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.
When the effects of transmission were included in the analysis, screening in the ED and STD clinic settings was cost-saving compared with inpatient testing.
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 (), given that index patients in both the ED and STD clinics initiated HAART at the same time following infection, i.e., when their CD4 counts decreased to 350 cells/µL. When we drew values from cumulative distributions around the median CD4 counts at diagnosis in the different settings in the probabilistic sensitivity analysis, screening of index patients in STD clinics became cost-effective compared with ED diagnosis (an ICER of $44,000 per QALY in ). Due to the nature of these distributions, individuals were tested in both settings at CD4 counts higher and lower than the median. For example, model results (not presented) showed that 25% of individuals in the ED setting were diagnosed at CD4 counts of 185 cells/µL or less compared with 309 cells/µL for STD clinics. Thus, individuals in the ED would, on average, have had a much more advanced disease stage at diagnosis compared with those diagnosed in the STD clinic setting, although both would be referred to treatment immediately after diagnosis. Therefore, if individuals in the STD clinic and ED settings are actually tested at CD4 counts that vary widely from the median, there can be a benefit to the index patients of testing and initiating HAART, on average, earlier in STD clinics than in emergency departments.
Limitations of the Analysis
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.
Decreasing the transmission probabilities in the sensitivity analysis reduced the number of transmissions per index partner but did not affect the overall cost-effectiveness results.
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.