Point-of-care HIV maternal viral load and early infant diagnosis testing around time of delivery at tertiary obstetric units in South Africa: a prospective study of coverage, results return and turn-around times

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Study Justification:
– Maternal viral load monitoring (mVL) and early infant diagnosis (EID) are crucial for preventing mother-to-child transmission of HIV.
– Point-of-care testing can provide better outcomes compared to centralized laboratory testing.
– This study aimed to implement point-of-care mVL and EID testing around delivery at high volume obstetric units in South Africa.
Highlights:
– The study was conducted at four high volume tertiary obstetric units in Gauteng, South Africa.
– A total of 8,147 live births to pregnant women living with HIV were recorded during the implementation period.
– The coverage of point-of-care mVL testing was 35.6%, and the coverage of point-of-care EID testing was 61.9%.
– Proportions of mothers and infants with results returned prior to discharge were 74.3% and 73.0%, respectively.
– Turn-around time for point-of-care testing was longer for EID compared to mVL testing.
– Point-of-care testing results were comparable to those from laboratory testing.
Recommendations:
– Further scale-up of point-of-care mVL and EID testing is needed to improve coverage and outcomes.
– Health system factors at the facility level should be addressed to ensure successful implementation.
– Efforts should be made to reduce analytical error rates.
Key Role Players:
– Routine staff at obstetric units for specimen collection and testing.
– Dedicated point-of-care operators for conducting testing.
– Counsellors for providing post-test counselling.
– Clinicians responsible for routine care.
– Data coordinator for data management.
Cost Items for Planning Recommendations:
– Personnel costs for routine staff, point-of-care operators, counsellors, clinicians, and data coordinator.
– Training and capacity building costs for staff.
– Equipment and supplies for point-of-care testing.
– Quality assurance measures.
– Data management and analysis costs.
– Communication and reporting costs.
– Monitoring and evaluation costs.
Please note that the above information is a summary of the study and its findings. For more detailed information, please refer to the publication in the Journal of the International AIDS Society, Volume 23, No. 4, Year 2020.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a prospective study conducted at four high volume tertiary obstetric units in South Africa. The study provides descriptive statistics and regression analysis to describe outcomes such as coverage of testing, results return, and turn-around time. The study also compares point-of-care testing to centralized laboratory testing. However, the abstract does not provide information on the sample size, representativeness of the study population, or potential limitations of the study. To improve the evidence, the abstract could include these missing details and provide a clear statement of the study’s limitations.

Introduction: Maternal viral load monitoring (mVL) and early infant diagnosis (EID) are necessary to achieve elimination of mother-to-child transmission of HIV. Point-of-care testing can achieve better outcomes compared to centralized laboratory testing (CLT). We describe the first implementation of point-of-care (POC) mVL and EID testing around delivery at four high volume tertiary obstetric units (TOUs) in Gauteng, South Africa. Methods: Prospective study of pregnant women living with HIV (WLHIV) and their infants. During the period 1 June 2018 to 31 March 2019, routine staff collected blood specimens from women and their infants around delivery. Specimen collection occurred throughout the week while dedicated POC operators, conducted testing during working hours on weekdays. Descriptive statistics and multivariable Poisson regression with robust error variance were used to describe outcomes and associated factors. Outcomes determined were (i) coverage of mVL and EID testing defined as a proportion of live births to WLHIV admitted at each facility (ii) results returned prior to discharge (iii) turn-around time (TAT) and iv) performance of POC testing compared to CLT. Results: In total, 8147 live births to pregnant WLHIV were recorded in the implementation period. Of these, 2912 mVL and 5074 EID specimens were included in the analysis, with 131 (4.5%) mVL and 715 (14.1%) EID specimens having initial invalid/error results. Overall coverage of POC mVL and EID testing was 35.6% (range 20.9% to 60.1%) and 61.9% (range 47.0% to 88.0%) respectively. Proportions of POC tested mothers and infants with results returned prior to discharge were 74.3% (range 39.0% to 95.7%) and 73.0% (range 50.0 to 97.9%). Return of results was independently associated with TOU, after-hours specimen collection, having an initial invalid or error result and period of implementation. Overall TAT for specimens collected from mother-infant pairs where both had POC testing, during weekdays was longer for EID compared to mVL testing (median 3.3 hours vs. 2.9 hours, p-value sign test <0.001). POC results were comparable to those from laboratory testing. Conclusion: Accurate and timely POC mVL and EID testing around delivery was implemented with variable success across TOUs. Further scale up would need to address health system factors at facility level and high analytical error rates.

This implementation study was conducted at four high volume TOUs in Gauteng during the period 1 June 2018 to 31 March 2019. The TOUs were located in Johannesburg Regions B, D, F, and in Tshwane district. All had average monthly total number of live births of 520 to 1650 in the preceding year, of which 119 to 353 were to pregnant WLHIV and delivered pregnant women referred from lower level obstetric units in their catchment areas. During implementation, routine VL testing around the time of delivery was a newly introduced practice while EID testing at birth for all HIV‐exposed infants had been established practice since 2015. Prospective study of pregnant or early post‐partum WLHIV admitted to labour or post‐delivery wards and their new‐born infants until return of results or discharge. All WLHIV admitted to labour or postnatal wards at the four TOUs during the study period were offered POC VL and or birth PCR testing by routine staff. To be eligible for enrolment and specimen collection for the study, WLHIV and or their infants had to be admitted in labour or postnatal wards and be willing to provide verbal consent. For both WLHIV and infants, two specimens were collected – one for POC and the other for CLT. Specimens were collected by doctors and nurses as part of their routine duties. Non‐study patients could access HIV EID and VL POC testing where clinically indicated and these were labelled “miscellaneous.” POC testing was conducted by a dedicated POC operator working in a designated POC testing room. While specimen collection took place throughout the week including weekends and after‐hours at all but one TOU (Johannesburg Region B), testing took place during working hours – 08:00 to 16:00 – on weekdays only. During the first three months of implementation, all specimens collected were processed and tested while afterwards only weekday specimens and those weekend specimens which had a reasonable probability of results being returned were tested. At two of the busier TOUs (Johannesburg region B and D), two dedicated counsellors hired through the study assisted with return of results and post‐test counselling before discharge. For POC VL testing, Xpert™ HIV‐1 VL was used while POC EID testing was conducted using either the Xpert™ HIV‐1 Qual or the m‐PIMA HIV‐1/2 Detect assays. Upon receiving appropriate samples, POC operators entered specimen details into the instruments’ information management system and tested the specimens according to manufacturers’ specifications and sample volumes. Mothers and infants whose results were reported as error, invalid or no result had the test repeated on the same sample and if there was still no conclusive result, a second sample was collected as soon as possible after the result. Infants with a positive POC EID result were repeat tested on the same assay using the same sample to ensure reproducibility, and a second sample was requested for confirmatory testing on the alternate EID assay. Additional information on specimen collection, testing procedures and quality assurance are provided in Supplementary Document SD1. POC operators printed results and gave them to TOU staff or counsellors for post‐test counselling. High maternal VL and positive EID results were automatically sent to the clinicians responsible for routine care by SMS for action. WLHIV who had high VLs were prescribed enhanced adherence counselling and repeat VL after three months while their EID negative infants were offered high‐risk prophylaxis, consisting of either (i) daily zidovudine (AZT) and nevirapine (NVP) for six weeks OR (ii) daily NVP for 12 weeks. Virally suppressed (VL  50 copies/mL, VL > 1000 copies/mL Bland‐Altman analysis. Kappa Statistic, sensitivity and specificity of POC testing compared to CLT EID, early infant diagnosis; mVL, maternal HIV viral load; POC, point‐of‐care test. The protocol for this implementation study was approved by the University of the Witwatersrand Human Subjects Research Ethics Committee (M1711115) and the University of Pretoria Research Ethics Committee (50/2018). Approvals were also obtained from the national and provincial Departments of Health as well as from the management at the tertiary hospitals where the obstetric units are situated. Verbal consent to perform POC testing was obtained from admitted women. Written informed consent for research purposes was waived to facilitate measuring routine implementation. Because of this waiver, patient details other than those required for return of results could not be collected. As the REDCap® database collected personal identifiable information such as name, surname, folder number and specimen barcodes required for return of results and linkage to centralized laboratory database, only POC operators and key study staff had access to the database.

The innovation described in the study is the implementation of point-of-care (POC) maternal viral load (mVL) and early infant diagnosis (EID) testing around the time of delivery at high volume tertiary obstetric units in South Africa. This approach aims to improve access to maternal health by providing accurate and timely testing for pregnant women living with HIV and their infants. The study found that POC testing had variable success across the obstetric units, with coverage rates of 35.6% for mVL testing and 61.9% for EID testing. The results were comparable to those from centralized laboratory testing. Factors associated with the return of results included the facility, after-hours specimen collection, initial invalid or error results, and the period of implementation. The study highlights the importance of addressing health system factors at the facility level and reducing analytical error rates for further scale-up of POC testing.
AI Innovations Description
The recommendation based on the study is to implement point-of-care (POC) maternal viral load (mVL) and early infant diagnosis (EID) testing around the time of delivery at tertiary obstetric units in South Africa. This approach can improve access to maternal health by providing accurate and timely testing for pregnant women living with HIV (WLHIV) and their infants. The study found that POC testing had variable success across different obstetric units, but overall coverage of POC mVL and EID testing was 35.6% and 61.9% respectively. The study also showed that POC results were comparable to those from centralized laboratory testing (CLT). To further improve access to maternal health, the implementation of POC testing would need to address health system factors at the facility level and reduce analytical error rates.
AI Innovations Methodology
Based on the provided description, the study implemented point-of-care (POC) maternal viral load (mVL) and early infant diagnosis (EID) testing at four high volume tertiary obstetric units in South Africa. The goal was to improve access to maternal health by providing accurate and timely testing for pregnant women living with HIV and their infants. The study collected blood specimens from women and infants around the time of delivery, and POC testing was conducted by dedicated POC operators during working hours on weekdays. The outcomes measured were the coverage of mVL and EID testing, the return of results prior to discharge, the turn-around time (TAT), and the performance of POC testing compared to centralized laboratory testing (CLT).

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify the recommendations: Based on the study findings and analysis, identify specific recommendations that could improve access to maternal health. For example, recommendations could include increasing the number of POC testing operators, extending testing hours to include weekends, improving training and quality assurance for POC testing, or implementing electronic result reporting systems.

2. Define the simulation parameters: Determine the variables and parameters that will be used to simulate the impact of the recommendations. This could include factors such as the number of POC testing operators, testing hours, testing capacity, and the proportion of women and infants who receive testing.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and the defined parameters. The model should simulate the process of specimen collection, testing, result return, and the overall impact on access to maternal health. The model could use statistical techniques such as Monte Carlo simulation or discrete event simulation.

4. Run the simulation: Use the developed model to run simulations with different scenarios, varying the parameters and recommendations. This will allow for the evaluation of the potential impact of each recommendation on improving access to maternal health. The simulations can provide estimates of coverage, return of results, TAT, and other relevant outcomes.

5. Analyze the results: Analyze the simulation results to assess the impact of the recommendations on improving access to maternal health. Compare the outcomes of different scenarios to determine which recommendations have the greatest potential for improvement. Consider factors such as cost-effectiveness, feasibility, and scalability when evaluating the recommendations.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and feedback from stakeholders. Validate the model by comparing the simulated outcomes with real-world data, if available, to ensure its accuracy and reliability.

By following this methodology, stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health. This information can inform decision-making and help prioritize interventions to enhance maternal health services.

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