Conceived and designed the experiments: VSP PGF ABH SS SLS. Performed the experiments: VSP PGF ABH SS SLS. Analyzed the data: VSP PGF ABH SS SLS. Wrote the paper: VSP PGF. Contributed to the writing of the paper: VSP PGF ABH SS JDH MRG JTB DR SLS.
The authors have declared that no competing interests exist.
Identifying and treating persons with human immunodeficiency virus (HIV) infection early in their disease stage is considered an effective means of reducing the impact of the disease. We compared the cost-effectiveness of HIV screening in three settings, sexually transmitted disease (STD) clinics serving men who have sex with men, hospital emergency departments (EDs), settings where patients are likely to be diagnosed early, and inpatient diagnosis based on clinical manifestations.
We developed the Progression and Transmission of HIV/AIDS model, a health state transition model that tracks index patients and their infected partners from HIV infection to death. We used program characteristics for each setting to compare the incremental cost per quality-adjusted life year gained from early versus late diagnosis and treatment. We ran the model for 10,000 index patients for each setting, examining alternative scenarios, excluding and including transmission to partners, and assuming HAART was initiated at a CD4 count of either 350 or 500 cells/µL. Screening in STD clinics and EDs was cost-effective compared with diagnosing inpatients, even when including only the benefits to the index patients. Screening patients in STD clinics, who have less-advanced disease, was cost-effective compared with ED screening when treatment with HAART was initiated at a CD4 count of 500 cells/µL. When the benefits of reduced transmission to partners from early diagnosis were included, screening in settings with less-advanced disease stages was cost-saving compared with screening later in the course of infection. The study was limited by a small number of observations on CD4 count at diagnosis and by including transmission only to first generation partners of the index patients.
HIV prevention efforts can be advanced by screening in settings where patients present with less-advanced stages of HIV infection and by initiating treatment with HAART earlier in the course of infection.
More than 1.1 million people in the U.S. are living with human immunodeficiency virus (HIV) infection, of whom approximately one fifth are undiagnosed and unaware of their infection.
The Centers for Disease Control and Prevention (CDC) and other public health agencies have promoted HIV testing in sexually transmitted disease (STD) clinics in the U.S. for almost two decades. To increase early diagnosis, the CDC now recommends that “diagnostic HIV testing and opt-out HIV screening be a part of routine clinical care in all health-care settings,” such as hospital emergency departments (EDs) and outpatient clinics.
Previous cost-effectiveness analyses of HIV testing have shown that population-based screening protocols are cost-effective except when there is very low HIV prevalence.
Recent literature indicates that early initiation of HAART may be both effective and cost-effective in preventing and treating HIV.
In this study, we evaluate the cost-effectiveness of HIV testing based on the CD4 cell count at diagnosis. To do this, we use illustrative examples comparing routine screening in STD clinics in urban areas with a large population of men who have sex with men (MSM); routine screening in hospital EDs; and diagnostic testing based on clinical manifestations of HIV infection in inpatient units. Routine screening is a process where age-eligible persons are offered point-of-care rapid HIV testing in accordance with CDC's revised recommendations for HIV testing in health care settings.
We developed the Progression and Transmission of HIV/AIDS (PATH) model to estimate the quality-adjusted life expectancy and costs of persons diagnosed with HIV infection at various stages of the disease. PATH is an individual Monte Carlo simulation health state transition model that tracks index patients through different phases of HIV from infection until death. It also includes transmission and follows the infected partners of the index patients until death. The model was developed in Microsoft Excel (Version 2003, Microsoft Corporation, Redmond, WA) with Visual Basic Applications (Version 6.3, Microsoft Corporation, Redmond, WA). Distributions, random numbers, and simulations were generated with @Risk (Version 4.5.7, industrial edition, Palisade Corporation, Ithaca, NY). The unit of time progression is a three-month period representing a quarter of a calendar year, with costs, quality-adjusted life years (QALYs) lost, and other outcomes computed for each quarter. A summary of key input parameters for the model is presented in
Variable | Base Case Value | Range | Source |
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CD4 cell count when infected (cells/µL) | 900 | 750–900 |
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HIV viral load set point (log10 copies/ml) | 4.5 | 4.0–5.0 | |
Cumulative quarterly probability of developing an opportunistic infection (%) | 0.3–35.3 |
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Minimum CD4 cell count to initiate HAART (cells/µL) | 350/500 |
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Suppressed HIV viral load level (log10 copies/ml) | 1.3 | 1.0–2.7 |
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Rebound HIV viral load level (log10 copies/ml) | 3.7 | 3.1–4.5 |
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Maximum number of HAART regimens | 4 |
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Probability of virologic suppression in HAART regimens 1–4 | 0.80 |
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Inpatient and outpatient resource utilization | 905–6,007 |
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Additional costs of opportunistic infections (each occurrence) | 3,492–20,542 |
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Additional cost of HAART (each quarter) | 4,143–13,699 |
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Acute | 0.751 | ||
Non-acute unaware | 0.093 | ||
Non-acute aware, not on HAART | 0.041 | ||
Non-acute aware, on HAART | 0.008 | ||
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Age at infection (years) | 35 | 30–40 |
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Discount rate for costs and quality-adjusted life years (QALYs) | 3% |
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Utility weights to estimate quality-adjusted life years (QALYs) | .935–.702 |
|
The lower and upper bounds reflect probabilities for CD4 cell counts of >500 cells/µL and 0–50 cells/µL, respectively.
Expert opinion (2009).
Costs vary by CD4 cell count, HAART usage, and history of AIDS-defining opportunistic infection.
These numbers represent costs for different opportunistic illnesses.
The lower and upper bounds reflect costs for the first and fourth HAART regimens. Costs for the other regimens lie in between these values.
Written communication, R. Song, Centers for Disease Control and Prevention, June, 2008.
Utility weights vary by CD4 cell count and presence of opportunistic infection.
We created three scenarios — one each for routine screening in STD clinics, routine screening in hospital EDs, and diagnoses made in inpatient settings. We ran the PATH model for 10,000 iterations for each scenario. Each iteration represented an individual, or an index person, whom we tracked from infection until death. The three scenarios differed only in CD4 cell count at diagnosis, undiagnosed seropositivity rate, associated screening costs, and assumptions made about the proportion of newly diagnosed persons who were linked to care.
For STD clinics, we assumed that persons visit a clinic for sexual education, health examinations, tests, and treatments, and that screening with a rapid HIV test is routinely conducted as a part of a program for STD prevention. We based our analysis on clinics located in an urban area with a large MSM population in which many persons are tested frequently. For diagnosis in an ED, we assumed that people visit an ED facility because they need urgent or emergency medical care and are routinely screened with a rapid test. For HIV diagnosis in inpatient facilities, we assumed that physicians conduct diagnostic testing (e.g., order HIV tests based on the clinical manifestations of patients) using conventional testing with an HIV enzyme immunoassay (EIA) of serum obtained by venipuncture. In all three settings, positive EIA and rapid tests were assumed to be confirmed with a Western blot.
For CD4 cell count at diagnosis in STD clinics, we used data from the One-on-One program of the Public Health – Seattle & King County (PHSKC) STD Clinic in Washington state from January 2006 to June 2008 (
Setting | Median CD4 Cell Count at Diagnosis (cells/µL) | Undiagnosed Seropositivity Rate in the Setting (%) | Cost of a Positive HIV Test/Negative HIV Test (2009 $) |
Total Program Cost per HIV-Infected Person (2009 $) | Linkage to Care Assumptions |
|
36 | 14.3 |
62.4/5.3 | 94.1 | 100% following diagnosis |
Range |
2–847 | ||||
Sample size |
69 |
||||
|
356 | 0.7 |
73.4/16.5 | 2,413.50 | 65% following diagnosis; 15% when CD4 cell count = 200 cells/µL; 20% as inpatients |
Range | 4–1,020 | ||||
Sample size | 55 |
||||
|
429 | 0.8 |
85.4/19.7 | 2,527.50 | 65% following diagnosis; 15% when CD4 cell count = 200 cells/µL; 20% as inpatients |
Range | 5–1,287 | ||||
Sample size | 398 |
Test costs were derived from
Range of CD4 cell count values in the source study.
Number of persons diagnosed in the source study.
Also, written communication with M. Golden, Public Health-Seattle & King County STD Clinic and the Center for AIDS & STD, University of Washington, Seattle, May, 2009.
For CD4 cell count at diagnosis in EDs, we used the results from a program of expanded HIV screening and on-site rapid testing primarily among adult Hispanic and non-Hispanic black patients in an urban academic ED in Oakland, CA (
For CD4 cell count at diagnosis in inpatient facilities, we used data on inpatients discharged with a new diagnosis of HIV or AIDS at two academic medical centers in Boston, MA (
We assumed that all patients diagnosed in the inpatient setting were linked to care in the quarter following diagnosis. For patients diagnosed in the ED and STD settings, we assumed that 65% were linked to care in the quarter following diagnosis, and that an additional 15% were linked to care by the time their CD4 cell count decreased to 200 cells/µL. The remaining 20% were assumed to be diagnosed as inpatients and were linked to care when their CD4 cell count decreased to 36 cells/µL, the median CD4 cell count at diagnosis in inpatient facilities.
We included the following phases of HIV infection as health states in the PATH model: acute infection, asymptomatic HIV infection, symptomatic HIV infection or acquired immunodeficiency syndrome (AIDS), and death. CD4 cell count and HIV viral load were the key determinants of disease progression in this model. When individuals were linked to care according to the above assumptions, they became eligible for HAART when their CD4 cell count decreased to a threshold of either 350 or 500 cells/µL to model previous and current treatment guidelines.
We predicted the probability of death during each quarter following diagnosis based on probabilities related to age and CD4 count at initiation of antiretroviral treatment.
We included both treatment costs and program costs in 2009 dollars estimated from the provider perspective. Treatment costs, derived from lifetime cost estimates by Schackman et al.,
To estimate program costs, we calculated only the marginal costs associated with testing and counseling in a particular setting for both HIV-infected and uninfected persons. We assumed that the settings evaluated already had HIV testing ability, so fixed and start-up costs were not included in our calculations. For inpatient facilities, we estimated the laboratory costs for conventional HIV testing and post-test counseling costs only for HIV-infected persons. For the ED and STD clinic settings, we included additional costs associated with rapid HIV testing, such as the costs for collecting specimens, the test kits, and post-test counseling for infected persons. We did not include the cost of administrative overhead and other costs that would have been incurred in the absence of a screening program. These program costs were varied in the sensitivity analysis, particularly for STD clinics, to reflect the repeat testing by MSM that often occurs in these settings.
To include the costs of persons who are tested but are not HIV infected, we computed program costs per HIV-infected person identified using the following formula for each setting: {[
We used a quarterly probability of HIV transmission per infected individual derived from an annual transmission rate to add HIV transmission from index patients to the model (
We evaluated secondary transmissions for a single generation of transmissions, i.e., transmission of HIV from index persons to their partners. We assumed that all partners who acquired infections from an index patient were diagnosed at a CD4 cell count of 500 cells/µL and were linked to care based on assumptions similar to those for persons diagnosed in ED and STD clinics. We standardized the linkage to care and treatment approach for infected partners because our primary interest is assessing the timing of diagnosis, linkage to care, and initiation of treatment of index patients on the cost-effectiveness of HIV diagnosis in different settings.
We estimated the costs (treatment and program) and QALYs lost to infection for each of the 10,000 index patients for each setting, and we computed the mean costs (
Incremental cost-effectiveness ratios may be negative, indicating cost-savings resulting from an increase in QALYs gained and a decrease in costs, or positive, showing that additional costs are required to achieve the gain in QALYs. For the latter, $100,000 per QALY gained represents a reasonable current estimate of the amount society is willing to pay to gain a QALY, although this amount may be even higher.
The base case simulation of the model for 10,000 iterations used point estimates for all variables in the model. A simulation of 10,000 iterations was necessary as the outcomes of the model reflected the probabilities of the occurrence of different events, such as the development of an opportunistic illness or the probability of dying during the quarter after HAART had been initiated, for each of the 10,000 index persons. Values of the model variables were drawn directly from the literature as noted previously and in
We then performed one-way sensitivity analyses of the impact of changes in selected variables on the STD-ED ICERs in the base case, assuming initiation of HAART at a CD4 count of 350 cells/µL. These variables included the undiagnosed HIV seropositivity rate in the different testing settings, overall program costs, STD clinic program costs, HIV treatment costs, age at infection, the probability of viral load suppression, and the transmission probabilities. The differences between testing in the STD and ED settings were analyzed in more detail by varying the CD4 count at diagnosis in the STD setting. The impact of linkage to care was examined by assuming that all index patients and their partners were immediately linked to care.
To reflect the overall uncertainty in decision analytic models, we also ran a probabilistic sensitivity analysis by assigning distributions around the point estimates of key variables based on accepted conventions.
In the base case analysis, assuming initial treatment with HAART at a CD4 count of 350 cells/µL and excluding the effects of HIV transmission (
Setting | Mean Discounted Costs (2009 $) | Mean Discounted Quality-Adjusted Life Years Lost to Infection (QALY) | Incremental Cost | Incremental QALY Gained | Incremental Cost-Effectiveness Ratio (ICER) ($/QALY) |
|
|||||
|
313,655 | 7.313 | – | – | – |
(95% CI) |
(310,854–316,456) | (7.229–7.397) | – | – | – |
|
398,833 | 4.851 | 85,178 | 2.462 | 34,597 |
(95% CI) | (395,898–401,768) | (4.767–4.935) | (81,121–89,235) | (2.343–2.581) | – |
|
399,844 | 4.851 | 1,012 | 0.000 | Undefined |
(95% CI) | (396,909–402,779) | (4.767–4.935) | (−3,140–5,162) | – | – |
|
|||||
|
817,419 | 14.097 | – | – | – |
(95% CI) | (809,196–825,642) | (13.904–14.290) | – | – | – |
|
816,824 | 10.130 | −595 | 3.967 | Cost-saving |
(95% CI) | (808,954–824,694) | (9.958–10.302) | (−11,977–10,787) | (3.708–4.226) | – |
|
800,716 | 9.866 | −16,108 | 0.264 | Cost-saving |
(95% CI) | (792,950–808,482) | (9.699–10.033) | (−27,164–−5,052) | (0.024–0.504) | – |
CI = confidence interval.
These ratios are undefined because there is no increase in QALYs between the emergency department and sexually transmitted disease clinic settings. The incremental cost would be divided by zero.
Screening in the setting is cost-saving compared with screening in the previous setting because there is an increase in QALYs and a decrease in costs.
In other model results (data not shown), index patients in both the ED and STD clinic settings started HAART at a median CD4 count of 345 cells/µL, had a mean time from infection to the start of HAART of 11.2 years, were on HAART for a mean time of 25.3 years, and experienced the onset of AIDS an average of 22.0 years from the time of infection. Mean life expectancy with infection was 36.5 years, which is consistent with the literature.
We estimated that persons diagnosed in STD clinics transmitted HIV to an average of 1.37 individuals compared with 1.44 individuals for those diagnosed in EDs and 1.83 individuals for those diagnosed in inpatient settings. When including the costs and QALYs gained that were associated with transmission, diagnosing persons in ED settings was found to be cost-saving compared with diagnosis in inpatient facilities (except at the upper bound of the 95% confidence interval for incremental costs). Diagnosis in STD clinics was also cost-saving when compared with ED settings and inpatient facilities (
In the case excluding transmission effects where treatment with HAART for the index patient was initiated at a CD4 count of 500 cells/µL (
Setting | Mean Discounted Costs (2009 $) | Mean Discounted Quality-Adjusted Life Years Lost to Infection (QALY) | Incremental Cost | Incremental QALY Gained | Incremental Cost-Effectiveness Ratio (ICER) ($/QALY) |
|
|||||
|
313,520 | 7.331 | – | – | – |
(95% CI) |
(310,726–316,314) | (7.247–7.415) | – | – | – |
|
396,164 | 4.942 | 82,644 | 2.389 | 34,594 |
(95% CI) | (393,273–399,055) | (4.859–5.025) | (78,624–86,664) | (2.271–2.507) | – |
|
417,883 | 4.580 | 21,719 | 0.362 | 59,997 |
(95% CI) | (414,935–420,831) | (4.498–4.662) | (17,590–25,848) | (0.245–0.479) | – |
|
|||||
|
867,404 | 13.519 | – | – | – |
(95% CI) | (858,483–876,325) | (13.334–13.704) | – | – | – |
|
859,993 | 9.712 | −7,411 | 3.807 | Cost-saving |
(95% CI) | (851,501–868,485) | (9.549–9.875) | (−19,728–4,906 | (3.560–4.054) | – |
|
856,432 | 8.986 | −3,561 | 0.726 | Cost-saving |
(95% CI) | (848,077–864,787) | (8.828–9.144) | (−15,474–8,352) | (0.499–0.953) | – |
CI = confidence interval.
Screening in the setting is cost-saving compared with screening in the previous setting because there is an increase in QALYs and a decrease in costs.
The results of the one-way sensitivity analyses in
Variable | Values | Incremental Cost-Effectiveness Ratio (ICER) | |
Excluding Transmission |
Including Transmission |
||
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Base Case | STD: 0.8%; ED: 0.7% | Undefined | Cost-saving |
Low | STD: 0.56%; ED: 0.5% | Undefined | Cost-saving |
High | STD: 3.0%; ED: 1.5% | Undefined | Cost-saving |
|
|
||
Base Case | 1.0 | Undefined | Cost-saving |
Low | 0.5 | Undefined | Cost-saving |
High | 2.0 | Undefined | Cost-saving |
|
|
||
Base Case | 1.0 | Undefined | Cost-saving |
Low | 0.5 | Undefined | Cost-saving |
High | 2.0 | Undefined | Cost-saving |
|
|
||
Base Case | 1.0 | Undefined | Cost-saving |
Low | 0.8 | Undefined | Cost-saving |
High | 1.2 | Undefined | Cost-saving |
|
|||
Base Case | 35 | Undefined | Cost-saving |
Low | 30 | Undefined | Cost-saving |
High | 40 | Undefined | Cost-saving |
|
|||
Base Case | 0.80 | Undefined | Cost-saving |
Low | 0.72 | Undefined | Cost-saving |
High | 0.88 | Undefined | Cost-saving |
|
|||
Base Case | Undefined | Cost-saving | |
Reduce by 25% | Undefined | Cost-saving | |
Reduce by 50% | Undefined | Cost-saving | |
|
|||
356 (same as ED) | Undefined | Undefined | |
376 | Undefined | Cost-saving | |
396 | Undefined | Cost-saving | |
416 | Undefined | Cost-saving | |
436 | Undefined | Cost-saving | |
|
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Base Case (65%, 15%, 20%) | Undefined | Cost-saving | |
100% | Undefined | Cost-saving |
These ratios are undefined because there is no increase in QALYs between the ED and STD clinic settings. The incremental cost would be divided by zero.
Screening in the STD clinic setting is cost-saving compared with screening in the ED setting because there is an increase in QALYs and a decrease in costs.
In sensitivity analysis (data not shown), the ED-inpatient ICERs were all in the same range as for the base case. Thus, the results for all the incremental cost-effectiveness ratios were robust in the sensitivity analysis.
When the model was run with a probabilistic sensitivity analysis around key variables (
Setting | Mean Discounted Costs (2009 $) | Mean Discounted Quality-Adjusted Life Years Lost to Infection (QALY) | Incremental Cost | Incremental QALY Gained | Incremental Cost-Effectiveness Ratio (ICER) ($/QALY) |
|
|||||
|
334,003 | 7.573 | – | – | – |
(95% CI) |
(330,517–337,489) | (7.468–7.678) | – | – | – |
|
401,807 | 5.506 | 67,804 | 2.067 | 32,803 |
(95% CI) | (398,584–405,030) | (5.413–5.599) | (63,056–72,552) | (1.927–2.207) | – |
|
409,952 | 5.320 | 8,145 | 0.186 | 43,790 |
(95% CI) | (406,744–413,160) | (5.228–5.412) | (3,598–12,692) | (0.056–0.316) | – |
|
|||||
|
794,190 | 13.491 | – | – | – |
(95% CI) | (785,663–802,717) | (13.296–13.686) | – | – | – |
|
793,861 | 10.330 | −329 | 3.161 | Cost-saving |
(95% CI) | (785,864–801,858) | (10.157–10.503) | (−12,019–11,361) | (2.900–3.422) | – |
|
783,900 | 9.896 | −9,961 | 0.434 | Cost-saving |
(95% CI) | (776,056–791,744) | (9.727–10.065) | (−21,163–1,241) | (0.192–0.676) | – |
CI = confidence interval.
Screening in the setting is cost-saving compared with screening in the previous setting because there is an increase in QALYs and a decrease in costs.
Setting | Mean Discounted Costs (2009 $) | Mean Discounted Quality-Adjusted Life Years Lost to Infection (QALY) | Incremental Cost | Incremental QALY Gained | Incremental Cost-Effectiveness Ratio (ICER) ($/QALY) |
|
|||||
|
339,830 | 7.498 | – | – | – |
(95% CI) |
(336,301–343,359) | (7.393–7.603) | – | – | – |
|
415,374 | 5.356 | 75,544 | 2.142 | 35,268 |
(95% CI) | (412,053–418,695) | (5.263–5.449) | (70,698–80,390) | (2.002–2.282) | – |
|
427,799 | 5.119 | 12,425 | 0.237 | 52,427 |
(95% CI) | (424,494–431,104) | (5.028–5.210) | (7,740–17,110) | (0.107–0.367) | – |
|
|||||
|
854,757 | 12.990 | – | – | – |
(95% CI) | (845,609–863,905) | (12.800–13.180) | – | – | – |
|
853,593 | 9.808 | −1,164 | 3.182 | Cost-saving |
(95% CI) | (844,936–862,250) | (9.641–9.975) | (−13,759–11,431) | (2.929–3.435) | – |
|
839,551 | 9.285 | −14,042 | 0.523 | Cost-saving |
(95% CI) | (830,981–848,121) | (9.125–9.445) | (−26,223–−1,861) | (0.292–0.754) | – |
CI = confidence interval.
Screening in the setting is cost-saving compared with screening in the previous setting because there is an increase in QALYs and a decrease in costs.
Although individuals should always be tested when they present with clinical manifestations in inpatient settings, HIV prevention efforts can be improved by screening in settings where people present with less-advanced stages of HIV infection and by initiating treatment with HAART at those earlier disease stages. Our results illustrate the cost-effectiveness of testing for HIV infection in settings where diagnosis at higher CD4 counts early in the course of disease is likely to occur and when treatment with HAART is initiated earlier in the course of infection.
If HAART is initiated at a CD4 count of 350 cells/µL, early diagnosis is cost-effective for index patients when comparing either the ED or STD clinic setting with inpatient diagnosis. Although the mean discounted program and treatment costs were higher in the ED and STD clinic settings compared with inpatient diagnosis because patients were on HAART regimens for longer periods, there were reduced QALYs lost to HIV infection due to the delayed onset of AIDS that resulted in incremental cost-effectiveness ratios of less than $100,000 per QALY gained.
In the base case analysis excluding transmission effects, diagnosis of index patients in STD clinics compared with the ED setting involved slightly higher costs because the earlier average diagnosis in STD clinics at a median CD4 count of 429 cells/µL (compared with 356 cells/µL in the ED setting) resulted in monitoring costs for an additional duration for the index patients. However, index patients in both settings were assumed to initiate a HAART regimen only when their CD4 counts decreased to 350 cells/µL. This fact accounted for the lack of differences in the disease progression variables, e.g., mean time from infection to start of HAART and mean time on HAART, for index patients in the STD clinic and ED settings and for the identical QALYs lost to infection in both settings.
However, earlier diagnosis in the STD clinic setting compared with the ED setting implies that index patients spend less time unaware of their serostatus in the non-acute phase of HIV infection, resulting in fewer transmissions per person. The costs of treating HIV infection comprise approximately 99% of the total costs associated with each setting. Even a small change in the number of transmissions per index patient (1.37 in STD clinics compared with 1.44 in EDs and 1.83 in the inpatient setting) results in significant treatment costs averted and makes screening in the ED setting cost-saving compared with inpatient diagnosis and screening in STD clinics cost-saving compared with the ED setting.
Thus, the cost-effectiveness issues change fundamentally when the benefits of reduced transmission are included in the model. Earlier diagnosis averts more secondary infections from the index patients. This outcome results from the modeled reduction in risky behavior following diagnosis and reduced transmission due to HIV viral load suppression achieved with HAART. These transmission effects resulted in a reduced number of secondary infections and reduced total costs (i.e., the combined costs of HIV infection for the index patient and their infected partners). Thus, settings where individuals were diagnosed earlier in their infections were cost-saving compared to settings with later diagnosis when transmission effects were included. These transmission benefits occurred even when there were very small differences in CD4 counts between index patients in the ED and STD clinic settings, given the treatment costs saved.
The analysis also changed when it was assumed that initiation of HAART began at a CD4 count of 500 cells/µL. Screening index patients in STD clinic settings was now cost-effective compared with ED settings because treatment for more patients began immediately when they were diagnosed with HIV, reducing the quality-adjusted life expectancy lost to HIV infection. Early treatment with HAART suppresses viral load, increases the patient's CD4 count and the maximum CD4 count attainable, and lowers the rate of CD4 count decline. All of these factors lower the probability of death for patients on HAART compared with HAART-naïve patients.
In our base case analysis, in which we assumed that all individuals in each setting were tested at the median CD4 count for that setting, 429 cells/µL for STD clinics, 356 cells/µL for EDs, and 36 cells/µL for inpatient settings, there were no changes in QALYs between the ED and STD clinic settings (
Our work is subject to a number of limitations. Data regarding disease status (CD4 cell count and HIV viral load at diagnosis) for the different HIV testing settings are very limited. In particular, the data we used for CD4 cell count at diagnosis were drawn from observations at a small number of locations. We, therefore, may not be able to generalize our findings to all EDs, STD clinics, and inpatient settings. Our analysis indicates that more data, particularly on CD4 count at diagnosis by setting, would be useful, given the differences between our base case results and those in the probabilistic sensitivity analysis where we allowed the CD4 count at diagnosis to vary around the median in each setting. On the other hand, our main finding, that diagnosing persons living with HIV at higher CD4 counts is cost-effective, is robust even with the limited data.
We may have under-estimated the costs for screening in STD clinics because we did not include any fixed costs and because many STD settings include clinics that strongly encourage repeat testing among their MSM clients. However, it would be inconsistent to use average costs (that include fixed costs) for STD clinics and marginal or incremental costs (that exclude fixed costs) for the ED and inpatient settings. Although repeat testing would increase STD clinic costs, we showed in the one-way sensitivity analysis that increasing STD screening costs by 100 percent did not change the results of the analysis. In a separate simulation (results not shown), we increased STD screening costs ten-fold from their base case value, and this variation also did not change the overall cost-effectiveness results of the analysis.
Data on linkage to care are sparse and may vary by subgroups in the population. The assumptions in this model are consistent with the existing literature, and our sensitivity analysis did not show any impact of changes in these assumptions. However, better data, particularly on linkage to care in different settings, will improve future modeling efforts.
The PATH model does not incorporate any measure of ongoing transmission beyond the first generation partners. Thus, we may underestimate the cost-effectiveness of early diagnosis as some additional secondary transmission might also be averted. On the other hand, some infections we consider to be averted might only be delayed. Use of a dynamic transmission model in an economic analysis could improve the estimates of the cost-effectiveness of different HIV screening programs, but would introduce additional complexity and uncertainty related to sexual mixing patterns, which are not well defined. Our estimates of the number of transmissions per index partner are consistent with those in the literature.
Our analysis with the PATH model showed that identifying persons with HIV while their CD4 counts are high is cost-effective and potentially cost-saving, when the effects of early diagnosis on transmission are considered. Although inpatient testing based on clinical manifestations of disease should always be undertaken, our results should prompt additional HIV case-finding efforts, particularly in venues such as STD clinics and emergency departments, where persons are likely to have higher CD4 counts at the time of diagnosis. The results can help guide decisions about implementing HIV screening and should be used to encourage the collection of additional data on CD4 count at diagnosis to identify more settings where persons are likely to be tested early in the course of disease. Our model also showed that initiating treatment with HAART earlier in the course of infection is cost-effective, making early diagnosis even more beneficial.
The Progression and Transmission of HIV/AIDS (PATH) Model.
(DOC)
We want to thank R. Scott Braithwaite, MD, for his comments on an earlier version of the appendix.