The Clinical and Economic Impact of Point-of-Care CD4 Testing in Mozambique and Other Resource-Limited Settings: A Cost-Effectiveness Analysis

: Emily Hyle and colleagues conduct a cost-effectiveness analysis to estimate the clinical and economic impact of point-of-care CD4 testing compared to laboratory-based tests in Mozambique. Background: Point-of-care CD4 tests at HIV diagnosis could improve linkage to care in resource-limited settings. Our objective is to evaluate the clinical and economic impact of point-of-care CD4 tests compared to laboratory-based tests in Mozambique. Methods and Findings: We use a validated model of HIV testing, linkage, and treatment (CEPAC-International) to examine two strategies of immunological staging in Mozambique: (1) laboratory-based CD4 testing (LAB-CD4) and (2) point-of-care CD4 testing (POC-CD4). Model outcomes include 5-y survival, life expectancy, lifetime costs, and incremental cost-effectiveness ratios (ICERs). Input parameters include linkage to care (LAB-CD4, 34%; POC-CD4, 61%), probability of correctly detecting antiretroviral therapy (ART) eligibility (sensitivity: LAB-CD4, 100%; POC-CD4, 90%) or ART ineligibility (specificity: LAB-CD4, 100%; POC-CD4, 85%), and test cost (LAB-CD4, US$10; POC-CD4, US$24). In sensitivity analyses, we vary POC-CD4-specific parameters, as well as cohort and setting parameters to reflect a range of scenarios in sub-Saharan Africa. We consider ICERs less than three times the per capita gross domestic product in Mozambique (US$570) to be cost-effective, and ICERs less than one times the per capita gross domestic product in Mozambique to be very cost-effective. Projected 5-y survival in HIV-infected persons with LAB-CD4 is 60.9% (95% CI, 60.9%–61.0%), increasing to 65.0% (95% CI, 64.9%–65.1%) with POC-CD4. Discounted life expectancy and per person lifetime costs with LAB-CD4 are 9.6 y (95% CI, 9.6–9.6 y) and US$2,440 (95% CI, US$2,440–US$2,450) and increase with POC-CD4 to 10.3 y (95% CI, 10.3–10.3 y) and US$2,800 (95% CI, US$2,790–US$2,800); the ICER of POC-CD4 compared to LAB-CD4 is US$500/year of life saved (YLS) (95% CI, US$480–US$520/YLS). POC-CD4 improves clinical outcomes and remains near the very cost-effective threshold in sensitivity analyses, even if point-of-care CD4 tests have lower sensitivity/specificity and higher cost than published values. In other resource-limited settings with fewer opportunities to access care, POC-CD4 has a greater impact on clinical outcomes and remains cost-effective compared to LAB-CD4. Limitations of the analysis include the uncertainty around input parameters, which is examined in sensitivity analyses. The potential added benefits due to decreased transmission are excluded; their inclusion would likely further increase the value of POC-CD4 compared to LAB-CD4. Conclusions: POC-CD4 at the time of HIV diagnosis could improve survival and be cost-effective compared to LAB-CD4 in Mozambique, if it improves linkage to care. POC-CD4 could have the greatest impact on mortality in settings where resources for HIV testing and linkage are most limited. Background: AIDS has already killed about 36 million people, and a similar number of people (mostly living in low- and middle-income countries) are currently infected with HIV, the virus that causes AIDS. HIV destroys immune system cells (including CD4 cells, a type of lymphocyte), leaving infected individuals susceptible to other infections. Early in the AIDS epidemic, HIV-infected individuals usually died within ten years of infection. After effective antiretroviral therapy (ART) became available in 1996, HIV infection became a chronic condition for people living in high-income countries, but because ART was expensive, HIV/AIDS remained a fatal disease in low- and middle-income countries. In 2003, the international community began to work towards achieving universal ART coverage, and by the end of 2012, 61% of HIV-positive people (nearly 10 million individuals) living low- and middle-income countries who were eligible for treatment—because their CD4 cell count had fallen below 350 cells/mm3 of blood or they had developed an AIDS-defining condition—were receiving treatment. Why Was This Study Done?: In sub-Saharan Africa nearly 50% of HIV-infected people eligible for treatment remain untreated, in part because of poor linkage between HIV diagnosis and clinical care. After patients receive a diagnosis of HIV infection, their eligibility for ART initiation is determined by sending a blood sample away to a laboratory for a CD4 cell count (the current threshold for treatment is a CD4 count below 500/mm3, although low- and middle-income countries have yet to update their national guidelines from the threshold CD4 count below 350/mm3). Patients have to return to the clinic to receive their test results and to initiate ART if they are eligible for treatment. Unfortunately, many patients are “lost” during this multistep process in resource-limited settings. Point-of-care CD4 tests at HIV diagnosis—tests that are done on the spot and provide results the same day—might help to improve linkage to care in such settings. Here, the researchers use a mathematical model to assess the clinical outcomes and cost-effectiveness of point-of-care CD4 testing at the time of HIV diagnosis compared to laboratory-based testing in Mozambique, where about 1.5 million HIV-positive individuals live. What Did the Researchers Do and Find?: The researchers used a validated model of HIV testing, linkage, and treatment called the Cost-Effectiveness of Preventing AIDS Complications–International (CEPAC-I) model to compare the clinical impact, costs, and cost-effectiveness of point-of-care and laboratory CD4 testing in newly diagnosed HIV-infected patients in Mozambique. They used published data to estimate realistic values for various model input parameters, including the probability of linkage to care following the use of each test, the accuracy of the tests, and the cost of each test. At a CD4 threshold for treatment of 250/mm3, the model predicted that 60.9% of newly diagnosed HIV-infected people would survive five years if their immunological status was assessed using the laboratory-based CD4 test, whereas 65% would survive five years if the point-of-care test was used. Predicted life expectancies were 9.6 and 10.3 years with the laboratory-based and point-of-care tests, respectively, and the per person lifetime costs (which mainly reflect treatment costs) associated with the two tests were US$2,440 and $US2,800, respectively. Finally, the incremental cost-effectiveness ratio—calculated as the incremental costs of one therapeutic intervention compared to another divided by the incremental benefits—was US$500 per year of life saved, when comparing use of the point-of-care test with a laboratory-based test. What Do These Findings Mean?: These findings suggest that, compared to laboratory-based CD4 testing, point-of-care testing at HIV diagnosis could improve survival for HIV-infected individuals in Mozambique. Because the per capita gross domestic product in Mozambique is US$570, these findings also indicate that point-of-care testing would be very cost-effective compared to laboratory-based testing (an incremental cost-effectiveness ratio less than one times the per capita gross domestic product is regarded as very cost-effective). As with all modeling studies, the accuracy of these findings depends on the assumptions built into the model and on the accuracy of the input parameters. However, the point-of-care strategy averted deaths and was estimated to be cost-effective compared to the laboratory-based test over a wide range of input parameter values reflecting Mozambique and several other resource-limited settings that the researchers modeled. Importantly, these “sensitivity analyses” suggest that point-of-care CD4 testing is likely to have the greatest impact on HIV-related deaths and be economically efficient in settings in sub-Saharan Africa with the most limited health care resources, provided point-of-care CD4 testing improves the linkage to care for HIV-infected people. Additional Information: Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001725.