Baggaley, R

Cambiano, V

Corbett, E

Figueroa, C

Hatzold, K

Johnson, C

Perez Gonzalez, M

Phillips, A

Prat, I

Sands, A

Taegtmeyer, M

Terris-Prestholt, F

Urassa, W

English

UCL Discovery

TUPEC0834 AUTHORS:  C. Johnson1,2, C. Figueroa1, V. Cambiano3, A. Phillips3, A. Sands4, W. Urassa4, M. Perez Gonzalez4, I. Prat4, F. Terris-Prestholt2, E. Corbett2,5, K. Hatzold6, M. Taegtmeyer7, R. Baggaley1 1. World Health Organization, Department of HIV, Geneva, Switzerland; 2. London School of Hygiene and Tropical Medicine, London, UK ; 3. University College London, London, UK; 4. World Health Organization, Essential Medicines and Health Products, Geneva, Switzerland; 5. Malawi Liverpool Wellcome Trust, Blantyre, Malawi; 6. Population Services International, Harare, Zimbabwe; 7. Liverpool School of Tropical Medicine, Liverpool, UK    A clinical utility risk-benefit analysis for HIV self-testing CONCLUSION  In the majority of scenarios, risks were exceeded by the benefits of diagnosis and linkage to HIV prevention and treatment services. While HIVST's clinical utility is greatest when performance is greatest, this analysis suggests that net benefit can be achieved even with performance below currently acceptable standards (≥90% specificity and ≥70% sensitivity) in most all settings considered; provided services linking self-testers to HIV prevention and treatment services are functional.  For very high prevalence settings, such as those among female sex workers in Johannesburg, South Africa (72%),  with very low linkage (23%) , ≥90% sensitivity and specificity would be needed to observe a net positive benefit. This emphasizes the need to focus on effective linkage following HIV self-testing, as with all testing services.The likelihood of achieving a high-level of clinical utility using HIVST should be high as studies have shown HIVST  kits can achieve sensitivity (80–100%) and specificity (95.1–100%).  BACKGROUND As countries work to achieve the United Nation's  “90-90-90” testing and treatment targets, many countries are adopting WHO's recommendation to offer HIVST as an additional HIV testing approach. Many self-testers can use an HIV rapid diagnostic test (RDT) correctly and achieve results similar to trained testers. Although HIVST does not provide an HIV-positive diagnosis some concern about potential false reactive and false non-reactive self-test results remain. Thus, we conducted a clinical utility risk benefit analysis to establish a minimum performance threshold for HIVST at which public health benefit can be  achieved. METHODS To assess HIVST’s clinical utility and weigh performance-related risks and benefits: sensitivity (65-99.8%), specificity (65-100%), HIV prevalence (0.01-15%), linkage to care (50-85%) and linkage to prevention (0-35%) were considered. Ranges were based on literature review and available programmatic data from South Africa. Different scenarios characterized by varying levels of these factors were simulated. We then sampled from distributions to generate each scenario; and re-ran simulations excluding scenarios in which no benefit was achieved. A  net benefit score was derived as Total Benefit (calculated as the sum of true reactive linked to care multiplied by three and; the true nonreactive linked to prevention) minus Total Risk (sum of false reactive and; false nonreactive multiplied by two). The weight for false nonreactives, false reactives, true reactives linked to care and true nonreactive linked to prevention were weighted based on expert consultation. The proportion of scenarios with positive net benefit was calculated.  RESULTS 61% of scenarios with ≥70% sensitivity and ≥90% specificity yielded greater benefit than risk. In high prevalence scenarios (prevalence ≥5-10%), positive net benefit was observed at ≥80% specificity and ≥70% sensitivity. For very low prevalence scenarios (prevalence 0.1-1%), net benefit marginally increased when sensitivity increased from 70% to 90%. Linkage to prevention drove net benefit; when moderate (20-30%) benefit was achieved at ≥80% specificity, when low (0-10%) ≥90% specificity was needed. Linkage to care (varied from 50-60% to 70-80%) had modest impact except in very high prevalence settings, e.g. among female sex workers in Johannesburg  a positive net benefit in all scenarios was not observed until linkage to care was ≥50%. Figure 3. Case Example female sex workers in Johannesburg, South Africa CURRENT SCENARIO  Population: 7,697   HIV Prevalance: 72%  Linkage to Prevention: 15%   Linkage to Care: 23%    Combinations with  Net Benefit: 75%  Very Low  HIV Prevalence (0.1% and 1%)       Specificity Levels  Total Aggregate 99% 95% 90% 80% 70% Sensitivity Levels 99% 88% 75% 63% 25% 0% 50% 95% 88% 75% 63% 25% 0% 50% 90% 88% 75% 63% 25% 0% 50% 80% 88% 75% 59% 25% 0% 49% 70% 78% 75% 53% 25% 0% 46%   Aggregate 85% 75% 60% 25% 0% 49% Sensitivity Levels       Specificity Levels  Total Aggregate 99% 95% 90% 80% 70% Sensitivity Levels 99% 88% 75% 56% 22% 3% 49% 95% 88% 75% 53% 19% 3% 48% 90% 88% 75% 53% 16% 0% 46% 80% 84% 72% 44% 6% 0% 41% 70% 78% 63% 31% 3% 0% 35% Aggregate 85% 72% 48% 13% 1% 44% Low Linkage to Prevention (0% and 10%) Low/Moderate Linkage to Care (50% and 60%)       Specificity Levels  Total Aggregate 99% 95% 90% 80% 70% Sensitivity Levels 99% 94% 88% 75% 44% 16% 63% 95% 94% 88% 75% 44% 9% 62% 90% 94% 88% 75% 44% 6% 61% 80% 91% 84% 69% 38% 6% 58% 70% 88% 78% 63% 31% 0% 52%  Aggregate 92% 85% 71% 40% 8% 59% Tables 2a-f.  Percent of situations with net positive results, according to HIV prevalence, linkage to prevention and care  High HIV Prevalence (5% and 10%)       Specificity Levels  Total Aggregate 99% 95% 90% 80% 70% 99% 100% 100% 94% 72% 38% 81% 95% 100% 100% 91% 69% 31% 78% 90% 100% 100% 91% 66% 25% 76% 80% 100% 97% 84% 56% 16% 71% 70% 100% 88% 78% 47% 6% 64%   Aggregate 100% 97% 88% 62% 23% 74%       Specificity Levels Total  Aggregate 99% 95% 90% 80% 70% Sensitivity Levels 99% 100% 100% 100% 75% 34% 82% 95% 100% 100% 100% 75% 28% 81% 90% 100% 100% 100% 75% 25% 80% 80% 100% 100% 100% 75% 16% 78% 70% 100% 100% 100% 69% 6% 75% Aggregate 100% 100% 100% 74% 22% 79% Moderate Linkage to Prevention (20% and 30%) High Linkage to Care (70% and 80%)    Specificity Levels Total  Aggregate 99% 95% 90% 80% 70% Sensitivity Levels 99% 94% 88% 81% 53% 22% 68% 95% 94% 88% 78% 50% 22% 66% 90% 94% 88% 78% 47% 19% 65% 80% 94% 88% 75% 44% 9% 62% 70% 91% 84% 69% 41% 6% 58% Aggregate 93% 87% 76% 47% 16% 64% 020,00040,00060,00080,000100,0001x 2x 3x 4xNet BenefitFigure 2. Varying weights of risk and benefit by HIV prevalence Scenario 2 HIV Prevalence: 5% Specificity: 90%; Sensitivity:70% Linkage to Care: 60%  Linkage to Prevention: 10% 020,00040,00060,00080,000100,0001x 2x 3x 4xNet BenefitWeighting020,00040,00060,00080,000100,0001x 2x 3x 4xNet BenefitFalse Non-ReactivesTrue Reactives Linked to CareWeightingScenario 1 HIV Prevalence: 1% Specificity: 90%; Sensitivity:70% Linkage to Care: 60%  Linkage to Prevention: 10% Scenario 3 HIV Prevalence: 60% Specificity: 90%; Sensitivity:70% Linkage to Care: 50%  Linkage to Prevention: 15% WeightingFigure 1.  Subset of scenarios with net positive results Base case scenario: Linkage to care 60%; Linkage to prevention 30% Specificity is highly impactful on false reactives and represents a large portion of total risks and benefits.  Increasing sensitivity leads to minor increases in true reactives linked to care and decreases in false non-reactives.   This is mainly impactful at higher prevalence levels IMPROVED LINKAGE  SCENARIO  Population: 7,697   HIV Prevalance: 72%   Linkage to Prevention: 15%   Linkage to Care: 50%    Combinations with  Net Benefit: 100%  Changes in the weighting of false non-reactives and true reactives linked to care will impact the net benefit of testing scenario differently depending on characteristics of the scenario, such as HIV prevalence and linkage rates.  LIMITATIONS  This clinical utility analysis assessed HIVST risk and benefit based on performance alone and did not consider additional social benefits or possible harm. In addition, the weightings utilized in the model were derived from expert opinion due to the absence of sufficient data. We are currently exploring updating this analysis using data from the HIV self-test Africa (STAR) project to more explicitly model the consequences of false reactive and non-reactive, as well as the benefits of correct results.        Specificity Levels  Total Aggregate  99% 95% 90% 80% 70% Sensitivity Levels 99% 94% 88% 78% 48% 19% 65% 95% 94% 88% 77% 47% 16% 64% 90% 94% 88% 77% 45% 13% 63% 80% 92% 86% 72% 41% 8% 60% 70% 89% 81% 66% 36% 3% 55%  Aggregate  93% 86% 74% 43% 12% 61% Table 1a.  Percent of situations with net positive results, overall     Specificity Levels  Total Aggregate 100% 99% 98% 97% 96% 95% 94% 93% 92% 91% 90% Sensitivity Levels 100% 100% 94% 91% 88% 88% 88% 88% 88% 86% 86% 78% 88% 99% 100% 94% 91% 88% 88% 88% 88% 88% 86% 86% 78% 88% 98% 100% 94% 91% 88% 88% 88% 88% 88% 86% 86% 78% 88% 97% 100% 94% 89% 88% 88% 88% 88% 88% 86% 84% 78% 88% 96% 100% 94% 89% 88% 88% 88% 88% 88% 86% 84% 78% 88% 95% 100% 94% 89% 88% 88% 88% 88% 86% 86% 84% 77% 88% 94% 100% 94% 89% 88% 88% 88% 88% 86% 86% 84% 77% 88% 93% 100% 94% 89% 88% 88% 88% 88% 86% 86% 84% 77% 88% 92% 100% 94% 89% 88% 88% 88% 88% 86% 84% 84% 77% 88% 91% 100% 94% 89% 88% 88% 88% 88% 86% 84% 84% 77% 88% 90% 100% 94% 88% 88% 88% 88% 88% 86% 84% 83% 77% 87%   Aggregate 100% 94% 89% 88% 88% 88% 88% 87% 86% 85% 77% 88% Table 1b.  Percent of situations with net positive results, overall  (90-100%)      Tables 1a - 2f show a sample of scenarios yielding a net positive benefit within various combinations of sensitivityand specificity.  In Tables 1a-b each combination of sensitivity and specificity contains a sample of 64 scenarios derived through combining four rates of HIV prevalence, four levels of linkage to care and four levels of linkage to prevention. In Tables 2a-f each combination of sensitivity and specificity contains 32 scenarios derived through combining two rates of HIV prevalence, four levels of linkage to care and four levels of linkage to prevention. Levels used in combinations:  HIV Prevalence:  0.1%, 1%, 5%, 10% Rate of Linkage to Care (of Reactive Tests):  50%, 60%, 70%, 80% Rate of Linkage Prevention (of Non-Reactive Tests):  0%, 10%, 20%, 30% 

A clinical utility risk-benefit analysis for HIV self-testing

https://discovery.ucl.ac.uk/10085797/1/clinical-utility-analysis-hivselftesting.pdf

A clinical utility risk-benefit analysis for HIV self-testing

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