Health is more than the absence of disease. It is a state of complete physical, mental and social well-being sustained through interactions among biological, social and environmental influences. Although articulated more than seven decades ago, this definition remains foundational. However, the contexts in which health is produced and maintained have changed substantially. Contemporary health systems now operate within societies shaped by population aging, growing chronic disease burdens, environmental pressures, global mobility and persistent social inequities. A new conceptual framework — Health Elements — offers a structured way to understand this complexity in the digital era.
Why Traditional Health Models Fall Short
Many dominant models in health research continue to emphasize single-domain risk factors or downstream clinical events. These approaches remain indispensable for biomedical discovery and clinical care. However, they are limited in their ability to represent nonlinear dynamics, feedback processes and dependencies across the life course. Health reflects the cumulative effects of ongoing interactions between individuals and their biological characteristics, behaviors, social relationships, physical environments and digital systems. No single-domain model captures this adequately. Consequently, a more process-oriented framework is needed — one that treats health as an emergent outcome of interacting systems rather than the sum of isolated causal pathways.
Lessons From COVID-19 on Health Complexity
The COVID-19 pandemic illustrated health complexity with particular clarity. While driven by a novel pathogen, its population-level impacts reflected interactions between biological susceptibility and social vulnerability, occupational exposure, housing conditions, mobility patterns, institutional trust and digital access to information and services. These influences shaped transmission dynamics, disease severity and mortality across populations. Furthermore, the pandemic reinforced a long-standing public health insight: health outcomes arise from interacting systems. No single variable explained why some populations fared dramatically worse than others. The pandemic made the case for complexity-informed health frameworks more urgent than any theoretical argument could.
Digital Transformation and Its Health Implications
Digital transformation has fundamentally reconfigured the health landscape. Algorithmic systems influence health-related behaviors and beliefs. Digital platforms mediate access to services. Ubiquitous sensing technologies generate continuous streams of health-relevant data. Additionally, advances in data science and artificial intelligence have enabled the integration of electronic health records, multiomics data, wearable sensors, environmental monitoring systems and administrative datasets to represent health across the life course. Machine learning methods capable of modeling high-dimensional and nonlinear relationships offer new opportunities to examine how diverse influences combine and evolve over time. However, they also raise important conceptual, ethical and governance questions that existing frameworks are not well equipped to address.
Introducing the Health Elements Framework
The Health Elements framework conceptualizes health as an emergent outcome of interacting biological, behavioral, social, environmental and technological domains. Rather than replacing determinant-based approaches, it extends them by explicitly emphasizing interaction, temporality and the growing role of digital systems in shaping health. The framework builds on systems epidemiology and complexity science — fields that established over a decade ago that health outcomes arise from nonlinear interactions, feedback loops and path-dependent trajectories. Health Elements applies these principles to a contemporary, digitally mediated health landscape in which the configuration of relevant domains has expanded substantially beyond what earlier frameworks anticipated.
Building on Social Determinants of Health
The social determinants of health framework has been central to demonstrating that social and economic conditions are fundamental drivers of population health and health inequities. Education, income, employment, housing and social policy shape exposure to risk and access to resources across the life course. However, SDH frameworks were largely developed in an era of low-frequency data collection, limited domain linkage and minimal digital mediation of daily life. Determinants were therefore often conceptualized as stable background conditions rather than dynamic processes. Health Elements builds on these foundations but introduces a process-oriented perspective. Rather than asking whether a given factor influences health, the framework asks how multiple elements interact, under what conditions, and across which time horizons.
The Five Domains of Health Elements
The Health Elements framework organizes health influences across five distinct domains. Biological elements include physiology, genetics, epigenetics, molecular and cellular pathways, immune function, microbiome composition and biological aging processes. Behavioral elements cover physical activity, diet, sleep, substance use, stress-related behaviors and health literacy — many of which can now be observed through digital devices and continuous monitoring technologies. Social elements arise from interpersonal relationships, socioeconomic conditions, education, cultural norms and institutional contexts. Environmental elements encompass natural and built environments including air and water quality, climate exposures, housing conditions, transportation systems and urban form. Technological elements capture digital access, data availability and interoperability, algorithmic systems and digital literacy — all of which shape how individuals and populations engage with health systems.
Why Technology Deserves Its Own Domain
The Health Elements framework makes a deliberate and consequential decision: it treats technological elements as a structurally independent first-order domain rather than a subcategory of social or environmental factors. Three reasons justify this position. First, technological systems introduce genuinely novel mechanisms of health influence — including algorithmic decision-making, platform-mediated behavioral environments, AI-enabled diagnostics and real-time data feedback loops — that have no functional analog in pre-digital frameworks. Second, technological elements possess a uniquely cross-domain modifying capacity. They do not merely add an additional exposure but actively restructure how biological, behavioral, social and environmental elements operate and interact. Third, technological systems occupy a reflexive position in modern health systems — functioning both as determinants of health outcomes and as the primary infrastructure through which other determinants are measured, interpreted and acted upon.
Importantly, technological elements are not synonymous with advanced digital infrastructure. In low- and middle-income countries and resource-constrained settings, the technological domain may be defined as much by absence or intermittency as by presence. Limited surveillance capacity, lack of interoperable health records and low digital health literacy are themselves constitutive features of the technological environment that shape health outcomes and constrain disease detection and response.
Health as Emergence, Not Addition
Health is conceptualized within this framework not as the additive sum of its elements but as an emergent property of their configurations. In complexity science, emergence refers to system-level outcomes that arise from interactions among components — outcomes that cannot be predicted from or reduced to the properties of any individual component. This differs fundamentally from complex additive effects in which multiple domains contribute independently to a decomposable outcome. Genuine emergence is characterized by nonlinearity, context dependence and feedback dynamics. Identical biological risk factors may produce different health trajectories depending on the configuration of social, environmental, behavioral and technological elements operating simultaneously over time. The shifting epidemiology of chronic kidney disease in China illustrates this distinction — the rise of diabetes as its leading cause cannot be attributed to biological change alone but reflects the convergence of urbanization, behavioral transition, environmental exposure and health system capacity. No single-domain model could have anticipated or explained that pattern. This relational perspective is what distinguishes Health Elements from frameworks that treat determinants as independent contributors.
