AHRI.Optimised electronic patient records to improve clinical monitoring of HIV-positive patients in rural South Africa (MONART Trial) 27 March 2020 to 30 June 2024
South Africa, 2023 - 2024
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Reference ID
AHRI.MONART.MainStudy.2024.v1
Producer(s)
Iwuji Collins
Metadata
Created on
Oct 18, 2024
Last modified
Oct 18, 2024
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Identification
Survey ID number
AHRI.MONART.MainStudy.2024.v1
Title
AHRI.Optimised electronic patient records to improve clinical monitoring of HIV-positive patients in rural South Africa (MONART Trial) 27 March 2020 to 30 June 2024
Country
| Name | Country code |
|---|---|
| South Africa | ZA |
Abstract
In our formative research, analysis of antiretroviral treatment (ART) data manually entered in the Three Interlinked Electronic Registers (TIER.Net) showed poor viral load monitoring (VLM) and inadequate management of virological failure in HIV-positive patients on ART in rural KwaZulu-Natal, South Africa. ART interruption was high, with nearly half of patients falling out of care within 5 years of starting ART. Non-Nucleoside reverse transcriptase pre-treatment drug resistance exceeds 10% in the setting; the threshold required to trigger in a change in first-line ART using the public health approach. These factors are contributory to increasing HIV drug resistance (HIVDR) in this setting. HIVDR is associated with increased morbidity and mortality with the risk of transmitting drug-resistant HIV to sexual partners. We presented these findings to healthcare providers, policy makers and community representatives with brainstorming of health system challenges and potential interventions. This study aims to complement these findings by investigating the clinical and process impediments in VLM within the health system and to develop a quality improvement package (QIP) to address the gaps. The stakeholders recommended such QIP would utilise the viral load (VL) champion model, a named healthcare provider who would be the focal point for ensuring proper administrative management of viral load tests and results through identification of those who need tests and triaging of results for action. This QIP will be supported by technological enhancement of the routine clinic-based TIER.Net software which will allow daily automatic import of results from the National Health Service Laboratory (NHLS) to TIER.Net and development of a dashboard system to support VLM. In addition, results of contact tracing will be recorded and followed up pro-actively if not initially successful.
We will evaluate the effectiveness of these interventions compared to current care for improving VLM and virological suppression using an innovative effectiveness-implementation hybrid cluster-randomised design in 10 clinics. A within-trial health economics analysis will be undertaken using recommended methods to examine the cost-effectiveness of the intervention compared to standard care. Finally, we will use a mixed-methods approach to undertake a process evaluation assessing acceptability, fidelity, adaptation and contexts in the implementation of the interventions.
Design: Cluster randomised trial of 10 primary health care clinics (5 x 2) located in the Africa Health Research Institute (AHRI) demographic surveillance area, uMkhanyakude district, South Africa.
Population and Intervention: Quality improvement package (QIP) delivered to healthcare staff including augmentation of an existing electronic ART database (TIER.Net)
Aim: We aim to demonstrate that a staff-centred QIP and technological augmentation of TIER.Net would result in optimal VLM of patients on ART, prompt clinical management of virological failure and an overall improvement in virological suppression
Objectives
1. To identify health system specific gaps in VLM.
2. To develop and train healthcare provider on a QIP using the VL champion model
3. To augment TIER.Net with a dashboard system that includes the latest laboratory results imported from the NHLS.
4. To evaluate the effectiveness of the QIP.
5. To evaluate the cost and cost effectiveness of the intervention compared to standard care
6. To undertake a process evaluation assessing acceptability, fidelity, adaptation and contexts in the implementation of the intervention.
7. To write up a best practice document, describing what is required and operationally, recommendations on how to successfully implement the QIP for a national scale-up if proven successful
Major variables: As documented in study questionnaire
We will evaluate the effectiveness of these interventions compared to current care for improving VLM and virological suppression using an innovative effectiveness-implementation hybrid cluster-randomised design in 10 clinics. A within-trial health economics analysis will be undertaken using recommended methods to examine the cost-effectiveness of the intervention compared to standard care. Finally, we will use a mixed-methods approach to undertake a process evaluation assessing acceptability, fidelity, adaptation and contexts in the implementation of the interventions.
Design: Cluster randomised trial of 10 primary health care clinics (5 x 2) located in the Africa Health Research Institute (AHRI) demographic surveillance area, uMkhanyakude district, South Africa.
Population and Intervention: Quality improvement package (QIP) delivered to healthcare staff including augmentation of an existing electronic ART database (TIER.Net)
Aim: We aim to demonstrate that a staff-centred QIP and technological augmentation of TIER.Net would result in optimal VLM of patients on ART, prompt clinical management of virological failure and an overall improvement in virological suppression
Objectives
1. To identify health system specific gaps in VLM.
2. To develop and train healthcare provider on a QIP using the VL champion model
3. To augment TIER.Net with a dashboard system that includes the latest laboratory results imported from the NHLS.
4. To evaluate the effectiveness of the QIP.
5. To evaluate the cost and cost effectiveness of the intervention compared to standard care
6. To undertake a process evaluation assessing acceptability, fidelity, adaptation and contexts in the implementation of the intervention.
7. To write up a best practice document, describing what is required and operationally, recommendations on how to successfully implement the QIP for a national scale-up if proven successful
Major variables: As documented in study questionnaire
Kind of Data
Clinical Data
Unit of Analysis
Individuals within a cluster defined by clinics
Version
Version Description
v1.0.0
Scope
Keywords
HIV, viral load monitoring, virological failure, drug resistance, viral load champion
Coverage
Geographic Coverage
14 clinics in Hlabisa subdistrict
Universe
People living with HIV on antiretroviral therapy
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Iwuji Collins | AHRI, Brighton and Sussex Medical School, University of Sussex, Brightin, UK |
Producers
| Name |
|---|
| Africa Health Research Institute |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Royal Academy of Engineering | RAEng | Funder |
Other Identifications/Acknowledgments
| Name | Affiliation | Role |
|---|---|---|
| Khumalo Sfundo | Africa Health Research Institute | Data Manager |
| Mazibuko Lusanda | Africa Health Research Institute | Statistician |
Sampling
Sampling Procedure
Cluster randomised sampling
Data collection
Dates of Data Collection
| Start | End |
|---|---|
| 2023-02-01 | 2024-06-30 |
Access policy
Access conditions
Access to the data requires accurate completion of the online data access application form accessible on the AHRI Data repository(<https://data.ahri.org/>). Data users are required to abide by the data use conditions stipulated on the application for access to the data. Failure to do so may result in their data access privileges being revoked by the Data Custodian. In order to recognise the effort and intellectual contributions of AHRI investigators in producing and curating the data, users of AHRI data must acknowledge the source of the data and abide by the terms and conditions under which the data is accessed and must cite the dataset in publication using the citation provided as part of this documentation. All analytical datasets published on the AHRI Data Repository are assigned digital object identifier (DOIs) and the DOIs can be found on the Data Repository under Study Description tab - Access policy. AHRI data users are required to always cite the dataset using the relevant DOI.
Citation requirements
Iwuji, C. (2024). AHRI.Optimised electronic patient records to improve clinical monitoring of HIV-positive patients in rural South Africa (MONART Trial) 27 March 2020 to 30 June 2024 [Data set]. Africa Health Research Institute.
DOI:https://doi.org/10.23664/AHRI.MONART.MAINSTUDY.2024
DOI:https://doi.org/10.23664/AHRI.MONART.MAINSTUDY.2024
Metadata production
DDI Document ID
DDI.AHRI.MONART.MainStudy.2024.v1
Producers
| Name | Abbreviation |
|---|---|
| Africa Health Research Institute | AHRI |