quality measurement and gender differences in managed care populations with chronic diseases ann f....
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Quality Measurement and Gender Differences in Managed Care
Populations with Chronic Diseases
Ann F. ChouCarol WeismanArlene Bierman
Sarah Hudson Scholle
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Background
• Institute of Medicine (IOM) outlined 6 attributes of health care: – Health care should be “safe, effective, patient-
centered, timely, efficient, equitable.”
• There are many reports of disparities in health care and health outcomes (IOM, Unequal Treatment).
• Overall health care quality has improved over time but it has not improved for all patient subgroups.
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Background: Disparities in Care for Cardiovascular Disease (CVD)
• Substantial literature documents gender differences in guideline indicated services and treatment.
• Women may need more aggressive risk factor management than men due to differences in risk factors and symptoms presentation.
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Managed Care Population
• A significant portion of the US population receives care through managed care organizations, where the quality of care may be more uniform.
• In particular, there are few studies that examined disparities among commercially managed care enrollees.
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Objective
• To examine possible gender and racial disparities in meeting quality performance indicators for cholesterol screening and control among commercial health plans and in commercial and Medicare managed care populations of patients with chronic conditions
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HEDIS®
• Healthcare Effectiveness Data and Information Set (HEDIS®) is a set of standardized performance measures designed to ensure that purchasers and consumers have the information to reliably compare the performance of managed health care plans.
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MEASURING QUALITY OF CARE
FOR HEART DISEASEHEDIS Measures:
– Comprehensive diabetes care• Screening for Cholesterol
• Good control of cholesterol (LDL <100 mg/dL)
– Cholesterol management after acute cardiovascular event • Cholesterol screening
• Good control of cholesterol (LDL <100 mg/dL)
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Study Population
• Submissions from Commercial plans (2005)– 46 plan level submissions
– 31 member level data submissions, including 11,813 patients
– Geocoding and surname analysis used to estimate race, ethnicity and socio-economic status (SES)
– Participating plans are larger, higher performing
• Submissions from Medicare plans (2004)– 96,055 patients from 148 health plans
– Race obtained from CMS enrollment data, geocoding used to estimate SES
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Analytic Approach
• Member Level– Hierarchical Generalized Linear Modeling (HGLM):
HEDIS outcomes were modeled as functions of gender, controlling for other socio-demographic characteristics at the first level, and plan’s clustering effect at the second level
– Adjusted rates were calculated
• Plan Level– Descriptive statistics – Calculation of disparities score (male-female
difference)– T-test to determine significance of the gender
difference in performance rates
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Results
Gender and Racial/Ethnic Disparities in Medicare
Managed Care Populations with Diabetes Mellitus
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Sample DescriptionCharacteristic Medicare Population
(%)Age:
• <65 12.2
• 65+ 87.8
Gender:
• Male 50.3
• Female 49.7
Race:
• White 86.6
• African American 13.4
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HGLM Results: Odds Ratios
HEDIS Outcomes Cholesterol Screening
LDL control <100 mg/dL
Female
/Male
1.06 0.75*
African American
/White
0.67* 0.69*
Enrolled in plans with >20% minority members
0.81 0.93
* Denotes statistical significance at p≤0.05
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Adjusted Rates for Gender and Race
30
40
50
60
70
80
90
100
AA Female
Wht Female
AA Men
Wht MenLDL Control <100
Cholesterol screening
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Gender Differences in Quality Measures Among Commercially
Insured Patients
Results
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Sample CharacteristicsCharacteristics %
Gender (women) 44.2
Race/Ethnicity
• African American 8.5
• Latino 7.7
• White/other 74.6
Low SES 10.3
Age
•Age 65 and older 6.7
•45-64 years 72.6
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Unadjusted Rates: Cholesterol Measures
Characteristics Diabetes Management Cholesterol Management
Screening LDL Control
Screening LDL Control
% t-test
% t-test
% t-test
% t-test
Gender
• Female 91.6 2.13*
37.7 6.04*
76.8 2.53*
45.3 6.22*• Male 92.7 43.3 79.9 55.0
African American
• Female 90.6 -1.39 29.9 2.03*
65.7 -0.24 34.2 1.55
• Male 87.7 36.1 64.2 44.8
White
• Female 91.5 2.84*
39.4 5.05*
77.6 2.32*
46.2 5.31*• Male 93.2 44.9 80.8 55.4
* Denotes statistical significance at p≤0.05
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HGLM Results: Odds RatiosDiabetes
ManagementCholesterol
Management
Covariate Cholesterol screening
LDL Control
Cholesterol screening
LDL Control
Female/Male
0.88 0.81* 0.88* 0.72*
African American/White
0.69* 0.74* 0.88 0.71*
Latino/White
1.13 0.73* 0.79* 0.87
Low SES/High SES
0.85 0.83* 0.77* 0.75*
Age 65+ /(<65)
1.10 1.52* 1.03 1.26*
* Denotes statistical significance at p≤0.05
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WOMEN LESS LIKELY CONTROL CHOLESTEROL;
AFRICAN-AMERICAN WOMEN EVEN LESS LIKELY
Cholesterol Control <100 mg/dl for Commercial CVD sampleCholesterol Control <100 mg/dl for Commercial CVD sample
21.2
White Males9.2
White Females
10.6
African-American Males
African-American Females
20
30
40
50
60
70
Un
ad
jus
ted
Ra
te (
%)
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Gender Differences in Quality Measures Cross a Sample of
Health Plans
Results
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Plans Characteristics
Measures N (%)
Medicare Plans(N=148)
Commercial Plans(N=46)
Not for Profit 53 (35.8) 12 (26.1)
Health Plan Model
Staff/Group Model 63 (42.6) 2 (4.3)
IPA/Network 79 (53.4) 25 (54.3)
Mixed Model 6 (4.0) 19 (41.3)
Region
Midwest 40 (27.0) 13 (28.3)
Northeast 38 (25.7) 11 (23.9)
South 33 (22.3) 15 (21.7)
West 37 (25.0) 7 (15.2)
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Overall Performance of Plans
Measures Commercial Plans
Medicare Plans
Male Female
Male Female
Cholesterol Screening-diabetes
92.9 91.7 90.9 91.8*
Lipid Control – diabetes 44.4 38.8* 44.4 38.0*
Cholesterol Screening-CVD event
84.2 81.6 81.1 79.5*
Lipid Control – CVD event
56.4 47.1* 52.1 43.6*
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MORE THAN HALF OF HEALTH PLANS SHOW DISPARITY FAVORING MEN ON
CHOLESTEROL CONTROL
0
10
20
30
40
50
60
70
BP Control CholesterolScreening(Diabetes)
CholesterolControl (<100)
(Diabetes)
CholesterolScreening
(CVD)
CholesterolControl (<100)
(CVD)
Favors Women Favors Men
COMMERCIAL PLANS (%) Striking disparities for
cholesterol control
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MORE THAN HALF OF HEALTH PLANS SHOW DISPARITY FAVORING MEN ON
CHOLESTEROL CONTROL
0
10
20
30
40
50
60
70
80
BP Control CholesterolScreening(Diabetes)
CholesterolControl (<100)
(Diabetes)
CholesterolScreening
(CVD)
CholesterolControl (<100)
(CVD)
Favors Women Favors Men
MEDICARE PLANS (%) Striking disparities for
cholesterol control
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Conclusion• Clinically important and statistically
significant gender differences and racial/ethnic disparities in LDL control measures in those with chronic conditions
• African-American and poor women at greatest disadvantage from additive effect of race, gender, socioeconomic status
• More than half of health plans have gender disparity of 5 percentage points or more for LDL control
• Differences persist despite fairly equitable access to care. This is concerning given these are populations with high risk due to the presence of co-morbidities
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Limitations
• Lack of information on patient clinical, behavioral, and attitudinal characteristics. Results were consistent after adjusting for region, SES and plan clustering
• No information on patterns of utilization and providers
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Recommendations for Quality Improvement
• Inform consumers about disparities and encourage active role in demanding high quality care
• Educate physicians about the patterns of gender disparities in CVD care and encourage for active management with patients as partners
• Engage employers, purchasers and health plans in promoting focus on disparities in quality improvement
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Recommendations for Policy and Research
• Build consideration of gender, racial/ethnic, and socioeconomic differences into quality improvement interventions, with incentives for health plans and providers to identify, address and monitor potential disparities in CVD care
• Gain consensus on the methods for collecting, examining, and reporting on key population-wide health measures by gender, racial/ethnic, and socioeconomic characteristics
• Improve training in the area of women’s health for the health care workforce
• Fund further research to disentangle influence of physician and patient factors on outcomes
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Acknowledgements
• Funded by the Agency for Healthcare Research and Quality, American Heart Association, the California Endowment
• NCQA staff for support in project and data management