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 SimoniWastila, Ilene Zuckerman
The Peter Lamy Center on Drug Therapy and Aging
University of Maryland Baltimore
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
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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
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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)
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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
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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
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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
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Descriptive Results
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Characteristics of the Study Population (N=1,956)
Characteristic
Frequency or
count (SE)
Demographics
Age (%)
Under 65
65 – 69
70 – 74
75 – 79
80+
14.8
17.1
24.7
20.8
22.6
Female sex (%)
54.8
Nonwhite (%)
20.7
Geographic residence (%)
East
Midwest
South
West
18.5
25.6
43.3
12.6
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Characteristics of the Study Population (N=1,956)
Economic variables
Annual income (%)
< $10,000
$10,000 – 20,000
$20,001 – 30,000
>$30,000
25.1
31.4
21.5
22.0
Supplementary medical insurance
No coverage (%)
Full year (%)
Part year (%)
Covered months (count)
8.5
88.0
3.5
6.4 (0.5)
Prescription drug coverage
No coverage (%)
Full year (%)
Part year (%)
Covered months (count)
25.0
65.1
9.9
6.0 (0.2)
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Characteristics of the Study Population (N=1,956)
Health status and disease severity factors
Self-reported health (%)
Poor
Fair
Good to excellent
13.0
28.7
57.6
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
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Characteristics of the Study Population (N=1,956)
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
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Figure 1. Prevalence of Selected Diseases among Medicare
Beneficiaries with Diabetes Stratified by Decile of Annual Medical
Spending, 2002
80.0%
70.0%
Prevalence (%)
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
1
2
3
4
5
6
7
8
9
10
Spending Decile
Ischemic Heart Disease
Cancer
COPD/Asthma
Stroke
Arthritis/non-traumatic joint disorders
Dementia incl Alzheimer's
Pneumonia
Peptic ulcer/dyspepsia
Depression/other mood disorders
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Disease Burden and Medication Use:
the Inverted “U”
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Figure 2. Unadjusted Annual Prevalence of Antidiabetic Medication
Use by Decile of Annual Medical Spending
100.0%
90.0%
Percentage of Diabetics
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
1
2
3
4
6
5
7
8
10
9
Decile
% using any antidiabetic medication
% using oral antidibetic medications
% using insulin
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Figure 3. Unadjusted Mean Annual Prescription Fills for Oral
Antidiabetic Medications by Users of These Drugs
12.0
10.0
Mean # of Fills
8.0
6.0
4.0
2.0
0.0
1
2
3
4
5
6
7
8
9
10
Spending Decile
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Figure 4. Unadjusted Mean Annual Pill Counts for Oral Antidiabetic
Medications Stratified by Users of These Drugs
800.0
700.0
Mean Number of Pills
600.0
500.0
400.0
300.0
200.0
100.0
0.0
1
2
3
4
5
6
7
8
9
10
Spending Decile
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Multivariate Results
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Adjusted Odds Ratios for Any Use of Antidiabetic
Medications by Study Subjects
Decile
1
2
3
4
5
6
7 (reference)
8
9
10
*p <.05
**p <.01
Decile Only Regression
Unadj. Odds (95% CI)
Full Model
Adj. Odds (95% CI)
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)
1.00
0.81 (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)
1.00
0.79 (0.50-1.25)
0.83 (0.52-1.35)
0.69 (0.41-1.16)
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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
•
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Adjusted Coefficients for Annual Prescription Fills for Study Subjects
Using Oral Antidiabetic Medications
Decile
1
2
3
4
5
6
7 (reference)
8
9
10
*p <.05
**p <.01
Decile Only Regression
Unadj. Coef. (95% CI)
Full Model
Adj. Coef. (95% CI)
-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)
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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
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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
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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?
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Thank You!