Gender Differences in Hospital Survival Rates For Medicare Beneficiaries Undergoing

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Gender Differences in Hospital Survival Rates
For Medicare Beneficiaries Undergoing
Coronary Artery Bypass Graft Surgery: Does
Hospital Performance Ranking Matter
Steven D. Culler, PhD
Associate Professor
Rollins School of Public Health
Emory University
April Simon MRN
President
Cardiac Data Solutions
Atlanta GA
Study Objectives
• To report on gender differences in riskadjusted mortality rates by hospital
performance classes based on CABG
outcomes among Medicare beneficiaries.
• To identify the number of female
Medicare beneficiary deaths that could
be avoided by improving outcomes in
bottom tier hospitals.
Methods: Data Sources
• Medicare Provide Analysis and
Review File (MedPAR): An
administrative database containing
demographic information, 9 diagnostic
and 6 procedure (ICD-9-CM) codes,
and the discharge status of all
Medicare beneficiaries admitted to any
U.S. hospital.
Methods: Study Period
• Study Period: October 1, 2002 to
September 30, 2004 (Fiscal Years
2003 & 2004).
Methods: Study Population
Inclusion Criteria:
• All Medicare beneficiaries undergoing a CABG surgery
(Procedure codes of 36.10-36.19 and 36.2).
Exclusion Criteria:
• Patients having any concomitant valve surgery (Procedure
codes of 35.00-35.04; 35.10-35.14; 35.20-35.28; &
35.31-35.39).
• All patients in hospitals performing less than 52 surgeries
per year or less than 17 surgeries on females per year.
Methods: Study Sample
Final Study Sample
Number of Hospitals
FY-2003
FY-2004
802
768
Average Hospital Volume
167±123
159±113
Number of Hospitalizations
134,407
122,231
66.5%
66.9%
% Male
Methods: Analytic Approach
• Step 1: Annual Risk-Adjusted Mortality:
A logistic regression equation (controlling
for up to 25 demographic and co-morbid
conditions) was estimated to predict each
Medicare beneficiary’s probability of
experiencing in-hospital mortality for
each fiscal year.
Methods: Analytic Approach
• Step 2: Annual Hospital Performance Tiers:
Hospitals were annually ranked into
quartiles based on the number of lives
saved (or lost) - the difference
between a hospital’s risk adjusted
expected number of deaths and its
observed number of deaths during the
fiscal year.
Methods: Analytic Approach
• Step 3: Annual Hospital Risk-Adjusted
Mortality Rate by Gender:
A male and female risk-adjusted
mortality rate was calculated for each
hospital for each fiscal year.
Results: Risk-Adjusted
CABG Mortality
FY-2003
FY-2004
All Study Hospitals:
3.68%
3.61%
Male Rate
Female Rate
Gender Differential (M-F)
3.17%
4.71%
-1.55%
3.09%
4.68%
-1.59%
Results: Risk-Adjusted
CABG Mortality
Overall Rates FY-2003
Hospital Performance Tier
I
II
III
IV
Male Rate
1.24%
2.19%
3.59%
5.68%
Female Rate
1.96%
3.40%
5.11%
8.39%
Differential (M-F)
-0.72%
-1.21% -1.52%
-2.71%
Male Rate
1.12%
2.16%
3.49%
5.52%
Female Rate
1.80%
3.31%
5.39%
8.19%
Differential (M-F)
-0.68%
-1.15% -1.90%
-2.67%
Overall Rates FY-2004
Results: Gender Difference
Between Top and Bottom Tier
Top
Bottom
p-Value
Male Rate
1.24%
5.68%
<0.001
Female Rate
1.96%
8.39%
<0.001
Differential (M-F)
-0.72%
-2.71%
<0.001
Male Rate
1.12%
5.52%
<0.001
Female Rate
1.80%
8.19%
<0.001
Differential (M-F)
-0.68%
-2.67%
<0.001
FY-2003:
FY-2004:3
Issues: Alternative Goals for
Bottom Tier Hospitals
1. The females and males have the same risk-adjusted
mortality rate in bottom tier hospitals;
2. The female risk-adjusted mortality rate in bottom tier
hospitals improves to the average female risk-adjusted
mortality rate; and
3. The female risk-adjusted mortality rate in bottom tier
hospitals improves to the female risk-adjusted mortality
rate in top tier hospitals.
Goal Three: Bottom Tier
Equals Top Tiers
Bottom Tier Females
Female Hospitalizations
Expected Female Deaths
(Current Practice)
FY-2003
FY-2004
Both Years
12,215
11,100
23,325
1,025
909
1,934
Goal: Female RA-Mortality rate the same in both tiers
Expected Deaths
151
133
284
Expected Deaths Avoided
874
776
1,650
85.3%
85.4%
85.3%
Percent Deaths Avoided
Summary:
• Female Medicare beneficiaries had
significantly higher risk-adjusted
hospital mortality rates than males.
• As one moves from the top quartile
to the bottom quartile, the gender
disparity in the risk-adjusted
mortality rate increases.
Summary:
• Improvement Goal:
85.3% of expected female beneficiaries
deaths could be avoided if bottom tier
hospitals achieved the same riskadjusted outcomes as top tier CABG
hospitals.
Limitations:
• Risk-adjusted models are based on co-morbid
conditions identified from ICD-9-CM codes
reported in an administrative dataset.
• Gender differences for Medicare beneficiaries
may not reflect gender differences for CABG
surgery among younger patients.
Conclusion
Female Medicare beneficiaries should
be much more selective in choosing
where to have their CABG surgery
performed!
The End
Goal One: No Gender Difference in
Bottom Tier Hospitals
Bottom Tier Females
Female Hospitalizations
Expected Female Deaths
(Current Practice)
FY-2003
FY-2004
Both Years
12,215
11,100
23,325
1,025
909
1,934
Goal: No Gender Difference in Rates in Bottom Tier
Expected Deaths
693
613
1,306
Expected Deaths Avoided
332
296
628
Percent Deaths Avoided
32.4%
32.6%
32.5%
Goal Two: Bottom Tier Hospitals
Improve to the Average Female Rate
Bottom Tier Females
Female Hospitalizations
Expected Female Deaths
(Current Practice)
FY-2003
FY-2004
Both Years
12,215
11,100
23,325
1,025
909
1,934
Goal: Female Rate in Bottom Tier Improves to Average
Expected Deaths
575
519
1,094
Expected Deaths Avoided
450
390
840
Percent Deaths Avoided
43.9%
42.9%
43.4%
Methods: Analytic Approach
Risk-Adjustment: Demographic Variables:
Variables
Answer
Age Group
Age 65 to 69,
Age 70 to 74,
Age 75 to 79, and
Age 80 or greater
Gender
Race
Male or Female
White or Non-white
Methods: Analytic Approach
Risk-Adjustment: History of Prior
Procedures or Conditions:
Variables
History of
History of
History of
History of
Prior CABG
Prior PCI
Prior MI
Hemodialysis
Answer
Yes or No
Yes or No
Yes or No
Yes or No
Methods: Analytic Approach
Risk-Adjustment – Co-Morbid Conditions:
Variables
Answer
Obesity
Yes or No
Diabetes
Yes or No
Chronic Obstructive Pulmonary Disease
Yes or No
Current Smoker
Yes or No
Chronic Renal Failure
Yes or No
Chronic Liver Disorder
Yes or No
Hypertension
Yes or No
Heart Failure
Yes or No
Cardiogenic Shock
Yes or No
Aortic Aneurysm
Yes or No
Methods: Analytic Approach
Risk-Adjustment: Co-Morbid Conditions
Variables
Answer
Atrial Fibrillation
Ventricular Fibrillation
Cardiac Arrest
Type of Primary Acute MI
Yes or No
Yes or No
Yes or No
STEMI
NSTEMI
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