity Relationship

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• High Intensity (+0.75)– Type II Insulin use– ≥ 3 oral anti-diabetics

• Low Intensity (-0.75):– No pharmaceuticals needed

Adapted and Modified from Shectman et al., Diabetes Care, 2002

Page 13: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Drug Adherence

• Reflects:– Self-Management– Drug Effectiveness

• Calculated with Avg. Days Supply Method

• (% Adherence – 82%) x (-1.5)

Adapted and Modified from Shectman et al., Diabetes Care, 2002

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Page 14: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Rules Summary

• Co-Morbidities:• 0: 6.77• 1: 7.40• 2: 8.06• 3: 8.17• 4: 10.11• 5: 11.81• 6: 13.80• 7: 16.10• 8: 18.70• 9: 21.59• No PCP nor Eye Appts for full

year: (+0.75)

• Pharmacy• Insulin: (+0.75)• ≥ 3 oral anti-diabetics: (+0.75)• None (-0.75)• (% Adherent – 82%) x (-1.5)

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Predicted HbA1c=(Co-Morbidity Index + Pharmacy Index)/2

Note: All adjustments are from 7.40

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Validity

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Page 16: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Paired T-Test All Inclusive Excluding Physician Visit Outliers

  Actual Predicted

Mean 7.116470588 7.216149433

Variance 1.131392157 0.431441838

Observations 85 85Pearson Correlation 0.289856571Hypothesized Mean Difference 0

df 84

t Stat -0.854070714

P(T<=t) two-tail 0.395494943

t Critical two-tail 1.988610165  

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  Predicted Actual

Mean 7.388 7.31215

Variance 2.275006 0.437331

Observations 100 100Pearson Correlation 0.338633Hypothesized Mean Difference 0

df 99

t Stat 0.531475

P(T<=t) two-tail 0.59628

t Critical two-tail 1.984217  

Predictions compared with 2005-2007 BCBSRI HEDIS Data

Page 17: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Predictive Power

Method 1 Method 2

Deviation from Mean -0.07585 +0.09968

Avg. Absolute Deviation 0.89341 0.75371

1.0 Deviation Confidence 77% 80%

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Page 18: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Limitations

• Variance

• Patients skipping full year of appointments

• Variables limited to data fields within pharmacy and insurance claims

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Applications

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Page 20: New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status:

Disease Management

Patient-Level

• Identify Actionable Members

• Measure Intervention Effectiveness

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Marketing

Population-Level

• Track and report group’s year over year changes in predicted mean HbA1c

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References

• NIH. National Diabetes Statistics 2007. http://diabetes.niddk.nih.gov/dm/pubs/statistics/

• American Diabetes Association. Direct and Indirect Costs of Diabetes in the United States. http://www.diabetes.org/diabetes-statistics/cost-of-diabetes-in-us.jsp

• de Brantes F, Wickland P, Williams J:The Value of Ambulatory Care Measures: A Review of Clinical and Financial Impact from an Employer/Payer Perspective. Am J of Managed Care 14: 360-368, 2008

• IMPACT Product: Meta-analysis of case-controlled, longitudinal studies• Schectman J, Nadkarni M, Voss J: The Association Between Diabetes

Metabolic Control and Drug Adherence in an Indigent Population. Diabetes Care 25: 1017-1021,2002

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Questions

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