The investigation (summarized in the attached slides) analyzed how at-risk obese/overweight patients interact with beneficial interventions (2013 AHA/ACC risk, cholesterol, obesity and lifestyle prevention guidelines). The study estimated the savings potential if overweight/obese patients in the ACC/AHA four statin benefit groups stepped-down one risk level. Title: Cost Of Obesity-Based Heart Risk In The Context Of Preventive And Managed Care Decision-Making: An NHANES Cross-Sectional Concurrent Study By: John Frias Morales

1. Impact of unhealthy behavior on per
capita costs
By Dr. John Frias Morales with committee: Dr. Judith Lee (chair), Dr. Robert Fulkerth,
& Dr. Lance Robins
(Reviewers: Dr. Walter Stevenson and Dr. Hamid Shomali)
February 24, 2015
1

2. Introduction
 The American College of Cardiology's (ACC) 2013 standards of primary care were created to
reduce individual heart risk (heart attack, stroke or cardiac death event within 10 and 30
years) and obesity-based chronic disease risk, but if taken together, may also represent
modifiable lab/exam levels that are more predictive of cost than claims-based billing code
sets.
 A clinical data set, representative of US “well-appearing” and impaired obese and
atherosclerotic cardiovascular disease (ASCVD) adults alike, was used to determine
prevalence, cost differences, and correlates per stage. This cross-sectional study used a public
health data set to investigate the relationship between obesity and heart risk and their
impact on treatment costs with general linear models.
 This research examined how obesity interacts with heart risk to raise costs, and how disease-
free or normal patients differ from moderate heart risk patients with obesity (pre-clinical
well-appearing). Exploratory analysis also studied the cost impact of heart risk with
comorbidities, medication adherence, weight loss, fitness, and binge drinking.
2

3. NHANES Dissertation Design & Methods
Problem.
Medical processes match at-risk patients with obesity and
pre-clinical heart disease to beneficial anti-cholesterol, weight
loss, and lifestyle therapies (per 2013 American College of
Cardiology guidelines), but financing & scaling rules that
enable risk-reduction haven’t been defined.
• Research question: How does the relationship between
obesity and heart risk impact total medical costs?
• Purpose. Determine how obesity and healthy weight
depend on heart risk to amplify costs, and how disease-
free/normal patients differ from moderate heart risk
patients with obesity (pre-clinical well-appearing).
Design:
Cross-sectional for baseline cost estimates and service non-
use, as naturally distributed in the population. Exploratory
analysis for hypothesis generation and definition of stage-
contingent rules.
Methods
Who:
Adults (20-74 years old) representing the US
non-institutionalized population
• Not pregnant without outlier/rare diseases
• Disease-free and obesity-based heart risk
Measures of effect
• Mean costs difference relative
to normal/disease-free
• Magnitude of dependency
trend
Data description
• Patient-level service use (NHANES
public health data 2003-2012) mapped to
market prices (Healthcare Bluebook &
Micromedex Redbook) and estimates of
non-service use; and
• Clinical lab, exam, and vital sign data
mapped to risk of heart attack/stroke (10-
year calculator benefit groups, then
defaulting to low lifetime risk categories)
and body size.
Defining cost types
• Disease-free versus moderate
heart risk (incubating, well-
appearing), stratified by obesity
• Sub-clinical heart risk
(≥7.5%diabetics & genetic high
cholesterol) versus clinical
ASCVD (had severe event),
stratified by obesity
Statistical evaluation/test:
• Model main effects and moderation
interaction effects with R Sq,
• Hypothesis equivalence testing of mean
total cost by Wald F & T test for subgroups
• Estimated marginal means difference from
disease-free baseline for magnitude of
effects with Wald F and T test.
Comparator criteria
• Cost difference of higher risk
(10 year calculator) relative to
lower risk (30 year calculator)
cost
• R square of obesity-based
heart risk model compared to
industry actuarial risk
adjustment R square (Milliman)
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
Body size
(BMI Category)
X
2
Medical costs
(Rx, visits, hosp.)
Y
Heart risk
(anti-cholesterol statin
benefit groups)
Z
Product term
moderator
XZ
5

4. 4
Dissertation findings applied to decision making
If heart risk is at this level…
…Then channel to a preventive program with these change
element.
Cost
difference in
behavioral
change
Heart attack/stroke survivor
(clinical atherosclerotic
cardiovascular disease)
1. Resolve depression, pain, gastric reflux, asthma, and thyroid
hormones issues (w/ 2 factors vs w/o factors)
2. Moderate or rigorous exercise at 120 minutes per week vs.
less than 120 to zero
3. Prescription medication adherence (anti-cholesterol statin
eligible) vs non-Rx adherence
1. $6,037
2. $4,601
3. $3,167
Familial high cholesterol
(bad cholesterol LDL ≥190)
1. Moderate or rigorous exercise at 120 minutes per week
(anti-cholesterol statin eligible) vs. less than 120 to zero
2. Moderation of alcohol binge drinking vs. binge drinkers
1. $3,088
2. $436
Diabetic and at risk for heart
attack/stroke in the short
term
(10-year ASCVD calculator
≥7.5%)
1. Resolve depression, pain, gastric reflux, asthma, and thyroid
hormones issues (anti-cholesterol statin eligible) (w/ 2
factors vs w/o factors)
2. Moderation of alcohol binge drinking vs. binge drinkers
3. Moderate or rigorous exercise at 120 minutes per week vs.
less than 120 to zero
1. $2,636
2. $2,062
3. $1,648
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS obesity
algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because other algorithms
are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.

5. 5
Dissertation findings applied to decision making
If heart risk is at this level…
…Then consider specific behavioral change and prevention order
sets (heart risk levels: with behavioral factor vs. w/o behavioral
factor)
Cost
difference
Not diabetic and at risk for
heart attack/stroke in the
short term
(10-year ASCVD calculator
≥7.5%)
1. Resolve depression, pain, gastric reflux, asthma, and thyroid
hormones issues (w/ 2 factors vs w/o factors)
2. Moderation of alcohol binge drinking vs. binge drinkers
1. $4,748
2. $1,157
Diabetic and at risk for heart
attack/stroke in the long term
(30-year CVD calculator ≥39%)
1. Depression, pain, gastric reflux, asthma, and thyroid
hormones management (w/ 2 factors vs w/o factors)
2. Moderation of alcohol binge drinking vs. binge drinkers
3. Prescription medication adherence vs non-Rx adherence
4. Weight maintenance vs weight gain
1. $3,107
2. $1,885
3. $2,390
4. $3,325
Not diabetic and at risk for
heart attack/stroke in the long
term
(30-year CVD calculator ≥39%)
1. Resolve depression, pain, gastric reflux, asthma, and thyroid
hormones issues (w/ 2 factors vs w/o factors)
2. Prescription medication adherence vs non-Rx adherence
3. Weight maintenance vs weight gain
1. $1,490
2. $1,611
3. $552
Normal
(not diabetic, and 10-year
ASCVD calculator <7.5%, and
30-year CVD calculator <39%,
and did not have heart
attack/stroke)
1. Moderate or rigorous exercise at 120 minutes per week vs.
less than 120 to zero
2. Weight maintenance vs weight gain
1. $825
2. $409
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS obesity
algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because other algorithms
are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.

6. 6
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
Difference between Rx adherence & non-adherence
(exploratory analysis for hypothesis generation)
Average:
(heart disease calculator used to find normal and severe disease stages)

7. 7
Heart risk & obesity difference from disease-free
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.

8. 8
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
Difference between binge drinkers & modest drinkers
(exploratory analysis for hypothesis generation)

9. 9
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
Difference between fit and non-fit heart risk
(exploratory analysis for hypothesis generation)

10. 10
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
Difference between weight gain and maintenance
(exploratory analysis for hypothesis generation)

11. 11
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
(exploratory analysis for hypothesis generation)
Impact of obesity complications

12. 12
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
(exploratory analysis for hypothesis generation)
Impact of obesity complications

13. Obesity costs are dependent on strength of heart risk
Share of variance explained by
model (R Square)
• 11% Total costs
• 19% Prescription drug costs
• 4% Hospital costs
• 4% Office visit costs
Model effects (Wald F mean)
• Heart risk calculators (10-yr w/ 30-
yr) adds 20% to total costs and adds
147% to prescription costs
Model effects (Wald F mean)
• Obesity algorithm adds 1%
to Total Costs and adds 3%
to prescription costs
Results
Obesity explains 2% of cost by itself,
together with heart risk some -10% is
explained, and interaction effects at
0.2% has the least potency on costs.
• Hypothesized differences in
obesity-based heart risk are
statistically significant
• Specific obesity-based heart risk
levels have strong interaction
effects.
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. Sources: NHANES 2003-2012, Healthcare Bluebook, Micromedex Redbook, AHA/ACC/TOS
obesity algorithm (Jensen and Ryan, 2013), AHA/ACC ASCVD 10-yr calculator (Goff, et. al., 2013) , and lifetime calculator (Lloyd-Jones, et. al., 2006) . The following are ineligible for inclusion because
other algorithms are more accurate for outlier populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.
Provider
choices
35%
Obesity
2%
10%
Other
Condition
34%
Unknown
19%
Total Cost R Sq
Conclusions
• 61% of Americans are obese/overweight
and could benefit from weight loss (≥5%),
and 27% with heart risk could benefit from
anti-cholesterol statins.
• Experimental sub-obesity definition (obese
with depression, analgesic, & gastric
reflux) and heart risk explains more
variance: 45% R Square total cost and
82% R Square prescription cost
Heart
risk
6

14. 14
Cost inflection points
John Frias Morales (2015) dissertation for Golden Gate University doctorate in business administration. The following are ineligible for inclusion because other algorithms are more accurate for outlier
populations: transplant, HIV, MS, dialysis/CKD, hepatitis, rheumatic, pregnant, <20 or 76+; and participants must have survey and exam.