nhanes ascvd cost study

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This study uses consecutive National Health and Nutrition Examination Surveys (NHANES) data from

2003-2012 to concurrently model obese body size (c.f., normal weight) main effects, moderated by non-

diabetic moderate 10-year ASCVD risk (c.f., 30-year and diabetic), on total medical cost outcomes.

• Minors, seniors 76+, outlier diseases, and pregnant women were excluded, resulting in 192,447,424

weighted or 22,510 unweighted participants.

Findings are that obesity explains 2% of cost by itself, together with heart risk some 10% contribution is

explained, and interaction effects at 0.2% has the least potency on costs.

• Heart risk, 10-year and 30-year alike, exponentially compound costs at the onset of diabetes and heart

attack/stroke; this means the speed of heart disease progression in patients differs but mean costs rise

identically with new diabetes or heart events.

Promising experiments beyond this study’s research showed that new sub-obesity algorithms (viz., adding

depression, pain, and gastric reflux), with heart risk (ASCVD), could predict 82% of prescription costs and

45% of total cost.

• The exploratory research found that if a non-diabetic patient moderately at-risk of heart attack or stroke

partnered with their physician to resolve depression and pain, there could be a savings opportunity of

$4,748 per capita.

• Patient costs in the “healthy obesity” category slowly increase when combined with low and moderate

heart risk, until the co-occurrence of depression and pain. When these behavioral health factors start,

costs rise.

Abstract

Design and 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

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Cardio-metabolic obesity exposure

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Pharmacy prescription drug

costs

Unit cost: Average

Wholesale Price

(lowest dose & cost)

Service use:

Number of days taken medicine

Service use: Number of

prescription medicines

taken

(Micromedex

Redbook, 2015)

Actuarial model of cost (DV)

(NHANES, 2015)

Hospital inpatient costs

Clinician professional consult costs

Service use: Number of

times received

healthcare over past year

Service use:Number of times

overnight hospital patient/

last year

(NHANES,

2015)

Unit cost: Mean hospital costs per stay by patient age group $5,000-

13,000

Unit cost: Average office

Visit, Established Patient, Level 2 Total Fair Price:

$84(Moore, Levit,

Elixhauser, 2014)

(Healthcare

Bluebook, 2015)

x x x x

(NHANES, 2015)

.

Total costs

Red text= adaptation to actuarial model’s cost categories

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.

Unweighted sample sizes

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Started with the full unweighted NHANES 2003-2012 samples (n=50,912). Pregnant subjects, minors, and rare diseases will be

excluded because special BMI algorithms are needed. Subjects must have NHANES questionnaire and examination data.

22,481 final sample of heart risk eligible who are exam and interview.

Full data set

Eligible set

Ineligible

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Outlier condition Unweighted Sample records impacted

Rheumatism (on anti-rheumatic Rx) 62

Anti-viral Rx (HIV, herpes, hepatitis) 161

Dialysis 23

Hepatitis (all types) 215

HIV 19

Kidney failure 158

Immunostimulant Rx

(immunodeficiency, autoimmunity, &

cancer patients)

46

Immunostimulant Rx (transplant

patients

23

Total outliers excluded 516

Outlier diseases not eligible for inclusion in the analysis, 2003-2012 NHANES adult

subpopulation with rare disease requiring special algorithms.

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Heart risk & obesity difference from disease-free




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Exploratory research findings

0 cardio-metabolic based Rx1 cardio-metabolic based Rx

2+ cardio-metabolic based Rx

0 obesity-based Rx1 obesity-based Rx

2+ obesity-based RxModerating

variables:

Prescription

adherence

Independent

variable:

prescription mix

Dependent variables:

prescription drug costs

(log transformed)

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Pharmacy cost

(Non)-adherence to cardio-metabolic & obesity-based Rx model

Anticoaguant

Antiplatelet

Antianginal

Antiarrhymic

Antihyperlipidemic

Antidiabetic & glucose elevating

HTN1 =Agents for hypertensive emergencies, pulmonary

hypertension, antihypertensive, antihypertensive combinations

HTN2 = Aldosterone receptor antagonist, antiotensin converting

enzyme inhibitors, antiotensin II inhibitors, antiadrenergic centrally

acting, antiadrenergic peripherally, beta andrenergic blocking,

calcium channel blocking, diuretics, peripheral vasodialators,

renin inhibitors, vasodilators

Asthma

Analgesic

Depression

Gout

Gastric reflux

Thyroid hormones

68% R square

Taking cardio-metabolic medicines

Not taking cardio-metabolic medicines

Rx for diabetes, hypertensions and/or

cholesterol

Theory: adaptive capacity

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(exploratory analysis for hypothesis generation)

Impact of obesity complications

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Impact of normal weight with complications

Behavioral health with mean cost per subgroup


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.


≥7.5% ASCVD Risk (not diabetic), obese, & obesity-based Rx

• Not on analgesics $3,243

• On analgesics $6,237 (15% on analgesics)

• Not on anti-depressants $3,457

• On anti-depressants $5,832 (10% on anti-depressants)

• Not on gastric reflux Rx $3,413

• On gastric reflux Rx $5,832 (11% on gastric reflux Rx)

• Have cholesterol Rx, but not taking = $4,959

(14% non-adherent)

• Have cholesterol Rx and taking = $6,006

Clinical ASCVD, obese, & on obesity-based Rx

• Not on analgesics $9,713

• On analgesics $13,815 (29% on analgesics)

• Not on anti-depressants $9,669

• On anti-depressants $15,639 (21% on anti-

depressants)

• Not on gastric reflux Rx $9,746

• On gastric reflux Rx $15,490 (20% on gastric

reflux Rx

• Have cholesterol Rx, but not taking = $15,917

(22% non-adherent)

• Have cholesterol Rx and taking = $12,750

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)

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

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

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

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

nhanes ascvd cost study

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