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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
5
Cardio-metabolic obesity exposure
4
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
5
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
6
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.
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
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)
3
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
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.
(exploratory analysis for hypothesis generation)
Impact of obesity complications
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 normal weight with complications
Behavioral health with mean cost per subgroup
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)
≥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
16
)
13
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)
14
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)
15
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)
16
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)