Using queueing models as a decision support tool in allocating point-of-care HIV viral load testing machines in Kisumu County, Kenya

Early HIV viral suppression associated with subsequent 12-month treatment success among people living with HIV in South Africa

BACKGROUND

We analyzed the STREAM (Simplifying HIV TREAtment and Monitoring) study to determine risk factors associated with HIV viraemia and poor retention 18 months after initiation of antiretroviral therapy (ART).

METHODS

The STREAM study was an open-label randomized controlled trial in Durban, South Africa, that enrolled 390 people living with HIV presenting for their first HIV viral load measurement ~6 months after ART initiation. We used modified Poisson regression with robust standard errors to describe associations between baseline characteristics and three HIV outcomes 18 months after ART initiation: HIV viraemia (>50 copies/mL), poor retention in HIV care, and a composite outcome of poor retention in care and/or HIV viraemia.

RESULTS

Approximately 18 months after ART initiation, 45 (11.5%) participants were no longer retained in care and 43 (11.8%) had viraemia. People with CD4 counts <200 and those with viraemia 6 months after ART initiation were significantly more likely to have viraemia 18 months after ART initiation (adjusted relative risk [aRR] 4.0; 95% confidence interval [CI] 2.1-7.5 and aRR 5.5; 95% CI 3.3-9.0, respectively). People who did not disclose their HIV status and had viraemia after ART initiation were more likely to not be retained in care 12 months later (aRR 2.6; 95% CI 1.1-6.1 and aRR 2.2; 95% CI 1.0-4.8). People with a CD4 count <200 and those with viraemia were more likely to not achieve the composite outcome 18 months after ART initiation.

CONCLUSIONS

Viraemia after ART initiation was the strongest predictor of subsequent viraemia and poor care retention. Understanding early indicators can help target our interventions to better engage people who may be more likely to experience persistent viraemia or disengage from HIV care.

Optimising HIV drug resistance testing laboratory networks in Kenya: insights from systems engineering modelling

BACKGROUND

HIV drug resistance (DR) is a growing threat to the durability of current and future HIV treatment success. DR testing (DRT) technologies are very expensive and specialised, relying on centralised laboratories in most low and middle-income countries. Modelling for laboratory network with point-of-care (POC) DRT assays to minimise turnaround time (TAT), is urgently needed to meet the growing demand.

METHODS

We developed a model with user-friendly interface using integer programming and queueing theory to improve the DRT system in Kisumu County, Kenya. We estimated DRT demand based on both current and idealised scenarios and evaluated a centralised laboratory-only network and an optimised POC DRT network. A one-way sensitivity analysis of key user inputs was conducted.

RESULTS

In a centralised laboratory-only network, the mean TAT ranged from 8.52 to 8.55 working days, and the system could not handle a demand proportion exceeding 1.6%. In contrast, the mean TAT for POC DRT network ranged from 1.13 to 2.11 working days, with demand proportion up to 4.8%. Sensitivity analyses showed that expanding DRT hubs reduces mean TAT substantially while increasing the processing rate at national labs had minimal effect. For instance, doubling the current service rate at national labs reduced the mean TAT by only 0.0%-1.9% in various tested scenarios, whereas doubling the current service rate at DRT hubs reduced the mean TAT by 37.5%-49.8%. In addition, faster batching modes and transportation were important factors influencing the mean TAT.

CONCLUSIONS

Our model offers decision-makers an informed framework for improving the DRT system using POC in Kenya. POC DRT networks substantially reduce mean TAT and can handle a higher demand proportion than a centralised laboratory-only network, especially for children and pregnant women living with HIV, where there is an immediate push to use DRT results for patient case management.