Appendix: The Lifetime Medical Cost Savings from Preventing HIV in the United States
CEPAC Model Inputs
Entry into Care. Simulated patients enter HIV care with a mean CD4 count that varies by
sex, race/ethnicity, and transmission category (Appendix Table 1). These inputs were derived
from data on entry into care in 2007 from the North American-AIDS Cohort Collaboration on
Research and Design (NA-ACCORD) [1].
Adherence and virologic suppression. Simulated patients are assigned an intrinsic
predisposition to adhere (e.g. ≥ 95%), based on data from contemporary US data from population
based studies [2, 3]. A high predisposition to adhere results in a high probability of achieving
virologic suppression and a low probability of subsequent virologic failure, while a low
predisposition results in a low probability of achieving suppression and a high probability of
virologic failure. For example, in the base case, on average an individual has an 83.5%
probability of achieving viral suppression 6 months after initiating first-line ART [4].
Hypothetical individuals in the base case, however, have different probabilities of achieving viral
suppression depending on their adherence e.g. an individual with <5% adherence would have no
probability of achieving viral suppression and an individual with 95% adherence would have a
91% probability of achieving suppression. A similar approach is used to model the rate of
virologic failure after achieving initial virologic suppression. The resulting predicted odds of
achieving viral suppression based on number of previous regimen failures are consistent with
those reported in the literature [5].
Loss to Follow-up. We used 2001-2009 data from the HIVRN on the number of patients
lost to follow-up, defined as not receiving any care from the HIVRN site during the previous 12
months with no record of having died during this period, to derive loss to follow-up and return to
1
care inputs for the CEPAC model. We estimated monthly probabilities of loss to follow-up for 6month intervals since initiation of care. Model inputs are based on the average probability of loss
to follow-up in the period 24 to 96 months after entry into care, in order not to bias our estimate
due to higher losses early after initiation of care. This average loss to follow-up rate (7.5 per 100
person years) is assumed to vary depending on adherence (i.e. individuals with higher adherence
are less likely to be lost to follow-up). For example, less than 5% of those with greater than 95%
adherence will be lost to follow-up. The rate of return to care after loss to follow-up (16.9 per
100 person years) was derived from the same HIVRN data. We also assumed that simulated
individuals in the CEPAC model who experience an opportunistic infection while lost to followup return to care independent of this return rate.
Life Table Adjustments for Non-HIV Mortality
Individuals who engage in behavior that puts them at risk for HIV have a higher
likelihood of smoking, alcohol and drug use, and other behaviors that could put them at greater
risk of dying, even in the absence of HIV. To account for this excess mortality we applied
standardized mortality ratios (SMRs) derived from the HIVNET Vaccine Preparedness Study to
race/ethnicity and sex-specific life tables from the Centers for Disease Control and Prevention
(CDC) [6, 7]. The SMRs represent the ratio of actual deaths among individuals in 5 specified
subpopulations (MSM, male IDU, male heterosexual at high risk for HIV infection, female IDU,
female heterosexual at high risk for HIV infection) to the deaths that would be expected in the
general population among individuals with the same age, sex, race/ethnicity characteristics. Male
and female heterosexuals at high risk for HIV infection include individuals with multiple sex
partners, who are engaged in commercial sex work, and/or have a history of sexually transmitted
2
infections [6]. This life table adjustment method was previously used by CEPAC investigators
in an evaluation of differences in life expectancy by race/ethnicity and sex for HIV-infected
individuals in the US [8]. In the current analysis, these non-HIV mortality adjustments were
applied to both HIV-infected and HIV-uninfected individuals.
HIVRN Annual Cost Calculations
Seven HIVRN sites participating in 2009 provided data for cost estimation. All HIVRN
sites provide information on outpatient utilization, CD4 and HIV-1 RNA tests, and antiretroviral
and opportunistic infection prophylaxis medications. However, the seven sites selected for this
study also provided data on prescription medications in addition to antiretroviral and
opportunistic infection prophylaxis medications. Sites are located in the Eastern (3), Southern
(2), and Western U.S. (2). Six sites have academic affiliations; one is community-based.
Cost calculations were performed from the perspective of a large-scale purchaser of
services, such as the Federal government, which can often negotiate discounts from standard
charges. Because information on payments was not directly available, we estimated expenditures
by multiplying utilization data for inpatient days, outpatient visits, emergency department visits,
and lab tests (CD4, HIV resistance, and HIV-1 RNA) by an appropriate unit cost.
Inpatient Costs per Day. Medical record data do not contain expenditure information,
and most patients cannot accurately report the costs of their care. Consequently, we obtained
inpatient cost information from the Healthcare Cost and Utilization Project (HCUP) State
Inpatient Database (SID), which contains hospital discharge abstract data covering inpatient
stays from all short-term non-federal community hospitals in participating states [9, 10]. SID
data include primary and all secondary diagnoses for each inpatient stay, the length of stay (LOS,
3
calculated as the difference between the admission and discharge date) and the total charges for
the hospitalization. We used data for calendar year 2009 from 11 states: California, Colorado,
Florida, Illinois, Maryland, Michigan, New Jersey, New York, Oregon, Pennsylvania, and
Washington.
We identified hospitalizations of HIV-infected patients who were > 18 years old at
admission by examining all primary and secondary diagnoses listed in the discharge abstract.
These hospitalizations were identified by a primary or secondary International Classification of
Diseases, ninth edition (ICD-9 CM) diagnosis code that included 042 [11, 12].
To estimate a unit cost per inpatient day, we used data on charges and cost-to-charge
ratios for HIV-related inpatient admissions. Hospital charges for each admission were converted
to costs by multiplying by an inpatient expenditure-to-charge (ICC) ratio [13]. All-payer
hospital-specific ICC ratios were based on data from standard accounting files of the Centers for
Medicare and Medicaid Services. If a hospital-specific ICC was not available, then a group
average ICC was used, where the grouping was based on the hospital’s state, ownership, urban or
rural location, and size. The group average ICC was used for 8% of the admissions, and the
hospital-specific ICC was used for the rest. For 82,619 admissions identified in this way in the
SID with data available for total charges and LOS, the mean cost per day was $2,115.07.
For HIVRN patients, total inpatient expenditures were obtained by multiplying the
number of inpatient days for any reason in 2009 by $2115.07. If a patient was admitted in 2008
or discharged in 2010, only those inpatient days in 2009 were included in the calculation of
inpatient costs.
Pharmaceutical Costs. Price discounts for medications are available to certain entities.
For example, AIDS Drug Assistance Programs (ADAPs) can purchase drugs using the 340B
4
Drug Pricing Program, which provides drug price ceilings [14]. The Congressional Budget
Office estimated that the 340B ceiling price averaged 51% of the average wholesale price in
2003, although this estimate was not focused on antiretroviral medications [15]. Not all state
ADAPs take advantage of 340B prices, however [14]. Prices for specific drugs under the 340B
program are not publicly available.
Computation of monthly costs for each medication was based on 2009 Red Book average
wholesale price (AWP). It is recognized that the AWP overestimates actual pharmaceutical costs.
A report by the Office of the Inspector General for the Department of Health and Human
Services compared AWP to the average manufacturer’s price (AMP) [16], which is the average
unit price paid to manufacturers by wholesalers for retail drugs, calculated from actual sales
transactions. For single source brands, at the median the AMP was 23% less than the AWP. We
discounted the published AWP by 23% for all antiretroviral and opportunistic infection
prophylaxis and treatment medications, as most are not available in generic form. We assumed
that standard dosages were prescribed, calculated the number of units administered in a month,
and used this to derive a monthly cost for each drug. For sulfamethoxazole/trimethoprim,
dapsone, and azithromycin, which are available in generic form, we used monthly costs of $6.84,
$39.00, and $149.28, respectively.
For non-HIV medications (i.e., other than antiretroviral and opportunistic infection
prophylaxis and treatment medications), we excluded over-the-counter medications, those
usually taken for less than 30 days (including anti-infectives and pain medications), and those
with an irregular dosing schedule, such as medications administered topically, by inhaler, or on
an as-needed (PRN) basis. Where generic versions were available, we used the generic price.
5
The discounted monthly cost was multiplied by the number of months a patient was
prescribed the medication in 2009. These products were then summed across drugs for each
patient to obtain total antiretroviral and non-antiretroviral medication expenditures.
Outpatient Visit Expenditures. The estimated unit cost for an outpatient visit with the
HIV primary care provider was based on the Medicare National Physician Fee Schedule for 2009
[17]. The facility unit cost for an outpatient visit, based on CPT-4 code 99215 for the most
complex level of patient evaluation and management, was $116.46. We multiplied the number of
visits to the primary HIV care provider by the cost per visit to obtain total outpatient visit
expenditures. Ambulatory visits to other providers at HIVRN sites (e.g., other specialists, or
clinics other than the HIV clinic) were not consistently reported and were not included in this
calculation, nor were visits to other providers not participating in the HIVRN.
ED Expenditures. Emergency department (ED) visits were based on estimates from the
2009 Medical Expenditure Panel Survey (MEPS) [18]. We used $686 as the cost of an ED visit,
which was the mean Medicare payment for the 1,146 ED visits in MEPS that had a nonzero
facility or physician payment from Medicare.
Laboratory Expenditures. Based on the Medicare Clinical Diagnostic Laboratory Fee
Schedule for 2009, we used $68.60 as the cost of a CD4 test, and $124.24 as the cost of an HIV1 RNA test.
Total Expenditures. We summed outpatient, inpatient, antiretroviral, opportunistic
infection prophylaxis and treatment, CD4, HIV-1 RNA test, ED visit, and non-HIV medication
expenditures to obtain total expenditures in 2009 for each patient. The unit costs are themselves
estimates and may only approximate true opportunity costs. Variations in the intensity of
inpatient treatment or the length of outpatient visits, which could affect costs, were not
6
incorporated into the analyses. Estimates may be biased downwards to the extent that HIVRN
data do not capture all aspects of service utilization, such as emergency department or inpatient
use or ambulatory visits to non-HIVRN providers.
Analyses. Multiple regression models included gender, race/ethnicity, HIV transmission
risk factor, age, CD4 category, an indicator for any ART prescription in 2009, and indicators for
HIVRN site. Risk factor was coded as injection drug use (IDU, including IDU in conjunction
with other risk factors), men who have sex with men (MSM), heterosexual (HET), or other.
Race/ethnicity categories were White, Black, Hispanic, and other. Age and median CD4 count
were categorized to capture possible non-linear associations with annual costs. Age as of July 1,
2009 was categorized as 18-29, 30-39, 40-49, and 50 or older. We categorized the median CD4
count recorded in 2009 as < 50, 51-200, 201-350, 351-500, and > 500 cells/mm3.
We identified 12,155 adult patients at the 7 sites who had at least one outpatient visit
and one CD4 test in 2009. Sample sizes were not sufficiently large to support cost estimates for
those with “other” race/ethnicity (n=247) and those with “other” HIV risk (n=455); these
individuals were removed from the analyses. Transgender patients with an MSM risk factor
(n=59) were reclassified as male; 23 additional transgender patients were removed from the
analysis, as were two patients under age 17. Based on preliminary analyses, one person appeared
to be an outlier, with a notably large discrepancy between actual and predicted expenditures; this
person was removed from the analysis. The resulting analytic sample numbered 11,427.
Most patients (86%) had enrolled in their respective clinics prior to 2009; 1,595
enrolled during 2009, and 109 died in 2009. We annualized costs for patients who enrolled in
2009, as they had partial-year data; we did not annualize costs for patients who died during 2009,
as this would represent extrapolating beyond death. (Three patients both enrolled in 2009 and
7
died in 2009.) As in other analyses of medical care costs [19], we compensated for annualizing
costs by using weights for partial-year patients in analyses, where the weight was the proportion
of months in 2009 that the patient provided data. Patients with full-year data (and decedents) had
analytic weights equal to one.
Ordinary least-squares (OLS) regression can perform sub-optimally when used to analyze
data that are skewed or have heavy tails, a feature characteristic of expenditure data.[20, 21]
Generalized linear models have been recommended as an alternative mode of estimation for such
data [21, 22]. We used a generalized linear model with a log link and a Poisson distribution.
Predicted costs from this model correlated slightly more strongly with observed costs, compared
to a model with a gamma distribution. The model included three interactions (1) CD4 category
by any ART, (2) age category by any ART, and (3) race/ethnicity by age category. We used a
combined gender/risk variable, categorized as male/MSM, male/IDU, male/heterosexual,
female/IDU and female/heterosexual. Based on this model’s estimated coefficients, we
calculated mean predicted costs for each of the 600 combinations of gender/risk (5 categories),
race/ethnicity (3 categories), median CD4 category (5 categories), age category (4 categories),
and any ART in 2009 (2 categories), averaging over site. Analyses were conducted using Stata
11.2. Model prediction results are presented in Appendix Table 2.
Life Expectancy and Lifetime Cost Results by Subpopulation and Age at Infection
We conducted analyses separately for 15 subpopulations: 5 transmission risk groups
(MSM, male IDU, male heterosexual, female IDU, female heterosexual) stratified by
race/ethnicity (White, Black, Hispanic). We report mean time from infection to entry into care
and mean costs with empiric standard errors.
8
We used pseudo-bootstrapping (randomly drawing 1,000 iterations from a normal
distributions with specified mean and standard error) to derive empiric standard deviations of the
distribution of mean costs, which are empiric standard errors. A limitation of this method is that
we based our analysis on random draws from a normal distribution. Means and standard errors
were derived from HIV Research Network or MEPS data.
Appendix Table 3 reports the results for each subpopulation in the base case, with HIV
infection occurring at age 35. Results reported in the main text are derived from the results for
each subpopulation weighted in proportion to that subpopulation’s contribution to HIV incidence
in the US on average between 2006 of 2009 that are reported in Appendix Table 4 [23]. These
weights were applied to both HIV-infected and HIV-uninfected populations. Appendix Figures 1
and 2 report these weighted results for discounted costs assuming HIV infection occurs at age 25
(estimated life expectancy 34.9 years) and at age 55 (estimated life expectancy 18.8 years),
respectively.
9
Appendix Table 1. CEPAC model inputs for CD4 cells/μL at entry to HIV care*
White
Black
Hispanic
Men who have sex with men
376
314
322
Male heterosexual
262
280
273
Female heterosexual
383
360
368
Male injection drug user
366
331
310
Female injection drug user
482
384
348
* Calibrated to match observed mean CD4 count at entry to HIV care from the North AmericanAIDS Cohort Collaboration on Research and Design (NA-ACCORD) [1, 24].
CEPAC: Cost-Effectiveness of Preventing AIDS Complications.
10
Appendix Table 2. Estimated average monthly costs by HIV-infected subpopulation (2012 US dollars)
Age, disease stage, and race
MSM
Male
Female
Heterosexual Heterosexual
Male IDU
Female
IDU
18-29 years
CD4 cells (/μL)
≤ 50
Black
3,093
2,831
2,970
3,041
3,322
Hispanic
3,133
2,868
3,008
3,081
3,365
White
2,725
2,494
2,616
2,680
2,926
Black
2,362
2,162
2,268
2,322
2,536
Hispanic
2,393
2,190
2,297
2,353
2,570
White
2,081
1,904
1,998
2,046
2,235
50-200
201-350
11
Black
2,007
1,837
1,927
1,974
2,156
Hispanic
2,034
1,861
1,952
2,000
2,184
White
1,768
1,619
1,698
1,739
1,899
Black
1,831
1,676
1,758
1,801
1,966
Hispanic
1,855
1,698
1,781
1,824
1,992
White
1,613
1,477
1,549
1,586
1,732
Black
1,726
1,580
1,657
1,697
1,854
Hispanic
1,748
1,600
1,679
1,719
1,878
White
1,520
1,392
1,460
1,495
1,633
351-500
>500
30-39 years
CD4 cells (/μL)
12
≤ 50
Black
3,393
3,106
3,258
3,337
3,644
Hispanic
3,393
3,105
3,257
3,336
3,643
White
3,497
3,201
3,358
3,439
3,756
Black
2,591
2,372
2,488
2,548
2,783
Hispanic
2,591
2,371
2,487
2,547
2,782
White
2,671
2,444
2,564
2,626
2,868
Black
2,202
2,016
2,114
2,166
2,365
Hispanic
2,202
2,015
2,114
2,165
2,365
White
2,270
2,077
2,179
2,232
2,438
50-200
201-350
351-500
13
Black
2,009
1,839
1,929
1,975
2,157
Hispanic
2,008
1,838
1,928
1,975
2,157
White
2,070
1,895
1,988
2,036
2,224
Black
1,893
1,733
1,818
1,862
2,033
Hispanic
1,893
1,733
1,818
1,862
2,033
White
1,952
1,786
1,874
1,919
2,096
Black
3,702
3,389
3,554
3,641
3,976
Hispanic
3,986
3,648
3,827
3,920
4,281
White
3,746
3,428
3,596
3,683
4,023
>500
40-49 years
CD4 cells (/μL)
≤ 50
14
50-200
Black
2,827
2,588
2,714
2,780
3,036
Hispanic
3,044
2,786
2,922
2,993
3,269
White
2,860
2,618
2,746
2,813
3,072
Black
2,403
2,199
2,307
2,363
2,580
Hispanic
2,587
2,368
2,484
2,544
2,778
White
2,431
2,225
2,334
2,390
2,611
Black
2,192
2,006
2,104
2,155
2,354
Hispanic
2,360
2,160
2,266
2,321
2,534
White
2,217
2,030
2,129
2,180
2,381
201-350
351-500
>500
15
Black
2,066
1,891
1,983
2,031
2,219
Hispanic
2,224
2,036
2,136
2,187
2,389
White
2,090
1,913
2,007
2,055
2,245
Black
4,126
3,777
3,962
4,058
4,432
Hispanic
4,026
3,685
3,865
3,959
4,324
White
4,016
3,676
3,856
3,950
4,313
Black
3,151
2,884
3,025
3,099
3,384
Hispanic
3,074
2,814
2,951
3,023
3,301
White
3,067
2,807
2,945
3,016
3,294
50+ years
CD4 cells (/μL)
≤ 50
50-200
16
201-350
Black
2,678
2,451
2,571
2,634
2,876
Hispanic
2,613
2,391
2,508
2,569
2,806
White
2,607
2,386
2,503
2,563
2,799
Black
2,443
2,236
2,345
2,402
2,624
Hispanic
2,383
2,181
2,288
2,344
2,560
White
2,378
2,176
2,283
2,338
2,554
Black
2,303
2,108
2,211
2,264
2,473
Hispanic
2,246
2,056
2,157
2,209
2,413
White
2,241
2,051
2,152
2,204
2,407
351-500
>500
17
Appendix Table 3.Time from infection to entry into care and medical cost saved by avoiding one HIV infection by
subpopulation, excluding secondary transmission effects (2012 US dollars)
Subpopulation
Time from Infection
to Entry into Care
Mean per Person Lifetime Cost
(SD)
Lifetime Cost Saved
(SD)*
Discounted
Undiscounted
Discounted
Undiscounted
MSM
Black
5.6
319,500
(4,300)
589,500
(8,300)
230,000
(8,400)
405,300
(16,800)
Hispanic
5.7
354,200
(5,200)
682,300
(11,700)
283,800
(7,800)
526,400
(18,400)
White
5.1
352,500
(5,700)
663,900
(12,200)
235,000
(7,700)
411,500
(16,700)
Black
5.1
218,200
(2,800)
365,900
(5,000)
154,600
(6,200)
248,600
(11,100)
Hispanic
5.7
267,200
(4,200)
481,300
(8,400)
210,100
(6,300)
364,600
(13,200)
White
5.7
246,500
(3,900)
435,000
(8,100)
154,000
(5,700)
252,500
(12,000)
Male Heterosexual
Female Heterosexual
18
Black
4.8
282,600
(3,500)
501,300
(6,600)
177,300
(8,000)
303,500
(14,900)
Hispanic
5.0
332,000
(5,000)
619,800
(9,900)
239,700
(8,400)
429,400
(17,400)
White
4.8
314,900
(5,100)
575,100
(10,400)
188,700
(6,900)
327,400
(13,500)
Black
4.2
204,400
(3,000)
318,100
(4,600)
154,500
(5,300)
233,500
(8,500)
Hispanic
5.0
262,600
(4,000)
445,400
(7,600)
214,200
(5,700)
353,000
(10,700)
White
4.4
252,900
(4,400)
416,100
(7,400)
176,000
(5,700)
274,400
(9,400)
Black
3.3
173,700
(3,300)
246,100
(4,600)
121,900
(5,400)
170,800
(7,500)
Hispanic
4.3
251,300
(4,800)
394,600
(8,200)
194,300
(6,400)
299,400
(11,000)
White
3.2
243,500
(4,900)
364,500
(7,600)
169,100
(5,600)
247,500
(8,700)
Male IDU
Female IDU
*Lifetime cost saved is equal to the difference between the mean person lifetime cost for HIV positive and HIV negative individuals;
however, these numbers do not sum exactly due to rounding.
SD: standard deviation; MSM: men who have sex with men; IDU: injection drug use.
19
Appendix Table 4. Percent of incident HIV infections by risk group and race/ethnicity*
Sex-risk group
White
Black
Hispanic
Total
Men who have sex with men
24.7
23.4
13.0
61.0
Male heterosexual
1.2
5.2
1.4
7.8
Female heterosexual
3.7
11.7
3.7
19.0
Male injection drug user
2.8
3.4
1.7
7.9
Female injection drug user
1.4
2.0
0.8
4.2
Total
33.8
45.7
20.6
100.0
*Adapted from [23].
20
Appendix Figure 1. Cumulative discounted lifetime costs from time of infection at age 25 (2012 US dollars)
350,000
HIV-Infected
HIV-Infected (5-year delay)
300,000
42,500
HIV-Uninfected
250,000
248,700
200,000
Cumulative
Cost
(2012 US dollars)
150,000
100,000
50,000
0
25
35
45
55
65
Age (years)
75
85
95
21
Appendix Figure 2. Cumulative discounted lifetime costs from time of infection at age 55 (2012 US dollars)
350,000
HIV-Infected
300,000
HIV-Infected (5-year delay)
HIV-Uninfected
250,000
46,100
200,000
Cumulative
Cost
(2012 US dollars)
146,300
150,000
100,000
50,000
0
55
60
65
70
75
80
Age (years)
85
90
95
100
22
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