Defining Aspects of Mechanisms: Evidence-Based Mechanism (Evidence for a Mechanism), Mechanism-Based Evidence (Evidence from a Mechanism), and Mechanistic Reasoning

Client experiences with "Dynamic Choice Prevention," a model for flexible patient-centred HIV prevention delivery in rural Eastern Africa

INTRODUCTION

Identifying the optimal approaches to offering HIV prevention to meet the needs of those at risk is a high priority, particularly given the expanding toolkit of biomedical HIV prevention options. An ongoing study in rural East African communities evaluated the uptake of choices in product, testing mode and location of care delivery through a structured patient-centred HIV prevention delivery model. In this qualitative study, we sought to understand clients' experiences of this "dynamic choice prevention model" (DCP) and highlight pathways of action to inform HIV prevention delivery models.

METHODS

In-depth semi-structured interviews were conducted from November 2021 through March 2022 with a purposively selected sample of n = 56 participants in DCP trials (across outpatient departments, antenatal clinics and community settings), and n = 21 healthcare providers (total n = 77). A seven-person multi-regional team translated and inductively coded transcript data. We used a framework analysis approach to identify emergent themes.

RESULTS

Individuals taking up HIV pre-exposure prophylaxis (PrEP) reported feelings of relief, liberation from fears of acquiring HIV and satisfaction with being able to take action despite partners' behaviours. Couples used a range of approaches afforded by the study to persuade partners to get tested and opt for PrEP. Post-exposure prophylaxis (PEP) use was less common, although women welcomed it in the event of sexual coercion or assault. Participants discussed switching from PEP to PrEP after familiarizing themselves with usage and ascertaining ongoing risk. Participants felt respected by providers, trusted them and appreciated being able to contact them directly for telephone support. Prevention uptake was hindered by stigma, limited experience with and knowledge of prevention methods, gendered and generational power dynamics within intimate partnerships and families, and negative perceptions of methods due to the products themselves. Participants anticipated long-acting injectable PrEP could solve their challenges regarding pill size, daily pill burden and the likelihood of unwanted disclosure.

CONCLUSIONS

Diverse preferences and barriers to uptake of prevention require a choice of HIV prevention options, locations and delivery modalities-but in addition, flexible, competent and friendly care provision is crucial to promote uptake. Helping clients feel valued, and addressing their unique needs and challenges, enables their agency to prioritize their health.

Causal Pluralism in Medicine and its Implications for Clinical Practice

The existing philosophical views on what is the meaning of causality adequate to medicine are vastly divided. We approach this question and offer two arguments in favor of pluralism regarding concepts of causality. First, we analyze the three main types of research designs (randomized-controlled trials, observational epidemiology and laboratory research). We argue, using examples, that they allow for making causal conclusions that are best understood differently in each case (in agreement with a version of manipulationist, probabilistic and mechanistic definitions, respectively). Second, we analyze clinical practice and argue that these manipulationist, probabilistic and mechanistic causal claims can be used as evidence for different therapeutic decisions. We differentiate among 'predicting' that does not change the relata of causal claims, (mechanistic) 'interferences', and 'interventions' in the strict sense that act on causes to change effects. The central conclusion is that causal claims agreeing with diverse concepts of causality can deliver evidence for different types of therapeutic decisions.

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

INTRODUCTION AND OBJECTIVE

Signals of adverse drug reactions (ADRs) can be supported by reports of ADRs and by interventional and non-interventional studies. The evidence base and features of ADR reports that are used to support signals remain to be comprehensively described. To this end, we have undertaken a scoping review.

METHODS

We searched the following databases: PubMed, EMBASE, PsycINFO, Web of Science, and Google Scholar, without language or time restrictions. We also hand searched the bibliographies of relevant studies. We included studies of any design if the results were described as signals. We assessed the levels of evidence using the Oxford Centre for Evidence-Based Medicine (OCEBM) criteria and coded features of reports of ADRs using the Bradford Hill guidelines.

RESULTS

Overall, 1974 publications reported 2421 studies of signals; 1683/2421 were clinical assessments of anecdotal reports of ADRs, but only 225 (13%) of these included explicit judgments on which features of the ADR reports were supportive of a signal. These 225 studies yielded 228 signals; these were supported by features, which were: 'experimental evidence' (i.e., positive dechallenge or rechallenge, 154 instances [68%]), 'temporality' (i.e., time to onset, 130 [57%]), 'exclusion of competing causes' (49 [21%]), and others (40 [17%]). Positive dechallenge/rechallenge often co-occurred with temporality (77/228). OCEBM 4 (i.e., case series and case-control studies) was the most frequent level of evidence (2078 studies). Between 2013 and 2019, there was a three-fold increase in clinical assessments of reports of ADRs compared with a less than two-fold increase in studies supported by higher levels of evidence (i.e., OCEBM 1-3). We identified an increased rate between 2013 and 2019 in disproportionality analyses (about 15 studies per year), mostly from academia.

CONCLUSIONS

Most signals were supported by temporality and dechallenge/rechallenge, but clear reporting of judgments on causality remains infrequent. The number of studies supported only by anecdotal reports of ADRs increased from year to year. The impact of a growing number of signals of disproportionate reporting communicated without an accompanying clinical assessment should be evaluated.

Clinical recommendations: The role of mechanisms in the GRADE framework

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework has become one of the most influential frameworks for assessing quality of research and developing clinical recommendations. The GRADE framework has been presented as an evolution in the Evidence-Based Medicine (EBM) movement. Both GRADE and EBM emphasize effect estimates derived from population-level clinical trials and, as a consequence, devalue the role of mechanisms as the basis for clinical decisions. Although mechanisms do not hold the epistemic privilege of rigorous clinical trials in EBM reasoning, this paper will argue that mechanisms appear to be important in the use and application of GRADE, as described in the literature. The seemingly necessary role of mechanisms in the development of clinical recommendations has, so far, received little attention and is not explicitly featured in the literature describing GRADE. The analysis of the GRADE framework presented in this paper reveals an apparent tension between EBM's willingness to downplay mechanisms and what seems their inevitable use in GRADE. In this paper, we take the position that if mechanistic reasoning is inevitable in the use of GRADE, then the instructional literature on the framework would benefit from more explicit discussion of how to consider and integrate mechanisms.

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with those who are. Modern concepts of AI date from Alan Turing's work, especially his paper on "the imitation game", in the late 1940s and early 1950s. Its scope today includes computational skills, including the formulation of mathematical proofs; visual perception, including facial recognition and virtual reality; decision making by expert systems; aspects of language, such as language processing, speech recognition, creative composition, and translation; and combinations of these, e.g. in self-driving vehicles. Machines can be programmed with the ability to learn, using neural networks that mimic cognitive actions of the human brain, leading to deep structural learning. Limitations of AI include difficulties with language, arising from the need to understand context and interpret ambiguities, which particularly affect translation, and inadequacies of databases, requiring careful preparation and curation. New techniques may cause unforeseen difficulties via unexpected malfunctioning. Relevant terms and concepts include different types of machine learning, neural networks, natural language programming, ontologies, and expert systems. Adoption of the tools of AI in pharmacovigilance has been slow. Machine learning, in conjunction with natural language processing and data mining, to study adverse drug reactions in databases such as those found in electronic health records, claims databases, and social media, has the potential to enhance the characterization of known adverse effects and reactions and detect new signals.