INTEGrating Ag-RDTs for COVID-19 in MNCH,HIV and TB Services in Cameroon and Kenya

Overview

Integration of antigen-detecting rapid diagnostic tests (Ag-RDT) for COVID-19 into services that provide care for vulnerable populations such as pregnant women, children, people with HIV infection, and patients with tuberculosis (TB) will identify more people with Coronavirus infection. This will allow for earlier treatment and tracing of contacts to decrease the spread of the coronavirus. This study is looking at two models for providing the testing in Maternal, Newborn and Child Health (MNCH), Tuberculosis (TB) and HIV clinics in Cameroon and Kenya. In some clinics, attendees with be screened for Coronavirus symptoms and history of exposure and if positive they will receive the rapid coronavirus test right in the clinic. In other facilities, all people attending the clinic with be provided with the coronavirus testing even if they screen negative to see how many people are infected but do not show any symptoms. Hospitalized and non-hospitalized patients with the coronavirus infection will be followed to document their illness and health outcomes. We will also ask health care workers about how well the testing in these clinics is working and what are some of their challenges, and collect information about the costs associated with both the models of testing.

Rationale: The use of simple, rapid and affordable antigen-detecting rapid diagnostic tests (Ag-RDT) to expand access to SARS-CoV-2 testing is being incorporated in many national COVID-19 response plans including Cameroon and Kenya. Targeting populations at high risk of COVID-19 disease, more severe outcomes, and onward transmission to other vulnerable populations such as pregnant women, people living with HIV, and patients with TB has the potential to mitigate the adverse effects of the SARS-CoV-2 pandemic. Data on SARS-CoV-2 infection in these populations in Africa and the feasibility of integrating Ag-RDT within MNCH, HIV, and TB services are limited. Most current programs use a screen and test strategy to identify symptomatic infection and those at risk due to exposure because of the limited availability and costs of broader testing. However, this strategy does not identify those with asymptomatic infection who also contribute to the spread of SARS-CoV-2 infection. Design: We will conduct a pragmatic cluster randomized trial to determine the SARS-CoV-2 case detection rate in facilities randomized to the standard "screen and test" model of SARS-CoV-2 Ag-RDT compared to a "test all" model of SARS-CoV-2 Ag-RDT in MNCH, HIV and TB clinics. We will describe clinical outcomes of SARS-CoV-2 infection and determine the feasibility and costs associated with each model. In each country, we propose to randomly allocate 10 purposively chosen facilities to the "test all" arm and standard of care arm in a 1:1 ratio. We plan to enroll at least 6000 patients per arm. Screening and testing data on all individuals attending MNCH, TB and HIV clinics will be recorded in clinic records and captured into an electronic database that will be accessed for this study. SARS-CoV-2 positive patients will be followed prospectively to determine the SARS-CoV-2 cascade from testing, ascertainment of disease status, linkage to care, disease progression, to treatment and outcomes for hospitalized and non-hospitalized patients. Disease progression and outcome data will be collected through phone interviews from patients who are not hospitalized and through interview and medical record extraction for hospitalized patients. SARS-CoV-2 positive study participants will be given contact tracing and testing forms for their contacts, which will capture anonymous testing data when contacts access the Ag-RDT testing at the health facility. The feasibility of the two models of integration will be captured through a structured questionnaire administered to health care workers and the collection of time-motion data to determine provider and patient time required to conduct the Ag-RDT testing. The service delivery costs of each model (such as personnel, training, equipment, tests, documentation) will be determined. Outcomes: Primary outcomes include the SARS-CoV-2 case detection rates and the number/ proportion of contacts tested and diagnosed with SARS-CoV-2 infection. Secondary outcomes include testing rates, linkage to care, disease progression, treatment and final outcome for SARS-CoV-2 infected patients and the feasibility and cost of integration in the two models. The primary analysis will summarize SARS-CoV-2 detection rates and associated 95% confidence intervals for each facility. The overall case identification rates for each arm will be estimated using inverse variance weighted method to combine rates across facilities. The effect of the intervention and associated 95% confidence intervals will be estimated using Poisson regression and expressed as a relative risk. Lower 95% confidence interval limit above 1 will indicate significant increase in the case detection rates due to "test all" intervention. Additional sub group analyses will examine effects of the "test all" intervention across the different clinics and different countries. To account for potential clustering of outcomes by health facility, we will estimate robust standard errors in the regression models. Duration: it is anticipated that study enrolment will take approximately 4 months with an additional 1 month to complete study follow-up, and 3-4 months for data analysis and reporting.