impact of unhealthy behavior on per capita costs

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

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.

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NHANES Dissertation Design & MethodsProblem.

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

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 drinkers3. 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.

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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%)


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 drinkers3. Prescription medication adherence vs non-Rx adherence4. 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%)


2. Prescription medication adherence vs non-Rx adherence3. 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.

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Difference between Rx adherence & non-adherence(exploratory analysis for hypothesis generation)

Average:

(heart disease calculator used to find normal and severe disease stages)

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Heart risk & obesity difference from disease-free




8




Difference between binge drinkers & modest drinkers(exploratory analysis for hypothesis generation)

9




Difference between fit and non-fit heart risk(exploratory analysis for hypothesis generation)

10




Difference between weight gain and maintenance(exploratory analysis for hypothesis generation)

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(exploratory analysis for hypothesis generation)

Impact of obesity complications

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(exploratory analysis for hypothesis generation)

Impact of obesity complications

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 costsModel 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.




Provider choices

35%

Obesity2%

10%

Other Condition

34%

Unknown19%

Total Cost R SqConclusions

• 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 costHeart

risk

6

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.

impact of unhealthy behavior on per capita costs

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