Discordance between measured and perceived vision in older adults: the need for objective screening despite health consciousness.

Implementation science is the study of methods to promote the adoption and integration of evidence-based practices into routine clinical and public health settings to improve population health []. As interventions move from efficacy trials into complex real-world environments, challenges arise in understanding how interventions should be delivered, under what conditions they succeed or fail, and how they can be adapted to maximize impact. Addressing these challenges often requires methodological approaches that help researchers anticipate implementation barriers, understand contextual dynamics, and evaluate potential implementation strategies prior to large-scale implementation. For example, there is robust evidence supporting the efficacy of antiretroviral pre-exposure prophylaxis (PrEP) for HIV prevention: multiple randomized controlled trials have shown that persons with high adherence to PrEP have a substantially reduced risk of acquiring HIV [,]. These findings led to the global adoption of PrEP guidelines and implementation of diverse service delivery models. However, despite strong evidence of clinical efficacy, real-world implementation has often produced lower-than-expected reductions in HIV incidence at the population level []. Understanding why these gaps persist and addressing the challenges that emerge during the scale-up of evidence-based interventions are critical for translating PrEP efficacy into sustained public health impact. Epidemiologists are well equipped to address these challenges by emphasizing rigor in defining relationships, controlling for confounding, and assessing effect modification, all key concepts for understanding who benefits from interventions and under what conditions. With causal inference tools like directed acyclic graphs (DAGs) and g-methods, and expertise in modeling population-level dynamics, epidemiologists are also uniquely positioned to integrate systems approaches into the evaluation of implementation strategies in complex settings. These strengths position us to bridge disciplines and enhance implementation science with stronger causal reasoning and deeper contextual insights. Yet, traditional epidemiologic training often emphasizes isolating variables and estimating independent effects, treating surrounding complexity as background noise to be controlled. In doing so, epidemiologists may overlook the systemic dynamics that shape implementation outcomes such as feasibility (i.e., “the extent to which a new treatment, or an innovation, can be successfully used or carried out within a given agency or setting”) and adoption (i.e., “the intention, initial decision, or action to try or employ an innovation or evidence-based practice”) [,]. This paper explores how integrating epidemiologic methods with principles of systems thinking can strengthen implementation science and inform implementation strategies for evidence-based interventions in complex, real-world settings. A core insight from intervention research in public health is that implementation is never one-size-fits-all. The success of an intervention in real-world settings often depends on the intervention itself (i.e., the evidence-based action, treatment, or policy intended to improve health), the implementation strategy (“methods or techniques used to enhance the adoption, implementation, and sustainability of a clinical program or practice”), and the context in which it is delivered []. In implementation science, context refers to the broader set of organizational, social, and policy conditions that shape how interventions are implemented and sustained, including both time-stable factors (e.g., health system structure, geography) and time-varying influences (e.g., staffing levels, policy changes), some of which may be directly measured while others remain unobserved. A program that is effective in one setting may fail in another due to differences in contextual conditions rather than flaws in the intervention itself. For example, a community-based PrEP delivery model might succeed in a region with strong medication supply chains and adequate staffing but prove ineffective elsewhere due to resource constraints. This variability highlights why some of the most pressing questions in implementation science—how, for whom, and under what conditions interventions work—are also the most challenging. Addressing them requires more deliberate engagement with the dynamic systems in which interventions operate. To do this, we need a deep understanding of context as an interconnected system of variables that may modify or mediate implementation outcomes [].