Where is the evidence? The use of routinely-collected patient data to retain adults on antiretroviral treatment in low and middle income countries–a state of the evidence review

Abstract

Retention rates in antiretroviral treatment (ART) in low- and middle-income countries are suboptimal for meeting global "90-90-90" treatment targets. Interventions using routinely collected patient data to follow up with ART defaulters is recommended to improve retention; yet, little is documented on how these data are used in practice. This state of the evidence review summarizes how facilities and programmes use patient data to retain adults on ART in low- and middle-income countries, and what effect, if any, these interventions have on retention. The authors searched peer-reviewed and grey literature in PubMed, POPLINE, OVID, Google Scholar, and select webpages; screened publications for relevance; and applied eligibility criteria to select articles for inclusion. Over 4,000 records were found, of which 19 were eligible. Interventions assessed within the studies were sorted into three categories: patient tracing (18), data reviews (3), and improved data capture systems (9). Nine studies demonstrated increased retention or reduced lost to follow-up; however, the quality of evidence was weak. We recommend that future research investigates how various combinations of these interventions are being implemented and their effectiveness on ART retention across diverse country contexts, taking into account cultural, social and economic barriers and differences in countries' HIV epidemics and health information systems.

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response

“…Such sources comprise local data inputs, which can provide critical information regarding real-world uptake and engagement with HIV services and characteristics of ongoing inequities that continue to define the HIV response [11•]. While the systematic collection and collation of surveillance data for key populations have increased over the last decade, the use of routinely collected program data and community-led monitoring has traditionally been less utilized by institutions such as UNAIDS and others tasked with making epidemic projections [20,79,80]. For example, program data are routinely collected servicedelivery data from implementing partners and community organizations, and may include client files, registries, and reporting indicators to support service management/implementation and evaluate routine program activities.…”

Implementation and applications of EMOD, an individual-based multi-disease modeling platform

“… 2015 ). To constrain these uncertainties and make informed forecasts, models must to rapidly adapt to incorporate new evidence about the outcomes of those who appear to be lost to follow-up based on clinic records (Haas Andreas et al 2018 ; Holmes et al 2018 ), interventions to retain (Murray et al 2017 ) or re-engage lost patients (Bershteyn et al 2017 ; do Nascimento, Barker and Brodsky 2018 ), and the impact of other health system changes such as decentralization and differentiated care on patient retention (Bor et al 2017 ; Murray et al 2017 ). Making changes to health care patterns in the EMOD source code required re-compiling and re-testing the model, which limited the speed and frequency of updates to the health system model.…”

The impact of digital communication and data exchange on primary health service delivery in a small island developing state setting

“…The unanimous approval by WHO Member States of the ’Resolution on Digital Health’ [ 7 ] at the 2018 World Health Assembly and the Regional Committee for the Western Pacific’s endorsement of the Regional Action Agenda on Harnessing e-Health for Improved Health Service Delivery [ 8 ] demonstrates a recognition of the value digital technology offers health service development and the commitment of decision-makers to exploring its use. There are many examples of the use of ICT to support health care delivery in low- and middle-income settings including for delivery of telemedicine [ 9 – 11 ], targeted patient communication [ 12 – 15 ], supply chain management [ 16 , 17 ], workforce training [ 18 – 20 ], clinical decision-making support, [ 21 , 22 ] data collection and management [ 16 , 18 , 19 , 23 , 24 ], and healthcare workers-to-healthcare worker communication and data exchange [ 25 – 27 ] including during disaster situations [ 28 ].…”