Disease Burden and Intensity of Antidiabetic Drug Use by Medicare Beneficiaries with Diabetes: Will...

Disease Burden and Intensity of Antidiabetic Drug Use by Medicare Beneficiaries with Diabetes: Will Part D Make a Difference?

AcademyHealth June 25, 2006

Bruce Stuart, Thomas Shaffer, Linda Simoni-Wastila, Ilene Zuckerman

The Peter Lamy Center on Drug Therapy and Aging University of Maryland Baltimore

The Peter Lamy Center on Drug Therapy and Aging

Outline

• Sponsor acknowledgment: funding provided by The Commonwealth Fund under grant Benchmarking the Quality of Medication Use by Medicare Beneficiaries

• Background: the growing controversy over the importance of glycemic control for frail diabetics

• Study objectives and hypotheses

• Data and study sample

• Measures

• Statistical strategy

• Results

• Discussion and study implications for policy


The Importance of Glycemic Control among Frail Older Persons

Traditional diabetes guidelines

• Glycemic control is the mainstay of traditional guidelines for treatment of diabetes promulgated by the ADA, AHRQ, NCQA, AMDA, and other organizations.

• Most guidelines recognize the importance of comorbidity and frailty in making clinical treatment decisions, but offer no quantitative guidance

New thinking

• Current approaches to assessing quality of diabetes treatment do not account for heterogeneity among older patients

• Less intense targets for glycemic control are warranted for older diabetics in poor health and low life expectancy


Study Objectives and Hypotheses

Objectives

• Benchmark prevalence and level of use of antidiabetic agents (oral hypoglycemic agents and insulins) among community-dwelling Medicare beneficiaries with diabetes stratified by burden of illness

• Identify factors associated with differences in antidiabetic use by burden of illness

Hypotheses

• Factors hypothesized to be associated with higher antidiabetic drug use

- Income, prescription drug coverage, severity of diabetes, obesity, health system contacts (surveillance hypothesis)

• Factors hypothesized to be associated with lower antidiabetic drug use

- Increasing disease burden, kidney disease, hospitalization, health system contacts (competing demands hypothesis)


Data and Study Sample

Data

• 2002 MCBS Cost and Use files (N=12,697)

Study Sample

• Inclusion criteria:

- Self report of diabetes and/or ICD-9 diagnosis codes: 250.xx, 357.2, 362.01, 362.02, 366.41. Two or more Dx of diabetes on outpatient and carrier claims or one diagnosis on an impatient claim

• Exclusion criteria

- In MCBS facility sample all or part year, Part A and/or B part year, any Medicare HMO enrollment, incomplete surveys

• Final study sample: N=1,956


Measures

Overall Burden of Illness

• Stratify study sample into 10 equal sized groups (deciles) by cumulative spending for all medical services including drugs

Dependent Variables

• Any antidiabetic drug use in 2002 by type (orals, insulin), number of prescription fills per year, total pill count for oral hypoglycemics

Explanatory Variables

• 6 domains: (1) decile assignment, (2) demographics (age, sex, race, census region), (3) economic variables (income, prescription coverage), (4) health (self-reported, ADLs, BMI, self-reported DM/no claims, diabetes complication, chronic kidney disease) (5) contacts with health system (inpatient hospital, SNF, HHA, hospice, count of physician E&M visits, count of different physicians seen in office visits), (6) denominator days


Statistical Strategy

Descriptive Charts

• Prevalence rates for common comorbidities by decile of medical spending

• Prevalence and level of antidiabetic drug use by decile of medical spending

Regression Analysis

• Stepwise logistic and OLS regressions with explanatory variables entered in blocks representing the 6 domains of explanatory variables. Objective is to determine whether differences in drug use observed in the unadjusted stratification by decile lose significance with additional covariates, and if so which are responsible

Digging Deeper

• Within-decile regression analysis


Descriptive Results


Characteristics of the Study Population (N=1,956)

Characteristic Frequency or count (SE)

Demographics

Age (%) Under 65 65 – 69 70 – 74 75 – 79 80+

14.817.124.720.822.6

Female sex (%) 54.8

Nonwhite (%) 20.7

Geographic residence (%) East Midwest South West

18.525.643.312.6



Economic variables

Annual income (%) < $10,000 $10,000 – 20,000 $20,001 – 30,000 >$30,000

25.131.421.522.0

Supplementary medical insurance No coverage (%) Full year (%) Part year (%) Covered months (count)

8.588.03.5

6.4 (0.5)

Prescription drug coverage No coverage (%) Full year (%) Part year (%) Covered months (count)

25.065.19.9

6.0 (0.2)



Health status and disease severity factors

Self-reported health (%) Poor Fair Good to excellent

13.028.757.6

3 or more activity limitations (ADLs) (%) 5.5

BMI >= 30 (%) 39.1

Self reported diabetes/no claim diagnosis (%) 10.5

Diabetes complication (ICD-9 250.1 – 250.9) (%)

52.5

Chronic kidney disease (ICD-9 ) (%) 7.1

Died (%) 4.6

Denominator days* (count) 353.3



Health system contact variables

Any hospital admission (%) 27.8

Any SNF stay (%) 4.3

Any home health visit (%) 11.4

Any hospice stay (%) 1.2

Number of office visits for evaluation and management

(E&M) (count)

9.1

Number of different physicians seen for E&M visits (count)

4.8


Figure 1. Prevalence of Selected Diseases among Medicare Beneficiaries with Diabetes Stratified by Decile of Annual Medical Spending, 2002

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

1 2 3 4 5 6 7 8 9 10

Spending Decile

Prev

alen

ce (%

)

Ischemic Heart Disease Cancer COPD/Asthma

Stroke Arthritis/non-traumatic joint disorders Dementia incl Alzheimer's

Pneumonia Peptic ulcer/dyspepsia Depression/other mood disorders


Disease Burden and Medication Use: the Inverted “U”


Figure 2. Unadjusted Annual Prevalence of Antidiabetic Medication Use by Decile of Annual Medical Spending

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

1 2 3 4 5 6 7 8 9 10Decile

Per

cen

tag

e o

f D

iab

etic

s

% using any antidiabetic medication % using oral antidibetic medications % using insulin


Figure 3. Unadjusted Mean Annual Prescription Fills for Oral Antidiabetic Medications by Users of These Drugs

0.0

2.0

4.0

6.0

8.0

10.0

12.0

1 2 3 4 5 6 7 8 9 10

Spending Decile

Mea

n #

of F

ills


Figure 4. Unadjusted Mean Annual Pill Counts for Oral Antidiabetic Medications Stratified by Users of These Drugs

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

1 2 3 4 5 6 7 8 9 10

Spending Decile

Mea

n N

um

ber

of

Pill

s


Multivariate Results


Adjusted Odds Ratios for Any Use of Antidiabetic Medications by Study Subjects

Decile Decile Only RegressionUnadj. Odds (95% CI)

Full ModelAdj. Odds (95% CI)

1 2 3 4 5 6 7 (reference) 8 9 10

0.40 (0.26-0.60)**0.61 (0.40-0.91)*0.79 (0.52-1.20)0.82 (0.54-1.24)

0.64 (0.42-0.97)*0.87 (0.57-1.33)

1.000.81 (0.53-1.23)0.83 (0.55-1.27)

0.53 (0.36-0.81)**

0.56 (0.34-0.91)*0.71 (0.45-1.13)0.81 (0.51-1.28)0.87 (0.56-1.37)0.66 (0.42-1.03)0.80 (0.51-1.24)

1.000.79 (0.50-1.25)0.83 (0.52-1.35)0.69 (0.41-1.16)

*p <.05

**p <.01


Factors Affecting Treatment Odds (or not)

• None of the demographic or economic factors was a consistent predictor of likelihood of using antidiabetic medicine. Prescription coverage plays no significant role.

• Significant (p <.01) health and diabetes severity indicators (range of odds ratios in models 5 and 6): BMI >=30 (1.51 to 1.53), self reported diabetes/no claim (0.20), diabetes complications (1.75 to 1.80), chronic kidney disease (0.55)

• Only one significant (p <.05) health system contact variables (odds ratios in model 6): number of different physician seen in office visits (0.95)

• The adjusted treatment odds in the full model are 16 points higher in each tail of the distribution, but the inverted “U” pattern remains

•


Adjusted Coefficients for Annual Prescription Fills for Study Subjects

Using Oral Antidiabetic Medications

Decile Decile Only RegressionUnadj. Coef. (95% CI)

Full ModelAdj. Coef. (95% CI)

1 2 3 4 5 6 7 (reference) 8 9 10

-3.79 (-5.30; -2.28)**-2.30 (-3.74; -0.86)**

-0.60 (-2.20; 1.00)-0.78 (-2.37; 0.80)-0.99 (-2.66; 0.69)-0.32 (-1.98; 1.33)

0.00-1.30 (-2.95; 0.35)-1.11 (-2.81; 0.59)

-2.77 (-4.36; -1.19)**

-4.37 (-6.07; -2.67)**-3.05 (-4.67; -1.43)**

-1.32 (-3.01; 0.38)-1.58 (-3.27; 0.12)-1.60 (-3.32; 0.13)-0.94 (-2.59; 0.72)

0.00-0.71 (-2.39; 0.98)0.43 (-1.43; 2.30)-0.38 (-2.26; 1.50)

*p <.05

**p <.01


Factors Affecting Oral Hypoglycemic Treatment Levels (or not)

• Full year prescription coverage has a small effect in increasing antidiabetic drug use in models 4 and 5, but loses significance in the full model 6. Lower incomes are associated with higher drug use but the effect is small and not consistently significant

• Significant (p <.05) health and diabetes severity indicators (range of coefficients in models 5 and 6): BMI >=30 (0.90 to 0.93), self reported diabetes/no claim (-1.49 to -1.80), hospitalization (-2.37)

• Only two significant health system contact variables in model 6: any hospitalization (-2.37; p<.01), and number of different physicians seen in office visits (0.02; p <.05)

• Covariate control essentially eliminates the upper tail in the inverted “U” for level of antidiabetic drug use, but unexplained variance in the lower tail increases significantly


Discussion: Main Points

High level of heterogeneity in antidiabetic drug use • Almost 1 in 4 Medicare beneficiaries with diabetes took no antidiabetic drugs

in 2002• All 6 variable domains combined could explain only a small fraction of the

likelihood and level of antidiabetic use among beneficiaries

The inverted “U” pattern in medication intensity by disease burden• Increasing disease burden is associated with a sharp rise in antidiabetic

medication intensity among the least sick, plateaus, and then falls sharply among those with the greatest disease burden

• Study limitations• No measure of blood glucose levels• Cross-sectional design precludes causal inferences


Conclusions: Implications for Part D

Drug coverage makes no significant difference in antidiabetic drug use

Quality of care and the medication intensity curve• All three zones in the inverted “U” have potential clinical significance but

require additional study- Does the low level of antidiabetic use among diabetics with the least disease

burden reflect lack of need or underuse?

- Does the plateau in prevalence and level of antidiabetic use among those in the middle of the range of disease burden represent a meaningful target for improving the quality of diabetic treatment in the Medicare program?

- Is the sharp drop in medication use in the upper tail of the distribution of disease burden justified by new thinking about the need for glycemic control among frail older people or does it represent underuse?

Thank You!

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