Integrated surveillance of HIV care in low-income and middle-income countries

The utility of population-based surveys to describe the continuum of HIV services for key and general populations

Monitoring the cascade or continuum of HIV services - ranging from outreach services to anti-retroviral treatment - has become increasingly important as the focus in prevention moves toward biomedical interventions, in particular, 'Treatment as Prevention.' The HIV continuum typically utilises clinic-based care and treatment monitoring data and helps identify gaps and inform programme improvements. This paper discusses the merits of a population-based survey-informed continuum of services. Surveys provide individual-level, population-based data by sampling persons both in and outside the continuum, which facilitate the estimation of population fractions, such as the proportion of people living with HIV in care, as well as the examination of determinants for being in or outside the continuum. Survey-informed cascades of services may especially benefit key populations at increased risk for HIV infection for who social marginalisation, criminalisation, and stigma result in barriers to access and retention in services, a low social visibility, mobility, and outreach-based services can compromise clinic-based monitoring. Adding CD4+ T-cell count and viral load measurements to such surveys may provide population-level information on viral load suppression, stage of disease, treatment needs, and population-level transmission potential. While routine clinic-based reporting will remain the mainstay of monitoring, a survey-informed service cascade can address some of its limitations and offer additional insights.

Treatment adherence and health outcomes in MSM with HIV/AIDS: patients enrolled in "one-stop" and standard care clinics in Wuhan China

BACKGROUND

Conducted in Wuhan China, this study examined follow-up and health markers in HIV patients receiving care in two treatment settings. Participants, all men who have sex with men, were followed for 18-24 months.

METHOD

Patients in a "one-stop" service (ACC; N = 89) vs those in standard care clinics (CDC; N = 243) were compared on HIV treatment and retention in care outcomes.

RESULTS

Among patients with CD4 cell count ≦350 cells/µL, the proportion receiving cART did not differ across clinic groups. The ACC was favored across five other indicators: proportion receiving tests for CD4 cell count at the six-month interval (98.2% vs. 79.4%, 95% CI 13.3-24.3, p = 0.000), proportion with HIV suppression for patients receiving cART for 6 months (86.5% vs. 57.1%, 95% CI 14.1-44.7, p = 0.000), proportion with CD4 cell recovery for patients receiving cART for 12 months (55.8% vs. 22.2%, 95% CI 18.5-48.6, p = 0.000), median time from HIV confirmation to first test for CD4 cell count (7 days, 95% CI 4-8 vs. 10 days, 95% CI 9-12, log-rank p = 0.000) and median time from first CD4 cell count ≦350 cells/µL to cART initiation (26 days, 95% CI 16-37 vs. 41.5 days, 95% CI 35-46, log-rank p = 0.031). Clinic groups did not differ on any biomedical indicator at baseline, and no baseline biomedical or demographic variables remained significant in the multivariate analysis. Nonetheless, post-hoc analyses suggest the possibility of self-selection bias.

CONCLUSIONS

Study findings lend preliminary support to a one-stop patient-centered care model that may be useful across various HIV care settings.

Beyond indicators: advances in global HIV monitoring and evaluation during the PEPFAR era

Monitoring and evaluation (M&E) is fundamental to global HIV program implementation and has been a cornerstone of the President's Emergency Plan for AIDS Relief (PEPFAR). Rapid results were crucial to demonstrating feasibility and scalability of HIV care and treatment services early in PEPFAR. When national HIV M&E systems were nascent, the rapid influx of funds and the emergency expansion of HIV services contributed to the development of uncoordinated "parallel" information systems to serve donor demands for information. Close collaboration of PEPFAR with multilateral and national partners improved harmonization of indicators, standards, methods, tools, and reports. Concurrent PEPFAR investments in surveillance, surveys, program monitoring, health information systems, and human capacity development began to show signs of progress toward sustainable country-owned systems. Awareness of the need for and usefulness of data increased, far beyond discussions of indicators and reporting. Emphasis has turned toward ensuring the quality of data and using available data to improve the quality of care. Assessing progress toward an AIDS-free generation requires that the global community can measure the reduction of new HIV infections in children and adults and monitor the coverage, quality, and outcomes of highly efficacious interventions in combination. Building national M&E systems requires sustained efforts over long periods of time with effective leadership and coordination. PEPFAR, in close collaboration with its global and national partners, is well positioned to transform the successes and challenges associated with early rapid scale-up into future opportunities for sustainable, cost-effective, country-owned programs and systems.

Modeling the cost-effectiveness of health programs: HIV testing and early treatment in the USA

The recent results from the NIH HIV Prevention Trials Network (HPTN) 052 trial, confirmed the relationship between plasma viral load and degree of infectivity of people living with HIV (PLHIV); when PLHIV are treated with antiretroviral therapy (ART), their infectivity is significantly reduced. This reiterates the importance of 'treatment for prevention', as well as the therapeutic function of ART. This and other studies raise a number of important questions, including when to start ART. Given the substantial number of PLHIV that are unaware that they are infected, should policy-makers set-up specific programs to identify these PLHIV and get them into treatment early? Long et al. tried to answer this question by modeling the cost-effectiveness of an expanded screening and treatment program in the USA but how good was their model, how can modeling exercises assist policy-makers in answering these difficult questions and what are some of the broader implications?