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A Cost-Effectiveness Analysis of Alternative HIV Retesting Strategies in Sub-Saharan Africa

Waters, Richard C MSc*; Ostermann, Jan PhD*†; Reeves, Travis D MD‡; Masnick, Max F BA*†; Thielman, Nathan M MD, MPH*†‡; Bartlett, John A MD*‡§‖; Crump, John A MB, ChB, DTM&H*‡§‖

JAIDS Journal of Acquired Immune Deficiency Syndromes: 15 April 2011 - Volume 56 - Issue 5 - pp 443-452
doi: 10.1097/QAI.0b013e3182118f8c
Epidemiology and Prevention

Background: Guidelines in sub-Saharan Africa on when HIV-seronegative persons should retest range from never to annually for lower-risk populations and from annually to every 3 months for high-risk populations.

Methods: We designed a mathematical model to compare the cost-effectiveness of alternative HIV retesting frequencies. Cost of HIV counseling and testing, linkage to care, treatment costs, disease progression, and mortality, and HIV transmission are modeled for three hypothetical cohorts with posited annual HIV incidence of 0.8%, 1.3%, and 4.0%, respectively. The model compared costs, quality-adjusted life-years gained, and secondary infections averted from testing intervals ranging from 3 months to 30 years. Input parameters from sub-Saharan Africa were used and explored in sensitivity analyses.

Results: Accounting for secondary infections averted, the most cost-effective testing frequency was every 7.5 years for 0.8% incidence, every 5 years for 1.3% incidence, and every 2 years for 4.0% incidence. Optimal testing strategies and their relative cost-effectiveness were most sensitive to assumptions about HIV counseling and testing and treatment costs, rates of CD4 decline, rates of HIV transmission, and whether tertiary infections averted were taken into account.

Conclusions: While higher risk populations merit more frequent HIV testing than low risk populations, regular retesting is beneficial even in low-risk populations. Our data demonstrate benefits of tailoring testing intervals to resource constraints and local HIV incidence rates.

From the *Duke Global Health Institute, Duke University, Durham, NC; †Center for Health Policy and Inequalities Research, Duke University, Durham, NC; ‡Division of Infectious Diseases and International Health, Duke University Medical Center, Durham, NC; §Kilimanjaro Christian Medical Centre, Moshi, Tanzania; and ‖Kilimanjaro Christian Medical College, Tumaini University, Moshi, Tanzania.

Received for publication May 3, 2010; accepted January 20, 2011.

Funding provided by the US National Institutes of Health. Investigator support was obtained from the Fogarty International Center (D43 PA-03-018, J.A.B., N.M.T., J.A.C.), the Duke Clinical Trials Unit and Clinical Research Sites (U01 AI069484-01 JAB, N.M.T., J.A.C.), the International Studies on AIDS Associated Co- infections award (U01 AI-03-036 J.A.B., N.M.T., J.A.C.), Center for HIV/AIDS Vaccine Immunology (U01 AI067854 J.A.B., J.A.C.), and the Duke University Center for AIDS Research (P30 AI 64518 J.O., J.A.B., N.M.T.).

Presented in part at the 16th Conference on Retroviruses and Opportunistic Infections, Montreal, Canada, February 8-11, 2009, abstract W-203.

R.C.W. and J.O. contributed equally to this work.

J.A.C., J.O., T.D.R., and N.M.T. originated the work. J.O., T.D.R., and R.C.W. developed the analytic framework. T.D.R. and R.C.W. identified the background information and input parameters for the model. M.F.M. and R.C.W. streamlined the original analyses and implemented the model in Matlab. J.A.C., J.O., and R.C.W. wrote the final article. J.A.B. and N.M.T. contributed to study interpretation and article editing. All authors contributed to the final version of the article.

The authors have no conflicts of interest to disclose.

Correspondence to: John A. Crump, MB, ChB, DTM&H, Division of Infectious Diseases and International Health, Duke University Medical Center, Box 102359, Durham, NC 27701 (email:

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF version of this article on the journal's web site (

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HIV counseling and testing (HCT) is promoted to increase serostatus awareness and entry into HIV care and treatment programs, particularly in low- and middle-income countries.1,2 Although uncertainty remains about its efficacy in producing behavior change,3 the role of HCT in linking HIV-infected persons to care and treatment services is undisputed.4-7 Moreover, an increased understanding of the relationship between plasma HIV-1 RNA concentration and risk for HIV transmission8,9 has prompted consideration of antiretroviral therapy as an HIV prevention strategy,10-12 further increasing the importance of HCT as an entry point into care.

The generalized nature of the HIV epidemic in sub-Saharan Africa has led to the promotion of universal HCT.13-16 Although many campaigns and strategies appropriately emphasize HCT for persons who have never tested,17-24 the risk for HIV infection for a given individual typically persists beyond the initial HCT encounter,3 raising the question of when, if at all, seronegative testers should retest.25

For nonpregnant HIV-seronegative testers, recommendations on when to retest for HIV are varied. Several national guidelines make no mention of the frequency with which a seronegative tester should continue to test;20,23,24 others specify a single test after 1 to 3 months in case the initial HIV antibody test was performed before development of HIV antibodies;24,26,27 some promote testing every 3 months or "periodically" for those who engage in high-risk behaviors19,22 and annual testing for the general population.22 The World Health Organization recently released guidelines on retesting, advising annual testing for persons living in countries with generalized HIV epidemics who are at high risk for HIV, who do not know the HIV status of their partner, or who have any other ongoing risk behavior.28 Testing every 3 months is discouraged in these recommendations as is retesting for individuals who have not had new potential exposures to HIV.

Mathematical models have been used previously to study the cost-effectiveness of one-time and repeated HIV screening in the United States, Russia, and South Africa.29-34 In simulating repeated screening for HIV, these models take into account long-standing undiagnosed prevalent cases, recent incident cases, and variable uptake of HCT, and the results have contributed to the formation of new guidelines for HIV screening.35,36 However, there has been little evaluation of the cost-effectiveness of different frequencies of retesting for persons who test HIV-seronegative, in which retesting concerns the detection of incident cases only. With national testing campaigns gradually raising rates of HIV serostatus awareness,13,21 cost-effectiveness analyses need to be extended to address the detection of incident HIV infections and the costs and benefits of alternative retesting frequencies.

To evaluate the question of when a seronegative tester should retest for HIV, we designed a mathematical model that compares the cost-effectiveness of alternative frequencies of HIV retesting using input parameters from sub-Saharan Africa when available.

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The model follows a cohort of individuals assumed to have initially tested HIV-seronegative; i.e., HIV prevalence at the start of the model is 0%. The cohort is followed for 45 years in the base-case scenario. Three different annual incidence rates mimic different HIV-risk environments comparable to those seen in previous studies in sub-Saharan Africa: 0.8% (low), 1.3% (medium), 4.0% (high).37-41 Twelve HCT strategies compare testing intervals ranging from every 3 months to once after 30 years. No further HIV risk or testing is assumed to occur during the final 15 years of the model. The base-case scenario uses a starting age of 20 years and age-specific mortality data for uninfected persons from South Africa.42 The primary outcome of interest was the cost per quality-adjusted life-year (QALY) gained from each testing strategy when compared with a scenario without HIV testing or treatment. Cost and QALY calculations account for secondary infections averted from effective antiretroviral therapy and behavior change (see subsequently). Incremental cost-effectiveness ratios for each strategy were calculated in comparison with the respective next longer retesting interval, with a single repeat test after 30 years compared with no retesting. The model is estimated iteratively in 3 month cycles; costs and benefits are discounted at 3% per year and expressed in year 2011 US dollars ($).43 Table 1 summarizes the base-case assumptions and sensitivity analysis ranges used in the model. Further discussion of the model and input parameters are presented in the Supplemental Appendix (see Supplemental Digital Content 1, The model was estimated in MATLAB Version R2009a (MathWorks Inc, Natick, MA); formulae are available from the authors on request.

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HIV Infection and Disease Progression Without Treatment

Individuals become infected with HIV at the given incidence rate but remain undiagnosed until testing. HIV disease progression is modeled by changes in CD4 counts with associated changes in quality-of-life values and mortality rates over time. The base case scenario assumes a median time from seroconversion to AIDS of 10.3 years and a 10-year cumulative mortality of 39%.44-49

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HIV Testing, Linkage to Care, and Treatment

Testing frequencies range from retesting every 3 months to one test after 30 years (Table 1). To avoid biases resulting from different lengths of follow-up after the last test, testing frequencies were chosen such that the last test for all strategies takes place 30 years after the start of the model. To compare the relative cost-effectiveness of each strategy, all individuals tested according to the given frequencies (see Appendix, Supplemental Digital Content 1, HIV tests were assumed to be rapid, point-of-care tests and have 100% sensitivity and specificity. Individuals testing seronegative continue to test at the specified frequency; individuals testing seropositive do not retest but are linked to care and then started on first-line highly active antiretroviral therapy (HAART) if the CD4 count is 350 cells/mm3 or less.50 After initiating HAART, a person may be lost to follow-up at a rate of 10% per year (range, 5-20% yearly in sensitivity analysis).51 Following HAART initiation and virologic suppression, a patient's CD4 count gradually increases as a function of the CD4 count at the start of HAART.29,52,53 Failure rates and mortality on first- and second-line HAART were assumed to be greatest immediately after initiation of HAART.54-58 It was assumed to take 6 months for virologic failure to be detected and patients to be switched to second-line HAART. To avoid unrealistic increases of CD4, CD4 counts were assumed to remain constant during effective second-line therapy in the base case; the effect of this assumption was explored in sensitivity analysis (see Appendix, Supplemental Digital Content 1, During nonsuppressive therapy, CD4 counts were assumed to drop again. Patients failing second-line therapy are kept on nonsuppressive therapy, consistent with guidelines.50,59,60

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With the most substantial increase in persons on HAART likely to occur in South Africa,21 costs for HAART therapy were derived for the drug regimens indicated by the South Africa 2010 guidelines for patients newly starting therapy, tenofovir + emtracitabine/lamivudine + efavirenz/nevirapine, and averaging the costs for the four possible regimens.61 Costs were similarly modeled for a second-line therapy of zidovudine + lamivudine + ritonavir-boosted lopinavir, consistent with the same guidelines. HCT cost per tester, laboratory costs, cost of prophylaxis for opportunistic infections, and cost per person for treatment of opportunistic infections were derived from studies in sub-Saharan Africa.62,63 Costs for healthcare facilities overhead, salaries of healthcare workers, and costs to the patient for time spent obtaining care are not explicitly included in the model, although significantly higher costs for HAART-where overhead costs can be implicit-were explored in the sensitivity analysis.

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Quality-of-Life Estimates

The quality-of-life value for HIV-uninfected persons was assumed to be 1. Quality-of-life values (Table 1) for an HIV-infected individual were assumed to be dependent on CD4 counts: CD4 <200, 200-349, and ≥350 cells/mm3 with values of 0.70, 0.82, and 0.94, respectively.64 Table 1 shows the base-case and sensitivity analysis values used.

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Secondary Transmission of HIV

Differential transmission rates were modeled for the acute, subacute (2-9 months following infection), chronic, and AIDS phases. Because it was assumed that a test for HIV is 100% sensitive and specific, it was also assumed that any diagnosis of HIV occurs after the acute phase. Combined with the mortality estimates for untreated and undiagnosed HIV disease, the base-case transmission rates, shown in Table 1, result in an undiscounted lifetime average of 0.94 infections per HIV-infected person per lifetime.8,65 Rates of HIV transmission were assumed to decline by 20% in the base-case scenario (range, 0-50% in sensitivity analysis) if an individual is aware of his or her HIV-infected status, a conservative estimate based on several studies in sub-Saharan Africa and the United States.66-70

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Sensitivity Analysis

Comprehensive sensitivity analyses for each of the three incidence scenarios evaluated the effect of alternative assumptions for the model input parameters. For each variation of a single input parameter, the most cost-effective testing strategy was identified and compared with that of the base-case scenario. The sensitivity of the primary outcome of cost per QALY to a 1% change in each input parameter was also evaluated. The sensitivity of the results to downstream infections prevented due to testing and treatment in the primary cohort was studied by taking into account tertiary in addition to secondary infections averted.

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Base-Case Scenario

In low-risk environments, the most cost-effective testing frequency was testing every 7.5 years (Table 2). The total cost per QALY gained from this testing frequency was $998. When cost savings and QALYs gained from preventing secondary HIV infections were taken into account, the overall cost per QALY gained was $701. For testing every 7.5 years, the total cost per HIV-infected case identified was $2030. Of the total cost, 4.5%, 68.1%, and 27.3% were from HCT costs, HAART costs, and laboratory costs, respectively.

In a medium-risk environment, the most cost-effective testing frequency was every 6 years with a total cost per QALY gained of $977. Factoring in benefits derived from transmission reductions resulted in testing every 5 years being most cost-effective with a total cost per QALY gained of $681 (Table 2). The cost per HIV-infected case identified for this testing frequency was $2123. Of the intervention cost, 4.0%, 68.5%, and 27.4% were from HCT costs, HAART costs, and laboratory costs, respectively.

In a high-risk environment, testing every 5 years was most cost-effective with a cost per QALY of $942. Including secondary infections averted into the analysis resulted in testing ever 2 years being the most cost-effective strategy with a total cost per QALY gained of $635. For this frequency, cost per HIV-infected case identified was $2325 with 3.2%, 69.2%, and 27.6% of the total cost from HCT costs, HAART costs, and laboratory costs, respectively. Annual testing and testing every 6 months resulted in incremental cost-effectiveness ratios of $833/QALY and $1899/QALY gained, respectively, when compared with the next least effective strategy and when benefits from secondary infections averted are accounted for.

Without testing, counseling, diagnosis, or treatment, the average number of undiscounted secondary infections per HIV-infected individual is 0.94 for the base-case scenarios. Values for reproductive numbers greater than 1.0 were assessed in the sensitivity analysis. Reductions in rates of HIV transmission resulting from testing, counseling, and treatment ranged from 5.4% (testing once after 30 years, 4.0% incidence) to 26.3% (testing every 3 months, 0.8% incidence). The percent reduction in transmission of HIV for each testing scenario is shown in Table 2.

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Sensitivity Analysis

Tables 3 through 5 display the effects of varying the input parameters, one at a time, on the most cost-effective testing frequency taking into account benefits from secondary infections averted. For the low, medium, and high HIV-risk scenarios, the variations studied in the sensitivity analysis produced ranges of every 3 to 30 years, every 2 to 15 years, and every 6 months to 7.5 years, respectively, as the most cost-effective testing frequencies. For no scenario evaluated in the sensitivity analysis was testing every 3 months the most cost-effective frequency. The greatest variation was produced by varying assumptions about HCT cost, annual declines in CD4 counts for untreated HIV, and rates of HIV transmission; decreasing HCT costs, faster CD4 count decline, and greater reductions in HIV transmissions from diagnosis and treatment favored more frequent testing. Importantly, although some variations of parameters did not change which frequency was most cost-effective, all variations affected the cost per QALY gained from each testing strategy. When the cost savings and benefits due to tertiary infections averted in addition to secondary infections averted were factored into the analysis using base-case parameters, the most cost-effective testing intervals shortened further (data not shown).

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Using a mathematical model, we compared alternative retesting strategies for HIV with best estimates for input parameters from sub-Saharan Africa. Expectedly, the most cost-effective testing frequency depended on the risk environment, with higher risk indicating more frequent testing.

Our sensitivity analysis shows that the most cost-effective strategy can vary substantially with changes in the input parameters. HCT cost, assumptions about the effect of a seropositive diagnosis on HIV transmission, and the cost of first-line HAART had the greatest effects across risk settings; rates of linkage to care, rates of CD4 count decline for untreated HIV, assumptions about quality-of-life values, rates of HIV transmission for untreated HIV, and cost of second-line HAART altered the optimal testing strategy for some risk scenarios.

The effect of the cost of HAART on Tables 3 through 5 merits further attention. Although HAART and laboratory costs are the primary drivers of overall cost in most scenarios considered, the percentage of total cost from HCT is what most influences the relative cost-effectiveness of the testing strategies. Lower treatment costs result in HCT costs comprising a greater percentage of the total intervention cost, approaching 67% for some testing strategies (data not shown), which creates a bias against frequent testing despite overall lower cost per QALY gained. For example, testing once after 30 years is the most cost-effective strategy for low-risk settings when first-line HAART is set at $50 per patient-year (Table 3), yet for this variation, even annual testing costs less per QALY gained than the most cost-effective strategy in the base case.

The results for the high-risk scenario approximate the recommendation for annual testing for high-risk individuals recently released from the World Health Organization,29 particularly if the cost of HCT per tester can be minimized and certainly in settings where the epidemic is rapidly growing, where including tertiary infections averted into the analysis is reasonable. The World Health Organization guidelines also discourage retesting for individuals who have no new exposure after a seronegative HIV test. However, knowing that no new exposure occurred may be difficult in the setting of a generalized epidemic, particularly for married women.13-16 In such circumstances, our results suggest that even populations of lower risk would benefit from continuing to retest for HIV.

Aside from uncertainties introduced by the input parameters, our model has several structural limitations and could be extended in several ways. Behavior change associated with HCT for seronegative testers, for which there is mixed evidence,3,71 would alter our cost-effectiveness estimates. Including a background of ongoing symptom-based case identification or exposure-related self-initiated testing at interim time points would affect the cost-effectiveness of the strategies as would allowing a mechanism for those who are lost to follow-up to later return to care. Treatment side effects and development of resistant strains that alter the effectiveness of available HAART options are not currently modeled. False-positive and false-negative test results are not taken into account, which would gain importance at more frequent testing intervals. Although studies have shown that CD4 counts at seroconversion vary with age, sex, and exposure group,44,45 and that rates of CD4 decline vary significantly with HIV-1 subtype,45 these factors were not included in the model and would be pertinent for certain subpopulations of testers. Although costs for healthcare personnel and overhead were not built into the model, these costs can be absorbed into the treatment costs, higher costs of which are studied in the sensitivity analysis. An extension of the model to incorporate these factors as well as modeling of disease progression and cost at the individual rather than cohort level is required for a comprehensive cost-effectiveness analysis of retesting strategies. Given the large number of assumptions and the structural limitations of the model, the specific results presented here and the precise values of estimates should be interpreted with caution. However, the broad trends observed in these data relating to higher risk populations meriting more frequent testing and the benefits of retesting even in lower risk populations are likely to be robust.

For our findings to provide guidance, policymakers need to understand variations in local HIV incidence rates and resource availability to select optimal testing strategies. This includes differential rates of HIV infection in population subgroups and an understanding of the cost and uptake of alternative HCT options such as provider-initiated testing, mobile HCT, fixed-venue HCT, and home-based testing. Selection biases associated with seeking HIV testing and variation in the cost per test have the potential to greatly influence the relative cost-effectiveness of each strategy in different venues. With limited resources, there is also a need to balance retesting against the more pressing need to detect as yet undiagnosed prevalent cases. Although guidelines on retesting can assist clients already presenting for HIV testing, significant obstacles remain in decreasing stigma and promoting uptake of HIV testing by those who have yet to ever test.13 Care also needs to be taken to ensure that HIV retesting recommendations do not present testing barriers for individuals who merit more frequent testing.

Although our model is theoretical, it has direct implications for HIV testing and treatment policy and practice. The results suggest substantial benefits from periodic retesting for HIV even for groups other than high-risk populations. These findings also show substantial savings in cost and QALYs from reduced HIV transmission as a result of periodic retesting and linkage to care, consistent with the paradigm of treatment as prevention.10-12 Recognizing the need for most sexually active adults, as opposed to only certain risk groups, to retest for HIV at regular intervals may help to decrease stigma and normalize HIV testing if testing is no longer viewed as “acknowledgement of bad behavior,” a reason that keeps some individuals from ever testing.72,73 Further work in this area and the convergence of several models on similar outcomes as presented here would provide more robust evidence in support of the adoption of guidelines for HIV retesting for lower-risk populations to complement those currently in place for higher-risk populations.

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We gratefully acknowledge the Hubert-Yeargan Center for Global Health for critical infrastructure support for the Kilimanjaro Christian Medical Centre-Duke University collaboration.

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HIV counseling and testing; retesting; cost-effectiveness; guidelines; sub-Saharan Africa

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