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How to estimate the cost of point-of-care CD4 testing in program settings: an example using the Alere Pima Analyzer in South Africa.

ABSTRACT: Integrating POC CD4 testing technologies into HIV counseling and testing (HCT) programs may improve post-HIV testing linkage to care and treatment. As evaluations of these technologies in program settings continue, estimates of the costs of POC CD4 tests to the service provider will be needed and estimates have begun to be reported. Without a consistent and transparent methodology, estimates of the cost per CD4 test using POC technologies are likely to be difficult to compare and may lead to erroneous conclusions about costs and cost-effectiveness. This paper provides a step-by-step approach for estimating the cost per CD4 test from a provider's perspective. As an example, the approach is applied to one specific POC technology, the Pima Analyzer. The costing approach is illustrated with data from a mobile HCT program in Gauteng Province of South Africa. For this program, the cost per test in 2010 was estimated at $23.76 (material costs = $8.70; labor cost per test = $7.33; and equipment, insurance, and daily quality control = $7.72). Labor and equipment costs can vary widely depending on how the program operates and the number of CD4 tests completed over time. Additional costs not included in the above analysis, for on-going training, supervision, and quality control, are likely to increase further the cost per test. The main contribution of this paper is to outline a methodology for estimating the costs of incorporating POC CD4 testing technologies into an HCT program. The details of the program setting matter significantly for the cost estimate, so that such details should be clearly documented to improve the consistency, transparency, and comparability of cost estimates.

SUBMITTER: Larson B

PROVIDER: S-EPMC3331987 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Project description:BackgroundPoint-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 findingsWe 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.ConclusionsPOC-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. Please see later in the article for the Editors' Summary.

Project description:BACKGROUND: Point-of-care CD4 testing can provide immediate CD4 reporting at HIV-testing sites. This study evaluated performance of capillary blood sampling using the point-of-care Pima™ CD4 device in representative primary health care clinics doing HIV testing. METHODS: Prior to testing, prescribed capillary-sampling and instrument training was undertaken by suppliers across all sites. Matching venous EDTA samples were drawn throughout for comparison to laboratory predicate methodology (PLG/CD4). In Phase I, Pima™ cartridges were pipette-filled with EDTA venous blood in the laboratory (N = 100). In Phase II (N = 77), Pima™ CD4 with capillary sampling was performed by a single operator in a hospital-based antenatal clinic. During subsequent field testing, Pima™ CD4 with capillary sampling was performed in primary health care clinics on HIV-positive patients by multiple attending nursing personnel in a rural clinic (Phase-IIIA, N = 96) and an inner-city clinic (Phase-IIIB, N = 139). RESULTS: Pima™ CD4 compared favourably to predicate/CD4 when cartridges were pipette-filled with venous blood (bias -17.3 ± STDev = 36.7 cells/mm3; precision-to-predicate %CV < 6%). Decreased precision of Pima™ CD4 to predicate/CD4 (varying from 17.6 to 28.8%SIM CV; mean bias = 37.9 ± STDev = 179.5 cells/mm3) was noted during field testing in the hospital antenatal clinic. In the rural clinic field-studies, unacceptable precision-to-predicate and positive bias was noted (mean 28.4%SIM CV; mean bias = +105.7 ± STDev = 225.4 cells/mm3). With additional proactive manufacturer support, reliable performance was noted in the subsequent inner-city clinic field study where acceptable precision-to-predicate (11%SIM CV) and less bias of Pima™ to predicate was shown (BA bias ~11 ± STDev = 69 cells/mm3). CONCLUSIONS: Variable precision of Pima™ to predicate CD4 across study sites was attributable to variable capillary sampling. Poor precision was noted in the outlying primary health care clinic where the system is most likely to be used. Stringent attention to capillary blood collection technique is therefore imperative if technologies like Pima™ are used with capillary sampling at the POC. Pima™ CD4 analysis with venous blood was shown to be reproducible, but testing at the point of care exposes operators to biohazard risk related to uncapping vacutainer samples and pipetting of blood, and is best placed in smaller laboratories using established principles of Good Clinical Laboratory Practice. The development of capillary sampling quality control methods that assure reliable CD4 counts at the point of care are awaited.

Project description:BackgroundDespite the upsurge in support and intervention of donor agencies in HIV care and treatment programing in Sub-Sahara African, antiretroviral (ART) programs are still confronted with access and coverage challenges which influence enrolment of new patients. This study investigated the validity of point of care BD FACSPresto™ CD4 analyzer for CD4+ cell count, overall agreement, correlation, sensitivity, and specificity in comparison to a reference standard flow cytometry method. We also assessed the feasibility of use among non-laboratorians.MethodsBlood samples from 300 HIV infected individuals were analyzed for CD4+ T cell and CD4%, using finger prick capillary sample from 150 PMTCT clients and 150 ART clients at Murtala Mohammed Specialist Hospital, Kano, Nigeria. Their venous samples were compared on a flow cytometry reference method using BD FACSCount CD4+ count system. The accuracy of the BD FACSPresto machine in comparison to BD FACSCount was evaluated. Statistical analysis was carried out using STATA (version 12). Bland-Altman method and correlation analysis were used to analyze agreement between both measurements. In addition, sensitivity and specificity of both measurements were determined. Statistical significance was set at p-value <0.05.ResultsThe mean bias and limit of agreement for CD4+ count between BD FACSPresto and BD FACS count machine were 7.49 (95% CI: 2.44 to 12.54) and -8.14 to 96.39 respectively. Further analysis revealed close agreement between BD FACSPresto and BD FACSCount with no significant difference between the two methods (p = .0.95). Using a threshold of 500 cells/μL, sensitivity and specificity of BD FACSPresto were 95.1% and 97.1% respectively, compared to BD FACSCount. There was no statistically significant difference in the misclassification between BD FACSPresto and BD FACSCount results (p = 0.23). Furthermore, sensitivity and specificity were similar when BD FACSPresto machine was operated by a nurse or laboratory scientist, there was no substantial difference in testing variability observed between laboratory and non-laboratory operators using the BD FACSPresto analyzer.ConclusionsOverall, BD FACSPresto Point of Care CD4+ count finger stick capillary blood results is a reliable method in comparison to venous sample cytometry method and no significant difference variability observed between laboratory personnel and non-laboratory operators. The BD FACSPresto is simple, more robust and easy to use equipment without significant variability in reliability by non-laboratory health care workers hence will be a valuable instrument in increasing access and coverage of CD4 estimations in developing countries. The introduction of the BD FACSPresto POC analyzer has a high potential in reducing patients waiting time and improving the overall quality of ART service and clients' satisfaction especially in rural settings.

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How to estimate the cost of point-of-care CD4 testing in program settings: an example using the Alere Pima Analyzer in South Africa.

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